Animal disease monitoring data statistical analysis system and method

Abstract

The present invention provides a system and method for statistical analysis of animal disease monitoring data, wherein the method comprises: an animal disease monitoring data acquisition module for acquiring animal disease monitoring data; a statistical analysis intention acquisition module for interactively acquiring the statistical analysis intention of a user; a statistical analysis strategy generation module for generating a statistical analysis strategy based on the statistical analysis intention; a statistical analysis module for performing statistical analysis on the animal disease monitoring data based on the statistical analysis strategy; and a statistical analysis result output module for outputting the statistical analysis result. The present invention interactively acquires the statistical analysis intention of the user, generates a statistical analysis strategy corresponding to the statistical analysis intention, and performs corresponding statistical analysis on the animal disease monitoring data, thereby saving workload and reducing labor costs. In addition, the statistical analysis efficiency of the animal disease monitoring data is greatly improved, and the timeliness of determining the animal disease risk, the animal disease epidemic risk, etc. is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of computer data processing, and in particular to a statistical analysis system and method for animal disease monitoring data. Background Art

[0002] In the animal disease prevention and control system, it is usually necessary to monitor animal disease data within the controlled area, and then conduct statistical analysis on the animal disease monitoring data obtained to determine animal disease risks, animal disease epidemic risks, etc.

[0003] However, the statistical analysis of animal disease monitoring data is usually done manually by specialized statistical analysts. The workload of statistical analysis is large, resulting in high labor costs. In addition, the efficiency of statistical analysts in manually performing statistical analysis on animal disease monitoring data is also low, which may affect the timeliness of determining animal disease risks, animal disease epidemic risks, etc.

[0004] Therefore, it is necessary to propose a system and method for statistical analysis of animal disease monitoring data to at least partially solve the problems existing in the prior art. Summary of the invention

[0005] One of the purposes of the present invention is to provide an animal disease monitoring data statistical analysis system. After acquiring the animal disease monitoring data, the user's statistical analysis intention is interactively acquired, a statistical analysis strategy corresponding to the statistical analysis intention is generated, and corresponding statistical analysis is performed on the animal disease monitoring data for the user to view the statistical analysis results. There is no need to set up special statistical analysts to manually perform statistical analysis on the animal disease monitoring data, which saves workload and reduces labor costs. In addition, the statistical analysis efficiency of the animal disease monitoring data is greatly improved, and the timeliness of determining animal disease risks, animal disease epidemic risks, etc. is improved.

[0006] An embodiment of the present invention provides an animal disease monitoring data statistical analysis system, comprising:

[0007] Animal disease monitoring data acquisition module, used to obtain animal disease monitoring data;

[0008] A statistical analysis intention acquisition module is used to interactively acquire the user's statistical analysis intention;

[0009] A statistical analysis strategy generation module, used to generate a statistical analysis strategy based on the statistical analysis intention;

[0010] Statistical analysis module, used to perform statistical analysis on animal disease monitoring data based on statistical analysis strategies;

[0011] The statistical analysis result output module is used to output the statistical analysis results.

[0012] Preferably, the animal disease monitoring data acquisition module acquires the animal disease monitoring data, including:

[0013] Acquire first candidate data from a preset active scene;

[0014] Determining whether the first candidate data meets the quasi-acquisition condition;

[0015] When the conditions are met, the first candidate data will be used as animal disease monitoring data;

[0016] and / or,

[0017] Acquire second candidate data from a preset passive scene;

[0018] Based on a preset first basis acquisition template corresponding to the data monitoring type of the second candidate data, an authenticity verification basis of the second candidate data is acquired from a passive scene;

[0019] Based on the authenticity verification basis, the second candidate data is verified for authenticity;

[0020] When the verification passes, the second candidate data is used as animal disease monitoring data.

[0021] Preferably, the quasi-acquisition conditions include one or more combinations of the following:

[0022] There is a correlation between the first candidate data and the scene attribute information of the active scene;

[0023] There are at least M target-related events in the active scene; the target-related event is an associated event associated with the first candidate data that is generated within a preset time before and after the first generation time of the first candidate data, and the event type of the associated event exists in a preset standard event type library; M is a positive integer;

[0024] The active scene exists in a preset trusted scene library or the scene credibility of the active scene is greater than or equal to a preset credibility threshold.

