Breeding decision-making system and method based on bombyx batryticatus growth cycle prediction
Through the growth cycle prediction system of the zombie growth cycle, the growth cycle model is trained using machine learning algorithms, combined with breeding adjustment early warning analysis and hazard evaluation, the problem of the growth cycle prediction and breeding management difficulty of the zombie growth cycle is solved, and the optimization of the growth environment of the zombie growth and the accuracy of the management strategy is achieved.
Patent Information
- Application Number
- CN202510421833.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-07
- Publication Date
- 2025-07-11
AI Technical Summary
The existing technology is difficult to automatically predict the growth cycle of silkworms, and it is impossible to accurately evaluate the degree of breeding hazards and control of silkworms sand, resulting in high difficulty in breeding management and low intelligence level.
A breeding decision-making system based on the prediction of the growth cycle of a slag silkworm is adopted, including a growth monitoring and transmission unit, a growth cycle prediction unit, a breeding decision support unit, a breeding regulation management and control unit and an intelligent supervision end. The growth cycle prediction model is trained using machine learning algorithms, and combined with a breeding regulation early warning analysis and hazard evaluation, automatic regulation and monitoring are achieved.
It has achieved accurate prediction of the growth cycle of silkworms, optimized the breeding environment and management strategies, reduced management difficulty, improved the cleanliness of the breeding environment and the suitability of silkworms' growth, and improved the level of intelligence.
Smart Images
Figure CN120297499A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of monitoring the cultivation of Bombyx batryticatus, and specifically to a cultivation decision-making system and method based on predicting the growth cycle of Bombyx batryticatus. Background Art
[0002] Bombyx batryticatus, also known as white Bombyx batryticatus, dead silkworm or celestial worm, is the dried body of the 4th to 5th instar larvae of the silkworm moth Bombyx mori infected with Beauveria bassiana and died of disease. Traditional Bombyx batryticatus is mostly a natural infection product, while modern production can be carried out on a large scale through artificial inoculation technology. The core lies in the parasitism of Beauveria bassiana, which hardens the body wall of the larvae and causes death, forming Bombyx batryticatus with extremely high medicinal value, having medicinal values such as calming endogenous wind and stopping convulsions, expelling wind and relieving pain, resolving phlegm and dissipating nodules;
[0003] During the cultivation process of Bombyx batryticatus, it is necessary to formulate corresponding cultivation management plans based on the growth cycle of Bombyx batryticatus. At present, it is difficult to automatically predict the growth cycle of Bombyx batryticatus, reasonably adjust the environment and control feeding, and it is impossible to accurately evaluate the degree of cultivation hazards suffered by Bombyx batryticatus during the cultivation process and the performance of controlling the cleaning of silkworm excrement, which is not conducive to the growth of Bombyx batryticatus and increases the difficulty of cultivating and managing Bombyx batryticatus, with a low level of intelligence;
[0004] In view of the above technical defects, a solution is now proposed. Summary of the Invention
[0005] The purpose of the present invention is to provide a cultivation decision-making system and method based on predicting the growth cycle of Bombyx batryticatus, which solves the problems that it is difficult in the prior art to automatically predict the growth cycle of Bombyx batryticatus, reasonably adjust the environment and control feeding, and it is impossible to accurately evaluate the degree of cultivation hazards suffered by Bombyx batryticatus during the cultivation process and the performance of controlling the cleaning of silkworm excrement, which is not conducive to the growth of Bombyx batryticatus and increases the difficulty of cultivating and managing Bombyx batryticatus.
[0006] To achieve the above purpose, the present invention provides the following technical solutions:
[0007] A cultivation decision-making system based on predicting the growth cycle of Bombyx batryticatus includes a growth monitoring and transmission unit, a growth cycle prediction unit, a cultivation decision-making support unit, a cultivation regulation and control unit, and an intelligent supervision terminal; the growth monitoring and transmission unit monitors the cultivation process of Bombyx batryticatus, collects cultivation environment data and Bombyx batryticatus growth data, and sends the collected data to the growth cycle prediction unit and the cultivation regulation and control unit;
[0008] The growth cycle prediction unit trains a Bombyx batryticatus growth cycle prediction model using machine learning algorithms, optimizes the model performance through cross-validation and parameter adjustment, predicts the growth cycle of Bombyx batryticatus based on the Bombyx batryticatus growth cycle prediction model, and sends the Bombyx batryticatus growth cycle prediction result to the cultivation decision-making support unit and the intelligent supervision terminal;
[0009] Based on the prediction results of the growth cycle of Bombyx batryticatus, the aquaculture decision support unit formulates an aquaculture environment decision-making plan and a feeding management decision-making plan, and sends the aquaculture decision-making information to the aquaculture regulation and control unit and the intelligent supervision terminal; the aquaculture regulation and control unit judges whether it is in an aquaculture risk state through aquaculture regulation early warning analysis. When it is judged to be in an aquaculture risk state, it automatically adjusts the aquaculture environment and sends the adjustment information to the intelligent supervision terminal.
