Automatic weighing and identifying system for dairy cows

Abstract

The present application belongs to the field of automatic weighing and identification of dairy cows, and relates to an automatic weighing and identification system for dairy cows. By setting a target dairy cow guiding module, a target dairy cow automatic identification module, a pneumatic door closing module, a target dairy cow dynamic weighing module, a cloud database, a dairy cow periodic body weight data extraction module, and a dairy cow periodic body weight data analysis module, the present application effectively realizes identity recognition and real-time body weight data monitoring of dairy cows, solves the problem of human resource loss, facilitates efficient management of farms, and makes up for the lack of real-time weighing of dairy cows in the prior art. According to the target dairy cow body weight comprehensive index, the health status of dairy cows is analyzed, scientific and reasonable basis is provided for the health monitoring and management of dairy cows, the problem of incomplete analysis of the health status of dairy cows is solved, more high-quality milk can be collected, and higher breeding economic benefits can be achieved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of automatic weighing and identification of dairy cows, and relates to an automatic weighing and identification system for dairy cows. BACKGROUND

[0002] With the improvement of the living standards of residents in China and the change of eating habits, the demand for milk has increased rapidly, and the breeding farm has gradually expanded the breeding scale of dairy cows. In order to improve the economic benefits of dairy farming, the feeding and management work at each stage needs to be done well to ensure the healthy growth of dairy cows, so as to produce more high-quality milk. The body weight change of dairy cows is one of the important parameters reflecting the health status of dairy cows, so measuring the body weight of dairy cows is essential in the work of breeding farms.

[0003] At present, the main way to weigh dairy cows is the traditional weighing method, that is, the dairy cows are driven to the livestock scale, and the body weight is registered manually. However, this method has obvious shortcomings:

[0004] 1. The current process of weighing dairy cows is complicated and has a low frequency. A large amount of human resources is consumed in driving the dairy cows and registering the body weight during the weighing process, which is not conducive to the efficient management of the breeding farm and cannot realize real-time monitoring of the body weight data of the dairy cows, so as to obtain the health status of the dairy cows in time.

[0005] 2. The current record and processing of the weighing data of dairy cows are one-sided and single, without considering the interference of external factors, such as the movement of dairy cows on the livestock scale and the leaning of dairy cows on the fence during weighing, etc. There is a problem of inaccurate weighing data caused by weighing error of dairy cows, so as to ensure the accuracy and reliability of the analysis of the health status of dairy cows.

[0006] 3. The current way of statistical analysis of the body weight of dairy cows belongs to a relatively conventional analysis method, and does not analyze the body weight change of dairy cows combined with the body weight change of other dairy cows, which has certain limitations. The analysis of the health status of dairy cows has low credibility, which is not convenient for the accurate understanding of the health status of dairy cows by the management personnel, so that the dairy cows cannot produce high-quality milk due to poor health status, thereby reducing the yield of high-quality milk and affecting the economic benefits of breeding. SUMMARY

[0007] In order to overcome the shortcomings in the background art, the embodiments of the present application provide an automatic weighing and identification system for dairy cows, which can effectively solve the problems involved in the above background art.

[0008] The purpose of the present application can be achieved by the following technical scheme: an automatic weighing and identification system for dairy cows, comprising: a target dairy cow guiding module for guiding the target dairy cow to a dairy cow identification waiting area through a dairy cow guiding device.

[0009] A target cow automatic identification module is configured to identify the identity of the target cow when the target cow reaches a cow identity recognition waiting area, the identity recognition including ear identification and body posture identification, and to open a pneumatic door after successful identification.

[0010] A pneumatic door closing module is configured to detect the distance between the tail of the target cow and the pneumatic door, analyze a safety distance coefficient, and execute a closing instruction based on the safety distance coefficient.

[0011] A target cow dynamic weighing module is configured to monitor the weighing information of the target cow within a set time period when the pneumatic door is closed, the weighing information including the body weight at each unit time point and posture action images, to process the real-time body weight data of the target cow, and to transmit the real-time body weight data to a cloud database.

