CN117035388A - Risk assessment method and system for target place, terminal equipment and storage medium - Google Patents
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Abstract
The disclosure relates to the technical field of data analysis, in particular to a risk assessment method and system for a target place, a storage medium and terminal equipment. The method comprises the following steps: acquiring report data corresponding to a target place, analyzing the report data to acquire structured data, and determining the number of associated objects corresponding to the target place; analyzing the structured data according to preset rules to extract place features and determining feature values corresponding to the place features; inputting the number of associated objects corresponding to the target place, the place characteristics and the corresponding characteristic values as input parameters into a GAM model to obtain the weight coefficient of the place characteristics; and determining the risk parameters of the target place by combining the place characteristics and the weight coefficients of the place characteristics. According to the method, when the risk parameters of the target places are calculated, the association relation among different features is considered, the regional characteristics of the target places can be considered, and the epidemic risk of the epidemic place can be accurately estimated.
Description
Technical Field
The disclosure relates to the technical field of data analysis, and in particular relates to a risk assessment method of a target place, a risk assessment system of the target place, a storage medium and terminal equipment.
Background
In the treatment of infectious diseases, risk grades of infectious disease patients and visited places of the infectious disease patients need to be evaluated, and corresponding management and control strategies are formulated. In the prior art, a certain period of action track analysis is required for the definite patient with the infectious disease and the related close contact person, and epidemiological investigation is carried out to determine specific risk places; for different risk sites, the risk level is evaluated manually using medical knowledge based on epidemiological findings. However, in the related technical solutions, reference to foreign cases is generally required, and expert personnel are required to specify the management and control policy according to previous experience. However, there are cases of "one-cut" in this way. In addition, when the existing calculation model is used for carrying out risk assessment on a place, influences among model variables cannot be accurately considered, so that risk assessment grades aiming at the place are inaccurate and unreasonable, and the problem of fitting easily occurs in the calculation process.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The disclosure aims to provide a risk assessment method and system for a target site, a storage medium and a terminal device, so as to overcome limitations and defects of related technologies at least to a certain extent and accurately assess the risk of infectious disease transmission of the target site.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
According to a first aspect of the present disclosure, there is provided a risk assessment method for a target site, including:
acquiring report data corresponding to a target place, analyzing the report data to acquire structured data, and determining the number of associated objects corresponding to the target place;
analyzing the structured data according to a preset rule to extract place features and determining feature values corresponding to the place features;
inputting the number of associated objects corresponding to the target place, the place characteristics and the corresponding characteristic values as input parameters into a GAM model to obtain the weight coefficient of the place characteristics;
And determining the risk parameters of the target place by combining the place characteristics and the weight coefficients of the place characteristics.
In an exemplary embodiment of the present disclosure, the obtaining report data corresponding to the target location, and analyzing the report data to obtain structured data includes:
analyzing the collected report data to identify location information associated with the target object; and selecting the target location according to the location information; extracting report data associated with the target location and parsing to obtain structured data; or alternatively
And acquiring associated report data based on the determined target place, and analyzing the acquired report data to acquire structural data.
In an exemplary embodiment of the present disclosure, the report data includes: epidemiological survey report data, target subject's intimate contact person data, target site data, crowd activity data.
In an exemplary embodiment of the disclosure, the analyzing the structured data according to a preset rule to extract a location feature includes:
identifying characteristic items in the structured data, and respectively calculating quantization parameters of the characteristic items; and screening the place features of the preset data dimension according to the quantization parameter calculation results corresponding to the feature items.
In one exemplary embodiment of the present disclosure, the venue feature comprises: the number of people in the place, the area of the place, the stay time of the people in the place and the wearing rate of the mask;
the determining the feature value corresponding to the place feature comprises the following steps:
estimating the moving state of an object to be analyzed in the target place, and determining the quantity of the object to be analyzed corresponding to each contact type according to the estimation result of the moving state;
calculating the average contact number of the objects to be analyzed based on the number of the objects to be analyzed corresponding to each contact type;
determining a propagation rate by combining the single sensing probability and the propagation attenuation rate;
based on the number of preset initial propagators, average contact number, propagation rate and generation time, determining propagation estimated number, and configuring the propagation estimated number into characteristic values of combination of place number, place area, in-place person stay time and mask wearing rate.
In one exemplary embodiment of the present disclosure, the venue feature comprises: the mask wearing state of the related object at the target place, the place ventilation type and the place type;
the determining the feature value corresponding to the place feature comprises the following steps:
and identifying the place information in the structured data and the activity information of the associated object at the place so as to determine the mask wearing state, the place ventilation type and the place type of the associated object at the target place, and carrying out quantization processing so as to configure corresponding characteristic values.