[0010] Furthermore, the specific analysis process of the aquaculture regulation early warning analysis is as follows:
[0011] Collect the temperature data, humidity data and light data of the aquaculture environment, and based on the aquaculture environment decision-making plan, retrieve the optimal environment data range, optimal humidity data range and optimal light data range. Compare the temperature data, humidity data and light data with the optimal environment data range, optimal humidity data range and optimal light data range respectively. If the temperature data, humidity data or light data is not within the corresponding preset optimal range, it is judged that the current is in an aquaculture risk state.
[0012] Furthermore, if the temperature data, humidity data and light data are all within the corresponding preset optimal ranges, calculate the difference between the temperature data and the median of the optimal temperature data range and take the absolute value to obtain the Bombyx batryticatus aquaculture temperature condition value. Similarly, obtain the Bombyx batryticatus aquaculture humidity condition value and the Bombyx batryticatus aquaculture light condition value;
[0013] Calculate the weighted sum of the Bombyx batryticatus aquaculture temperature condition value, the Bombyx batryticatus aquaculture humidity condition value and the Bombyx batryticatus aquaculture light condition value to obtain the aquaculture regulation early warning value. Compare the aquaculture regulation early warning value with the preset aquaculture regulation early warning threshold. If the aquaculture regulation early warning value exceeds the preset aquaculture regulation early warning threshold, it is judged that the current is in an aquaculture risk state.
[0014] Furthermore, the aquaculture regulation and control unit is communicatively connected to the aquaculture hazard assessment unit. The aquaculture hazard assessment unit is used to set the assessment period, evaluate and analyze the aquaculture hazard manifestations during the assessment period, generate a high aquaculture hazard signal or a low aquaculture hazard signal through analysis, and send the high aquaculture hazard signal or the low aquaculture hazard signal to the intelligent supervision terminal. When the intelligent supervision terminal receives the high aquaculture hazard signal, it issues a corresponding warning.
[0015] Furthermore, the aquaculture hazard assessment unit is communicatively connected to the feeding management monitoring unit. The feeding management monitoring unit is communicatively connected to the aquaculture decision support unit. The aquaculture decision support unit sends the aquaculture decision-making information to the feeding management monitoring unit. The feeding management monitoring unit conducts the feeding management of the growth of Bombyx batryticatus based on the feeding management decision-making plan and monitors the feeding process, and sends the feeding management monitoring information to the aquaculture hazard assessment unit.
[0016] Furthermore, the specific analysis process of the aquaculture hazard assessment unit includes:
[0017] When the breeding staff feeds the cadaveric silkworms, the feeding amount in the corresponding feeding process is collected and marked as the feeding detection value, and the deviation value of the start time of the corresponding feeding process from the corresponding standard feeding time is marked as the feeding deviation value. The feeding detection value and the feeding deviation value are weighted and summed to calculate the feeding monitoring value. The feeding monitoring value is numerically compared with the preset feeding monitoring threshold. If the feeding monitoring value exceeds the preset feeding monitoring threshold, the non-standard feeding symbol WP-1 is assigned.
[0018] The total number of times the non-standard feeding symbol WP-1 is assigned during the evaluation period is obtained and marked as the non-standard feeding value, and the total duration in the breeding risk state during the evaluation period is marked as the breeding risk value. The non-standard feeding value and the breeding risk value are numerically compared with the preset non-standard feeding threshold and the preset breeding risk threshold respectively. If the non-standard feeding value or the breeding risk value exceeds the corresponding preset threshold, a high breeding hazard signal is generated.
[0019] Furthermore, if both the non-standard feeding value and the breeding risk value do not exceed the corresponding preset thresholds, when it is judged to be in the breeding risk state, timing is carried out until the end of the current breeding risk state. Based on this, the risk duration characteristic value is obtained. The risk duration characteristic value is numerically compared with the preset risk duration characteristic threshold. If the risk duration characteristic value exceeds the preset risk duration characteristic threshold, the corresponding risk duration characteristic value is marked as the risk duration abnormal value. The number of risk duration abnormal values during the evaluation period is obtained and marked as the risk duration abnormal frequency value.
[0020] And all the feeding monitoring values during the evaluation period are obtained and their mean value is calculated to obtain the feeding monitoring table value. By weighted summing the non-standard feeding value, the breeding risk value, the risk duration abnormal frequency value and the feeding monitoring table value, the breeding hazard assessment coefficient is calculated. The breeding hazard assessment coefficient is numerically compared with the preset breeding hazard assessment coefficient threshold. If the breeding hazard assessment coefficient exceeds the preset breeding hazard assessment coefficient threshold, a high breeding hazard signal is generated; if the breeding hazard assessment coefficient does not exceed the preset breeding hazard assessment coefficient threshold, a low breeding hazard signal is generated.
