CN115798735A - Epidemic situation early warning method and device based on hierarchical analysis - Google Patents
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Abstract
The application relates to an epidemic situation early warning method and device based on hierarchical analysis, and belongs to the technical field of computers. According to the method and the device, index items of each level and index weights corresponding to the index items can be determined according to a preset risk early warning model; grading epidemic situation data of a target area by using the index items of each level to obtain index grades corresponding to each index item aiming at the epidemic situation data; and generating early warning information corresponding to the epidemic situation data based on the index weight and the index score corresponding to each index item. Therefore, timely early warning of epidemic risks can be achieved.
Description
Technical Field
The application belongs to the technical field of computers, and particularly relates to an epidemic situation early warning method and device based on hierarchical analysis.
Background
At present, governments in various regions fully play system advantages since the outbreak of the new coronary epidemic situation, obtain great achievements in the field of epidemic situation prevention and control, and effectively avoid the wide-range spread of the new coronary pneumonia epidemic situation in China.
In practice, the earlier the epidemic situation is found, the earlier the epidemic situation is treated, the lower the price is paid, and the impact on the development of social economy and the normal life of people is the lowest. Therefore, how to realize the timely early warning of epidemic risks becomes a problem which needs to be solved urgently.
Disclosure of Invention
Therefore, the epidemic situation early warning method and device based on the hierarchical analysis are provided, and timely early warning of epidemic situation risks is facilitated.
In order to achieve the purpose, the following technical scheme is adopted in the application:
in a first aspect, the present application provides an epidemic situation early warning method based on hierarchical analysis, the method comprising:
determining index items of each level and index weight corresponding to each index item according to a preset risk early warning model;
grading epidemic situation data of a target area by using the index items of each level to obtain index grades corresponding to each index item aiming at the epidemic situation data;
and generating early warning information corresponding to the epidemic situation data based on the index weight and the index score corresponding to each index item.
Further, based on each index weight and index score corresponding to the index item, generating early warning information corresponding to the epidemic situation data, including:
calculating a weighted score according to the index weight and the index score corresponding to each index item;
summing the weighted scores of the index items to obtain an early warning score;
determining at least one early warning parameter matching the early warning score; wherein the at least one early warning parameter comprises at least one of: early warning level parameters, early warning degree parameters and early warning description parameters;
and determining the early warning score and the at least one early warning parameter as the early warning information corresponding to the epidemic situation data.
Further, before determining the index items of each level and the index weight corresponding to each index item according to a preset risk early warning model, the method further includes:
acquiring a hierarchical structure among the index items;
generating a judgment matrix for each index item based on the hierarchical structure;
carrying out consistency check on the judgment matrix to obtain a target check result; wherein the target test result is that the test is passed or not passed;
and if the target detection result is that the target detection passes the detection, generating the index weight of each index item based on the judgment matrix.
Further, the method further comprises:
and if the target detection result is that the detection is not passed, adjusting the judgment matrix until the judgment matrix passes the consistency detection, and generating the index weight of each index item based on the judgment matrix passing the consistency detection.
Further, the generating a determination matrix for each index item based on the hierarchical structure includes:
determining each index item corresponding to each level based on the hierarchical structure;
and for each level, generating a judgment matrix corresponding to the level based on each index item corresponding to the level and the index item of the previous level corresponding to the level.
Further, the performing consistency check on the judgment matrix to obtain a target check result includes:
for a judgment matrix corresponding to each level, determining an initial test result of the judgment matrix based on the single-sequence consistency ratio of the judgment matrix;
and determining a target test result of the judgment matrix based on the initial test result of the judgment matrix corresponding to each hierarchy and the index item corresponding to each hierarchy.
Further, the determining, for the judgment matrix corresponding to each hierarchy, an initial test result of the judgment matrix based on a single-rank consistency ratio of the judgment matrix includes:
calculating the maximum eigenvalue of the judgment matrix for the judgment matrix corresponding to each level;
determining a consistency index value of the judgment matrix based on the maximum characteristic value and the order of the judgment matrix;
determining a single-sequencing consistency ratio according to the consistency index value and a preset random consistency index value;
if the single-sequencing consistency ratio is smaller than a preset first threshold value, determining that the initial test result of the judgment matrix is a pass test;
and if the single-sequencing consistency ratio is greater than or equal to the preset first threshold, determining that the initial test result of the judgment matrix is failed in test.
