Influence factor intelligent analysis method based on nasosinusitis

Abstract

The invention relates to the technical field of nasosinusitis analysis, and discloses a nasosinusitis-based influence factor intelligent analysis method and system, and the method comprises the steps: constructing a spatial distribution diagram of nasosinusitis patients, analyzing the spatial distribution correlation of the nasosinusitis patients, carrying out the spatial distribution division of the nasosinusitis patients, and obtaining regional distribution patients; querying patient growth data of the regional distribution patients, constructing a seasonal distribution model of the regional distribution patients by using the patient growth data, and identifying environmental conditions of the regional distribution patients so as to construct an environmental pathological model of the regional distribution patients; a seasonal distribution model and an environmental pathology model are utilized to analyze the disease influence degree of environmental variables in a geographical environment where the nasosinusitis patient is located on the nasosinusitis patient, target variables in the environmental variables are extracted based on the disease influence degree, pathology characteristics of the nasosinusitis patient are analyzed, and influence factors of the pathology characteristics are determined by utilizing the target variables. According to the method, the comprehensiveness of analysis of the influence factors of the nasosinusitis can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of low-carbon optimization of bridges, and particularly to an intelligent analysis method for influencing factors based on sinusitis. Background Art

[0002] The sinuses are recessed parts in the head bones, connected to the nasal cavity, and play a role in filtering, humidifying, and warming the air. When the sinus mucosa is infected, damaged, or blocked, mucus cannot be discharged, leading to the growth of bacteria and the occurrence of inflammation, thus resulting in the formation of sinusitis. Common symptoms of sinusitis include nasal congestion, nasal discharge, facial pressure or pain, sore throat, headache, etc. Sinusitis can be acute or chronic, and severe sinusitis may cause complications such as sinus empyema, eye infections, intracranial infections, etc. Since there are many influencing factors for inducing sinusitis, analyzing the influencing factors of sinusitis can determine the main causes of the disease, and then corresponding preventive measures can be formulated.

[0003] Currently, the analysis of influencing factors for sinusitis is generally achieved through the physical manifestation characteristics of patients. These physical manifestation characteristics are such as the patient's own poor resistance (over-fatigue, getting cold and wet), the patient having diseases in the nasal cavity (allergic rhinitis, nasal polyps), and external contacts (water entering the nose when swimming, nasal packing for too long). The solution for analyzing the influencing factors of sinusitis through physical manifestation characteristics does not consider the influence brought by environmental changes caused by geographical differences, resulting in an incomplete analysis of the influencing factors of sinusitis. Summary of the Invention

[0004] To solve the above problems, the present invention provides an intelligent analysis method for influencing factors based on sinusitis, which can improve the comprehensiveness of the analysis of influencing factors of sinusitis.

[0005] In a first aspect, the present invention provides an intelligent analysis method for influencing factors based on sinusitis, including:

[0006] Obtaining the spatial distribution information of sinusitis patients, and using the spatial distribution information to construct a spatial distribution map of the sinusitis patients;

[0007] According to the spatial distribution map, analyzing the spatial distribution correlation of the sinusitis patients, and based on the spatial distribution correlation, dividing the spatial distribution of the sinusitis patients to obtain regionally distributed patients;

[0008] Querying the patient growth data of the regionally distributed patients, using the patient growth data to construct a seasonal distribution model of the regionally distributed patients, identifying the environmental conditions where the regionally distributed patients are located, and constructing an environmental pathology model of the regionally distributed patients based on the environmental conditions;

[0009] Using the seasonal distribution model and the environmental pathology model, analyze the influence degree of environmental variables in the geographical environment where the sinusitis patient is located on the disease of the sinusitis patient, extract the target variables in the environmental variables based on the influence degree, analyze the pathological characteristics of the sinusitis patient, and use the target variables to determine the influencing factors of the pathological characteristics.

[0010] In a possible implementation manner of the first aspect, the constructing the spatial distribution map of the sinusitis patient by using the spatial distribution information includes:

[0011] Determine the distribution range of the sinusitis patient based on the spatial distribution information;

[0012] Construct a view framework of the sinusitis patient according to the distribution range;

[0013] Identify the geographical distribution information of the sinusitis patient;

[0014] Perform geographical information annotation on the view framework based on the geographical distribution information to obtain an annotated view;

[0015] Perform three-dimensional display on the annotated view to obtain a spatial distribution map.

