CN117057822A - Silkworm cocoon purchasing supervision method - Google Patents
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Mining
Abstract
The application relates to the technical field of silkworm cocoon purchasing flow control, in particular to a silkworm cocoon purchasing supervision method, which comprises the following steps: acquiring cocoon acquisition data fed back by a plurality of acquisition station servers in a first time period; based on the number of silkworm eggs picked up by each farmer in the early period of cultivation, detecting whether the corresponding silkworm cocoon yield value is abnormal or not, and under the condition that the silkworm eggs are abnormal, calling the acquisition video data corresponding to the cocoon frame numbers corresponding to the abnormal farmers to carry out multiple-disk examination.
Description
Technical Field
The application relates to the technical field of silkworm cocoon purchasing flow control, in particular to a silkworm cocoon purchasing supervision method.
Background
The existing silkworm cocoon purchasing process is that after a silkworm merchant arrives at a cultivation area, a raiser is notified, the raiser uses gunny bags and the like to bear own silkworm cocoons, the purchasing unit price is estimated by on-site weighing, then the silkworm cocoons are put into storage and settled, however, the whole process is totally manually led, and corresponding process retrospective supervision such as supervision of weighing links or supervision data of silkworm cocoon quality estimation links is lacked, so that after silkworm cocoons are put into storage in a concentrated mode, even if abnormal silkworm cocoons are found in the later review process, repeated plate review cannot be carried out.
Disclosure of Invention
The application aims to provide a silkworm cocoon acquisition supervision method for improving the problems. In order to achieve the above purpose, the technical scheme adopted by the application is as follows:
in a first aspect, the application provides a method for supervising silkworm cocoon acquisition, comprising the following steps: acquiring silkworm cocoon acquisition data fed back by a plurality of acquisition station servers in a first time period, wherein the silkworm cocoon acquisition data comprise cocoon frame numbers and first weighing values corresponding to each cocoon frame, and the cocoon frames are distributed by silkworm vendors according to the number of silkworm eggs acquired by each farmer in the early period of cultivation; classifying the cocoon frame numbers according to the farmer numbers, further obtaining a plurality of cocoon frame numbers corresponding to each farmer number, and calculating a silkworm cocoon yield value corresponding to each farmer based on a first weighing value and an evaluation unit price corresponding to each cocoon frame number; detecting whether the corresponding cocoon yield value of each farmer is abnormal or not based on the number of silkworm eggs picked up by each farmer in the early stage of cultivation, and calling the cocoon frame number corresponding to the abnormal farmer under the condition that the cocoon yield value is abnormal, and marking the cocoon frame number as a first abnormal cocoon frame label; and feeding back the first abnormal cocoon frame mark number to a corresponding acquisition station server so that the acquisition station server feeds back corresponding abnormal acquisition video data.
Optionally, the detecting whether the corresponding cocoon yield value is abnormal based on the number of silkworm eggs picked up by each farmer in the early stage of cultivation, and calling the cocoon frame number corresponding to the abnormal farmer as the first abnormal cocoon frame mark number under the condition that the abnormality exists, includes:
after the silk cocoon purchase is finished, calculating an average silk cocoon yield value corresponding to each silk seed in the first culture area based on the total silk cocoon yield value in the first culture area and the total silk seed number obtained by farmers in the first culture area in the early stage of culture, and marking the average silk cocoon yield value as a first standard silk cocoon yield value, wherein the influence factors of the silk cocoon yield value comprise silk cocoon purchase unit price and yield, the silk cocoon purchase unit price and weight are related to the moisture content of the silk cocoons, and the moisture content is greatly influenced by local climatic conditions;
calculating a silkworm cocoon yield error coefficient by a weighting method based on first historical weather data during silkworm cocoon purchase and second historical weather data within 3-5 days before silkworm cocoon purchase, and calculating a first silkworm cocoon yield range corresponding to each silkworm species based on the silkworm cocoon yield error coefficient and a first standard silkworm cocoon yield;
based on the number of silkworm eggs picked up by each farmer in the early period of cultivation and the first cocoon yield value range corresponding to each silkworm egg, calculating a second cocoon yield value range corresponding to each farmer, detecting whether the cocoon yield value corresponding to each farmer is in the second cocoon yield value range, if not, marking the corresponding farmer as an abnormal farmer, marking the warehouse-in cocoon frame corresponding to the abnormal farmer as a first abnormal cocoon frame, and further obtaining a first abnormal cocoon frame mark corresponding to the first abnormal cocoon frame.