[0025] Preferably, the statistical analysis intention acquisition module interactively acquires the user's statistical analysis intention, including:

[0026] Based on the preset second basis acquisition template, multiple intention prediction bases of the user are acquired;

[0027] Preprocessing the intention prediction basis to obtain a preprocessing result;

[0028] Based on a preset feature description template, feature description is performed on the preprocessing result to obtain a feature description vector;

[0029] Determining at least one predicted intent corresponding to the feature description vector from a preset predicted intent library;

[0030] Based on the predicted intent, generate an intent selection table;

[0031] Receive the predicted intent selected by the user from the intent selection table and use it as the statistical analysis intent;

[0032] Among them, the intention prediction basis is preprocessed to obtain the preprocessing results, including:

[0033] Based on the second generation time of the intention prediction basis, setting the intention prediction basis on a preset time axis;

[0034] Determine a plurality of continuous basis sequences that meet the continuous basis sequence condition from the time axis;

[0035] Integrate each continuous basis sequence to obtain the preprocessing result.

[0036] Preferably, the continuous sequence conditions include:

[0037] There are at least N intention prediction bases in the continuous basis sequence; N is a positive integer;

[0038] The maximum time interval between two intention prediction bases in the continuous base sequence is less than or equal to a preset first interval threshold;

[0039] The time interval between two adjacent intention prediction bases in the continuous base sequence is less than or equal to a preset second interval threshold; the first interval threshold is greater than the second interval threshold;

[0040] The basis types of the intention prediction basis in the continuous basis sequence are the same, or different basis types of the intention prediction basis in the continuous basis sequence have corresponding combinable relationships in the preset combinable relationship library.

[0041] Preferably, the statistical analysis result output module outputs the statistical analysis result, including:

[0042] A first information type set for parsing statistical analysis results;

[0043] Retrieve the preset visualization model corresponding to the information type set;

[0044] Map statistical analysis results into visual models;

[0045] Output the current visualization model.

[0046] Preferably, the animal disease monitoring data statistical analysis system further includes:

[0047] Additional review recommended modules for including:

[0048] Get the user's gaze content in the visualization model;

[0049] Determine candidate content related to the observation content from animal disease monitoring data;

[0050] Parsing a second information type set of the content to be selected;

[0051] Based on a preset template for generating a supplementary viewing recommendation list, generating a supplementary viewing recommendation list according to a second information type set;

[0052] Mapping the supplementary viewing recommendation list into the visualization model;

[0053] receiving a target information type selected by a user from a supplementary viewing recommendation list;

[0054] Determine local content of the target information type from the content to be selected;

[0055] Map local content into the visual model.

[0056] Preferably, the animal disease monitoring data statistical analysis system is characterized by further comprising:

[0057] The content position adjustment module is used to include:

[0058] Based on the content position adjustment condition, the content position of the fixation content, the supplementary viewing recommendation list and the local content is continuously adjusted in the visualization model;

[0059] The content position adjustment conditions include:

[0060] The shortest distance between the center position of the first content of the gazed content and the target straight line falls within a preset distance interval; the target straight line includes: a line between the center position of the second content of the supplementary viewing recommendation list and the center position of the third content of the local content;

[0061] The length of the target straight line falls within the preset length range;

[0062] The vector angle between the first direction vector and the second direction vector is greater than or equal to a preset angle threshold; the first direction vector is constructed based on the center position of the first content and the last moving direction of the gazed content; the second direction vector is constructed based on the midpoint position of the target straight line and the straight line direction from the midpoint position to the center position of the first content.

[0063] An embodiment of the present invention provides a method for statistical analysis of animal disease monitoring data, comprising:

[0064] Step S1: Obtain animal disease monitoring data;

[0065] Step S2: interactively obtaining the user's statistical analysis intention;

[0066] Step S3: generating a statistical analysis strategy based on the statistical analysis intention;

[0067] Step S4: performing statistical analysis on the animal disease monitoring data based on the statistical analysis strategy;

[0068] Step S5: Output statistical analysis results.

