Intelligent management method for medicinal silkworm breeding

Abstract

The present invention discloses an intelligent management method for medicinal silkworm farming, which belongs to the field of intelligent management technology. The method comprises the following steps: placing silkworm pupae inoculated with Beauveria bassiana from the same batch at a specimen collection point, acquiring a monitoring data sequence, a monitoring image set, and Beauveria bassiana inoculation parameters corresponding to the specimen collection point in real time; using an image recognition model to identify the silkworm pupae, outputting a sequence of the degree of rigidity of the silkworm pupae, and calculating the rigidity period of the silkworm pupae; inputting all sequence sets into a monitoring model for analysis, generating a state management model, generating a state estimation table corresponding to the specimen collection point, and constructing a state condition list for the silkworm pupae; pre-processing all collected data of the silkworm pupae to be monitored and inputting them into the state management model, outputting a rigidity state, judging whether the rigidity state is qualified based on the state condition list, and repeating this step until the silkworm pupae to be monitored complete the rigidity process and form medicinal silkworm pupae. The present invention can improve the intelligence and accuracy of medicinal silkworm farming management.

Description

Technical Field

[0001] The invention belongs to the technical field of intelligent management, and in particular relates to an intelligent management method for breeding medicinal silkworms. Background Art

[0002] The cultivation and collection of the medicinal silkworm, a biological resource with significant medicinal value, has long been a crucial component of the Traditional Chinese Medicine (TCM) industry. Traditionally, silkworm cultivation has relied primarily on the natural environment and manual experience, lacking systematic scientific management. This has resulted in low efficiency, unstable product quality, and susceptibility to climate and environmental changes, making precise regulation difficult, thus impacting the growth quality and yield of the silkworm. To improve the efficiency and product quality of medicinal silkworm cultivation, modern information technologies, such as the Internet of Things, big data, and artificial intelligence, are needed to enable intelligent management of the cultivation process. This will improve resource utilization, reduce costs, enhance industry competitiveness, achieve precise control of the cultivation environment, optimize disease prevention and control strategies, and automate data collection and analysis. Ultimately, this will improve the overall efficiency and product quality of silkworm cultivation to meet market demand for high-quality medicinal silkworms.

[0003] Similar prior art includes Chinese patent application publication number CN118710176A, which discloses a smart dry cocoon storage environment management system. The system includes: regularly obtaining cocoon patterns fed back by a sampling inspection system, and identifying whether the cocoons have abnormalities based on the cocoon patterns. If an abnormality is present, the system identifies the type of abnormality corresponding to the cocoon pattern; based on the abnormality type, the system adjusts the corresponding local environmental parameters within the dry cocoon storage warehouse to mitigate the deterioration of the corresponding abnormality. The sampling inspection system is used to regularly obtain dry cocoon storage patterns from different areas of the dry cocoon storage warehouse. Employees regularly place dry cocoons from different areas of the dry cocoon storage warehouse at corresponding positions on the sampling inspection table, so that the sampling inspection system generates corresponding cocoon patterns captured by a camera. The cocoon patterns are then fed back to the dry cocoon storage environment management system, which automatically adjusts the local environmental parameters within the dry cocoon storage warehouse in a timely manner based on the abnormalities reflected by the cocoon patterns. There is also Chinese patent application publication number CN118333462A, which provides a digital platform and management method for a modern silkworm industrial park, relating to the field of aquaculture management technology. This invented platform is centered around information collection terminals, radio frequency identification terminals, and cloud-based digital management units, enabling real-time monitoring and management of various functional areas. Silkworm breeding, strain cultivation, spray inoculation, silkworm cultivation, finished product screening, and quality analysis all have dedicated management modules to monitor and update various status information to respond to possible abnormal situations. This comprehensive digital management system improves the transparency, efficiency, and quality of the production process, reduces production risks and losses, and enhances the competitiveness and sustainable development capabilities of the industrial park. Through the use of scientific and technological means, the platform has brought new possibilities to modern breeding management, helping the industry move towards a more intelligent, efficient, and controllable direction.

[0004] However, the above-mentioned existing technologies cannot automatically identify the abnormal types corresponding to the cocoon images. The digital platform requires more complex equipment and cannot timely monitor the abnormal types of silkworm breeding. In actual situations, due to the different degrees of influence of conditional variables, the rigidity state of silkworm pupae will also be different. It is necessary to use conditional probability to improve the accuracy of medicinal silkworm breeding management. Summary of the Invention

[0005] In response to the above-mentioned technical problems, the present invention provides an intelligent management method for breeding medicinal silkworms to solve the problems in the prior art.

[0006] The present invention provides an intelligent management method for cultivating medicinal silkworms, comprising:

[0007] Multiple specimen collection points are set up in the breeding area, and silkworm pupae inoculated with Beauveria bassiana from the same batch are placed at the specimen collection points. The sensor device combination acquires monitoring data sequences, monitoring image sets, and Beauveria bassiana inoculation parameters corresponding to the specimen collection points in real time based on preset collection intervals.

[0008] constructing an image recognition model, the image recognition model identifying silkworm pupae based on the monitoring image set, generating a classified and annotated image set, outputting a sequence of rigidity levels of silkworm pupae based on the classified and annotated image set, calculating a rigidity period of silkworm pupae based on the rigidity sequence, and combining the monitoring data sequence, the rigidity sequence, and the classified and annotated image set into a sequence set based on the rigidity period;

[0009] Constructing a monitoring model based on a Bayesian network, inputting all the sequence sets into the monitoring model for analysis based on the collection interval, the sensor device combination, and the inoculation parameters, generating a state management model, generating a state estimation table corresponding to the specimen collection point, and constructing a state condition list of silkworm pupae based on all the state estimation tables;

[0010] All collected data corresponding to the current collection time period of the silkworm pupae to be monitored are obtained, the collected data are pre-processed and then input into the state management model, the rigidification state of the silkworm pupae to be monitored is output, and whether the rigidification state is qualified is judged based on the state condition list. If so, all the collected data corresponding to the next collection time period are analyzed in real time. Otherwise, an unqualified condition type is output, and the control device corresponding to the sensor device combination is adjusted based on the unqualified condition type. This step is repeated until the silkworm pupae to be monitored complete the rigidification process and form medicinal silkworm pupae.

