Emergency treatment method and system for dealing with infectious diseases

Abstract

The invention belongs to the field of emergency treatment of infectious diseases, and particularly relates to an emergency treatment method and system for dealing with infectious diseases, and the method comprises the steps: extracting patient symptom features according to the treatment information of patients to form a symptom feature matrix; calculating a calculated Euclidean distance between the symptom feature matrix and the infectious disease feature vector, extracting a patient whose calculated Euclidean distance is less than a preset distance as a target patient, and calculating an infectious disease intensity coefficient; when the infectious disease intensity coefficient is greater than a preset coefficient, calculating a propagation index according to the activity track data set, and calculating an area proportion of the high-risk region according to the propagation index; when the area proportion is greater than the preset proportion, executing a second interference measure, and calculating an infection growth rate after the second interference measure is executed; and if the infection growth rate is greater than the preset growth rate, modifying the second interference measure according to the infection growth rate to obtain a third interference measure. Reasonable management can be carried out according to different infectious disease intensities and transmission areas, and manpower and material resources are saved.

Description

Technical Field

[0001] The present invention belongs to the field of emergency treatment of infectious diseases, and specifically relates to an emergency treatment method and system for dealing with infectious diseases. Background Art

[0002] Infectious diseases are diseases caused by pathogens (such as bacteria, viruses, parasites, and fungi) that can spread between organisms. These diseases are spread through direct or indirect contact with the source of infection, air, water, food, insect bites, and other pathways, potentially causing health problems for individuals or groups. Whenever an infectious disease occurs, emergency measures are needed to prevent its widespread spread and control it.

[0003] In related technologies, infectious diseases are controlled mainly through management and isolation. By detecting the activity trajectories of patients with infectious diseases, the areas where the patients have passed through or the areas around them are isolated.

[0004] Regarding the above-mentioned related technologies, when controlling infectious diseases, a one-size-fits-all isolation method is adopted, and the transmission intensity and transmission area of the infectious diseases are not taken into consideration to adopt targeted control measures, thereby saving manpower and material resources. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide an emergency treatment method and system for infectious diseases, which can select different control measures according to the intensity and infection area of different infectious diseases to save manpower and material resources.

[0006] An emergency treatment method for infectious diseases, comprising:

[0007] Obtaining patient medical information, extracting patient symptom characteristics based on the medical information, and forming a symptom characteristic matrix with multiple patient symptom characteristics;

[0008] Obtaining a preset infectious disease feature vector, calculating a calculated Euclidean distance between the symptom feature matrix and the preset infectious disease feature vector, and extracting patients whose calculated Euclidean distance is less than the preset distance as target patients;

[0009] Calculating the infectious disease intensity coefficient of the target patient;

[0010] When the infectious disease intensity coefficient is greater than the preset coefficient, obtaining the activity trajectory dataset of the target patient before the preset time period;

[0011] Calculating a propagation index based on the activity trajectory dataset, screening high-risk areas based on the propagation index, and calculating the area ratio of the high-risk areas;

[0012] When the area ratio is less than or equal to a preset ratio, executing a first interference measure;

[0013] When the area ratio is greater than a preset ratio, executing a second intervention measure, and calculating the infection growth rate after executing the second intervention measure;

[0014] If the infection growth rate is greater than the preset growth rate, the second interference measure is modified according to the infection growth rate to obtain a third interference measure, and the control intensity and control radius of the first interference measure, the second interference measure and the third interference measure gradually increase.

