Silkworm disease prediction method and system based on molecular biology

Abstract

The present application relates to the field of data processing technology, and in particular to a method and system for predicting silkworm diseases based on molecular biology, obtaining the working status of silkworm tools in each sub-silkworm room in a silkworm room to be detected, and capturing the behavior information and molecular biological information of live silkworms in the silkworm room to be detected; based on the behavior information of live silkworms captured in the past, the molecular biological information and the working status of silkworm tools in each sub-silkworm room in the silkworm room to be detected, a molecular biological silkworm disease prediction network algorithm is generated, and the influence of multiple silkworm tool working status and feed status on silkworm disease infection is analyzed; based on the past measurement information of live silkworms raised in the silkworm room to be detected, the silkworm disease infection in the silkworm room to be detected is predicted, and the silkworm disease infection value is analyzed; through the real-time feed status and the captured behavior information of live silkworms, the analyzed silkworm disease infection value is presented to the breeding personnel through a mobile terminal. The present invention can predict the occurrence of silkworm diseases caused by real-time molecular biology of silkworm breeding, guide the precise disinfecting of silkworm rooms, silkworm tools, and surrounding molecular biology, and effectively improve the quality and benefits of silkworm breeding.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and system for predicting silkworm diseases based on molecular biology. Background Art

[0002] Silkworm disease is one of the main reasons for the economic loss of silkworm breeding. The existing visual microscopic examination method can not recover the economic losses that have been caused by the outbreak of silkworm disease. Only by predicting the outbreak of silkworm disease can we truly achieve early detection, early discovery, early response and early treatment. Polymerase chain reaction (PCR) fluorescence quantitative detection technology can amplify the genetic information contained in pathogenic microorganisms through the amplification of pathogenic microorganism DNA and RNA characteristic sequences, and quickly and efficiently detect the types of pathogenic microorganisms.

[0003] The present invention introduces molecular biological detection methods into the detection and diagnosis of molecular biological pathogens in silkworm breeding in Liangshan for the first time. With the "sampling-detection-prevention and control" integrated silkworm disease detection and prevention linkage platform as the carrier, it qualitatively and quantitatively detects the occurrence trends of molecular biological pathogens in silkworm breeding at all stages, scenes and links of sericulture production, predicts the occurrence of silkworm diseases caused by molecular biological pathogens in silkworm breeding in real time, guides the precise disinfection of silkworm rooms, silkworm tools and surrounding molecular organisms, effectively improves the quality and benefits of silkworm breeding, and is of great significance for increasing silkworm farmers' income, stabilizing the industry and developing the industry. Summary of the invention

[0004] To achieve the above objectives, this application provides the following technical solutions:

[0005] According to a first aspect of the present invention, the present invention claims protection for a method for predicting silkworm diseases based on molecular biology, the method comprising the following steps:

[0006] S10, obtaining the working status of silkworm tools in each sub-silkworm room in the silkworm room to be detected, and capturing the behavioral information and molecular biological information of live silkworms in the silkworm room to be detected;

[0007] S20, based on the behavior information of live silkworms captured in the past, molecular biological information and the working status of silkworm tools in each sub-silkworm room in the silkworm room to be tested, generate a molecular biological silkworm disease prediction network algorithm, and analyze the influence of multiple silkworm tool working status and feed status on silkworm disease infection;

[0008] S30, predicting the silkworm disease infection in the silkworm room to be tested based on the past measurement information of the live silkworms raised in the silkworm room to be tested, and analyzing the silkworm disease infection value;

[0009] S40, presenting the analyzed silkworm disease infection value to the breeding staff through the mobile terminal based on the real-time feed status and the captured live silkworm behavior information;

[0010] The live silkworm behavior information is the live silkworm parameter elements that will affect the breeding status of the live silkworms; and the molecular biological information is the live silkworm blood information in the silkworm room to be detected.

[0011] Furthermore, the silkworm room to be detected is a non-integrated shed with multiple silkworm platforms, feeding machines and shed features; the silkworm room to be detected is divided into n sub-silkworm rooms through the multiple sheds of the silkworm room to be detected, and P1, P2, ..., P n ; When capturing the behavior information of live silkworms in the silkworm room to be detected, the dynamic behavior information of live silkworms corresponding to each silkworm room is determined by the planned silkworm room in units of days, and the behavior information of live silkworms is synchronized in real time; Among them, P i is the i-th sub-silkworm room corresponding to the plan; i=1, 2, ..., n; n is the number of sub-silkworm rooms planned;

[0012] The working state of the silkworm tool is that the age of live silkworms cultured in the silkworm room to be detected and the influence of artificial factors on the behavior information of live silkworms; when the artificial factors influence the behavior information of live silkworms in the silkworm tool, the captured behavior information of live silkworms is synchronized; wherein the breeding sparseness of live silkworms in the silkworm room to be detected is the same.

