Animal epidemic disease virus dilution multiple statistical analysis system

Abstract

The invention relates to the field of virus dilution multiple statistical analysis, in particular to an animal epidemic disease virus dilution multiple statistical analysis method which comprises the following steps: receiving parameters input by a user, calculating a virus dilution multiple through a preset calculation formula, and outputting the virus dilution multiple; in combination with mathematical modeling and a programming technology, the method has the advantages of accuracy, rapidness, storage, statistics, query, analysis and the like, the conditions of experiment failure, experiment repetition, experiment expenditure increase and subject (project) delay due to incomplete experiment progress caused by inaccurate calculation of the virus dosage are avoided, and the method is convenient, accurate and efficient; the method has the advantages that the method is simple and convenient to operate, has the functions of data reference and comparison and analysis of multiple groups of data, can accurately, quickly and indiscriminately give the dilution multiple of the virus needing to be used, can store the calculation result, and can be used only by query without repeated calculation for the same experiment result under the same condition in the future.

Description

Technical Field

[0001] This invention relates to the field of statistical analysis of virus dilution ratios, and more particularly to a statistical analysis system for animal disease virus dilution ratios. Background Technology

[0002] Currently, in the field of disease prevention and control, when it comes to immunizing or challenging animals with viruses, researchers rely entirely on their familiarity with the virus being used and their past work experience to roughly determine the amount of virus injected into the animal. Based on the roughly estimated dose, they then calculate the required dilution factor of the virus.

[0003] Given the current state of technology, it is easy to cause significant discrepancies between experimental and actual results, sometimes even falling outside the margin of error. Furthermore, inaccurate virus injection dosage can easily prevent the desired experimental effects from being achieved. Excessive injection can lead to animal death, causing unnecessary reproducibility issues; insufficient injection will not have the intended effect on challenged (immunized) animals, failing to achieve the desired experimental results. Experimental failures due to inaccurate dosage are frequent occurrences.

[0004] Some of the animals used in experiments are highly seasonal, and the experimental cycle is measured in years. If the experiment fails due to inaccurate calculation of the virus dosage, the entire experiment will have to be delayed. This chain reaction will cause incalculable losses to the normal acceptance of the project.

[0005] For example, estimating the appropriate dose for immunization or challenge in animals based on years of experience and familiarity with all viruses is more or less a matter of luck. The first attempt serves as a trial-and-error experiment, and the dosage is adjusted based on the results of the first experiment.

[0006] Alternatively, manual calculation can yield results that are very close, but not perfectly accurate, which can affect the results of subsequent experiments. This method not only fails to achieve the best results but is also time-consuming and carries the risk of errors and inaccuracies in manual calculations.

[0007] In view of this, we propose a statistical analysis system for animal disease virus dilution ratios to solve the existing problems. Summary of the Invention

[0008] The purpose of this invention is to provide a statistical analysis system for the dilution factor of animal disease viruses to solve the problems mentioned in the background art.

[0009] To achieve the above objectives, the present invention provides the following technical solution: an animal disease virus dilution factor statistical analysis system, comprising a parameter input module, a data verification module, a calculation module, a result output module, a storage module, a query module, an analysis module, and a system integration interface; The parameter input module receives user-input parameters via a graphical user interface. These parameters include TCID. 50 Numerical values, the volume of virus to be injected into each target individual, the numerical value per unit volume, and the number of viruses to be injected into each target individual; The data validation module performs real-time validation of input parameters to ensure that the parameter format is correct, the value is valid, and the volume unit is consistent. If the validation fails, the user is prompted to re-enter the data. The calculation module calculates the virus dilution factor based on the verified parameters using a predetermined formula: Virus dilution factor = [(1 / TCID)] 50 [(Volume of virus to be injected into each target individual) / [Value per unit volume × Number of viruses to be injected into each target individual]; The results output module outputs the calculated virus dilution factor to the user interface and supports exporting the results as an experimental report format. The storage module associates and stores input parameters, calculation results, timestamps, and experimental context information in the database to form a traceable historical record; The query module retrieves historical records based on user-input conditions and supports multi-field combination retrieval; The analysis module performs statistical analysis on multiple sets of historical records, generates trend charts of the correlation between virus dilution factor and experimental parameters, comparison reports or optimization suggestions, and visualizes the analysis results. The system integration interface connects to the experimental equipment to achieve automatic parameter acquisition and result feedback control.