[0021] Furthermore, the breeding hazard assessment unit is communicatively connected to the silkworm excrement cleaning and tracking assessment unit. The breeding hazard assessment unit sends the low breeding hazard signal to the silkworm excrement cleaning and tracking assessment unit. When the silkworm excrement cleaning and tracking assessment unit receives the low breeding hazard signal, it analyzes the cleaning and control performance of the silkworm excrement during the cadaveric silkworm breeding process. By analyzing, a qualified silkworm excrement cleaning and control signal or an abnormal silkworm excrement cleaning and control signal is generated, and the qualified silkworm excrement cleaning and control signal or the abnormal silkworm excrement cleaning and control signal is sent to the intelligent supervision terminal. When the intelligent supervision terminal receives the abnormal silkworm excrement cleaning and control signal, a corresponding warning is issued.
[0022] Further, the specific analysis process of the silkworm excrement cleaning tracking and evaluation unit is as follows:
[0023] Collect the time of the previous adjacent silkworm excrement cleaning and mark it as the adjacent silkworm excrement cleaning time. Start timing from the adjacent silkworm excrement cleaning time to obtain the silkworm excrement cleaning interval duration. Compare the silkworm excrement cleaning interval duration with the preset silkworm excrement cleaning interval duration threshold. If the silkworm excrement cleaning interval duration exceeds the preset silkworm excrement cleaning interval duration threshold, it is determined that the current is in a high emergency state of silkworm excrement cleaning;
[0024] If the silkworm excrement cleaning interval duration does not exceed the preset silkworm excrement cleaning interval duration threshold, collect the monitoring images in the environment for the growth of dead silkworms, obtain the ratio of the silkworm excrement distribution area based on the monitoring images in the environment for the growth of dead silkworms and mark it as the silkworm excrement diffusion value. Compare the silkworm excrement diffusion value with the preset silkworm excrement diffusion threshold. If the silkworm excrement diffusion value exceeds the preset silkworm excrement diffusion threshold, it is determined that the current is in a high emergency state of silkworm excrement cleaning;
[0025] When it is determined that the current is in a high emergency state of silkworm excrement cleaning, start timing until the silkworm excrement cleaning begins, and obtain the silkworm excrement cleaning waiting duration accordingly. Compare the silkworm excrement cleaning waiting duration with the preset silkworm excrement cleaning waiting duration threshold. If the silkworm excrement cleaning waiting duration exceeds the preset silkworm excrement cleaning waiting duration threshold, mark the corresponding silkworm excrement cleaning waiting duration as a negative duration;
[0026] Obtain the number of negative durations during the evaluation period and mark it as the negative time frequency value. Compare the negative time frequency value with the preset negative time frequency threshold. If the negative time frequency value exceeds the preset negative time frequency threshold, generate a silkworm excrement cleaning control abnormal signal;
[0027] If the negative time frequency value does not exceed the preset negative time frequency threshold, mark the excess value of the corresponding negative duration compared with the preset silkworm excrement cleaning waiting duration threshold as the negative impact value, and mark the average value and the maximum value of all negative impact values during the evaluation period as the negative performance value and the negative performance amplitude value respectively;
[0028] Calculate the weighted sum of the negative time frequency value, the negative performance value and the negative performance amplitude value to obtain the silkworm excrement cleaning control coefficient. Compare the silkworm excrement cleaning control coefficient with the preset silkworm excrement cleaning control coefficient threshold. If the silkworm excrement cleaning control coefficient exceeds the preset silkworm excrement cleaning control coefficient threshold, generate a silkworm excrement cleaning control abnormal signal; if the silkworm excrement cleaning control coefficient does not exceed the preset silkworm excrement cleaning control coefficient threshold, generate a silkworm excrement cleaning control qualified signal.
[0029] Further, the present invention also proposes a breeding decision-making method based on the prediction of the growth cycle of dead silkworms, including the following steps:
[0030] Step 1: Monitor the breeding process of dead silkworms, and collect breeding environment data and dead silkworm growth data;
[0031] Step 2: Predict the growth cycle of Bombyx Batryticatus based on the growth cycle prediction model of Bombyx Batryticatus;
[0032] Step 3: Generate matching breeding decision information according to the prediction result of the growth cycle of Bombyx Batryticatus;
[0033] Step 4: Judge whether it is in the breeding risk state through breeding adjustment early warning analysis;
[0034] Step 5: Automatically adjust the breeding environment when it is judged to be in the breeding risk state.
