Comprehensive analysis, prevention and control system for resident chronic obstructive pulmonary disease

Abstract

The invention discloses a comprehensive analysis, prevention and control system for resident chronic obstructive pulmonary disease, and relates to the technical field of chronic obstructive pulmonary disease analysis, prevention and control. Comprising a survey acquisition module, a survey analysis module, a statistical analysis module and a measure execution module. The investigation and analysis module comprehensively investigates and analyzes the chronic obstructive pulmonary disease of residents and outputs cognitive scores of the residents; the statistical analysis module deeply analyzes the survey data and outputs an evaluation result according to a preset algorithm; the measure execution module marks a signal value according to the signal generated by the statistical analysis module, and calculates a measure execution coefficient through a preset algorithm; matching an execution coefficient according to a preset measure range, and respectively carrying out corresponding steps; important data support is provided for a community to know the cognitive degree of residents on the chronic obstructive pulmonary disease and potential risk factors, the system is beneficial to formulating a targeted prevention and control strategy, the morbidity and aggravation risk of the chronic obstructive pulmonary disease are reduced, and the overall health level of community residents is improved.

Description

Technical Field

[0001] This invention relates to the field of COPD analysis and prevention technology, specifically a comprehensive analysis and prevention system for COPD in residents. Background Technology

[0002] Chronic obstructive pulmonary disease (COPD) is a common, preventable, and treatable disease characterized by persistent respiratory symptoms and airflow limitation, usually caused by significant exposure to toxic particles or gases leading to airway and / or alveolar abnormalities. While the existing COPD analysis and control system covers multiple aspects, including but not limited to early diagnostic systems based on medical imaging, intelligent health monitoring devices, personalized treatment plan generation algorithms, and telemedicine consultation platforms, it does not encompass comprehensive preventative measures. Preventing COPD requires broader interventions, including public education to raise awareness of risk factors such as smoking, air pollution, and occupational exposure, as well as promoting policies to reduce exposure to these risk factors. Therefore, although the existing system has some value in managing patients already diagnosed with COPD, it fails to adequately understand the individual's COPD status, resulting in limitations and low levels of intelligence. Summary of the Invention

[0003] The purpose of this invention is to address the problem that existing systems cannot better understand the COPD status of residents, resulting in an inability to respond effectively to actual conditions, and thus the system has limitations and low intelligence. Therefore, this invention proposes a comprehensive analysis and prevention system for COPD in residents.

[0004] The objective of this invention can be achieved through the following technical solution: a comprehensive analysis and prevention system for COPD in residents, comprising a survey acquisition module, a survey analysis module, a statistical analysis module, and a response implementation module; the survey acquisition module is used to collect resident information of community residents and send it to the survey analysis module;

[0005] The statistical analysis module is used to count the number of residents in the community, divide them by age, and calculate the proportions to obtain proportion one N1 and proportion two N2. If N1 > 2 × N2, the first resident count is output using survey method mode one; if N1 < 2 × N2, the second resident count is output using survey method mode two; and if N1 = 2 × N2, the third resident count is output using survey method mode three. The first, second, and third resident counts are then sent to the statistical analysis module.

[0006] The statistical analysis module is used to analyze the first, second, and third population data and output the first control value, the ratio of heavy to light cases, and the implementation value. The first control value, the ratio of heavy to light cases, and the implementation value are matched with the corresponding preset control value intervals to generate corresponding signals and send them to the emergency response execution module.

[0007] The response execution module is used to process the received signal and output the response execution coefficient. If the response execution coefficient belongs to the preset response range Yy1, then step one is executed; if the response execution coefficient belongs to the preset response range Yy2, then step two is executed; if the response execution coefficient belongs to the preset response range Yy3, then step three is executed.