[0010] Furthermore, the method by which the parameter input module receives user input parameters through the graphical user interface includes: displaying a parameter configuration interface containing at least one configurable parameter in a first area of ​​the graphical user interface; displaying a preview result generated based on the current parameter value in a second area of ​​the graphical user interface; obtaining an updated value of the target parameter in response to the user's input operation on the target parameter in the parameter configuration interface; updating the preview result in real time according to the updated value, and displaying the updated preview result in the second area.

[0011] Furthermore, the parameter verification method of the data verification module includes: responding to the user's input operation on the parameter input interface, obtaining the input parameter value and its associated unit; performing real-time verification of the parameter value, including format verification and numerical validity verification; and monitoring the unit consistency among multiple parameters.

[0012] Furthermore, the experimental data traceability storage method of the storage module includes: receiving an experiment execution request, which includes input parameters and experimental context information; executing the experiment and generating calculation results; generating a timestamp; and associating and storing the input parameters, calculation results, timestamp, and experimental context information in the database.

[0013] Furthermore, the query history method of the query module includes: receiving query conditions input by the user through the interface, which contain at least one query field; assigning weights to each query field in the received query conditions according to predefined field weight rules, wherein fields used for exact matching are assigned higher weights than fields used for fuzzy matching; performing a hierarchical database query operation based on the weighted query conditions, wherein a first result subset is obtained by searching based on query fields with higher weights than the set weights, and then a final result set is obtained by searching based on query fields with lower weights than the set weights within the first result subset; sorting the records in the final result set according to their relevance based on the weights of each field in the query conditions; and outputting the sorted final result set.

[0014] Furthermore, the analysis and optimization methods of the analysis module include: acquiring multiple sets of historical virus experiment records, each record containing at least the correspondence between virus dilution factor, experimental parameters, and experimental results; performing multi-dimensional statistical analysis on the historical experiment records to identify the correlation trend between virus dilution factor and at least one experimental parameter; generating a comparison report or experimental parameter optimization suggestions based on the correlation trend; and displaying the correlation trend, comparison report, or optimization suggestions graphically.

[0015] Furthermore, the automated data acquisition and feedback control method for the system integration interface includes: encapsulating the communication protocols and interfaces of various heterogeneous experimental devices and providing a unified device operation interface for upper-layer applications; receiving and distributing data messages between modules within the system; parsing and executing user-defined experimental process configurations, which define the execution order of experimental steps, parameter acquisition rules, and triggering conditions for control commands; subscribing to real-time acquired data and generating control commands based on a preset intelligent algorithm model, which are then sent to the target experimental device through the device driver abstraction layer.

[0016] Furthermore, when real-time verification fails or inconsistencies in units are detected, corresponding prompts are provided on the parameter input interface.

[0017] Furthermore, when inconsistencies in units are detected, a unit unification operation prompt is generated and presented. The unit unification operation prompt includes at least one option to unify different units into the target unit. In response to the user's selection of the option, the units of multiple parameters are unified into the target unit, and the parameter values ​​are converted accordingly.

[0018] Furthermore, the storage step includes: calculating the data hash value of the input parameters and / or calculation results; and storing the data hash value in association with the corresponding input parameters and / or calculation results.

[0019] Compared with the prior art, the beneficial effects of the present invention are: This invention combines mathematical modeling and programming technology to solve the problem of researchers accurately determining the correct dosage of virus injected into animals during clinical or scientific experiments in the field of animal disease prevention and control, when immunizing or challenging animals. It has many advantages, including accuracy, speed, storage, statistics, querying, and analysis. It avoids situations where inaccurate virus dosage calculations lead to experimental failures, repeated experiments, increased experimental costs, and project delays due to incomplete experimental progress. It is not only convenient, accurate, and efficient, but also has data retrieval capabilities and the ability to compare and analyze multiple sets of data. It can accurately, quickly, and indiscriminately provide the required dilution factor of the virus, and can store the calculation results so that the same experimental results under the same conditions can be retrieved without repeated calculations. Attached Figure Description

[0020] Figure 1 This is a flowchart of the statistical analysis system for dilution factors of animal disease viruses according to the present invention; Figure 2 Image of a 96-well plate used in the experiments of this invention. Detailed Implementation

[0021] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments. Example 1

[0022] like Figure 1 As shown, the workflow of an animal disease virus dilution factor statistical analysis system includes parameter input, data verification and preprocessing, dilution factor calculation, result output and storage, query and analysis.

