Cloud architecture clustering management method for pet allergen detection result big data

Abstract

The invention relates to the technical field of data processing, and particularly discloses a cloud architecture clustering management method for pet allergen detection result big data, which comprises an acquisition step, a generalized function set construction step, a neutral virtual pet model construction step, a clustering step and an output step. The method comprises the following steps: collecting pet allergen detection data and constructing a pet allergy responsivity function to form a generalized function set; using the neutral virtual pet function set as a reference, executing single-valued description and clustering analysis on the distance between different function sets, and obtaining a clustering interval and allergy judgment result concentration; and finally outputting a pet allergy judgment result and a corresponding confidence coefficient. The scheme can improve the assessment accuracy of the pet allergy risk, has both flexibility and expandability, is suitable for large-scale pet allergy detection and data management scenes, and can realize data sharing through a unified cloud architecture.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a cloud architecture clustering management method for big data of pet allergen detection results. Background Art

[0002] In recent years, with the improvement of people's living standards and the popularity of pet keeping, pets have gradually become an important part of family members. At the same time, the demand for pet health management is also increasing. Among them, pet allergen detection, as an important diagnostic method, is widely used in pet hospitals, research institutions and related testing centers. Pet allergic reactions are usually caused by environmental factors (such as pollen, dust mites, mold, etc.) or food ingredients (such as beef, chicken, grains, etc.), manifested as skin itching, ear inflammation, gastrointestinal discomfort, respiratory problems and other symptoms. Therefore, accurately identifying the allergens of individual pets is of great significance to their health management and improvement of their quality of life.

[0003] At present, pet allergen detection methods mainly include serum IgE antibody detection, skin prick test (SPT), food elimination test and molecular diagnostic technology. Among them, serum IgE detection is a more common method, which determines the sensitivity of pets to specific allergens by analyzing the level of specific immunoglobulins in the blood of pets. However, due to the differences in pet species, genetic background, living environment and feeding methods, the mechanism of allergic reactions is relatively complex, and traditional detection methods still face many challenges in data analysis, information integration and personalized application. With the development of big data and artificial intelligence technology, how to effectively manage, analyze and utilize pet allergen detection results to make them more clinically valuable has become an urgent problem to be solved in the current industry.

[0004] At present, pet allergen detection still faces the following major problems: 1. Difficulty in data storage and sharing: Current pet allergen detection data are usually stored in independent databases of different laboratories or pet hospitals. There is a lack of unified standards for data formats and storage methods, which makes data integration difficult. At the same time, due to the limitations of privacy protection and technical barriers, cross-institutional data sharing and interoperability are poor, and a complete allergen database cannot be formed, thus affecting the overall utilization value of the data.

[0005] 2. Limited data analysis capabilities: Traditional analysis methods mainly rely on linear statistics or simple rule matching, which makes it difficult to fully explore the correlation between multidimensional data and accurately identify the allergy patterns of different pet groups. For example, certain breeds of pets may be more susceptible to certain allergens, and this information is difficult to effectively extract through traditional methods.

[0006] 3. Lack of intelligent diagnosis supported by big data: The occurrence of pet allergic reactions is affected by many factors, such as genetic background, breeding environment, climate change, etc. Existing detection methods usually only focus on individual data and fail to combine large-scale data for intelligent analysis, making it difficult to achieve accurate allergy risk prediction and diagnosis and treatment recommendations.

[0007] 4. Insufficient real-time update and feedback mechanism: Current test reports are usually presented in the form of static documents, lacking dynamic update and trend prediction capabilities. For example, some pets may react differently to specific allergens with age, immune status or environmental changes, but the existing system cannot achieve long-term data tracking, resulting in a lack of scientific basis and real-time adjustment capabilities for allergy management strategies.

[0008] To address the above issues, there is an urgent need to build a big data clustering management method based on cloud architecture, integrate pet allergen detection data into the cloud, and classify, cluster and trend the data through machine learning algorithms, so as to achieve efficient storage, intelligent analysis and real-time feedback of detection data, thereby providing more accurate, scientific and personalized solutions for pet allergy management. Summary of the invention

[0009] In view of the above problems, the purpose of the present invention is to propose a cloud architecture clustering management method for big data of pet allergen detection results, comprising the following steps: S1. Collection step: collecting pet allergen detection data to obtain an allergen detection data set; S2, step of constructing a generalized function set: constructing a pet allergy response function, and combining the pet allergy response function and the allergen detection data set to form a generalized function set; S3, step of constructing a neutral virtual pet model: constructing a neutral virtual pet model with reference to the form of a generalized function set, wherein the neutral virtual pet model has a neutral virtual pet function set; S4, clustering step: performing single-value characterization and cluster analysis on the distances of different generalized function sets relative to the neutral virtual pet function set, and obtaining the generalized function distance, clustering interval and allergy determination result concentration; S5. Output step: output the pet allergy determination result and confidence level based on the generalized functional distance, cluster interval and allergy determination result concentration.