[0035] Compared with the prior art, the beneficial effects of the present invention are as follows:
[0036] 1. In the present invention, the growth cycle prediction unit predicts the growth cycle of Bombyx Batryticatus based on the growth cycle prediction model of Bombyx Batryticatus. The breeding decision support unit scientifically formulates a breeding decision plan according to the prediction result of the growth cycle of Bombyx Batryticatus. And through breeding adjustment early warning analysis to identify the breeding risk state and realize the automatic adaptive adjustment of the breeding environment, and through the breeding hazard assessment unit to evaluate and analyze the breeding hazard performance during the evaluation period in time to strengthen the breeding monitoring and control, which is beneficial to the growth of Bombyx Batryticatus and reduces the difficulty of breeding management;
[0037] 2. In the present invention, the breeding hazard assessment unit sends the breeding low-hazard signal to the silkworm excrement cleaning tracking assessment unit. When the silkworm excrement cleaning tracking assessment unit receives the breeding low-hazard signal, it analyzes the cleaning and control performance of the silkworm excrement during the breeding process of Bombyx Batryticatus during the evaluation period. When generating an abnormal signal for silkworm excrement cleaning and control, strengthen the subsequent silkworm excrement cleaning and control to ensure the cleanliness of the breeding environment and prevent the accumulation of germs from affecting the normal growth of Bombyx Batryticatus, with a high level of intelligence. Description of the Drawings
[0038] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings;
[0039] Figure 1 It is the system block diagram of Embodiment 1 in the present invention;
[0040] Figure 2 It is the system block diagram of Embodiment 2 in the present invention;
[0041] Figure 3 It is the method flow chart of Embodiment 3 in the present invention. Detailed Embodiments
[0042] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.
[0043] Embodiment 1: As Figure 1 shown, the breeding decision-making system based on the prediction of the growth cycle of Bombyx batryticatus proposed by the present invention includes a growth monitoring and transmission unit, a growth cycle prediction unit, a breeding decision support unit, a breeding regulation and control unit, a feeding management monitoring unit, a breeding hazard assessment unit, and an intelligent supervision terminal; the growth monitoring and transmission unit monitors the breeding process of Bombyx batryticatus, collects breeding environment data and Bombyx batryticatus growth data, and sends the collected data to the growth cycle prediction unit and the breeding regulation and control unit.
[0044] It should be noted that high-precision temperature and humidity sensors are selected for collecting breeding environment data, such as SHT3x series sensors, with measurement ranges of 0-100% RH and -40-125°C respectively, and accuracies of ±2% RH and ±0.3°C respectively, and the BH1750 light sensor is used, with a measurement range of 1-65535 lx; and a high-resolution industrial camera is used for collecting Bombyx batryticatus growth data, such as Basler acA2040-90uc, with a resolution of 2048×2048 and a frame rate of 90 fps, for shooting the growth state of Bombyx batryticatus.
[0045] The growth cycle prediction unit uses machine learning algorithms (such as decision trees, support vector machines, neural networks, etc.) to train the Bombyx batryticatus growth cycle prediction model, optimizes the model performance through cross-validation and parameter adjustment, etc., predicts the Bombyx batryticatus growth cycle based on the Bombyx batryticatus growth cycle prediction model, and sends the Bombyx batryticatus growth cycle prediction result to the breeding decision support unit and the intelligent supervision terminal; among them, the Bombyx batryticatus growth cycle includes the egg stage, larval stage, pupal stage, adult stage, etc.
[0046] The breeding decision support unit formulates a breeding environment decision-making plan (including the most suitable temperature, humidity, light intensity, etc.) and a feeding management decision-making plan (including feeding frequency, feeding amount, etc.) according to the Bombyx batryticatus growth cycle prediction result, and sends the breeding decision-making information to the breeding regulation and control unit, the feeding management monitoring unit, and the intelligent supervision terminal, and optimizes the breeding environment and management strategies by accurately predicting the growth cycle of Bombyx batryticatus, reducing the breeding cost and the difficulty of breeding management.
[0047] The breeding regulation and control unit determines whether the current state is a breeding risk state through breeding regulation early warning analysis. When it is determined that the state is a breeding risk state, it automatically adjusts the breeding environment, realizes automatic monitoring and analysis of the breeding environment and adaptive adjustment, ensures the suitability of the breeding environment, is conducive to the growth of Bombyx batryticatus and reduces the difficulty of its breeding management, and sends the adjustment information to the intelligent supervision terminal, which is conducive to detailed understanding of the breeding environment adjustment situation. The specific analysis process of the breeding regulation early warning analysis is as follows:
[0048] Temperature data, humidity data, and light data (i.e., light intensity) of the breeding environment are collected. Based on the breeding environment decision-making scheme, the optimal environmental data range, the optimal humidity data range, and the optimal light data range are retrieved. The temperature data, humidity data, and light data are respectively compared numerically with the optimal environmental data range, the optimal humidity data range, and the optimal light data range. If the temperature data, humidity data, or light data is not within the corresponding preset optimal range, it indicates that the current breeding environment condition is poor and is not conducive to the growth of Bombyx batryticatus, then it is judged that the current state is a breeding risk state.
[0049] Furthermore, if the temperature data, humidity data, and light data are all within the corresponding preset optimal ranges, then the difference between the temperature data and the median of the optimal temperature data range is calculated and the absolute value is taken to obtain the temperature condition value for Bombyx batryticatus breeding. The difference between the humidity data and the median of the optimal humidity data range is calculated and the absolute value is taken to obtain the humidity condition value for Bombyx batryticatus breeding. The difference between the light data and the median of the optimal light data range is calculated and the absolute value is taken to obtain the light condition value for Bombyx batryticatus breeding.