[0015] Optionally, extracting patient symptom characteristics based on the medical information includes:

[0016] According to the medical information, obtaining body temperature characteristics, respiratory information, pathogen detection values and rash area;

[0017] The body temperature feature is normalized to obtain a normalized body temperature feature, which is expressed as:

[0018]

[0019] Among them, f1 is the normalized temperature characteristic, T fever The patient's highest body temperature;

[0020] According to the respiratory information, the cough frequency level, the degree of shortness of breath, and the duration of sore throat are obtained. The cough frequency level, the degree of shortness of breath, and the duration of sore throat are weighted and summed to obtain the respiratory characteristics, which are expressed as:

[0021]

[0022] Among them, w k is the weight coefficient of cough frequency level, shortness of breath degree and pharyngeal pain duration, s k is the respiratory information, f2 is the respiratory characteristics;

[0023] The pathogen characteristics are calculated based on the pathogen detection value and expressed as:

[0024]

[0025] Among them, f3 is the pathogen characteristic, c ref is the reference threshold, c t is the nucleic acid test value;

[0026] The rash distribution density characteristic is calculated based on the rash area and is expressed as:

[0027]

[0028] Among them, f4 is the distribution density characteristic of the rash;

[0029] According to the normalized temperature feature, the respiratory tract feature, the pathogen feature, and the rash distribution density feature, the patient symptom feature is formed and expressed as:

[0030] M s =[f1, f2, f3, f4,…] T ;

[0031] in, is the transposed matrix.

[0032] Optionally, the obtaining of a preset infectious disease feature vector and calculating the Euclidean distance between the symptom feature matrix and the preset infectious disease feature vector includes:

[0033] The infectious disease feature vector is expressed as:

[0034]

[0035] Among them, V base is the infectious disease feature vector, is the patient's symptom characteristics, N is the number of historical infectious disease case samples;

[0036] Get the Euclidean distance formula;

[0037] According to the Euclidean distance formula and the patient's symptom characteristics, the Euclidean distance is calculated and expressed as:

[0038]

[0039] Among them, v i V base The i-th feature in , γ i is the feature weight, f i is the symptom characteristic of the i-th patient, D(M s , V base ) is the Euclidean distance.

[0040] Optionally, calculating a propagation index based on the activity trajectory dataset, screening high-risk areas based on the propagation index, and calculating an area ratio of the high-risk areas includes:

[0041] Constructing a transmission risk field model and a patient activity area based on the activity trajectory dataset;

[0042] The transmission risk field model is expressed as:

[0043]

[0044] Among them, (x k ,yk ) is the geographic coordinate of the Kth trajectory point, d k is the residence time, Δ t is the time window from the onset of the patient, σ is the spatial diffusion radius, m is the number of trajectory points, and Risk(x, y) is the intensity of the infectious disease spread risk;

[0045] According to the transmission risk field model, the transmission index of the patient activity area is calculated as follows:

[0046]

[0047] Among them, A j is the area of the jth administrative region, ρ(x,y) is the population density distribution function, Z j is the spread index;

[0048] Determining whether the propagation index is greater than a preset index;

[0049] If the transmission index is greater than a preset index, the patient activity area where the transmission index is greater than the preset index is regarded as a high-risk area;

[0050] Get the area of high-risk areas;

[0051] According to the area of the region and the total area, the area ratio of the high-risk area is obtained.

[0052] Optionally, obtaining a preset index includes:

[0053] Obtain the historical average and standard deviation of the diffusion index;

[0054] Acquire the area type of the patient's activity area, where the area type includes an urban type and a rural type;

[0055] Obtaining a first preset index according to the historical propagation index average value, the historical propagation index standard deviation, and the risk sensitivity coefficient;

[0056] Obtaining a region weight according to the region type;

[0057] The first preset index is modified according to the regional weight to obtain a preset index.

[0058] Optionally, when the area ratio is greater than a preset ratio, executing the second interference measure includes:

[0059] The second intervention measure is to implement traffic control in high-risk areas, and the control intensity of the traffic control is:

[0060]

[0061] Among them, B1 is the control intensity, Z j is the spread index, Z high is the preset index, Z max This is the historical extreme value of the transmission index.

[0062] Optionally, the second intervention measure is modified according to the infection growth rate to obtain a third intervention measure including:

[0063] The control radius of the second intervention measure is adjusted according to the infection growth rate to obtain a third intervention measure, which is expressed as:

[0064] r=r0*(1+k*λ(t));

[0065] Among them, r is the control radius of the adjusted third intervention measure, r0 is the control radius of the second intervention measure, λ(t) is the infection growth rate, and k is the adjustment coefficient.

