An intelligent dynamic assessment method and system for chronic disease risks of the elderly

Abstract

The present invention relates to the field of data processing, and particularly provides a method and system for dynamically evaluating the risks of chronic diseases for the elderly. By presetting risk assessment rules, risk assessment is carried out on the basic physical and chemical index data of the target elderly individual, so as to instantaneously and dynamically calculate the basic risk assessment score of the target elderly individual. From the perspective of score orientation, multiple types of chronic diseases are evaluated to improve the overall evaluation efficiency. Then, through preset key-value pair matching rules, risk assessment is carried out on the comprehensive risk index data of the target elderly individual to conduct a comprehensive evaluation from the perspective of result orientation, avoiding the limitations of single-orientation evaluation. Then, by comparing different orientations for weighted refinement, the overall evaluation accuracy is further improved. The present invention greatly improves the efficiency and accuracy of the method for dynamically evaluating the risks of chronic diseases.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and particularly to an intelligent dynamic assessment method and system for chronic disease risks of the elderly. Background Art

[0002] With the rapid development of data intelligence, data analysis and assessment have been widely applied to various scenarios. Among them, especially for the assessment of chronic disease risks of the elderly, due to the complex data correlation and excessive data correlation factors, the cost and difficulty of manual assessment are relatively high. Therefore, intelligent data analysis and assessment methods have gradually become an important part of the assessment of chronic disease risks of the elderly.

[0003] In the prior art, the assessment of chronic disease risks of the elderly mainly adopts a specific single chronic disease type assessment method, which pre-sets specific data as the target and only screens relevant data to avoid the problem of simultaneously processing multiple complex attribution factors. However, in actual assessment, usually a single target elderly individual has multiple chronic disease types. Since the existing method can only judge a single chronic disease type in one assessment, the overall assessment efficiency is too low, and for each chronic disease type, targeted assessment model parameter settings need to be carried out separately, resulting in relatively high development costs and operation and maintenance costs, which cannot meet the requirements of actual assessment.

[0004] Therefore, how to design an intelligent chronic disease risk assessment method for the elderly to meet the requirements of actual assessment is an urgent problem to be solved. Summary of the Invention

[0005] Based on this, the object of the present invention is to provide an intelligent dynamic assessment method and system for chronic disease risks of the elderly. By presetting risk assessment rules, risk assessment is carried out on the basic physical and chemical index data of the target elderly individual, so as to calculate the basic risk assessment score of the target elderly individual in real-time and dynamically, and multiple chronic disease types are evaluated from the perspective of score orientation to improve the overall assessment efficiency. Then, by presetting key-value pair matching rules, risk assessment is carried out on the comprehensive risk index data of the target elderly individual, and comprehensive assessment is carried out from the perspective of result orientation to avoid the limitations of single-orientation assessment. Then, weighted refinement is carried out by comparing different orientations to further improve the accuracy of the overall assessment. The present invention improves the efficiency and accuracy of the dynamic assessment method for chronic disease risks.

[0006] An intelligent dynamic assessment method for chronic disease risks of the elderly proposed by the present invention includes:

[0007] Obtain the basic physical and chemical index data and comprehensive risk index data of the target individual from the preset individual index database and perform preprocessing. Both the basic physical and chemical index data and the comprehensive risk index data are stored in the preset individual index database. When obtaining the basic physical and chemical index data and comprehensive risk index data of the target individual, data screening and data scraping are performed on the preset individual index database according to the individual identity information;

[0008] Determine the basic risk assessment score according to the basic physical and chemical index data and the preset risk assessment rules. The preset risk assessment rules are dynamic parsing expressions;

[0009] Determine the comprehensive risk assessment result of the target individual according to the comprehensive risk index data and the preset key-value pair matching rules. The preset key-value pair matching rules include the comprehensive risk index data and the corresponding comprehensive risk assessment results. When matching the comprehensive risk assessment results, data retrieval and data matching are performed on the comprehensive risk assessment result value table according to the keywords in the comprehensive risk index data;

[0010] If it is determined that there are rough target items in the basic risk assessment score and the comprehensive risk assessment result, perform weighted refinement processing on the rough target items according to the preset weighted refinement rules. The preset weighted refinement rules judge whether to perform weighted enhancement or refinement deletion in the weighted refinement processing according to the rough state of the rough target items;

[0011] Then generate a final risk assessment report according to the basic risk assessment score and the comprehensive risk assessment result, save the final risk assessment report to the risk assessment database and perform weight update. When saving the final risk assessment report to the risk assessment database, data screening is performed on the risk assessment database according to the types of chronic diseases, and then weight update is performed on the risk assessment database according to the index data change label of the target individual.

[0012] In summary, according to the above-mentioned dynamic risk assessment method for the elderly's intelligent chronic diseases, by presetting risk assessment rules, the basic physical and chemical index data of the target elderly individual is risk-assessed to instantaneously and dynamically calculate the basic risk assessment score of the target elderly individual. From the perspective of score orientation, multiple chronic disease types are evaluated to improve the overall assessment efficiency. Then, through the preset key-value pair matching rules, the comprehensive risk index data of the target elderly individual is risk-assessed to conduct a comprehensive assessment from the perspective of result orientation, avoiding the limitations of single-orientation assessment. Then, by comparing different orientations for weighted refinement, the overall assessment accuracy is further improved. The present invention improves the efficiency and accuracy of the dynamic risk assessment method for chronic diseases. Specifically, the basic physical and chemical index data and comprehensive risk index data of the target individual are obtained and preprocessed. According to the basic physical and chemical index data and the preset risk assessment rules, the basic risk assessment score is determined. The preset risk assessment rules are dynamic parsing expressions. Through the preset risk assessment rules of the dynamic parsing expressions, the basic risk assessment score of the target elderly individual is instantaneously and dynamically calculated. From the perspective of score orientation, multiple chronic disease types are evaluated to improve the overall assessment efficiency. According to the comprehensive risk index data and the preset key-value pair matching rules, the comprehensive risk assessment result of the target individual is determined. The preset key-value pair matching rules include the comprehensive risk index data and the corresponding comprehensive risk assessment result, to conduct a comprehensive assessment from the perspective of result orientation, avoiding the limitations of single-orientation assessment and improving the overall assessment accuracy at the same time. If it is determined that there are rough target items in the basic risk assessment score and the comprehensive risk assessment result, the rough target items are weighted and refined according to the preset weighted refinement rules. The preset weighted refinement rules judge whether to perform weighted enhancement or refinement deletion in the weighted refinement process according to the rough state of the rough target items, compare the results of different orientations to enhance the rough assessment items between different orientations, and further improve the overall assessment accuracy. Then, according to the basic risk assessment score and the comprehensive risk assessment result, a final risk assessment report is generated. The final risk assessment report is saved to the risk assessment database and the weights are updated. The present invention improves the efficiency and accuracy of the dynamic risk assessment method for chronic diseases.