[0016] In a possible implementation manner of the first aspect, the analyzing the spatial distribution correlation of the sinusitis patient according to the spatial distribution map includes:

[0017] Perform spatial partitioning on the spatial distribution map to obtain partitioned spaces;

[0018] Calculate the spatial correlation index of the sinusitis patient based on the partitioned spaces in combination with the following formula:

[0019]

[0020] where y represents the spatial correlation index, m represents the number of partitioned spaces, μ ij represents the weight matrix of the partitioned spaces, A i represents the number of sinusitis patients in the i-th partitioned space, A j represents the number of sinusitis patients in the j-th partitioned space, and A represents the average number of sinusitis patients in the partitioned spaces;

[0021] Analyze the spatial distribution correlation of the sinusitis patient according to the spatial correlation index.

[0022] In a possible implementation manner of the first aspect, the analyzing the spatial distribution correlation of the sinusitis patient according to the spatial correlation index includes:

[0023] Calculate the correlation score value of the spatial correlation index by using the following formula:

[0024]

[0025] And,

[0026] where S represents the relevant score value, y represents the spatial correlation index, m represents the number of divided spaces, E[y] represents the spatial aggregation degree, and V[y] represents the spatial dispersion degree;

[0027] Analyze the spatial distribution correlation of the patients with sinusitis according to the relevant score value.

[0028] In a possible implementation manner of the first aspect, the spatial distribution of the patients with sinusitis is divided based on the spatial distribution correlation to obtain regionally distributed patients, including:

[0029] Identify the spatial distribution characteristics of the patients with sinusitis according to the spatial distribution correlation;

[0030] Construct a topological map of the patients with sinusitis based on the spatial distribution characteristics;

[0031] Use the topological map to divide the spatial distribution of the patients with sinusitis to obtain regionally distributed patients.

[0032] In a possible implementation manner of the first aspect, constructing a seasonal distribution model of the regionally distributed patients by using the patient growth data includes:

[0033] Construct an initial seasonal distribution model of the regionally distributed patients;

[0034] Use the patient growth data to determine the differential time constant of the initial seasonal distribution model;

[0035] Based on the differential time constant, calculate the parameter adjustment value of the initial seasonal distribution model by using the following formula:

[0036]

[0037] where α represents the adjustment parameter value, β represents the initial parameter of the initial seasonal distribution model, T1 represents the integration time constant, T D represents the differential time constant, t represents time, and d represents the parameter adjustment direction;

[0038] Perform a parameter adjustment operation on the initial seasonal distribution model based on the parameter adjustment value to obtain a seasonal distribution model.

[0039] In a possible implementation manner of the first aspect, constructing an environmental pathology model of the regionally distributed patients based on the environmental conditions includes:

[0040] Collect the geographical and ecological data of the patients distributed in the area according to the environmental conditions;

[0041] Construct an initial environmental pathology model for the patients distributed in the area. After inputting the geographical and ecological data into the initial environmental pathology model, calculate the model accuracy of the initial environmental pathology model;

[0042] Based on the model accuracy, adjust the initial environmental pathology model, and calculate the model accuracy after the model adjustment. When the model accuracy after the model adjustment meets the preset accuracy, obtain the environmental pathology model.

[0043] In a possible implementation manner of the first aspect, calculating the model accuracy of the initial environmental pathology model includes:

[0044] Calculate the model accuracy of the initial environmental pathology model by using the following formula:

[0045]

[0046] where precision represents the model accuracy, MP represents the percentage error of the initial environmental pathology model, RS represents the root mean square error of the initial environmental pathology model, ME represents the mean absolute error of the initial environmental pathology model, θ represents the fitting accuracy of the initial environmental pathology model, U represents the amount of geographical and ecological data input into the initial environmental pathology model, B i represents the predicted value of the i-th geographical and ecological data in the initial environmental pathology model, represents the true value of the i-th geographical and ecological data of the initial environmental pathology model, represents the mean value of the true values of the i-th geographical and ecological data of the initial environmental pathology model.

