CN109886826B - Block chain-based livestock breeding circulation tracking method, electronic device and storage medium - Google Patents
Block chain-based livestock breeding circulation tracking method, electronic device and storage medium Download PDFInfo
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- CN109886826B CN109886826B CN201910008196.1A CN201910008196A CN109886826B CN 109886826 B CN109886826 B CN 109886826B CN 201910008196 A CN201910008196 A CN 201910008196A CN 109886826 B CN109886826 B CN 109886826B
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Abstract
The invention relates to a livestock breeding circulation tracking method based on a blockchain, an electronic device and a storage medium, wherein the method comprises the following steps: the facial feature information of each livestock to be ensured is obtained through an imaging device and is used as the identity ID of the livestock, a data node is generated for each livestock in a blockchain system, each data node comprises a database of the biological feature information to be ensured, the health state to be ensured and the environment information to be ensured, the growth data of the livestock are periodically updated into the blockchain system, the livestock meeting slaughter requirements are slaughtered and segmented to generate unique two-dimensional codes, the association relation of the data nodes corresponding to the livestock is established, and meanwhile, the related data of the livestock sold in transportation and stores are uploaded into the blockchain system to form a complete livestock database. The invention utilizes the blockchain technology to track the life cycle of the guaranteeing livestock, thereby realizing the symmetry and safety of livestock information.
Description
Technical Field
The present invention relates to the field of blockchain technologies, and in particular, to a blockchain-based livestock breeding circulation tracking method, an electronic device, and a storage medium.
Background
At present, the high-speed development of economy is rapid, the industrialization and town development of China still exist, but the development of agricultural modernization is obviously behind other countries, the food safety cannot be guaranteed, the agricultural resource is wasted, and the like, and the problems are ultimately caused by unbalanced supply and demand of agricultural products, and the unbalance is caused by the fact that in three links of agricultural production, circulation and consumption, producers and consumers cannot realize information symmetry.
The development and application of the blockchain technology can effectively solve the problem, and the blockchain is a technical scheme for collectively maintaining a reliable database through computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like in a decentralization and belief-removal mode.
The blockchain has the characteristic of non-falsification, can ensure that all generated data are truly recorded in the whole process of agricultural production, circulation and consumption, and can be traced, thereby being safe and reliable.
Disclosure of Invention
The invention aims to provide a livestock breeding circulation tracking method, an electronic device and a storage medium, which aim to track the life cycle of an ensured livestock by using a blockchain technology and realize the symmetry and safety of livestock information.
In order to achieve the above object, the present invention provides an electronic device, including a memory and a processor, wherein the memory stores an livestock breeding circulation tracking program capable of running on the processor, and the following steps are implemented when the livestock breeding circulation tracking program is executed by the processor:
the node construction step: acquiring facial feature information of each insurance-applied livestock by an imaging device as an identity ID of the livestock, generating a data node for each livestock in a blockchain system based on the identity ID, wherein each data node comprises a database of insurance-applied biological feature information, insurance-applied health state and insurance-applied cultivation environment information;
a first state recording step: the method comprises the steps that face recognition is carried out on each livestock through a pre-trained livestock face recognition system to confirm an identity ID, growth data of each livestock with the identity ID is updated into a database corresponding to a data node represented by the identity ID by the blockchain system, and the growth data of each livestock comprise activity conditions, feeding conditions, physique conditions and medication conditions of the livestock;
a second state recording step: after livestock information states after livestock quarantine are recorded, slaughtering and dividing the livestock meeting slaughtering requirements, generating a unique two-dimensional code for each decomposition part of the livestock, and establishing an association relationship between the two-dimensional code and corresponding data nodes of the slaughtered livestock in the blockchain system;
information summarizing step: and recording the transportation data of each decomposition part of each slaughtered livestock in the transportation process and the sales data after arriving at a sales store, and uploading the transportation data and the sales data to a database of corresponding data nodes in the blockchain system to form a complete livestock database.
Preferably, the pre-trained livestock face recognition system is a twin neural network model, and comprises a first convolutional neural network model and a second convolutional neural network model, and the step of performing face recognition on each livestock by the pre-trained livestock face recognition system to confirm the identity ID comprises the following steps of:
extracting features of a livestock face image acquired in the previous time by using a first convolutional neural network model to obtain a first face feature vector;
extracting features of the current facial image of the livestock by using the second convolutional neural network model, and outputting a corresponding second facial feature vector;
calculating the vector distance between the first facial feature vector and the second facial feature vector, taking the vector distance as a similarity value, and when the similarity value exceeds a preset threshold value, confirming that the current livestock facial image and the livestock facial image acquired at the previous time are facial images of livestock with the same identity ID;
when the identity ID of the livestock is confirmed, the face image of the livestock obtained in the previous time is replaced by the current face image of the livestock, and an updated second face feature vector is extracted from the current face image of the livestock.