[0069] Preferably, step S1: obtaining animal disease monitoring data includes:

[0070] Acquire first candidate data from a preset active scene;

[0071] Determining whether the first candidate data meets the quasi-acquisition condition;

[0072] When the conditions are met, the first candidate data will be used as animal disease monitoring data;

[0073] and / or,

[0074] Acquire second candidate data from a preset passive scene;

[0075] Based on a preset first basis acquisition template corresponding to the data monitoring type of the second candidate data, an authenticity verification basis of the second candidate data is acquired from a passive scene;

[0076] Based on the authenticity verification basis, the second candidate data is verified for authenticity;

[0077] When the verification passes, the second candidate data is used as animal disease monitoring data.

[0078] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0079] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0081] Figure 1 A schematic diagram of a statistical analysis system for animal disease monitoring data in an embodiment of the present invention;

[0082] Figure 2 Schematic diagram of a method for statistical analysis of animal disease monitoring data in an embodiment of the present invention. DETAILED DESCRIPTION

[0083] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0084] The embodiment of the present invention provides a statistical analysis system for animal disease monitoring data, such as Figure 1 As shown, including:

[0085] The animal disease monitoring data acquisition module 1 is used to acquire animal disease monitoring data; the animal disease monitoring data includes: the species of animals suffering from the disease, the location of the disease, the time of the disease, the type of disease, the number of sick animals, etc. in the control area;

[0086] Statistical analysis intention acquisition module 2 is used to interactively acquire the statistical analysis intention of users; users include: staff of animal disease prevention and control departments, etc.; statistical analysis intentions include: the intentions of users on how to analyze animal disease monitoring data, such as: analyzing the growth rate of the number of sick animals of a certain disease type, analyzing the location distribution of sick animals of the same disease type, etc.;

[0087] The statistical analysis strategy generation module 3 is used to generate a statistical analysis strategy based on the statistical analysis intention; generate a statistical analysis strategy corresponding to the statistical analysis intention, for example: if the statistical analysis intention is to analyze the growth rate of the number of sick animals of a certain disease type, then the statistical analysis strategy is to calculate the growth rate of the number of sick animals of the disease type;

[0088] Statistical analysis module 4, used for performing statistical analysis on animal disease monitoring data based on statistical analysis strategies;

[0089] The statistical analysis result output module 5 is used to output the statistical analysis result. After output, the user can view it.

[0090] The working principle and beneficial effects of the above technical solution are:

[0091] After acquiring the animal disease monitoring data, the present application interactively acquires the user's statistical analysis intention, generates a statistical analysis strategy corresponding to the statistical analysis intention, and performs corresponding statistical analysis on the animal disease monitoring data for the user to view the statistical analysis results. There is no need to set up special statistical analysts to manually perform statistical analysis on the animal disease monitoring data, which saves workload and reduces labor costs. In addition, it greatly improves the statistical analysis efficiency of the animal disease monitoring data and improves the timeliness of determining animal disease risks, animal disease epidemic risks, etc.

[0092] In one embodiment, the animal disease monitoring data acquisition module 1 acquires animal disease monitoring data, including:

[0093] The first candidate data is obtained from a preset active scenario; the active scenario is a scenario that actively provides animal disease monitoring data, such as a work record database of various local animal disease monitoring stations within the control area;

[0094] Determining whether the first candidate data meets the quasi-acquisition condition;

[0095] When the conditions are met, the first candidate data is used as animal disease monitoring data; a quasi-acquisition condition is set, and when the first candidate data meets the conditions, it is used as animal disease monitoring data to improve the acquisition quality of animal disease monitoring data;

[0096] and / or,

[0097] The second candidate data is obtained from a preset passive scenario; the passive scenario is a scenario that passively provides animal disease monitoring data, such as an animal breeding exchange forum, a Weibo discussion area with the topic of "animal breeding exchange", etc. Animal breeders may share the disease conditions of their own breeding animals in such passive scenarios;

[0098] Based on the preset first basis acquisition template corresponding to the data monitoring type of the second candidate data, the authenticity verification basis of the second candidate data is obtained from the passive scene; for example, if the data monitoring type is animal disease, the first basis acquisition template is to obtain a result picture of a positive animal disease test as the authenticity verification basis;