[0010] Furthermore, in step S1, the allergen detection dataset is formally represented as ,in The pet serial number. , Allergen detection dataset Dimensions; For the Allergen testing data for pets.

[0011] Further, in step S2, the pet allergy response function is formally expressed as , the generalized function set is formally expressed as ,satisfy:

[0012]

[0013]

[0014]

[0015] Among them, a and b are the lower threshold and upper threshold of the reaction, respectively, both are positive real numbers, and satisfy a <b; and Respectively Allergen testing data for pets The detection value and weight value of the data component; is the pet allergy response function; This is the result of allergy diagnosis.

[0016] Furthermore, step S2 also includes: introducing a default allergen detection data set , the default allergen detection dataset As a generalized function set The first set of data sets in constructs with initial weight values The initial generalized function set ; Initial generalized function set satisfy:

[0017] Among them, the default allergen detection dataset It refers to the collection of allergen test data of a certain pet when there is no test result or data missing on some allergens; , and They respectively represent the allergen detection data, pet allergy response function and allergy determination results of a certain pet when there is no detection result or data missing on some allergens; satisfy: T1: Assuming that the pet with a history record is exposed to a certain allergen for the first time and has no allergic reaction, then ; T2: Assuming that the pet with a historical record is exposed to a certain allergen for the first time and has an allergic reaction, then ; T3: Assuming that the pet with a history record has been exposed to a certain allergen multiple times and has experienced allergic reactions and no allergic reactions, then ,in, is the number of exposures, The number of allergic reactions.

[0018] Further, step S3 is specifically as follows: referring to the generalized function set In the form of, construct a neutral virtual pet function set, with a neutral virtual pet function set .

[0019] Further, in step S3, referring to the generalized function set In the form of a neutral virtual pet function set ,satisfy:

[0020] in, Indicates a neutral virtual pet with no allergic reaction to all the test items. is a neutral assignment function, which means that all detection items in which the neutral virtual pet participates are assigned the lower reaction threshold.

[0021] Furthermore, step S4 specifically includes: S41, characterizing the distances of different generalized function sets relative to the neutral virtual pet function set by single values, and obtaining the generalized function distance; S42. Perform cluster analysis on the generalized functional distance to obtain a cluster interval, and determine the concentration of the allergy determination result based on the cluster interval.

[0022] Furthermore, step S41 specifically includes: Introduce the distance function to characterize the distance between different generalized function sets and the neutral virtual pet function set. With neutral virtual pet feature set The generalized functional distance satisfy:

[0023] in, and denote the generalized function set and the neutral virtual pet function set in the The value of a feature dimension.

[0024] Further, step S42 specifically includes: S421. Generalized functional distance using single value representation Perform cluster analysis to obtain a set of cluster groups ,satisfy:

[0025] in, is the cluster group number, Indicates the serial number is The clustering group, is the total number of cluster groups; S422. For different cluster groups, capture the allergy determination results of the elements in the group , calculate the concentration of allergy determination result, satisfying:

[0026] in, , and Respectively indicate the serial number The number of allergic reactions and severe allergic reactions in the cluster group, the total number of elements in the cluster group, and the concentration of allergy judgment results.

[0027] Beneficial Effects The present invention proposes a cloud architecture clustering management method for big data of pet allergen detection results, which has the following beneficial effects compared with the prior art: 1. Clearer baseline reference: By constructing a "neutral virtual pet model" and assigning a neutral virtual pet function set as a unified baseline or reference object, the allergen detection data of different pets can be compared and clustered under the same reference system, thereby greatly reducing the deviation of test results caused by individual differences of pets.

[0028] 2. Cluster analysis is more accurate: By calculating the distance between any generalized function set and the neutral virtual pet function set, and performing single-value characterization and cluster analysis on the distance, it is not only possible to effectively discover the similarities and differences in allergies between different pets, but also to further refine the clustering intervals and combine the concentration of allergy determination results (such as mild allergies, severe allergic reactions, etc.) for accurate classification, thereby improving the discrimination and accuracy of allergen detection results.