[0008] As a preferred embodiment of the present invention, the specific process of outputting the two-country number using survey method mode two is as follows:

[0009] The system analyzes and determines residents' occupations to obtain occupational values; then it analyzes residents' physical conditions to obtain health habit values, disease preset values, and mental health preset values; finally, it analyzes residents' home environment and surrounding environment to obtain a residential environment value; and finally, it substitutes the occupational values, health habit values, disease preset values, mental health preset values, and residential environment value into Residential Recognition Model 1 to output Residential Recognition Model 1. As a preferred embodiment of the present invention, Residential Recognition Model 1 is as follows:

[0010] Where CJI1 is the residence index, CPβ is the occupational value, JXφ is the health habit value, JBχ is the disease preset value, XLδ is the mental health preset value, HJε is the residence value; a1, a2, a3, a4 and a5 are preset correction factors.

[0011] As a preferred embodiment of the present invention, the specific process of outputting the two-country number using survey method mode two is as follows:

[0012] Residents' educational levels are categorized to obtain educational level values ​​Xp, and questionnaire scores are statistically analyzed to obtain resident questionnaire scores Df; these are then substituted into resident recognition model two:

[0013] Output the recognized binary number CJI2, where a7, a8, and a9 are preset correction factors, and Da1 and Da2 are preset weighting factors; the recognized binary number output when Df≥5 is labeled as CJI2. Z The binary number output when Df < 5 is labeled as CJ I2. Q .

[0014] As a preferred embodiment of the present invention, the specific process of outputting the three-number consensus using the survey method mode three is as follows:

[0015] Through the three-dimensional recognition model The output values ​​CJI3, c1, and c2 are preset correction factors.

[0016] As a preferred embodiment of the present invention, the specific process of analyzing and outputting the control value one from the first, second, and third population numbers is as follows:

[0017] Analyzing the first reference value yields the corresponding effective reference value. Multiplying all effective reference values ​​by their corresponding weighting factors and summing them yields the first control value. Substituting the second and third reference values ​​into the corresponding preset formulas, the weight-to-light ratio and the implementation value are output. In a preferred embodiment of the present invention, the specific process of performing step one is as follows:

[0018] The preset response range Yy1 is divided into three equal parts to obtain three sub-ranges. The response execution coefficients are matched with the three sub-ranges respectively, and the corresponding prevention and response measures are executed for the three sub-ranges.

[0019] The specific process of executing step two is as follows: the preset response range Yy2 is split into two equal parts to obtain two sub-ranges. The response execution coefficients are matched with the two sub-ranges respectively, and the corresponding prevention and response measures are executed for the two sub-ranges.

[0020] The specific process of executing step three is as follows: online and offline activities are carried out simultaneously, controlling online devices to play content and generating offline promotional instructions to be sent to the smart terminals of the corresponding personnel.

[0021] The beneficial effects of this invention are:

[0022] 1. This invention utilizes a survey and analysis module to conduct a comprehensive survey and analysis of COPD among residents. The survey method is determined by age grouping based on the number of residents in the community. Questionnaires are received via mobile terminals to obtain residents' awareness and prevention data regarding COPD. Data on occupation, health habits, medical history, and mental health are also collected and analyzed. Finally, the system outputs residents' awareness scores based on data collected from their homes and surrounding environments. This survey and analysis module provides crucial data support for communities to understand residents' level of awareness of COPD and potential risk factors, helping to develop targeted prevention and control strategies, reduce the incidence and aggravation risk of COPD, and improve the overall health level of community residents.

[0023] 2. This invention uses a statistical analysis module to deeply analyze survey data and outputs evaluation results based on a preset model; it calculates age range control numbers through resident cognitive scores, enabling the system to quickly identify problems and propose prevention and control measures, thereby improving residents' health and reducing the risk of COPD.