[0050] The breeding regulation early warning value is obtained by performing a weighted sum calculation on the temperature condition value for Bombyx batryticatus breeding, the humidity condition value for Bombyx batryticatus breeding, and the light condition value for Bombyx batryticatus breeding. That is, corresponding preset weight coefficients are assigned to the temperature condition value for Bombyx batryticatus breeding, the humidity condition value for Bombyx batryticatus breeding, and the light condition value for Bombyx batryticatus breeding. The temperature condition value for Bombyx batryticatus breeding, the humidity condition value for Bombyx batryticatus breeding, and the light condition value for Bombyx batryticatus breeding are respectively multiplied by the corresponding preset weight coefficients, and the sum value of the three product results is marked as the breeding regulation early warning value. And, the larger the value of the breeding regulation early warning value, the worse the comprehensive breeding environment condition is currently.
[0051] The breeding regulation early warning value is compared numerically with the preset breeding regulation early warning threshold. If the breeding regulation early warning value exceeds the preset breeding regulation early warning threshold, it indicates that the current breeding environment condition is poor comprehensively and is not conducive to the growth of Bombyx batryticatus, then it is judged that the current state is a breeding risk state.
[0052] The feeding management monitoring unit conducts the feeding management of the growth of Bombyx batryticatus based on the feeding management decision-making scheme, monitors the feeding process, and sends the feeding management monitoring information to the aquaculture hazard assessment unit to provide information support for the analysis process of the aquaculture hazard assessment unit, ensuring the comprehensiveness of its analysis and the accuracy of the analysis results; the aquaculture hazard assessment unit is used to set the assessment period, evaluate and analyze the aquaculture hazard manifestations within the assessment period, and generate a high aquaculture hazard signal or a low aquaculture hazard signal through analysis;
[0053] And send the high aquaculture hazard signal or the low aquaculture hazard signal to the intelligent supervision terminal. When the intelligent supervision terminal receives the high aquaculture hazard signal, it issues a corresponding warning to remind the management personnel to strengthen the aquaculture monitoring and control in the follow-up, ensure the suitability of the environment where the Bombyx batryticatus is located and maintain the feeding standardization, which is beneficial to the growth of the Bombyx batryticatus; the specific analysis process of the aquaculture hazard assessment unit is as follows:
[0054] When the aquaculture personnel feed the Bombyx batryticatus, the feeding amount of the corresponding feeding process is collected and marked as the feeding detection value, and the deviation value of the start time of the corresponding feeding process compared with the corresponding standard feeding time is marked as the feeding deviation value;
[0055] The feeding detection value and the feeding deviation value are weighted and summed to obtain the feeding monitoring value, that is, the corresponding preset weight coefficients are assigned to the feeding detection value and the feeding deviation value respectively, the feeding detection value and the feeding deviation value are multiplied by the corresponding preset weight coefficients respectively, and the sum value of the two product results is marked as the feeding monitoring value; moreover, the larger the value of the feeding monitoring value, the more the corresponding feeding process does not meet the standard; the feeding monitoring value is compared with the preset feeding monitoring threshold value. If the feeding monitoring value exceeds the preset feeding monitoring threshold value, it indicates that the corresponding feeding process does not meet the standard, and then the feeding non-standard symbol WP-1 is assigned;
[0056] Obtain the total number of times the feeding non-standard symbol WP-1 is assigned within the assessment period and mark it as the feeding non-standard value, and mark the total duration in the aquaculture risk state within the assessment period as the aquaculture risk value. The feeding non-standard value and the aquaculture risk value are compared with the preset feeding non-standard threshold value and the preset aquaculture risk threshold value respectively. If the feeding non-standard value or the aquaculture risk value exceeds the corresponding preset threshold value, it indicates that the aquaculture process within the assessment period brings a deeper harm degree to the Bombyx batryticatus and the growth risk of the Bombyx batryticatus is higher, and then a high aquaculture hazard signal is generated.