Preferably, the second state recording step further includes:
confirming the identity ID of the livestock through the livestock face identification system, and updating the life state of the livestock into slaughtered livestock in the database of the data node after the livestock meeting slaughter requirements are slaughtered;
if the quarantine result of the livestock does not meet the slaughtering requirement, the life state of the livestock is updated to be quarantine disqualification in the database of the data node.
Preferably, when the livestock breeding circulation tracking program is executed by the processor, the following steps are further executed before the second status recording step:
if the livestock dies in the breeding process, determining the Identity (ID) of the dead livestock by utilizing a livestock face recognition system, and modifying the life state of the dead livestock into dead livestock; a kind of electronic device with high-pressure air-conditioning system
If the death cause of the dead livestock reaches the claim settlement standard, the amount of the claim settlement party is credited to the applicant party through the intelligent contract of the blockchain.
In addition, in order to achieve the above object, the present invention also provides a livestock breeding circulation tracking method based on a blockchain, which is applied to an electronic device, and the method comprises:
s1: acquiring facial feature information of each insurance-applied livestock by an imaging device as an identity ID of the livestock, generating a data node for each livestock in a blockchain system based on the identity ID, wherein each data node comprises a database of insurance-applied biological feature information, insurance-applied health state and insurance-applied cultivation environment information;
s2: the method comprises the steps that face recognition is carried out on each livestock through a pre-trained livestock face recognition system to confirm an identity ID, growth data of each livestock with the identity ID is updated into a database corresponding to a data node represented by the identity ID by the blockchain system, and the growth data of each livestock comprise activity conditions, feeding conditions, physique conditions and medication conditions of the livestock;
s3: after livestock information states after livestock quarantine are recorded, slaughtering and dividing the livestock meeting slaughtering requirements, generating a unique two-dimensional code for each decomposition part of the livestock, and establishing an association relationship between the two-dimensional code and corresponding data nodes of the slaughtered livestock in the blockchain system;
s4: and recording the transportation data of each decomposition part of each slaughtered livestock in the transportation process and the sales data after arriving at a sales store, and uploading the transportation data and the sales data to a database of corresponding data nodes in the blockchain system to form a complete livestock database.
Preferably, the pre-trained livestock face recognition system is a twin neural network model, and comprises a first convolutional neural network model and a second convolutional neural network model, and the step of performing face recognition on each livestock by the pre-trained livestock face recognition system to confirm the identity ID comprises the following steps of:
extracting features of a livestock face image acquired in the previous time by using a first convolutional neural network model to obtain a first face feature vector;
extracting features of the current facial image of the livestock by using the second convolutional neural network model, and outputting a corresponding second facial feature vector;
calculating the vector distance between the first facial feature vector and the second facial feature vector, taking the vector distance as a similarity value, and when the similarity value exceeds a preset threshold value, confirming that the current livestock facial image and the livestock facial image acquired at the previous time are facial images of livestock with the same identity ID;
when the identity ID of the livestock is confirmed, the face image of the livestock obtained in the previous time is replaced by the current face image of the livestock, and an updated second face feature vector is extracted from the current face image of the livestock.
Preferably, the second state recording step further includes:
confirming the identity ID of the livestock through the livestock face identification system, and updating the life state of the livestock into slaughtered livestock in the database of the data node after the livestock meeting slaughter requirements are slaughtered;
if the quarantine result of the livestock does not meet the slaughtering requirement, the life state of the livestock is updated to be quarantine disqualification in the database of the data node.
Preferably, before the step S3, the method further includes the following steps:
if the livestock dies in the breeding process, determining the Identity (ID) of the dead livestock by utilizing a livestock face recognition system, and modifying the life state of the dead livestock into dead livestock; a kind of electronic device with high-pressure air-conditioning system
If the death cause of the dead livestock reaches the claim settlement standard, the amount of the claim settlement party is credited to the applicant party through the intelligent contract of the blockchain.
Preferably, the method further comprises:
responding to a request of a user for scanning the two-dimension code, reading related information of slaughtered livestock matched with the two-dimension code from a database of a corresponding data node of the blockchain system, and displaying the related information to the user.