[0024] 3. This invention uses an emergency response execution module to mark signal values ​​based on signals generated by a statistical analysis module, and calculates emergency response execution coefficients using a preset algorithm. The system matches the execution coefficients to a preset emergency response range and performs corresponding steps: for execution coefficients within a subdivided range, the system provides services such as COPD-related knowledge dissemination, online consultations, and appointment scheduling through a community mini-program to improve residents' understanding and awareness of COPD prevention; through offline health activities and online promotion, the system helps community residents understand and manage COPD, providing comprehensive support for their health. Attached Figure Description

[0025] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0026] Figure 1 This is a schematic diagram of the principle of the present invention;

[0027] Figure 2 This is a schematic diagram illustrating the effective reference values ​​for the present invention;

[0028] Figure 3 This is a schematic diagram of the COPD high-risk population screening questionnaire of the present invention. Detailed Implementation

[0029] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. 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. Embodiment 1

[0030] Please see Figure 1 As shown, a comprehensive analysis and prevention system for COPD in residents includes a survey acquisition module, a survey analysis module, a statistical analysis module, and a response implementation module.

[0031] The survey data acquisition module comprehensively collects data on residents' awareness and prevention of COPD, obtaining resident information. This information covers residents' age, occupation, environment, and physical condition. Age groups are segmented to better understand awareness across different age groups. Regarding occupation, it determines whether residents are engaged in occupations associated with COPD risk, such as miners, construction workers, new industrial workers, and carpenters. Environmental data includes surrounding factories, transportation hubs, farmland, pesticide and fertilizer use, as well as factors that may affect residents' health, such as humidity, indoor gas and coal use, and construction work. Physical condition data includes information on health habits, medical history, genetic factors, and mental health. The survey data acquisition module uses questionnaires or direct interviews, scanning questionnaire data and capturing interview wording through smart devices to ensure accuracy and completeness. The collected data is stored and sent to the survey analysis module for further analysis and processing.

[0032] The survey and analysis module counts the number of residents in the community. The total number of residents in the community is marked as R, and S people are randomly surveyed. They are divided into different intervals according to age, including interval 1, interval 2, interval 3, interval 4, interval 5 and interval 6. Specifically, the age group of 18-25 years old (excluding 25) is marked as interval 1, 25-32 years old (excluding 32) is marked as interval 2, 32-40 years old (excluding 40) is marked as interval 3, 40-50 years old (excluding 50) is marked as interval 4, 50-60 years old (excluding 60) is marked as interval 5, and 60 years old and above is marked as interval 6.

[0033] Next, the number of people in different age ranges is compared; the number of people in range 1, range 2 and range 3 is added together, and the percentage of the surveyed people is calculated to obtain percentage 1, which is marked as N1; the number of people in range 4, range 5 and range 6 is added together, and the percentage of the surveyed people is calculated to obtain percentage 2, which is marked as N2; by comparing the size of N1 and N2, the survey method mode is determined.

[0034] If N1 > 2 × N2, then use survey method mode one; otherwise, use survey method mode two; when N1 = 2 × N2, then use survey method mode three.

[0035] The specific survey process for Survey Method Model 1 is as follows: First, questionnaires are received via mobile terminals to collect data on residents' awareness and prevention. Then, the collected data is analyzed. In terms of occupation, residents' occupations are collected, and it is determined whether they are engaged in occupations related to COPD risk, such as miners, construction workers, new industry workers, carpenters, and tobacco workers. For different occupations, corresponding preset occupation values ​​are assigned, specifically: tobacco worker is assigned CP1, miner is assigned CP2, new industry worker is assigned CP3, construction worker is assigned CP4, incinerator worker is assigned CP5, carpenter is assigned CP6, restaurant chefs and waiters are assigned CP7, transportation workers are assigned CP8, and textile workers are assigned CP9. The occupation values ​​are collectively referred to as CPβ, β = 1, 2, ..., 9. The size of the occupation value is assigned according to the risk level of the occupation, specifically: CP1 > CP2 > CP3 > CP4 > CP5 > CP6 > CP7 > CP8 > CP9.