[0057] Furthermore, if both the feeding non-standard value and the breeding insurance value do not exceed the corresponding preset thresholds, when it is determined that the breeding is in a risk state, timing is carried out until the end of the current breeding risk state, and accordingly, a risk duration characteristic value is obtained. The risk duration characteristic value is numerically compared with the preset risk duration characteristic threshold. If the risk duration characteristic value exceeds the preset risk duration characteristic threshold, the corresponding risk duration characteristic value is marked as a risk duration abnormal value, and the number of risk duration abnormal values within the evaluation period is obtained and marked as the risk duration abnormal frequency value;
[0058] And all the feeding monitoring values within the evaluation period are obtained and their mean value is calculated to obtain the feeding monitoring table value. By performing a weighted sum calculation on the feeding non-standard value, the breeding insurance value, the risk duration abnormal frequency value, and the feeding monitoring table value, a breeding hazard assessment coefficient is obtained, that is, corresponding preset weight coefficients are assigned to the feeding non-standard value, the breeding insurance value, the risk duration abnormal frequency value, and the feeding monitoring table value respectively, and the feeding non-standard value, the breeding insurance value, the risk duration abnormal frequency value, and the feeding monitoring table value are multiplied by the corresponding preset weight coefficients respectively, and the sum value of the four groups of product results is marked as the breeding hazard assessment coefficient; moreover, the larger the value of the breeding hazard assessment coefficient, the higher the comprehensive growth risk of the silkworm larvae within the evaluation period;
[0059] The breeding hazard assessment coefficient is numerically compared with the preset breeding hazard assessment coefficient threshold. If the breeding hazard assessment coefficient exceeds the preset breeding hazard assessment coefficient threshold, indicating that the comprehensive growth risk of the silkworm larvae within the evaluation period is relatively high, a breeding high-hazard signal is generated; if the breeding hazard assessment coefficient does not exceed the preset breeding hazard assessment coefficient threshold, indicating that the comprehensive growth risk of the silkworm larvae within the evaluation period is relatively low, a breeding low-hazard signal is generated.
[0060] Embodiment 2: As Figure 2 shown, the difference between this embodiment and Embodiment 1 is that the breeding hazard assessment unit is communicatively connected to the silkworm excrement cleaning and tracking assessment unit. The breeding hazard assessment unit sends the breeding low-hazard signal to the silkworm excrement cleaning and tracking assessment unit. When the silkworm excrement cleaning and tracking assessment unit receives the breeding low-hazard signal, it analyzes the cleaning and control performance of the silkworm excrement during the breeding process of the silkworm larvae within the evaluation period, and generates a silkworm excrement cleaning and control qualified signal or a silkworm excrement cleaning and control abnormal signal through the analysis;
[0061] And the silkworm excrement cleaning and control qualified signal or the silkworm excrement cleaning and control abnormal signal is sent to the intelligent supervision terminal. When the intelligent supervision terminal receives the silkworm excrement cleaning and control abnormal signal, it issues a corresponding warning to remind the management personnel to strengthen the subsequent cleaning and control of the silkworm excrement to ensure the cleanliness of the breeding environment and prevent the accumulation of germs from affecting the normal growth of the silkworm larvae; the specific analysis process of the silkworm excrement cleaning and tracking assessment unit is as follows:
[0062] Collect the time of the previous adjacent cleaning of silkworm excrement and mark it as the adjacent cleaning time of silkworm excrement. Start timing from the adjacent cleaning time of silkworm excrement, and thus obtain the cleaning interval duration of silkworm excrement; Compare the cleaning interval duration of silkworm excrement with the preset cleaning interval duration threshold of silkworm excrement. If the cleaning interval duration of silkworm excrement exceeds the preset cleaning interval duration threshold of silkworm excrement, it indicates that the silkworm excrement needs to be cleaned in time to ensure the cleanliness of the breeding environment, and then it is judged that the current is in a high emergency state of silkworm excrement cleaning;
[0063] If the cleaning interval duration of silkworm excrement does not exceed the preset cleaning interval duration threshold of silkworm excrement, collect the monitoring images in the growth environment of stiff silkworms, obtain the ratio of the distribution area of silkworm excrement in the growth environment of stiff silkworms and mark it as the diffusion value of silkworm excrement, compare the diffusion value of silkworm excrement with the preset diffusion threshold of silkworm excrement. If the diffusion value of silkworm excrement exceeds the preset diffusion threshold of silkworm excrement, it indicates that the silkworm excrement needs to be cleaned in time to ensure the cleanliness of the breeding environment, and then it is judged that the current is in a high emergency state of silkworm excrement cleaning;
[0064] When it is judged that the current is in a high emergency state of silkworm excrement cleaning, start timing until the silkworm excrement cleaning starts, and thus obtain the waiting duration for silkworm excrement cleaning. Compare the waiting duration for silkworm excrement cleaning with the preset waiting duration threshold for silkworm excrement cleaning. If the waiting duration for silkworm excrement cleaning exceeds the preset waiting duration threshold for silkworm excrement cleaning, it indicates that the cleaning of silkworm excrement for this time is not timely, and then mark the corresponding waiting duration for silkworm excrement cleaning as a negative duration;
[0065] Obtain the number of negative durations during the evaluation period and mark it as the negative time frequency value. Compare the negative time frequency value with the preset negative time frequency threshold. If the negative time frequency value exceeds the preset negative time frequency threshold, it indicates that the management and control performance of silkworm excrement cleaning during the evaluation period is poor and is not conducive to the growth of stiff silkworms, and then generate an abnormal signal for the management and control of silkworm excrement cleaning;
[0066] If the negative time frequency value does not exceed the preset negative time frequency threshold, mark the excess value of the corresponding negative duration compared with the preset waiting duration threshold for silkworm excrement cleaning as the negative impact value, and mark the average value and the maximum value of all negative impact values during the evaluation period as the negative performance value and the negative performance amplitude value respectively;
[0067] Calculate the management and control coefficient of silkworm excrement cleaning by weighted summation of the negative time frequency value, the negative performance value and the negative performance amplitude value; that is, assign corresponding preset weight coefficients to the negative time frequency value, the negative performance value and the negative performance amplitude value respectively, multiply the negative time frequency value, the negative performance value and the negative performance amplitude value by the corresponding preset weight coefficients respectively, and mark the sum value of the three product results as the management and control coefficient of silkworm excrement cleaning; Moreover, the larger the value of the management and control coefficient of silkworm excrement cleaning, the worse the comprehensive management and control performance of silkworm excrement cleaning during the evaluation period;
[0068] Compare the coefficient of silkworm excrement cleaning control with the preset threshold value of the coefficient of silkworm excrement cleaning control. If the coefficient of silkworm excrement cleaning control exceeds the preset threshold value of the coefficient of silkworm excrement cleaning control, it indicates that the comprehensive performance of silkworm excrement cleaning control during the evaluation period is poor, and then generate an abnormal signal for silkworm excrement cleaning control; if the coefficient of silkworm excrement cleaning control does not exceed the preset threshold value of the coefficient of silkworm excrement cleaning control, it indicates that the comprehensive performance of silkworm excrement cleaning control during the evaluation period is better, and then generate a qualified signal for silkworm excrement cleaning control.