In addition, in order to achieve the above object, the present invention also provides a computer-readable storage medium storing an livestock breeding circulation tracking program executable by a processor to perform the blockchain-based livestock breeding circulation tracking method as set forth in any one of the above.
The beneficial effects of the invention are as follows: the intelligent tracking is realized on the whole life cycle of the applied livestock through the block chain system and the livestock face recognition system based on deep learning training, the real data is ensured not to be tampered in the livestock breeding process, and the information symmetry and the data safety of producers and consumers are realized.
Drawings
FIG. 1 is a schematic diagram of an electronic device according to an embodiment of the invention;
FIG. 2 is a flowchart of a block chain based livestock breeding circulation tracking method according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that the description of "first", "second", etc. in this disclosure is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implying an indication of the number of technical features being indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present invention.
Referring to fig. 1, a schematic diagram of an electronic device according to an embodiment of the invention is shown. The electronic apparatus 1 is a device capable of automatically performing numerical calculation and/or information processing in accordance with a preset or stored instruction. The electronic device 1 may be a computer, a server group formed by a single network server, a plurality of network servers, or a cloud formed by a large number of hosts or network servers based on cloud computing, wherein the cloud computing is one of distributed computing, and is a super virtual computer formed by a group of loosely coupled computer sets.
In the present embodiment, the electronic device 1 may include, but is not limited to, a memory 11, a processor 12, and a network interface 13, which may be communicatively connected to each other through a system bus, and the memory 11 stores a livestock breeding circulation tracking program 10 that may be run on the processor 12. It is noted that fig. 1 only shows an electronic device 1 with components 11-13, but it is understood that not all shown components are required to be implemented, and that more or fewer components may alternatively be implemented.
Wherein the storage 11 comprises a memory and at least one type of readable storage medium. The memory provides a buffer for the operation of the electronic device 1; the readable storage medium may be a non-volatile storage medium such as flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the readable storage medium may be an internal storage unit of the electronic device 1, such as a hard disk of the electronic device 1; in other embodiments, the nonvolatile storage medium may also be an external storage device of the electronic apparatus 1, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic apparatus 1. In this embodiment, the readable storage medium of the memory 11 is generally used for storing an operating system and various application software installed in the electronic device 1, for example, storing a livestock breeding circulation tracking program in an embodiment of the present invention. Further, the memory 11 may be used to temporarily store various types of data that have been output or are to be output.
The processor 12 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 12 is typically used for controlling the overall operation of the electronic apparatus 1, e.g. for performing control and processing related to data interaction or communication with the other devices, etc. In this embodiment, the processor 12 is configured to execute the program code or process data stored in the memory 11, such as running a livestock breeding circulation tracking program.
The network interface 13 may comprise a wireless network interface or a wired network interface, which network interface 13 is typically used for establishing a communication connection between the electronic apparatus 1 and other electronic devices.
The livestock farming circulation tracking program is stored in a memory 11, including computer readable instructions stored in the memory 11 that are executable by the processor 12 to implement the methods of the various embodiments of the present application.
In one embodiment, the livestock breeding circulation tracking program is executed by the processor 12 to perform the following steps:
the node construction step: acquiring facial feature information of each of the insuring livestock by an imaging device (such as a camera, a CCD camera and the like) as an identity ID of the livestock, generating a data node for each of the livestock in a blockchain system based on the identity ID, wherein each data node comprises a database of insuring biological feature information, insuring health state and insuring culture environment information;
wherein the database is organized, stored, and managed according to a data structure and is built on a computer storage device. The block chain system further verifies the authenticity of the data through a consensus mechanism, reminds the existing information, avoids re-recording the information, feeds back incomplete information, does not store the error information, and cannot be tampered after the information data is consensus.
The consensus mechanism is a mathematical algorithm for establishing trust and obtaining rights and interests between different nodes in a block chain system.
A first state recording step: the method comprises the steps that face recognition is carried out on each livestock through a pre-trained livestock face recognition system to confirm an identity ID, growth data of each livestock with the identity ID is updated into a database corresponding to a data node represented by the identity ID by the blockchain system, and the growth data of each livestock comprise activity conditions, feeding conditions, physique conditions and medication conditions of the livestock;
wherein data update (data update) is the process of replacing a data file or database with a new data item or record, the old data item or record corresponding thereto. Data updating can be implemented using both an "object resource manager" and a T-SQL statement.