[0011] Furthermore, the image recognition model identifies silkworm pupae based on the monitoring image set, comprising the following steps:

[0012] Installing a camera device directly above the specimen collection point, the camera device captures and generates monitoring images of the silkworm pupa based on preset shooting parameters and the collection interval, sequentially combining all the monitoring images to generate the monitoring image set, extracting a plurality of the monitoring images from the monitoring image set as initial images, and combining them to generate the initial image set;

[0013] Converting the initial image into a grayscale image based on the shooting parameters, respectively obtaining RGB values ​​and pixel coordinates of any pixel in the grayscale image, constructing a three-dimensional coordinate system based on the RGB values, plotting any pixel in the grayscale image based on the RGB values ​​in the three-dimensional coordinate system to generate a spatial point, clustering all the spatial points based on spatial distribution positions using a uniform clustering algorithm to generate multiple clusters, calculating a first distance between any spatial point in the cluster and a cluster center point, and calculating an average and a standard deviation of all the first distances;

[0014] Mapping all the spatial points contained in the clusters to the grayscale image corresponding to the initial image based on the pixel coordinates, and performing category marking on the pixels corresponding to all the clusters to generate category labels;

[0015] The image recognition model is constructed based on a convolutional neural network. The image recognition model is trained on the initial image containing the category label and the remaining monitoring images, and the category labels contained in all the monitoring images are identified based on the recognition scores. The pixels in the monitoring images whose category labels are silkworm pupa labels are identified as silkworm pupae, the monitoring images with the category labels are set as classified annotated images, and all the classified annotated images are combined based on the acquisition interval and set as the classified annotated image set.

[0016] Furthermore, the step of outputting a sequence of rigidity levels of silkworm pupae based on the classified and labeled image set comprises the following steps:

[0017] Obtaining pixels identified as silkworm pupae in the classified and labeled image and setting them as monitoring pixels, sequentially connecting adjacent monitoring pixels based on the pixel coordinates to generate a monitoring area, and setting the total number of pixels included in the monitoring area to a first value;

[0018] Acquire a center pixel corresponding to the monitoring area contained in a first classified and annotated image in the classified and annotated image set based on the acquisition interval, obtain coordinates of the center pixel and set them as fixed coordinates, obtain a second distance between edge pixels of the monitoring area and the fixed coordinates based on the pixel coordinates, calculate an average value corresponding to all second distances in the monitoring area, and set the average value as a second value;

[0019] Taking the fixed coordinate as a constant point, sequentially calculating the monitoring areas corresponding to the constant point in the remaining classified and labeled images to generate the first and second values, sequentially accumulating all the first values ​​in the classified and labeled images to generate an area value, and setting the average of all the second values ​​in the classified and labeled images as the growth value;

[0020] The area values ​​and the growth values ​​corresponding to all the classified and labeled images are sequentially counted based on the acquisition interval to generate a first area sequence and a first growth sequence, and the first area sequence and the first growth sequence are combined to set as the rigidity degree sequence of silkworm pupae.

[0021] Furthermore, the step of identifying the category labels contained in all the monitoring images based on the recognition scores includes the following steps:

[0022] Calculate the recognition score F between any pixel in the grayscale image and the category label based on a first formula, wherein the first formula is: Wherein, D is the first distance of the spatial point corresponding to any pixel in the grayscale image, α is the average value of all the first distances in the cluster, and β is the standard deviation of all the first distances;

[0023] If the recognition score is less than or equal to the first threshold, it is determined that the classification of the spatial points contained in the cluster is accurate, and any pixel in the grayscale image belongs to the same category label as the cluster; otherwise, the parameters of the uniform clustering algorithm are adjusted and all the spatial points are re-clustered until the recognition score is less than or equal to the first threshold.

[0024] Furthermore, the calculating of the rigidification period of silkworm pupae based on the rigidification degree sequence includes:

[0025] The rigidity degree sequence is fitted to generate a first type of curve, an inflection point of the first type of curve is marked, and the first type of curve is divided into a plurality of rigidity extension regions and rigidity drying regions based on the inflection point. A time period between the rigidity extension region and the next adjacent rigidity drying region is obtained, and the time period is set as the rigidity period.

[0026] Furthermore, the state management model is generated based on the following steps:

[0027] The monitoring model extracts multiple conditional variables based on the data information contained in the sequence set, performs feature selection on the data information based on the data type to generate a feature quantity set, sets the conditional variables as child nodes, calculates the conditional probability between any of the child nodes and a preset parent node based on the feature quantity set, and performs modeling based on the conditional probability, the preset parent node and the child node to generate the state management model.

[0028] Furthermore, a list of status conditions of silkworm pupae is constructed based on the following steps:

[0029] Respectively obtaining a category label and a total number of silkworm pupae with the category label in the classified and labeled image and setting them as a third value and a fourth value, setting a plurality of growth state types based on a ratio between the fourth value and the third value, and setting the growth state type as the preset parent node;

[0030] Obtaining state information of the child node, summing up a first occurrence count of any of the preset parent nodes and a second occurrence count of the same state information corresponding to any of the preset parent nodes, and setting a ratio between the second occurrence count and the first occurrence count as a conditional probability between the child node and the preset parent node;

[0031] Based on the time characteristics, the preset parent node, the child node and the conditional probability are combined to generate the state estimation table, all the state estimation tables are summarized to generate a first data table, the conditional probabilities corresponding to the same preset parent node and the same child node are averaged and summarized to generate a second data table, and the preset parent node in the normal rigid state is extracted from the second data table and combined into a state condition list of silkworm pupae.