[0099] Based on the authenticity verification basis, the second candidate data is verified for authenticity;

[0100] When the verification is passed, the second candidate data is used as animal disease monitoring data; after the authenticity verification is passed, the second candidate data is used as animal disease monitoring data to improve the acquisition quality of animal disease monitoring data;

[0101] Among them, the quasi-access conditions include one or more combinations of the following:

[0102] There is a correlation between the first candidate data and the scene attribute information of the active scene; the scene attribute information includes: the monitoring scope and monitoring responsibilities of the animal disease monitoring site; for example: if the first candidate data is that animals are suffering from an epidemic at location A, and the monitoring scope of the animal disease monitoring site includes location A, then there is a correlation; setting this quasi-acquisition condition can fully ensure that the first candidate data belongs to the self-monitoring data of the active scene, avoid information crosstalk, and improve acquisition quality;

[0103] There are at least M target-related events in the active scene; the target-related event is an associated event that is generated within a preset time before and after the first generation time of the first candidate data and is associated with the first candidate data, and the event type of the associated event exists in a preset standard event type library; M is a positive integer; the preset time can be, for example: 3 days; the standard event type in the standard event type library is an event type that can represent the truth of the first candidate data, for example: the first candidate data is an animal epidemic at location B, then the standard event type is a work record of going to location B to investigate the animal epidemic, a work record of communicating with the animal breeder at location B by phone, etc.; M can be set in advance by the technical staff according to actual needs; setting this quasi-acquisition condition can fully guarantee the authenticity of the first candidate data and further improve the acquisition quality;

[0104] The active scenario exists in the preset trusted scenario library or the scenario credibility of the active scenario is greater than or equal to the preset credibility threshold. There are a large number of credible active scenarios in the trusted scenario library; the credibility threshold can be, for example, 70; generally, animal disease monitoring can also be outsourced to a third-party monitoring agency. If the third-party monitoring agency is credible, it can be stored in the trusted scenario library or set a credibility greater than or equal to the credibility threshold; setting this quasi-acquisition condition improves the applicability of the system;

[0105] The above technical solutions obtain animal disease monitoring data from active scenarios and passive scenarios respectively, thereby improving the richness of animal disease monitoring data acquisition.

[0106] In one embodiment, the statistical analysis intention acquisition module 2 interactively acquires the statistical analysis intention of the user, including:

[0107] Based on the preset second basis acquisition template, multiple intention prediction bases of the user are obtained; the intention prediction bases include: the animal disease monitoring data statistical analysis task records undertaken by the user in the recent period, the animal disease prevention and control task records undertaken, and the animal disease monitoring data visited in the past;

[0108] Preprocessing the intention prediction basis to obtain a preprocessing result;

[0109] Based on a preset feature description template, the preprocessing result is characterized to obtain a feature description vector; the feature description template is to extract features such as the requirements for statistical analysis of animal disease monitoring data, the requirements for animal disease prevention and control tasks, and the types of animal disease monitoring data accessed historically from the preprocessing result, and these features are constructed into a feature description vector in the form of vectors;

[0110] Determine at least one prediction intent corresponding to the feature description vector from a preset prediction intent library; the prediction intent library contains prediction intents corresponding to different feature description vectors, for example: the feature constructed into the feature description vector is the statistical analysis task requirement of animal disease monitoring data and is to analyze the growth rate of swine fever pathology, then the prediction intent is to statistically analyze the growth rate of swine fever pathology in the control area;

[0111] Based on the predicted intent, an intent selection table is generated; an intent selection table is generated for the user to select his actual intent;

[0112] Receive the predicted intent selected by the user from the intent selection table and use it as the statistical analysis intent;

[0113] Among them, the intention prediction basis is preprocessed to obtain the preprocessing results, including:

[0114] Based on the second generation time of the intention prediction basis, the intention prediction basis is set on a preset time axis; when setting, the intention prediction basis is set at a time axis position on the time axis corresponding to the second generation time;

[0115] Determine a plurality of continuous basis sequences that meet the continuous basis sequence condition from the time axis;

[0116] Integrate each continuous basis sequence to obtain the preprocessing result;