The object resource manager is SQL Server Management Studio provides functionality for managing objects in database engines, analysis Services, integration Services, and Reporting Services instances;
T-SQL, transact-SQL, is an enhancement of SQL on Microsoft SQL Server, which is the primary language used to let applications communicate with SQL Server.
A second state recording step: after livestock information states after livestock quarantine are recorded, slaughtering and dividing the livestock meeting slaughtering requirements, generating a unique two-dimensional code for each decomposition part of the livestock, and establishing an association relationship between the two-dimensional code and corresponding data nodes of the slaughtered livestock in the blockchain system;
the two-dimensional code records data symbol information by using a certain specific geometric figure distributed on a plane (in a two-dimensional direction) according to a certain rule and a black-white alternate figure; the concept of 0 and 1 bit streams forming the internal logic foundation of a computer is skillfully utilized in code programming, a plurality of geometric shapes corresponding to binary are used for representing literal numerical information, and the literal numerical information is automatically read through an image input device or an optoelectronic scanning device to realize automatic information processing: it has some commonalities in barcode technology: each code has its specific character set; each character occupies a certain width; has a certain checking function and the like. Meanwhile, the system also has the function of automatically identifying information of different rows and processes the rotation change points of the graphics.
Information summarizing step: recording the transportation data of each decomposition part of each slaughtered livestock in the transportation process and the sales data after arriving at a sales store, and uploading the transportation data and the sales data to a database of corresponding data nodes in the blockchain system to form a complete livestock database;
wherein the transportation data comprises: departure place, destination, real-time location, vehicle, pork refrigeration status;
the sales data includes: the pork is in the area, weight, price and time from slaughter to sale.
Further, in this embodiment, the pre-trained livestock face recognition system is a twin neural network model, including a first convolutional neural network model and a second convolutional neural network model, and the step of performing face recognition on each livestock by the pre-trained livestock face recognition system to confirm the identity ID includes the following steps:
extracting features of a livestock face image acquired in the previous time by using a first convolutional neural network model to obtain a first face feature vector;
extracting features of the current facial image of the livestock by using the second convolutional neural network model, and outputting a corresponding second facial feature vector;
calculating the vector distance between the first facial feature vector and the second facial feature vector, taking the vector distance as a similarity value, and when the similarity value exceeds a preset threshold value, confirming that the current livestock facial image and the livestock facial image acquired at the previous time are facial images of livestock with the same identity ID;
when the identity ID of the livestock is confirmed, the face image of the livestock obtained in the previous time is replaced by the current face image of the livestock, and an updated second face feature vector is extracted from the current face image of the livestock.
Further, in this embodiment, the second state recording step further includes:
confirming the identity ID of the livestock through the livestock face identification system, and updating the life state of the livestock into slaughtered livestock in the database of the data node after the livestock meeting slaughter requirements are slaughtered;
if the quarantine result of the livestock does not meet the slaughtering requirement, the life state of the livestock is updated to be quarantine disqualification in the database of the data node.
The quarantine comprises pre-slaughter quarantine and post-slaughter quarantine, wherein the pre-slaughter quarantine comprises 4 aspects, namely:
it is seen that the examination of the sensitivity response is one of the more accurate methods for judging the disease and health. The hair and skin of healthy livestock are glossy, psychoactive, sensitive in response, looking around, mental depression of the ill livestock, slow response, drooping ears, non-lifting head, semi-closing eyes, dry and non-wetting nose glasses, slow walking or standing still;
listening mainly includes breathing sounds, calling sounds and cough sounds. Auscultation is carried out by means of a stethoscope, the alveolar breath of the general healthy livestock is similar to the sound of furfure, the inspiration is clear, and the expiration is weak; alveolar sound breathing abnormality when suffering from pneumonia, emphysema and pleuropneumonia; healthy livestock call sounds are humming gently, and the call sounds of the ill livestock are hoarseness and subsidence, moan and the like; cough is high and thick during cold, and continuous sound is generally sprayed without sputum;
touching the body surface with hand to sense subcutaneous skin change, the skin of healthy livestock is soft and elastic, and has no edema, eruption, emphysema, etc. Superficial lymph nodes are mainly examined in livestock throat and shallow cervical lymph nodes, and are examined according to size, morphology, temperature, sensitivity and mobility. For example, the lymph nodes of livestock suffering from streptococcicosis have obvious swelling and pain, and the surface has fluctuation sensation. Clinically, the inguinal lymph nodes of livestock are usually checked for enlargement;
the focus of detection is to detect body temperature, as an increase or decrease in body temperature is an important marker for livestock illness. Laboratory routine tests are required for foul livestock that cannot be diagnosed, and the method has the advantage of rapid diagnosis in targeted immunology. The livestock checked by quarantine personnel, which is confirmed to be required to be slaughtered urgently for non-malignant infectious diseases, should be provided with an urgent slaughter proof and sent to an urgent slaughter workshop for urgent slaughter, so that convenience is provided for checking during post-slaughter inspection.