[0012] A cloud database is configured to receive the real-time body weight data of the target cow, store the body weight data of each cow within a set period, standard ear tag information of each cow, standard body posture information of each cow, standard weighing posture action images of the cow, and a comprehensive set numerical range of the body weight of the cow.

[0013] A cow period body weight data extraction module is configured to extract the body weight of the target cow on each day within a set period from the cloud database, and analyze the body weight growth health index and the body weight growth compliance index of the target cow.

[0014] A cow period body weight data analysis module is configured to combine the body weight growth health index and the body weight growth compliance index of the target cow to obtain a comprehensive index of the body weight of the target cow, analyze the health condition of the body weight change of the target cow based on the comprehensive index, and report the identity information of the target cow to a manager if the health condition of the body weight change of the target cow is abnormal, to perform early warning.

[0015] Preferably, the target cow is identified by an electronic ear tag identifier arranged on the weighing machine pneumatic door to identify the electronic ear tag worn by the target cow, to obtain ear tag information of the target cow, the ear tag information including the breed, code, and age of the cow, and to match the ear tag information of the target cow with the standard ear tag information of each cow in the cloud database, to determine the identity information of the target cow if the ear tag information of the target cow is consistent with the standard ear tag information of a certain cow in the cloud database, and to perform body posture identification authentication of the target cow if the ear tag information of the target cow is inconsistent with the standard ear tag information of each cow in the cloud database.

[0016] Preferably, the target cow is authenticated by body shape recognition, and the specific analysis method is as follows: an electronic camera is arranged on the pneumatic door of the weighing machine to shoot a full-view video of the target cow, a full-view image of the target cow is obtained by digital video sampling technology, thereby obtaining the body shape information of the target cow, the body shape information includes the contour volume and the body surface pattern image, the number of black areas α and the number of white areas ε in the body surface pattern image of the target cow are counted, the standard body shape information of each cow in the cloud database is extracted, the standard contour volume and the standard number of black areas and the standard number of white areas in the body surface pattern image of each cow are obtained, and the body shape matching degree of the target cow and each cow is analyzed, and the calculation formula is: wherein λ represents the contour volume of the target cow, λ q represents the standard contour volume of the qth cow, α q represents the standard number of black areas in the body surface pattern image of the qth cow, ε q represents the standard number of white areas in the body surface pattern image of the qth cow, q=1, 2, …, a, β1, β2, and β3 represent the body shape matching proportion weight corresponding to the set contour volume, the number of black areas in the body surface pattern image, and the number of white areas, respectively, Δλ, Δα, and Δε represent the allowable difference of the set contour volume, the number of black areas in the body surface pattern image, and the number of white areas, respectively, κ represents the set body shape matching index, and e represents the natural constant; the body shape matching degrees of the target cow and each cow are arranged in order of size, and the identity information of the cow with the highest body shape matching degree of the target cow is taken as the identity information of the target cow.

[0017] Preferably, the analysis of the safety distance coefficient is performed according to the following steps: A1, a laser radar is arranged on the pneumatic door of the weighing machine to perform laser irradiation on the tail of the target cow, the interval t between the signal emission time and the signal receiving time is recorded, and the distance between the tail of the target cow and the pneumatic door is obtained according to the formula s=vt, wherein v represents the speed of light.

[0018] A2, the safety distance coefficient is analyzed, and the calculation formula is: wherein s0 represents the set safety distance between the tail of the cow and the pneumatic door.

[0019] A3, when the safety distance coefficient reaches the set safety distance coefficient, the pneumatic door executes the closing instruction; when the safety distance coefficient does not reach the set safety distance coefficient, the target cow is driven by the internal driving device of the weighing machine, and the above steps are repeated until the pneumatic door executes the closing instruction.