[0117] Continuous based on sequence conditions include:

[0118] There are at least N intention prediction bases in the continuous basis sequence; N is a positive integer; N can be set in advance by the technical staff according to actual needs; setting this continuous basis sequence condition can ensure that the data volume of the preprocessing results used to predict the user's prediction intention is sufficient, thereby improving the prediction efficiency and effect;

[0119] The maximum time interval between two intention prediction bases in the continuous basis sequence is less than or equal to the preset first interval threshold; the time interval between two adjacent intention prediction bases in the continuous basis sequence is less than or equal to the preset second interval threshold; the first interval threshold is greater than the second interval threshold; the first interval threshold can be, for example: 1 hour; the second time interval threshold can be, for example: 10 minutes; setting these two continuous basis sequence conditions can ensure that the intention prediction bases in the continuous basis sequence are continuously generated in a short period of time. Generally, the intention prediction bases generated continuously in a short period of time can be more used to predict the user's predicted intention. For example: the intention prediction bases in the continuous basis sequence are respectively receiving a statistical analysis task for analyzing the epidemic speed of swine fever, accessing historical statistical analysis records of the epidemic speed of swine fever, receiving a prevention and control task of swine fever, etc., then it can be quickly determined that the user wants to analyze the epidemic speed of swine fever in the control area. Therefore, setting this continuous basis sequence condition can further improve the prediction efficiency and effect;

[0120] The same basis type of the intention prediction basis in the continuous basis sequence or different basis types of the intention prediction basis in the continuous basis sequence have corresponding combinable relationships in the preset combinable relationship library. The combinable relationship means that intention prediction bases of different basis types can be used together to predict the user's prediction intention, such as: receiving a statistical analysis task to analyze the epidemic rate of swine fever, accessing historical statistical analysis records of the epidemic rate of swine fever, etc.; therefore, by setting this continuous basis sequence condition, the combinable intention prediction bases can be combined in advance, further improving the prediction efficiency and effect, and improving the applicability of the system;

[0121] The above technical solution predicts the user's predicted intention based on the preprocessing results of the intention prediction basis, allows the user to select his or her true intention, and realizes interactive acquisition of the user's statistical analysis intention, thereby improving the user's work efficiency and being more intelligent and user-friendly.

[0122] In one embodiment, the statistical analysis result output module 5 outputs the statistical analysis result, including:

[0123] A first information type set for parsing the statistical analysis results; the first information type set includes various information types included in the statistical analysis results, such as: the daily growth rate of the prevalence of swine fever, the weekly growth rate of the prevalence of swine fever, the monthly growth rate of the prevalence of swine fever, etc.;

[0124] Retrieve a preset visualization model corresponding to the information type set; the visualization model contains information boxes for placing information of each information type included in the statistical analysis results, and during mapping, place the information into the corresponding information boxes;

[0125] Map statistical analysis results into visual models;

[0126] Output the current visualization model.

[0127] The above technical solution visualizes the output of statistical analysis results, improves the intuitiveness of users' understanding of statistical analysis results, and improves users' work efficiency to a certain extent.

[0128] In one embodiment, the animal disease monitoring data statistical analysis system further includes:

[0129] Additional review recommended modules for including:

[0130] Get the user's gaze content in the visualization model;

[0131] Determine the candidate content related to the watched content from the animal disease monitoring data; the candidate content is related to the watched content, for example, if the watched content is the daily growth rate of the swine fever epidemic, then the candidate content is the location involved in the newly added swine fever on the day with the largest growth rate among the daily growth rates of the swine fever epidemic, the time of occurrence, information of the breeder, etc.;

[0132] Parsing a second information type set of the content to be selected; the second information type set includes various information types included in the content to be selected;

[0133] Based on the preset supplementary viewing recommendation list generation template, a supplementary viewing recommendation list is generated according to the second information type set; the supplementary viewing recommendation list is marked with a title above the supplementary viewing recommendation list that reads “Do you have any additional information you would like to know? Please select.”, and the information types in the second information type set are arranged from top to bottom below;

[0134] Mapping the supplementary viewing recommendation list into the visualization model;

[0135] receiving a target information type selected by a user from a supplementary viewing recommendation list; when the user needs to view supplementary information, selecting the target information type from the list;

[0136] Determine local content of the target information type from the content to be selected;

[0137] Map local content into the visual model.