In one exemplary embodiment of the present disclosure, the venue feature comprises: the number of other visit cases and the number of visit cases during other toxin expelling periods;
the determining the feature value corresponding to the place feature comprises the following steps: and carrying out quantification processing on the epidemiological investigation report data to determine the characteristic values corresponding to the other visit object numbers and the visit object numbers in other toxin expelling periods.
In one exemplary embodiment of the present disclosure, the venue feature comprises: the symptom type, duration of onset, and viral load of the associated case;
the determining the feature value corresponding to the place feature comprises the following steps: analyzing and quantifying the report data to determine the symptom type and the attack duration of the associated case when entering the target place and the characteristic value corresponding to the grade of the viral load.
In an exemplary embodiment of the present disclosure, the method further comprises:
configuring a corresponding risk level for the target place according to the risk parameter; and adding corresponding risk identification information for the target place in the electronic map.
In an exemplary embodiment of the present disclosure, the method further comprises:
and determining an object to be analyzed which is associated with the target place in the first window time, and distributing a control strategy to the object to be analyzed by combining the risk level of the target place.
According to a second aspect of the present disclosure, there is provided a risk assessment system for a target site, comprising:
the basic data acquisition module is used for acquiring report data corresponding to a target place, analyzing the report data to acquire structural data, and determining the number of associated objects corresponding to the target place;
the feature processing module is used for analyzing the structured data according to preset rules to extract place features and determining feature values corresponding to the place features;
the GAM model execution module is used for taking the number of associated objects corresponding to the target place, the place characteristics and the corresponding characteristic values as input parameters, and inputting the input parameters into a GAM model to obtain the weight coefficient of the place characteristics;
and the risk parameter calculation module is used for determining the risk parameter of the target place by combining the place characteristics and the weight coefficients of the place characteristics.
According to a third aspect of the present disclosure, there is provided a storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described risk assessment method for a target site.
According to a fourth aspect of the present disclosure, there is provided an electronic device comprising:
a processor; and
A memory for storing executable instructions of the processor;
wherein the processor is configured to perform the above-described risk assessment method of the target site via execution of the executable instructions.
In the risk assessment method for the target location provided by the embodiment of the disclosure, the collected report data corresponding to the target location is analyzed, location features are extracted, feature values corresponding to the location features are statistically determined, and the number of associated cases corresponding to the target location is counted; inputting the number of associated cases corresponding to the target place, the place characteristics and the corresponding characteristic values as input parameters into a GAM model, and determining weight coefficients corresponding to the characteristics serving as model independent variables by using the GAM model; and calculating risk parameters of the target place according to the place characteristics and the weight coefficients corresponding to the place characteristics. When the risk parameters of the target place are calculated, the association relation among different features is considered, the regional characteristics of the target place can be considered, and the accurate assessment of the infectious disease transmission risk of the target place is realized; the method is beneficial to rapidly carrying out risk assessment on the related places and places at the early stage of infection outbreak and is beneficial to carrying out regional and hierarchical accurate management and control.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.
FIG. 1 schematically illustrates a schematic diagram of a risk assessment method for a target site in an exemplary embodiment of the present disclosure;
FIG. 2 schematically illustrates a schematic diagram of a system architecture in an exemplary embodiment of the present disclosure;
FIG. 3 schematically illustrates a schematic diagram of a method of determining preset features and feature values of an object under evaluation in an exemplary embodiment of the present disclosure;
fig. 4 schematically illustrates a composition diagram of a risk assessment apparatus of a target site in an exemplary embodiment of the present disclosure;
fig. 5 schematically illustrates a composition diagram of a terminal device in an exemplary embodiment of the present disclosure;
Fig. 6 schematically illustrates a composition diagram of a storage medium in an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
In order to solve the technical problems in the prior art, the present exemplary embodiment provides a risk assessment method for a target site, which can be applied to analysis and assessment of epidemic propagation risk of the target site in an epidemic situation of an infectious disease, and obtain an accurate assessment result. Referring to fig. 1, the target site risk assessment method described above may include the steps of:
step S11, acquiring report data corresponding to a target place, analyzing the report data to acquire structural data, and determining the number of associated objects corresponding to the target place;
Step S12, analyzing the structured data according to preset rules to extract place features and determining feature values corresponding to the place features;
step S13, inputting the number of associated objects corresponding to the target place, the place characteristics and the corresponding characteristic values as input parameters into a GAM model to obtain the weight coefficients of the place characteristics;
and S14, combining the location features and the weight coefficients of the location features to determine risk parameters of the target location.