[0066] An emergency response system for infectious diseases, comprising:

[0067] The first acquisition module is used to obtain patient medical information, extract patient symptom characteristics based on the medical information, and form a symptom characteristic matrix based on the patient symptom characteristics;

[0068] A second acquisition module is configured to acquire a preset infectious disease feature vector, calculate a calculated Euclidean distance between the symptom feature matrix and the preset infectious disease feature vector, and extract patients whose calculated Euclidean distance is less than the preset distance as target patients;

[0069] A first calculation module is used to calculate the infectious disease intensity coefficient of the target patient;

[0070] A third acquisition module is configured to acquire an activity trajectory dataset of a target patient before a preset time period when the infectious disease intensity coefficient is greater than the preset coefficient;

[0071] A second calculation module is configured to calculate a propagation index based on the activity trajectory dataset, screen high-risk areas based on the propagation index, and calculate an area ratio of the high-risk areas;

[0072] A first execution module, configured to execute a first interference measure when the area ratio is less than or equal to a preset ratio;

[0073] A first execution module is configured to execute a second interference measure when the area ratio is greater than a preset ratio, and calculate an infection growth rate after the second interference measure is executed;

[0074] The adjustment module is used to modify the second interference measure according to the infection growth rate to obtain a third interference measure if the infection growth rate is greater than the preset growth rate, and the control intensity and control radius of the first interference measure, the second interference measure and the third interference measure gradually increase.

[0075] A terminal device includes a memory and a processor. The memory stores a computer program that can be run on the processor. When the processor loads and executes the computer program, an emergency treatment method for infectious diseases is adopted.

[0076] A computer-readable storage medium stores a computer program. When the computer program is loaded and executed by a processor, an emergency treatment method for infectious diseases is adopted.

[0077] The beneficial effects of the present invention are:

[0078] Obtain patient medical information, extract patient symptom characteristics based on the medical information, and form a symptom characteristic matrix of several patient symptom characteristics; obtain a preset infectious disease characteristic vector, calculate the Euclidean distance between the symptom characteristic matrix and the preset infectious disease characteristic vector, and extract patients whose calculated Euclidean distance is less than the preset distance as target patients; calculate the infectious disease intensity coefficient of the target patient; when the infectious disease intensity coefficient is greater than the preset coefficient, obtain the activity trajectory data set of the target patient before the preset time period; calculate the transmission index based on the activity trajectory data set, screen high-risk areas based on the transmission index and calculate the area ratio of the high-risk areas; when the area ratio is less than or equal to the preset ratio, execute the first interference measure; when the area ratio is greater than the preset ratio, execute the second interference measure, and calculate the infection growth rate after executing the second interference measure; if the infection growth rate is greater than the preset growth rate, modify the second interference measure according to the infection growth rate to obtain the third interference measure. Compared with the existing infectious disease emergency response method, this application takes into account the impact of different infectious disease intensities and different transmission areas, selects different treatment measures to respond, can avoid one-size-fits-all lockdowns, and saves manpower and material resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 It is a structural schematic diagram of the present invention.

[0080] Figure 2 This is an attempt to standardize symptoms of the present invention. DETAILED DESCRIPTION

[0081] An emergency response method for infectious diseases, such as Figure 1 As shown, the present invention includes:

[0082] S1. Obtain the patient's medical information, extract the patient's symptom characteristics based on the medical information, and form a symptom feature matrix based on the symptom characteristics of several patients.

[0083] According to the medical information, the patient's symptom characteristics are extracted, including:

[0084] Based on the medical information, body temperature characteristics, respiratory information, pathogen detection values and rash area are obtained.

[0085] Normalize the body temperature feature to obtain the normalized body temperature feature, which is expressed as:

[0086]

[0087] Among them, f1 normalizes the temperature characteristics, T fever The patient's highest body temperature.