The post-slaughter quarantine includes 12 steps, respectively: 1. dissecting and examining submaxillary lymph nodes at two sides of the head; 2. removing external occipital muscle on two sides of the anterior head; 3. dissecting and examining the bronchial lymph nodes; 4. dissecting and examining the ventricle and pericardium; 5. dissecting and checking hepatic lymph nodes; 6. dissecting mesenteric lymph nodes; 7. dissecting and examining inguinal shallow lymph nodes; 8. dissecting deep inguinal lymph node and intra-iliac lymph node; 9. dissecting and examining the medial femoral muscle; 10. dissecting and examining lumbar muscle; 11. dissecting the kidney; 12. and (6) checking trichina.
Further, in this embodiment, when the livestock breeding circulation tracking program is executed by the processor, the following steps are further executed before the second status recording step:
if the livestock dies in the breeding process, determining the Identity (ID) of the dead livestock by utilizing a livestock face recognition system, and modifying the life state of the dead livestock into dead livestock; a kind of electronic device with high-pressure air-conditioning system
If the death cause of the dead livestock reaches the claim settlement standard, the amount of the claim settlement party is credited to the applicant party through the intelligent contract of the blockchain.
The smart contract is a set of digitally defined commitments that control digital assets and contain rights and obligations contracted by contract participants, which are automatically executed by the computer system.
The intelligent contract program is not only a computer program which can be automatically executed, but also a system participant, responds to the received information, can receive and store the value, and can also send the information and the value outwards. It will be appreciated that this program, like a person who can be trusted, can temporarily store assets, always performing operations according to the rules in advance.
Compared with the prior art, the intelligent tracking system has the advantages that intelligent tracking is realized on the whole life cycle of the guaranteeing livestock through the blockchain system and the livestock face recognition system based on deep learning training, real data is not tampered in the livestock breeding process, and information symmetry and data safety of producers and consumers are realized.
Referring to fig. 2, a flowchart of an embodiment of a blockchain-based livestock breeding circulation tracking method according to the present invention is shown. The method comprises the following steps:
s1: acquiring facial feature information of each of the insuring livestock by an imaging device (such as a camera, a CCD camera and the like) as an identity ID of the livestock, generating a data node for each of the livestock in a blockchain system based on the identity ID, wherein each data node comprises a database of insuring biological feature information, insuring health state and insuring culture environment information;
wherein the database is organized, stored, and managed according to a data structure and is built on a computer storage device. The block chain system further verifies the authenticity of the data through a consensus mechanism, reminds the existing information, avoids re-recording the information, feeds back incomplete information, does not store the error information, and cannot be tampered after the information data is consensus.
The consensus mechanism is a mathematical algorithm for establishing trust and obtaining rights and interests between different nodes in a block chain system.
S2: the method comprises the steps that face recognition is carried out on each livestock through a pre-trained livestock face recognition system to confirm an identity ID, growth data of each livestock with the identity ID is updated into a database corresponding to a data node represented by the identity ID by the blockchain system, and the growth data of each livestock comprise activity conditions, feeding conditions, physique conditions and medication conditions of the livestock;
wherein data update (data update) is the process of replacing a data file or database with a new data item or record, the old data item or record corresponding thereto. Data updating can be implemented using both an "object resource manager" and a T-SQL statement.
The object resource manager is SQL Server Management Studio provides functionality for managing objects in database engines, analysis Services, integration Services, and Reporting Services instances;
T-SQL, transact-SQL, is an enhancement of SQL on Microsoft SQL Server, which is the primary language used to let applications communicate with SQL Server.
S3: after livestock information states after livestock quarantine are recorded, slaughtering and dividing the livestock meeting slaughtering requirements, generating a unique two-dimensional code for each decomposition part of the livestock, and establishing an association relationship between the two-dimensional code and corresponding data nodes of the slaughtered livestock in the blockchain system;
the two-dimensional code records data symbol information by using a certain specific geometric figure distributed on a plane (in a two-dimensional direction) according to a certain rule and a black-white alternate figure; the concept of 0 and 1 bit streams forming the internal logic foundation of a computer is skillfully utilized in code programming, a plurality of geometric shapes corresponding to binary are used for representing literal numerical information, and the literal numerical information is automatically read through an image input device or an optoelectronic scanning device to realize automatic information processing: it has some commonalities in barcode technology: each code has its specific character set; each character occupies a certain width; has a certain checking function and the like. Meanwhile, the system also has the function of automatically identifying information of different rows and processes the rotation change points of the graphics.