[0047] In a possible implementation manner of the first aspect, analyzing the influence degree of environmental variables in the geographical environment where the sinusitis patients are located on the incidence of the sinusitis patients by using the seasonal distribution model and the niche model includes:

[0048] Construct a regression function of the seasonal distribution model and the environmental pathology model;

[0049] Calculate the regression coefficient of the regression function by using the following formula:

[0050]

[0051] where γ represents the regression coefficient, represents the analysis value of the regression function, represents the average value of the regression function, C i represents the mean square deviation of the average value;

[0052] Calculate the fitted value of the regression function according to the regression coefficient and in combination with the following formula:

[0053]

[0054] where ε represents the fitted value, M represents the number of independent variables in the regression function, N represents the number of dependent variables in the regression function, γ represents the regression coefficient, f() represents the regression function, R i represents the i-th independent variable in the regression function, and T j represents the j-th dependent variable in the regression function;

[0055] Analyze the influence degree of the environmental variables in the geographical environment where the sinusitis patients are located on the incidence of sinusitis according to the fitted value.

[0056] In a second aspect, the present invention provides an intelligent analysis system based on influencing factors of sinusitis. The system includes:

[0057] A distribution map construction module, configured to obtain the spatial distribution information of sinusitis patients, and use the spatial distribution information to construct a spatial distribution map of the sinusitis patients;

[0058] A region division module, configured to analyze the spatial distribution correlation of the sinusitis patients according to the spatial distribution map, and based on the spatial distribution correlation, divide the spatial distribution of the sinusitis patients to obtain regionally distributed patients;

[0059] A model construction module, configured to query the patient growth data of the regionally distributed patients, use the patient growth data to construct a seasonal distribution model of the regionally distributed patients, identify the environmental conditions where the regionally distributed patients are located, and based on the environmental conditions, construct an environmental pathology model of the regionally distributed patients;

[0060] An influence analysis module, configured to use the seasonal distribution model and the environmental pathology model to analyze the influence degree of the environmental variables in the geographical environment where the sinusitis patients are located on the incidence of sinusitis, extract the target variables in the environmental variables based on the influence degree, analyze the pathological characteristics of the sinusitis patients, and use the target variables to determine the influencing factors of the pathological characteristics.

[0061] Compared with the prior art, the technical principle and beneficial effects of the present solution are as follows:

[0062] In the embodiments of the present invention, by obtaining the spatial distribution information of patients with sinusitis, the distribution characteristics of the patients with sinusitis among different locations or regions can be understood, such as the distribution range, density, aggregation degree, etc. And according to the spatial distribution map, analyzing the spatial distribution correlation of the patients with sinusitis can help users understand the geographical connections between different things and the possible interactions between them, such as the population quantity, natural resource distribution, and disease distribution, etc. Further, in the embodiments of the present invention, based on the spatial distribution correlation, the spatial distribution of the patients with sinusitis is divided to obtain the patients in regional distribution, and the geographical location data of the patients with sinusitis can be associated and analyzed with other environmental, population or geographical factors, and by querying the patient growth data of the patients in regional distribution, the growth quantity of the patients with sinusitis in a certain period of time in this region can be understood. Further, in the embodiments of the present invention, by using the patient growth data, a seasonal distribution model of the patients in regional distribution is constructed to understand the change trend of the patients with sinusitis in different seasons, and further understand the influence of factors such as temperature, humidity, and wind direction on the sinusitis, and by using the seasonal distribution model and the niche model, analyzing the influence degree of the environmental variables in the geographical environment where the patients with sinusitis are located on the incidence of sinusitis can reveal the influence degree of different environmental factors on the disease risk, which helps to understand the pathogenesis of sinusitis and provide more accurate prevention and treatment guidance for patients. An intelligent analysis method and system for influencing factors based on sinusitis proposed in the embodiments of the present invention can improve the comprehensiveness of the analysis of influencing factors of sinusitis. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present invention and used together with the specification to explain the principles of the present invention.

[0064] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without creative efforts.

[0065] Figure 1 It is a schematic flowchart of an intelligent analysis method for influencing factors based on sinusitis provided by an embodiment of the present invention;

[0066] Figure 2 It is a schematic diagram of modules of an intelligent analysis system for influencing factors based on sinusitis provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0067] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0068] An embodiment of the present invention provides an intelligent analysis method for influencing factors based on sinusitis. The execution subject of the intelligent analysis method for influencing factors based on sinusitis includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present invention. In other words, the intelligent analysis method for influencing factors based on sinusitis can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, Content Delivery Network (CDN), and big data and artificial intelligence platforms.