[0088] Specifically, generally speaking, the body temperature range is 36-41 degrees, 36 degrees Celsius corresponds to the healthy baseline, and 41 degrees Celsius is the human body's tolerance limit.

[0089] According to the respiratory information, the cough frequency level, shortness of breath degree, and sore throat duration are obtained. The cough frequency level, shortness of breath degree, and sore throat duration are weighted and summed to obtain the respiratory characteristics, which are expressed as:

[0090]

[0091] Among them, w k is the weight coefficient of cough frequency level, shortness of breath degree and pharyngeal pain duration, s k is the respiratory information, and f2 is the respiratory characteristics.

[0092] Specifically, the specific calculation method of respiratory characteristics can be expressed as:

[0093] Different weight coefficients are taken according to different levels.

[0094] The pathogen characteristics are calculated based on the pathogen detection values and expressed as:

[0095]

[0096] Among them, f3 is the pathogen characteristic, c ref is the reference threshold, c t It is the nucleic acid detection value.

[0097] The rash distribution density characteristics are calculated based on the rash area and expressed as:

[0098]

[0099] Among them, f4 is the rash distribution density characteristic, and the erythema intensity is usually set to 3 levels, and different levels correspond to different scores.

[0100] According to the unified temperature characteristics, respiratory characteristics, pathogen characteristics and rash distribution density characteristics, the patient symptom characteristics are composed and expressed as:

[0101] M s =[f1, f2, f3, f4,…] T .

[0102] in, is the transposed matrix.

[0103] S2. Obtain a preset infectious disease feature vector, calculate the Euclidean distance between the symptom feature matrix and the preset infectious disease feature vector, and extract patients whose calculated Euclidean distance is less than the preset distance as target patients.

[0104] Obtaining a preset infectious disease feature vector and calculating the Euclidean distance between the symptom feature matrix and the preset infectious disease feature vector includes:

[0105] The infectious disease feature vector is expressed as:

[0106]

[0107] Among them, V base is the infectious disease feature vector, is the patient's symptom characteristics, and N is the number of historical infectious disease case samples.

[0108] Specifically, the baseline vector is not static, and the baseline feature vector can be updated. When the number of newly confirmed cases reaches the update threshold, the update is triggered. The update method is:

[0109]

[0110] Among them, α is the historical data retention coefficient, usually 0.7, is the historical eigenvector, Here are the symptom characteristics of patients with new cases, N new For the number of new people.

[0111] Get the Euclidean distance formula.

[0112] According to the Euclidean distance formula and the patient's symptom characteristics, the Euclidean distance is calculated and expressed as:

[0113]

[0114] Among them, v i V base The i-th feature in , γ iis the feature weight, f i is the symptom characteristic of the i-th patient, D(M s , V base ) is the Euclidean distance.

[0115] Specifically, feature weights can be obtained through principal component analysis. Principal component analysis is a statistical method for data dimensionality reduction and feature importance quantification. It converts original features into linearly independent principal components (PCs) through orthogonal transformation. Its core lies in:

[0116] Variance maximization: Each principal component captures the direction of maximum variation in the original data

[0117] Weight explicitness: The principal components are composed of linear combinations of the original features, and the coefficients reflect the contribution of the features.

[0118] S3. Calculate the infectious disease intensity coefficient of the target patient.

[0119] Specifically, the infectious disease intensity coefficient is used to measure the transmissibility of infectious diseases, that is, whether the transmissibility is strong or weak. The infectious disease intensity coefficient is related to multiple factors, usually including the viral load test value, the number of close contacts, the symptom severity score, and the maximum symptom score. It is calculated using the following formula:

[0120]

[0121] Among them, R c is the epidemic intensity coefficient, α and β are weight coefficients, T c is the viral load detection value, N c is the number of close contacts, S P Score for symptom severity, S max The maximum value of the symptom score. The viral load test value is obtained through nucleic acid testing, and the number of close contacts can be obtained by scanning the venue code, etc. The symptom severity score is scored based on different symptoms, including fever duration, respiratory symptoms and blood oxygen saturation. For example, fever is 0-3 points, respiratory symptoms are 0-4 points, and blood oxygen saturation is 0-3 points. The specific score is obtained by checking the standardized symptom table, which is as follows: Figure 2 The maximum symptom score is the total score when all symptoms reach the most severe level, such as persistent high fever + severe dyspnea + blood oxygen <90%.