Optionally, the feeding back the first abnormal cocoon frame number to the corresponding acquisition station server, so that the acquisition station server feeds back the corresponding abnormal acquisition video data, including:
acquiring a first purchase unit price and a first weight corresponding to a first abnormal cocoon frame mark, and calculating to obtain a standard weight reference range corresponding to each silkworm seed based on the number of silkworm seeds picked up in the early period of abnormal farmer cultivation and the average pupation rate of silkworms in the current cultivation period;
constructing a cocoon water content evaluation model, and evaluating cocoon water content parameters corresponding to each purchase day during the silk cocoon purchase period based on third historical weather data of the silk cocoon purchase day and 7-10 days before the silk cocoon purchase day;
correcting the standard weight reference range based on the cocoon water content parameter, detecting whether the first weight is in the corrected standard weight reference range, and if not, calling weighing data of a weighing video metering scale corresponding to the first abnormal cocoon frame number to a corresponding acquisition station server;
and judging whether the first purchase price is abnormal or not based on the average purchase price of the silkworm cocoon culturing area corresponding to the purchase station, and if so, calling an instrument evaluation video corresponding to the first abnormal cocoon frame number to the purchase station server.
Optionally, after the invoking of the appearance video corresponding to the first abnormal cocoon frame mark from the acquisition station server, the method further includes:
intercepting a plurality of sample images in an evaluation video of the instrument, identifying the effective cocoon number in each sample image based on an image feature extraction algorithm, and identifying the corresponding sample image as a pattern to be detected under the condition that the effective cocoon number is greater than a minimum observed number threshold value;
converting the pattern to be detected into a first gray image, wherein the cocoons are white, so that the exposure intensity needs to be reduced, and further, the extraction and evaluation of the surface textures of the cocoons in the later period are facilitated;
determining a brightness adjustment factor based on the exposure of the pattern to be detected, adjusting the brightness in the first gray level image based on the brightness adjustment factor through a linear transformation algorithm, and recording the brightness as a second gray level image;
extracting texture features of the surfaces of each cocoon in the second gray level image based on a texture analysis algorithm, carrying out texture feature vectorization on the texture features of the surfaces of each cocoon based on a SIFT algorithm, further obtaining a texture vector feature value of the surfaces of each cocoon in the second gray level image, marking cocoons with feature values higher than a first feature threshold value as abnormal cocoons, and further obtaining abnormal cocoon rate in each second gray level image;
removing corresponding abnormal cocoons from the first gray level image and the pattern to be detected, further obtaining a plurality of first effective cocoon evaluation sub-patterns from the first gray level image, and obtaining a plurality of second effective cocoon evaluation sub-patterns from the pattern to be detected;
sequentially calculating the sizes of cocoons corresponding to each first effective cocoon evaluation sub-pattern, and further obtaining first average size values of a plurality of cocoons in the first gray level image;
sequentially analyzing the color and the uniform color distribution condition of the cocoons in each second effective cocoon evaluation sub-pattern, and evaluating the quality index of the cocoons in the second effective cocoon evaluation sub-pattern based on the color and the uniform color distribution condition of the cocoons, so as to obtain average quality indexes of a plurality of cocoons in the pattern to be inspected;
based on the abnormal cocoon rate, the first average size value of cocoons, the average quality index of cocoons and the cocoon water content parameter of the day of purchase, calculating the corresponding cocoon purchase evaluation value of the video through a weighting algorithm, and calling the staff information of the day of purchase responsible instrument evaluation post and sending the staff information to a responsibility department terminal under the condition that the cocoon purchase evaluation value is obviously lower than the first purchase unit price.