[0069] Refer to Figure 1 As shown, it is a schematic flowchart of an intelligent analysis method for influencing factors based on sinusitis provided by an embodiment of the present invention. Among them, Figure 1 The intelligent analysis method for influencing factors based on sinusitis described in

[0070] S1. Obtain the spatial distribution information of sinusitis patients, and use the spatial distribution information to construct a spatial distribution map of the sinusitis patients.

[0071] Through the above-mentioned obtaining the spatial distribution information of sinusitis patients, the embodiment of the present invention can understand the distribution characteristics of the sinusitis patients among different locations or regions, such as the distribution range, density, aggregation degree, etc.

[0072] Furthermore, through the above-mentioned using the spatial distribution information to construct the spatial distribution map of the sinusitis patients, the embodiment of the present invention can help us understand the spatial patterns and distribution laws of geographical phenomena, so as to better understand the natural environment and human activities on the earth. Among them, the spatial distribution map refers to a visualization map that shows the phenomena or attributes of spatial positions.

[0073] As an embodiment of the present invention, the using the spatial distribution information to construct the spatial distribution map of the sinusitis patients includes: determining the distribution range of the sinusitis patients based on the spatial distribution information, constructing a view framework of the sinusitis patients according to the distribution range, identifying the geographical distribution information of the sinusitis patients, performing geographical information annotation on the view framework based on the geographical distribution information to obtain an annotated view, and performing three-dimensional display on the annotated view to obtain a spatial distribution map.

[0074] Among them, the distribution range refers to the size of the distributed area, the view framework refers to the basic structure of the view such as size and hierarchical structure, and the geographical distribution information refers to information such as geographical names, locations, and climates.

[0075] Optionally, determining the distribution range of the sinusitis patients based on the spatial distribution information is determined by querying the geographical distribution interval of the sinusitis patients. Constructing the view framework of the sinusitis patients according to the distribution range is to construct the view framework of the sinusitis patients through equal ratio scaling using the mysql tool with the distribution range. Identifying the geographical distribution information of the sinusitis patients is identified through a geographical view tool. Geographically annotating the view framework based on the geographical distribution information, and obtaining the annotated view is annotated through a script constructed by java. Three-dimensionally displaying the annotated view, and obtaining the spatial distribution map is displayed through GIS software.

[0076] S2. According to the spatial distribution map, analyze the spatial distribution correlation of the sinusitis patients, and based on the spatial distribution correlation, divide the spatial distribution of the sinusitis patients to obtain regionally distributed patients.

[0077] In an embodiment of the present invention, analyzing the spatial distribution correlation of the sinusitis patients according to the spatial distribution map can help users understand the geographical connections between different things and the possible interactions between them, such as population quantity, natural resource distribution, and disease distribution. Among them, the geographical distribution correlation refers to the relationship between the distribution patterns of two or more things in the geographical space.

[0078] As an embodiment of the present invention, analyzing the spatial distribution correlation of the sinusitis patients according to the spatial distribution map includes: performing spatial partitioning on the spatial distribution map to obtain partitioned spaces, and calculating the spatial correlation index of the sinusitis patients based on the partitioned spaces in combination with the following formula:

[0079]

[0080] Among them, y represents the spatial correlation index, m represents the number of partitioned spaces, μ ij represents the weight matrix of the partitioned spaces, A i represents the number of sinusitis patients in the i-th partitioned space, A j represents the number of sinusitis patients in the j-th partitioned space, and A represents the average number of sinusitis patients in the partitioned spaces.

[0081] Analyze the spatial distribution correlation of the sinusitis patients according to the spatial correlation index.

[0082] Optionally, the spatial partitioning of the spatial distribution map to obtain partitioned spaces is achieved by performing location partitioning using the geographical locations corresponding to the spatial distribution map.

[0083] Further, in another optional embodiment of the present invention, the analysis of the spatial distribution correlation of the sinusitis patients based on the spatial correlation index includes: calculating the correlation score value of the spatial correlation index using the following formula:

[0084]

[0085] And,

[0086] where S represents the correlation score value, y represents the spatial correlation index, m represents the number of partitioned spaces, E[y] represents the spatial aggregation degree, and V[y] represents the spatial dispersion degree;

[0087] Analyze the spatial distribution correlation of the sinusitis patients based on the score value.

[0088] Among them, the correlation score value represents the degree of spatial correlation. When the score value is 0.6, it indicates a strong correlation, and it can also be set according to the actual application scenario.