[0013] Furthermore, the method steps of step S20 are:

[0014] S201, determine the age H and sparseness F of live silkworms cultured in the silkworm room to be tested, and determine the number m of live silkworm parameter elements to be analyzed; determine the dynamic data of the number of live silkworms of each live silkworm parameter element in each sub-silkworm room with period t through the live silkworm behavior information captured in the past, and obtain ;pass , analyze the resource utilization rate V of multiple live silkworm parameter elements when the live silkworm breeding sparseness is F when there is no artificial factor and accidental influence on the live silkworm behavior information in the silkworm tools;

[0015] ;

[0016] in, is the dynamic data of the number of live silkworms of the jth live silkworm parameter element in the i-th sub-silkworm room with period t; t is the period under multiple days; is the resource utilization rate of the jth live silkworm parameter element when the sparseness of live silkworm breeding is F; For analysis The fth period corresponding to the jth live silkworm parameter element in the silkworm tool when there is no artificial factor or accidental influence on the live silkworm behavior information in the silkworm tool; is the parameter element of the j-th type of live silkworm in the period Dynamic quantity of live silkworms; For analysis The number of periodic segments in which the j-th type of live silkworm parameter elements meet the conditions;

[0017] S202, generating a molecular biological silkworm disease prediction network algorithm, using PCR technology, deploying the same shed molecular biology as the silkworm room to be tested in the network algorithm; , analyze the influence of feed status on the behavior information of live silkworms in silkworm tools; determine the number of days with unexpected cycles through past molecular biological information, and determine the feed status L corresponding to that number of days t and the presence of feed state L t Dynamic data of live silkworm quantity of each live silkworm parameter element in each silkworm room and ; Analyze the impact of feed status on the behavior of live silkworms in silkworm tools ,

[0018] ;

[0019] S203, and L t As the input of the network algorithm, As the output of the network algorithm, the number of live silkworms under various live silkworm parameter elements under multiple feeding conditions is simulated in the network algorithm to obtain the dynamics of L t , and The functional relationship is: ; Simulate the network algorithm to determine Function of silkworm age and coefficient values.

[0020] Furthermore, the method steps of step S30 are:

[0021] S301, measuring the information of the breeding state of live silkworms H, analyzing the breeding state of live silkworms H under multiple concentrations of live silkworm parameter elements; calculating the number of live silkworms x1, x2, ..., x corresponding to the m kinds of live silkworm parameter elements in the breeding molecular biology of live silkworms H m as the independent variable and the amount of raw silk from live silkworms Y as the dependent variable, and the recursive network algorithm was fitted;

[0022] ;

[0023] Where j = 1, 2, ..., m; The number of live silkworms corresponding to the j-th live silkworm parameter element is The impact of time on the raw silk production of live silkworms; is the error term;

[0024] Determine w1, w2, ..., w in the recursive network algorithm m The impact value of

[0025] S302, pass and , predict the silkworm disease infection in the silkworm room to be tested, and analyze the silkworm disease infection value,

[0026] ;

[0027] in, is the silkworm disease infection value analyzed under the corresponding cycle day t; is the area of ​​the i-th sub-silkworm room.

[0028] Furthermore, the number of live silkworms of each live silkworm parameter element in each sub-silkworm room in real-time period t0 is determined by the captured live silkworm behavior information and molecular biological information. and feed status size ;pass and , substituted into the molecular biological silkworm disease prediction network algorithm, and analyzed the impact of feed status on the behavior information of live silkworms in silkworm tools ; Predict the number of live silkworms of each live silkworm parameter element in each silkworm room after an accident ;

[0029] ;

[0030] Through the predicted , analyze the real-time silkworm disease infection value , and the analyzed silkworm disease infection value The predicted , thereby artificially influencing the behavior information of live silkworms in each silkworm room;

[0031] and Refers to determine the feed status for the corresponding days and the presence of feed status Dynamic data of the number of live silkworms of each live silkworm parameter element in each silkworm room before and after.

[0032] According to the second aspect of the present invention, the present invention claims protection for a silkworm disease prediction system based on molecular biology, which is used to execute the silkworm disease prediction method based on molecular biology, and is characterized in that: the system comprises an information capture and processing device, a network algorithm control and analysis device, a molecular biology silkworm disease prediction device and an interactive presentation device;

[0033] The information capture and processing device is used to obtain the working status of silkworm tools in each sub-silkworm room in the silkworm room to be detected, and to capture the behavior information and molecular biological information of live silkworms in the silkworm room to be detected; the network algorithm control and analysis device is used to generate a molecular biological silkworm disease prediction network algorithm, and analyze the influence of multiple silkworm tool working status and feed status on silkworm disease infection; the molecular biological silkworm disease prediction device is used to predict the silkworm disease infection in the silkworm room to be detected based on the past measurement information of live silkworms raised in the silkworm room to be detected, and analyze the silkworm disease infection value; the interactive presentation device is used to present the analyzed silkworm disease infection value to the breeding personnel through the mobile terminal through the real-time feed status and the captured live silkworm behavior information;

[0034] The live silkworm behavior information is the live silkworm parameter elements that will affect the breeding status of the live silkworms; and the molecular biological information is the live silkworm blood information in the silkworm room to be detected.