The beneficial effects of the application are as follows:
according to the method, the cocoon frames with the corresponding numbers and the identification codes are sent to farmers according to the number of silkworm eggs picked up by each farmer in the early period of cultivation, when the farmers purchase, corresponding purchase service stations are set up in important silkworm cocoon producing areas, the purchase service stations are partitioned based on purchase links, corresponding monitoring devices are arranged in each area, and the cocoon frames with the identification codes are matched, so that effective supervision of the whole silkworm cocoon purchase flow is realized, when abnormal silkworm cocoons are found in the later period, effective multi-disc examination can be performed through cocoon frame coding and effective tracing of relevant data such as weighing monitoring pictures, weighing data of the corresponding cocoon frames, silkworm cocoon quality inspection pictures and the like, for example, reasonable production values corresponding to the farmers are deduced according to the number of silkworm eggs picked up by the farmers in the early period of cultivation, and when the production values of purchase record reactions are obviously inconsistent with the reasonable production values, the multi-disc examination can be performed through modulating supervision data of the corresponding purchase links.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the embodiments of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for supervising silkworm cocoon acquisition according to an embodiment of the application;
fig. 2 is a schematic diagram of a cocoon acquisition supervision apparatus according to an embodiment of the present application.
The marks in the figure: 800. silkworm cocoon purchasing supervision equipment; 801. a processor; 802. a memory; 803. a multimedia component; 804. an I/O interface; 805. a communication component.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Example 1:
the embodiment provides a supervision method for silkworm cocoon purchasing, which needs to briefly explain a supervision system matched with the method before illustration, wherein the supervision system comprises a plurality of purchasing stations distributed in different cultivation areas, each purchasing station is divided into a weighing area, an instrument evaluation area, a financial settlement area and the like according to silkworm cocoon purchasing flows, each area is provided with a corresponding supervision system, corresponding purchasing data, such as weighing of each cocoon frame, cocoon quality sampling inspection pictures corresponding to each cocoon frame and the like, are recorded through cocoon frame codes, and the corresponding relation exists between the cocoon frames and farmers, so that the production value of each farmer in the current cultivation period can be calculated.
Referring to fig. 1, the method is shown to include step S100, step S200, step S300, and step S400.
Step S100, acquiring cocoon acquisition data fed back by a plurality of acquisition station servers in a first time period, wherein the cocoon acquisition data comprise cocoon frame numbers and first weighing values corresponding to each cocoon frame, and the cocoon frames are distributed by silkworm vendors according to the number of silkworm eggs acquired by each farmer in the early period of cultivation;
step 200, classifying the cocoon frame numbers according to the farmer numbers, further obtaining a plurality of cocoon frame numbers corresponding to each farmer number, and calculating a silkworm cocoon yield value corresponding to each farmer based on a first weighing value and an evaluation unit price corresponding to each cocoon frame number;
step S300, detecting whether the corresponding cocoon yield value of each farmer is abnormal based on the number of silkworm eggs picked up by each farmer in the early stage of cultivation, and calling the cocoon frame number corresponding to the abnormal farmer to be marked as a first abnormal cocoon frame label when the cocoon yield value is abnormal;
and step 400, feeding back the first abnormal cocoon frame mark to a corresponding acquisition station server so that the acquisition station server feeds back corresponding abnormal acquisition video data.
In this embodiment, by sending a corresponding number of cocoon frames with identification codes to farmers according to the number of silkworm eggs received by each farmer in the early period of cultivation, setting up corresponding purchasing service stations in important silkworm cocoon producing areas during purchasing, partitioning the purchasing service stations based on purchasing links, setting up corresponding monitoring devices in each area, and simultaneously matching cocoon frames with identification codes, effective supervision of the whole silkworm cocoon purchasing process is achieved, when abnormal silkworm cocoons are found in the later period, effective compound plate examination can be performed by cocoon frame coding and effective tracing of relevant data during purchasing, such as weighing monitoring pictures, weighing data of corresponding cocoon frames, silkworm cocoon quality inspection pictures and the like, for example, reasonable production values corresponding to farmers are deduced according to the number of silkworm eggs received by farmers in the early period of cultivation, and when the production values of purchasing record reactions are obviously different from the reasonable production values, compound plate examination can be performed by taking corresponding supervision data of purchasing links.