[0069] Embodiment 3: As Figure 3 shown, the difference between this embodiment and Embodiment 1 and Embodiment 2 is that the breeding decision-making method based on the prediction of the growth cycle of stiff silkworms proposed by the present invention includes the following steps:
[0070] Step 1, monitor the breeding process of stiff silkworms, and collect breeding environment data and growth data of stiff silkworms;
[0071] Step 2, predict the growth cycle of stiff silkworms based on the growth cycle prediction model of stiff silkworms;
[0072] Step 3, generate matching breeding decision-making information according to the prediction result of the growth cycle of stiff silkworms;
[0073] Step 4, judge whether it is in a breeding risk state through breeding adjustment early warning analysis;
[0074] Step 5, automatically adjust the breeding environment when it is judged to be in a breeding risk state.
[0075] The working principle of the present invention: When in use, the growth cycle prediction unit predicts the growth cycle of stiff silkworms based on the growth cycle prediction model of stiff silkworms, and the breeding decision-making support unit formulates a breeding environment decision-making plan and a feeding management decision-making plan according to the prediction result of the growth cycle of stiff silkworms, which can accurately predict the growth cycle of stiff silkworms to optimize the breeding environment and management strategies, reduce the breeding cost and the difficulty of breeding management, and judge whether it is in a breeding risk state through breeding adjustment early warning analysis, and automatically adjust the breeding environment when it is judged to be in a breeding risk state, realizing automatic monitoring and analysis of the breeding environment and adaptive adjustment, and evaluating and analyzing the breeding hazard performance during the evaluation period through the breeding hazard assessment unit, strengthening the breeding monitoring and control when generating a high breeding hazard signal, ensuring the suitability of the environment where the stiff silkworms are located and maintaining the feeding standardization, which is beneficial to the growth of stiff silkworms and further reduces the difficulty of breeding management of stiff silkworms.
[0076] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only the specific implementation manners. Obviously, according to the content of this specification, many modifications and variations can be made. These embodiments are selected and specifically described in this specification in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.
Claims
1. A breeding decision-making system based on the prediction of the growth cycle of Bombyx Batryticatus, characterized in that, It includes a growth monitoring and transmission unit, a growth cycle prediction unit, a breeding decision support unit, a breeding regulation and control unit, and an intelligent supervision terminal; the growth monitoring and transmission unit monitors the breeding process of Bombyx batryticatus and sends the collected data to the growth cycle prediction unit and the breeding regulation and control unit; the growth cycle prediction unit predicts the growth cycle of Bombyx batryticatus based on the Bombyx batryticatus growth cycle prediction model; The breeding decision support unit formulates a breeding environment decision-making plan and a feeding management decision-making plan according to the Bombyx batryticatus growth cycle prediction result, and sends the breeding decision information to the breeding regulation and control unit and the intelligent supervision terminal; the breeding regulation and control unit judges whether it is in a breeding risk state through breeding regulation early warning analysis, and automatically adjusts the breeding environment when it is judged to be in a breeding risk state.
2. The breeding decision-making system based on the prediction of the growth cycle of Bombyx Batryticatus according to claim 1, wherein The specific analysis process of the breeding regulation early warning analysis is as follows: The temperature data, humidity data, and light data of the breeding environment are collected. If the temperature data, humidity data, or light data is not within the corresponding preset optimal range, it is judged that the current is in a breeding risk state.
3. The breeding decision-making system based on the prediction of the growth cycle of Bombyx Batryticatus according to claim 2, wherein, If the temperature data, humidity data, and light data are all within the corresponding preset optimal range, the breeding regulation early warning value is calculated by weighted summation of the Bombyx batryticatus breeding temperature condition value, the Bombyx batryticatus breeding humidity condition value, and the Bombyx batryticatus breeding light condition value. If the breeding regulation early warning value exceeds the preset breeding regulation early warning threshold, it is judged that the current is in a breeding risk state.