[0036] The study considers residents' physical condition, including health habits, medical history, and mental health. Health habits are categorized into four main types: quitting or avoiding smoking, maintaining a healthy weight and a balanced diet, and avoiding air pollution and respiratory infections. These four categories are assigned habit values, JX1, JX2, JX3, and JX4, collectively referred to as the health habit value JXφ, where φ = 1, 2, 3, 4. Medical history includes conditions such as rhinitis, asthma, chronic bronchitis, and emphysema, ranked according to severity and matched with corresponding preset values, JB1, JB2, ..., JBχ, where JB1 > JB2 > ... > JBχ. Mental health includes anxiety, depression, and social isolation. If the condition is anxiety, depression, or social isolation, a corresponding preset value is assigned, XL1, XL2, and XL3, where XL2 > XL3 > XL1, collectively referred to as the mental health preset value XLδ, where δ = 1, 2, 3.

[0037] Finally, the residential environment and surrounding environment of residents are analyzed. The surrounding environment or residential environment is set as factories, and the corresponding preset values ​​are matched as follows: industrial area is set as HJ2, transportation hub is set as HJ3, pesticide and fertilizer use in farmland or agricultural land is set as HJ4, dampness is set as HJ5, indoor gas and coal use is set as HJ6, building construction and renovation is set as HJ7, indoor pollution is set as HJ8, and climate change is set as HJ9. These are collectively referred to as the residential environment value HJε, where ε = 1, 2, ..., 9.

[0038] Substitute occupational values, healthy habit values, disease preset values, mental health preset values, and living environment values ​​into the resident recognition model. Output a number, where a1, a2, a3, a4 and a5 are preset correction factors corresponding to occupation value, disease preset value, mental health preset value, living environment value and health habit value, respectively. Their values ​​can be set by the user, such as 1.3, 9, 2, 1.7 and 2.5 respectively.

[0039] Please see Figure 3 As shown, the specific survey process for survey method mode two is as follows: First, obtain residents' COPD awareness and prevention data through telephone surveys, face-to-face interviews, or questionnaires; then analyze and process these data; during the analysis process, consider factors such as residents' education level, occupation, medical history, and questionnaire scores.

[0040] Residents' educational levels are categorized into primary school and below, junior high school, senior high school and vocational school, junior college, bachelor's degree and master's degree and above. Primary school and below are matched with a preset educational level value of X1, junior high school with X2, senior high school and vocational school with X3, junior college with X4, bachelor's degree with X5, and master's degree and above with X6. The educational level values ​​are collectively referred to as Xp, p = 1, 2, ..., 6, and X1 < X2 < X3 < X4 < X5 < X6.

[0041] Statistical analysis was performed on the questionnaire scores; if a resident's total questionnaire score was ≥5, that resident was marked as a target heavy resident R. Z And match it with the preset weighting factor Da1; if a resident's total questionnaire score is <5, mark the resident as a target light resident R. Q And it is matched with the preset weighting factor Da2; the residents' questionnaire scores are collectively referred to as Df;

[0042] Substitution of Residence Model 2 Output the recognized binary number, where a7, a8, and a9 are preset correction factors; the recognized binary number output when Df≥5 is labeled as CJI2. Z The binary number output when Df < 5 is labeled as CJI2. Q .

[0043] Survey Method Model 3 combines Survey Method Model 1 and Survey Method Model 2, using the Resident Recognition Model 3. The output is a set of three numbers, where c1 and c2 are preset correction factors.

[0044] Please see Figure 2 As shown, the statistical analysis module is used to analyze the first, second, and third population statistics output by the survey analysis module, specifically as follows:

[0045] CUI V =∑CJI1+3.12

[0046] Analyze the population data and output the age range control value CUI for each age range. V V = 1, 2, 3, 4, 5, 6; corresponding to interval 1, interval 2, interval 3, interval 4, interval 5, and interval 6, respectively. A threshold W is preset for interval 1. CUI1 is compared with threshold W1. When CUI1 is less than W1, it is considered an invalid reference value 1; when CUI1 is greater than or equal to W1, it is considered a valid reference value 1. A threshold W2 is preset for interval 2. CUI2 is compared with threshold W2. When CUI2 is less than W2, it is considered an invalid reference value 2; when CUI2 is greater than or equal to W2, it is considered a valid reference value 2. This process continues, resulting in valid reference values ​​1, 2, 3, 4, 5, and 6. The preset weighting factors for the valid reference values ​​are yx1, yx2, yx3, yx4, yx5, and yx6, respectively. Substituting these values ​​into the formula... Output control value 1 YK1; preset control value range is Aa1, Aa2 and Aa3; when YK1∈Aa1, generate mild response signal 1; when YK1∈Aa2, the statistical analysis module generates moderate response signal 1; when YK1∈Aa3, generate severe response signal 1.

