Disease early warning method and system for silkworm breeding data

Abstract

The invention relates to the technical field of silkworm breeding monitoring, in particular to a disease early warning method and system for silkworm breeding data. Outputting regular silk quality analysis data, thallus bombyx batryticatus degree periodic trend data, disinfection quality periodic trend data and thallus aggregation and attachment state quantitative analysis data; performing aggregated pyosis state simulation calculation on the thallus aggregation attachment state quantitative analysis data, and outputting aggregated pyosis state data; based on the thallus aggregation attachment state quantitative analysis data and the aggregation pyosis state data, performing silkworm disease diagnosis early warning deployment of the silkworm room to be early warned, outputting silkworm disease diagnosis early warning data of the silkworm room to be early warned and a silkworm disease diagnosis intelligent early warning direction of the silkworm room to be early warned, and sending the silkworm disease diagnosis intelligent early warning direction of the silkworm room to be early warned to a cloud platform; and performing disease early warning for the silkworm breeding data. According to the invention, the disinfection operation in the silkworm breeding process can be accurately and reasonably controlled so as to ensure the smooth transition of the disease period.

Description

Technical Field

[0001] The present application relates to the field of silkworm breeding monitoring technology, and in particular to a disease early warning method and system for silkworm breeding data. Background Art

[0002] Diseases that are very common in the silkworm breeding process: midgut purulent disease and hollow-headed softening disease: midgut purulent disease and hollow-headed softening disease are both caused by viruses and can occur in any batch of silkworms, but the most serious occurrence is in the 3rd to 6th batches of silkworms, which is a high temperature season; Bombyx batryticatus disease, in which silkworms are infected with Beauveria bassiana when they are fifth instar, causing the silkworms to be infected with Beauveria bassiana and die.

[0003] At present, the methods of controlling diseases and insect pests of silkworms are almost similar to those of silkworm breeding, and are mostly controlled by spreading chlorine-containing lime for disinfection, feeding pueraria, killing flies, etc. However, spreading chlorine-containing lime for disinfection may lead to excessive ash content, and the impact of feeding other pesticides on the effective ingredients of silkworms has not been investigated. Therefore, how to more accurately control the disinfection in the silkworm breeding process to ensure a smooth transition of the disease period is an urgent problem to be solved. Summary of the invention

[0004] To achieve the above objectives, this application provides the following technical solutions: According to a first aspect of the present invention, the present invention claims a disease early warning method for silkworm breeding data, comprising: A: Based on the silk yield meter, regular silk quality analysis is performed in the alarm silkworm room, and regular silk quality analysis data is output; the regular silk quality analysis data is used to collect the periodic trend of the degree of silkworm pupae and the disinfection quality, and the periodic trend data of the degree of silkworm pupae and the periodic trend data of the disinfection quality are output in turn; B: Perform quantitative analysis on the bacterial aggregation and attachment state of the bacterial silkworm degree periodic trend data and the disinfection quality periodic trend data, and output the quantitative analysis data on the bacterial aggregation and attachment state; perform simulation calculation on the aggregation and pus disease state of the bacterial aggregation and attachment state quantitative analysis data, and output the aggregation and pus disease state data; C: Based on the quantitative analysis data of bacterial aggregation and attachment status and the data of aggregated pus disease status, the diagnosis and early warning deployment of silkworm diseases in the silkworm room to be warned is carried out, and the diagnosis and early warning data of silkworm diseases in the silkworm room to be warned is output; D: Deploy intelligent warning directions based on the warning data of silkworm diseases in the silkworm room to be warned, output intelligent warning directions of silkworm diseases in the silkworm room to be warned, and send the intelligent warning directions of silkworm diseases in the silkworm room to be warned to the cloud platform to execute disease warning for silkworm breeding data.

[0005] Furthermore, A includes: A1: Perform regular silk quality analysis on the silkworm room to be warned based on the silk yield meter, and output regular silk quality analysis data; A2: Correct the abnormal value of the timed silk quality analysis data and output the corrected silk quality analysis data; A3: Perform periodic state analysis on the silk quality analysis correction data, and output the silk quality analysis periodic state data; A4: Based on the silk quality analysis periodic status data, the silkworm degree and disinfection quality periodic trend data are collected for the timed silk quality analysis data, and the bacterial silkworm degree periodic trend data and the disinfection quality periodic trend data are output in sequence.

[0006] Furthermore, B includes: B1: Collect the framework of the silkworm room to be warned; perform three-dimensional layout analysis on the framework of the silkworm room to be warned, and output the three-dimensional layout data of the silkworm room to be warned; B2: Based on the three-dimensional layout data of the silkworm room to be warned, the regular natural silkworm disease diagnosis structure analysis is performed to output the regular natural silkworm disease diagnosis time series data; B3: Based on the regular natural silkworm disease diagnosis time series data, quantitative analysis of the aggregation and attachment state of the silkworm body is performed on the periodic trend data of the silkworm body degree and the periodic trend data of the disinfection quality, and the quantitative analysis data of the aggregation and attachment state of the silkworm body is output; B4: Perform aggregation and pus disease state simulation calculation on the quantitative analysis data of bacterial aggregation and attachment state, and output the aggregation and pus disease state data.