According to the risk assessment method for the target places, on one hand, collected report data corresponding to the target places are analyzed, place features are extracted, feature values corresponding to the place features are statistically determined, and the number of associated cases corresponding to the target places are counted; inputting the number of associated cases corresponding to the target place, the place characteristics and the corresponding characteristic values as input parameters into a GAM model, and determining weight coefficients corresponding to the characteristics serving as model independent variables by using the GAM model; and calculating risk parameters of the target place according to the place characteristics and the weight coefficients corresponding to the place characteristics. When the risk parameters of the target place are calculated, the association relation among different features is considered, the regional characteristics of the target place can be considered, and the accurate evaluation of epidemic risk of the target place is realized.
Hereinafter, each step of the target place risk assessment method in the present exemplary embodiment will be described in more detail with reference to the accompanying drawings and examples.
In this exemplary embodiment, referring to fig. 2, a system architecture is provided, which may be used to execute and implement the target site risk assessment method described above. Referring to fig. 2, the system architecture may include a terminal device 201, a network 202, and a server 203. The terminal device 201 may be one or more of a smart phone, a tablet computer, a desktop computer, and a portable computer; network 202 may be the medium used to provide communication links between terminal devices and servers. The network 202 may include various connection types, such as wired communication links, wireless communication links, and the like. It should be understood that the number of terminal devices, networks and servers in fig. 2 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation. For example, the server 202 may be a server cluster formed by a plurality of servers. Specifically, the above-mentioned evaluation method may be executed by the server side, or by the terminal device and the server side cooperatively. For example, based on the system architecture described above, a user may submit a data analysis request to the server through the terminal device 201; in response to the data analysis request, the server 203 extracts the associated data from the associated database, calculates the associated data, and feeds back the data evaluation result to the terminal device 201.
In step S11, report data corresponding to a target location is acquired, the report data is parsed to acquire structured data, and the number of associated cases corresponding to the target location is determined.
In the present exemplary embodiment, the above-described target site may be a public site involved in epidemic propagation in epidemiological investigation, for example: supermarkets, malls, stores, schools, parks, communities, villages, and the like. The report data may include: epidemiological survey report data, target subject's intimate contact person data, target site data, crowd activity data. The target may be a patient who has a definite case or an asymptomatic infection.
Specifically, the step S11 may include: analyzing the collected report data to identify location information associated with the target object; and selecting the target location according to the location information; report data associated with the target locale is extracted and parsed to obtain structured data.
For example, the target object may be a diagnosis case. For example, when a specific case is first identified, report data related to the specific case may be queried, the relevant report data may be analyzed, and locations related to the specific case may be identified as target locations; and corresponding risk assessment tasks can be created for each place respectively to obtain epidemic situation spreading risk assessment results of each target place.
Alternatively, the step S11 may include: and acquiring associated report data based on the determined target place, and analyzing the acquired report data to acquire structural data.
For example, after determining the epidemic place to be evaluated, the relevant report data may be queried according to the name and address of the target place. And then analyzing the report data.
The epidemic place may be a public place or a private place where the diagnosed case and the suspected case go in a designated monitoring time period. For example, the time period may be 24 hours, 48 hours, 72 hours, 7 days, 10 days, 15 days, or the like before the diagnosis date of the case diagnosis. After determining the case to be analyzed or the target site to be analyzed, the database may be queried for all of the data such as epidemiological survey reports, contact person information, action tracks, building detail records of the visited sites, and social survey data of crowd activities, which are associated. After the above basic data are collected, the report data can be identified and structured by using a natural language processing model, and the structured data containing the characteristics of keywords, names of people, names of places, time and the like can be extracted. In addition, after the target place is determined, people who come in, go out, move and stay in the place within a period of time can be counted and used as the related objects of the target place. For example, the number of associated objects may be determined by counting the associated objects based on the entry and exit registration information of the epidemic place.
Alternatively, in some exemplary embodiments, a data template of the place to be analyzed may be designed in advance, text recognition may be performed on the report data, and specified feature information of the template may be extracted.
In step S12, the structured data is analyzed according to a preset rule to extract a location feature, and a feature value corresponding to the location feature is determined.
In this example embodiment, feature items in the structured data may be identified, and quantization parameters of each feature item may be calculated separately; and screening the place features of the preset data dimension according to the quantization parameter calculation results corresponding to the feature items.