[0032] Furthermore, judging whether the rigid state is qualified based on the state condition list includes the following steps:

[0033] The collected data includes a data sequence corresponding to the sensor device combination, obtaining a rigid state corresponding to the collected data based on the image recognition model and the state management model, and checking list information corresponding to the rigid state in the second data table based on the collection time period;

[0034] If the list information belongs to the normal rigid state of the state condition list, the rigid state is determined to be qualified; otherwise, different sub-node items of the list information and the normal rigid state are filtered out based on the current collection time period, and the condition variables corresponding to the different sub-node items are set to the unqualified condition type.

[0035] Compared with the prior art, the beneficial effects of the present invention are at least as follows:

[0036] The present invention first collects data from each specimen collection point by setting up a combination of multiple sensor devices, which can improve the precision of the specimen collection points. Then, the monitoring image set is classified and identified through an image recognition model, and a sequence of the rigidity of silkworm pupae is generated, which can improve the accuracy of identifying silkworm pupae in the monitoring images. Finally, a monitoring model is constructed through a Bayesian network, which can accurately analyze the conditional probability of silkworm pupae in the rigidity breeding process and improve the accuracy of intelligent management.

[0037] The present invention also combines an image recognition model and a state management model to analyze the collected data of the monitored silkworm pupae in the current collection time period, outputs the rigidification state, and verifies the eligibility of the rigidification state in combination with a state condition list to adjust the control equipment, thereby improving the intelligence of medicinal silkworm breeding. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a flow chart of the steps of an intelligent management method for medicinal silkworm breeding according to the present invention;

[0039] Figure 2 Schematic diagram of the first type of curve in the present invention;

[0040] Figure 3 Schematic diagram of the state management model in the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0042] It is understood that the terms "first," "second," etc., used herein may be used to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script without departing from the scope of this application.

[0043] like Figure 1 As shown, an intelligent management method for medicinal silkworm breeding includes:

[0044] S1: Multiple specimen collection points are set up in the breeding area, and silkworm pupae inoculated with Beauveria bassiana from the same batch are placed at the specimen collection points. The sensor device combination obtains the monitoring data sequence, monitoring image set, and Beauveria bassiana inoculation parameters corresponding to the specimen collection points in real time based on the preset collection interval.

[0045] Specifically, in this embodiment, the breeding area refers to the indoor production space, and the specimen collection point refers to the location area in the breeding area for placing the silkworm pupa device, wherein the number of the specimen collection points can be multiple. The same batch refers to the batch number of silkworm pupae inoculated with Beauveria bassiana at the same time, wherein silkworm pupae is a general term, and therefore, the number of silkworm pupae is multiple. The sensor device combination refers to a collection of equipment types used to collect various environmental data, including but not limited to humidity sensors, temperature sensors, cameras and other equipment types. The acquisition interval refers to the time information composed of acquisition time points composed of the same time interval, the monitoring data sequence refers to the data sequence that the non-image sensor device combination monitors and collects the specimen collection points in sequence by the acquisition interval, the monitoring image set refers to the collection of monitoring images generated by the camera shooting the specimen collection points in sequence by the acquisition interval, and the inoculation parameters of Beauveria bassiana refer to the performance values ​​of the inoculated Beauveria bassiana, including but not limited to spore yield, enzyme activity, biotransformation efficiency, etc.

[0046] S2: Construct an image recognition model. The image recognition model identifies silkworm pupae based on the monitoring image set, generates a classified and labeled image set, and outputs a rigidity degree sequence of silkworm pupae based on the classified and labeled image set. The rigidity period of silkworm pupae is calculated based on the rigidity degree sequence, and the monitoring data sequence, the rigidity degree sequence and the classified and labeled image set are combined into a sequence set based on the rigidity period.

[0047] Specifically, in this embodiment, the image recognition model is a model constructed by a convolutional neural network for performing image classification and image recognition on a monitoring image set. Identifying silkworm pupae refers to identifying pixel areas with a category label of silkworm pupae in the monitoring image set, and the classified and labeled image set refers to a collection of classified and labeled images containing various category label information. The rigidity degree sequence refers to a data sequence of state changes of silkworm pupae generated by analyzing the classified and labeled image set. The rigidity period refers to the length of time required for silkworm pupae to form medicinal silkworm pupae after being inoculated with Beauveria bassiana. The sequence set refers to a data set generated by combining the monitoring data sequence corresponding to the same rigidity period, the rigidity degree sequence, and the classified and labeled images in the classified and labeled image set through the acquisition interval.

[0048] S3: Build a monitoring model based on the Bayesian network, input all sequence sets into the monitoring model for analysis based on the collection interval, sensor equipment combination and inoculation parameters, generate a state management model, and generate a state estimation table corresponding to the specimen collection point, and build a list of silkworm pupae state conditions based on all state estimation tables.

[0049] Specifically, in this embodiment, a Bayesian network is a probabilistic graphical model used to represent conditional dependencies between variables. It combines probability theory and graph theory to graphically display the probabilistic dependency structure between variables, facilitating probabilistic reasoning and decision analysis. A monitoring model constructed using a Bayesian network can analyze the relationship between the rigidification state of silkworm pupae after inoculation with Beauveria bassiana, environmental data corresponding to sensor device combinations, and Beauveria bassiana inoculation parameters. Sequence sets corresponding to the same rigidification cycle are sequentially input into the monitoring model based on the time information corresponding to the acquisition interval. The analyzed monitoring model can then be set as a state management model. A state estimation table is a list of relevant information used to describe the various stages of the actual rigidification process of silkworm pupae. A state condition list is a list of relevant information generated by summarizing all state estimation tables to describe the various stages of the ideal rigidification process of silkworm pupae. Therefore, the difference between a state estimation table and a state condition list is that the ideal rigidification process is generated from the actual rigidification process, which can improve the accuracy of medicinal silkworm breeding.