[0035] Furthermore, the information capture and processing device comprises a silkworm tool control and recording module, an information processing and classification module, and a molecular biology control and recording module;

[0036] The silkworm tool control and recording module is used to obtain the age of live silkworms cultured in the silkworm room to be detected and the influence of artificial factors on the behavior information of live silkworms; when the artificial factors influence the behavior information of live silkworms in the silkworm tool, the captured behavior information of live silkworms is synchronized;

[0037] The information processing classification module is used to capture the behavior information of live silkworms in the silkworm room to be detected, determine the dynamic behavior information of live silkworms corresponding to each sub-silkworm room every day through the planned sub-silkworm rooms, and synchronize the behavior information of live silkworms in real time.

[0038] The molecular biological control and recording module is used for detecting the blood information of live silkworms in the silkworm room.

[0039] Further, the network algorithm control and analysis device comprises a resource usage analysis module, a network algorithm control analysis module and a network algorithm simulation analysis module;

[0040] The resource utilization analysis module is used to analyze the resource utilization rate of multiple live silkworm parameter elements when there is no artificial factor or accidental influence on the live silkworm behavior information in the silkworm tool;

[0041] The network algorithm control analysis module is used to generate a molecular biological silkworm disease prediction network algorithm, using PCR technology to deploy the same shed molecular biology as the silkworm room to be detected in the network algorithm; analyze the impact value of feed status on the behavior information of live silkworms in silkworm tools;

[0042] The network algorithm simulation and analysis module is used to simulate the dynamics of the number of live silkworms of various live silkworm parameter elements under multiple feed conditions in the network algorithm.

[0043] Furthermore, the molecular biological silkworm disease prediction device comprises an information measurement and analysis module and a silkworm disease infection value analysis module;

[0044] The information measurement and analysis module is used to measure the information of the breeding state of live silkworms, analyze the breeding state of live silkworms under multiple concentrations of live silkworm parameter elements; and determine the influence value of the live silkworm quantity corresponding to each live silkworm parameter element on the live silkworm raw silk quantity;

[0045] The silkworm disease infection value analysis module is used to predict the silkworm disease infection in the silkworm room to be detected and analyze the silkworm disease infection value.

[0046] Furthermore, the interactive presentation device comprises a silkworm disease infection value synchronization module and a breeding staff end control module;

[0047] The silkworm disease infection value synchronization module is used to determine the number of live silkworms and the feed status of each live silkworm parameter element in each sub-silkworm room in the real-time period through the captured live silkworm behavior information and molecular biological information; analyze the impact value of the feed status on the live silkworm behavior information in the silkworm tools; predict the number of live silkworms of each live silkworm parameter element in each sub-silkworm room after an accident; and analyze the real-time silkworm disease infection value;

[0048] The breeder-side control module is used to present the analyzed silkworm disease infection value to the breeder via a mobile terminal.

[0049] The present application relates to the field of data processing technology, and in particular to a method and system for predicting silkworm diseases based on molecular biology, obtaining the working status of silkworm tools in each sub-silkworm room in a silkworm room to be detected, and capturing the behavior information and molecular biological information of live silkworms in the silkworm room to be detected; based on the behavior information of live silkworms captured in the past, the molecular biological information and the working status of silkworm tools in each sub-silkworm room in the silkworm room to be detected, a molecular biological silkworm disease prediction network algorithm is generated, and the influence of multiple silkworm tool working status and feed status on silkworm disease infection is analyzed; based on the past measurement information of live silkworms raised in the silkworm room to be detected, the silkworm disease infection in the silkworm room to be detected is predicted, and the silkworm disease infection value is analyzed; through the real-time feed status and the captured behavior information of live silkworms, the analyzed silkworm disease infection value is presented to the breeding personnel through a mobile terminal. The present invention can predict the occurrence of silkworm diseases caused by real-time molecular biology of silkworm breeding, guide the precise disinfecting of silkworm rooms, silkworm tools, and surrounding molecular biology, and effectively improve the quality and benefits of silkworm breeding. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a workflow diagram of the molecular biology-based silkworm disease prediction method claimed in the embodiments of the present application.