Specifically, the statistical parameter p may be calculated as the quantization parameter by identifying feature items in the structured data according to the specified keywords. For example, a feature term with p value < 0.05 may be selected as the location feature. In addition, a plurality of data dimensions may be preconfigured, and the location feature may be selected from among the data dimensions. The data dimension may include: an environment dimension, an infected person dimension, a dynamic risk dimension. The feature items corresponding to the environment dimensions can comprise screening: the area of the place, the ventilation property of the place, the type of the place, the number of people in the place, the stay time of the people in the place and the wearing rate of the mask. Characteristic items of the infected person dimension may include: whether to wear a mask, symptom type, day of onset, viral load Ct. The feature items of the dynamic risk dimension may include: other visit case numbers and other toxin expelling period visit case data.
In this exemplary embodiment, for the feature item corresponding to the target location, that is, the location feature, the corresponding feature value may be obtained by analyzing the structured data obtained after analysis, or analyzing and counting the original report data. Specifically, among the above-mentioned site characteristics, the number of site persons, the site area, the residence time of the persons in the site, the mask wearing rate, the number of days of onset at the time of visit of the associated object (case), the viral load Ct of the associated object (case), the number of other visited objects (cases), and the number of visited objects (cases) during other detoxification are numerical characteristics; whether the mask is worn during case visit is characterized by Boolean type; site ventilation, site type, and symptom type at case visit are classified features.
In some exemplary embodiments, specifically, referring to fig. 3, for the above-mentioned place characteristics of the number of people in the place, the area of the place, the stay time of the people in the place, and the mask wearing rate, the method for obtaining the corresponding characteristic values may include:
step S31, estimating the moving state of the object to be analyzed in the target place, and determining the quantity of the object to be analyzed corresponding to each contact type according to the estimation result of the moving state;
Step S32, calculating the average contact number of the objects to be analyzed based on the number of the objects to be analyzed corresponding to each contact type;
step S33, determining a propagation rate by combining the single sensing probability and the propagation attenuation rate;
step S34, determining the estimated number of people to be transmitted based on the preset initial number of people to be transmitted, the average contact number, the transmission rate and the generation time, and configuring the estimated number of people to be transmitted as a characteristic value combining the number of people in a place, the area of the place, the stay time of the people in the place and the wearing rate of the mask.
Specifically, the four characteristics of the number of people in the place, the area of the place, the stay time of the people in the place and the wearing rate of the mask can have a certain finger taking range. For example, the number of locales may be 25-1000; the area of the place can be 100-1000 square meters; the residence time of the personnel in the place can be 1-12 hours; the wearing rate of the mask can be 0-100%. The location area can be obtained by the structured data after the analysis of the target location data. The number of people in the place can be obtained by setting a statistics time window and counting the number of people in the time window; for example, one week before the date of case diagnosis, statistics may be performed for each of a plurality of different time periods in which the target site is located, and then an average value may be calculated from a plurality of statistics results as the number of site persons at the target site. The residence time of personnel in the place can also be counted by continuous observation of the target place.
In addition, the characteristic values corresponding to the four site characteristics can be represented by final infectious people through the result values after individual movement and contact propagation simulation by combining the agent-base model with the SIR cabin model. Specifically, assuming that there is an initial case in the target site, the agent-base model is input to simulate the free movement of the person in the target site by using the number of persons in the site, the area of the site, the mask wearing rate, and the person stay time as input parameters.
Specifically, when individual movement simulation is performed, in the simulation operation, it is assumed that the step size of movement of each agent (the object to be analyzed, i.e., the person in the target site) is subject to normal distribution, and according to the simulation of actual adult walking, the simulation sets the mean value=step size, standard deviation=step size/2 of the normal distribution. The normal distribution sampling of the step length comprises the steps of sampling personnel in a target place respectively to obtain the current moving step length L of each agent corresponding to independence. The walking angle is also random, and the walking angle theta is also randomly sampled (from 0 degree to 359 degrees) during simulation. Since each agent does not necessarily move at each iteration, we again randomly sample the results s from the [0,1] distribution to determine if the current agent moves. Therefore, after each iteration, the actual displacement of each agent in the x and y coordinate system is:
x＝cos(θ)×L×s
y＝sin(θ)×L×s
Wherein L is a moving step length, s is a random sampling result of whether to move, and θ is a walking angle.
Based on the above formula, the actual displacement of the x axis and the y axis of the personnel movement after each iteration can be finally obtained. From the displacement simulation results for the person, the type of contact between the person and the case, such as normal contact or deep contact (intimate contact), can be evaluated.