Next, in step S300, based on the number of silkworm eggs picked up by each farmer in the early stage of cultivation, detecting whether there is an abnormality in the corresponding silkworm cocoon yield value, and calling the cocoon frame number corresponding to the abnormal farmer if there is an abnormality, where the specific implementation manner may be as follows:
step S310, after the silk cocoon purchase is finished, calculating an average silk cocoon yield value corresponding to each silk seed in the first culture area based on the total silk cocoon yield value in the first culture area and the total silk seed number acquired by farmers in the first culture area in the early stage of culture, and marking the average silk cocoon yield value as a first standard silk cocoon yield value, wherein the influence factors of the silk cocoon yield value comprise silk cocoon purchase unit price and yield, the silk cocoon purchase unit price and weight are related to the water content of silk cocoons, and the water content is greatly influenced by local climatic conditions, so that the climatic factors need to be considered in the analysis of the silk cocoon yield value;
step S320, calculating a silkworm cocoon yield value error coefficient by a weighting method based on first historical weather data during silkworm cocoon purchase and second historical weather data within 3-5 days before silkworm cocoon purchase, and calculating a first silkworm cocoon yield value range corresponding to each silkworm species based on the silkworm cocoon yield value error coefficient and a first standard silkworm cocoon yield value;
step S330, calculating a second cocoon yield range corresponding to each farmer based on the number of silkworm seeds picked up by each farmer in the early period of cultivation and the first cocoon yield range corresponding to each silkworm seed, detecting whether the cocoon yield corresponding to each farmer is in the second cocoon yield range, if not, marking the corresponding farmer as an abnormal farmer, marking a warehouse-in cocoon frame corresponding to the abnormal farmer as a first abnormal cocoon frame, and further obtaining a first abnormal cocoon frame mark corresponding to the first abnormal cocoon frame.
Secondly, in step S400, the first abnormal cocoon frame number is fed back to the corresponding acquisition station server, so that the acquisition station server feeds back the corresponding abnormal acquisition video data, in the process of feeding back the corresponding abnormal acquisition video data by the acquisition station server, the factors causing the abnormal silkworm cocoons need to be diagnosed in advance, the acquisition unit price and the acquisition weight are the factors causing the abnormal silkworm cocoons, the acquisition unit price corresponds to the evaluation link, the acquisition weight corresponds to the weighing link, and therefore, the corresponding abnormal acquisition video data, which is the supervision video of the corresponding link, need to be directly and oppositely adjusted according to the factors causing the abnormal silkworm cocoons, and secondly, when the acquisition unit price and the acquisition weight are evaluated, the evaluation error caused by the water content of the silkworm cocoons due to weather needs to be considered, and the specific implementation mode is as follows:
step S410, acquiring a first purchase price and a first weight corresponding to a first abnormal cocoon frame mark, and calculating to obtain a standard weight reference range corresponding to each silkworm egg based on the number of silkworm eggs acquired in the early period of abnormal farmer cultivation and the average pupation rate of silkworms in the current cultivation period;
step S420, constructing a cocoon water content evaluation model, and evaluating cocoon water content parameters corresponding to each purchase day during the silk cocoon purchase period based on third historical weather data of the silk cocoon purchase day and 7-10 days before the silk cocoon purchase day;
step S430, correcting the standard weight reference range based on the cocoon water content parameter, detecting whether the first weight is in the corrected standard weight reference range, and if not, calling weighing data of a weighing video metering scale corresponding to the first abnormal cocoon frame number to a corresponding acquisition station server;
and S440, judging whether the first purchasing price is abnormal according to the average purchasing price of the silkworm cocoon culturing area corresponding to the purchasing station, and if so, calling an instrument evaluation video corresponding to the first abnormal cocoon frame mark to the purchasing station server.