[0051] Figure 2 This is a structural module diagram of the molecular biology-based silkworm disease prediction system claimed in the embodiments of the present application. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field without creative work based on the embodiments in the present application are within the scope of protection of this application.

[0053] The terms "first", "second" and "third" in this application are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined as "first", "second" and "third" can explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications (such as up, down, left, right, front, back...) in the embodiments of this application are only used to explain the relative position relationship, motion state, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or modules is not limited to the listed steps or modules, but optionally also includes steps or modules that are not listed, or optionally also includes other steps or modules inherent to these processes, methods, products or devices.

[0054] Reference to "embodiments" herein means that a particular feature, structure, or organism described in conjunction with the embodiments may be included in at least one embodiment of the present application. The presence of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0055] See also Figure 1 The present invention provides a technical solution: a method for predicting silkworm diseases based on molecular biology, the method comprising the following steps:

[0056] S10, obtaining the working status of silkworm tools in each sub-silkworm room in the silkworm room to be detected, and capturing the behavioral information and molecular biological information of live silkworms in the silkworm room to be detected;

[0057] S20, based on the behavior information of live silkworms captured in the past, molecular biological information and the working status of silkworm tools in each sub-silkworm room in the silkworm room to be tested, generate a molecular biological silkworm disease prediction network algorithm, and analyze the influence of multiple silkworm tool working status and feed status on silkworm disease infection;

[0058] S30, predicting the silkworm disease infection in the silkworm room to be tested based on the past measurement information of the live silkworms raised in the silkworm room to be tested, and analyzing the silkworm disease infection value;

[0059] S40, presenting the analyzed silkworm disease infection value to the breeding staff through the mobile terminal based on the real-time feed status and the captured live silkworm behavior information;

[0060] The live silkworm behavior information is the live silkworm parameter elements that will affect the breeding status of the live silkworms; and the molecular biological information is the live silkworm blood information in the silkworm room to be detected.

[0061] The silkworm room to be detected is a non-integrated shed with multiple silkworm platforms, feeding machines and shed features; the silkworm room to be detected is divided into n sub-silkworm rooms through the multiple sheds of the silkworm room to be detected, and P1, P2, ..., P n ; When capturing the behavior information of live silkworms in the silkworm room to be detected, the dynamic behavior information of live silkworms corresponding to each silkworm room is determined by the planned silkworm room in units of days, and the behavior information of live silkworms is synchronized in real time; Among them, P i is the i-th sub-silkworm room corresponding to the plan; i=1, 2, ..., n; n is the number of sub-silkworm rooms planned;

[0062] The working state of the silkworm tool is that the age of live silkworms cultured in the silkworm room to be detected and the influence of artificial factors on the behavior information of live silkworms; when the artificial factors influence the behavior information of live silkworms in the silkworm tool, the captured behavior information of live silkworms is synchronized; wherein the breeding sparseness of live silkworms in the silkworm room to be detected is the same.

[0063] The method steps of step S20 are:

[0064] S201, determine the age H and sparseness F of live silkworms cultured in the silkworm room to be tested, and determine the number m of live silkworm parameter elements to be analyzed; determine the dynamic data of the number of live silkworms of each live silkworm parameter element in each sub-silkworm room with period t through the live silkworm behavior information captured in the past, and obtain ;pass , analyze the resource utilization rate V of multiple live silkworm parameter elements when the live silkworm breeding sparseness is F when there is no artificial factor and accidental influence on the live silkworm behavior information in the silkworm tools;

[0065] ;

[0066] in, is the dynamic data of the number of live silkworms of the jth live silkworm parameter element in the i-th sub-silkworm room with period t; t is the period under multiple days; is the resource utilization rate of the jth live silkworm parameter element when the sparseness of live silkworm breeding is F; For analysis The fth period corresponding to the jth live silkworm parameter element in the silkworm tool when there is no artificial factor or accidental influence on the live silkworm behavior information in the silkworm tool; is the parameter element of the j-th type of live silkworm in the period Dynamic quantity of live silkworms; For analysis The number of periodic segments in which the j-th type of live silkworm parameter elements meet the conditions;

[0067] S202, generating a molecular biological silkworm disease prediction network algorithm, using PCR technology, deploying the same shed molecular biology as the silkworm room to be tested in the network algorithm; , analyze the influence of feed status on the behavior information of live silkworms in silkworm tools; determine the number of days with unexpected cycles through past molecular biological information, and determine the feed status L corresponding to that number of days t and the presence of feed state L t Dynamic data of live silkworm quantity of each live silkworm parameter element in each silkworm room and ; Analyze the impact of feed status on the behavior of live silkworms in silkworm tools ,

[0068] ;

[0069] S203, and L t As the input of the network algorithm, As the output of the network algorithm, the number of live silkworms under various live silkworm parameter elements under multiple feeding conditions is simulated in the network algorithm to obtain the dynamics of L t , and The functional relationship is: ; Simulate the network algorithm to determine Function of silkworm age and coefficient values.