[0138] Generally, when viewing statistical analysis results, users may need to view some additional content related to the analysis results. At this time, users need to retrieve it by themselves, which is cumbersome. The above technical solution makes additional viewing recommendations, and users can directly select the target information type and view the corresponding local content, which improves convenience and further enhances the humanization of the system.

[0139] In one embodiment, the animal disease monitoring data statistical analysis system further includes:

[0140] The content position adjustment module is used to include:

[0141] Based on the content position adjustment condition, the content position of the fixation content, the supplementary viewing recommendation list and the local content is continuously adjusted in the visualization model;

[0142] The content position adjustment conditions include:

[0143] The shortest distance between the center position of the first content of the gaze content and the target straight line falls within a preset distance interval; the target straight line includes: the line between the center position of the second content of the supplementary viewing recommendation list and the center position of the third content of the local content; the distance interval can be, for example: 8 to 25 centimeters; the supplementary viewing recommendation list must be available for users to select other supplementary viewing content at any time, and the local content is a supplement to the gaze content, and it must be ensured that it is visually related to the gaze content. Setting this content position adjustment condition can ensure that the supplementary viewing recommendation list and the local content are not too far from the gaze content, nor too close, and meet the requirements of the supplementary viewing recommendation list and the local content;

[0144] The length of the target straight line falls within a preset length range; the length range can be, for example, 35 to 55 centimeters. Setting this content position adjustment condition can ensure that the supplementary viewing recommendation list and the local content are neither too far nor too close, visually ensuring that the user clearly knows the content types of the two, making the viewing clear;

[0145] The vector angle between the first direction vector and the second direction vector is greater than or equal to the preset angle threshold; the first direction vector is constructed based on the center position of the first content and the last moving direction of the gazed content; the second direction vector is constructed based on the midpoint position of the target straight line and the straight line direction from the midpoint position to the center position of the first content. The angle threshold can be, for example, 160 degrees; the user can drag the gazed content, so the gazed content will move in the visualization model, that is, the last moving direction will be generated; generally, when the user drags the gazed content, the gazed content may block the content that the user wants to view. To avoid blocking, all content should be in the last moving direction of the gazed content. For example, if the user drags the gazed content from the center position of the visualization model to the lower right, it means that there is content that the user wants to view in the center of the visualization model. To avoid blocking, the supplementary viewing recommendation list and local content need to be moved to the lower right of the gazed content after being dragged. Therefore, setting this content position adjustment condition can meet the needs of such situations, further improve humanization, and to a certain extent improve the user's work efficiency.

[0146] The present invention provides a method for statistical analysis of animal disease monitoring data. Figure 2 As shown, including:

[0147] Step S1: Obtain animal disease monitoring data;

[0148] Step S2: interactively obtaining the user's statistical analysis intention;

[0149] Step S3: generating a statistical analysis strategy based on the statistical analysis intention;

[0150] Step S4: performing statistical analysis on the animal disease monitoring data based on the statistical analysis strategy;

[0151] Step S5: Output statistical analysis results.

[0152] Step S1: Obtain animal disease monitoring data, including:

[0153] Acquire first candidate data from a preset active scene;

[0154] Determining whether the first candidate data meets the quasi-acquisition condition;

[0155] When the conditions are met, the first candidate data will be used as animal disease monitoring data;

[0156] and / or,

[0157] Acquire second candidate data from a preset passive scene;

[0158] Based on a preset first basis acquisition template corresponding to the data monitoring type of the second candidate data, an authenticity verification basis of the second candidate data is acquired from a passive scene;

[0159] Based on the authenticity verification basis, the second candidate data is verified for authenticity;

[0160] When the verification passes, the second candidate data is used as animal disease monitoring data.