[0020] Preferably, the weighing information of the target cow in the set time period is monitored according to the following steps: B1, a weighing sensor arranged in the cow weighing area is used to measure the body weight of the target cow at each unit time point in the set time period to obtain the body weight of the target cow at each unit time point, denoted as bj , j represents the number of the jth unit time point, j = 1, 2, …, m.

[0021] B2, the electronic camera set up by the weighing area of the dairy cow, captures the posture action image of the target dairy cow at each unit time point within the set time period, obtains the posture action image of the target dairy cow at each unit time point, compares the posture action image of the target dairy cow at each unit time point with the standard weighing posture action image of the dairy cow in the cloud database, if the posture action image of the target dairy cow at a certain unit time point is consistent with the standard weighing posture action image of the dairy cow in the cloud database, the body weight at the unit time point is retained, if the posture action image of the target dairy cow at a certain unit time point is inconsistent with the standard weighing posture action image of the dairy cow in the cloud database, the body weight at the unit time point is excluded, and the retained body weight of the target dairy cow at each unit time point within the set time period is counted and recorded as the retained body weight of the target dairy cow at each unit time point bi', i represents the number of the ith retained unit time point, i = 1, 2, …, g.

[0022] Preferably, the real-time body weight data of the target dairy cow is obtained by screening the maximum and minimum values of the retained body weight of the target dairy cow at each unit time point according to the formula , wherein max(b i ') represents the maximum value of the retained body weight of the target dairy cow at each unit time point, min(b i ') represents the minimum value of the retained body weight of the target dairy cow at each unit time point, and g represents the total number of retained unit time points within the set time period.

[0023] Preferably, the growth and health index of the target dairy cow is obtained by extracting the body weight of the target dairy cow in the cloud database at each day within the set period according to the formula f , f represents the number of the fth day, f = 1, 2, …, p, and the growth rate of the target dairy cow at each day within the set period is obtained according to the formula , wherein r f-1 represents the body weight of the target dairy cow at the f-1th day within the set period, the number of days c1 of positive growth of the target dairy cow and the number of days c2 of negative growth of the target dairy cow are counted from the growth rate of the target dairy cow at each day within the set period, and the growth and health index η1 of the target dairy cow is analyzed, and the calculation formula is: , wherein γ1 represents the proportion weight of the growth and health index corresponding to the set number of days of positive growth of the target dairy cow, γ2 represents the proportion weight of the growth and health index corresponding to the set number of days of negative growth of the target dairy cow, θ represents the proportion weight factor of the set growth and health index, and p represents the total number of days of measuring body weight within the set period.

[0024] Preferably, the target cow weight growth conforms to the exponential, and the specific analysis method is: by extracting the standard ear tag information of each cow in the cloud database and the weight data of each cow in the set period, including the weight of each day, according to the breed and age of the target cow ear tag information, the same breed and age of each cow as the target cow is screened, which is each similar cow, and the weight χ zf of each similar cow in the set period is compared, z represents the number of the zth similar cow, z=1, 2, …, k, and the average daily weight gain of each similar cow is obtained, and the calculation formula is: χ z(f-1) z represents the weight of the zth similar cow on the f-1th day in the set period, so as to analyze the target cow weight growth exponential η2, and the calculation formula is: wherein k represents the total number of similar cows.

[0025] Preferably, the target cow weight comprehensive index, and the calculation formula is: μ=ln(1+η1*Δx+η2*Δy), wherein Δx represents the set target cow weight growth health index proportion factor, and Δy represents the set target cow weight growth exponential proportion factor.

[0026] Preferably, the target cow weight change health condition, and the specific analysis method is: by extracting the cow weight comprehensive set value range in the cloud database, comparing the target cow weight comprehensive index with the cow weight comprehensive set value range, if the target cow weight comprehensive index is in the cow weight comprehensive set value range, no warning signal is sent, and if the target cow weight comprehensive index is not in the cow weight comprehensive set value range, a warning signal is sent, and the identity information of the target cow is sent to the management personnel in the form of a short message through the background system connection, prompting manual intervention.