[0023] In the parameter input section, users input the key parameters required for the calculation through the system interface. These parameters include: TCID 50 Numerical values, for example, the TCID of the viral stock solution. 50 10 -4.5 If the volume is 0.1ml, then this value is 10. -4.5 ; The volume of virus to be injected into each target individual, for example, if the plan is to inject 2 ml of diluted virus solution into each animal, then this value is 2 (ml); The value corresponding to each unit volume, this value is related to TCID. 50 The definition is relevant, for example, if TCID 50 Defined as a titer per 0.1 ml, this value is 0.1 (ml); The number of viruses to be injected into each target individual, for example, if the experiment requires each animal to be infected with 200 virus particles, then this number is 200.

[0024] In data validation and preprocessing, the system verifies the format and validity of input parameters, such as checking whether the value is positive and whether the units match, to prevent calculation deviations caused by input errors.

[0025] In the dilution factor calculation, the system calls the built-in calculation engine and applies the formula for automatic calculation. Taking the aforementioned parameters as an example: Virus dilution factor = [(1 / 10 -4.5 [0.1×2] / [0.1×200]=[(10 4.5 The system will automatically perform the exponential calculation ()×2] / 20 to obtain the accurate dilution factor.

[0026] In the result output and storage, after the calculation is completed, the system will clearly display the dilution factor result to the user. At the same time, the system will automatically store all input parameters, calculation results, calculation timestamps and other information of this calculation to the database. This step lays the foundation for subsequent query, statistics and analysis functions.

[0027] In the query and analysis section, users can query historical calculation records through the system. The system also provides statistical analysis functions, such as comparing and analyzing multiple dilutions of the same virus under different experimental conditions, or displaying the influence trend of different parameters on the dilution factor in the form of charts, providing data insights for researchers.

[0028] A statistical analysis system for dilution ratios of animal disease viruses includes a parameter input module, a data verification module, a calculation module, a result output module, a storage module, a query module, an analysis module, and a system integration interface. The parameter input module receives user-input parameters via a graphical user interface. These parameters include TCID. 50 Numerical values, the volume of virus to be injected into each target individual, the numerical value per unit volume, and the number of viruses to be injected into each target individual; The data validation module performs real-time validation of input parameters to ensure that the parameter format is correct, the value is valid, and the volume unit is consistent. If the validation fails, the user is prompted to re-enter the data. The calculation module calculates the virus dilution factor based on the verified parameters using a predetermined formula: Virus dilution factor = [(1 / TCID)] 50 [(Volume of virus to be injected into each target individual) / [Value per unit volume × Number of viruses to be injected into each target individual]; The results output module outputs the calculated virus dilution factor to the user interface and supports exporting the results as an experimental report format. The storage module associates and stores input parameters, calculation results, timestamps, and experimental context information in the database to form a traceable historical record; The query module retrieves historical records based on user-input conditions and supports multi-field combination retrieval; The analysis module performs statistical analysis on multiple sets of historical records, generates trend charts of the correlation between virus dilution factor and experimental parameters, comparison reports or optimization suggestions, and visualizes the analysis results. The system integration interface connects to the experimental equipment to achieve automatic parameter acquisition and result feedback control.

[0029] The working principle of the animal disease virus dilution factor statistical analysis system based on Example 1 is as follows: The 96-well plate used in the experiment, such as Figure 2 As shown, each hole is a target hole.

[0030] TCID 50 The unit is "dilution per unit volume", TCID of the original solution. 50 =10 -4.5 If 0.1ml of diluted solution needs to be injected into each target individual at a rate of 2ml, and each target individual needs to contain 200 viruses, then how many times should the original solution be diluted to achieve the final goal?

[0031] In original solution, 10 -4.5 The dilution level corresponds to one TCID per 0.1 ml. 50 The unit of virus requires each target individual to contain 200 viruses. Let X be the number of target individuals that need to be injected with the liquid. Then the total number of viruses contained in all target individuals is 200X, and the total volume of liquid to be injected into all target individuals is 2X ml.

[0032] Let the dilution factor be Y, then the number of viruses per ml after dilution is (10) 4.5 / 0.1) / Y=10 4.5 / (Y*0.1), because 10 -4.5 Dilution means that the original solution is diluted 10 times. 4.5 After doubling, each 0.1 ml contains 1 TCID. 50 Let's assume one TCID 50 This corresponds to one infectious viral particle.