Further, the determining a target test result of the determination matrix based on the initial test result of the determination matrix corresponding to each hierarchy and the index item corresponding to each hierarchy includes:
if the initial test result of the judgment matrix corresponding to each hierarchy is that the test is passed, generating the initial weight of the index item of each hierarchy according to the judgment matrix;
for the index item of each hierarchy, determining a synthetic weight of the index item relative to a preset target based on the initial weight;
determining a synthesis consistency ratio for the synthesis weights;
if the synthesized consistency ratio is smaller than a preset second threshold value, determining that the target test result of the judgment matrix is passed through the test;
and if the composite consistency ratio is larger than or equal to the preset second threshold value, determining that the target test result of the judgment matrix is failed in test.
Further, the method further comprises:
generating prevention and control decision information according to the early warning information;
and outputting the prevention and control decision information.
In a second aspect, the present application provides an epidemic situation early warning device based on hierarchical analysis, the device includes:
the index determining unit is used for determining index items of all levels and index weights corresponding to the index items according to a preset risk early warning model;
the grading determination unit is used for grading the epidemic situation data of the target area by using the index items of each level to obtain an index grade corresponding to each index item aiming at the epidemic situation data;
and the information generating unit is used for generating early warning information corresponding to the epidemic situation data based on the index weight and the index score corresponding to each index item.
Further, the information generating unit is specifically configured to:
calculating a weighted score according to the index weight and the index score corresponding to each index item;
summing the weighted scores of the index items to obtain an early warning score;
determining at least one early warning parameter matching the early warning score; wherein the at least one early warning parameter comprises at least one of: early warning level parameters, early warning degree parameters and early warning description parameters;
and determining the early warning score and the at least one early warning parameter as the early warning information corresponding to the epidemic situation data.
Further, the apparatus further comprises:
the index construction unit is used for acquiring a hierarchical structure among the index items before determining the index items of each hierarchy and the index weight corresponding to each index item according to a preset risk early warning model; generating a judgment matrix for each index item based on the hierarchical structure; carrying out consistency check on the judgment matrix to obtain a target check result; wherein the target test result is that the test is passed or not passed; and if the target detection result is that the target detection passes the detection, generating the index weight of each index item based on the judgment matrix.
Further, the index construction unit is further configured to:
and if the target detection result is that the detection is not passed, adjusting the judgment matrix until the judgment matrix passes the consistency detection, and generating the index weight of each index item based on the judgment matrix passing the consistency detection.
Further, the index constructing unit is specifically configured to:
determining each index item corresponding to each hierarchy based on the hierarchical structure;
and for each level, generating a judgment matrix corresponding to the level based on each index item corresponding to the level and the index item of the previous level corresponding to the level.
Further, the index constructing unit is specifically configured to:
for a judgment matrix corresponding to each level, determining an initial test result of the judgment matrix based on the single-sequence consistency ratio of the judgment matrix;
and determining a target test result of the judgment matrix based on the initial test result of the judgment matrix corresponding to each hierarchy and the index item corresponding to each hierarchy.
Further, the index constructing unit is specifically configured to:
calculating the maximum eigenvalue of the judgment matrix for the judgment matrix corresponding to each level;
determining a consistency index value of the judgment matrix based on the maximum characteristic value and the order of the judgment matrix;
determining a single-sequencing consistency ratio according to the consistency index value and a preset random consistency index value;
if the single-sequencing consistency ratio is smaller than a preset first threshold, determining that the initial test result of the judgment matrix is a test pass;
and if the single-sequencing consistency ratio is greater than or equal to the preset first threshold, determining that the initial test result of the judgment matrix is failed in test.
Further, the index constructing unit is specifically configured to:
if the initial test result of the judgment matrix corresponding to each hierarchy is that the test is passed, generating the initial weight of the index item of each hierarchy according to the judgment matrix;
for the index item of each hierarchy, determining a synthetic weight of the index item relative to a preset target based on the initial weight;
determining a synthesis consistency ratio for the synthesis weights;
if the synthesized consistency ratio is smaller than a preset second threshold value, determining that the target test result of the judgment matrix is passed through the test;
and if the synthesized consistency ratio is larger than or equal to the preset second threshold value, determining that the target detection result of the judgment matrix is failed in detection.