[0027] Compared with the prior art, the embodiments of the present application have at least the following advantages or beneficial effects: (1) the present application guides the target cow to the cow identity recognition waiting area through the cow guiding device, and drives the target cow to the pneumatic door to execute the closing instruction through the internal driving device of the weighing machine, effectively realizing the automatic driving of the cow, solving the problem of human resource loss, and facilitating efficient management of the farm.

[0028] (2) The present application processes the weighing information of the target cow in the set time period to obtain the real-time weight data of the target cow, and stores the real-time weight data to the cloud database, effectively realizing the real-time monitoring of the cow weight data, making up for the lack of real-time in the prior art in weighing the cow, and facilitating the management personnel to timely master the health status of the cow.

[0029] (3) The present application realizes the identity recognition of the dairy cow from ear tag information and body posture information, which can quickly and accurately obtain the identity of the dairy cow, facilitate the storage and extraction of the weighing data, and avoid the problems caused by human operation errors to a certain extent.

[0030] (4) The present application obtains the average weighing data of the dairy cow in the standard weighing posture by analyzing the body weight at each unit time point and the posture action image at each unit time point of the dairy cow in a set time period, thereby greatly reducing the weighing error of the dairy cow, improving the accuracy of the weighing data, and avoiding the analysis error of the health condition caused by inaccurate data measurement.

[0031] (5) The present application analyzes the body weight growth health index and the body weight growth compliance index of the dairy cow respectively, and then calculates the comprehensive index of the target dairy cow, thereby analyzing the health condition of the body weight change of the dairy cow, providing scientific and reasonable basis for the health monitoring and management of the dairy cow, avoiding the phenomenon of unhealthy growth of the dairy cow caused by the supervision of the dairy cow, and reducing the hidden danger of the disease infection of the dairy cow to a certain extent, thereby being beneficial to collect more high-quality milk and realize higher breeding economic benefits. BRIEF DESCRIPTION OF DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed for the description of the embodiments will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0033] Figure 1 It is a system structure schematic diagram of the present application.

[0034] Figure 2 It is a structure schematic diagram of the weighing machine of the present application. DETAILED DESCRIPTION

[0035] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0036] Please refer to Figure 1 and Figure 2As shown, the present application provides a kind of automatic weighing recognition system of dairy cow, and specific module distribution is as follows: target dairy cow guiding module, target dairy cow automatic identification module, pneumatic door closing module, target dairy cow dynamic weighing module, cloud database, dairy cow periodic body weight data extraction module, dairy cow periodic body weight data analysis module.The connection between the modules is that target dairy cow guiding module is connected with target dairy cow automatic identification module, target dairy cow automatic identification module is connected with pneumatic door closing module, pneumatic door closing module is connected with target dairy cow dynamic weighing module, target dairy cow automatic identification module, target dairy cow dynamic weighing module and dairy cow periodic body weight data extraction module are all connected with cloud database, and dairy cow periodic body weight data extraction module is connected with dairy cow periodic body weight data analysis module.

[0037] The target dairy cow guiding module is used to guide the target dairy cow to the dairy cow identity identification waiting area through the dairy cow guiding device.

[0038] Specifically, the target dairy cow is guided by the dairy cow guiding device, and the specific analysis process is as follows: a horn type channel, i.e., an outer wide and inner narrow type channel, is set up, and a fence provided with a vibration sensor is arranged on both sides of the channel, when the target dairy cow stops and leans against the fence, the fence is vibrated by the vibration sensor, and the dairy cow is driven to move forward to the dairy cow identity identification waiting area, and the dairy cow guiding device is composed of the horn type channel and the dairy cow identity identification waiting area.

[0039] The embodiment of the present application guides the target dairy cow by the dairy cow guiding device, effectively realizes the automatic driving of dairy cow, solves the problem of human resource loss, and facilitates efficient management of the farm.