[0047] Analyze the two numbers and substitute them into the formula. Output the weight ratio, where b1 and b2 are preset weighting factors, and S Z =1, 2, ..., n1; S Q =1, 2, ..., n2; n1 is the target population R of the community survey. Z The total number of people, n2 is the target population of the community survey R. Q The number of people; preset ratio intervals, namely Bb1, Bb2 and Bb3; when CUR∈Bb1, generate mild response signal 2; when CUR∈Bb2, generate moderate response signal 2; when CUR∈Bb3, generate severe response signal 2.

[0048] Analyze the three numbers and substitute them. Output response value CUE, G = 1, 2, ..., S; d1 and d2 are preset weighting factors; the preset response value interval is Cc1, Cc3 and Cc3; if CUE ∈ Cc1, generate mild response signal 3; if CUE ∈ Cc2, generate moderate response signal 3; if CUE ∈ Cc3, generate severe response signal 3.

[0049] The emergency response execution module processes the signals generated by the statistical analysis module. Specifically, it matches the mild emergency response signal 1, moderate emergency response signal 1, severe emergency response signal 1, mild emergency response signal 2, moderate emergency response signal 2, severe emergency response signal 2, mild emergency response signal 3, moderate emergency response signal 3, and severe emergency response signal 3 with their corresponding preset signal values, QC1, QC2, QC3, QC4, QC5, QC6, QC7, QC8, and QC9, respectively; these are collectively referred to as signal values ​​and labeled as QCk, k = 1, 2, ..., 9; then, the signal value XHZ is substituted into the formula. Output the response implementation coefficient YCZX, where e is a natural number and d3 is a preset correction factor; then match the response implementation coefficient with the preset response range; the preset response range is Yy1, Yy2 and Yy3; when YCZX∈Yy1, execute step one; when YCZX∈Yy2, execute step two; when YCZX∈Yy3, execute step three.

[0050] Step 1: Divide the preset response range Yy1 into three equal parts, resulting in three sub-ranges, labeled XF1, XF2, and XF3 respectively. If YCZX∈XF1, disseminate relevant knowledge about COPD and its prevention and response measures through the community mini-program. If YCZX∈XF2, the community mini-program will redirect to an online consultation page for a more comprehensive understanding of one's health. If YCZX∈XF3, schedule an appointment for COPD-related examinations through the community mini-program.

[0051] Step 2: Divide the range Yy2 into two equal parts, resulting in two sub-ranges, labeled XV1 and XV2 respectively. If YCZX∈XV1, generate an offline activity prompt tag for the corresponding smart device, which will then execute the activity. Offline activities include organizing health lectures, seminars, or health knowledge training sessions, as well as inviting professional doctors or health experts to introduce the symptoms, diagnosis, treatment, and prevention of COPD. If YCZX∈XV2, generate a prompt tag for holding health check-ups and free clinics in the residential community and send it to the corresponding smart device, which will then execute the activity. Free lung function testing services will also be provided to help residents understand their health status.

[0052] Step 3: Conduct online and offline activities simultaneously. Control online devices, including televisions, the internet, and other multimedia platforms, to play promotional videos and short health knowledge videos about COPD. Establish official accounts or community groups for COPD health education through social media platforms to share COPD knowledge, case studies, and the latest research progress. Generate offline promotional instructions and send them to the smart terminals of relevant personnel to promote informational materials such as brochures, posters, and booklets. Personnel will then promote basic knowledge, symptoms, early identification, and management methods of COPD. Train a group of COPD health volunteers to carry out promotional activities and provide consultation services in the community to help residents better understand and manage COPD.