[0029] 3. Improved big data processing efficiency: The present invention uniformly manages pet allergen detection data under a cloud architecture, and integrates multi-dimensional information such as detection values, weight values, and allergy determination results in the form of a "generalized function set", which is convenient for efficient calculation and query on a big data scale, and can also be quickly called in subsequent historical data tracing, statistical analysis, and result prediction, thereby improving overall processing efficiency.

[0030] 4. Wide range of applicable scenarios: Due to the introduction of mechanisms such as the default allergen detection data set and the initial generalized function set, it is compatible with data missing or initially incomplete in a variety of scenarios; and through the statistics of multiple exposures to the same allergen (the ratio of the number of exposures to the number of allergic reactions), the sensitivity of pets is comprehensively evaluated, which greatly enhances the robustness and applicability of the method. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0032] In order to deepen the understanding of the present invention, the present invention will be further described in detail below in conjunction with examples. The examples are only used to explain the present invention and do not constitute a limitation on the protection scope of the present invention.

[0033] Embodiment 1 according to Figure 1 As shown, this embodiment provides a cloud architecture clustering management method for pet allergen detection result big data, including the following steps: S1. Collection step: collecting pet allergen detection data to obtain an allergen detection data set; S2, step of constructing a generalized function set: constructing a pet allergy response function, and combining the pet allergy response function and the allergen detection data set to form a generalized function set; S3, step of constructing a neutral virtual pet model: constructing a neutral virtual pet model with reference to the form of a generalized function set, wherein the neutral virtual pet model has a neutral virtual pet function set; S4, clustering step: performing single-value characterization and cluster analysis on the distances of different generalized function sets relative to the neutral virtual pet function set, and obtaining the generalized function distance, clustering interval and allergy determination result concentration; S5. Output step: output the pet allergy determination result and confidence level based on the generalized functional distance, cluster interval and allergy determination result concentration.

[0034] Furthermore, in step S1, the allergen detection dataset is formally represented as ,in The pet serial number. , Allergen detection dataset Dimensions; For the Allergen testing data for pets.

[0035] Further, in step S2, the pet allergy response function is formally expressed as , the generalized function set is formally expressed as ,satisfy:

[0036]

[0037]

[0038]

[0039] Among them, a and b are the lower threshold and upper threshold of the reaction, respectively, both are positive real numbers, and satisfy a <b; and Respectively Allergen testing data for pets The detection value and weight value of the data component; is the pet allergy response function; This is the result of allergy diagnosis.

[0040] Furthermore, step S2 also includes: introducing a default allergen detection data set , the default allergen detection dataset As a generalized function set The first set of data sets in constructs with initial weight values The initial generalized function set ; Initial generalized function set satisfy:

[0041] Among them, the default allergen detection dataset It refers to the collection of allergen test data of a certain pet when there is no test result or data missing on some allergens; , and They respectively represent the allergen detection data, pet allergy response function and allergy determination results of a certain pet when there is no detection result or data missing on some allergens; satisfy: T1: Assuming that the pet with a history record is exposed to a certain allergen for the first time and has no allergic reaction, then ; T2: Assuming that the pet with a historical record is exposed to a certain allergen for the first time and has an allergic reaction, then ; T3: Assuming that the pet with a history record has been exposed to a certain allergen multiple times and has experienced allergic reactions and no allergic reactions, then ,in, is the number of exposures, The number of allergic reactions.

[0042] Further, step S3 is specifically as follows: referring to the generalized function set In the form of, construct a neutral virtual pet function set, with a neutral virtual pet function set .

[0043] Further, in step S3, referring to the generalized function set In the form of a neutral virtual pet function set ,satisfy:

[0044] in, Indicates a neutral virtual pet with no allergic reaction to all the test items. is a neutral assignment function, which means that all detection items in which the neutral virtual pet participates are assigned the lower reaction threshold.

[0045] Furthermore, step S4 specifically includes: S41, characterizing the distances of different generalized function sets relative to the neutral virtual pet function set by single values, and obtaining the generalized function distance; S42. Perform cluster analysis on the generalized functional distance to obtain a cluster interval, and determine the concentration of the allergy determination result based on the cluster interval.

[0046] Furthermore, step S41 specifically includes: Introduce the distance function to characterize the distance between different generalized function sets and the neutral virtual pet function set. With neutral virtual pet feature set The generalized functional distance satisfy:

[0047] in, and denote the generalized function set and the neutral virtual pet function set in the The value of a feature dimension.