[0040] The target dairy cow automatic identification module is used to identify the identity of the target dairy cow when it reaches the dairy cow identity identification waiting area, and the identity identification includes ear identification and body posture identification, and the pneumatic door is opened after successful identification.

[0041] Specifically, the identity of the target dairy cow is identified, and the specific analysis process is as follows: an electronic ear tag identifier is arranged on the weighing machine pneumatic door to identify the electronic ear tag worn by the target dairy cow, and the ear tag information of the target dairy cow is obtained, including the breed, code and age of the dairy cow, the ear tag information of the target dairy cow is one-to-one corresponding to the standard ear tag information of each dairy cow in the cloud database, if the ear tag information of the target dairy cow is consistent with the standard ear tag information of a certain dairy cow in the cloud database, the identity information of the dairy cow is taken as the identity information of the target dairy cow, and if the ear tag information of the target dairy cow is inconsistent with the standard ear tag information of each dairy cow in the cloud database, the body posture identity of the target dairy cow is identified and authenticated.

[0042] Further, the body shape identity recognition of the target dairy cow is specifically analyzed as follows: an electronic camera is arranged on a pneumatic door of a weighing machine to shoot a full-view video of the target dairy cow, a full-view image of the target dairy cow is obtained by digital video sampling technology, and thus the body shape information of the target dairy cow is obtained, the body shape information including a contour volume and a body surface pattern image, the number of black regions α and the number of white regions ε in the body surface pattern image of the target dairy cow are counted, the standard body shape information of each dairy cow in the cloud database is extracted, the standard number of black regions and the standard number of white regions in the contour volume and the body surface pattern image of each dairy cow are obtained, and the body shape matching degrees of the target dairy cow and each dairy cow are analyzed, and the calculation formula is: wherein λ represents the contour volume of the target dairy cow, λ q represents the standard contour volume of the qth dairy cow, α q represents the standard number of black regions in the body surface pattern image of the qth dairy cow, ε q represents the standard number of white regions in the body surface pattern image of the qth dairy cow, q = 1, 2, …, a, β1, β2, and β3 represent the body shape matching proportion weights corresponding to the set contour volume, the number of black regions in the body surface pattern image, and the number of white regions respectively, Δλ, Δα, and Δε represent the allowable differences of the set contour volume, the number of black regions in the body surface pattern image, and the number of white regions respectively, κ represents the set body shape matching index, and e represents a natural constant.

[0043] It should be noted that the full-view image of the target dairy cow obtained by the digital video sampling technology is that the full-view video of the target dairy cow is uploaded to the free studio software in the background, every 10 frames are extracted, and the full-view image of the target dairy cow is obtained.

[0044] Further, the number of black regions α and the number of white regions ε in the body surface pattern image of the target dairy cow are counted as follows: step one, the body surface pattern image of the target dairy cow is placed in the YUV color space, and the component space in which the black and white regions of the target dairy cow are more easily distinguished is found by comparing each component space, so as to realize image preprocessing.

[0045] Step two, a target dairy cow image histogram is drawn according to the preprocessed body surface pattern image of the target dairy cow, the image gray threshold is determined, the gray value of each pixel in the body surface pattern image of the target dairy cow is compared with the gray threshold, if the gray value of a certain pixel is less than the gray threshold, the pixel is recorded as a black pixel, and if the gray value of a certain pixel is greater than the gray threshold, the pixel is recorded as a white pixel.

[0046] Step 3: Divide the target cow's body pattern image into several equal sections. Count the number of black pixels and white pixels in each section. If the number of black pixels in a section is greater than the number of white pixels, then the section is designated as a black area section. The counted number of black area sections is the number of black areas α in the target cow's body pattern image. If the number of black pixels in a section is less than the number of white pixels, then the section is designated as a white area section. The counted number of white area sections is the number of white areas ε in the target cow's body pattern image.