[0053] Example 2

[0054] Based on Example 1, the response execution module is also used to monitor and follow up COPD patients. The specific process is as follows: obtain authorization from COPD patients. After obtaining authorization, collect physical indicator data of COPD patients regularly. The physical indicator data includes weight, respiratory rate, calculated blood eosinophil count, and lung ratio. The lung ratio is the ratio of forced expiratory volume in one second (FEV1) to forced vital capacity (FVC).

[0055] Extract all respiratory rate values ​​within a preset time period and sort them according to the chronological order of collection. Compare the magnitudes of two adjacent respiratory rates; if a later respiratory rate is greater than an earlier one, calculate the difference between the two values ​​to obtain the respiratory rate duration. Sum all respiratory rate duration values ​​to obtain the total respiratory rate value. Calculate the average frequency by averaging all respiratory rate values. Obtain the highest respiratory rate and mark it as the maximum respiratory rate value. Convert the maximum respiratory rate value, total respiratory rate value, and average frequency values ​​into straight line lengths according to a preset ratio. Construct a triangle with the straight line lengths of the maximum respiratory rate value and the total respiratory rate value as the two legs. Select any point on the face of the triangle and construct the altitude with a length equal to the straight line length corresponding to the average frequency. Construct a triangular pyramid using the triangle and the altitude. Calculate the volume of the triangular pyramid and mark its value as the shortness of breath value. If the shortness of breath value is greater than a set threshold, obtain all the patient's weights within a preset time period, sort the weight values ​​according to time order, and compare the weights of the first and last ranked values. If the weight of the last ranked value is greater than the weight of the first ranked value, calculate the difference between the two to obtain the weight loss value, and mark the smallest weight as the minimum weight value. Mark the weight loss value and the minimum weight value as TJ1 and TJ2, respectively. Substitute these values ​​into the formula ZY = TJ1 × fa1 + fa2 / TJ2 to output the patient's weight anomaly value ZY, where fa1 and fa2 are set weighting factors. Normalize the shortness of breath value, weight anomaly value, calculated blood eosinophil count, and lung ratio, and take the values ​​of these four values, then substitute them into the formula. Output the patient's follow-up value SF; where PP1 is the dyspnea value, PP2 is the calculated blood eosinophil count, PP3 is the lung ratio, and ηb1, ηb2, ηb3, and ηb4 are the weighting factors corresponding to the dyspnea value, the hyperisotropy value, the calculated blood eosinophil count, and the lung ratio, respectively. Their values ​​are user-defined and are greater than one.

[0056] If a patient's follow-up value is greater than the first follow-up threshold and less than or equal to the second follow-up threshold, the follow-up value is divided by a preset fixed value and rounded to obtain the follow-up frequency. The patient and the follow-up frequency are then sent to the corresponding medical staff's smart terminal. When the follow-up value is greater than the second follow-up threshold, a monitoring instruction for the patient is generated and sent to the medical staff's smart terminal. The medical staff then take the patient to the hospital for monitored treatment. This addresses the lack of intelligent follow-up and monitoring in the existing system for managing COPD patients, which leads to relapses or worsening of the patient's condition.