[0007] Furthermore, B3 includes: B31: Perform real-time infection risk analysis on the time series data of natural silkworm disease diagnosis, and output the real-time infection risk data of natural silkworm disease diagnosis; B32: Calculate disinfectant concentration for disinfection quality cycle trend data and output disinfection quality disinfectant concentration data; B33: Calculate the frequency change of the disinfection quality cycle trend data based on the disinfection quality disinfectant concentration data, and output the frequency change data; B34: Predict the silkworm age distribution ratio based on the periodic trend data of the degree of Bombyx mori, and output the silkworm age distribution ratio of the degree of Bombyx mori; B35: Based on the distribution ratio of silkworm age to the degree of Bombyx mori, the disinfectant effect amount is predicted for the frequency change data, and the disinfectant effect data is output; B36: Predict the attachment degree of the bacteria based on the disinfectant action data and the periodic trend data of the degree of the bacterial silkworm, and output the attachment degree data; B37: Based on the real-time infection risk data of natural silkworm disease diagnosis, the disinfectant action data and attachment degree data are used to quantitatively analyze the bacterial aggregation and attachment status, and output the quantitative analysis data of the bacterial aggregation and attachment status.

[0008] Furthermore, B36 includes: Conduct disinfection influence relationship judgment among multiple disinfectants on disinfectant action data, and output disinfection influence relationship judgment data; Based on the disinfectant disinfection impact relationship judgment data and the bacterial Bombyx batryticatus degree period trend data, the disinfectant attachment state infection risk analysis is performed, and the disinfectant attachment state infection risk data is output; Based on the periodic trend data of the degree of Bombyx mori, the infection risk data of the disinfectant attachment state is analyzed for the degree of Bombyx mori - the proportion of the attachment state, and the degree of Bombyx mori - the proportion of the attachment state is output; Based on the degree of Bombyx Batryticatus-the ratio of attachment state, the attachment state infection risk data of the disinfectant are sampled according to the normal distribution, and the attachment state normal distribution sampling data is output; The attachment degree of bacteria is predicted for the normally distributed sampling data of the attachment state, and the attachment degree data is output.

[0009] Furthermore, B4 includes: B41: Analyze the aggregation hyphae rate based on the quantitative analysis data of bacterial aggregation and attachment status, and output the aggregation hyphae rate data; B42: Based on the aggregation hyphae rate data, the quantitative analysis data of bacterial aggregation and attachment state is analyzed for real-time bacterial infection distribution, and the real-time bacterial infection distribution data is output; B43: Perform real-time offset calculation on the real-time bacterial infection distribution data and output the real-time offset data of Bombyx batryticatus; B44: Based on the real-time offset data of Bombyx batryticatus, simulate and calculate the state of aggregated pus disease and output the state data of aggregated pus disease.

[0010] Furthermore, C includes: C1: Perform bacterial aggregation quality analysis on the quantitative analysis data of bacterial aggregation and attachment status, and output bacterial aggregation quality data; C2: Analyze the change of bacterial aggregation size based on the data of aggregated pus disease status, and output the data of bacterial aggregation size change; C3: Based on the bacterial aggregation quality data and the bacterial aggregation size change data, the regular natural silkworm disease diagnosis time series data is deployed for the silkworm room silkworm disease diagnosis warning to be warned, and the silkworm room silkworm disease diagnosis warning data to be warned is output.

[0011] Furthermore, C3 includes: C31: Analyze the change trend of bacterial aggregation size change data and output bacterial aggregation change trend data; C32: Based on the bacterial aggregation quality data, the trend quality silkworm tool identification is performed on the bacterial aggregation change trend data, and the trend quality silkworm tool identification data is output; C33: Based on the trend quality silkworm tool distinction data, the silkworm disease diagnosis bacteria diffusion simulation between multiple silkworm tools is performed on the time series data of natural silkworm disease diagnosis, and the silkworm disease diagnosis bacteria diffusion simulation data is output; C34: Perform cyclic learning on the silkworm disease diagnosis bacterial diffusion simulation data, and output the silkworm disease diagnosis bacterial diffusion simulation cyclic data; C35: Deploy the early warning data of silkworm disease diagnosis in the silkworm room based on the simulated circulation data of silkworm disease diagnosis bacteria diffusion, and output the early warning data of silkworm disease diagnosis in the silkworm room.

[0012] Furthermore, C33 includes: Collect control parameters of silkworm disease diagnosis equipment; Based on the trend quality silkworm tool distinction data, the silkworm disease diagnosis equipment control parameters and the timing natural silkworm disease diagnosis time series data are matched between multiple silkworm tools for bacterial diffusion output, and the bacterial diffusion output data is output; Based on the trend quality silkworm tool distinction data, the control parameters of the silkworm disease diagnosis equipment and the timing natural silkworm disease diagnosis time series data are matched with disinfectant ratios among multiple silkworm tools, and the disinfectant ratio matching data is output; Based on the trend quality silkworm tool distinction data, disinfectant ratio matching data and bacterial diffusion output data, the silkworm disease diagnosis output compensation is performed on the time-based natural silkworm disease diagnosis time series data, and the silkworm disease diagnosis output compensation data is output; Based on the bacterial diffusion output data, disinfectant ratio matching data and silkworm disease diagnosis output compensation data, silkworm disease diagnosis bacterial diffusion simulation is performed among multiple silkworm tools, and silkworm disease diagnosis bacterial diffusion simulation data is output.