[0033] The number of viruses in 2X ml is 2X*

[10] 4.5 If [ / (Y*0.1)]=200X, then Y=(2*10 4.5 ) / (0.1*200), and finally Y=3162.28. Therefore, the virus dilution factor is 3162.28 times. This means that the original virus solution needs to be diluted 3162.28 times before each animal is injected with 2ml to ensure that each animal receives 200 virus particles.

[0034] Therefore, the viral dilution factor is independent of the required number of target individuals. The formula for calculating the viral dilution factor can be derived as: Virus dilution factor = [(1 / TCID)] 50 [(Volume of virus to be injected into each target individual) / [Value per unit volume × Number of viruses to be injected into each target individual]

[0035] By combining specific biomathematical formulas with computer technology, the problems of traditional methods relying on experience, large errors, and low efficiency are effectively solved, providing a precise, efficient, and reliable dose calculation solution for immunization and challenge experiments in animal disease research. Example 2

[0036] The method for the parameter input module to receive user input parameters through a graphical user interface includes: displaying a parameter configuration interface containing at least one configurable parameter in a first area of ​​the graphical user interface; displaying a preview result generated based on the current parameter value in a second area of ​​the graphical user interface; obtaining an updated value of the target parameter in response to the user's input operation on the target parameter in the parameter configuration interface; updating the preview result in real time according to the updated value, and displaying the updated preview result in the second area.

[0037] The working principle of the animal disease virus dilution factor statistical analysis system based on Example 2 is as follows: The parameter configuration interface includes multiple configuration steps arranged in sequence; the method also includes: in response to the user's operation of completing the current configuration step, enabling the interface element for the next configuration step.

[0038] In response to user actions on parameters in the current configuration step, dynamically adjust the available options or value range of parameters in at least one subsequent configuration step.

[0039] In response to the user's input operation on the target parameter in the parameter configuration interface, after obtaining the updated value of the target parameter, the method also includes: validating the updated value; if the validation fails, displaying an error message next to the input control of the target parameter, and keeping the preview result unchanged or displaying a preview result with an error status.

[0040] The preview results are updated in real time based on the updated values, including: sending the updated values ​​and all other current parameter values ​​to the background processing engine; receiving the preview data calculated by the background processing engine based on all parameter values; and refreshing the display content of the second area using the preview data.

[0041] Before sending the updated value to the background processing engine, the process also includes: debouncing the user's input, and only triggering the sending of the updated value to the background processing engine when there is no new input within a preset time interval. Example 3

[0042] The parameter verification method of the data verification module includes: responding to the user's input operation on the parameter input interface, obtaining the input parameter value and its associated unit; performing real-time verification of the parameter value, including format verification and numerical validity verification; monitoring the unit consistency among multiple parameters; providing corresponding prompts on the parameter input interface when real-time verification fails or unit inconsistency is detected; wherein, when unit inconsistency is detected, generating and presenting a unit unification operation prompt, which includes at least one option to unify different units to the target unit; responding to the user's selection of the option, unifying the units of multiple parameters to the target unit and performing corresponding conversions on the parameter values.

[0043] The working principle of the animal disease virus dilution factor statistical analysis system based on Example 3 is as follows: Real-time verification also includes correlation verification, which is used to verify whether the numerical logical relationship between multiple parameters conforms to preset business rules.

[0044] After real-time verification of parameter values, the process also includes: if a formatting error that can be automatically corrected is detected in the parameter value, the parameter value is automatically corrected, and a correction confirmation prompt is generated and presented to the user.

[0045] The corresponding prompts include: providing first-level prompts through changes in the visual attributes of the corresponding parameter input boxes in the parameter input interface; and providing second-level detailed prompts through text information floating on the parameter input boxes.

[0046] The verification rules used for real-time verification come from a configurable rule base; the method also includes adding, deleting or modifying verification rules in the rule base through a rule management interface. Example 4

[0047] The experimental data traceability storage method of the storage module includes: receiving an experiment execution request, which includes input parameters and experimental context information; executing the experiment and generating calculation results; generating a timestamp; and associating and storing the input parameters, calculation results, timestamp, and experimental context information in the database. The storage steps include: calculating the data hash value of the input parameters and / or calculation results; and associating and storing the data hash value with the corresponding input parameters and / or calculation results.