Further, the apparatus further comprises:
the information output unit is used for generating prevention and control decision information according to the early warning information; and outputting the prevention and control decision information.
In a third aspect, the present application provides an epidemic situation early warning apparatus based on hierarchical analysis, comprising:
one or more memories having executable programs stored thereon;
and the one or more processors are used for executing the executable programs in the memory so as to realize the steps of the epidemic situation early warning method based on the hierarchical analysis.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the epidemic situation early warning method based on hierarchical analysis.
This application adopts above technical scheme, possesses following beneficial effect at least:
according to the epidemic situation early warning method and system based on the hierarchical analysis, an index system with a hierarchical relationship can be constructed in advance, so that index items and index weights of all levels are determined based on the index system, epidemic situation data of a target area are scored, early warning information is automatically generated based on scoring, and timely early warning of epidemic situation risks is achieved.
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 application.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flow chart diagram illustrating a method for epidemic situation early warning based on hierarchical analysis according to an exemplary embodiment;
FIG. 2 is a schematic illustration of a hierarchy between various metric items shown in accordance with an exemplary embodiment;
FIG. 3 is a block diagram of an epidemic situation early warning device based on hierarchical analysis according to an exemplary embodiment;
fig. 4 is a block diagram of an epidemic situation early warning device based on hierarchical analysis according to an exemplary embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail below. It should be apparent that the described embodiments are only a few embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, fig. 1 is a flowchart illustrating an epidemic situation early warning method based on hierarchical analysis according to an exemplary embodiment, where the epidemic situation early warning method based on hierarchical analysis includes the following steps:
s11, determining index items of each level and index weight corresponding to each index item according to a preset risk early warning model.
In this embodiment, the execution subject may be an electronic device such as a terminal device or a server.
In this embodiment, the execution subject may pre-construct a risk early warning model, and the risk early warning model may process each item of index data of each region related to the epidemic situation, and generate early warning information for the epidemic situation of each region. The risk early warning model can comprise a plurality of levels of index items, the levels correspond to different index granularities from the bottom level to the top level, and the index granularity of the index items of the levels closer to the top level is larger. The index items closer to the bottom level have smaller index granularity. And moreover, index weights for all index items can be preset in the risk early warning model. For each index item, the index weight corresponding to the index item is determined based on the index level where the index item is located.
As an alternative embodiment, the index items of multiple levels may be constructed by the following steps: sending index consultation information to the terminal corresponding to the target expert, and receiving index items returned by the terminal corresponding to each target expert; for each index item in the index items, determining an index level corresponding to the index item based on semantic analysis; and summarizing the index items of each index level to obtain a corresponding index system. By implementing the optional implementation mode, the more reasonable index system can be automatically generated by combining the expert experience of the target expert and the semantic analysis technology, and the determination accuracy of the index system is improved.
As another alternative implementation, the index weight corresponding to each index item may be determined by the following steps: for each index item, determining the index level where the index item is located, and determining the inter-level weight corresponding to the index level; and determining intra-level weights of the index items in the corresponding index levels; and generating the index weight corresponding to the index item based on the inter-level weight and the intra-level weight. By implementing the optional implementation mode, the index weight can be generated based on the relative importance degree between levels and the relative importance degree of the index in the level, so that the generation accuracy of the index weight is improved.
And S12, scoring the epidemic situation data of the target area by using the index items of each level to obtain an index score corresponding to each index item aiming at the epidemic situation data.
In this embodiment, the execution subject may first acquire epidemic situation data of a target area that needs epidemic situation early warning. The epidemic situation data at least comprises the index data corresponding to the index items. For example, the index item may include the number of people at risk who enter the target area within a certain period, and the epidemic situation data of the target area may include a corresponding number of people. Then, for the index item of each level, the index value corresponding to the index item can be determined from the epidemic situation data, the scoring category to which the index value belongs is determined, and the score corresponding to the scoring category is determined as the index score corresponding to the index item.
And S13, generating early warning information corresponding to the epidemic situation data based on the index weight and the index score corresponding to each index item.
In this embodiment, the execution subject may obtain a total score for epidemic situation data of the target area based on the index weight and the index score corresponding to each index item, and generate the early warning information corresponding to the total score according to a preset association relationship between the total score and the early warning information. The early warning information may include any combination of total score, early warning level, early warning degree and early warning description. Wherein, the higher the total score is, the higher the corresponding early warning level is, it indicates that epidemic situation spreads the risk more greatly. The corresponding early warning description is used for describing the corresponding epidemic situation spreading risk condition.