[0013] According to a second aspect of the present invention, the present invention claims a disease early warning system for silkworm breeding data, which is used to execute the disease early warning method for silkworm breeding data, and the disease early warning system for silkworm breeding data comprises: The environmental data collection unit is used to perform regular silk quality analysis on the early warning silkworm room based on the silk production meter, and output regular silk quality analysis data; collect the periodic trend data of the silkworm degree and the disinfection quality on the regular silk quality analysis data, and output the periodic trend data of the bacterial silkworm degree and the periodic trend data of the disinfection quality in turn; The bacterial aggregation analysis unit is used to perform quantitative analysis on the bacterial aggregation and attachment state of the bacterial silkworm degree periodic trend data and the disinfection quality periodic trend data, and output the quantitative analysis data of the bacterial aggregation and attachment state; perform aggregation and pus disease state simulation calculation on the quantitative analysis data of the bacterial aggregation and attachment state, and output the aggregation and pus disease state data; A silkworm disease diagnosis and early warning deployment unit is used to perform early warning deployment of silkworm disease diagnosis in a silkworm room to be warned based on quantitative analysis data of bacterial aggregation and attachment status and data of aggregated pus disease status, and output early warning data of silkworm disease diagnosis in a silkworm room to be warned; The intelligent early warning direction deployment unit is used to deploy intelligent early warning directions based on the early warning data of silkworm disease diagnosis in the silkworm room to be warned, output the intelligent early warning directions of silkworm disease diagnosis in the silkworm room to be warned, and send the intelligent early warning directions of silkworm disease diagnosis in the silkworm room to be warned to the cloud platform to execute disease early warning for silkworm breeding data.

[0014] The present application relates to the field of sericulture monitoring technology, and in particular to a disease early warning method and system for sericulture data, which performs regular silk quality analysis on a silkworm room to be warned based on a silk production meter, outputs regular silk quality analysis data, periodic trend data of bacterial silkworm degree, periodic trend data of disinfection quality, and quantitative analysis data of bacterial aggregation and attachment state; performs simulation calculation of aggregation and pus disease state on the quantitative analysis data of bacterial aggregation and attachment state, and outputs aggregation and pus disease state data; performs silkworm disease diagnosis and early warning deployment on the silkworm room to be warned based on the quantitative analysis data of bacterial aggregation and attachment state and the aggregation and pus disease state data, outputs silkworm disease diagnosis and early warning data and intelligent early warning direction of silkworm disease diagnosis in the silkworm room to be warned, and sends the intelligent early warning direction of silkworm disease diagnosis in the silkworm room to be warned to a cloud platform to execute disease early warning for sericulture data. The present invention can accurately and reasonably control the disinfection operation in the silkworm breeding process to ensure a smooth transition of the disease period. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A workflow diagram of a disease early warning method for silkworm breeding data claimed in an embodiment of the present application; Figure 2 A second work flow chart of a disease early warning method for silkworm breeding data claimed in the embodiment of the present application; Figure 3 This is the third workflow diagram of a disease warning method for silkworm breeding data for which protection is requested in an embodiment of the present application. DETAILED DESCRIPTION

[0016] 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. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0017] 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. Thus, the features defined as "first", "second" and "third" may 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, movement, 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 or units is not limited to the listed or units, but optionally also includes the unlisted or units, or optionally also includes other or units inherent to these processes, methods, products or devices.

[0018] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance 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.

[0019] In the embodiment of the present invention, reference Figure 1 The above is a workflow diagram of a disease early warning method for silkworm breeding data of the present invention. In this example, the disease early warning method for silkworm breeding data includes: A: Based on the silk yield meter, regular silk quality analysis is performed in the alarm silkworm room, and regular silk quality analysis data is output; the regular silk quality analysis data is used to collect the periodic trend of the degree of silkworm pupae and the disinfection quality, and the periodic trend data of the degree of silkworm pupae and the periodic trend data of the disinfection quality are output in turn; B: Perform quantitative analysis on the bacterial aggregation and attachment state of the bacterial silkworm degree periodic trend data and the disinfection quality periodic trend data, and output the quantitative analysis data on the bacterial aggregation and attachment state; perform simulation calculation on the aggregation and pus disease state of the bacterial aggregation and attachment state quantitative analysis data, and output the aggregation and pus disease state data; C: Based on the quantitative analysis data of bacterial aggregation and attachment status and the data of aggregated pus disease status, the diagnosis and early warning deployment of silkworm diseases in the silkworm room to be warned is carried out, and the diagnosis and early warning data of silkworm diseases in the silkworm room to be warned is output; In the embodiment of the present invention, after collecting the quantitative analysis data of the aggregation and attachment state of the thalli and the data of the aggregation and pus disease state, the early warning deployment of the silkworm disease diagnosis of the silkworm room to be warned is started. This deployment is based on the state of thalli aggregation, formulates the strategy of the silkworm disease diagnosis system, and optimizes the empty thalli passage and thalli exchange efficiency. In the deployment process, firstly, based on the thalli aggregation quality, the silkworm disease diagnosis needs of multiple silkworm tools in the silkworm room are analyzed. The empty thalli movement of the silkworm room is simulated and optimized. Based on the detailed modeling of the silkworm room structure (such as walls, roofs, etc.), the thalli diffusion, infection speed distribution and silkworm degree trend of each silkworm tool are calculated. The aggregation and pus disease state data are used as a reference to formulate the silkworm disease diagnosis intensity and frequency of multiple silkworm tools to ensure that the empty thalli can effectively eliminate the impact of thalli aggregation on air quality. Based on the thalli aggregation quality, the silkworm disease diagnosis diffusion and infection speed are further adjusted. If the thalli concentration of some silkworm tools is high or the aggregation state is strong, it is necessary to enhance the silkworm disease diagnosis intensity of the silkworm tool to ensure that harmful thalli are removed in time and fresh empty thalli are guided in. The final result is to generate silkworm disease diagnosis and early warning data for the silkworm room to be warned, including the specific silkworm disease diagnosis and control strategies for each silkworm tool.