When calculating the average number k of contacts with infection possibility generated by each agent, the calculation formula may include:
wherein, the contact number of the common contact is defined as 1, the contact number of the deep contact is defined as 2, and n represents the number of personnel; l (L) 1 Representing a person in ordinary contact, L 2 Indicating the person in deep contact.
The SIR-based cabin model can also be reused to calculate the single contact infection probability b. Specifically, the calculation formula of the model may include:
where β is the average probability that a susceptible person will become infected and enter latency after a case with infectivity comes into contact with a susceptible population. Wherein β=kb; k is the average number of cases, for example, k can be configured to be 5 or the initial value of k to be 5 according to some statistics; b is the probability of infection at a single contact.
Further, considering that the initial infected person is 1 person, i.e., I (0) =1, it is possible to obtain:
The estimation may yield b= 0.0543009418817.
In addition, the influence of wearing the mask on epidemic spread can be considered. For example, assume a mask wearerThe spread possibility of epidemic situation after wearing is reduced to 30 percent. The actual attenuation rate of the single contact propagation rate b under the influence of the mask can be obtained by combining the wearing rate of the mask, and is recorded as theta 1 . The simulation is performed on the variant strain, and the sowing efficiency alpha of the variant strain compared with the ordinary strain can be obtained based on the prior epidemiological statistics. Under the influence of the conditions, the final propagation rate under the current simulation condition can be obtained. Based on the above conditions, the final propagation rate under the current simulation conditions can be obtained:
b )ina+ ＝α×θ 1 ×b
based on this, R is based on 0 Can be obtained: r is R 0 C b x k x T. Where b is the spread per contact and k is the average number of contacts between the case and the susceptible; t is the time of generation (latency period+onset to diagnosis period), and T is 1 per unit time in this scenario.
By combining the setting of the initial propagator population base, the total population propagated in the scene can be obtained, which comprises the following steps:
Infete＝base×k×b )inal ×T
the result value estimated by the agent-base is a floating point value and can also be used for discretization. The combination of four characteristics of the number of people in the place, the area of the place, the stay time of the people in the place and the wearing rate of the mask is represented by the number of the final infectious agents, so that the strong and direct data correlation between the number of the final infectious agents and the four characteristics in the target place is fully considered, and the corresponding characteristic values can be accurately represented. In particular, the four features may all use the same feature value in subsequent calculations.
In some exemplary embodiments, for one of the venue features described above: the mask wearing state, the place ventilation type and the place type of the related object in the target place can be identified, the place information in the structured data and the activity information of the related object in the place can be identified, the mask wearing state, the place ventilation type and the place type of the related object in the target place can be determined, and quantitative processing is carried out to configure corresponding characteristic values.
For example, as a feature item based on objective facts, a corresponding feature value may be determined from the tonal data. For example, if the case is in the mask wearing state of the target site, the characteristic value is set to 1, and if not, the characteristic value is set to 0. For the ventilation type, three types may be preconfigured: outdoor wind, indoor wind and no ventilation, the corresponding type can be determined according to the information class of the place. For a venue type, a number of alternative types may be configured: nucleic acid sampling points, malls, schools, medical institutions, dining facilities, training facilities, workplaces, convenience shopping stores, recreational facilities, and public service places. For the three feature items, the corresponding feature values can be determined in a one-hot mode.
In some exemplary embodiments, for one of the above-described venue features: the number of other visit objects and the number of visit objects in other toxin expelling periods can refer to the number of other visit cases and the number of visit cases in other toxin expelling periods; specifically, the epidemiological investigation report data may be quantitatively processed to determine the characteristic values corresponding to the other visit case numbers and the visit case numbers during other toxin expelling periods.
For example, the feature values of the feature term may be counted by searching for the associated diagnosed case and the related case through the target site. For example, the corresponding statistics may be obtained by searching and counting the tonal data. As statistical data, it may also be categorized by discretization type.
In some exemplary embodiments, for one of the above-described venue features: the symptom type, the attack duration and the virus load of the case can be analyzed and quantified, so that the symptom type, the attack duration and the characteristic value corresponding to the grade of the virus load when the case enters the target place can be determined.