[0050] S4: Obtain all collected data corresponding to the current collection time period of the silkworm pupae to be monitored, pre-process the collected data and input them into the state management model, output the rigidification state of the silkworm pupae to be monitored, and judge whether the rigidification state is qualified based on the state condition list. If so, analyze all collected data corresponding to the next collection time period in real time. Otherwise, output the unqualified condition type, and adjust the control device corresponding to the sensor device combination based on the unqualified condition type. Repeat this step until the silkworm pupae to be monitored complete the rigidification process and form medicinal silkworm pupae.

[0051] Specifically, in this embodiment, the collected data originates from the same source and has the same collection interval as the aforementioned monitoring data sequence, monitoring image set, and inoculation parameters, but is collected at different times and for different objects. The current collection time period refers to the time period currently requiring intelligent management, i.e., the collected data includes the monitoring data sequence, monitoring image set, and Beauveria bassiana inoculation parameters corresponding to the monitored silkworm pupae during the current collection time period. Preprocessing involves processing the collected data through the same steps described above, for example, using an image recognition model to perform image classification and image recognition on the monitoring image set in the collected data.

[0052] The silkworm pupae to be monitored refer to a new batch of pupae inoculated with Beauveria bassiana, and there are multiple of them. The rigidity state refers to the distribution of morphological changes that occur after inoculation. Whether the pupae are qualified or not is used to determine whether intelligent control and management of the pupae to be monitored is necessary. If qualified, the pupae to be monitored are in a normal rigidity state during the current collection period, and only real-time analysis of all collected data corresponding to the next collection period is required. If unqualified, the rigidity state of the pupae to be monitored during the current collection period is affected by factors, i.e., unqualified condition types. Therefore, the control equipment or inoculation parameters corresponding to the condition type need to be adjusted using the state condition list. This step is repeated to monitor and analyze the pupae to be monitored until the pupae complete the rigidity process and become medicinal silkworm pupae. For example, if the rigidity state of group A of the pupae to be monitored is "rigidity delayed state," the state condition list indicates that the rigidity state is unqualified, and the unqualified condition type is "temperature less than 30°C." Therefore, the temperature control equipment is controlled to adjust the temperature to 30°C.

[0053] As a preferred technical solution of the present invention, the image recognition model identifies silkworm pupae based on the monitoring image set, including the following steps:

[0054] A camera device is installed directly above the specimen collection point. The camera device generates monitoring images of silkworm pupae based on preset shooting parameters and collection intervals. All monitoring images are combined in sequence to generate a monitoring image set. Multiple monitoring images are extracted from the monitoring image set as initial images, and are combined to generate the initial image set.

[0055] The initial image is converted into a grayscale image based on the shooting parameters, the RGB value and pixel coordinates of any pixel in the grayscale image are obtained respectively, a three-dimensional coordinate system is constructed based on the RGB value, and any pixel in the grayscale image is plotted in the three-dimensional coordinate system based on the RGB value to generate a spatial point. A uniform clustering algorithm is used to cluster all spatial points based on their spatial distribution positions to generate multiple clusters. The first distance between any spatial point in the cluster and the cluster center point is calculated, and the average and standard deviation of all first distances are calculated.

[0056] All spatial points contained in the clusters are mapped to the grayscale image corresponding to the initial image based on the pixel coordinates, and the pixels corresponding to all clusters are marked to generate category labels.

[0057] An image recognition model is constructed based on a convolutional neural network. The image recognition model is trained on the initial image containing the category label and the remaining monitoring images, and the category labels contained in all monitoring images are identified based on the recognition scores. The pixels in the monitoring images with the category label of silkworm pupa are identified as silkworm pupae, and the monitoring images with the category label are set as classified annotated images. All classified annotated images are combined based on the acquisition interval and set as a classified annotated image set.

[0058] Specifically, in this embodiment, the camera device refers to the camera in the sensor device assembly. The shooting parameters refer to the imaging performance parameters of the camera device and the vertical distance from the specimen collection point when the camera device is installed. The initial image refers to a randomly selected monitoring image from the monitoring image set, and the initial image set refers to the collection of multiple selected initial images.

[0059] The brightness and contrast of the initial image can be controlled by adjusting shooting parameters such as exposure, ISO, and aperture. Grayscale conversion is then performed to generate a grayscale image. RGB values ​​include R, G, and B values. In a grayscale image, the R, G, and B values ​​of any pixel are equal, meaning the RGB value is equal to the grayscale value. Pixel coordinates refer to the row and column coordinates corresponding to any pixel in a grayscale image. A three-dimensional coordinate system refers to the spatial coordinates where the x, y, and z axes represent the R, G, and B values, respectively. A spatial point refers to the location of each pixel in a grayscale image plotted in the three-dimensional coordinate system based on its RGB values. A uniform clustering algorithm generally refers to a clustering method that divides spatial points into multiple clusters, where points within each cluster are close to each other and relatively far from points in other clusters. Examples include the K-Means algorithm and hierarchical clustering. Spatial distribution refers to the coordinate distribution of spatial points in the three-dimensional coordinate system. Clusters are the results of a uniform clustering algorithm; therefore, there are multiple clusters. The first distance refers to the spatial distance between a spatial point and a cluster center point in a three-dimensional coordinate system, wherein the cluster center point refers to the spatial point located in the center of the cluster, the average value refers to the average value of the first distances corresponding to all spatial points contained in the cluster, and the standard deviation value refers to the degree of dispersion of the average value of the first distances corresponding to all spatial points contained in the cluster. The average value and standard deviation can be used subsequently to evaluate the clustering effect of the cluster, that is, the identification score.