S4: recording the transportation data of each decomposition part of each slaughtered livestock in the transportation process and the sales data after arriving at a sales store, and uploading the transportation data and the sales data to a database of corresponding data nodes in the blockchain system to form a complete livestock database;
wherein the transportation data comprises: departure place, destination, real-time location, vehicle, pork refrigeration status;
the sales data includes: the pork is in the area, weight, price and time from slaughter to sale.
Further, in this embodiment, the pre-trained livestock face recognition system is a twin neural network model, including a first convolutional neural network model and a second convolutional neural network model, and the step of performing face recognition on each livestock by the pre-trained livestock face recognition system to confirm the identity ID includes the following steps:
extracting features of a livestock face image acquired in the previous time by using a first convolutional neural network model to obtain a first face feature vector;
extracting features of the current facial image of the livestock by using the second convolutional neural network model, and outputting a corresponding second facial feature vector;
calculating the vector distance between the first facial feature vector and the second facial feature vector, taking the vector distance as a similarity value, and when the similarity value exceeds a preset threshold value, confirming that the current livestock facial image and the livestock facial image acquired at the previous time are facial images of livestock with the same identity ID;
when the identity ID of the livestock is confirmed, the face image of the livestock obtained in the previous time is replaced by the current face image of the livestock, and an updated second face feature vector is extracted from the current face image of the livestock.
Further, in this embodiment, the step S3 further includes:
confirming the identity ID of the livestock through the livestock face identification system, and updating the life state of the livestock into slaughtered livestock in the database of the data node after the livestock meeting slaughter requirements are slaughtered;
if the quarantine result of the livestock does not meet the slaughtering requirement, the life state of the livestock is updated to be quarantine disqualification in the database of the data node.
The quarantine comprises pre-slaughter quarantine and post-slaughter quarantine, wherein the pre-slaughter quarantine comprises 4 aspects, namely:
it is seen that the examination of the sensitivity response is one of the more accurate methods for judging the disease and health. The hair and skin of healthy livestock are glossy, psychoactive, sensitive in response, looking around, mental depression of the ill livestock, slow response, drooping ears, non-lifting head, semi-closing eyes, dry and non-wetting nose glasses, slow walking or standing still;
listening mainly includes breathing sounds, calling sounds and cough sounds. Auscultation is carried out by means of a stethoscope, the alveolar breath of the general healthy livestock is similar to the sound of furfure, the inspiration is clear, and the expiration is weak; alveolar sound breathing abnormality when suffering from pneumonia, emphysema and pleuropneumonia; healthy livestock call sounds are humming gently, and the call sounds of the ill livestock are hoarseness and subsidence, moan and the like; cough is high and thick during cold, and continuous sound is generally sprayed without sputum;
touching the body surface with hand to sense subcutaneous skin change, the skin of healthy livestock is soft and elastic, and has no edema, eruption, emphysema, etc. Superficial lymph nodes are mainly examined in livestock throat and shallow cervical lymph nodes, and are examined according to size, morphology, temperature, sensitivity and mobility. For example, the lymph nodes of livestock suffering from streptococcicosis have obvious swelling and pain, and the surface has fluctuation sensation. Clinically, the inguinal lymph nodes of livestock are usually checked for enlargement;
the focus of detection is to detect body temperature, as an increase or decrease in body temperature is an important marker for livestock illness. Laboratory routine tests are required for foul livestock that cannot be diagnosed, and the method has the advantage of rapid diagnosis in targeted immunology. The livestock checked by quarantine personnel, which is confirmed to be required to be slaughtered urgently for non-malignant infectious diseases, should be provided with an urgent slaughter proof and sent to an urgent slaughter workshop for urgent slaughter, so that convenience is provided for checking during post-slaughter inspection.
The post-slaughter quarantine includes 12 steps, respectively: 1. dissecting and examining submaxillary lymph nodes at two sides of the head; 2. removing external occipital muscle on two sides of the anterior head; 3. dissecting and examining the bronchial lymph nodes; 4. dissecting and examining the ventricle and pericardium; 5. dissecting and checking hepatic lymph nodes; 6. dissecting mesenteric lymph nodes; 7. dissecting and examining inguinal shallow lymph nodes; 8. dissecting deep inguinal lymph node and intra-iliac lymph node; 9. dissecting and examining the medial femoral muscle; 10. dissecting and examining lumbar muscle; 11. dissecting the kidney; 12. and (6) checking trichina.