As an optional implementation manner, the generating of the early warning information corresponding to the epidemic situation data based on the index weight and the index score corresponding to each index item includes:
calculating a weighted score according to the index weight and the index score corresponding to each index item;
summing the weighted scores of the index items to obtain an early warning score;
determining at least one early warning parameter matching the early warning score; wherein the at least one early warning parameter comprises at least one of: early warning level parameters, early warning degree parameters and early warning description parameters;
and determining the early warning score and the at least one early warning parameter as the early warning information corresponding to the epidemic situation data.
In this embodiment, when calculating the early warning score, the following formula may be used:
wherein R is the early warning score, X is the index score, Y is the index weight, and m is the total number of the index items.
Specifically, for the ith index item in the m index items, the weighting score XY corresponding to the ith index item may be calculated according to the index weight and the index score corresponding to the ith index item, and then the weighting scores of the m index items are summed up to obtain the early warning score R.
The value of the early warning score can be any value between 0 and 100, and the closer to 0, the lower the epidemic situation propagation risk of the local area key group is; conversely, closer to 100, the higher the risk of propagation.
Optionally, the warning information may be presented in a form of a table, specifically as shown in table one:
watch 1
As an optional implementation manner, before determining, according to a preset risk early warning model, index items of each hierarchy and an index weight corresponding to each index item, the method further includes:
acquiring a hierarchical structure among the index items;
generating a judgment matrix for each index item based on the hierarchical structure;
carrying out consistency check on the judgment matrix to obtain a target check result; wherein the target test result is that the test is passed or not passed;
and if the target detection result is that the target detection passes the detection, generating the index weight of each index item based on the judgment matrix.
In this embodiment, the executing entity may first obtain the risk pre-warning data from a plurality of data sources, wherein the plurality of data sources may include, but are not limited to, wei Jian commission, public security agency, operator, and other government department business systems closely related to key personnel. Wherein, the risk early warning data can be early warning data related to key population for epidemic situation prevention and control. And the execution main body can acquire initial risk early warning data, and then perform data cleaning, data management and data development on the initial risk early warning data to obtain final risk early warning data. And then, constructing a risk early warning model containing index items of each level based on the analysis of the final risk early warning data.
Specifically, the executive agent may determine an index system for the risk early warning data based on interaction with the target expert. Wherein, the index system is a hierarchical structure among all index items. Referring to fig. 2, fig. 2 is a schematic diagram illustrating a hierarchical structure among index items according to an exemplary embodiment, as shown in fig. 2, the hierarchical structure includes four levels, where the first level is an important group epidemic propagation risk, i.e., a target level constructed by an index system. The second level comprises five index items of popular situation, propagation risk, discovery report, prevention and control capability and medical treatment; the third level comprises an international epidemic situation and a domestic epidemic situation corresponding to the epidemic situation, an international transmission risk corresponding to the transmission risk, a domestic transmission risk and a working field contact risk, a first case and social surface epidemic situation control corresponding to the discovery report, public prevention and control and industrial field prevention and control corresponding to prevention and control capacity, and public medical treatment effect and industrial treatment effect corresponding to medical treatment.
Optionally, for each index item of the third hierarchy, the corresponding index item may be further subdivided. Each index item corresponding to the second level is a first-level index, each index item corresponding to the third level is a second-level index, each index item corresponding to the fourth level divided from the third level downwards is a third-level index, and a specific model index grading table is as follows:
watch 2
Wherein, index assignment can be carried out on the three-level indexes in the table two to obtain an index assignment table, as shown in the table three:
watch III
In the process of scoring the epidemic situation data of the target area by using the index items of each level to obtain the index score corresponding to each index item aiming at the epidemic situation data, the index score corresponding to each index item can be determined by using the corresponding relation between the index data and the index score in the index assignment table.