[0122] S4. When the infectious disease intensity coefficient is greater than the preset coefficient, obtain the activity trajectory dataset of the target patient before the preset time period.

[0123] Specifically, the preset coefficient is a critical value used to determine whether the infectious disease has strong or weak transmission ability. When the infectious disease intensity coefficient is greater than the preset coefficient, it means that the infectious disease has strong transmission ability.

[0124] S5. Calculate the propagation index based on the activity trajectory dataset, screen high-risk areas based on the propagation index, and calculate the area ratio of high-risk areas.

[0125] Specifically, the activity trajectory data is represented as:

[0126] L={((x k ,y k , t k , d k ))|k=1,2,……m}.

[0127] Among them, (x k ,y k ) is the geographic coordinate of the Kth trajectory point, d k is the residence time, t k is the arrival time.

[0128] The spread index is calculated based on the activity trajectory dataset. High-risk areas are screened based on the spread index and the area ratio of high-risk areas is calculated.

[0129] Based on the activity trajectory dataset, a transmission risk field model and patient activity area are constructed.

[0130] The propagation risk field model is expressed as:

[0131]

[0132] Among them, (x k ,y k ) is the geographic coordinate of the Kth trajectory point, d k is the residence time, Δ t is the time window from the onset of the patient, σ is the spatial diffusion radius, m is the number of trajectory points, and Risk(x, y) is the intensity of the infectious disease spread risk.

[0133] According to the transmission risk field model, the transmission index of the patient activity area is calculated as follows:

[0134]

[0135] Among them, A j is the area of the jth administrative region, ρ(x,y) is the population density distribution function, Z j The transmission index.

[0136] Determine whether the propagation index is greater than the preset index.

[0137] If the transmission index is greater than the preset index, the patient activity area where the transmission index is greater than the preset index will be regarded as a high-risk area.

[0138] Get the area of high-risk regions.

[0139] Based on the regional area and the total area, the area ratio of the high-risk area is obtained.

[0140] Specifically, when the area ratio is greater than the preset ratio, it means that the infectious disease is spreading over a large area. When the area ratio is less than the preset ratio, it means that the infectious disease is spreading over a small area.

[0141] Obtaining preset indices includes:

[0142] Get the historical average and standard deviation of the spread index.

[0143] Get the regional type of the patient's activity area, which includes urban type and rural type.

[0144] A first preset index is obtained based on the historical communication index average value, the historical communication index standard deviation and the risk sensitivity coefficient.

[0145] Gets the region weight based on the region type.

[0146] The first preset index is modified according to the regional weight to obtain a preset index.

[0147] Specifically, the historical average transmission index refers to the baseline value when there is no infectious disease, and the transmission index Z of all regions in normal times is j The arithmetic mean of the incidence rate reflects the natural transmission intensity of the disease under normal conditions.

[0148] Specifically, the preset index is obtained as follows:

[0149] Z high =μ+k·σ.

[0150] Among them, Z high is the preset index, μ is the average value of the historical transmission index, σ is the standard deviation of the transmission index, and K is the risk sensitivity coefficient, which is usually 2 to 3.

[0151] S6. When the area ratio is less than or equal to the preset ratio, execute the first interference measure.

[0152] S7. When the area ratio is greater than the preset ratio, execute the second interference measure and calculate the infection growth rate after executing the second interference measure.

[0153] When the area ratio is greater than the preset ratio, executing the second interference measure includes:

[0154] The second intervention measure is to implement traffic control in high-risk areas. The intensity of traffic control is:

[0155]

[0156] Among them, B1 is the control intensity, Z j is the spread index, Z high is the preset index, Z max This is the historical extreme value of the transmission index.