[0020] D: Deploy intelligent warning directions based on the warning data of silkworm diseases in the silkworm room to be warned, output intelligent warning directions of silkworm diseases in the silkworm room to be warned, and send the intelligent warning directions of silkworm diseases in the silkworm room to be warned to the cloud platform to execute disease warning for silkworm breeding data.

[0021] In the embodiment of the present invention, based on the silkworm disease diagnosis and warning data in C, the deployment of intelligent warning direction is carried out.

[0022] A includes: A1: Perform regular silk quality analysis on the silkworm room to be warned based on the silk yield meter, and output regular silk quality analysis data; A2: Correct the abnormal value of the timed silk quality analysis data and output the corrected silk quality analysis data; A3: Perform periodic state analysis on the silk quality analysis correction data, and output the silk quality analysis periodic state data; A4: Based on the silk quality analysis periodic status data, the silkworm degree and disinfection quality periodic trend data are collected for the timed silk quality analysis data, and the bacterial silkworm degree periodic trend data and the disinfection quality periodic trend data are output in sequence.

[0023] B includes: B1: Collect the framework of the silkworm room to be warned; perform three-dimensional layout analysis on the framework of the silkworm room to be warned, and output the three-dimensional layout data of the silkworm room to be warned; B2: Based on the three-dimensional layout data of the silkworm room to be warned, the regular natural silkworm disease diagnosis structure analysis is performed to output the regular natural silkworm disease diagnosis time series data; B3: Based on the regular natural silkworm disease diagnosis time series data, quantitative analysis of the aggregation and attachment state of the silkworm body is performed on the periodic trend data of the silkworm body degree and the periodic trend data of the disinfection quality, and the quantitative analysis data of the aggregation and attachment state of the silkworm body is output; B4: Perform aggregation and pus disease state simulation calculation on the quantitative analysis data of bacterial aggregation and attachment state, and output the aggregation and pus disease state data.

[0024] As an example of the present invention, refer to Figure 2 As shown, in this example, B includes: B1: Collect the framework of the silkworm room to be warned; perform three-dimensional layout analysis on the framework of the silkworm room to be warned, and output the three-dimensional layout data of the silkworm room to be warned; B2: Based on the three-dimensional layout data of the silkworm room to be warned, the regular natural silkworm disease diagnosis structure analysis is performed to output the regular natural silkworm disease diagnosis time series data; Analyze the movement of empty thalli when the silkworm disease diagnosis window is open; simulate the efficiency of the silkworm disease diagnosis system in winter when the temperature is low. Finally, the time series data of natural silkworm disease diagnosis is output based on calculation, including the efficiency, thalli volume, air exchange frequency and other indicators of each silkworm disease diagnosis port. This data set can describe in detail the movement pattern of empty thalli in the silkworm room and the efficiency of silkworm disease diagnosis under multiple weather conditions, providing a scientific basis for the subsequent intelligent adjustment of silkworm disease diagnosis.

[0025] B3: Based on the regular natural silkworm disease diagnosis time series data, quantitative analysis of the aggregation and attachment state of the silkworm body is performed on the periodic trend data of the silkworm body degree and the periodic trend data of the disinfection quality, and the quantitative analysis data of the aggregation and attachment state of the silkworm body is output; B4: Perform aggregation and pus disease state simulation calculation on the quantitative analysis data of bacterial aggregation and attachment state, and output the aggregation and pus disease state data.

[0026] B3 includes: B31: Perform real-time infection risk analysis on the time series data of natural silkworm disease diagnosis, and output the real-time infection risk data of natural silkworm disease diagnosis; B32: Calculate disinfectant concentration for disinfection quality cycle trend data and output disinfection quality disinfectant concentration data; B33: Calculate the frequency change of the disinfection quality cycle trend data based on the disinfection quality disinfectant concentration data, and output the frequency change data; B34: Predict the silkworm age distribution ratio based on the periodic trend data of the degree of Bombyx mori, and output the silkworm age distribution ratio of the degree of Bombyx mori; B35: Based on the distribution ratio of silkworm age to the degree of Bombyx mori, the disinfectant effect amount is predicted for the frequency change data, and the disinfectant effect data is output; B36: Predict the attachment degree of the bacteria based on the disinfectant action data and the periodic trend data of the degree of the bacterial silkworm, and output the attachment degree data; B37: Based on the real-time infection risk data of natural silkworm disease diagnosis, the disinfectant action data and attachment degree data are used to quantitatively analyze the bacterial aggregation and attachment status, and output the quantitative analysis data of the bacterial aggregation and attachment status.