[0048] Further, step S42 specifically includes: S421. Generalized functional distance using single value representation Perform cluster analysis to obtain a set of cluster groups ,satisfy:

[0049] in, is the cluster group number, Indicates the serial number is The clustering group, is the total number of cluster groups; S422. For different cluster groups, capture the allergy determination results of the elements in the group , calculate the concentration of allergy determination result, satisfying:

[0050] in, , and Respectively indicate the serial number The number of allergic reactions and severe allergic reactions in the cluster group, the total number of elements in the cluster group, and the concentration of allergy judgment results.

[0051] Embodiment 2 This embodiment is based on the distributed cloud platform of the pet medical center to uniformly manage and cluster the allergen detection data of pets from different regions and species to assess the allergy risk level of pets. The implementation process and algorithm are described as follows: 1. Data Collection and Preprocessing 1.1 Data Collection: Each pet hospital or testing point uploads the test results to the cloud server to form the original data set .

[0052] in The pet serial number. , Allergen detection dataset Dimensions; For the Allergen detection data for pets, including multiple dimensions or feature values , Each dimension can correspond to a specific allergen (such as cats' allergies to dust mites, cat scratching, pollen, etc.), or to different physiological indicators.

[0053] 1.2 Data cleaning and default processing If a pet has no test results or missing data for some allergens, it can be set as , and initialize the corresponding weights according to historical exposure records (whether allergies have ever occurred) ; For duplicate or abnormal values, threshold rules or statistical methods (such as extreme value elimination) can be set for pre-cleaning; Finally, a relatively complete and usable The data set is ready to enter the subsequent generalized feature set construction phase.

[0054] 2. Generalized feature set construction 2.1 Pet allergy response function In order to measure the comprehensive sensitivity of each pet to multiple allergens, the pet allergy response function is introduced ,satisfy:

[0055]

[0056] in is the pet’s detection value for the kth allergen, is the corresponding weight, indicating the importance of this dimension in the comprehensive sensitivity; the sum of the weights is 1 for easy normalization.

[0057] 2.2 Allergy determination results Use the following formula to determine allergies:

[0058] Among them, a and b are the lower response threshold and the upper response threshold, respectively, which can be determined based on large-scale statistics or clinical experience.

[0059] 2.3 Forming a generalized function set For each pet, Multiple information is uniformly packaged to form a generalized function set ; This generalized function set includes both the original detection data and multi-dimensional elements such as comprehensive sensitivity and judgment results, which facilitates subsequent unified analysis.

[0060] 3. Neutral virtual pet model creation 3.1 Neutral pet assumption In actual testing, the differences in characteristics of different pets often lead to large differences in the response values ​​to the same allergen. In order to reduce the impact of individual differences, this embodiment constructs a "neutral virtual pet" , assuming that it is at the "lower threshold" for all test items and has no allergic reaction.

[0061] 3.2 Neutral Virtual Pet Feature Set Reference The form of definition:

[0062] in, Indicates that the detection value of each dimension is assigned (or a smaller neutral value), Then a fixed constant can be taken so that the model is always in a "non-allergic" state, that is, No allergic reaction; this feature set is used to compare the feature set of real pets to provide a unified reference benchmark.

[0063] 4. Generalized functional distance calculation and cluster analysis 4.1 Distance calculation (single value characterization) In order to measure the difference between the real pet and the neutral model, a distance function is introduced:

[0064] This formula can be regarded as the weighted Euclidean distance of multidimensional test values. On the one hand, it comprehensively considers the data of different allergens, and on the other hand, it can be standardized (such as dividing by ) to control the distance magnitude.

[0065] Clustering algorithm implementation: Data preparation: Distance values ​​corresponding to all pets Collect into a set of single-value sequences; Clustering method: can be used , hierarchical clustering ( ) or clustering based on distance thresholds (such as )wait; Process example: If using ,Will It is regarded as a one-dimensional eigenvalue, the number of clusters is set, the center of each cluster is iteratively updated and assigned to the nearest cluster until convergence, and finally a set of cluster groups is obtained.

[0066] 4.2 Allergy determination result concentration After obtaining each cluster group, the distribution of allergic reaction types of elements in the group is statistically analyzed: Calculate the proportion of severe allergic reactions and general allergic reactions, that is:

[0067] in, is the total number of pets in the group, The number of pets that have allergic reactions (including general and severe allergies); this can quickly assess the concentration or risk level of the cluster group in the "allergy" dimension, providing a valuable reference for clinical diagnosis and management.