[0157] Specifically, whether it is the first, second, or third intervention measure, all involve traffic control, differing in the intensity and radius of control. The more serious the infectious disease, the stricter the traffic control and the larger the control radius.

[0158] The control intensity describes the degree of control over traffic. For example, if the control intensity is 38%, it means that 38% of the traffic is blocked.

[0159] S8. If the infection growth rate is greater than the preset growth rate, the second interference measure is modified according to the infection growth rate to obtain the third interference measure, and the control intensity and control radius of the first interference measure, the second interference measure and the third interference measure are gradually increased.

[0160] The second intervention measure is modified according to the infection growth rate, and the third intervention measure includes:

[0161] The control radius of the second intervention measure is adjusted according to the infection growth rate, and the third intervention measure is obtained, which is expressed as:

[0162] r=r0*(1+k*λ(t));

[0163] Where r is the adjusted control radius of the third intervention measure, r0 is the control radius of the second intervention measure, λ(t) is the infection growth rate, and k is the adjustment coefficient. Both the first and second intervention measures are based on a preset basic control radius.

[0164] An emergency response system for infectious diseases, comprising:

[0165] The first acquisition module is used to obtain patient medical information, extract patient symptom characteristics based on the medical information, and form a symptom characteristic matrix with a plurality of patient symptom characteristics.

[0166] The second acquisition module is used to obtain a preset infectious disease feature vector, calculate the Euclidean distance between the symptom feature matrix and the preset infectious disease feature vector, and extract patients whose calculated Euclidean distance is less than the preset distance as target patients.

[0167] The first calculation module is used to calculate the infectious disease intensity coefficient of the target patient.

[0168] The third acquisition module is used to obtain the activity trajectory dataset of the target patient before a preset time period when the infectious disease intensity coefficient is greater than a preset coefficient.

[0169] The second calculation module is used to calculate the propagation index based on the activity trajectory dataset, screen high-risk areas based on the propagation index, and calculate the area ratio of the high-risk areas.

[0170] The first execution module is configured to execute a first interference measure when the area ratio is less than or equal to a preset ratio.

[0171] The first execution module is configured to execute a second interference measure when the area ratio is greater than a preset ratio, and calculate an infection growth rate after the second interference measure is executed.

[0172] The adjustment module is used to modify the second interference measure according to the infection growth rate to obtain the third interference measure if the infection growth rate is greater than the preset growth rate, and the control intensity and control radius of the first interference measure, the second interference measure and the third interference measure gradually increase.

[0173] An embodiment of the present application also discloses a terminal device, including a memory and a processor. The memory stores a computer program that can be run on the processor. When the processor loads and executes the computer program, it adopts an emergency treatment method for infectious diseases.

[0174] Among them, the terminal device can be a computer device such as a desktop computer, a laptop computer or a cloud server, and the terminal device includes but is not limited to a processor and a memory. For example, the terminal device can also include input and output devices, network access devices and buses, etc.

[0175] Among them, the processor can adopt a central processing unit (CPU). Of course, according to actual usage, other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. can also be adopted. The general-purpose processor can adopt a microprocessor or any conventional processor, etc., and this application does not impose any restrictions on this.

[0176] Among them, the memory can be an internal storage unit of the terminal device, such as the hard disk or memory of the terminal device, or it can be an external storage device of the terminal device, such as a plug-in hard disk, smart memory card (SMC), secure digital card (SD) or flash memory card (FC) equipped on the terminal device, etc., and the memory can also be a combination of the internal storage unit and the external storage device of the terminal device. The memory is used to store computer programs and other programs and data required by the terminal device. The memory can also be used to temporarily store data that has been output or is to be output. This application does not impose any restrictions on this.

[0177] Among them, through this terminal device, an emergency treatment method for infectious diseases in the above embodiment is stored in the memory of the terminal device, and is loaded and executed on the processor of the terminal device for easy use.

[0178] An embodiment of the present application further discloses a computer-readable storage medium, and the computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, an emergency treatment method for infectious diseases in the above embodiment is adopted.