[0027] In an embodiment of the present invention, a real-time infection risk analysis of silkworm disease diagnosis is performed on the time series data of regular natural silkworm disease diagnosis, with the purpose of studying the infection risk situation of natural silkworm disease diagnosis in the silkworm room to be warned in real time due to the trend of the external environment. The specific implementation process includes: first, collecting regular natural silkworm disease diagnosis time series data. Then, these data are differentially processed, and the differences between each time point are collected to determine the real-time infection risk amplitude of natural silkworm disease diagnosis. Based on the calculation of these difference values, the real-time infection risk amplitude at each moment is output and recorded, and a set of real-time infection risk data of natural silkworm disease diagnosis is output, reflecting the influence of infection speed infection risk or air pressure infection risk at multiple time points on the movement of bacteria in the silkworm room to be warned.

[0028] B36 includes: Conduct disinfection influence relationship judgment among multiple disinfectants on disinfectant action data, and output disinfection influence relationship judgment data; Based on the disinfectant disinfection impact relationship judgment data and the bacterial Bombyx batryticatus degree period trend data, the disinfectant attachment state infection risk analysis is performed, and the disinfectant attachment state infection risk data is output; Based on the periodic trend data of the degree of Bombyx mori, the infection risk data of the disinfectant attachment state is analyzed for the degree of Bombyx mori - the proportion of the attachment state, and the degree of Bombyx mori - the proportion of the attachment state is output; Based on the degree of Bombyx Batryticatus-the ratio of attachment state, the attachment state infection risk data of the disinfectant are sampled according to the normal distribution, and the attachment state normal distribution sampling data is output; The attachment degree of bacteria is predicted for the normally distributed sampling data of the attachment state, and the attachment degree data is output.

[0029] Based on the disinfection impact intensity between multiple disinfectants, combined with the periodic trend data of the degree of Bombyx mori, the relationship between the attachment state of the disinfectant and the environmental conditions is established; based on the calculation of the infection risk of the attachment state of the bacteria at each moment (i.e., the response of the attachment state under the trend of the degree of Bombyx mori), the infection risk data of the attachment state are output; based on the infection risk data of the attachment state, the attachment behavior of the bacteria under multiple environmental conditions is analyzed to reflect the real-time attachment state of the bacteria. Based on the periodic trend data of the degree of Bombyx mori and the infection risk data of the attachment state of the disinfectant, the ratio between the degree of Bombyx mori and the attachment state is analyzed. First, based on the influence of the trend of the degree of Bombyx mori on the attachment state, the quantitative relationship between the degree of Bombyx mori and the attachment state is analyzed. Specifically: based on the analysis of the periodic trend data of the degree of Bombyx mori and the infection risk data of the attachment state of the disinfectant, the influence of the trend of the degree of Bombyx mori on the attachment state is identified; the proportional relationship between the trend of the degree of Bombyx mori and the attachment state is calculated, i.e., the trend of the attachment state caused by each unit of the trend of the degree of Bombyx mori; based on the establishment of the functional relationship between the degree of Bombyx mori and the attachment state, the proportion data of the degree of Bombyx mori-attachment state is output. Based on the proportion data of the degree of Bombyx mori-attachment state, the infection risk data of the attachment state of the disinfectant are sampled in a normal distribution. First, based on the statistical characteristics of the infection risk of the attachment state, it is assumed that it follows a normal distribution. On this basis, the Bombyx mori degree-attachment state ratio is used to perform normal distribution sampling and output sample data of the bacterial attachment state. Specifically, the parameters of the normal distribution are set based on the mean and standard deviation of the attachment state infection risk data; the attachment state infection risk data is normalized based on the Bombyx mori degree-attachment state ratio; the attachment state infection risk data is sampled using the normal distribution formula, and the attachment state data samples that conform to the normal distribution are output. This process can quantitatively analyze the infection risk characteristics of the attachment state of bacteria in multiple environments, and further provide data support for subsequent predictions.

[0030] B4 includes: B41: Analyze the aggregation hyphae rate based on the quantitative analysis data of bacterial aggregation and attachment status, and output the aggregation hyphae rate data; B42: Based on the aggregation hyphae rate data, the quantitative analysis data of bacterial aggregation and attachment state is analyzed for real-time bacterial infection distribution, and the real-time bacterial infection distribution data is output; B43: Perform real-time offset calculation on the real-time bacterial infection distribution data and output the real-time offset data of Bombyx batryticatus; B44: Based on the real-time offset data of Bombyx batryticatus, simulate and calculate the state of aggregated pus disease and output the state data of aggregated pus disease.