Then, for each level of index items, a judgment matrix can be generated, and the index weight is determined by performing consistency check on the judgment matrix. The judgment matrix is a set of relative importance of the index item of each level relative to the index item of the previous level. Specifically, for the hierarchical structure shown in fig. 2, the determination matrix corresponding to the index entry (first-level index) of the second layer may be as follows:
watch four
The judgment matrix a corresponding to table four is a matrix with 5 rows and 5 columns, and for the determination method of each value in the matrix, the determination method can be given by a scale method of 1-9 of Saaty, and the scale method is shown in the following table:
watch five
Then, the execution subject may perform consistency check on the determination matrices corresponding to the index items of the respective levels, and determine, for a determination matrix that passes the consistency check, an index weight of an index item of each level based on the determination matrix corresponding to the index item of the level.
As an optional implementation, the method further comprises:
and if the target detection result is that the detection is not passed, adjusting the judgment matrix until the judgment matrix passes the consistency detection, and generating the index weight of each index item based on the judgment matrix after the consistency detection is passed.
In this embodiment, the adjustment of the determination matrix is an adjustment of each numerical value in the determination matrix.
As an optional implementation manner, the generating a determination matrix for each index item based on the hierarchical structure includes:
determining each index item corresponding to each level based on the hierarchical structure;
for each level, a judgment matrix corresponding to the level is generated based on each index item corresponding to the level and the index item of the previous level corresponding to the level.
As an optional implementation manner, the performing consistency check on the judgment matrix to obtain a target check result includes:
for a judgment matrix corresponding to each level, determining an initial test result of the judgment matrix based on the single-sequence consistency ratio of the judgment matrix;
and determining a target test result of the judgment matrix based on the initial test result of the judgment matrix corresponding to each hierarchy and the index item corresponding to each hierarchy.
As an optional implementation manner, the determining, for the judgment matrix corresponding to each hierarchy, an initial test result of the judgment matrix based on a single-rank consistency ratio of the judgment matrix includes:
calculating the maximum eigenvalue of the judgment matrix for the judgment matrix corresponding to each level;
determining a consistency index value of the judgment matrix based on the maximum characteristic value and the order of the judgment matrix;
determining a single-sequencing consistency ratio according to the consistency index value and a preset random consistency index value;
if the single-sequencing consistency ratio is smaller than a preset first threshold, determining that the initial test result of the judgment matrix is a test pass;
and if the single-sequencing consistency ratio is greater than or equal to the preset first threshold, determining that the initial test result of the judgment matrix is failed in test.
In this embodiment, taking the single-level determination matrix of table four as an example, the maximum eigenvalue λ max and the order n of the determination matrix a may be determined for the determination matrix a corresponding to table four, and the consistency index value corresponding to the determination matrix a may be determined based on the following determination consistency calculation formula:
wherein, CI is a consistency index value corresponding to the judgment matrix A, and when CI =0, the judgment matrix has complete consistency; when CI is close to 0, there is satisfactory consistency; the larger the CI, the more severe the inconsistency.
Meanwhile, the execution main body can also obtain a preset random consistency index value, 500 judgment matrixes are randomly generated according to orders of 3 orders, 4 orders, 5 orders and the like in a Saaty mode to obtain a random consistency index value RI, and the calculation process of the RI for a certain order is as follows:
wherein the content of the first and second substances,
a consistency index value representing a1 st judgment matrix generated at random,
up to
That is, there are 500 consistency index values in total for the consistency index values of the corresponding determination matrix.
Represents the maximum eigenvalue of the 1 st decision matrix that is randomly generated,
up to
That is, the maximum eigenvalue of the corresponding decision matrix, there are 500 maximum eigenvalues of the decision matrix in total.
Thereafter, a single rank consistency ratio may be calculated based on the following formula:
when CR is less than 0.1, the judgment matrix is considered to have satisfactory consistency; if CR is more than or equal to 0.1, more epidemic situation expert opinions need to be introduced, and the judgment matrix is corrected. The first threshold may be 0.1.
As an optional implementation manner, the determining, based on the initial test result of the determination matrix corresponding to each hierarchy and the index item corresponding to each hierarchy, a target test result of the determination matrix includes:
if the initial test result of the judgment matrix corresponding to each hierarchy is that the test is passed, generating the initial weight of the index item of each hierarchy according to the judgment matrix;
for the index item of each hierarchy, determining a synthetic weight of the index item relative to a preset target based on the initial weight;
determining a synthesis consistency ratio for the synthesis weights;
if the synthesized consistency ratio is smaller than a preset second threshold value, determining that the target test result of the judgment matrix is passed through the test;
and if the composite consistency ratio is larger than or equal to the preset second threshold value, determining that the target test result of the judgment matrix is failed in test.