Secondly, after the step S440 of calling the appearance evaluation video corresponding to the first abnormal cocoon frame mark to the purchasing station server, the method further comprises automatically evaluating reasonable purchasing price corresponding to the abnormal cocoon frame through an artificial intelligence algorithm based on the appearance evaluation video, and specifically comprises the following steps:
step S441, a plurality of sample images in an evaluation video of the instrument are intercepted, the effective cocoon number in each sample image is identified based on an image feature extraction algorithm, and the corresponding sample image is identified as a pattern to be detected under the condition that the effective cocoon number is larger than a minimum observation number threshold value;
step S442, converting the pattern to be detected into a first gray image, wherein the cocoons are white, so that the exposure intensity needs to be reduced, and further, the extraction and evaluation of the surface textures of the cocoons in the later period are facilitated;
step S443, determining a brightness adjustment factor based on the exposure of the pattern to be detected, and adjusting the brightness in the first gray level image based on the brightness adjustment factor through a linear transformation algorithm, and recording the brightness as a second gray level image;
step 444, extracting texture features of the surfaces of every cocoon in the second gray level image based on a texture analysis algorithm, vectorizing the texture features of the surfaces of every cocoon based on a SIFT algorithm, further obtaining a texture vector feature value of the surfaces of every cocoon in the second gray level image, marking cocoons with feature values higher than a first feature threshold value as abnormal cocoons, and further obtaining abnormal cocoons rate in every second gray level image;
step S445, removing the corresponding abnormal cocoons from the first gray level image and the pattern to be detected, so as to obtain a plurality of first effective cocoon evaluation sub-patterns in the first gray level image, and obtaining a plurality of second effective cocoon evaluation sub-patterns in the pattern to be detected;
step S446, sequentially calculating the sizes of cocoons corresponding to each first effective cocoon evaluation sub-pattern, and further obtaining first average size values of a plurality of cocoons in a first gray level image;
step S447, sequentially analyzing the color and the uniform color distribution condition of the cocoons in each second effective cocoon evaluation sub-pattern, and evaluating the quality index of the cocoons in the second effective cocoon evaluation sub-pattern based on the color and the uniform color distribution condition of the cocoons, thereby obtaining the average quality index of a plurality of cocoons in the pattern to be inspected;
step S448, based on the abnormal cocoon rate, the first average size value of cocoons, the average quality index of cocoons and the cocoon water content parameter of the day of purchase, calculating the cocoon purchase evaluation corresponding to the video through a weighting algorithm, and when the cocoon purchase evaluation is obviously lower than the first purchase price, calling the staff information of the day of purchase responsible instrument evaluation, and sending to a responsibility department terminal.
Example 2:
corresponding to the above method embodiment, a cocoon purchasing supervision apparatus is further provided in this embodiment, and a cocoon purchasing supervision apparatus described below and a cocoon purchasing supervision method described above may be referred to correspondingly to each other.
Fig. 2 is a block diagram of a cocoon acquisition monitor apparatus 800, according to an exemplary embodiment. As shown in fig. 2, the cocoon acquisition supervision apparatus 800 may include: a processor 801, a memory 802. The cocoon acquisition monitor device 800 may further include one or more of a multimedia component 803, an I/O interface 804, and a communication component 805.
The processor 801 is configured to control the overall operation of the cocoon harvesting monitoring apparatus 800, so as to complete all or part of the steps in the cocoon harvesting monitoring method. The memory 802 is used to store various types of data to support the operation of the cocoon harvesting monitoring device 800, which may include, for example, instructions for any application or method operating on the cocoon harvesting monitoring device 800, as well as application-related data, such as contact data, messages, pictures, audio, video, and the like. The Memory 802 may be implemented by any type or combination of volatile or non-volatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM for short), electrically erasable programmable Read-Only Memory (ElectricallyErasable Programmable Read-Only Memory, EEPROM for short), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM for short), programmable Read-Only Memory (Programmable Read-Only Memory, PROM for short), read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. Wherein the screen may be, for example, a touch screen, the audio component being for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted through the communication component 805. The audio assembly further comprises at least one speaker for outputting audio signals. The I/O interface 804 provides an interface between the processor 801 and other interface modules, which may be a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is configured to perform wired or wireless communication between the cocoon acquisition monitor apparatus 800 and other apparatuses. Wireless communication, such as Wi-Fi, bluetooth, near field communication (Near FieldCommunication, NFC for short), 2G, 3G or 4G, or a combination of one or more thereof, the respective communication component 805 may thus comprise: wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the cocoon acquisition supervisory device 800 may be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASIC), digital signal processors (DigitalSignal Processor, abbreviated as DSP), digital signal processing devices (Digital SignalProcessing Device, abbreviated as DSPD), programmable logic devices (Programmable Logic Device, abbreviated as PLD), field programmable gate arrays (Field Programmable Gate Array, abbreviated as FPGA), controllers, microcontrollers, microprocessors, or other electronic components for performing the cocoon acquisition supervisory methods described above.