[0179] Among them, the computer program can be stored in a computer-readable medium, the computer program includes computer program code, the computer program code can be in the form of source code, object code, executable file or certain middleware, etc. The computer-readable medium includes any entity or device that can carry computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that computer-readable medium includes but is not limited to the above-mentioned components.

[0180] Among them, through this computer-readable storage medium, an emergency treatment method for infectious diseases in the above embodiment is stored in a computer-readable storage medium, and is loaded and executed on a processor to facilitate the storage and application of the above method.

[0181] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of protection of the present application is limited to these examples. In line with the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0182] The one or more embodiments of this application are intended to encompass all such substitutions, modifications, and variations that fall within the broad scope of this application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of this application should be included in the scope of protection of this application.

Claims

1. An emergency treatment method for infectious diseases, characterized by: include: Obtaining patient medical information, extracting patient symptom characteristics based on the medical information, and forming a symptom characteristic matrix with multiple patient symptom characteristics; Obtaining a preset infectious disease feature vector, calculating a calculated Euclidean distance between the symptom feature matrix and the preset infectious disease feature vector, and extracting patients whose calculated Euclidean distance is less than the preset distance as target patients; Calculating the infectious disease intensity coefficient of the target patient; When the infectious disease intensity coefficient is greater than the preset coefficient, obtaining the activity trajectory dataset of the target patient before the preset time period; Calculating a propagation index based on the activity trajectory dataset, screening high-risk areas based on the propagation index, and calculating the area ratio of the high-risk areas; When the area ratio is less than or equal to a preset ratio, executing a first interference measure; When the area ratio is greater than a preset ratio, executing a second intervention measure, and calculating the infection growth rate after executing the second intervention measure; If the infection growth rate is greater than the preset growth rate, the second interference measure is modified according to the infection growth rate to obtain a third interference measure, and the control intensity and control radius of the first interference measure, the second interference measure and the third interference measure gradually increase.

2. The method for emergency treatment of infectious diseases according to claim 1, characterized in that: Extracting patient symptom characteristics based on the medical information includes: According to the medical information, obtaining body temperature characteristics, respiratory information, pathogen detection values and rash area; The body temperature feature is normalized to obtain a normalized body temperature feature, which is expressed as: Among them, F1 normalized temperature characteristics, T fever The patient's highest body temperature; According to the respiratory information, the cough frequency level, the degree of shortness of breath, and the duration of sore throat are obtained. The cough frequency level, the degree of shortness of breath, and the duration of sore throat are weighted and summed to obtain the respiratory characteristics, which are expressed as: Among them, w k is the weight coefficient of cough frequency level, shortness of breath degree and pharyngeal pain duration, s k is the respiratory information, f2 is the respiratory characteristics; The pathogen characteristics are calculated based on the pathogen detection value and expressed as: Among them, f3 is the pathogen characteristic, c ref is the reference threshold, c t is the nucleic acid test value; The rash distribution density characteristic is calculated based on the rash area and is expressed as: Among them, f4 is the distribution density characteristic of the rash; According to the normalized temperature feature, the respiratory tract feature, the pathogen feature, and the rash distribution density feature, the patient symptom feature is formed and expressed as: M s =[f1,f2,f3,f4,……] T ; Where T is the transposed matrix.

3. The method for emergency treatment of infectious diseases according to claim 1, wherein: The step of obtaining a preset infectious disease feature vector and calculating the Euclidean distance between the symptom feature matrix and the preset infectious disease feature vector includes: The infectious disease feature vector is expressed as: Among them, V base is the infectious disease feature vector, is the symptom characteristic of the patient, N is the number of historical infectious disease case samples; Get the Euclidean distance formula; According to the Euclidean distance formula and the patient's symptom characteristics, the Euclidean distance is calculated and expressed as: Among them, v i V base The i-th feature in , γ i is the feature weight, f i is the symptom characteristic of the i-th patient, D(M s , V base ) is the Euclidean distance.