In the embodiment, by constructing the index weight vectors layer by layer, the weight vectors of the third level relative to a certain index of the second level and the second level relative to a certain index of the first level in the epidemic situation prevention and control risk early warning model can be calculated. However, for the epidemic situation prevention and control risk early warning model index system, the influence weight of the lowest-level index on the final target, namely the synthetic weight, needs to be determined. In a multi-layer index system, the influence of the k-layer index system on a final target is equal to the influence of the k-1-layer index system on the final target multiplied by the influence of the k-layer index system on the k-1-layer index system, and the transitivity of the index system on the final target is fully reflected in the calculation process.
Specifically, if the initial test result of the determination matrix corresponding to each hierarchy is a pass test, the initial weight of the index item of each hierarchy may be generated according to the determination matrix corresponding to each hierarchy. The initial weight represents the weight of each index item between the same levels, and the sum of the weights of each index item between the same levels is 1.
Then, the result of the single-level sorting is used for calculating the composite weight of each element in each level according to the top-down sequence. The composite weight is used for representing the weight of each index item relative to the previous level and the weight of each index item relative to a preset target between the same levels. The preset target may be the first layer target in fig. 2. After the synthesis weights are obtained, a synthesis consistency ratio of the synthesis weights may be determined; if the synthesized consistency ratio is smaller than a preset second threshold value, determining that the target test result of the judgment matrix is passed through the test; and if the synthesized consistency ratio is larger than or equal to the preset second threshold value, determining that the target detection result of the judgment matrix is failed in detection. The principle level list ordering consistency verification of consistency verification is similar, and is not described herein again.
For example, if the level B is a lower-level indicator, the level a is an upper-level indicator, the level B indicator is CIm for the level-single-sort consistency indicator of a1, a2, a.. And a3 (j =1,2, …, m) in the level a indicator, and the random consistency indicator is RIm, the calculation formula of the composite consistency ratio CR is:
when CR <0.1, the calculated result of the total hierarchical ordering is considered to pass consistency check and have satisfactory consistency, otherwise, the judgment matrix needs to be readjusted to enable the judgment matrix to accord with consistency check rules.
As an optional implementation, the method further comprises:
generating prevention and control decision information according to the early warning information;
and outputting the prevention and control decision information.
In this embodiment, a corresponding relationship between the early warning information and the prevention and control decision information may be established in advance, so as to generate the prevention and control decision information according to the early warning information, and output the prevention and control decision information to implement hierarchical management.
The index system with the hierarchical relationship can be constructed in advance, so that the index items and the index weights of all levels are determined based on the index system, epidemic situation data of a target area are scored, early warning information is automatically generated based on scoring, and timely early warning of epidemic situation risks is achieved.
Referring to fig. 3, fig. 3 is a block diagram of an epidemic situation early warning apparatus based on hierarchical analysis according to an exemplary embodiment, and as shown in fig. 3, the epidemic situation early warning apparatus based on hierarchical analysis includes:
the index determining unit 301 is configured to determine, according to a preset risk early warning model, index items of each hierarchy and an index weight corresponding to each index item;
the score determining unit 302 is configured to score epidemic situation data in a target area by using the index items of each hierarchy to obtain an index score corresponding to each index item for the epidemic situation data;
and the information generating unit 303 is configured to generate early warning information corresponding to the epidemic situation data based on the index weight and the index score corresponding to each index item.
As an optional implementation manner, the information generating unit 303 is specifically configured to:
calculating a weighted score according to the index weight and the index score corresponding to each index item;
summing the weighted scores of the index items to obtain an early warning score;
determining at least one early warning parameter matching the early warning score; wherein the at least one early warning parameter comprises at least one of: the early warning level parameters, the early warning degree parameters and the early warning description parameters;
and determining the early warning score and the at least one early warning parameter as the early warning information corresponding to the epidemic situation data.
As an optional implementation, the apparatus further comprises:
the index construction unit is used for acquiring a hierarchical structure among the index items before determining the index items of each hierarchy and the index weight corresponding to each index item according to a preset risk early warning model; generating a judgment matrix for each index item based on the hierarchical structure; carrying out consistency check on the judgment matrix to obtain a target check result; wherein the target test result is that the test is passed or not passed; and if the target detection result is that the target detection passes the detection, generating the index weight of each index item based on the judgment matrix.