[0013] Furthermore, the step of obtaining the basic physical and chemical index data and comprehensive risk index data of the target individual and performing preprocessing specifically includes:

[0014] After detecting the basic physical and chemical indexes of the target individual, it is retrieved in the preset individual index database whether there is a historical record of the basic physical and chemical index detection of the target individual;

[0015] If there is no historical record of the basic physical and chemical index detection of the target individual, the data obtained from the current basic physical and chemical index detection is used as the basic physical and chemical index data of the target individual;

[0016] If there is a historical record of basic physical and chemical index detection for the target individual, the data obtained from the current basic physical and chemical index detection is used as the basic physical and chemical index data of the target individual, and an index data change label is generated based on the historical basic physical and chemical index data obtained from the historical basic physical and chemical index detection;

[0017] After performing a comprehensive risk index detection on the target individual, the comprehensive risk index data is classified and obtained according to the data type in the preset individual index database to obtain comprehensive risk index data of multiple different data types, and the comprehensive risk index data is grouped according to the digital floating-point type and the text character type.

[0018] Further, the steps of generating the index data change label specifically include:

[0019] Compare the basic physical and chemical index data item by item with the historical basic physical and chemical index data to obtain single or multiple different index data change items and the index data change values corresponding to the index data change items;

[0020] Screen the key change items and non-key change items among the index data change items;

[0021] Judge whether the index data change value corresponding to the key change item is greater than or equal to the preset key change item change threshold. If the index data change value corresponding to the key change item is greater than or equal to the preset key change item change threshold, generate a key item severe change label. If the index data change value corresponding to the key change item is less than the preset key change item change threshold, generate a key item mild change label;

[0022] Judge whether the index data change value corresponding to the non-key change item is greater than or equal to the preset non-key change item change threshold. If the index data change value corresponding to the non-key change item is greater than or equal to the preset non-key change item change threshold, generate a non-key item change label;

[0023] Insert the key item severe change label, the key item mild change label, and the non-key item change label as the index data change label into the basic physical and chemical index data.

[0024] Further, the steps of determining the basic risk assessment score according to the basic physical and chemical index data and the preset risk assessment rule specifically include:

[0025] Divide the basic physical and chemical index data into assessment groups according to the preset risk assessment rule. Each assessment group includes multiple pieces of the basic physical and chemical index data, and each piece of the basic physical and chemical index data has a unique corresponding assessment group;

[0026] Convert all the basic physical and chemical index data in each of the evaluation groups into corresponding evaluation values, and calculate the evaluation scores for the corresponding evaluation groups according to the evaluation values. The evaluation score calculation includes positive score calculation and inverted score calculation. In the positive score calculation, perform positive accumulation processing on the evaluation values to obtain the evaluation scores. In the inverted score calculation, perform inversion processing on the evaluation values and then perform accumulation processing to obtain the evaluation scores;

[0027] Obtain the basic risk assessment score according to the evaluation scores of all the evaluation groups;

[0028] The evaluation group includes a traditional Chinese medicine constitution evaluation group, and the traditional Chinese medicine constitution evaluation group includes qi deficiency constitution items and peaceful constitution items;

[0029] The rules for calculating the evaluation scores corresponding to the qi deficiency constitution items are as follows:

[0030] #qdc_2 + #qdc_3 + #qdc_4 + #qdc_14,

[0031] where #qdc represents the qi deficiency constitution items, and the numerical label is the guiding symbol of the basic physical and chemical index data of the corresponding item. The calculation of the evaluation score corresponding to the qi deficiency constitution items is a positive score calculation, and positive accumulation processing is performed on each qi deficiency constitution item to obtain the evaluation score of the traditional Chinese medicine constitution [qi deficiency constitution];

[0032] The rules for calculating the evaluation scores corresponding to the peaceful constitution items are as follows:

[0033] #bc_1 + (6 - #qdc_2) + (6 - #qdc_4) + (6 - #qsc_5) + (6 - #yadc_13),

[0034] where #bc represents the peaceful constitution items, #qsc represents the qi stagnation constitution items, #yadc represents the yang deficiency constitution items, and 6 is the upper limit of the inverted score. The calculation of the evaluation score corresponding to the peaceful constitution items is an inverted score calculation. After performing inversion processing on the items other than the peaceful constitution items and then performing accumulation processing, the evaluation score of the traditional Chinese medicine constitution [peaceful constitution] is obtained.

[0035] Further, the step of determining the comprehensive risk assessment result of the target individual according to the comprehensive risk index data and the preset key-value pair matching rule specifically includes:

[0036] Extract keywords from the comprehensive risk index data according to the preset key-value pair matching rule to obtain the comprehensive risk variable name;

[0037] Generate an index key based on the comprehensive risk variable name to retrieve the comprehensive risk assessment result value table. The index key is a single string, and the string uniquely corresponds to the comprehensive risk assessment result value in the comprehensive risk assessment result value table;

[0038] Retrieve the comprehensive risk assessment result value, and divide the comprehensive risk assessment result value into evaluation groups according to the preset risk assessment rules. Each evaluation group includes multiple comprehensive risk assessment result values, and each comprehensive risk assessment result value has a uniquely corresponding evaluation group;

[0039] Judge the evaluation result according to the comprehensive risk assessment result value. The evaluation result judgment includes a first preset evaluation result range threshold and a second evaluation result range threshold. The first preset evaluation result range threshold and the second evaluation result range threshold are different values. Three different evaluation result regions are formed according to the first preset evaluation result range threshold and the second evaluation result range threshold, and each evaluation result region has a uniquely corresponding risk assessment result;

[0040] Obtain the comprehensive risk assessment result according to the risk assessment results of all the evaluation groups;

[0041] The evaluation group includes a traditional Chinese medicine physical constitution evaluation group, and the traditional Chinese medicine physical constitution evaluation group includes a qi deficiency constitution item. The preset key-value pair matching rule of the qi deficiency constitution item is as follows:

[0042] (#qdcScore>=11)?1:(#qdcScore>8and#qdcScore<11)?2:(#qdcScore<=8)?4:''

[0043] Among them, #qdcScore represents the comprehensive risk assessment result value of the qi deficiency constitution item. In the preset key-value pair matching rule of the qi deficiency constitution item, when the comprehensive risk assessment result value of the qi deficiency constitution item is greater than or equal to the first preset evaluation result range threshold, when the comprehensive risk assessment result value of the qi deficiency constitution item is less than the first preset evaluation result range threshold and greater than the second preset evaluation result range threshold, and when the comprehensive risk assessment result value of the qi deficiency constitution item is less than or equal to the second preset evaluation result range threshold, the corresponding risk assessment results are obtained respectively. The first preset evaluation result range threshold and the second preset evaluation result range threshold in the preset key-value pair matching rule of the qi deficiency constitution item are 11 and 8 respectively, and the guiding symbols of the risk assessment results in the preset key-value pair matching rule of the qi deficiency constitution item are 1, 2, and 4 respectively.

[0044] Further, the step of, if it is determined that there are rough target items in the basic risk assessment score and the comprehensive risk assessment result, performing weighted refinement processing on the rough target items according to the preset weighted refinement rule specifically includes:

[0045] Perform weighted refinement judgment based on the basic risk assessment score and the comprehensive risk assessment result;

[0046] Determine whether there is a deviation item in the basic risk assessment score from the comprehensive risk assessment result. If it is determined that there is a deviation item in the basic risk assessment score from the comprehensive risk assessment result, perform refinement processing according to the comprehensive risk assessment result. The refinement processing includes data deletion or data replacement according to the evaluation result value in the comprehensive risk assessment result;

[0047] Determine whether there is an offset item in the basic risk assessment score from the comprehensive risk assessment result. If it is determined that there is an offset item in the basic risk assessment score from the comprehensive risk assessment result, perform weighted processing according to the comprehensive risk assessment result and the index data change label in the basic physical and chemical index data. The weighted processing includes calculating the offset weight according to the index data change label to obtain the offset weight and weighting the evaluation result value of the comprehensive risk assessment result corresponding to the offset item to obtain the offset-corrected basic risk assessment score.