[0161] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A statistical analysis system for animal disease monitoring data, characterized in that: include: Animal disease monitoring data acquisition module, used to obtain animal disease monitoring data; A statistical analysis intention acquisition module is used to interactively acquire the user's statistical analysis intention; A statistical analysis strategy generation module, used to generate a statistical analysis strategy based on the statistical analysis intention; A statistical analysis module, used for performing statistical analysis on the animal disease monitoring data based on the statistical analysis strategy; Statistical analysis result output module, used for outputting statistical analysis results; The statistical analysis intention acquisition module interactively acquires the statistical analysis intention of the user, including: Based on the preset second basis acquisition template, multiple intention prediction bases of the user are obtained; the intention prediction bases include: animal disease monitoring data statistical analysis task records undertaken by the user in the recent period, animal disease prevention and control task records undertaken, and animal disease monitoring data visited in the past; Preprocessing the intention prediction basis to obtain a preprocessing result; Based on a preset feature description template, the preprocessing result is characterized to obtain a feature description vector; the feature description template is to extract the type characteristics of the animal disease monitoring data statistical analysis task requirements, animal disease prevention and control task requirements, and historically accessed animal disease monitoring data from the preprocessing result, and construct the feature description vector in the form of a vector; Determining at least one predicted intent corresponding to the feature description vector from a preset predicted intent library; Based on the predicted intent, generating an intent selection table; receiving the predicted intent selected by the user from the intent selection table and using it as the statistical analysis intent; Wherein, the preprocessing of the intention prediction basis to obtain the preprocessing result includes: Based on the second generation time of the intention prediction basis, the intention prediction basis is set on a preset time axis; when setting, the intention prediction basis is set at a time axis position on the time axis corresponding to the second generation time; Determine a plurality of continuous basis sequences satisfying the continuous basis sequence condition from the time axis; Integrate each of the continuous basis sequences to obtain the preprocessing result; The continuous sequence conditions include: There are at least N intention prediction bases in the continuous basis sequence; N is a positive integer; The maximum time interval between any two of the intention prediction bases in the continuous base sequence is less than or equal to a preset first interval threshold; The time interval between two adjacent intention prediction bases in the continuous base sequence is less than or equal to a preset second interval threshold; the first interval threshold is greater than the second interval threshold; The basis types of the intention prediction basis in the continuous basis sequence are the same, or different basis types of the intention prediction basis in the continuous basis sequence have corresponding combinable relationships in a preset combinable relationship library; the combinable relationship represents that intention prediction bases of different basis types can be used together to predict the user's predicted intention.

2. The animal disease monitoring data statistical analysis system according to claim 1, characterized in that: The animal disease monitoring data acquisition module acquires animal disease monitoring data, including: Acquire first candidate data from a preset active scene; Determining whether the first candidate data meets a quasi-acquisition condition; When the conditions are met, the first candidate data is used as the animal disease monitoring data; and / or, Acquire second candidate data from a preset passive scene; Based on a preset first basis acquisition template corresponding to the data monitoring type of the second candidate data, obtaining the authenticity verification basis of the second candidate data from the passive scene; Based on the authenticity verification basis, performing authenticity verification on the second candidate data; When the verification passes, the second selected data is used as the animal disease monitoring data.

3. The animal disease monitoring data statistical analysis system according to claim 2, characterized in that: The quasi-acquisition conditions include one or more combinations of the following: There is a correlation between the first candidate data and the scene attribute information of the active scene; There are at least M target-related events in the active scene; the target-related events are related events that are generated within a preset time before and after the first generation time of the first candidate data and are associated with the first candidate data, and the event type of the related event exists in a preset standard event type library; M is a positive integer; The active scenario exists in a preset credible scenario library or the scenario credibility of the active scenario is greater than or equal to a preset credibility threshold.

4. The animal disease monitoring data statistical analysis system according to claim 1, characterized in that: The statistical analysis result output module outputs the statistical analysis result, including: Parsing a first information type set of the statistical analysis result; Retrieving a preset visualization model corresponding to the information type set; Mapping the statistical analysis results into the visualization model; Output the current visualization model.

5. The animal disease monitoring data statistical analysis system according to claim 4, characterized in that: Also includes: Additional review recommended modules for including: Obtaining the gaze content of the user in the visualization model; Determining, from the animal disease monitoring data, candidate content related to the gaze content; Parsing a second information type set of the to-be-selected content; Based on a preset template for generating a supplementary viewing recommendation list, generating a supplementary viewing recommendation list according to the second information type set; mapping the supplemental viewing recommendation list into the visualization model; receiving a target information type selected by the user from the supplementary viewing recommendation list; Determining partial content of the target information type from the selected content; The local content is mapped into the visualization model.