[0070] The method steps of step S30 are:

[0071] S301, measuring the information of the breeding state of live silkworms H, analyzing the breeding state of live silkworms H under multiple concentrations of live silkworm parameter elements; calculating the number of live silkworms x1, x2, ..., x2 corresponding to the m kinds of live silkworm parameter elements in the breeding molecular biology of live silkworms H; m as the independent variable and the amount of raw silk from live silkworms Y as the dependent variable, and the recursive network algorithm was fitted;

[0072] ;

[0073] Where j = 1, 2, ..., m; The number of live silkworms corresponding to the j-th live silkworm parameter element is The impact of time on the raw silk production of live silkworms; is the error term;

[0074] Determine w1, w2, ..., w in the recursive network algorithm m The impact value of

[0075] S302, pass and , predict the silkworm disease infection in the silkworm room to be tested, and analyze the silkworm disease infection value,

[0076] ;

[0077] in, is the silkworm disease infection value analyzed under the corresponding cycle day t; is the area of ​​the i-th sub-silkworm room.

[0078] The number of live silkworms of each live silkworm parameter element in each sub-silkworm room in real-time period t0 is determined by the captured live silkworm behavior information and molecular biological information. and feed status size ;pass and , substituted into the molecular biological silkworm disease prediction network algorithm, and analyzed the impact of feed status on the behavior information of live silkworms in silkworm tools ; Predict the number of live silkworms of each live silkworm parameter element in each silkworm room after an accident ;

[0079] ;

[0080] Through the predicted , analyze the real-time silkworm disease infection value , and the analyzed silkworm disease infection value The predicted , thereby artificially influencing the behavioral information of live silkworms in each silkworm room.

[0081] According to the second embodiment of the present invention, the present invention claims protection for a silkworm disease prediction system based on molecular biology, referring to Figure 2 ,The system includes an information capture and processing device, a network algorithm control and analysis device, a molecular biological silkworm disease prediction device and an interactive presentation device;

[0082] The information capture and processing device is used to obtain the working status of silkworm tools in each sub-silkworm room in the silkworm room to be detected, and to capture the behavior information and molecular biological information of live silkworms in the silkworm room to be detected; the network algorithm control and analysis device is used to generate a molecular biological silkworm disease prediction network algorithm, and analyze the influence of multiple silkworm tool working status and feed status on silkworm disease infection; the molecular biological silkworm disease prediction device is used to predict the silkworm disease infection in the silkworm room to be detected based on the past measurement information of live silkworms raised in the silkworm room to be detected, and analyze the silkworm disease infection value; the interactive presentation device is used to present the analyzed silkworm disease infection value to the breeding personnel through the mobile terminal through the real-time feed status and the captured live silkworm behavior information;

[0083] The live silkworm behavior information is the live silkworm parameter elements that will affect the breeding status of the live silkworms; and the molecular biological information is the live silkworm blood information in the silkworm room to be detected.

[0084] The information capture and processing device comprises a silkworm tool control and recording module, an information processing and classification module, and a molecular biology control and recording module;

[0085] The silkworm tool control and recording module is used to obtain the age of live silkworms cultured in the silkworm room to be detected and the influence of artificial factors on the behavior information of live silkworms; when the artificial factors influence the behavior information of live silkworms in the silkworm tool, the captured behavior information of live silkworms is synchronized;

[0086] The information processing classification module is used to capture the behavior information of live silkworms in the silkworm room to be detected, determine the dynamic behavior information of live silkworms corresponding to each sub-silkworm room every day through the planned sub-silkworm rooms, and synchronize the behavior information of live silkworms in real time.

[0087] The molecular biological control and recording module is used for detecting the blood information of live silkworms in the silkworm room.

[0088] The network algorithm control and analysis device comprises a resource usage analysis module, a network algorithm control analysis module and a network algorithm simulation analysis module;

[0089] The resource utilization analysis module is used to analyze the resource utilization rate of multiple live silkworm parameter elements when there is no artificial factor or accidental influence on the live silkworm behavior information in the silkworm tool;

[0090] The network algorithm control analysis module is used to generate a molecular biological silkworm disease prediction network algorithm, using PCR technology to deploy the same shed molecular biology as the silkworm room to be detected in the network algorithm; analyze the impact value of feed status on the behavior information of live silkworms in silkworm tools;

[0091] The network algorithm simulation and analysis module is used to simulate the dynamics of the number of live silkworms of various live silkworm parameter elements under multiple feed conditions in the network algorithm.