[0048] The working principle of the animal disease virus dilution factor statistical analysis system based on Example 4 is as follows: The steps for generating a timestamp include: obtaining a trusted timestamp from a trusted time source, binding the trusted timestamp with a data hash value, and then storing it.

[0049] The experimental context information includes at least one of the following: software environment information, including operating system version and dependent library version; hardware environment information; the version identifier of the code on which the experiment is based; and the version identifier of the data used in the experiment.

[0050] The storage process employs a tiered storage strategy, which includes: storing metadata used for fast queries in a relational database; storing result files larger than a preset threshold in object storage or a file system; and storing access paths to the result files in the relational database. Example 5

[0051] The query history method of the query module includes: receiving query conditions input by the user through the interface, which contain at least one query field; assigning weights to each query field in the received query conditions according to predefined field weight rules, wherein fields used for exact matching are assigned higher weights than fields used for fuzzy matching; performing hierarchical database query operations based on the weighted query conditions, wherein a first result subset is obtained by searching based on query fields with higher weights than the set weights, and then a final result set is obtained by searching based on query fields with lower weights than the set weights within the first result subset; sorting the records in the final result set according to their relevance based on the weights of each field in the query conditions; and outputting the sorted final result set.

[0052] The working principle of the animal disease virus dilution factor statistical analysis system based on Example 5 is as follows: In addition to outputting the sorted final result set, the system also generates and outputs multi-dimensional statistical information associated with the final result set, which includes at least statistical charts distributed by time and operation type.

[0053] Before receiving user input, the process also includes: recording and storing the user's historical valid query condition combinations; during the process of receiving user input, recommending complete query condition combinations from the stored historical valid query condition combinations for the user to choose from, based on the currently entered query fields.

[0054] The step of receiving user input also includes: in response to the value entered or selected by the user in the first query field, automatically determining and updating the range of selectable values ​​in the second query field, wherein the first query field and the second query field have a predefined association relationship.

[0055] The system automatically determines and updates the range of selectable values ​​in the second query field. Specifically, based on the value of the first query field, it sends a request to the server to obtain a list of candidate values ​​for the associated second query field and populates the drop-down selection box of the second query field with the list of candidate values.

[0056] Based on the weighted query conditions, a hierarchical database query operation is performed, which includes: combining high-weight fields used for exact matching into the first query statement and prioritizing the use of database indexes to execute the query; caching the first subset of results obtained from the query in memory; and using low-weight fields used for fuzzy matching as filtering conditions to traverse and match the first subset of results in memory. Example 6

[0057] The analysis and optimization methods of the analysis module include: acquiring multiple sets of historical virus experiment records, with each record containing at least the correspondence between virus dilution factor, experimental parameters, and experimental results; performing multi-dimensional statistical analysis on the historical experiment records to identify the correlation trend between virus dilution factor and at least one experimental parameter; generating a comparison report or experimental parameter optimization suggestions based on the correlation trend; and displaying the correlation trend, comparison report, or optimization suggestions in a graphical manner. Example 7

[0058] The automated data acquisition and feedback control method for the system integration interface includes: encapsulating the communication protocols and interfaces of various heterogeneous experimental devices and providing a unified device operation interface for upper-layer applications; receiving and distributing data messages between modules within the system; parsing and executing user-defined experimental process configurations, which define the execution order of experimental steps, parameter acquisition rules, and triggering conditions for control commands; subscribing to real-time acquired data and generating control commands based on a preset intelligent algorithm model, which are then sent to the target experimental device through the device driver abstraction layer.

[0059] The above specific embodiments are merely several preferred embodiments of the present invention. Based on the technical solutions of the present invention and the relevant teachings of the above embodiments, those skilled in the art can make various alternative improvements and combinations to the above specific embodiments.

Claims

1. A statistical analysis system for dilution factors of animal disease viruses, characterized in that: It includes a parameter input module, a data validation module, a calculation module, a result output module, a storage module, a query module, an analysis module, and a system integration interface; The parameter input module receives user-input parameters via a graphical user interface. These parameters include TCID. 50 Numerical values, the volume of virus to be injected into each target individual, the numerical value per unit volume, and the number of viruses to be injected into each target individual; The data validation module performs real-time validation of input parameters to ensure that the parameter format is correct, the value is valid, and the volume unit is consistent. If the validation fails, the user is prompted to re-enter the data. The calculation module calculates the virus dilution factor based on the verified parameters using a predetermined formula: Virus dilution factor = [(1 / TCID)] 50 [(Volume of virus to be injected into each target individual) / [Value per unit volume × Number of viruses to be injected into each target individual]; The results output module outputs the calculated virus dilution factor to the user interface and supports exporting the results as an experimental report format. The storage module associates and stores input parameters, calculation results, timestamps, and experimental context information in the database to form a traceable historical record; The query module retrieves historical records based on user-input conditions and supports multi-field combination retrieval; The analysis module performs statistical analysis on multiple sets of historical records, generates trend charts of the correlation between virus dilution factor and experimental parameters, comparison reports or optimization suggestions, and visualizes the analysis results. The system integration interface connects to the experimental equipment to achieve automatic parameter acquisition and result feedback control.