In another exemplary embodiment, there is also provided a computer readable storage medium including program instructions which, when executed by a processor, implement the steps of the cocoon acquisition supervision method described above. For example, the computer readable storage medium may be the memory 802 described above including program instructions executable by the processor 801 of the cocoon harvesting supervision device 800 to perform the cocoon harvesting supervision method described above.
Example 3:
corresponding to the above method embodiment, a readable storage medium is also provided in this embodiment, and a readable storage medium described below and a cocoon acquisition supervision method described above may be referred to correspondingly.
A readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the cocoon acquisition supervision method of the above method embodiment.
The readable storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RandomAccess Memory, RAM), a magnetic disk, or an optical disk, and the like.
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (4)
1. The silkworm cocoon acquisition supervision method is characterized by comprising the following steps of:
acquiring silkworm cocoon acquisition data fed back by a plurality of acquisition station servers in a first time period, wherein the silkworm cocoon acquisition data comprise cocoon frame numbers and first weighing values corresponding to each cocoon frame, and the cocoon frames are distributed by silkworm vendors according to the number of silkworm eggs acquired by each farmer in the early period of cultivation;
classifying the cocoon frame numbers according to the farmer numbers, further obtaining a plurality of cocoon frame numbers corresponding to each farmer number, and calculating a silkworm cocoon yield value corresponding to each farmer based on a first weighing value and an evaluation unit price corresponding to each cocoon frame number;
detecting whether the corresponding cocoon yield value of each farmer is abnormal or not based on the number of silkworm eggs picked up by each farmer in the early stage of cultivation, and calling the cocoon frame number corresponding to the abnormal farmer under the condition that the cocoon yield value is abnormal, and marking the cocoon frame number as a first abnormal cocoon frame label;
and feeding back the first abnormal cocoon frame mark number to a corresponding acquisition station server so that the acquisition station server feeds back corresponding abnormal acquisition video data.
2. The method for supervising the purchase of cocoons according to claim 1, wherein the step of detecting whether or not there is an abnormality in the cocoon production value corresponding to each farmer based on the number of silkworm eggs picked up by each farmer in the early stage of cultivation, and calling the cocoon frame number corresponding to the abnormal farmer as the first abnormal cocoon frame number if there is an abnormality, comprises:
after the silkworm cocoon purchase is finished, calculating an average silkworm cocoon yield value corresponding to each silkworm seed in the first culture area based on the total silkworm cocoon yield value in the first culture area and the total silkworm seed number obtained by farmers in the first culture area in the early period of culture, and marking the average silkworm cocoon yield value as a first standard silkworm cocoon yield value, wherein the factors influencing the silkworm cocoon yield value comprise silkworm cocoon purchase unit price and yield;
calculating a silkworm cocoon yield error coefficient by a weighting method based on first historical weather data during silkworm cocoon purchase and second historical weather data within 3-5 days before silkworm cocoon purchase, and calculating a first silkworm cocoon yield range corresponding to each silkworm species based on the silkworm cocoon yield error coefficient and a first standard silkworm cocoon yield;
based on the number of silkworm eggs picked up by each farmer in the early period of cultivation and the first cocoon yield value range corresponding to each silkworm egg, calculating a second cocoon yield value range corresponding to each farmer, detecting whether the cocoon yield value corresponding to each farmer is in the second cocoon yield value range, if not, marking the corresponding farmer as an abnormal farmer, marking the warehouse-in cocoon frame corresponding to the abnormal farmer as a first abnormal cocoon frame, and further obtaining a first abnormal cocoon frame mark corresponding to the first abnormal cocoon frame.