For example, the symptom type at the time of case visit and the number of days of onset at the time of visit can be comprehensively considered, and the infection degree classification is defined: strong, medium, weak. Wherein, if the case is visited, the symptoms (dry cough, fever, etc.) are present: the number of days from the time of case visit to the time of onset is numerical, and the infection ability is defined by classification by discretization of the number of onset days according to the reference. Strong infectivity: 2 to 4 days; infectivity is moderate: 4 to 7 days; weak infectivity: not in the first two days. If no symptoms are present at the time of case visit, but there is a definite last exposure (infection) time, according to the reference: strong infectivity: 3 to 7 days; among the infectious agents: 7 to 21 days; weak infectivity: after 21 days. If the case has no symptoms and no definite onset time, the onset time is determined by using the controlled time, and the calculation is carried out according to the onset conditions. Case viral load Ct is a continuous numerical value, combined with the reference, and can be classified into 6 classes of grades by single factor analysis of the results: 0 to 10, 10 to 15, 15 to 20, 20 to 25 and 25 to 30.
In step S13, the number of associated objects corresponding to the target location, the location feature and the corresponding feature value are used as input parameters, and a GAM model is input to obtain a weight coefficient of the location feature.
In this example embodiment, the GAM (Generalized Additive Model ) model represents a class of non-parametric smooth regression framework generalizations that serve a set of dependent variables X from an exponential distribution family (e.g., normal distribution, exponential distribution, poisson distribution, binomial distribution, negative binomial distribution, etc.) as follows:
g(μ Y )＝β 0 +f 1 (X 1 )+f 2 (X 2 )+...+f n (X n )
wherein f n (X n ) A function that is non-parametric; g (mu) Y ) Representing a function (a join function) of the mean of the dependent variable Y conditions. In general, additive models are in fact of a special form of generalized additive model in normal dependent variables, where g (μ) Y ) =y. X is as described above n Is the corresponding characteristic value of the place characteristic. Y is the number of cases associated with a target place in a certain time period; for example: restaurant A, 15 patients occurred simultaneously during the near session.
Specifically, for the above formula, it is understood that all the independent variables X (i.e., the location features corresponding to the three data dimensions) will ultimately affect the dependent variable Y (the number of associated cases at the location). After X and Y are both well determined, the coefficient of each independent variable X can be calculated as a weight coefficient of each location feature by the generalized additive model described above.
In step S14, a risk parameter of the target location is determined in combination with the location feature and a weight coefficient of the location feature.
In this example embodiment, after determining the weight coefficient of each location feature by using the GAM model, the feature values of each location feature may be collected, and the value of the risk parameter may be calculated; specifically, the method comprises the following steps:
wherein X is i Is the characteristic value of the site characteristic, W i Is a weighting coefficient for the venue feature.
Based on the foregoing, in some exemplary embodiments of the present disclosure, the method may further include: configuring a corresponding risk level for the target place according to the risk parameter; and adding corresponding risk identification information for the target place in the electronic map.
Specifically, after calculating the risk parameter value for the target site, the risk level for the site may be determined. For example, high, medium, and low risk levels, and risk parameter value intervals corresponding to the respective risk levels may be predefined. After the risk level of the target place is determined, places with different risk levels can be marked in the electronic map by using different colors. For example, a red marking is used to indicate a high risk location, an orange color is used to indicate a medium risk location, and a yellow color is used to indicate a low risk location.
For example, in the system architecture shown in fig. 2, after the server calculates the risk parameters of the target location, the risk parameters are issued to the terminal device. On the terminal device, the risk parameter may be obtained by an application program, and color rendering is performed on a corresponding region in the electronic map based on the risk parameter, and then the map after color rendering is displayed in the interactive interface.
Based on the foregoing, in some exemplary embodiments of the present disclosure, the method may further include: and determining an object to be analyzed which is associated with the target place in the first window time, and distributing a control strategy to the object to be analyzed by combining the risk level of the target place.
Specifically, the first window time may be a monitoring duration with the case entering the target location as a time start point, or a monitoring duration with the case entering the target location as a time midpoint. According to the flow regulation report, all objects (personnel) to be analyzed which stay in the target place after the objects pass through or enter the target place in the monitoring time period can be identified. And configuring corresponding management and control strategies for each object to be analyzed respectively. For example, people related to a target place 14 days before and after the determination of a case are tracked based on the risk level of the place, and hierarchical management is performed. For example, if two or more high risk areas are present within approximately 500 meters of each other, then the areas need to be focused on by the visiting personnel and the closely connected personnel within 14 days of the confirmed case findings. The epidemic personnel can be managed in a grading and policy-classifying manner by adjusting the dynamic threshold value.