[0031] C includes: C1: Perform bacterial aggregation quality analysis on the quantitative analysis data of bacterial aggregation and attachment status, and output bacterial aggregation quality data; C2: Analyze the change of bacterial aggregation size based on the data of aggregated pus disease status, and output the data of bacterial aggregation size change; C3: Based on the bacterial aggregation quality data and the bacterial aggregation size change data, the regular natural silkworm disease diagnosis time series data is deployed for the silkworm room silkworm disease diagnosis warning to be warned, and the silkworm room silkworm disease diagnosis warning data to be warned is output.

[0032] As an example of the present invention, refer to Figure 3 As shown, in this example, C includes: C1: Perform bacterial aggregation quality analysis on the quantitative analysis data of bacterial aggregation and attachment status, and output bacterial aggregation quality data; C2: Analyze the change of bacterial aggregation size based on the data of aggregated pus disease status, and output the data of bacterial aggregation size change; C3: Based on the bacterial aggregation quality data and the bacterial aggregation size change data, the regular natural silkworm disease diagnosis time series data is deployed for the silkworm room silkworm disease diagnosis warning to be warned, and the silkworm room silkworm disease diagnosis warning data to be warned is output.

[0033] In the embodiment of the present invention, the regular natural silkworm disease diagnosis time series data is used to perform the early warning deployment of silkworm disease diagnosis in the silkworm room based on the thallus aggregation quality data and the thallus aggregation size change data. The silkworm disease diagnosis deployment needs to adjust the working mode of the silkworm disease diagnosis system based on the impact of the thallus aggregation process on the environment to ensure the control of the empty thallus and the degree of dead silkworms.

[0034] C3 includes: C31: Analyze the change trend of bacterial aggregation size change data and output bacterial aggregation change trend data; C32: Based on the bacterial aggregation quality data, the trend quality silkworm tool identification is performed on the bacterial aggregation change trend data, and the trend quality silkworm tool identification data is output; C33: Based on the trend quality silkworm tool distinction data, the silkworm disease diagnosis bacteria diffusion simulation between multiple silkworm tools is performed on the time series data of natural silkworm disease diagnosis, and the silkworm disease diagnosis bacteria diffusion simulation data is output; C34: Perform cyclic learning on the silkworm disease diagnosis bacterial diffusion simulation data, and output the silkworm disease diagnosis bacterial diffusion simulation cyclic data; C35: Deploy the early warning data of silkworm disease diagnosis in the silkworm room based on the simulated circulation data of silkworm disease diagnosis bacteria diffusion, and output the early warning data of silkworm disease diagnosis in the silkworm room.

[0035] C33 includes: Collect control parameters of silkworm disease diagnosis equipment; Based on the trend quality silkworm tool distinction data, the silkworm disease diagnosis equipment control parameters and the timing natural silkworm disease diagnosis time series data are matched between multiple silkworm tools for bacterial diffusion output, and the bacterial diffusion output data is output; Based on the trend quality silkworm tool distinction data, the control parameters of the silkworm disease diagnosis equipment and the timing natural silkworm disease diagnosis time series data are matched with disinfectant ratios among multiple silkworm tools, and the disinfectant ratio matching data is output; Based on the trend quality silkworm tool distinction data, disinfectant ratio matching data and bacterial diffusion output data, the silkworm disease diagnosis output compensation is performed on the time-based natural silkworm disease diagnosis time series data, and the silkworm disease diagnosis output compensation data is output; Based on the bacterial diffusion output data, disinfectant ratio matching data and silkworm disease diagnosis output compensation data, silkworm disease diagnosis bacterial diffusion simulation is performed among multiple silkworm tools, and silkworm disease diagnosis bacterial diffusion simulation data is output.

[0036] The present invention also provides a disease early warning system for silkworm breeding data, which is used to execute the disease early warning method for silkworm breeding data as described above. The disease early warning system for silkworm breeding data comprises: The environmental data collection unit is used to perform regular silk quality analysis on the early warning silkworm room based on the silk production meter, and output regular silk quality analysis data; collect the periodic trend data of the silkworm degree and the disinfection quality on the regular silk quality analysis data, and output the periodic trend data of the bacterial silkworm degree and the periodic trend data of the disinfection quality in turn; The bacterial aggregation analysis unit is used to perform quantitative analysis on the bacterial aggregation and attachment state of the bacterial silkworm degree periodic trend data and the disinfection quality periodic trend data, and output the quantitative analysis data of the bacterial aggregation and attachment state; perform aggregation and pus disease state simulation calculation on the quantitative analysis data of the bacterial aggregation and attachment state, and output the aggregation and pus disease state data; A silkworm disease diagnosis and early warning deployment unit is used to perform early warning deployment of silkworm disease diagnosis in a silkworm room to be warned based on quantitative analysis data of bacterial aggregation and attachment status and data of aggregated pus disease status, and output early warning data of silkworm disease diagnosis in a silkworm room to be warned; The intelligent early warning direction deployment unit is used to deploy intelligent early warning directions based on the early warning data of silkworm disease diagnosis in the silkworm room to be warned, output the intelligent early warning directions of silkworm disease diagnosis in the silkworm room to be warned, and send the intelligent early warning directions of silkworm disease diagnosis in the silkworm room to be warned to the cloud platform to execute disease early warning for silkworm breeding data.