[0048] Further, the step of generating the final risk assessment report according to the basic risk assessment score and the comprehensive risk assessment result specifically includes:

[0049] Calculate the chronic disease risk probability of the target individual according to the basic risk assessment score, and then retrieve the corresponding doctor-side feedback text in the preset chronic disease database to generate a doctor-side feedback report;

[0050] Retrieve the corresponding patient-side suggestion text in the preset chronic disease database according to the comprehensive risk assessment result, and perform text filling according to the preset evaluation report text template to generate a comprehensive risk assessment feedback report;

[0051] Obtain the final risk assessment report according to the doctor-side feedback report and the comprehensive risk assessment feedback report, save the basic risk assessment score and the comprehensive risk assessment result corresponding to the final risk assessment report to the preset chronic disease database, and update the index data change label of the target individual.

[0052] An intelligent chronic disease risk dynamic assessment system for the elderly proposed by the present invention includes:

[0053] A preprocessing module for obtaining the basic physical and chemical index data and the comprehensive risk index data of the target individual and performing preprocessing. The basic physical and chemical index data and the comprehensive risk index data are both stored in the preset individual index database;

[0054] A basic risk assessment module, configured to determine a basic risk assessment score according to the basic physical and chemical index data and a preset risk assessment rule, where the preset risk assessment rule is a dynamic parsing expression;

[0055] A comprehensive risk assessment module, configured to determine a comprehensive risk assessment result of the target individual according to the comprehensive risk index data and a preset key-value pair matching rule, where the preset key-value pair matching rule includes the comprehensive risk index data and the corresponding comprehensive risk assessment result;

[0056] A weighted refinement module, configured to, if it is determined that there are rough target items in the basic risk assessment score and the comprehensive risk assessment result, perform weighted refinement processing on the rough target items according to a preset weighted refinement rule, where the preset weighted refinement rule determines whether to perform weighted enhancement or refinement deletion in the weighted refinement processing according to the rough state of the rough target items;

[0057] A report generation module, configured to generate a final risk assessment report according to the basic risk assessment score and the comprehensive risk assessment result, save the final risk assessment report to a risk assessment database, and update the weights.

[0058] The present invention also provides a storage medium storing one or more programs, where when the programs are executed by a processor, the intelligent dynamic risk assessment method for elderly people with chronic diseases as described above is implemented.

[0059] The present invention also provides a computer device, where the computer device includes a memory and a processor, and:

[0060] The memory is used to store a computer program;

[0061] When the processor executes the computer program stored in the memory, the intelligent dynamic risk assessment method for elderly people with chronic diseases as described above is implemented. Description of the Drawings

[0062] Figure 1 It is a flowchart of the intelligent dynamic risk assessment method for elderly people with chronic diseases proposed in the first embodiment of the present invention;

[0063] Figure 2 It is a flowchart of the intelligent dynamic risk assessment method for elderly people with chronic diseases proposed in the second embodiment of the present invention;

[0064] Figure 3 It is a schematic structural diagram of the intelligent dynamic risk assessment system for elderly people with chronic diseases proposed in the third embodiment of the present invention.

[0065] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. Specific Embodiments

[0066] To facilitate the understanding of the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. Several embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, these embodiments are provided to make the disclosure of the present invention more thorough and comprehensive.

[0067] It should be noted that when an element is referred to as being "fixedly provided on" another element, it can be directly on the other element or there may also be an intermediate element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intermediate element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are only for illustrative purposes.

[0068] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0069] Please refer to Figure 1 Please refer to Figure 1 As shown, it is a flowchart of the intelligent chronic disease risk dynamic assessment method for the elderly proposed in the first embodiment of the present invention. This intelligent chronic disease risk dynamic assessment method for the elderly includes steps S01 to S05, where:

[0070] Step S01: Obtain the basic physical and chemical index data and comprehensive risk index data of the target individual and perform preprocessing;

[0071] It should be noted that in this embodiment, both the basic physical and chemical index data and the comprehensive risk index data are stored in the preset individual index database. The preset individual index database is classified based on individuals, and the data classified for each individual includes records of the individual's current index data and historical index data. After detecting the basic physical and chemical indexes of the target individual, check whether there is a historical record of the basic physical and chemical index detection of the target individual in the preset individual index database;

[0072] If there is no historical record of the basic physical and chemical index detection of the target individual, the data obtained from this basic physical and chemical index detection will be used as the basic physical and chemical index data of the target individual;

[0073] If there are historical records of basic physical and chemical index detections for the target individual, the data obtained from the current basic physical and chemical index detection is used as the basic physical and chemical index data of the target individual, and an index data change label is generated based on the historical basic physical and chemical index data obtained from the historical basic physical and chemical index detections;

[0074] After performing a comprehensive risk index detection on the target individual, in the preset individual index database, classification acquisition of comprehensive risk index data is carried out according to the data type to obtain comprehensive risk index data of multiple different data types, and the comprehensive risk index data is grouped and processed according to the digital floating-point type and the text character type;

[0075] In this embodiment, the steps of generating the index data change label specifically include:

[0076] Compare the basic physical and chemical index data item by item with the historical basic physical and chemical index data to obtain single or multiple different index data change items and the index data change values corresponding to the index data change items;

[0077] Screen the key change items and non-key change items among the index data change items;

[0078] Judge whether the index data change value corresponding to the key change item is greater than or equal to the preset key change item change threshold. If the index data change value corresponding to the key change item is greater than or equal to the preset key change item change threshold, generate a key item severe change label. If the index data change value corresponding to the key change item is less than the preset key change item change threshold, generate a key item mild change label;

[0079] Judge whether the index data change value corresponding to the non-key change item is greater than or equal to the preset non-key change item change threshold. If the index data change value corresponding to the non-key change item is greater than or equal to the preset non-key change item change threshold, generate a non-key item change label;

[0080] Insert the key item severe change label, the key item mild change label, and the non-key item change label as the index data change label into the basic physical and chemical index data.

[0081] Step S02: Determine the basic risk assessment score according to the basic physical and chemical index data and the preset risk assessment rules;

[0082] It should be noted that in this embodiment, the basic physical and chemical index data is divided into evaluation groups according to the preset risk assessment rules. Each evaluation group includes multiple pieces of the basic physical and chemical index data, and each piece of the basic physical and chemical index data has a unique corresponding evaluation group;

[0083] Convert all the basic physical and chemical index data in each of the evaluation groups into corresponding evaluation values, and calculate the evaluation scores for the corresponding evaluation groups according to the evaluation values. The evaluation score calculation includes positive score calculation and inverted score calculation. In the positive score calculation, perform positive accumulation processing on the evaluation values to obtain the evaluation scores. In the inverted score calculation, perform inversion processing on the evaluation values and then perform accumulation processing to obtain the evaluation scores;

[0084] Obtain the basic risk assessment score according to the evaluation scores of all the evaluation groups.