According to the target place risk assessment method provided by the exemplary embodiment of the disclosure, natural language processing can be performed on report data, characteristic items contained in the report data are identified, and place characteristics of a plurality of data dimensions suitable for evaluating epidemic situation spreading risks of epidemic places are selected according to a certain rule; the structural data is analyzed and counted, so that the characteristic values of part of site characteristics, such as whether an infected person wears a mask, the number of other visited cases and the like, can be directly extracted; the characteristic values of the characteristics of other places can be quantified in a model simulation mode, such as stay time of personnel in the places, mask wearing rate and the like. By selecting the partial characteristics with the highest correlation with the risk parameters for calculation, the accuracy of the risk parameter calculation result can be effectively improved. In addition, by utilizing a generalized additive model, a potential nonlinear relation between an independent variable X (place characteristic) and a dependent variable Y (place-related case number) is mined, so that the data content in the streaming data is accurately analyzed. Compared with the prior art, the method and the device can effectively fuse interaction influence among respective variable characteristics into a calculation process, and avoid the problem that fitting is easy to occur in the calculation process. By calculating the risk value of the place by utilizing the characteristic value of the place characteristic and the weight value of the place characteristic, the definition of accurately describing the risk from multiple dimensions is realized; and can realize the horizontal comparison of the risk values of different places. For example, for different restaurants, the patient is concentrated in restaurant A, and the adjacent restaurant B does not have the patient concentrated, so that two restaurants can be accurately managed and controlled by adopting different prevention and control policies respectively, and not all restaurants on a certain street are closed completely. And further can realize the requirements of rapid and accurate regional and hierarchical management and control.
Further, referring to fig. 4, in the embodiment of the present example, there is further provided a target site risk assessment system 40, including: a basic data acquisition module 401, a feature processing module 402, a GAM model execution module 403, and a risk parameter calculation module 404. Wherein,
the basic data collection module 401 may be configured to obtain report data corresponding to a target location, analyze the report data to obtain structured data, and determine the number of associated objects corresponding to the target location.
The feature processing module 402 may be configured to analyze the structured data according to a preset rule to extract a location feature, and determine a feature value corresponding to the location feature.
The GAM model execution module 403 may be configured to input the number of associated objects corresponding to the target location, the location feature and the corresponding feature value as input parameters, into a GAM model to obtain a weight coefficient of the location feature.
The risk parameter calculation module 404 may be configured to determine a risk parameter for the target venue in conjunction with the venue feature, a weight coefficient for the venue feature.
The specific details of each module in the target site risk assessment system are described in detail in the corresponding target site risk assessment method, so that details are not repeated here.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
In an exemplary embodiment of the present disclosure, a computer system capable of implementing the above method is also provided.
Those skilled in the art will appreciate that the various aspects of the invention may be implemented as a system, method, or program product. Accordingly, aspects of the invention may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.
A terminal device 500 according to this embodiment of the present invention is described below with reference to fig. 5. The composition of the terminal device 500 shown in fig. 5 is only an example, and should not impose any limitation on the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 5, the components of the terminal device 500 may include, but are not limited to: the at least one processing unit 510, the at least one memory unit 520, and a bus 530 connecting the various system components (including the memory unit 520 and the processing unit 510).
Wherein the storage unit stores program code that is executable by the processing unit 510 such that the processing unit 510 performs steps according to various exemplary embodiments of the present invention described in the above section of the "exemplary method" of the present specification. For example, the processing unit 510 may perform the steps as shown in fig. 1.
The storage unit 520 may include readable media in the form of volatile storage units, such as Random Access Memory (RAM) 5201 and/or cache memory unit 5202, and may further include Read Only Memory (ROM) 5203.
The storage unit 520 may also include a program/utility 5204 having a set (at least one) of program modules 5205, such program modules 5205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 530 may be one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
A terminal may also communicate with one or more external devices 300 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the terminal, and/or with any device (e.g., router, modem, etc.) that enables the terminal to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 550. And, the terminal may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet, via network adapter 560. As shown, network adapter 560 communicates with other modules of the terminal over bus 530. The processing unit 510 is connected to the display unit 540 via a bus 530. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in connection with the terminal, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a terminal device, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, a computer-readable storage medium having stored thereon a program product capable of implementing the method described above in the present specification is also provided. In some possible embodiments, the various aspects of the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the invention as described in the "exemplary methods" section of this specification, when said program product is run on the terminal device.