[0089] Further, in the embodiment of the present invention, through the spatial distribution correlation, the spatial distribution of the sinusitis patients is partitioned to obtain regional distribution patients, and the geographical location data of the sinusitis patients can be associated and analyzed with other environmental, population, or geographical factors.

[0090] As an embodiment of the present invention, the partitioning of the spatial distribution of the sinusitis patients based on the spatial distribution correlation to obtain regional distribution patients includes: identifying the spatial distribution characteristics of the sinusitis patients according to the spatial distribution correlation, constructing a topological graph of the sinusitis patients based on the spatial distribution characteristics, and using the topological graph to perform spatial distribution partitioning on the sinusitis patients to obtain regional distribution patients. Among them, the spatial distribution characteristics refer to the characteristics of the distribution of the sinusitis patients in space, such as continuous type, dense type, etc., and the topological graph refers to a graphical representation used to describe the connection relationships between elements in a system or network.

[0091] Optionally, the identification of the spatial distribution characteristics of the sinusitis patients according to the spatial distribution correlation is obtained by setting those with a correlation reaching a preset requirement as similar classes and identifying the distribution characteristics of the similar classes. The construction of the topological graph of the sinusitis patients based on the spatial distribution characteristics is obtained by connecting regions with the same distribution characteristics.

[0092] S3. Query the patient growth data of the patients distributed in the region. Using the patient growth data, construct a seasonal distribution model for the patients distributed in the region, identify the environmental conditions of the patients distributed in the region, and construct an environmental pathology model for the patients distributed in the region based on the environmental conditions.

[0093] In the embodiment of the present invention, by querying the patient growth data of the patients distributed in the region, the growth quantity of the sinusitis patients in a certain period of time in this region can be understood.

[0094] Furthermore, in the embodiment of the present invention, by using the patient growth data to construct a seasonal distribution model for the patients distributed in the region, the change trend of the sinusitis patients in different seasons can be understood, and thus the influence of factors such as temperature, humidity, and wind direction on the sinusitis can be understood. Among them, the seasonal distribution model refers to an analysis method that can compare and analyze data in different time periods or regions to understand their seasonal differences. The seasonal distribution model includes a data input module, a data processing module, a data analysis module, and a data output module. The input module can receive the patient data of the sinusitis patients. The data processing module can perform data cleaning, duplicate removal, and classification processing on the patient data of the sinusitis patients. The data analysis module can analyze the seasonal influence relationship of the sinusitis patients. The data output module can output the data analysis result of the sinusitis patients.

[0095] As an embodiment of the present invention, using the patient growth data to construct a seasonal distribution model for the patients distributed in the region includes: constructing an initial seasonal distribution model for the patients distributed in the region, using the patient growth data to determine the differential time constant of the initial seasonal distribution model, and based on the differential time constant, calculating the parameter adjustment value of the initial seasonal distribution model using the following formula:

[0096]

[0097] where α represents the adjustment parameter value, β represents the initial parameter of the initial seasonal distribution model, T1 represents the integration time constant, T D represents the differential time constant, t represents time, and d represents the parameter adjustment direction;

[0098] Based on the parameter adjustment value, perform the parameter adjustment operation of the initial seasonal distribution model to obtain the seasonal distribution model.

[0099] Among them, the differential time constant refers to the time required for the output signal to change after the input signal changes in the model.

[0100] Optionally, the initial seasonal distribution model of the patients distributed in the region can be constructed by a time series model.

[0101] In the embodiments of the present invention, by identifying the environmental conditions of the patients in the regional distribution, the correlation between the high-incidence areas of sinusitis and certain specific environmental factors can be found, so as to formulate relevant health policies and guiding bases.

[0102] Optionally, the environmental conditions of the patients in the regional distribution are identified by querying the geographical environment information of the patients in the regional distribution.

[0103] In the embodiments of the present invention, by constructing the environmental pathology model of the patients in the regional distribution based on the environmental conditions, the contribution degree of different environmental factors to the disease can be evaluated, and the prevention and control strategies of the disease can be optimized, etc. Among them, the environmental pathology model refers to a simulation or description model for studying the environmental factors of the disease in the process of the occurrence, development and spread of the disease.

[0104] As an embodiment of the present invention, constructing the environmental pathology model of the patients in the regional distribution based on the environmental conditions includes: according to the environmental conditions, collecting the geographical ecological data of the patients in the regional distribution, constructing the initial environmental pathology model of the patients in the regional distribution, after inputting the geographical ecological data into the initial environmental pathology model, calculating the model accuracy of the initial environmental pathology model, adjusting the initial environmental pathology model based on the model accuracy, calculating the model accuracy after the model adjustment, and obtaining the environmental pathology model when the model accuracy after the model adjustment meets the preset accuracy.