As an optional implementation manner, the index constructing unit is further configured to:
and if the target detection result is that the detection is not passed, adjusting the judgment matrix until the judgment matrix passes the consistency detection, and generating the index weight of each index item based on the judgment matrix after the consistency detection is passed.
As an optional implementation manner, the index constructing unit is specifically configured to:
determining each index item corresponding to each level based on the hierarchical structure;
and for each level, generating a judgment matrix corresponding to the level based on each index item corresponding to the level and the index item of the previous level corresponding to the level.
As an optional implementation manner, the index constructing unit is specifically configured to:
for a judgment matrix corresponding to each level, determining an initial test result of the judgment matrix based on the single-sequence consistency ratio of the judgment matrix;
and determining a target test result of the judgment matrix based on the initial test result of the judgment matrix corresponding to each hierarchy and the index item corresponding to each hierarchy.
As an optional implementation manner, the index constructing unit is specifically configured to:
calculating the maximum eigenvalue of the judgment matrix for the judgment matrix corresponding to each level;
determining a consistency index value of the judgment matrix based on the maximum characteristic value and the order of the judgment matrix;
determining a single-sequencing consistency ratio according to the consistency index value and a preset random consistency index value;
if the single-sequencing consistency ratio is smaller than a preset first threshold, determining that the initial test result of the judgment matrix is a test pass;
and if the single-sequencing consistency ratio is greater than or equal to the preset first threshold, determining that the initial test result of the judgment matrix is failed in test.
As an optional implementation manner, the index constructing unit is specifically configured to:
if the initial test result of the judgment matrix corresponding to each level is passed through the test, generating the initial weight of the index item of each level according to the judgment matrix;
for the index item of each hierarchy, determining a synthetic weight of the index item relative to a preset target based on the initial weight;
determining a synthesis consistency ratio for the synthesis weights;
if the synthesized consistency ratio is smaller than a preset second threshold value, determining that the target test result of the judgment matrix is passed through the test;
and if the composite consistency ratio is larger than or equal to the preset second threshold value, determining that the target test result of the judgment matrix is failed in test.
As an optional implementation, the apparatus further comprises:
the information output unit is used for generating prevention and control decision information according to the early warning information; and outputting the prevention and control decision information.
With regard to the epidemic situation early warning apparatus based on hierarchical analysis in the above embodiment, the specific manner in which each module performs operations has been described in detail in the above embodiment of the related method, and will not be described in detail here.
Referring to fig. 4, fig. 4 is a block diagram of an epidemic situation early warning apparatus based on hierarchical analysis according to an exemplary embodiment, where the epidemic situation early warning apparatus based on hierarchical analysis includes:
one or more memories 401 having executable programs stored thereon;
one or more processors 402 configured to execute the executable program in the memory 401 to implement the steps of the epidemic situation early warning method based on hierarchical analysis.
The epidemic situation early warning device based on hierarchical analysis in the above embodiment may be a server in practical application, and the specific way of executing the program in the memory 401 by the processor 402 thereof has been described in detail in the embodiment related to the method, and will not be elaborated herein.
In addition, the present application provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps of the epidemic situation early warning method based on hierarchical analysis are implemented.
The storage medium may be a magnetic Disk, an optical Disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD) or a Solid State Drive (SSD), etc.; the storage medium may also comprise a combination of memories of the kind described above.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar parts in other embodiments may be referred to for the content which is not described in detail in some embodiments.
It should be noted that, in the description of the present application, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, in the description of the present application, the meaning of "plurality" means at least two unless otherwise specified.
It will be understood that when an element is referred to as being "secured to" or "disposed on" another element, it can be directly on the other element or intervening elements may also be present; when an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present, and further, as used herein, connected may include wirelessly connected; the term "and/or" is used to include any and all combinations of one or more of the associated listed items.
Any process or method descriptions in the flow charts or otherwise described herein may be understood as: represents modules, segments or portions of code which include one or more executable instructions for implementing specific logical functions or steps of a process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried out in the method for implementing the above embodiment may be implemented by hardware that is related to instructions of a program, and the program may be stored in a computer readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (10)
1. An epidemic situation early warning method based on hierarchical analysis is characterized by comprising the following steps:
determining index items of each level and index weight corresponding to each index item according to a preset risk early warning model;
grading epidemic situation data of a target area by using the index items of each level to obtain index grades corresponding to each index item aiming at the epidemic situation data;
and generating early warning information corresponding to the epidemic situation data based on the index weight and the index score corresponding to each index item.