6. The animal disease monitoring data statistical analysis system according to claim 5, characterized in that: Also includes: The content position adjustment module is used to include: Based on a content position adjustment condition, continuously adjusting the content position of the gaze content, the supplementary viewing recommendation list, and the local content in the visualization model; The content position adjustment conditions include: The shortest distance between the first content center position of the gaze content and the target straight line falls within a preset distance interval; the target straight line includes: a line between the second content center position of the supplementary viewing recommendation list and the third content center position of the local content; The length of the target straight line falls within a preset length range; The vector angle between the first direction vector and the second direction vector is greater than or equal to a preset angle threshold; the first direction vector is constructed based on the center position of the first content and the last moving direction of the gaze content; the second direction vector is constructed based on the midpoint position of the target straight line and the straight line direction from the midpoint position to the center position of the first content.

7. A method for statistical analysis of animal disease monitoring data, characterized in that: include: Step S1: Obtain animal disease monitoring data; Step S2: interactively obtaining the user's statistical analysis intention; Step S3: generating a statistical analysis strategy based on the statistical analysis intention; Step S4: Based on the statistical analysis strategy, statistical analysis is performed on the animal disease monitoring data; Step S5: output statistical analysis results; The statistical analysis intention acquisition module interactively acquires the statistical analysis intention of the user, including: Based on the preset second basis acquisition template, multiple intention prediction bases of the user are obtained; the intention prediction bases include: animal disease monitoring data statistical analysis task records undertaken by the user in the recent period, animal disease prevention and control task records undertaken, and animal disease monitoring data visited in the past; Preprocessing the intention prediction basis to obtain a preprocessing result; Based on a preset feature description template, the preprocessing result is characterized to obtain a feature description vector; the feature description template is to extract the type characteristics of the animal disease monitoring data statistical analysis task requirements, animal disease prevention and control task requirements, and historically accessed animal disease monitoring data from the preprocessing result, and construct the feature description vector in the form of a vector; Determining at least one predicted intent corresponding to the feature description vector from a preset predicted intent library; Based on the predicted intent, generating an intent selection table; receiving the predicted intent selected by the user from the intent selection table and using it as the statistical analysis intent; Wherein, the preprocessing of the intention prediction basis to obtain the preprocessing result includes: Based on the second generation time of the intention prediction basis, the intention prediction basis is set on a preset time axis; when setting, the intention prediction basis is set at a time axis position on the time axis corresponding to the second generation time; Determine a plurality of continuous basis sequences satisfying the continuous basis sequence condition from the time axis; Integrate each of the continuous basis sequences to obtain the preprocessing result; The continuous sequence conditions include: There are at least N intention prediction bases in the continuous basis sequence; N is a positive integer; The maximum time interval between any two of the intention prediction bases in the continuous base sequence is less than or equal to a preset first interval threshold; The time interval between two adjacent intention prediction bases in the continuous base sequence is less than or equal to a preset second interval threshold; the first interval threshold is greater than the second interval threshold; The basis types of the intention prediction basis in the continuous basis sequence are the same, or different basis types of the intention prediction basis in the continuous basis sequence have corresponding combinable relationships in a preset combinable relationship library; the combinable relationship represents that intention prediction bases of different basis types can be used together to predict the user's predicted intention.

8. The method for statistical analysis of animal disease monitoring data according to claim 7, characterized in that: The step S1: obtaining animal disease monitoring data includes: Acquire first candidate data from a preset active scene; Determining whether the first candidate data meets a quasi-acquisition condition; When the conditions are met, the first candidate data is used as the animal disease monitoring data; and / or, Acquire second candidate data from a preset passive scene; Based on a preset first basis acquisition template corresponding to the data monitoring type of the second candidate data, obtaining the authenticity verification basis of the second candidate data from the passive scene; Based on the authenticity verification basis, performing authenticity verification on the second candidate data; When the verification passes, the second selected data is used as the animal disease monitoring data.

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