4. The method for emergency treatment of infectious diseases according to claim 1, wherein: Calculating the propagation index according to the activity trajectory dataset, screening high-risk areas according to the propagation index, and calculating the area ratio of the high-risk areas include: Constructing a transmission risk field model and a patient activity area based on the activity trajectory dataset; The transmission risk field model is expressed as: Among them, (x k ,y k ) is the geographic coordinate of the Kth trajectory point, d k is the residence time, Δ t is the time window from the onset of the patient, σ is the spatial diffusion radius, m is the number of trajectory points, and Risk(x, y) is the intensity of the infectious disease spread risk; According to the transmission risk field model, the transmission index of the patient activity area is calculated as follows: Among them, A j is the area of the jth administrative region, ρ(x,y) is the population density distribution function, Z j is the spread index; Determining whether the propagation index is greater than a preset index; If the transmission index is greater than a preset index, the patient activity area where the transmission index is greater than the preset index is regarded as a high-risk area; Get the area of high-risk areas; According to the area of the region and the total area, the area ratio of the high-risk area is obtained.

5. The method for emergency treatment of infectious diseases according to claim 4, characterized in that: Obtaining preset indices includes: Obtain the historical average and standard deviation of the diffusion index; Acquire the area type of the patient's activity area, where the area type includes an urban type and a rural type; Obtaining a first preset index according to the historical propagation index average value, the historical propagation index standard deviation, and the risk sensitivity coefficient; Obtaining a region weight according to the region type; The first preset index is modified according to the regional weight to obtain a preset index.

6. The method for emergency treatment of infectious diseases according to claim 4, characterized in that: When the area ratio is greater than the preset ratio, executing the second interference measure includes: The second intervention measure is to implement traffic control in high-risk areas, and the control intensity of the traffic control is: Among them, B1 is the control intensity, Z j is the spread index, Z high is the preset index, Z max This is the historical extreme value of the transmission index.

7. The method for emergency treatment of infectious diseases according to claim 1, wherein: The second intervention measure is modified according to the infection growth rate, so that the third intervention measure includes: The control radius of the second intervention measure is adjusted according to the infection growth rate to obtain a third intervention measure, which is expressed as: r=r0*(1+k*λ(t)); Among them, r is the control radius of the adjusted third intervention measure, r0 is the control radius of the second intervention measure, λ(t) is the infection growth rate, and k is the adjustment coefficient.

8. An emergency treatment system for infectious diseases, characterized by: include: The first acquisition module is used to obtain patient medical information, extract patient symptom characteristics based on the medical information, and form a symptom characteristic matrix based on the patient symptom characteristics; A second acquisition module is configured to acquire a preset infectious disease feature vector, calculate a calculated Euclidean distance between the symptom feature matrix and the preset infectious disease feature vector, and extract patients whose calculated Euclidean distance is less than the preset distance as target patients; A first calculation module is used to calculate the infectious disease intensity coefficient of the target patient; A third acquisition module is configured to acquire an activity trajectory dataset of a target patient before a preset time period when the infectious disease intensity coefficient is greater than the preset coefficient; A second calculation module is configured to calculate a propagation index based on the activity trajectory dataset, screen high-risk areas based on the propagation index, and calculate an area ratio of the high-risk areas; A first execution module, configured to execute a first interference measure when the area ratio is less than or equal to a preset ratio; A first execution module is configured to execute a second interference measure when the area ratio is greater than a preset ratio, and calculate an infection growth rate after the second interference measure is executed; The adjustment module is used to modify the second interference measure according to the infection growth rate to obtain a third interference measure if the infection growth rate is greater than the preset growth rate, and the control intensity and control radius of the first interference measure, the second interference measure and the third interference measure gradually increase.

9. A terminal device comprising a memory and a processor, characterized in that: The memory stores a computer program that can be run on the processor. When the processor loads and executes the computer program, the method according to any one of claims 1 to 7 is adopted.

10. A computer-readable storage medium storing a computer program, wherein: When the computer program is loaded and executed by a processor, the method according to any one of claims 1 to 7 is adopted.

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