3. The method for supervising the purchase of cocoons according to claim 1, wherein the feeding back the first abnormal cocoon frame number to the corresponding purchase station server, so that the purchase station server feeds back the corresponding abnormal purchase video data, comprises:
acquiring a first purchase unit price and a first weight corresponding to a first abnormal cocoon frame mark, and calculating to obtain a standard weight reference range corresponding to each silkworm seed based on the number of silkworm seeds picked up in the early period of abnormal farmer cultivation and the average pupation rate of silkworms in the current cultivation period;
constructing a cocoon water content evaluation model, and evaluating cocoon water content parameters corresponding to each purchase day during the silk cocoon purchase period based on third historical weather data of the silk cocoon purchase day and 7-10 days before the silk cocoon purchase day;
correcting the standard weight reference range based on the cocoon water content parameter, detecting whether the first weight is in the corrected standard weight reference range, and if not, calling weighing data of a weighing video metering scale corresponding to the first abnormal cocoon frame number to a corresponding acquisition station server;
and judging whether the first purchase price is abnormal or not based on the average purchase price of the silkworm cocoon culturing area corresponding to the purchase station, and if so, calling an instrument evaluation video corresponding to the first abnormal cocoon frame number to the purchase station server.
4. The method for supervising the purchase of cocoons according to claim 3, wherein after the step of retrieving the appearance video corresponding to the first abnormal cocoon frame number from the purchase station server, the method further comprises:
intercepting a plurality of sample images in an evaluation video of the instrument, identifying the effective cocoon number in each sample image based on an image feature extraction algorithm, and identifying the corresponding sample image as a pattern to be detected under the condition that the effective cocoon number is greater than a minimum observed number threshold value;
converting the pattern to be detected into a first gray level image;
determining a brightness adjustment factor based on the exposure of the pattern to be detected, adjusting the brightness in the first gray level image based on the brightness adjustment factor through a linear transformation algorithm, and recording the brightness as a second gray level image;
extracting texture features of the surfaces of each cocoon in the second gray level image based on a texture analysis algorithm, carrying out texture feature vectorization on the texture features of the surfaces of each cocoon based on a SIFT algorithm, further obtaining a texture vector feature value of the surfaces of each cocoon in the second gray level image, marking cocoons with feature values higher than a first feature threshold value as abnormal cocoons, and further obtaining abnormal cocoon rate in each second gray level image;
removing corresponding abnormal cocoons from the first gray level image and the pattern to be detected, further obtaining a plurality of first effective cocoon evaluation sub-patterns from the first gray level image, and obtaining a plurality of second effective cocoon evaluation sub-patterns from the pattern to be detected;
sequentially calculating the sizes of cocoons corresponding to each first effective cocoon evaluation sub-pattern, and further obtaining first average size values of a plurality of cocoons in the first gray level image;
sequentially analyzing the color and the uniform color distribution condition of the cocoons in each second effective cocoon evaluation sub-pattern, and evaluating the quality index of the cocoons in the second effective cocoon evaluation sub-pattern based on the color and the uniform color distribution condition of the cocoons, so as to obtain average quality indexes of a plurality of cocoons in the pattern to be inspected;
based on the abnormal cocoon rate, the first average size value of the cocoons, the average quality index of the cocoons and the cocoon water content parameter of the cocoons on the day of purchase, the corresponding cocoon purchase evaluation value of the video is evaluated through a weighting algorithm.
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| CN117422483A (en) * | 2023-12-19 | 2024-01-19 | 四川主干信息技术有限公司 | Cocoon industrial chain tracing platform and method |
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| CN117422483A (en) * | 2023-12-19 | 2024-01-19 | 四川主干信息技术有限公司 | Cocoon industrial chain tracing platform and method |
| CN117422483B (en) * | 2023-12-19 | 2024-03-19 | 四川主干信息技术有限公司 | Silkworm cocoon industrial chain tracing method |
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