[0085] Step S03: Determine the comprehensive risk assessment result of the target individual according to the comprehensive risk index data and the preset key-value pair matching rule;

[0086] It should be noted that in this embodiment, keyword extraction is performed on the comprehensive risk index data according to the preset key-value pair matching rule to obtain the comprehensive risk variable name;

[0087] Generate an index key according to the comprehensive risk variable name to retrieve the comprehensive risk assessment result value table. The index key is a single string, and the string uniquely corresponds to the comprehensive risk assessment result value in the comprehensive risk assessment result value table;

[0088] Retrieve the comprehensive risk assessment result value, and perform evaluation group division on the comprehensive risk assessment result value according to the preset risk assessment rule. Each evaluation group includes multiple comprehensive risk assessment result values, and each comprehensive risk assessment result value has a uniquely corresponding evaluation group;

[0089] Perform evaluation result judgment according to the comprehensive risk assessment result value. The evaluation result judgment includes a first preset evaluation result range threshold and a second evaluation result range threshold. The first preset evaluation result range threshold and the second evaluation result range threshold are different values. Three different evaluation result regions are formed according to the first preset evaluation result range threshold and the second evaluation result range threshold, and each evaluation result region has a uniquely corresponding risk assessment result;

[0090] Obtain the comprehensive risk assessment result according to the risk assessment results of all the evaluation groups.

[0091] Step S04: If it is determined that there are rough target items in the basic risk assessment score and the comprehensive risk assessment result, perform weighted refinement processing on the rough target items according to the preset weighted refinement rule;

[0092] It should be noted that in this embodiment, weighted refinement judgment is performed according to the basic risk assessment score and the comprehensive risk assessment result;

[0093] Determine whether there are any items in the basic risk assessment scores that deviate from the comprehensive risk assessment results. If it is determined that there are items in the basic risk assessment scores that deviate from the comprehensive risk assessment results, perform refinement processing according to the comprehensive risk assessment results. The refinement processing includes data deletion or data replacement according to the assessment result values in the comprehensive risk assessment results;

[0094] Determine whether there are any offset items in the basic risk assessment scores that deviate from the comprehensive risk assessment results. If it is determined that there are offset items in the basic risk assessment scores that deviate from the comprehensive risk assessment results, perform weighting processing according to the comprehensive risk assessment results and the index data change tags in the basic physical and chemical index data. The weighting processing includes calculating the offset weight according to the index data change tags to obtain the offset weight and weighting the assessment result value of the comprehensive risk assessment result corresponding to the offset item to obtain the basic risk assessment score corrected for offset.

[0095] Step S05: Then generate a final risk assessment report according to the basic risk assessment score and the comprehensive risk assessment result, save the final risk assessment report to the risk assessment database and update the weights;

[0096] It should be noted that in this embodiment, the chronic disease risk probability of the target individual is calculated according to the basic risk assessment score, and then the corresponding doctor-side feedback text is retrieved from the preset chronic disease database according to the chronic disease risk probability to generate a doctor-side feedback report;

[0097] Retrieve the corresponding patient-side suggestion text from the preset chronic disease database according to the comprehensive risk assessment result, and fill in the text according to the preset assessment report text template to generate a comprehensive risk assessment feedback report;

[0098] Obtain the final risk assessment report according to the doctor-side feedback report and the comprehensive risk assessment feedback report, save the basic risk assessment score and the comprehensive risk assessment result corresponding to the final risk assessment report to the preset chronic disease database, and update the index data change tags of the target individual.

[0099] In summary, according to the above-mentioned dynamic risk assessment method for the elderly's intelligent chronic diseases, by presetting risk assessment rules, the basic physical and chemical index data of the target elderly individual is risk-assessed to instantaneously and dynamically calculate the basic risk assessment score of the target elderly individual. Multiple chronic disease types are evaluated from the perspective of score orientation to improve the overall assessment efficiency. Then, through the preset key-value pair matching rules, the comprehensive risk index data of the target elderly individual is risk-assessed to conduct a comprehensive assessment from the perspective of result orientation, avoiding the limitations of single-orientation assessment. Then, by comparing different orientations for weighted refinement, the overall assessment accuracy is further improved. The present invention improves the efficiency and accuracy of the dynamic risk assessment method for chronic diseases. Specifically, the basic physical and chemical index data and comprehensive risk index data of the target individual are obtained and preprocessed. According to the basic physical and chemical index data and the preset risk assessment rules, the basic risk assessment score is determined. The preset risk assessment rules are dynamic parsing expressions. Through the preset risk assessment rules of the dynamic parsing expressions, the basic risk assessment score of the target elderly individual is instantaneously and dynamically calculated. Multiple chronic disease types are evaluated from the perspective of score orientation to improve the overall assessment efficiency. According to the comprehensive risk index data and the preset key-value pair matching rules, the comprehensive risk assessment result of the target individual is determined. The preset key-value pair matching rules include the comprehensive risk index data and the corresponding comprehensive risk assessment result, so as to conduct a comprehensive assessment from the perspective of result orientation, avoiding the limitations of single-orientation assessment and improving the overall assessment accuracy at the same time. If it is determined that there are rough target items in the basic risk assessment score and the comprehensive risk assessment result, the rough target items are weighted and refined according to the preset weighted refinement rules. The preset weighted refinement rules judge whether to perform weighted enhancement or refinement deletion in the weighted refinement process according to the rough state of the rough target items, compare the results of different orientations to enhance the rough assessment items between different orientations, and further improve the overall assessment accuracy. Then, according to the basic risk assessment score and the comprehensive risk assessment result, a final risk assessment report is generated. The final risk assessment report is saved to the risk assessment database and the weights are updated. The present invention improves the efficiency and accuracy of the dynamic risk assessment method for chronic diseases.

[0100] Please refer to Figure 2 , which shows the flowchart of the dynamic risk assessment method for the elderly's intelligent chronic diseases proposed in the second embodiment of the present invention. This dynamic risk assessment method for the elderly's intelligent chronic diseases includes steps S11 to S15, where:

[0101] Step S11: After detecting the basic physical and chemical indexes of the target individual, retrieve whether there is a historical record of basic physical and chemical index detection for the target individual. If there is a historical record of basic physical and chemical index detection for the target individual, use the data obtained from this basic physical and chemical index detection as the basic physical and chemical index data of the target individual, and generate an index data change label based on the historical basic physical and chemical index data obtained from the historical basic physical and chemical index detection. After performing a comprehensive risk index detection on the target individual, classify the comprehensive risk index data according to the data type to obtain comprehensive risk index data of multiple different data types;

[0102] Step S12: Divide the basic physical and chemical index data into evaluation groups according to the preset risk assessment rules, convert all the basic physical and chemical index data in each evaluation group into corresponding evaluation values, calculate the evaluation scores of the corresponding evaluation groups according to the evaluation values, and obtain the basic risk assessment scores;

[0103] It should be noted that the preset risk assessment rule in this embodiment is a dynamic parsing expression, which is oriented to the evaluation score. Taking the traditional Chinese medicine constitution evaluation group as an example, it includes the following evaluation items.