2. The method according to claim 1, wherein generating early warning information corresponding to the epidemic situation data based on the index weight and the index score corresponding to each index item comprises:
calculating a weighted score according to the index weight and the index score corresponding to each index item;
summing the weighted scores of the index items to obtain an early warning score;
determining at least one early warning parameter matching the early warning score; the at least one early warning parameter comprises at least one of: early warning level parameters, early warning degree parameters and early warning description parameters;
and determining the early warning score and the at least one early warning parameter as the early warning information corresponding to the epidemic situation data.
3. The method according to claim 1, before determining the index items of each level and the index weight corresponding to each index item according to a preset risk early warning model, the method further comprises:
acquiring a hierarchical structure among the index items;
generating a judgment matrix for each index item based on the hierarchical structure;
carrying out consistency check on the judgment matrix to obtain a target check result; the target inspection result is that the inspection is passed or not passed; and if the target detection result is that the target detection result passes the detection, generating index weight of each index item based on the judgment matrix.
4. The method of claim 3, further comprising: and if the target detection result is that the detection is not passed, adjusting the judgment matrix until the judgment matrix passes the consistency detection, and generating the index weight of each index item based on the judgment matrix passing the consistency detection.
5. The method of claim 3, wherein generating a determination matrix for each of the indicator items based on the hierarchical structure comprises:
determining each index item corresponding to each level based on the hierarchical structure;
and for each level, generating a judgment matrix corresponding to the level based on each index item corresponding to the level and the index item of the previous level corresponding to the level.
6. The method of claim 5, wherein the performing the consistency check on the judgment matrix to obtain the target check result comprises:
for a judgment matrix corresponding to each level, determining an initial test result of the judgment matrix based on the single-sequence consistency ratio of the judgment matrix;
and determining a target test result of the judgment matrix based on the initial test result of the judgment matrix corresponding to each hierarchy and the index item corresponding to each hierarchy.
7. The method according to claim 6, wherein the determining, for each hierarchical level corresponding judgment matrix, an initial test result of the judgment matrix based on a single-rank consistency ratio of the judgment matrix comprises:
calculating the maximum eigenvalue of the judgment matrix for the judgment matrix corresponding to each level;
determining a consistency index value of the judgment matrix based on the maximum characteristic value and the order of the judgment matrix;
determining a single-sequencing consistency ratio according to the consistency index value and a preset random consistency index value;
if the single-sequencing consistency ratio is smaller than a preset first threshold, determining that the initial test result of the judgment matrix is a test pass; and if the single-sequence consistency ratio is larger than or equal to the preset first threshold, determining that the initial test result of the judgment matrix is failed in test.
8. The method according to claim 7, wherein the determining a target test result of the determination matrix based on the initial test result of the determination matrix corresponding to each hierarchy and the index item corresponding to each hierarchy comprises:
if the initial test result of the judgment matrix corresponding to each hierarchy is that the test is passed, generating the initial weight of the index item of each hierarchy according to the judgment matrix;
for the index item of each hierarchy, determining a synthetic weight of the index item relative to a preset target based on the initial weight;
determining a synthesis consistency ratio for the synthesis weights;
if the synthesized consistency ratio is smaller than a preset second threshold value, determining that the target test result of the judgment matrix is a test pass; and if the composite consistency ratio is larger than or equal to the preset second threshold value, determining that the target test result of the judgment matrix is failed in test.
9. The method according to any one of claims 1 to 8, further comprising: generating prevention and control decision information according to the early warning information; and outputting the prevention and control decision information.
10. The utility model provides an epidemic situation early warning device based on hierarchical analysis which characterized in that, the device includes:
the index determining unit is used for determining index items of all levels and index weights corresponding to the index items according to a preset risk early warning model;
the grading determination unit is used for grading the epidemic situation data of the target area by using the index items of each level to obtain an index grade corresponding to each index item aiming at the epidemic situation data;
and the information generating unit is used for generating early warning information corresponding to the epidemic situation data based on the index weight and the index score corresponding to each index item.
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