[0105] Optionally, the initial environmental pathology model of the patients in the regional distribution is obtained by training a large amount of historical sinusitis patient data through a deep learning model.

[0106] Furthermore, in another optional embodiment of the present invention, calculating the model accuracy of the initial environmental pathology model includes:

[0107]

[0108] Where precision represents the model accuracy, MP represents the percentage error of the initial environmental pathology model, RS represents the root mean square error of the initial environmental pathology model, ME represents the mean absolute error of the initial environmental pathology model, θ represents the fitting accuracy of the initial environmental pathology model, U represents the amount of geographical ecological data input into the initial environmental pathology model, B i represents the predicted value of the i-th geographical ecological data in the initial environmental pathology model, represents the true value of the i-th geographical ecological data in the initial environmental pathology model, represents the mean value of the true values of the i-th geographical ecological data in the initial environmental pathology model.

[0109] S4. Using the seasonal distribution model and the environmental pathology model, analyze the impact degree of environmental variables in the geographical environment where the sinusitis patient is located on the disease of the sinusitis patient, extract the target variables from the environmental variables based on the impact degree, analyze the pathological characteristics of the sinusitis patient, and use the target variables to determine the influencing factors of the pathological characteristics.

[0110] In the embodiment of the present invention, by using the seasonal distribution model and the niche model to analyze the impact degree of environmental variables in the geographical environment where the sinusitis patient is located on the disease of the sinusitis patient, the impact degree of different environmental factors on the disease risk can be revealed, which helps to understand the pathogenesis of sinusitis and provide more accurate prevention and treatment guidance for patients.

[0111] As an embodiment of the present invention, using the seasonal distribution model and the niche model to analyze the impact degree of environmental variables in the geographical environment where the sinusitis patient is located on the disease of the sinusitis patient includes: constructing a regression function of the seasonal distribution model and the environmental pathology model, and calculating the regression coefficient of the regression function by using the following formula:

[0112]

[0113] where γ represents the regression coefficient, represents the analysis value of the regression function, represents the average value of the regression function, C i represents the mean square deviation of the average value;

[0114] Calculate the fitted value of the regression function according to the regression coefficient and in combination with the following formula:

[0115]

[0116] where ε represents the fitted value, M represents the number of independent variables in the regression function, N represents the number of dependent variables in the regression function, γ represents the regression coefficient, f() represents the regression function, R i represents the i-th independent variable in the regression function, T j represents the j-th dependent variable in the regression function;

[0117] Analyze the impact degree of environmental variables in the geographical environment where the sinusitis patient is located on the disease of the sinusitis patient according to the fitted value.

[0118] Furthermore, in the embodiment of the present invention, by extracting the target variables from the environmental variables based on the impact degree, the specific environmental factors affecting the patient's condition in the environment where the sinusitis patient is located can be determined.

[0119] In the embodiments of the present invention, by analyzing the pathological characteristics of the sinusitis patients, the physical manifestations of the sinusitis patients during the onset of the disease can be understood, such as nasal congestion, nasal discharge, facial pressure or pain, sore throat, headache, etc.

[0120] Furthermore, in the embodiments of the present invention, by using the target variable to determine the influencing factors of the pathological characteristics, the specific impacts of different disease-causing factors on the patients can be determined, and then corresponding disease prevention measures can be formulated.

[0121] As Figure 2 shown, it is a functional module diagram of an intelligent analysis system for influencing factors based on sinusitis according to the present invention.

[0122] The intelligent analysis system 200 for influencing factors based on sinusitis according to the present invention can be installed in an electronic device. According to the functions achieved, the intelligent analysis system for influencing factors based on sinusitis may include a distribution map construction module 201, a region division module 202, a model construction module 203, and an impact analysis module 204. The modules in the present invention may also be referred to as units, which refer to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0123] In the embodiments of the present invention, the functions of each module / unit are as follows:

[0124] The distribution map construction module 201 is used to obtain the spatial distribution information of sinusitis patients and construct a spatial distribution map of the sinusitis patients by using the spatial distribution information.