[0092] The molecular biological silkworm disease prediction device comprises an information measurement and analysis module and a silkworm disease infection value analysis module;

[0093] The information measurement and analysis module is used to measure the information of the breeding state of live silkworms, analyze the breeding state of live silkworms under multiple concentrations of live silkworm parameter elements; and determine the influence value of the live silkworm quantity corresponding to each live silkworm parameter element on the live silkworm raw silk quantity;

[0094] The silkworm disease infection value analysis module is used to predict the silkworm disease infection in the silkworm room to be detected and analyze the silkworm disease infection value.

[0095] The interactive presentation device comprises a silkworm disease infection value synchronization module and a breeding staff end control module;

[0096] The silkworm disease infection value synchronization module is used to determine the number of live silkworms and the feed status of each live silkworm parameter element in each sub-silkworm room in the real-time period through the captured live silkworm behavior information and molecular biological information; analyze the impact value of the feed status on the live silkworm behavior information in the silkworm tools; predict the number of live silkworms of each live silkworm parameter element in each sub-silkworm room after an accident; and analyze the real-time silkworm disease infection value;

[0097] The breeder-side control module is used to present the analyzed silkworm disease infection value to the breeder via a mobile terminal.

[0098] In this embodiment:

[0099] The system is specifically a molecular biological silkworm disease prediction and control system for agricultural production; in this system, the planned number of silkworm rooms n=10; the number of live silkworm parameter elements analyzed m=5; the age of live silkworms raised in the silkworm room to be tested is seed silkworms; the breeding sparseness F=1.8;

[0100] Through the behavior information of live silkworms captured in the past, the dynamic data of the number of live silkworms of each live silkworm parameter element in each sub-silkworm room with period t is determined, and the ;pass , analyze the resource utilization rate V of multiple live silkworm parameter elements when the live silkworm breeding sparseness is F when there is no artificial factor and accidental influence on the live silkworm behavior information in the silkworm tools;

[0101] ;

[0102] Generate a molecular biological silkworm disease prediction network algorithm, use PCR technology, and deploy the same shed molecular organisms as the silkworm room to be tested in the network algorithm; , analyze the influence of feed status on the behavior information of live silkworms in silkworm tools; determine the number of days with unexpected cycles through past molecular biological information, and determine the feed status L corresponding to that number of days t and the presence of feed state L tDynamic data of live silkworm quantity of each live silkworm parameter element in each silkworm room and ; Analyze the impact of feed status on the behavior of live silkworms in silkworm tools ,

[0103] ;

[0104] Will and L t As the input of the network algorithm, As the output of the network algorithm, the number of live silkworms under various live silkworm parameter elements under multiple feeding conditions is simulated in the network algorithm to obtain the dynamics of L t , and The functional relationship is: ;

[0105] The information of the breeding status of seed silkworms was measured, and the breeding status of seed silkworms under multiple concentrations of live silkworm parameter elements was analyzed; the number of live silkworms x1, x2, x3, x4, and x5 corresponding to the five live silkworm parameter elements in the molecular biology of seed silkworm breeding were used as independent variables, and the amount of live silkworms Y was used as the dependent variable to fit the recursive network algorithm;

[0106] ;

[0107] Determine the impact values ​​of w1, w2, w3, w4, and w5 in the recursive network algorithm;

[0108] At time t0:

[0109] Determine the number of live silkworms of each live silkworm parameter element in each sub-silkworm room in real-time period t0 and feed status size ;pass and , substituted into the molecular biological silkworm disease prediction network algorithm, and analyzed the impact of feed status on the behavior information of live silkworms in silkworm tools ; Predict the number of live silkworms of each live silkworm parameter element in each silkworm room after an accident ;

[0110] ;

[0111] ;

[0112] The analyzed silkworm disease infection value The predicted , thereby artificially influencing the behavioral information of live silkworms in each silkworm room.

[0113] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the planning of the modules is only for logical function planning. There may be other planning methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection presented or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0114] In addition, each functional module in each embodiment of the present application can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. The above is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the specification and drawings of this application, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

[0115] The specific implementation methods of the invention are described in detail above, but they are only examples, and the present application is not limited to the specific implementation methods described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the present application, and therefore, the equalization, modification, and improvement made without departing from the spirit and principle of the present application should be included in the scope of the present application.