[0104] Evaluation score rule for traditional Chinese medicine constitution [Qi deficiency constitution]:

[0105] #qdc_2 + #qdc_3 + #qdc_4 + #qdc_14,

[0106] Among them, #qdc represents the item of Qi deficiency constitution, and the numerical label is the leading symbol of the basic physical and chemical index data of the corresponding item;

[0107] The evaluation score calculation in the above evaluation score rule for traditional Chinese medicine constitution [Qi deficiency constitution] is a positive score calculation, and positive accumulation processing is performed on each item of Qi deficiency constitution to obtain the evaluation score of traditional Chinese medicine constitution [Qi deficiency constitution];

[0108] Evaluation score rule for traditional Chinese medicine constitution [Balanced constitution]:

[0109] #bc_1 + (6 - #qdc_2) + (6 - #qdc_4) + (6 - #qsc_5) + (6 - #yadc_13),

[0110] Among them, #bc represents the item of balanced constitution, #qsc represents the item of qi stagnation constitution, and #yadc represents the item of yang deficiency constitution;

[0111] The evaluation score calculation in the above evaluation score rule for traditional Chinese medicine constitution [Balanced constitution] is a reversed score calculation. For non-target items, that is, non-balanced constitution items, after reverse processing, accumulation processing is performed to obtain the evaluation score of traditional Chinese medicine constitution [Balanced constitution]. The upper limit of the reversed score in this embodiment is 6.

[0112] Step S13: Extract keywords from the comprehensive risk indicator data according to the preset key-value pair matching rule to obtain the comprehensive risk variable name, generate an index key based on the comprehensive risk variable name to retrieve the comprehensive risk assessment result value table, retrieve the comprehensive risk assessment result value, divide the comprehensive risk assessment result value into evaluation groups according to the preset risk assessment rule, and judge the evaluation result based on the comprehensive risk assessment result value to obtain the comprehensive risk assessment result;

[0113] It should be noted that in this embodiment, the comprehensive risk indicator data in the preset key-value pair matching rule is used as the index key, and the comprehensive risk assessment result is used as the paired value. Specifically, taking the traditional Chinese medicine constitution evaluation group as an example, the following evaluation items are included:

[0114] Preset key-value pair matching rule for traditional Chinese medicine constitution [Qi deficiency constitution]:

[0115] (#qdcScore>=11)?1:(#qdcScore>8and#qdcScore<11)?2:(#qdcScore<=8)?4:''

[0116] Among them, #qdcScore represents the comprehensive risk assessment result value of the Qi deficiency constitution item. Specifically, when the comprehensive risk assessment result value of the Qi deficiency constitution item is greater than or equal to the first preset evaluation result range threshold, when the comprehensive risk assessment result value of the Qi deficiency constitution item is less than the first preset evaluation result range threshold and greater than the second preset evaluation result range threshold, and when the comprehensive risk assessment result value of the Qi deficiency constitution item is less than or equal to the second preset evaluation result range threshold, the corresponding risk assessment results are obtained respectively. The first preset evaluation result range threshold and the second preset evaluation result range threshold in this embodiment are 11 and 8 respectively, and the guiding symbols of the risk assessment results in this embodiment are 1, 2, and 4 respectively;

[0117] Taking the hypertension evaluation group as an example, the following evaluation items are included:

[0118] Preset key-value pair matching rule for hypertension [blood pressure value]:

[0119] (#sbp_num>= 180 or #dbp_num>=110)? 5 :

[0120] (#sbp_num>= 160 or #dbp_num>= 100)? 4 :

[0121] (#sbp_num>= 140 or #dbp_num>= 90)? 3 :

[0122] (#sbp_num>= 120 or #dbp_num>= 80)? 2 :1

[0123] Among them, #sbp_num represents the high blood pressure value item, and #dbp_num represents the low blood pressure value item. In this embodiment, multiple pairs of preset evaluation result range thresholds are set to judge the evaluation results corresponding to the evaluation items. Specifically, when the high blood pressure value item is greater than or equal to the first preset evaluation result range threshold or the low blood pressure value item is greater than or equal to the second preset evaluation result range threshold, the corresponding risk assessment result is obtained. The first preset evaluation result range threshold and the second preset evaluation result range threshold in this embodiment are 180 and 110 pairs, 160 and 100 pairs, 140 and 90 pairs, 120 and 80 pairs, and the null value pair respectively. The null value pair represents other thresholds that are not preset evaluation result range thresholds. The guiding symbols of the risk assessment results in this embodiment are 1, 2, 3, 4, 5. Among them, the guiding symbol 1 corresponds to normal blood pressure, the guiding symbol 2 corresponds to slightly high blood pressure, the guiding symbol 3 corresponds to grade 1 hypertension, the guiding symbol 4 corresponds to grade 2 hypertension, and the guiding symbol 5 corresponds to grade 3 hypertension.

[0124] Step S14: Perform weighted refinement judgment based on the basic risk assessment score and the comprehensive risk assessment result. If it is determined that there is a deviation item between the basic risk assessment score and the comprehensive risk assessment result, refinement processing is performed according to the comprehensive risk assessment result. If it is determined that there is an offset item between the basic risk assessment score and the comprehensive risk assessment result, weighted processing is performed according to the comprehensive risk assessment result and the index data change label in the basic physical and chemical index data;

[0125] Step S15: Calculate the chronic disease risk probability of the target individual according to the basic risk assessment score, and then retrieve the corresponding doctor-side feedback text in the preset chronic disease database to generate a doctor-side feedback report. Retrieve the corresponding patient-side suggestion text in the preset chronic disease database according to the comprehensive risk assessment result, and perform text filling according to the preset evaluation report text template to generate a comprehensive risk assessment feedback report. Obtain the final risk assessment report according to the doctor-side feedback report and the comprehensive risk assessment feedback report, and save the basic risk assessment score and the comprehensive risk assessment result corresponding to the final risk assessment report to the preset chronic disease database, and update the index data change label of the target individual.

[0126] In summary, according to the above-mentioned dynamic risk assessment method for the elderly's intelligent chronic diseases, by presetting risk assessment rules, the basic physical and chemical index data of the target elderly individual is risk-assessed to instantaneously and dynamically calculate the basic risk assessment score of the target elderly individual. From the perspective of score orientation, multiple chronic disease types are evaluated to improve the overall assessment efficiency. Then, through the preset key-value pair matching rules, the comprehensive risk index data of the target elderly individual is risk-assessed to conduct a comprehensive assessment from the perspective of result orientation, avoiding the limitations of single-orientation assessment. Then, by comparing different orientations for weighted refinement, the overall assessment accuracy is further improved. The present invention improves the efficiency and accuracy of the dynamic risk assessment method for chronic diseases. Specifically, the basic physical and chemical index data and comprehensive risk index data of the target individual are obtained and preprocessed. According to the basic physical and chemical index data and the preset risk assessment rules, the basic risk assessment score is determined. The preset risk assessment rule is a dynamic parsing expression. Through the preset risk assessment rule of the dynamic parsing expression, the basic risk assessment score of the target elderly individual is instantaneously and dynamically calculated. From the perspective of score orientation, multiple chronic disease types are evaluated to improve the overall assessment efficiency. According to the comprehensive risk index data and the preset key-value pair matching rules, the comprehensive risk assessment result of the target individual is determined. The preset key-value pair matching rules include the comprehensive risk index data and the corresponding comprehensive risk assessment result, so as to conduct a comprehensive assessment from the perspective of result orientation, avoiding the limitations of single-orientation assessment and improving the overall assessment accuracy at the same time. If it is determined that there are rough target items in the basic risk assessment score and the comprehensive risk assessment result, the rough target items are weighted and refined according to the preset weighted refinement rules. The preset weighted refinement rules judge whether to perform weighted enhancement or refinement deletion in the weighted refinement process according to the rough state of the rough target items, compare the results of different orientations to enhance the rough assessment items between different orientations, and further improve the overall assessment accuracy. Then, according to the basic risk assessment score and the comprehensive risk assessment result, a final risk assessment report is generated, and the final risk assessment report is saved to the risk assessment database and the weights are updated. The present invention improves the efficiency and accuracy of the dynamic risk assessment method for chronic diseases.