[0125] The region division module 202 is used to analyze the spatial distribution correlation of the sinusitis patients according to the spatial distribution map, and based on the spatial distribution correlation, divide the spatial distribution of the sinusitis patients to obtain regionally distributed patients.

[0126] The model construction module 203 is used to query the patient growth data of the regionally distributed patients, construct a seasonal distribution model of the regionally distributed patients by using the patient growth data, identify the environmental conditions where the regionally distributed patients are located, and construct an environmental pathology model of the regionally distributed patients based on the environmental conditions.

[0127] The impact analysis module 204 is used to analyze the impact degree of environmental variables in the geographical environment where the sinusitis patients are located on the incidence of the sinusitis patients by using the seasonal distribution model and the environmental pathology model, extract the target variables in the environmental variables based on the impact degree of the disease, analyze the pathological characteristics of the sinusitis patients, and determine the influencing factors of the pathological characteristics by using the target variables.

[0128] Specifically, when the modules in the intelligent analysis system 200 for influencing factors of sinusitis according to the embodiments of the present invention are used, they adopt the same technical means as those in the Figure 1 intelligent analysis method for influencing factors of sinusitis described above, and can produce the same technical effects, which will not be elaborated here.

[0129] In several embodiments provided by the present invention, it should be understood that the disclosed system and method can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0130] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0131] In addition, in each embodiment of the present invention, the functional modules can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware, or in the form of a combination of hardware and software functional modules.

[0132] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-mentioned exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0133] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be encompassed by the present invention. Any associated drawing marks in the claims should not be regarded as limiting the claimed rights.

[0134] It should be noted that in this document, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising said element.

[0135] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. An intelligent analysis method for influencing factors based on sinusitis, characterized in that, The method includes: Obtaining the spatial distribution information of patients with sinusitis, and using the spatial distribution information to construct a spatial distribution map of the patients with sinusitis; Analyzing the spatial distribution correlation of the patients with sinusitis according to the spatial distribution map, and based on the spatial distribution correlation, dividing the spatial distribution of the patients with sinusitis to obtain regionally distributed patients; Querying the patient growth data of the regionally distributed patients, using the patient growth data to construct a seasonal distribution model of the regionally distributed patients, identifying the environmental conditions where the regionally distributed patients are located, and constructing an environmental pathology model of the regionally distributed patients based on the environmental conditions; Using the seasonal distribution model and the environmental pathology model to analyze the degree of influence of environmental variables in the geographical environment where the patients with sinusitis are located on the incidence of the patients with sinusitis, extracting target variables from the environmental variables based on the degree of influence, analyzing the pathological characteristics of the patients with sinusitis, and using the target variables to determine the influencing factors of the pathological characteristics.

2. The method according to claim 1, characterized in that, The constructing the spatial distribution map of the patients with sinusitis by using the spatial distribution information includes: Determining the distribution range of the patients with sinusitis based on the spatial distribution information; Constructing a view framework of the patients with sinusitis according to the distribution range; Identifying the geographical distribution information of the patients with sinusitis; Performing geographical information annotation on the view framework based on the geographical distribution information to obtain an annotated view; Performing three-dimensional display on the annotated view to obtain a spatial distribution map.

3. The method according to claim 1, wherein The analyzing the spatial distribution correlation of the patients with sinusitis according to the spatial distribution map includes: Performing spatial division on the spatial distribution map to obtain divided spaces; Calculating the spatial correlation index of the patients with sinusitis based on the divided spaces in combination with the following formula: Among them, y represents the spatial correlation index, m represents the number of divided spaces, and μ ij represents the weight matrix of the divided spaces, and A i represents the number of patients with sinusitis in the i-th divided space, and A j represents the number of patients with sinusitis in the j-th divided space, and A represents the average number of patients with sinusitis in the divided spaces; Analyzing the spatial distribution correlation of the patients with sinusitis according to the spatial correlation index.

4. The method according to claim 3, wherein The analyzing the spatial distribution correlation of the patients with sinusitis according to the spatial correlation index includes: Calculating the correlation score value of the spatial correlation index by using the following formula: And, where S represents the correlation score value, y represents the spatial correlation index, m represents the number of divided spaces, E[y] represents the spatial aggregation degree, and V[y] represents the spatial dispersion degree; Analyzing the spatial distribution correlation of the patients with sinusitis according to the correlation score value.