[0047] This invention identifies dairy cows by using both ear tag information and body posture information. This not only allows for the rapid and accurate acquisition of cow identities, facilitating the storage and retrieval of weighing data, but also avoids problems caused by human error to a certain extent.

[0048] The pneumatic door closing module is used to detect the distance between the target cow's tail and the pneumatic door, analyze the distance to obtain a safety distance coefficient, and execute a closing command accordingly.

[0049] Specifically, the safety distance coefficient is analyzed as follows: A1. A laser radar is installed on the pneumatic door of the weighing machine to irradiate the tail of the target cow with laser. The interval t between the signal transmission time and the signal reception time is recorded. The distance between the tail of the target cow and the pneumatic door is obtained according to the formula s = vt, where v represents the speed of light.

[0050] A2. Analyze the safety distance factor; its calculation formula is as follows: Where s0 represents the set safe distance between the cow's tail and the pneumatic door.

[0051] A3. When the safety distance coefficient reaches the set safety distance coefficient, the pneumatic door executes the closing command. When the safety distance coefficient does not reach the set safety distance coefficient, the target cow is driven by the internal drive device of the weighing machine, and the above steps are repeated until the pneumatic door executes the closing command.

[0052] It should be noted that the internal drive device of the aforementioned weighing machine refers to the fences equipped with vibration sensors installed on both sides of the cow weighing area. When the safety distance coefficient is not reached, the vibration sensors cause the fences to vibrate, driving the target cow to move towards the cow weighing area until the pneumatic gate executes the closing command.

[0053] This invention uses an internal drive device in a weighing machine to drive the target dairy cows until the pneumatic gate executes a closing command, effectively achieving automated cow driving, saving a great deal of human resources, and facilitating efficient farm management.

[0054] The target dairy cow dynamic weighing module is used for monitoring the weighing information of the target dairy cow in a set time period when the pneumatic door is closed, the weighing information including body weight at each unit time point and posture action image, and processing to obtain real-time body weight data of the target dairy cow and transmitting to a cloud database.

[0055] Specifically, the weighing information of the target dairy cow in the set time period is monitored, and the specific analysis process is as follows: B1, the body weight of the target dairy cow at each unit time point in the set time period is measured by the weighing sensor arranged in the dairy cow weighing area, to obtain the body weight of the target dairy cow at each unit time point, denoted as b j , j represents the number of the jth unit time point, and j = 1, 2,..., m.

[0056] B2, the posture action image of the target dairy cow at each unit time point in the set time period is captured by the electronic camera arranged in the dairy cow weighing area, to obtain the posture action image of the target dairy cow at each unit time point, and the posture action image of the target dairy cow at each unit time point is compared with the standard dairy cow weighing posture action image in the cloud database, if the posture action image of the target dairy cow at a certain unit time point is consistent with the standard dairy cow weighing posture action image in the cloud database, the body weight at the unit time point is retained, if the posture action image of the target dairy cow at a certain unit time point is inconsistent with the standard dairy cow weighing posture action image in the cloud database, the body weight at the unit time point is excluded, and the retained body weight of the target dairy cow at each unit time point in the set time period is counted, denoted as the retained body weight of the target dairy cow at each unit time point b i , i represents the number of the ith retained unit time point, and i = 1, 2,..., g.

[0057] It should be noted that the measurement of the body weight of the target dairy cow at each unit time point in the set time period refers to the measurement of the body weight of the target dairy cow at each unit time point in the set time period when the target dairy cow is empty in the morning and finishes milking.

[0058] The embodiment of the present application considers the influence of external factors on the weighing of the dairy cow, and the processing of the weighing data also has a certain degree of error, by analyzing the body weight of the dairy cow at each unit time point in the set time period and the posture action image at each unit time point, the average weighing data of the dairy cow in the standard weighing posture is obtained, the weighing error of the dairy cow is greatly reduced, the accuracy of the weighing data is improved, and the error of the health condition analysis caused by inaccurate data measurement in the later period is avoided.