2. The animal disease virus dilution factor statistical analysis system according to claim 1, characterized in that, The method for the parameter input module to receive user input parameters through a graphical user interface includes: displaying a parameter configuration interface containing at least one configurable parameter in a first area of ​​the graphical user interface; displaying a preview result generated based on the current parameter value in a second area of ​​the graphical user interface; obtaining an updated value of the target parameter in response to the user's input operation on the target parameter in the parameter configuration interface; updating the preview result in real time according to the updated value, and displaying the updated preview result in the second area.

3. The animal disease virus dilution factor statistical analysis system according to claim 1, characterized in that, The parameter validation methods of the data validation module include: responding to user input operations on the parameter input interface, obtaining the input parameter value and its associated unit; performing real-time validation of the parameter value, including format validation and numerical validity validation; and monitoring the unit consistency among multiple parameters.

4. The animal disease virus dilution factor statistical analysis system according to claim 1, characterized in that, The experimental data traceability storage method of the storage module includes: receiving an experiment execution request, which includes input parameters and experimental context information; executing the experiment and generating calculation results; generating a timestamp; and storing the input parameters, calculation results, timestamp, and experimental context information in the database.

5. The animal disease virus dilution factor statistical analysis system according to claim 1, characterized in that, The query history method of the query module includes: receiving query conditions input by the user through the interface, which contain at least one query field; assigning weights to each query field in the received query conditions according to predefined field weight rules, wherein fields used for exact matching are assigned higher weights than fields used for fuzzy matching; performing hierarchical database query operations based on the weighted query conditions, wherein a first result subset is obtained by searching based on query fields with higher weights than the set weights, and then a final result set is obtained by searching based on query fields with lower weights than the set weights within the first result subset; sorting the records in the final result set according to their relevance based on the weights of each field in the query conditions; and outputting the sorted final result set.

6. The animal disease virus dilution factor statistical analysis system according to claim 1, characterized in that, The analysis and optimization methods of the analysis module include: acquiring multiple sets of historical virus experiment records, with each record containing at least the correspondence between virus dilution factor, experimental parameters, and experimental results; performing multi-dimensional statistical analysis on the historical experiment records to identify the correlation trend between virus dilution factor and at least one experimental parameter; generating a comparison report or experimental parameter optimization suggestions based on the correlation trend; and displaying the correlation trend, comparison report, or optimization suggestions in a graphical manner.

7. The animal disease virus dilution factor statistical analysis system according to claim 1, characterized in that, The automated data acquisition and feedback control method for the system integration interface includes: encapsulating the communication protocols and interfaces of various heterogeneous experimental devices and providing a unified device operation interface for upper-layer applications; receiving and distributing data messages between modules within the system; parsing and executing user-defined experimental process configurations, which define the execution order of experimental steps, parameter acquisition rules, and triggering conditions for control commands; subscribing to real-time acquired data and generating control commands based on a preset intelligent algorithm model, which are then sent to the target experimental device through the device driver abstraction layer.

8. The animal disease virus dilution factor statistical analysis system according to claim 3, characterized in that: When real-time verification fails or inconsistencies in units are detected, corresponding prompts will be provided on the parameter input interface.

9. The animal disease virus dilution factor statistical analysis system according to claim 8, characterized in that: When inconsistencies in units are detected, a unit unification operation prompt is generated and presented. The unit unification operation prompt includes at least one option to unify different units into the target unit. In response to the user's selection of options, the units of multiple parameters are unified to the target unit, and the parameter values ​​are converted accordingly.

10. The animal disease virus dilution factor statistical analysis system according to claim 4, characterized in that, The storage steps include: calculating the data hash value of the input parameters and / or calculation results; and storing the data hash value in association with the corresponding input parameters and / or calculation results.

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