Claims

1. A method for predicting silkworm diseases based on molecular biology, characterized in that: The method comprises the following steps: S10, obtaining the working status of silkworm tools in each sub-silkworm room in the silkworm room to be detected, and capturing the behavioral information and molecular biological information of live silkworms in the silkworm room to be detected; S20, based on the behavior information of live silkworms captured in the past, molecular biological information and the working status of silkworm tools in each sub-silkworm room in the silkworm room to be tested, generate a molecular biological silkworm disease prediction network algorithm, and analyze the influence of multiple silkworm tool working status and feed status on silkworm disease infection; S30, predicting the silkworm disease infection in the silkworm room to be tested based on the past measurement information of the live silkworms raised in the silkworm room to be tested, and analyzing the silkworm disease infection value; S40, presenting the analyzed silkworm disease infection value to the breeding staff through the mobile terminal based on the real-time feed status and the captured live silkworm behavior information; Wherein, the live silkworm behavior information is the live silkworm parameter elements that will affect the live silkworm breeding status; the molecular biological information is the live silkworm blood information in the silkworm room to be detected; The method steps of step S20 are: S201, determine the age H and sparseness F of live silkworms cultured in the silkworm room to be tested, and determine the number m of live silkworm parameter elements to be analyzed; determine the dynamic data of the number of live silkworms of each live silkworm parameter element in each sub-silkworm room with period t through the live silkworm behavior information captured in the past, and obtain ;pass , analyze the resource utilization rate V of multiple live silkworm parameter elements when the live silkworm breeding sparseness is F when there is no artificial factor and accidental influence on the live silkworm behavior information in the silkworm tools; ； in, is the dynamic data of the number of live silkworms of the jth live silkworm parameter element in the i-th sub-silkworm room with period t; t is the period under multiple days; is the resource utilization rate of the jth live silkworm parameter element when the sparseness of live silkworm breeding is F; For analysis The fth period corresponding to the jth live silkworm parameter element in the silkworm tool when there is no artificial factor or accidental influence on the live silkworm behavior information in the silkworm tool; is the parameter element of the j-th type of live silkworm in the period Dynamic quantity of live silkworms; For analysis The number of periodic segments in which the j-th type of live silkworm parameter elements meet the conditions; S202, generating a molecular biological silkworm disease prediction network algorithm, using PCR technology, deploying the same shed molecular biology as the silkworm room to be tested in the network algorithm; , analyze the influence of feed status on the behavior information of live silkworms in silkworm tools; determine the number of days with unexpected cycles through past molecular biological information, and determine the feed status L corresponding to that number of days t and the presence of feed state L t Dynamic data of live silkworm quantity of each live silkworm parameter element in each silkworm room and ; Analyze the impact of feed status on the behavior of live silkworms in silkworm tools , ； S203, and L t As the input of the network algorithm, As the output of the network algorithm, the number of live silkworms under various live silkworm parameter elements under multiple feeding conditions is simulated in the network algorithm to obtain the dynamics of L t , and The functional relationship is: ; Simulate the network algorithm to determine Function of silkworm age and coefficient value; The method steps of step S30 are: S301, measuring the information of the breeding state of live silkworms H, analyzing the breeding state of live silkworms H under multiple concentrations of live silkworm parameter elements; calculating the number of live silkworms x1, x2, ..., x corresponding to the m kinds of live silkworm parameter elements in the breeding molecular biology of live silkworms H m as the independent variable and the amount of raw silk from live silkworms Y as the dependent variable, and the recursive network algorithm was fitted; ； Where j = 1, 2, ..., m; The number of live silkworms corresponding to the j-th live silkworm parameter element is The impact of time on the raw silk production of live silkworms; is the error term; Determine w1, w2, ..., w in the recursive network algorithm m The impact value of S302, pass and , predict the silkworm disease infection in the silkworm room to be tested, and analyze the silkworm disease infection value, ； in, is the silkworm disease infection value analyzed under the corresponding cycle day t; is the area of ​​the i-th sub-silkworm room.

2. The method for predicting silkworm diseases based on molecular biology according to claim 1, characterized in that: The silkworm room to be detected is a non-integrated shed with multiple silkworm platforms, feeding machines and shed features; the silkworm room to be detected is divided into n sub-silkworm rooms through the multiple sheds of the silkworm room to be detected, and P1, P2, ..., P n ; When capturing the behavior information of live silkworms in the silkworm room to be detected, the dynamic behavior information of live silkworms corresponding to each silkworm room is determined by the planned silkworm room in units of days, and the behavior information of live silkworms is synchronized in real time; Among them, P i is the i-th sub-silkworm room corresponding to the plan; i=1, 2, ..., n; n is the number of sub-silkworm rooms planned; The working state of the silkworm tool is that the age of live silkworms cultured in the silkworm room to be detected and the influence of artificial factors on the behavior information of live silkworms; when the artificial factors influence the behavior information of live silkworms in the silkworm tool, the captured behavior information of live silkworms is synchronized; wherein the breeding sparseness of live silkworms in the silkworm room to be detected is the same.