[0060] The pixels corresponding to each spatial point have pixel coordinates. Therefore, mapping refers to inverting all spatial points contained in any cluster into the initial image through pixel coordinates, which can form the category distribution of the same cluster in the initial image, and mark the same cluster with category labels in the initial image, wherein the category label refers to the label information of the material category in the initial image, including but not limited to silkworm pupae, insect pests, plants, background and other labels. Compared with directly using the edge segmentation of the image for image classification and recognition, this step can reduce the phenomenon that the edges are difficult to segment during the rigid breeding of silkworm pupae with high density, thereby reducing the accuracy of classification and recognition. At the same time, this step can improve the accuracy of the category labels in the initial image by manually performing category labeling.

[0061] Convolutional Neural Networks (CNNs) are deep learning models that have demonstrated outstanding performance in areas such as image recognition, video analysis, and natural language processing. The core of CNNs lies in their ability to automatically and efficiently learn spatial hierarchical features from data, making them particularly effective when processing image data with a distinct spatial hierarchy. Initial images containing class labels and remaining unlabeled surveillance images are combined into a training set and fed into an image recognition model for training. Semi-supervised learning is a machine learning paradigm that lies between fully supervised and fully unsupervised learning. In semi-supervised learning, image recognition models are trained using a combination of labeled data (with labels) and unlabeled data (without labels), making it easy to acquire a large amount of unlabeled data, i.e., the remaining surveillance images. The recognition score is an evaluation of the clustering accuracy of the uniform clustering algorithm. Silkworm pupae can be identified in any surveillance image using the class labels. Classified labeled surveillance images are then classified as labeled images. The labeled images are then combined based on the acquisition interval to form the labeled image set.

[0062] As a preferred technical solution of the present invention, outputting a sequence of rigidity levels of silkworm pupae based on a classified and labeled image set includes the following steps:

[0063] Pixels identified as silkworm pupae in the classified and labeled image are obtained and set as monitoring pixels, adjacent monitoring pixels are sequentially connected based on pixel coordinates to generate a monitoring area, and the total number of pixels included in the monitoring area is set to a first value.

[0064] Based on the acquisition interval, the center point pixel corresponding to the monitoring area contained in the first classified and labeled image is obtained in the classified and labeled image set, the coordinates of the center point pixel are obtained and set as fixed coordinates, the second distance between the edge pixels of the monitoring area and the fixed coordinates is obtained based on the pixel coordinates, and the average value corresponding to all the second distances in the monitoring area is calculated and set as the second value.

[0065] Taking the fixed coordinates as the constant points, the monitoring areas corresponding to the constant points in the remaining classified and labeled images are calculated in turn to generate the first value and the second value. All the first values ​​in the classified and labeled images are accumulated in turn to generate the area value, and the average value of all the second values ​​in the classified and labeled images is set as the growth value.

[0066] The area values ​​and growth values ​​corresponding to all classified and labeled images are counted sequentially based on the acquisition interval to generate a first area sequence and a first growth sequence, which are then combined to form a rigidity degree sequence of the silkworm pupae.

[0067] Specifically, in this embodiment, to monitor the rigidification process of silkworm pupae, analysis can be performed by monitoring the changing regions of the pixels. By sequentially extending the monitoring pixels to adjacent monitoring pixels, the range of the monitoring pixels, i.e., the monitoring region, can be depicted. Since non-adjacent monitoring pixels may exist in the classified and annotated image, there may be multiple monitoring regions in the classified and annotated image. The first value refers to the total number of pixels contained in any monitoring region.

[0068] The first classified and annotated image refers to an arrangement sequence consisting of the acquisition times corresponding to the acquisition intervals in sequence. Therefore, the first classified and annotated image represents the monitoring image corresponding to the initial stage of the silkworm pupa after inoculation with Beauveria bassiana. The center point pixel refers to the pixel located at the center point of the monitoring area (irregular), which can be directly obtained through computer vision technology, or calculated and generated by the pixel coordinates of the monitoring area. The fixed coordinates refer to the pixel coordinates of any center point pixel in the classified and annotated image. Since there may be multiple monitoring areas in the same classified and annotated image, there may also be multiple fixed coordinates. The edge pixel refers to the pixel located at the edge of the monitoring area, and the second distance refers to the straight-line distance between the pixel coordinates of the edge pixel and the fixed coordinate. In order to reduce the statistical value of the second distance, the edge pixels can be screened by adjacent equal intervals. For example, among all the edge pixels of the monitoring area, pixels are extracted at equal intervals according to the total number of all edge pixels as edge pixels. The second value refers to the average value after accumulating all the second distances contained in any monitoring area, which can be used as the initial judgment value.

[0069] A constant point refers to a pixel that maintains fixed coordinates. By using the same method as above, the first and second values ​​of the monitoring area corresponding to the constant point are calculated in sequence in the remaining classified and annotated images with the constant point as the center. The change sequence of the first and second values ​​corresponding to the constant point can be obtained in sequence according to the time information corresponding to the acquisition interval. Since there may be multiple constant points in any classified and annotated image, by adding up all the first values, the area occupied by the pixels belonging to the silkworm pupae in the classified and annotated image, that is, the area value, can be evaluated. The average value of all the second values ​​can be used to evaluate the change rate of the pixels belonging to the silkworm pupae in the classified and annotated image, that is, the growth value.