[0068] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A cloud architecture clustering management method for big data of pet allergen detection results, characterized by: The following steps are involved: S1. Collection step: collecting pet allergen detection data to obtain an allergen detection data set; S2, step of constructing a generalized function set: constructing a pet allergy response function, and combining the pet allergy response function and the allergen detection data set to form a generalized function set; S3, step of constructing a neutral virtual pet model: constructing a neutral virtual pet model with reference to the form of a generalized function set, wherein the neutral virtual pet model has a neutral virtual pet function set; S4, clustering step: performing single-value characterization and cluster analysis on the distances of different generalized function sets relative to the neutral virtual pet function set, and obtaining the generalized function distance, clustering interval and allergy determination result concentration; S5. Output step: output the pet allergy determination result and confidence level based on the generalized functional distance, cluster interval and allergy determination result concentration.

2. The cloud architecture clustering management method for pet allergen detection result big data according to claim 1, characterized in that: In step S1, the allergen detection dataset is formally represented as ,in is the pet serial number, , Allergen detection dataset Dimensions; For the Allergen testing data for pets.

3. The cloud architecture clustering management method for pet allergen detection result big data according to claim 1, characterized in that: In step S2, the pet allergy response function is formally expressed as , the generalized function set is formally expressed as ,satisfy: ； ； Among them, a and b are the lower threshold and upper threshold of the reaction, respectively, both are positive real numbers, and satisfy a <b; and Respectively Allergen testing data for pets The detection value and weight value of the data component; is the pet allergy response function; This is the result of allergy diagnosis.

4. A cloud architecture clustering management method for big data of pet allergen detection results according to claim 2, characterized in that: Step S2 also includes: introducing a default allergen detection data set , the default allergen detection dataset As a generalized function set The first set of data sets in constructs with initial weight values The initial generalized function set ; Initial generalized function set satisfy: ； Among them, the default allergen detection dataset It refers to the collection of allergen test data of a certain pet when there is no test result or data missing on some allergens; , and They respectively represent the allergen detection data, pet allergy response function and allergy determination results of a certain pet when there is no detection result or data missing on some allergens; satisfy: T1: Assuming that the pet with a history record is exposed to a certain allergen for the first time and has no allergic reaction, then ; T2: Assuming that the pet with a historical record is exposed to a certain allergen for the first time and has an allergic reaction, then ; T3: Assuming that the pet with a history record has been exposed to a certain allergen multiple times and has experienced allergic reactions and no allergic reactions, then ,in, is the number of exposures, The number of allergic reactions.

5. The cloud architecture clustering management method for pet allergen detection result big data according to claim 2, characterized in that: Step S3 specifically includes: referring to the generalized function set In the form of, construct a neutral virtual pet function set, with a neutral virtual pet function set .

6. A cloud architecture clustering management method for big data of pet allergen detection results according to claim 2, characterized in that: In step S3, referring to the generalized function set In the form of a neutral virtual pet function set ,satisfy: ； in, Indicates a neutral virtual pet with no allergic reaction to all the test items. is a neutral assignment function, which means that all detection items in which the neutral virtual pet participates are assigned the lower reaction threshold.

7. The cloud architecture clustering management method for big data of pet allergen detection results according to claim 4, characterized in that: Step S4 specifically includes: S41, characterizing the distances of different generalized function sets relative to the neutral virtual pet function set by single values, and obtaining the generalized function distance; S42. Perform cluster analysis on the generalized functional distance to obtain a cluster interval, and determine the concentration of the allergy determination result based on the cluster interval.

8. The cloud architecture clustering management method for pet allergen detection result big data according to claim 7, characterized in that: Step S41 specifically includes: Introduce the distance function to characterize the distance between different generalized function sets and the neutral virtual pet function set. With neutral virtual pet feature set The generalized functional distance satisfy: ； in, and denote the generalized function set and the neutral virtual pet function set in the The value of a feature dimension.

9. The cloud architecture clustering management method for pet allergen detection result big data according to claim 8, characterized in that: Step S42 specifically includes: S421. Generalized functional distance using single value representation Perform cluster analysis to obtain a set of cluster groups ,satisfy: ； in, is the cluster group number, Indicates the serial number is The clustering group, is the total number of cluster groups; S422. For different cluster groups, capture the allergy determination results of the elements in the group , calculate the concentration of allergy determination result, satisfying: ； in, , and Respectively indicate the serial number The number of allergic reactions and severe allergic reactions in the cluster group, the total number of elements in the cluster group, and the concentration of allergy judgment results.

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