Referring to fig. 6, a program product 600 for implementing the above-described method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
Furthermore, the above-described drawings are only schematic illustrations of processes included in the method according to the exemplary embodiment of the present application, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (13)
1. A risk assessment method for a target site, comprising:
Acquiring report data corresponding to a target place, analyzing the report data to acquire structured data, and determining the number of associated objects corresponding to the target place;
analyzing the structured data according to a preset rule to extract place features and determining feature values corresponding to the place features;
inputting the number of associated objects corresponding to the target place, the place characteristics and the corresponding characteristic values as input parameters into a GAM model to obtain the weight coefficient of the place characteristics;
and determining the risk parameters of the target place by combining the place characteristics and the weight coefficients of the place characteristics.
2. The risk assessment method of a target location according to claim 1, wherein the acquiring report data corresponding to the target location, parsing the report data to acquire structured data, includes:
analyzing the collected report data to identify location information associated with the target object; selecting the target place according to the place information; extracting report data associated with the target location and parsing to obtain structured data; or alternatively
And acquiring associated report data based on the determined target place, and analyzing the acquired report data to acquire structural data.
3. The risk assessment method of a target site according to claim 1 or 2, wherein the report data includes: epidemiological survey report data, target subject's intimate contact person data, target site data, crowd activity data.
4. The risk assessment method for a target site according to claim 1, wherein the analyzing the structured data according to a preset rule to extract site features comprises:
identifying characteristic items in the structured data, and respectively calculating quantization parameters of the characteristic items; and screening the place features of the preset data dimension according to the quantization parameter calculation results corresponding to the feature items.
5. The risk assessment method for a target site according to claim 1 or 4, wherein the site features include: the number of people in the place, the area of the place, the stay time of the people in the place and the wearing rate of the mask;
the determining the feature value corresponding to the place feature comprises the following steps:
estimating the moving state of an object to be analyzed in the target place, and determining the quantity of the object to be analyzed corresponding to each contact type according to the estimation result of the moving state;
Calculating the average contact number of the objects to be analyzed based on the number of the objects to be analyzed corresponding to each contact type;
determining a propagation rate by combining the single sensing probability and the propagation attenuation rate;
based on the number of preset initial propagators, average contact number, propagation rate and generation time, determining propagation estimated number, and configuring the propagation estimated number into characteristic values of combination of place number, place area, in-place person stay time and mask wearing rate.
6. The method of risk assessment of a target site of claim 1, wherein the site features comprise: the mask wearing state, the place ventilation type and the place type of the related object in the target place;
the determining the feature value corresponding to the place feature comprises the following steps:
and identifying the place information in the structured data and the activity information of the associated object at the place so as to determine the mask wearing state, the place ventilation type and the place type of the associated object at the target place, and carrying out quantization processing so as to configure corresponding characteristic values.
7. The method of risk assessment of a target site of claim 1, wherein the site features comprise: other visit object numbers and visit object numbers during other toxin expelling periods;
The determining the feature value corresponding to the place feature comprises the following steps: and carrying out quantification processing on the epidemiological investigation report data to determine the characteristic values corresponding to the other visit object numbers and the visit object numbers in other toxin expelling periods.
8. The method of risk assessment of a target site of claim 1, wherein the site features comprise: the symptom type, duration of onset, and viral load of the associated case;
the determining the feature value corresponding to the place feature comprises the following steps: analyzing and quantifying the report data to determine the symptom type and the attack duration of the associated case when entering the target place and the characteristic value corresponding to the grade of the viral load.
9. The method of risk assessment of a target site of claim 1, further comprising:
configuring a corresponding risk level for the target place according to the risk parameter;
and adding corresponding risk identification information for the target place in the electronic map.
10. The method of risk assessment of a target site of claim 9, further comprising:
and determining an object to be analyzed which is associated with the target place in the first window time, and distributing a control strategy to the object to be analyzed by combining the risk level of the target place.
11. A risk assessment system for a target site, comprising:
the basic data acquisition module is used for acquiring report data corresponding to a target place, analyzing the report data to acquire structural data, and determining the number of associated objects corresponding to the target place;
the feature processing module is used for analyzing the structured data according to preset rules to extract place features and determining feature values corresponding to the place features;
the GAM model execution module is used for taking the number of associated objects corresponding to the target place, the place characteristics and the corresponding characteristic values as input parameters, and inputting the input parameters into a GAM model to obtain the weight coefficient of the place characteristics;
and the risk parameter calculation module is used for determining the risk parameter of the target place by combining the place characteristics and the weight coefficients of the place characteristics.
12. A storage medium having stored thereon a computer program which, when executed by a processor, implements the risk assessment method of a target site according to any one of claims 1 to 10.
13. A terminal device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
Wherein the processor is configured to perform the risk assessment method of the target site of any one of claims 1 to 10 via execution of the executable instructions.
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