[0070] Based on the time information corresponding to the acquisition interval, the area values ​​and growth values ​​corresponding to all classified and labeled images can be arranged and combined to generate a first area sequence and a first growth sequence. The fluctuation trends corresponding to the first area sequence and the first growth sequence should be highly correlated. The first area sequence and the first growth sequence can be used to determine whether the silkworm pupae meet the requirements during the rigidification process. Therefore, the first area sequence and the first growth sequence are combined to form a rigidification degree sequence.

[0071] As a preferred technical solution of the present invention, identifying the category labels contained in all monitoring images based on the recognition scores includes the following steps:

[0072] The recognition score F between any pixel in the grayscale image and the category label is calculated based on the first formula. The first formula is: Where D is the first distance of the spatial point corresponding to any pixel in the grayscale image, α is the average value of all first distances in the cluster, and β is the standard deviation of all first distances.

[0073] If the recognition score is less than or equal to the first threshold, it is determined that the spatial points contained in the cluster are accurately classified, and any pixel in the grayscale image belongs to the same category label as the cluster. Otherwise, adjust the parameters of the uniform clustering algorithm and re-cluster all spatial points until the recognition score is less than or equal to the first threshold.

[0074] Specifically, in this embodiment, the first formula is used to evaluate the accuracy of the clusters generated after clustering using the uniform clustering algorithm. The smaller the recognition score F, the more concentrated the spatial point corresponding to the pixel in the grayscale image is with the cluster cluster, and the better the clustering effect of the spatial point. Therefore, if the recognition score is less than or equal to the first threshold, it means that the spatial point corresponding to the pixel belongs to the cluster cluster, that is, the category label marked by the cluster cluster is used as the category label of the pixel in the grayscale image corresponding to the spatial point. Otherwise, it is necessary to adjust the parameters of the uniform clustering algorithm, such as the number of clusters, initial centroid selection, convergence parameters, distance measurement and other parameters. Until the recognition score between all pixels in the grayscale image and the corresponding category labels is less than or equal to the first threshold.

[0075] As a preferred technical solution of the present invention, calculating the rigidity period of silkworm pupae based on the rigidity degree sequence includes:

[0076] The rigidity degree sequence is fitted to generate a first-class curve, the inflection point of the first-class curve is marked, and the first-class curve is divided into multiple rigidity expansion regions and rigidity drying regions based on the inflection point. The time period between the rigidity expansion region and the next adjacent rigidity drying region is obtained, and the time period is set as the rigidity period.

[0077] Specifically, in this embodiment, the first type of curve includes a fitting curve corresponding to the first area sequence in the rigidity degree sequence and a fitting curve corresponding to the first growth sequence, and a curve generated by changing the curvature of the two fitting curves. The inflection point refers to the point where the curvature on the first type of curve changes, that is, the point where the concavity of the first type of curve changes. The rigid expansion area refers to the time period when the first type of curve rises slowly, and the rigid drying area refers to the time period when the first type of curve falls back. The first type of curve can be used to illustrate the changing pattern of the rigidity process of silkworm pupae. The time period consisting of the starting time point of the rigid expansion area to the end time point of the next adjacent rigid drying area is the rigidity period. As shown in FIG. Figure 2 As shown, t1 to t2 are the starting and ending time points of the rigid expansion area, t2 to t3 are the next adjacent rigid drying area of ​​the rigid expansion area, and the first type of curve after t4 is the curve corresponding to the next batch of silkworm pupae.

[0078] As a preferred technical solution of the present invention, a state management model is generated based on the following steps:

[0079] The monitoring model extracts multiple conditional variables based on the data information contained in the sequence set, selects features of the data information based on the data type to generate a feature set, sets the conditional variables as child nodes, calculates the conditional probability between any child node and the preset parent node based on the feature set, and builds a model based on the conditional probability, preset parent node and child node to generate a state management model.

[0080] Specifically, in the present invention, conditional variables refer to the types of sensor device combinations corresponding to highly correlated data information in a sequence set, such as temperature, humidity, and inoculation parameters. A feature set refers to a collection of feature quantities corresponding to data information of different data types. A feature quantity is a numerical value extracted from the data information that can represent or describe certain characteristics of the data. Feature selection is the process of extracting feature quantities from data information, for example, feature selection methods such as principal component analysis and wavelet transform.

[0081] The preset parent node refers to a characteristic quantity that is associated with the conditional variable. In the present invention, the growth state type of the silkworm pupa is set as the preset parent node. The monitoring model generates the conditional probability between each child node and each preset parent node in a graphical manner by the Bayesian network, and then the Bayesian network uses statistical rules on the conditional probability, preset parent node, and child node to update the probability distribution of other variables in the network to establish a new model, namely the state management model. Figure 3 As shown, in the constructed state management model, the preset parent nodes and child nodes contain various data information, among which K1, K2, K3, K4, K5, ..., Kn are all conditional probabilities between the corresponding preset parent nodes and child nodes.

[0082] As a preferred technical solution of the present invention, a list of silkworm chrysalis status conditions is constructed based on the following steps:

[0083] The category labels and the total number of silkworm pupae with category labels in the classified annotation image are respectively obtained and set as the third value and the fourth value, multiple growth state types are set based on the ratio between the fourth value and the third value, and the growth state type is set as a preset parent node.

[0084] Obtain the status information of the child node, summarize the first occurrence number of any preset parent node and the second occurrence number of the same status information corresponding to any preset parent node, and set the ratio between the second occurrence number and the first occurrence number as the conditional probability between the child node and the preset parent node.

[0085] Based on the time characteristics, the preset parent nodes, child nodes and conditional probabilities are combined to generate a state estimation table, and all state estimation tables are summarized to generate a first data table. The conditional probabilities corresponding to the same preset parent nodes and the same child nodes are averaged and summarized to generate a second data table. In the second data table, the preset parent nodes in the normal rigid state are extracted and combined into a state condition list of silkworm pupae.