[0037] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented based on other methods. For example, the device embodiments described above are only schematic. For example, the division of units is only a division of warning functions. There may be other division methods in actual implementation, such as multiple units 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 shown or discussed can be based on some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0038] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. 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, is also included in the patent protection scope of the present application.

[0039] 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 disease early warning method for silkworm breeding data, characterized in that: include: A: Based on the silk yield meter, regular silk quality analysis is performed in the alarm silkworm room, and regular silk quality analysis data is output; Collect the periodic trend data of silkworm degree and disinfection quality for the timed silk quality analysis data, and output the periodic trend data of silkworm degree and disinfection quality in sequence; B: Perform quantitative analysis on the aggregation and attachment state of the bacterial silkworm degree periodic trend data and the disinfection quality periodic trend data, and output the quantitative analysis data on the aggregation and attachment state of the bacterial silkworm; Perform aggregation and pus disease state simulation calculation on the quantitative analysis data of bacterial aggregation and attachment state, and output the aggregation and pus disease state data; C: Based on the quantitative analysis data of bacterial aggregation and attachment status and the data of aggregated pus disease status, the diagnosis and early warning deployment of silkworm diseases in the silkworm room to be warned is carried out, and the diagnosis and early warning data of silkworm diseases in the silkworm room to be warned is output; D: Deploy intelligent warning directions based on the warning data of silkworm diseases in the silkworm room to be warned, output intelligent warning directions of silkworm diseases in the silkworm room to be warned, and send the intelligent warning directions of silkworm diseases in the silkworm room to be warned to the cloud platform to execute disease warning for silkworm breeding data.

2. The disease early warning method for silkworm breeding data according to claim 1, characterized in that: A include: A1: Perform regular silk quality analysis on the silkworm room to be warned based on the silk yield meter, and output regular silk quality analysis data; A2: Correct the abnormal value of the timed silk quality analysis data and output the corrected silk quality analysis data; A3: Perform periodic state analysis on the silk quality analysis correction data, and output the silk quality analysis periodic state data; A4: Based on the silk quality analysis periodic status data, the silkworm degree and disinfection quality periodic trend data are collected for the timed silk quality analysis data, and the bacterial silkworm degree periodic trend data and the disinfection quality periodic trend data are output in sequence.

3. The disease early warning method for silkworm breeding data according to claim 1 is characterized in that: B includes: B1: Collect the framework of the silkworm room to be warned; Perform three-dimensional layout analysis on the framework of the silkworm room to be warned, and output the three-dimensional layout data of the silkworm room to be warned; B2: Based on the three-dimensional layout data of the silkworm room to be warned, the regular natural silkworm disease diagnosis structure analysis is performed to output the regular natural silkworm disease diagnosis time series data; B3: Based on the regular natural silkworm disease diagnosis time series data, quantitative analysis of the aggregation and attachment state of the silkworm body is performed on the periodic trend data of the silkworm body degree and the periodic trend data of the disinfection quality, and the quantitative analysis data of the aggregation and attachment state of the silkworm body is output; B4: Perform aggregation and pus disease state simulation calculation on the quantitative analysis data of bacterial aggregation and attachment state, and output the aggregation and pus disease state data.

4. The disease early warning method for silkworm breeding data according to claim 3, characterized in that: B3 include: B31: Perform real-time infection risk analysis on the time series data of natural silkworm disease diagnosis, and output the real-time infection risk data of natural silkworm disease diagnosis; B32: Calculate disinfectant concentration for disinfection quality cycle trend data and output disinfection quality disinfectant concentration data; B33: Calculate the frequency change of the disinfection quality cycle trend data based on the disinfection quality disinfectant concentration data, and output the frequency change data; B34: Predict the silkworm age distribution ratio based on the periodic trend data of the degree of Bombyx mori, and output the silkworm age distribution ratio of the degree of Bombyx mori; B35: Based on the distribution ratio of silkworm age to the degree of Bombyx mori, the disinfectant effect amount is predicted for the frequency change data, and the disinfectant effect data is output; B36: Predict the attachment degree of the bacteria based on the disinfectant action data and the periodic trend data of the degree of the bacterial silkworm, and output the attachment degree data; B37: Based on the real-time infection risk data of natural silkworm disease diagnosis, the disinfectant action data and attachment degree data are used to quantitatively analyze the bacterial aggregation and attachment status, and output the quantitative analysis data of the bacterial aggregation and attachment status.

5. The disease early warning method for silkworm breeding data according to claim 4 is characterized in that: B36 includes: Conduct disinfection influence relationship judgment among multiple disinfectants on disinfectant action data, and output disinfection influence relationship judgment data; Based on the disinfectant disinfection impact relationship judgment data and the bacterial Bombyx batryticatus degree period trend data, the disinfectant attachment state infection risk analysis is performed, and the disinfectant attachment state infection risk data is output; Based on the periodic trend data of the degree of Bombyx mori, the infection risk data of the disinfectant attachment state is analyzed for the degree of Bombyx mori - the proportion of the attachment state, and the degree of Bombyx mori - the proportion of the attachment state is output; Based on the degree of Bombyx Batryticatus-the ratio of attachment state, the attachment state infection risk data of the disinfectant are sampled according to the normal distribution, and the attachment state normal distribution sampling data is output; The attachment degree of bacteria is predicted for the normally distributed sampling data of the attachment state, and the attachment degree data is output.