[0127] Please refer to Figure 3 , which shows a schematic structural diagram of an intelligent chronic disease risk dynamic assessment system for the elderly proposed in the third embodiment of the present invention. The system includes:

[0128] A preprocessing module 10, configured to obtain the basic physical and chemical index data and comprehensive risk index data of the target individual and perform preprocessing. The basic physical and chemical index data and comprehensive risk index data are both stored in a preset individual index database;

[0129] The basic risk assessment module 20 is used to determine a basic risk assessment score according to the basic physical and chemical index data and a preset risk assessment rule, and the preset risk assessment rule is a dynamic parsing expression;

[0130] The comprehensive risk assessment module 30 is used to determine the comprehensive risk assessment result of the target individual according to the comprehensive risk index data and a preset key-value pair matching rule, and the preset key-value pair matching rule includes the comprehensive risk index data and the corresponding comprehensive risk assessment result;

[0131] The weighted refinement module 40 is used to, if it is determined that there are rough target items in the basic risk assessment score and the comprehensive risk assessment result, perform weighted refinement processing on the rough target items according to a preset weighted refinement rule, and the preset weighted refinement rule determines whether to perform weighted enhancement or refinement deletion in the weighted refinement processing according to the rough state of the rough target items;

[0132] The report generation module 50 is used to generate a final risk assessment report according to the basic risk assessment score and the comprehensive risk assessment result, and save the final risk assessment report to a risk assessment database and update the weights.

[0133] The present invention also provides a computer storage medium, on which one or more programs are stored, and when the program is executed by a processor, the above-mentioned intelligent dynamic risk assessment method for chronic diseases of the elderly is implemented.

[0134] The present invention also provides a computer device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored on the memory to implement the above-mentioned intelligent dynamic risk assessment method for chronic diseases of the elderly.

[0135] Those skilled in the art can understand that the logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus or device and execute the instructions), or used in combination with these instruction execution systems, apparatus or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus or device.

[0136] More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which the program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then stored in a computer memory.

[0137] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0138] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0139] The above-described embodiments merely represent several implementation manners of the present invention, and their descriptions are relatively specific and detailed, but should not be construed as limiting the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.

Claims

1. An intelligent dynamic assessment method for chronic disease risks of the elderly, characterized in that Including: Obtain the basic physical and chemical index data and comprehensive risk index data of the target individual from the preset individual index database and perform preprocessing. Both the basic physical and chemical index data and the comprehensive risk index data are stored in the preset individual index database. When obtaining the basic physical and chemical index data and the comprehensive risk index data of the target individual, data screening and data scraping are performed on the preset individual index database according to the individual identity information; If there is a historical record of basic physical and chemical index detection for the target individual, use the data obtained from the current basic physical and chemical index detection as the basic physical and chemical index data of the target individual, and generate an index data change label based on the historical basic physical and chemical index data obtained from the historical basic physical and chemical index detection; Compare the basic physical and chemical index data item by item with the historical basic physical and chemical index data to obtain one or more different index data change items and the index data change values corresponding to the index data change items; Screen the key change items and non-key change items among the index data change items; Judge whether the index data change value corresponding to the key change item is greater than or equal to the preset key change item change threshold. If the index data change value corresponding to the key change item is greater than or equal to the preset key change item change threshold, generate a key item severe change label. If the index data change value corresponding to the key change item is less than the preset key change item change threshold, generate a key item mild change label; Judge whether the index data change value corresponding to the non-key change item is greater than or equal to the preset non-key change item change threshold. If the index data change value corresponding to the non-key change item is greater than or equal to the preset non-key change item change threshold, generate a non-key item change label; Insert the key item severe change label, key item mild change label, and non-key item change label as index data change labels into the basic physical and chemical index data; Determine the basic risk assessment score according to the basic physical and chemical index data and the preset risk assessment rule. The preset risk assessment rule is a dynamic parsing expression; The step of determining the basic risk assessment score according to the basic physical and chemical index data and the preset risk assessment rule specifically includes: Divide the basic physical and chemical index data into assessment groups according to the preset risk assessment rule. Each assessment group includes multiple pieces of the basic physical and chemical index data, and each piece of the basic physical and chemical index data has a unique corresponding assessment group; Convert all the basic physical and chemical index data in each assessment group into corresponding assessment values, and calculate the assessment score of the corresponding assessment group according to the assessment values. The assessment score calculation includes positive score calculation and reverse score calculation. In the positive score calculation, positive accumulation processing is performed on the assessment values to obtain the assessment score. In the reverse score calculation, reverse processing is performed on the assessment values and then accumulation processing is performed to obtain the assessment score; Obtain the basic risk assessment score according to the assessment scores of all the assessment groups; The assessment group includes a traditional Chinese medicine constitution assessment group, and the traditional Chinese medicine constitution assessment group includes a qi deficiency constitution item and a balanced constitution item; The rule for calculating the assessment score corresponding to the qi deficiency constitution item is as follows: #qdc_2 + #qdc_3 + #qdc_4 + #qdc_14, where #qdc represents the qi deficiency constitution item, and the numerical label is the guiding symbol of the basic physical and chemical index data of the corresponding item. The evaluation score corresponding to the qi deficiency constitution item is calculated as a positive score calculation. Positive accumulation processing is performed on each qi deficiency constitution item to obtain the evaluation score of traditional Chinese medicine constitution [qi deficiency constitution]; The rule for calculating the evaluation score corresponding to the balanced constitution item is as follows: #bc_1 + (6 - #qdc_2) + (6 - #qdc_4) + (6 - #qsc_5) + (6 - #yadc_13), where #bc represents the balanced constitution item, #qsc represents the qi stagnation constitution item, #yadc represents the yang deficiency constitution item, and 6 is the upper limit of the reversed score. The evaluation score corresponding to the balanced constitution item is calculated as a reversed score calculation. After performing reversed processing on the items other than the balanced constitution item and then performing accumulation processing, the evaluation score of traditional Chinese medicine constitution [balanced constitution] is obtained; According to the comprehensive risk index data and the preset key-value pair matching rule, determine the comprehensive risk assessment result of the target individual. The preset key-value pair matching rule includes the comprehensive risk index data and the corresponding comprehensive risk assessment result. When matching the comprehensive risk assessment result, data retrieval and data matching are performed on the comprehensive risk assessment result value table according to the keywords in the comprehensive risk index data; The step of determining the comprehensive risk assessment result of the target individual according to the comprehensive risk index data and the preset key-value pair matching rule specifically includes: Extract keywords from the comprehensive risk index data according to the preset key-value pair matching rule to obtain comprehensive risk variable names; Generate an index key according to the comprehensive risk variable name to retrieve the comprehensive risk assessment result value table. The index key is a single string, and the string corresponds uniquely to the comprehensive risk assessment result value in the comprehensive risk assessment result value table; Retrieve and obtain the comprehensive risk assessment result value. According to the preset risk assessment rule, divide the comprehensive risk assessment result value into evaluation groups. Each evaluation group includes multiple comprehensive risk assessment result values, and each comprehensive risk assessment result value has a uniquely corresponding evaluation group; Judge the evaluation result according to the comprehensive risk assessment result value. The evaluation result judgment includes a first preset evaluation result range threshold and a second evaluation result range threshold. The first preset evaluation result range threshold and the second evaluation result range threshold are different values. Three different evaluation result regions are formed according to the first preset evaluation result range threshold and the second evaluation result range threshold, and each evaluation result region has a uniquely corresponding risk assessment result; Obtain the comprehensive risk assessment result according to the risk assessment results of all the evaluation groups; The evaluation group includes a traditional Chinese medicine constitution evaluation group, and the traditional Chinese medicine constitution evaluation group includes qi deficiency constitution items. The preset key-value pair matching rule for the qi deficiency constitution items is as follows: (#qdcScore >= 11)? 1 : (#qdcScore > 8 && #qdcScore < 11)? 2 : (#qdcScore <= 8)? 4 : '' Among them, #qdcScore represents the comprehensive risk assessment result value of the qi deficiency constitution item. In the preset key-value pair matching rule of the qi deficiency constitution item, when the comprehensive risk assessment result value of the qi deficiency constitution item is greater than or equal to the first preset assessment result range threshold, when the comprehensive risk assessment result value of the qi deficiency constitution item is less than the first preset assessment result range threshold and greater than the second preset assessment result range threshold, and when the comprehensive risk assessment result value of the qi deficiency constitution item is less than or equal to the second preset assessment result range threshold, the corresponding risk assessment results are obtained respectively. The first preset assessment result range threshold and the second preset assessment result range threshold in the preset key-value pair matching rule of the qi deficiency constitution item are 11 and 8 respectively, and the guiding symbols of the risk assessment results in the preset key-value pair matching rule of the qi deficiency constitution item are 1, 2, and 4 respectively; If it is determined that there are rough target items in the basic risk assessment score and the comprehensive risk assessment result, the rough target items are weighted and refined according to the preset weighted refinement rule. The preset weighted refinement rule judges weighted enhancement or refinement deletion in the weighted refinement process according to the rough state of the rough target items; The step of, if it is determined that there are rough target items in the basic risk assessment score and the comprehensive risk assessment result, and the rough target items are weighted and refined according to the preset weighted refinement rule, specifically includes: Making a weighted refinement judgment according to the basic risk assessment score and the comprehensive risk assessment result; Judging whether there are deviation items in the basic risk assessment score from the comprehensive risk assessment result. If it is determined that there are deviation items in the basic risk assessment score from the comprehensive risk assessment result, refinement processing is performed according to the comprehensive risk assessment result. The refinement processing includes data deletion or data replacement according to the assessment result value in the comprehensive risk assessment result; Judging whether there are offset items in the basic risk assessment score from the comprehensive risk assessment result. If it is determined that there are offset items in the basic risk assessment score from the comprehensive risk assessment result, weighting is performed according to the comprehensive risk assessment result and the index data change label in the basic physical and chemical index data. The weighting processing includes calculating the offset weight according to the index data change label to obtain the offset weight and weighting the assessment result value of the comprehensive risk assessment result corresponding to the offset item to obtain the offset-corrected basic risk assessment score; Then, a final risk assessment report is generated according to the basic risk assessment score and the comprehensive risk assessment result, the final risk assessment report is saved to the risk assessment database and the weight is updated. When the final risk assessment report is saved to the risk assessment database, the risk assessment database is screened according to the chronic disease types, and then the weight of the risk assessment database is updated according to the index data change label of the target individual.