[0086] Specifically, in this embodiment, the third value refers to the total number of pixels in the classified and annotated image that have the category label, and the fourth value refers to the total number of pixels in the classified and annotated image that have the category label belonging to silkworm pupae. Therefore, calculating the ratio of the fourth value to the third value corresponding to all classified and annotated images can be used to subsequently identify and assess the rigidity state of silkworm pupae. The growth state type refers to the morphological type information that may exist during the rigidity process of silkworm pupae, including but not limited to plaque contamination, delayed rigidity, and normal rigidity. Setting the information corresponding to the growth state type as a preset parent node can facilitate the construction of a complete Bayesian network.

[0087] State information refers to the data associated with the characteristic quantities of a child node at different acquisition times. This state information can be multiple, for example, the state information corresponding to "temperature 30, humidity 55, inoculation parameter M1, and rigidity data G1." Because the same pre-set parent node may correspond to multiple types of child node state information, the first occurrence count refers to the number of child node state information associated with the same pre-set parent node, while the second occurrence count refers to the number of child node state information associated with the same pre-set parent node. The ratio of the second occurrence count to the first occurrence count is set as the conditional probability of the conditional variable associated with the child node.

[0088] A state estimation table refers to tabular data generated based on a combination of preset parent nodes, child nodes, and conditional probabilities. Since there are multiple sample collection points, there are also multiple state estimation tables. A time feature refers to time duration information. Different time features may have the same preset parent node, but their corresponding child nodes may be different. For example, the preset parent node "Normal Rigid State" is in the expanded state at time duration information T1 and in the dried state at time duration information T2. ​​Both the expanded and dried states belong to the "Normal Rigid State" of the preset parent node.

[0089] The state estimation tables corresponding to all sample collection points are aggregated to generate the first data table. Sum-average refers to a calculation method that first accumulates and then averages. If the information corresponding to the preset parent node and child node is the same, it indicates that the same silkworm pupae rigidification state exists at different sample collection points. Therefore, the corresponding conditional probabilities can be summed and averaged, and the first data table can be aggregated to generate the second data table. The normal rigidification state is one of the preset parent nodes corresponding to the growth state type. Therefore, the table information corresponding to the normal rigidification state in the second data table is extracted to generate the state condition list.

[0090] As a preferred technical solution of the present invention, judging whether the rigid state is qualified based on the state condition list includes the following steps:

[0091] The collected data includes a data sequence corresponding to the sensor device combination, the rigid state corresponding to the collected data is obtained based on the image recognition model and the state management model, and the list information corresponding to the rigid state is checked in the second data table based on the collection time period.

[0092] If the list information belongs to the normal rigid state of the state condition list, the rigid state is determined to be qualified; otherwise, different sub-node items of the list information and the normal rigid state are filtered out based on the current collection time period, and the condition variables corresponding to the different sub-node items are set to unqualified condition types.

[0093] Specifically, in this embodiment, the collected data is pre-processed by the image recognition model and then input into the state management model for probability analysis, so that the rigid state corresponding to the current time period can be output, and then the second data table selects the data information corresponding to the rigid state of the current time period, that is, the list information.

[0094] By comparing the list information with the state condition list, you can determine whether the rigid state is normal. Different subnode items refer to filtering different data information in the subnode based on the duration of the current collection period. For example, the subnode corresponding to the normal rigid state is "Temperature 35, Humidity 70," while the subnode corresponding to the rigid state is "Temperature 35, Humidity 50." Therefore, the data corresponding to the humidity in the subnode is a different subnode item, and the condition variable corresponding to the humidity data information is set as the condition type.

[0095] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0096] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The above-mentioned program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0097] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0098] The above embodiments merely illustrate several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, and all such variations and improvements fall within the scope of protection of the present invention.

[0099] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An intelligent management method for medicinal pupae cultivation, characterized in that: The method comprises the following steps: Multiple specimen collection points are set up in the breeding area, and silkworm pupae inoculated with Beauveria bassiana from the same batch are placed at the specimen collection points. The sensor device combination acquires monitoring data sequences, monitoring image sets, and Beauveria bassiana inoculation parameters corresponding to the specimen collection points in real time based on preset collection intervals. constructing an image recognition model, the image recognition model identifying silkworm pupae based on the monitoring image set, generating a classified and annotated image set, outputting a sequence of rigidity levels of silkworm pupae based on the classified and annotated image set, calculating a rigidity period of silkworm pupae based on the rigidity sequence, and combining the monitoring data sequence, the rigidity sequence, and the classified and annotated image set into a sequence set based on the rigidity period; Constructing a monitoring model based on a Bayesian network, inputting all the sequence sets into the monitoring model for analysis based on the collection interval, the sensor device combination, and the inoculation parameters, generating a state management model, generating a state estimation table corresponding to the specimen collection point, and constructing a state condition list of silkworm pupae based on all the state estimation tables; The state management model is generated based on the following steps: The monitoring model extracts a plurality of conditional variables based on the data information included in the sequence set, performs feature selection on the data information based on the data type to generate a feature quantity set, sets the conditional variables as child nodes, calculates the conditional probability between any of the child nodes and a preset parent node based on the feature quantity set, and performs modeling based on the conditional probability, the preset parent node, and the child nodes to generate the state management model; All collected data corresponding to the current collection time period of the silkworm pupae to be monitored are obtained, the collected data are pre-processed and then input into the state management model, the rigidification state of the silkworm pupae to be monitored is output, and whether the rigidification state is qualified is judged based on the state condition list. If so, all the collected data corresponding to the next collection time period are analyzed in real time. Otherwise, an unqualified condition type is output, and the control device corresponding to the sensor device combination is adjusted based on the unqualified condition type. This step is repeated until the silkworm pupae to be monitored complete the rigidification process and form medicinal rigid pupae.