Further, in this embodiment, before the step S3, the method may further include the following steps:
if the livestock dies in the breeding process, determining the Identity (ID) of the dead livestock by utilizing a livestock face recognition system, and modifying the life state of the dead livestock into dead livestock; a kind of electronic device with high-pressure air-conditioning system
If the death cause of the dead livestock reaches the claim settlement standard, the amount of the claim settlement party is credited to the applicant party through the intelligent contract of the blockchain.
The smart contract is a set of digitally defined commitments that control digital assets and contain rights and obligations contracted by contract participants, which are automatically executed by the computer system.
The intelligent contract program is not only a computer program which can be automatically executed, but also a system participant, responds to the received information, can receive and store the value, and can also send the information and the value outwards. It will be appreciated that this procedure, like a person who can be trusted, can temporarily store assets, always performing operations according to the rules of advance.
Further, in this embodiment, the method further includes the steps of:
responding to a request of a user for scanning the two-dimension code, reading related information of slaughtered livestock matched with the two-dimension code from a database of a corresponding data node of the blockchain system, and displaying the related information to the user.
For example, a consumer can obtain relevant information of the pig raising process associated with the two-dimensional code by scanning the two-dimensional code on a livestock decomposition part (such as pork) to be purchased through a mobile phone.
The present invention also provides a computer-readable storage medium storing an livestock farming circulation tracking program executable by a processor to perform a blockchain-based livestock farming circulation tracking method as described above.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (8)
1. An electronic device comprising a memory and a processor, wherein the memory stores an animal feed circulation tracking program operable on the processor, the animal feed circulation tracking program when executed by the processor performing the steps of:
the node construction step: acquiring facial feature information of each insurance-applied livestock by an imaging device as an identity ID of the livestock, generating a data node for each livestock in a blockchain system based on the identity ID, wherein each data node comprises a database of insurance-applied biological feature information, insurance-applied health state and insurance-applied cultivation environment information;
a first state recording step: the method comprises the steps that face recognition is carried out on each livestock through a pre-trained livestock face recognition system to confirm an identity ID, growth data of each livestock with the identity ID is updated into a database corresponding to a data node represented by the identity ID by the blockchain system, and the growth data of each livestock comprise activity conditions, feeding conditions, physique conditions and medication conditions of the livestock;
a second state recording step: after livestock information states after livestock quarantine are recorded, slaughtering and dividing the livestock meeting slaughtering requirements, generating a unique two-dimensional code for each decomposition part of the livestock, and establishing an association relationship between the two-dimensional code and corresponding data nodes of the livestock in the blockchain system;
information summarizing step: recording the transportation data of each decomposition part of each slaughtered livestock in the transportation process and the sales data after arriving at a sales store, and uploading the transportation data and the sales data to a database of corresponding data nodes in the blockchain system to form a complete livestock database;
the pre-trained livestock face recognition system is a twin neural network model and comprises a first convolutional neural network model and a second convolutional neural network model, and the face recognition of each livestock through the pre-trained livestock face recognition system to confirm the identity ID comprises the following steps: extracting features of a livestock face image acquired in the previous time by using a first convolutional neural network model to obtain a first face feature vector; extracting features of the current facial image of the livestock by using the second convolutional neural network model, and outputting a corresponding second facial feature vector; calculating the vector distance between the first facial feature vector and the second facial feature vector, taking the vector distance as a similarity value, and when the similarity value exceeds a preset threshold value, confirming that the current livestock facial image and the livestock facial image acquired in the previous time are facial images of livestock with the same identity ID; when the identity ID of the livestock is confirmed, the face image of the livestock obtained in the previous time is replaced by the current face image of the livestock, and an updated second face feature vector is extracted from the current face image of the livestock.
2. The electronic device of claim 1, wherein the second state recording step further comprises:
confirming the identity ID of the livestock through the livestock face identification system, and updating the life state of the livestock into slaughtered livestock in the database of the data node after the livestock meeting slaughter requirements are slaughtered;
if the quarantine result of the livestock does not meet the slaughtering requirement, the life state of the livestock is updated to be quarantine disqualification in the database of the data node.