2. The dynamic risk assessment method for chronic diseases of the elderly according to claim 1, characterized in that The steps of obtaining the basic physical and chemical index data and comprehensive risk index data of the target individual and preprocessing them specifically include: After detecting the basic physical and chemical indexes of the target individual, retrieve whether there is a historical record of basic physical and chemical index detection of the target individual in the preset individual index database; If there is no historical record of basic physical and chemical index detection for the target individual, use the data obtained from this basic physical and chemical index detection as the basic physical and chemical index data of the target individual; If there is a historical record of basic physical and chemical index detection for the target individual, use the data obtained from this basic physical and chemical index detection as the basic physical and chemical index data of the target individual, and generate an index data change label based on the historical basic physical and chemical index data obtained from the historical basic physical and chemical index detection; After detecting the comprehensive risk indexes of the target individual, classify and obtain the comprehensive risk index data according to the data type in the preset individual index database to obtain comprehensive risk index data of multiple different data types, and group and process the comprehensive risk index data according to the digital floating-point type and text character type.

3. The intelligent dynamic assessment method for chronic disease risks of the elderly according to claim 1, wherein, The steps of then generating a final risk assessment report based on the basic risk assessment score and the comprehensive risk assessment result specifically include: Calculate the chronic disease risk probability of the target individual based on the basic risk assessment score, and then retrieve the corresponding doctor-side feedback text in the preset chronic disease database according to the chronic disease risk probability to generate a doctor-side feedback report; Retrieve the corresponding patient-side suggestion text in the preset chronic disease database according to the comprehensive risk assessment result, and fill in the text according to the preset assessment report text template to generate a comprehensive risk assessment feedback report; Based on the doctor-side feedback report and the comprehensive risk assessment feedback report, obtain the final risk assessment report, save the corresponding basic risk assessment score and the comprehensive risk assessment result of the final risk assessment report to the preset chronic disease database, and update the index data change label of the target individual.