[0057] The preferred embodiments of the present invention disclosed above are only for the purpose of illustrating the present invention; the preferred embodiments do not describe all the details in detail, nor do they limit the invention to specific implementation methods; obviously, many modifications and variations can be made based on the content of this specification; this specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention; the present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A comprehensive analysis and prevention system for chronic obstructive pulmonary disease in residents, comprising a survey acquisition module, a survey analysis module, a statistical analysis module and a response execution module; characterized in that: The survey acquisition module is used to collect the resident information of the community residents and send it to the survey analysis module; The adjustment analysis module is used to count the number of residents in the community, divide them according to age, and calculate the proportion to obtain proportion one N1 and proportion two N2; if N1>2×N2, the survey method mode one is used to output the number of residents recognized one; if N1<2×N2, the survey method mode two is used to output the number of residents recognized two; when N1=2×N2, the survey method mode three is used to output the number of residents recognized three; the number of residents recognized one, the number of residents recognized two and the number of residents recognized three are sent to the statistical analysis module; The statistical analysis module is used to analyze the first, second and third numbers of the population recognition and output the control value one, the heavy-light ratio and the measure value, and respectively match the control value one, the heavy-light ratio and the measure value with the corresponding preset control value interval to generate corresponding signals and send them to the measure execution module; The response execution module is used to process the received signal and output the response execution coefficient. If the response execution coefficient belongs to the preset response range Yy1, step one is executed; if the response execution coefficient belongs to the preset response range Yy2, step two is executed; if the response execution coefficient belongs to the preset response range Yy3, step three is executed.

2. A comprehensive analysis and prevention system for chronic obstructive pulmonary disease in residents according to claim 1, characterized in that: The specific process of outputting the second number of residents using the survey method model 2 is as follows: Analyze the occupation of residents and determine the occupation value; then analyze the physical condition of residents to obtain the health habit value, disease preset value and mental health preset value; finally analyze the home environment and surrounding environment of residents to obtain the living environment value; substitute the occupation value, health habit value, disease preset value, mental health preset value and living environment value into the living recognition model to output the living recognition number.

3. A comprehensive analysis and prevention system for chronic obstructive pulmonary disease in residents according to claim 2, characterized in that: The first recognition model is: Among them, CJI1 is the household recognition number, CPβ is the occupational value, JXφ is the health habit value, JBχ is the disease preset value, XLδ is the mental health preset value, and HJε is the household value; a1, a2, a3, a4 and a5 are preset correction factors.

4. A comprehensive analysis and prevention system for chronic obstructive pulmonary disease in residents according to claim 3, characterized in that: The specific process of outputting the second number of residents using the survey method model 2 is as follows: The cultural level of residents is divided to obtain the cultural level value Xp, and the questionnaire score is statistically analyzed to obtain the resident questionnaire score Df; which is substituted into the second resident recognition model: Output the second number of recognition CJI2, where a7, a8 and a9 are preset correction factors, Da1 and Da2 are preset weight factors; the second number of recognition output when Df ≥ 5 is marked as CJI2 Z ; Mark the output number when Df<5 as CJI2 Q .

5. A comprehensive analysis and prevention system for chronic obstructive pulmonary disease in residents according to claim 4, characterized in that: The specific process of outputting the three numbers of residents using survey method model three is as follows: Through the recognition model three The output three numbers CJI3, c1, c2 are preset correction factors.

6. A comprehensive analysis and prevention system for chronic obstructive pulmonary disease in residents according to claim 1, characterized in that: The specific process of analyzing the first, second and third numbers and outputting the control value is as follows: Analyze the first number to get the corresponding effective reference value, multiply all the effective reference values ​​by the corresponding weight factors and sum them up to get the controlled value one; substitute the second number and the third number into the corresponding preset formula to output the weight ratio and the control value.

7. A comprehensive analysis and prevention system for chronic obstructive pulmonary disease in residents according to claim 1, characterized in that: The specific process of executing step 1 is as follows: The preset response range Yy1 is split into three equal parts to obtain three subdivided ranges, the response execution coefficients are matched with the three subdivided ranges respectively, and the preventive response measures corresponding to the three subdivided ranges are executed; the specific process of executing step 2 is: the preset response range Yy2 is split into two equal parts to obtain two subdivided ranges, the response execution coefficients are matched with the two subdivided ranges respectively, and the preventive response measures corresponding to the two subdivided ranges are executed; The specific process of executing step three is: carrying out online and offline operations simultaneously, controlling the online device to play, and generating offline promotion instructions and sending them to the smart terminal of the corresponding personnel.

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