3. The electronic device of claim 1, wherein the livestock farming circulation tracking program, when executed by the processor, performs the following steps prior to the second state recording step:
if the livestock dies in the breeding process, determining the Identity (ID) of the dead livestock by utilizing the livestock face recognition system, and modifying the life state of the dead livestock into dead livestock; a kind of electronic device with high-pressure air-conditioning system
If the death cause of the dead livestock reaches the claim settlement standard, the amount of the claim settlement party is credited to the applicant party through the intelligent contract of the blockchain.
4. A livestock breeding circulation tracking method based on a blockchain, which is applied to an electronic device, and is characterized by comprising the following steps:
s1: acquiring facial feature information of each insurance-applied livestock by an imaging device as an identity ID of the livestock, generating a data node for each livestock in a blockchain system based on the identity ID, wherein each data node comprises a database of insurance-applied biological feature information, insurance-applied health state and insurance-applied cultivation environment information;
s2: the method comprises the steps that face recognition is carried out on each livestock through a pre-trained livestock face recognition system to confirm an identity ID, growth data of each livestock with the identity ID is updated into a database corresponding to a data node represented by the identity ID by the blockchain system, and the growth data of each livestock comprise activity conditions, feeding conditions, physique conditions and medication conditions of the livestock;
s3: after livestock information states after livestock quarantine are recorded, slaughtering and dividing the livestock meeting slaughtering requirements, generating a unique two-dimensional code for each decomposition part of the livestock, and establishing an association relationship between the two-dimensional code and corresponding data nodes of the livestock in the blockchain system;
s4: recording the transportation data of each decomposition part of each slaughtered livestock in the transportation process and the sales data after arriving at a sales store, and uploading the transportation data and the sales data to a database of corresponding data nodes in the blockchain system to form a complete livestock database;
the pre-trained livestock face recognition system is a twin neural network model and comprises a first convolutional neural network model and a second convolutional neural network model, and the face recognition of each livestock through the pre-trained livestock face recognition system to confirm the identity ID comprises the following steps: extracting features of a livestock face image acquired in the previous time by using a first convolutional neural network model to obtain a first face feature vector; extracting features of the current facial image of the livestock by using the second convolutional neural network model, and outputting a corresponding second facial feature vector; calculating the vector distance between the first facial feature vector and the second facial feature vector, taking the vector distance as a similarity value, and when the similarity value exceeds a preset threshold value, confirming that the current livestock facial image and the livestock facial image acquired in the previous time are facial images of livestock with the same identity ID; when the identity ID of the livestock is confirmed, the face image of the livestock obtained in the previous time is replaced by the current face image of the livestock, and an updated second face feature vector is extracted from the current face image of the livestock.
5. The blockchain-based livestock farming circulation tracking method of claim 4, wherein the step S3 further comprises:
confirming the identity ID of the livestock through the livestock face identification system, and updating the life state of the livestock into slaughtered livestock in the database of the data node after the livestock meeting slaughter requirements are slaughtered;
if the quarantine result of the livestock does not meet the slaughtering requirement, the life state of the livestock is updated to be quarantine disqualification in the database of the data node.
6. The blockchain-based livestock breeding circulation tracking method of claim 4, comprising the following steps before the step S3:
if the livestock dies in the breeding process, determining the Identity (ID) of the dead livestock by utilizing the livestock face recognition system, and modifying the life state of the dead livestock into dead livestock; a kind of electronic device with high-pressure air-conditioning system
If the death cause of the dead livestock reaches the claim settlement standard, the amount of the claim settlement party is credited to the applicant party through the intelligent contract of the blockchain.
7. The blockchain-based livestock breeding circulation tracking method of claim 4, further comprising:
responding to a request of a user for scanning the two-dimension code, reading related information of slaughtered livestock matched with the two-dimension code from a database of a corresponding data node of the blockchain system, and displaying the related information to the user.
8. A computer readable storage medium storing an animal feed circulation tracking program executable by a processor to perform the blockchain-based animal feed circulation tracking method of any one of claims 4-7.
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| CN112364371B (en) * | 2020-10-16 | 2024-04-16 | 杭州甘道智能科技有限公司 | Vaccine transfer monitoring device and method based on block chain |
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| US9084411B1 (en) * | 2014-04-10 | 2015-07-21 | Animal Biotech Llc | Livestock identification system and method |
| CN107657459A (en) * | 2017-09-06 | 2018-02-02 | 翔创科技（北京）有限公司 | Auth method, settlement of insurance claim method, source tracing method, storage medium and the electronic equipment of livestock |
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