4. An intelligent dynamic assessment system for chronic disease risks of the elderly, characterized in that, Include: A preprocessing module for obtaining the basic physical and chemical index data and comprehensive risk index data of the target individual from the preset individual index database and performing preprocessing. Both the basic physical and chemical index data and the comprehensive risk index data are stored in the preset individual index database. When obtaining the basic physical and chemical index data and comprehensive risk index data of the target individual, perform data screening and data scraping on the preset individual index database according to the individual identity information; If there is a historical record of basic physical and chemical index detection for the target individual, use the data obtained from this basic physical and chemical index detection as the basic physical and chemical index data of the target individual, and generate an index data change label based on the historical basic physical and chemical index data obtained from the historical basic physical and chemical index detection; Compare the basic physical and chemical index data item by item with the historical basic physical and chemical index data to obtain single or multiple different index data change items and the index data change values corresponding to the index data change items; Screen the key change items and non-key change items among the index data change items; Determine whether the change value of the index data corresponding to the key change item is greater than or equal to the preset key change item change threshold. If the change value of the index data corresponding to the key change item is greater than or equal to the preset key change item change threshold, generate a key item severe change label. If the change value of the index data corresponding to the key change item is less than the preset key change item change threshold, generate a key item mild change label; Determine whether the change value of the index data corresponding to the non-key change item is greater than or equal to the preset non-key change item change threshold. If the change value of the index data corresponding to the non-key change item is greater than or equal to the preset non-key change item change threshold, generate a non-key item change label; Insert the key item severe change label, key item mild change label, and non-key item change label as index data change labels into the basic physical and chemical index data; A basic risk assessment module for determining a basic risk assessment score according to the basic physical and chemical index data and a preset risk assessment rule, where the preset risk assessment rule is a dynamic parsing expression; The step of determining the basic risk assessment score according to the basic physical and chemical index data and the preset risk assessment rule specifically includes: Divide the basic physical and chemical index data into assessment groups according to the preset risk assessment rule. Each assessment group includes multiple pieces of the basic physical and chemical index data, and each piece of the basic physical and chemical index data has a uniquely corresponding assessment group; Convert all the basic physical and chemical index data in each assessment group into corresponding assessment values, and calculate the assessment score of the corresponding assessment group according to the assessment values. The assessment score calculation includes positive score calculation and reverse score calculation. In the positive score calculation, positive accumulation processing is performed on the assessment values to obtain the assessment score. In the reverse score calculation, reverse processing is performed on the assessment values and then accumulation processing is performed to obtain the assessment score; Obtain the basic risk assessment score according to the assessment scores of all the assessment groups; The assessment group includes a traditional Chinese medicine constitution assessment group, and the traditional Chinese medicine constitution assessment group includes a qi-deficiency constitution item and a balanced constitution item; The rule for calculating the assessment score corresponding to the qi-deficiency constitution item is as follows: #qdc_2 + #qdc_3 + #qdc_4 + #qdc_14, where #qdc represents the qi-deficiency constitution item, and the digital label is the guide symbol of the basic physical and chemical index data corresponding to the item. The calculation of the assessment score corresponding to the qi-deficiency constitution item is positive score calculation, and positive accumulation processing is performed on each qi-deficiency constitution item to obtain the traditional Chinese medicine constitution [qi-deficiency constitution] assessment score; The rule for calculating the assessment score corresponding to the balanced constitution item is as follows: #bc_1 + (6 - #qdc_2) + (6 - #qdc_4) + (6 - #qsc_5) + (6 - #yadc_13), Among them, #bc represents the peaceful constitution item, #qsc represents the qi stagnation constitution item, #yadc represents the yang deficiency constitution item, 6 is the upper limit of the reverse score. The evaluation score corresponding to the peaceful constitution item is calculated as reverse score calculation. After reversing the items other than the peaceful constitution item and then performing an accumulation process to obtain the traditional Chinese medicine constitution [peaceful constitution] evaluation score; The comprehensive risk assessment module is used to determine the comprehensive risk assessment result of the target individual according to the comprehensive risk index data and the preset key-value pair matching rule. The preset key-value pair matching rule includes the comprehensive risk index data and the corresponding comprehensive risk assessment result. When matching the comprehensive risk assessment result, data retrieval and data matching are performed on the comprehensive risk assessment result value table according to the keywords in the comprehensive risk index data; The step of determining the comprehensive risk assessment result of the target individual according to the comprehensive risk index data and the preset key-value pair matching rule specifically includes: Extracting keywords from the comprehensive risk index data according to the preset key-value pair matching rule to obtain comprehensive risk variable names; Generating an index key according to the comprehensive risk variable name to retrieve the comprehensive risk assessment result value table. The index key is a single string, and the string corresponds uniquely to the comprehensive risk assessment result value in the comprehensive risk assessment result value table; Retrieving the comprehensive risk assessment result value, and dividing the comprehensive risk assessment result value into evaluation groups according to the preset risk assessment rule. Each evaluation group includes multiple comprehensive risk assessment result values, and each comprehensive risk assessment result value has a uniquely corresponding evaluation group; Judging the evaluation result according to the comprehensive risk assessment result value. The evaluation result judgment includes a first preset evaluation result range threshold and a second evaluation result range threshold. The first preset evaluation result range threshold and the second evaluation result range threshold are different values. Three different evaluation result regions are formed according to the first preset evaluation result range threshold and the second evaluation result range threshold, and each evaluation result region has a uniquely corresponding risk assessment result; Obtaining the comprehensive risk assessment result according to the risk assessment results of all the evaluation groups; The evaluation group includes a traditional Chinese medicine constitution evaluation group, and the traditional Chinese medicine constitution evaluation group includes a qi deficiency constitution item. The preset key-value pair matching rule for the qi deficiency constitution item is as follows: (#qdcScore>=11)?1:(#qdcScore>8and#qdcScore<11)?2:(#qdcScore<=8)?4:'' Among them, #qdcScore represents the comprehensive risk assessment result value of the qi deficiency constitution item. In the preset key-value pair matching rule of the qi deficiency constitution item, when the comprehensive risk assessment result value of the qi deficiency constitution item is greater than or equal to the first preset assessment result range threshold, when the comprehensive risk assessment result value of the qi deficiency constitution item is less than the first preset assessment result range threshold and greater than the second preset assessment result range threshold, and when the comprehensive risk assessment result value of the qi deficiency constitution item is less than or equal to the second preset assessment result range threshold, the corresponding risk assessment results are obtained respectively. In the preset key-value pair matching rule of the qi deficiency constitution item, the first preset assessment result range threshold and the second preset assessment result range threshold are 11 and 8 respectively, and the guiding symbols of the risk assessment results in the preset key-value pair matching rule of the qi deficiency constitution item are 1, 2, and 4 respectively; The weighted refinement module is used to, if it is determined that there are rough target items in the basic risk assessment score and the comprehensive risk assessment result, perform weighted refinement processing on the rough target items according to the preset weighted refinement rule. The preset weighted refinement rule is based on the rough state of the rough target items to judge weighted enhancement or refinement deletion in the weighted refinement processing; The step of, if it is determined that there are rough target items in the basic risk assessment score and the comprehensive risk assessment result, performing weighted refinement processing on the rough target items according to the preset weighted refinement rule specifically includes: Performing weighted refinement judgment based on the basic risk assessment score and the comprehensive risk assessment result; Judging whether there are deviation items in the basic risk assessment score from the comprehensive risk assessment result. If it is determined that there are deviation items in the basic risk assessment score from the comprehensive risk assessment result, refinement processing is performed according to the comprehensive risk assessment result. The refinement processing includes data deletion or data replacement according to the assessment result value in the comprehensive risk assessment result; Judging whether there are offset items in the basic risk assessment score from the comprehensive risk assessment result. If it is determined that there are offset items in the basic risk assessment score from the comprehensive risk assessment result, weighted processing is performed according to the comprehensive risk assessment result and the index data change label in the basic physical and chemical index data. The weighted processing includes calculating the offset weight according to the index data change label to obtain the offset weight and weighting the assessment result value of the comprehensive risk assessment result corresponding to the offset item to obtain the offset-corrected basic risk assessment score; The report generation module is used to generate a final risk assessment report based on the basic risk assessment score and the comprehensive risk assessment result, save the final risk assessment report to the risk assessment database and update the weight. When saving the final risk assessment report to the risk assessment database, data screening is performed on the risk assessment database according to the types of chronic diseases, and then the weight of the risk assessment database is updated according to the index data change label of the target individual.

5. A storage medium, characterized in that, The storage medium stores one or more programs, and when the program is executed by a processor, it implements the intelligent dynamic risk assessment method for the elderly with chronic diseases as described in any one of claims 1-3.

6. A computer device, characterized in that, The computer device includes a memory and a processor, wherein: The memory is used for storing a computer program; When the processor is used to execute the computer program stored on the memory, the dynamic risk assessment method for the elderly with chronic diseases is implemented as described in any one of claims 1-3.

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