6. The disease early warning method for silkworm breeding data according to claim 3 is characterized in that: B4 includes: B41: Analyze the aggregation hyphae rate based on the quantitative analysis data of bacterial aggregation and attachment status, and output the aggregation hyphae rate data; B42: Based on the aggregation hyphae rate data, the quantitative analysis data of bacterial aggregation and attachment state is analyzed for real-time bacterial infection distribution, and the real-time bacterial infection distribution data is output; B43: Perform real-time offset calculation on the real-time bacterial infection distribution data and output the real-time offset data of Bombyx batryticatus; B44: Based on the real-time offset data of Bombyx batryticatus, simulate and calculate the state of aggregated pus disease and output the state data of aggregated pus disease.

7. The disease early warning method for silkworm breeding data according to claim 3 is characterized in that: C includes: C1: Perform bacterial aggregation quality analysis on the quantitative analysis data of bacterial aggregation and attachment status, and output bacterial aggregation quality data; C2: Analyze the change of bacterial aggregation size based on the data of aggregated pus disease status, and output the data of bacterial aggregation size change; C3: Based on the bacterial aggregation quality data and the bacterial aggregation size change data, the regular natural silkworm disease diagnosis time series data is deployed for the silkworm room silkworm disease diagnosis warning to be warned, and the silkworm room silkworm disease diagnosis warning data to be warned is output.

8. The disease early warning method for silkworm breeding data according to claim 7, characterized in that C3 include: C31: Analyze the change trend of bacterial aggregation size change data and output bacterial aggregation change trend data; C32: Based on the bacterial aggregation quality data, the trend quality silkworm tool identification is performed on the bacterial aggregation change trend data, and the trend quality silkworm tool identification data is output; C33: Based on the trend quality silkworm tool distinction data, the silkworm disease diagnosis bacteria diffusion simulation between multiple silkworm tools is performed on the time series data of natural silkworm disease diagnosis, and the silkworm disease diagnosis bacteria diffusion simulation data is output; C34: Perform cyclic learning on the silkworm disease diagnosis bacterial diffusion simulation data, and output the silkworm disease diagnosis bacterial diffusion simulation cyclic data; C35: Deploy the early warning data of silkworm disease diagnosis in the silkworm room based on the simulated circulation data of silkworm disease diagnosis bacteria diffusion, and output the early warning data of silkworm disease diagnosis in the silkworm room.

9. The disease early warning method for silkworm breeding data according to claim 7, characterized in that: C33 includes: Collect control parameters of silkworm disease diagnosis equipment; Based on the trend quality silkworm tool distinction data, the silkworm disease diagnosis equipment control parameters and the timing natural silkworm disease diagnosis time series data are matched between multiple silkworm tools for bacterial diffusion output, and the bacterial diffusion output data is output; Based on the trend quality silkworm tool distinction data, the control parameters of the silkworm disease diagnosis equipment and the timing natural silkworm disease diagnosis time series data are matched with disinfectant ratios among multiple silkworm tools, and the disinfectant ratio matching data is output; Based on the trend quality silkworm tool distinction data, disinfectant ratio matching data and bacterial diffusion output data, the silkworm disease diagnosis output compensation is performed on the time-based natural silkworm disease diagnosis time series data, and the silkworm disease diagnosis output compensation data is output; Based on the bacterial diffusion output data, disinfectant ratio matching data and silkworm disease diagnosis output compensation data, silkworm disease diagnosis bacterial diffusion simulation is performed among multiple silkworm tools, and silkworm disease diagnosis bacterial diffusion simulation data is output.

10. A disease early warning system for silkworm breeding data, characterized in that: Used to execute the disease early warning method for silkworm breeding data as claimed in claim 1, the disease early warning system for silkworm breeding data comprises: The environmental data collection unit is used to perform regular silk quality analysis on the early warning silkworm room based on the silk production meter, and output regular silk quality analysis data; collect the periodic trend data of the silkworm degree and the disinfection quality on the regular silk quality analysis data, and output the periodic trend data of the bacterial silkworm degree and the periodic trend data of the disinfection quality in turn; The bacterial aggregation analysis unit is used to perform quantitative analysis on the bacterial aggregation and attachment state of the bacterial silkworm degree periodic trend data and the disinfection quality periodic trend data, and output the quantitative analysis data of the bacterial aggregation and attachment state; perform aggregation and pus disease state simulation calculation on the quantitative analysis data of the bacterial aggregation and attachment state, and output the aggregation and pus disease state data; A silkworm disease diagnosis and early warning deployment unit is used to perform early warning deployment of silkworm disease diagnosis in a silkworm room to be warned based on quantitative analysis data of bacterial aggregation and attachment status and data of aggregated pus disease status, and output early warning data of silkworm disease diagnosis in a silkworm room to be warned; The intelligent early warning direction deployment unit is used to deploy intelligent early warning directions based on the early warning data of silkworm disease diagnosis in the silkworm room to be warned, output the intelligent early warning directions of silkworm disease diagnosis in the silkworm room to be warned, and send the intelligent early warning directions of silkworm disease diagnosis in the silkworm room to be warned to the cloud platform to execute disease early warning for silkworm breeding data.

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