3. The method for predicting silkworm diseases based on molecular biology as claimed in claim 2, characterized in that: The number of live silkworms of each live silkworm parameter element in each sub-silkworm room in real-time period t0 is determined by the captured live silkworm behavior information and molecular biological information. and feed status size ;pass and , substituted into the molecular biological silkworm disease prediction network algorithm, and analyzed the impact of feed status on the behavior information of live silkworms in silkworm tools ; Predict the number of live silkworms of each live silkworm parameter element in each silkworm room after an accident ; ； Through the predicted , analyze the real-time silkworm disease infection value , and the analyzed silkworm disease infection value The predicted , thereby artificially influencing the behavior information of live silkworms in each silkworm room; and Refers to determine the feed status for the corresponding days and the presence of feed status Dynamic data of the number of live silkworms of each live silkworm parameter element in each silkworm room before and after.

4. A silkworm disease prediction system based on molecular biology, used to execute the silkworm disease prediction method based on molecular biology as claimed in any one of claims 1 to 3, characterized in that: The system includes an information capture and processing device, a network algorithm control and analysis device, a molecular biological silkworm disease prediction device and an interactive presentation device; The information capture and processing device is used to obtain the working status of silkworm tools in each sub-silkworm room in the silkworm room to be detected, and to capture the behavior information and molecular biological information of live silkworms in the silkworm room to be detected; the network algorithm control and analysis device is used to generate a molecular biological silkworm disease prediction network algorithm, and analyze the influence of multiple silkworm tool working status and feed status on silkworm disease infection; the molecular biological silkworm disease prediction device is used to predict the silkworm disease infection in the silkworm room to be detected based on the past measurement information of live silkworms raised in the silkworm room to be detected, and analyze the silkworm disease infection value; the interactive presentation device is used to present the analyzed silkworm disease infection value to the breeding personnel through the mobile terminal through the real-time feed status and the captured live silkworm behavior information; The live silkworm behavior information is the live silkworm parameter elements that will affect the breeding status of the live silkworms; and the molecular biological information is the live silkworm blood information in the silkworm room to be detected.

5. The silkworm disease prediction system based on molecular biology as claimed in claim 4, characterized in that: The information capture and processing device comprises a silkworm tool control and recording module, an information processing and classification module, and a molecular biology control and recording module; The silkworm tool control and recording module is used to obtain the age of live silkworms cultured in the silkworm room to be detected and the influence of artificial factors on the behavior information of live silkworms; when the artificial factors influence the behavior information of live silkworms in the silkworm tool, the captured behavior information of live silkworms is synchronized; The information processing classification module is used to capture the behavior information of live silkworms in the silkworm room to be detected, determine the dynamic behavior information of live silkworms corresponding to each sub-silkworm room every day through the planned sub-silkworm rooms, and synchronize the behavior information of live silkworms in real time. The molecular biological control and recording module is used for detecting the blood information of live silkworms in the silkworm room.

6. The silkworm disease prediction system based on molecular biology as claimed in claim 5, characterized in that: The network algorithm control and analysis device comprises a resource usage analysis module, a network algorithm control analysis module and a network algorithm simulation analysis module; The resource utilization analysis module is used to analyze the resource utilization rate of multiple live silkworm parameter elements when there is no artificial factor or accidental influence on the live silkworm behavior information in the silkworm tool; The network algorithm control analysis module is used to generate a molecular biological silkworm disease prediction network algorithm, using PCR technology to deploy the same shed molecular biology as the silkworm room to be detected in the network algorithm; analyze the impact value of feed status on the behavior information of live silkworms in silkworm tools; The network algorithm simulation and analysis module is used to simulate the dynamics of the number of live silkworms of various live silkworm parameter elements under multiple feed conditions in the network algorithm.

7. The silkworm disease prediction system based on molecular biology as claimed in claim 6, characterized in that: The molecular biological silkworm disease prediction device comprises an information measurement and analysis module and a silkworm disease infection value analysis module; The information measurement and analysis module is used to measure the information of the breeding state of live silkworms, analyze the breeding state of live silkworms under multiple concentrations of live silkworm parameter elements; and determine the influence value of the live silkworm quantity corresponding to each live silkworm parameter element on the live silkworm raw silk quantity; The silkworm disease infection value analysis module is used to predict the silkworm disease infection in the silkworm room to be detected and analyze the silkworm disease infection value.

8. The molecular biology-based silkworm disease prediction system according to claim 7, characterized in that: The interactive presentation device comprises a silkworm disease infection value synchronization module and a breeding staff end control module; The silkworm disease infection value synchronization module is used to determine the number of live silkworms and the feed status of each live silkworm parameter element in each sub-silkworm room in the real-time period through the captured live silkworm behavior information and molecular biological information; analyze the impact value of the feed status on the live silkworm behavior information in the silkworm tools; predict the number of live silkworms of each live silkworm parameter element in each sub-silkworm room after an accident; and analyze the real-time silkworm disease infection value; The breeder-side control module is used to present the analyzed silkworm disease infection value to the breeder via a mobile terminal.

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