4. The farming decision-making system based on the prediction of the growth cycle of Bombyx Batryticatus according to claim 1, characterized in that, The breeding regulation and control unit is communicatively connected to a breeding hazard assessment unit. The breeding hazard assessment unit is used to set the assessment period, evaluate and analyze the breeding hazard manifestations within the assessment period, generate a breeding high-hazard signal or a breeding low-hazard signal through analysis, and send the breeding high-hazard signal or the breeding low-hazard signal to the intelligent supervision terminal. When the intelligent supervision terminal receives the breeding high-hazard signal, it issues a corresponding warning.
5. The breeding decision-making system based on the prediction of the growth cycle of Bombyx Batryticatus according to claim 4, wherein The breeding hazard assessment unit is communicatively connected to a feeding management monitoring unit. The feeding management monitoring unit is communicatively connected to the breeding decision support unit. The breeding decision support unit sends the breeding decision information to the feeding management monitoring unit. The feeding management monitoring unit conducts the feeding management of the growth of Bombyx batryticatus based on the feeding management decision-making plan and monitors the feeding process, and sends the feeding management monitoring information to the breeding hazard assessment unit.
6. The breeding decision-making system based on the prediction of the growth cycle of Bombyx Batryticatus according to claim 5, characterized in that, The specific analysis process of the breeding hazard assessment unit includes: When the breeding personnel feed Bombyx batryticatus, the feeding monitoring value is calculated by weighted summation of the feeding detection value and the feeding deviation value at that time. If the feeding monitoring value exceeds the preset feeding monitoring threshold, the non-standard feeding symbol WP-1 is assigned; the total number of times the non-standard feeding symbol WP-1 is assigned within the assessment period is obtained and marked as the non-standard feeding value, and the total duration in the breeding risk state within the assessment period is marked as the breeding risk time value. If the non-standard feeding value or the breeding risk time value exceeds the corresponding preset threshold, a breeding high-hazard signal is generated.
7. The breeding decision-making system based on the prediction of the growth cycle of Bombyx Batryticatus according to claim 6, characterized in that, If both the feeding non-standard value and the breeding insurance value do not exceed the corresponding preset thresholds, the breeding hazard assessment coefficient is calculated by weighted summation of the feeding non-standard value, the breeding insurance value, the risk continuous different-frequency value, and the feeding monitoring form value. If the breeding hazard assessment coefficient exceeds the preset breeding hazard assessment coefficient threshold, a high breeding hazard signal is generated; otherwise, a low breeding hazard signal is generated.
8. The breeding decision-making system based on the prediction of the growth cycle of Bombyx Batryticatus according to claim 6, characterized in that, The breeding hazard assessment unit is communicatively connected to the silkworm excrement cleaning and tracking assessment unit. The breeding hazard assessment unit sends the low breeding hazard signal to the silkworm excrement cleaning and tracking assessment unit. When the silkworm excrement cleaning and tracking assessment unit receives the low breeding hazard signal, it analyzes the cleaning and control performance of the silkworm excrement during the breeding process of the stiff silkworms within the assessment period, generates a qualified silkworm excrement cleaning and control signal or an abnormal silkworm excrement cleaning and control signal through the analysis, and sends the qualified silkworm excrement cleaning and control signal or the abnormal silkworm excrement cleaning and control signal to the intelligent supervision terminal.
9. The breeding decision-making system based on the prediction of the growth cycle of Bombyx Batryticatus according to claim 8, characterized in that, The specific analysis process of the silkworm excrement cleaning and tracking assessment unit is as follows: Obtain the number of negative durations within the assessment period and mark it as the negative time-frequency value. If the negative time-frequency value exceeds the preset negative time-frequency threshold, an abnormal silkworm excrement cleaning and control signal is generated; if the negative time-frequency value does not exceed the preset negative time-frequency threshold, the silkworm excrement cleaning and control coefficient is calculated by weighted summation of the negative time-frequency value, the negative performance value, and the negative amplitude value. If the silkworm excrement cleaning and control coefficient exceeds the preset silkworm excrement cleaning and control coefficient threshold, an abnormal silkworm excrement cleaning and control signal is generated; if the silkworm excrement cleaning and control coefficient does not exceed the preset silkworm excrement cleaning and control coefficient threshold, a qualified silkworm excrement cleaning and control signal is generated.
10. A breeding decision-making method based on the prediction of the growth cycle of Bombyx Batryticatus, characterized in that, It includes the following steps: Step 1, Monitoring the breeding of stiff silkworms; Step 2, Predicting the growth cycle of stiff silkworms; Step 3, Generating matching breeding decision information; Step 4, Judging whether it is currently in a breeding risk state; Step 5, Automatically adjusting the breeding environment when it is judged to be in a breeding risk state.