2. The method according to claim 1, characterized in that The image recognition model identifies silkworm pupae based on the monitoring image set, comprising the following steps: Installing a camera device directly above the specimen collection point, the camera device captures and generates monitoring images of the silkworm pupa based on preset shooting parameters and the collection interval, sequentially combining all the monitoring images to generate the monitoring image set, extracting a plurality of the monitoring images from the monitoring image set as initial images, and combining them to generate the initial image set; Converting the initial image into a grayscale image based on the shooting parameters, respectively obtaining RGB values ​​and pixel coordinates of any pixel in the grayscale image, constructing a three-dimensional coordinate system based on the RGB values, plotting any pixel in the grayscale image based on the RGB values ​​in the three-dimensional coordinate system to generate a spatial point, clustering all the spatial points based on spatial distribution positions using a uniform clustering algorithm to generate multiple clusters, calculating a first distance between any spatial point in the cluster and a cluster center point, and calculating an average and a standard deviation of all the first distances; Mapping all the spatial points contained in the clusters to the grayscale image corresponding to the initial image based on the pixel coordinates, and performing category marking on the pixels corresponding to all the clusters to generate category labels; The image recognition model is constructed based on a convolutional neural network. The image recognition model is trained on the initial image containing the category label and the remaining monitoring images, and the category labels contained in all the monitoring images are identified based on the recognition scores. The pixels in the monitoring images whose category labels are silkworm pupa labels are identified as silkworm pupae, the monitoring images with the category labels are set as classified annotated images, and all the classified annotated images are combined based on the acquisition interval and set as the classified annotated image set.

3. The method according to claim 2, characterized in that Outputting a sequence of rigidity levels of silkworm pupae based on the classified and labeled image set comprises the following steps: Obtaining pixels identified as silkworm pupae in the classified and labeled image and setting them as monitoring pixels, sequentially connecting adjacent monitoring pixels based on the pixel coordinates to generate a monitoring area, and setting the total number of pixels included in the monitoring area to a first value; Acquire a center pixel corresponding to the monitoring area contained in a first classified and annotated image in the classified and annotated image set based on the acquisition interval, obtain coordinates of the center pixel and set them as fixed coordinates, obtain a second distance between edge pixels of the monitoring area and the fixed coordinates based on the pixel coordinates, calculate an average value corresponding to all second distances in the monitoring area, and set the average value as a second value; Taking the fixed coordinate as a constant point, sequentially calculating the monitoring areas corresponding to the constant point in the remaining classified and labeled images to generate the first and second values, sequentially accumulating all the first values ​​in the classified and labeled images to generate an area value, and setting the average of all the second values ​​in the classified and labeled images as the growth value; The area values ​​and the growth values ​​corresponding to all the classified and labeled images are sequentially counted based on the acquisition interval to generate a first area sequence and a first growth sequence, and the first area sequence and the first growth sequence are combined to set as the rigidity degree sequence of silkworm pupae.

4. The method according to claim 2, characterized in that The step of identifying the category labels contained in all the monitoring images based on the recognition scores comprises the following steps: The recognition score F between any pixel in the grayscale image and the category label is calculated based on a first formula, where the first formula is: , where D is the first distance of the spatial point corresponding to any pixel in the grayscale image, is the average value of all the first distances in the cluster, is the standard deviation of all the first distances; If the recognition score is less than or equal to the first threshold, it is determined that the classification of the spatial points contained in the cluster is accurate, and any pixel in the grayscale image belongs to the same category label as the cluster; otherwise, the parameters of the uniform clustering algorithm are adjusted and all the spatial points are re-clustered until the recognition score is less than or equal to the first threshold.

5. The method according to claim 1, wherein Calculating the rigidity period of silkworm pupae based on the rigidity degree sequence comprises: The rigidity degree sequence is fitted to generate a first type of curve, an inflection point of the first type of curve is marked, and the first type of curve is divided into a plurality of rigidity extension regions and rigidity drying regions based on the inflection point. A time period between the rigidity extension region and the next adjacent rigidity drying region is obtained, and the time period is set as the rigidity period.

6. The method according to claim 1, characterized in that Construct a list of silkworm chrysalis status conditions based on the following steps: Respectively obtaining a category label and a total number of silkworm pupae with the category label in the classified and labeled image and setting them as a third value and a fourth value, setting a plurality of growth state types based on a ratio between the fourth value and the third value, and setting the growth state type as the preset parent node; Obtaining state information of the child node, summing up a first occurrence count of any of the preset parent nodes and a second occurrence count of the same state information corresponding to any of the preset parent nodes, and setting a ratio between the second occurrence count and the first occurrence count as a conditional probability between the child node and the preset parent node; Based on the time characteristics, the preset parent node, the child node and the conditional probability are combined to generate the state estimation table, all the state estimation tables are summarized to generate a first data table, the conditional probabilities corresponding to the same preset parent node and the same child node are averaged and summarized to generate a second data table, and the preset parent node in the normal rigid state is extracted from the second data table and combined into a state condition list of silkworm pupae.

7. The method according to claim 6, characterized in that Determining whether the rigid state is qualified based on the state condition list includes the following steps: The collected data includes a data sequence corresponding to the sensor device combination, obtaining a rigid state corresponding to the collected data based on the image recognition model and the state management model, and checking list information corresponding to the rigid state in the second data table based on the collection time period; If the list information belongs to the normal rigid state of the state condition list, the rigid state is determined to be qualified; otherwise, different sub-node items of the list information and the normal rigid state are filtered out based on the current collection time period, and the condition variables corresponding to the different sub-node items are set to the unqualified condition type.

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