5. The method according to claim 1, wherein The dividing the spatial distribution of the patients with sinusitis based on the spatial distribution correlation to obtain regionally distributed patients includes: Identifying the spatial distribution characteristics of the patients with sinusitis according to the spatial distribution correlation; Constructing a topology map of the patients with sinusitis based on the spatial distribution characteristics; Using the topology map to divide the spatial distribution of the patients with sinusitis to obtain regionally distributed patients.

6. The method according to claim 1, wherein The constructing the seasonal distribution model of the regionally distributed patients by using the patient growth data includes: Constructing an initial seasonal distribution model of the regionally distributed patients; Determining the differential time constant of the initial seasonal distribution model by using the patient growth data; Calculating the parameter adjustment value of the initial seasonal distribution model based on the differential time constant by using the following formula: where α represents the adjustment parameter value, β represents the initial parameter of the initial seasonal distribution model, T1 represents the integration time constant, T D represents the differential time constant, t represents time, and d represents the parameter adjustment direction; Perform a parameter adjustment operation on the initial seasonal distribution model based on the parameter adjustment value to obtain a seasonal distribution model.

7. The method according to claim 1, characterized in that The constructing the environmental pathology model of the regionally distributed patients based on the environmental conditions includes: Collect the geographical ecological data of the regionally distributed patients according to the environmental conditions; Construct an initial environmental pathology model of the regionally distributed patients. After inputting the geographical ecological data into the initial environmental pathology model, calculate the model accuracy of the initial environmental pathology model; Perform model adjustment on the initial environmental pathology model based on the model accuracy, and calculate the model accuracy after the model adjustment. When the model accuracy after the model adjustment meets the preset accuracy, obtain an environmental pathology model.

8. The method according to claim 7, characterized in that The calculating the model accuracy of the initial environmental pathology model includes: Calculate the model accuracy of the initial environmental pathology model using the following formula: Among them, precision represents the model accuracy, MP represents the percentage error of the initial environmental pathology model, RS represents the root mean square error of the initial environmental pathology model, ME represents the mean absolute error of the initial environmental pathology model, θ represents the fitting accuracy of the initial environmental pathology model, U represents the amount of geographical ecological data input into the initial environmental pathology model, and B i represents the predicted value of the i-th geographical ecological data in the initial environmental pathology model, represents the true value of the i-th geographical ecological data of the initial environmental pathology model, represents the mean value of the true values of the i-th geographical ecological data of the initial environmental pathology model.

9. The method according to claim 1, wherein The analyzing the influence degree of environmental variables in the geographical environment where the sinusitis patients are located on the incidence of the sinusitis patients by using the seasonal distribution model and the niche model includes: Construct a regression function of the seasonal distribution model and the environmental pathology model; Calculate the regression coefficient of the regression function using the following formula: where γ represents the regression coefficient, represents the analytical value of the regression function, represents the average value of the regression function, C i represents the mean square deviation of the said average value; Calculate the fitted value of the regression function according to the regression coefficient and in combination with the following formula: Among them, ε represents the fitting value, M represents the number of independent variables in the regression function, N represents the number of dependent variables in the regression function, γ represents the regression coefficient, f() represents the regression function, R i represents the i-th independent variable in the regression function, T j represents the j-th dependent variable in the regression function; Analyze the influence degree of environmental variables in the geographical environment where the sinusitis patients are located on the incidence of the sinusitis patients according to the fitted value.

10. An intelligent analysis system for impact factors based on sinusitis, characterized in that, The system includes: A distribution map construction module, configured to obtain the spatial distribution information of sinusitis patients, and use the spatial distribution information to construct a spatial distribution map of the sinusitis patients; A region division module, configured to analyze the spatial distribution correlation of the sinusitis patients according to the spatial distribution map, and based on the spatial distribution correlation, perform spatial distribution division on the sinusitis patients to obtain regionally distributed patients; A model construction module, configured to query the patient growth data of the regionally distributed patients, use the patient growth data to construct a seasonal distribution model of the regionally distributed patients, identify the environmental conditions where the regionally distributed patients are located, and construct an environmental pathology model of the regionally distributed patients based on the environmental conditions; An influence analysis module, configured to analyze the influence degree of environmental variables in the geographical environment where the sinusitis patients are located on the incidence of the sinusitis patients by using the seasonal distribution model and the environmental pathology model, extract target variables in the environmental variables based on the influence degree of the incidence, analyze the pathological characteristics of the sinusitis patients, and determine the influencing factors of the pathological characteristics by using the target variables.

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