[0059] Further, the real-time body weight data of the target dairy cow is obtained by maximum value and minimum value screening of the retained body weight of the target dairy cow at each unit time point, according to the formula , wherein max(b irepresents the maximum value of the body weight of the target dairy cow at each reserved unit time point, g represents the minimum value of the body weight of the target dairy cow at each reserved unit time point, and g represents the total number of reserved unit time points in the set time period. i represents the maximum value of the body weight of the target dairy cow at each reserved unit time point, g represents the minimum value of the body weight of the target dairy cow at each reserved unit time point, and g represents the total number of reserved unit time points in the set time period.

[0060] The embodiment of the present application effectively realizes real-time monitoring of the body weight data of the dairy cow by monitoring the weighing information of the target dairy cow in the set time period, processing the real-time body weight data of the target dairy cow, and storing the real-time body weight data to the cloud database, which makes up for the lack of real-time in the prior art in weighing the dairy cow, and facilitates the management personnel to timely grasp the physical health condition of the dairy cow.

[0061] The dairy cow periodic body weight data extraction module is configured to extract the body weight of the target dairy cow on each day in a set period from the cloud database, and analyze the body weight growth health index and the body weight growth compliance index of the target dairy cow.

[0062] Specifically, the target dairy cow body weight growth health index has a specific analysis process as follows: the body weight of the target dairy cow on each day in the set period is extracted from the cloud database, and the body weight growth rate of the target dairy cow on each day in the set period is calculated according to the formula f , f represents the number of the fth day, f = 1, 2, …, p, and the body weight growth rate of the target dairy cow on each day in the set period is calculated according to the formula , wherein r f-1 represents the body weight of the target dairy cow on the f-1th day in the set period, the number of days of positive growth of the body weight of the target dairy cow c1 and the number of days of negative growth of the body weight of the target dairy cow c2 are counted from the body weight growth rate of the target dairy cow on each day in the set period, and the growth health index η1 of the target dairy cow is analyzed, and the calculation formula is: , wherein γ1 represents the proportion weight of the set number of days of positive growth of the body weight of the target dairy cow corresponding to the growth health index, γ2 represents the proportion weight of the set number of days of negative growth of the body weight of the target dairy cow corresponding to the growth health index, θ represents the set proportion weight factor of the growth health index, and p represents the total number of days of measuring the body weight in the set period.

[0063] It should be noted that the number of days of positive growth of the body weight of the target dairy cow c1 and the number of days of negative growth of the body weight of the target dairy cow c2 are counted, and the specific analysis process is as follows: when the body weight growth rate of the target dairy cow on a certain day in the set period is greater than 0, the day is recorded as a day of positive growth of the body weight of the target dairy cow, and the number of days of positive growth of the body weight of the target dairy cow is counted; when the body weight growth rate of the target dairy cow on a certain day in the set period is less than 0, the day is recorded as a day of negative growth of the body weight of the target dairy cow, and the number of days of negative growth of the body weight of the target dairy cow is counted.

[0064] In another specific embodiment, the body weight growth conforms to an index, and the specific analysis process is as follows: by extracting the standard ear tag information of each cow in the cloud database and the body weight data of each cow in a set period, including the body weight of each day, according to the breed and age of the target cow ear tag information, each cow of the same breed and age as the target cow is screened and recorded as each similar cow, and the body weight χ zf of each similar cow in each day in the set period is compared, z represents the number of the zth similar cow, z = 1, 2, …, k, the average daily weight gain of each similar cow is obtained, and the calculation formula is: wherein χ z(f-1) represents the weight of the zth similar cow on the f-1th day in the set period, and thus the body weight growth index η2 of the target cow is analyzed, and the calculation formula is: wherein k represents the total number of similar cows.

[0065] The cow cycle body weight data analysis module is used to combine the target cow body weight growth health index and the body weight growth index to obtain a target cow body weight comprehensive index, and the health condition of the target cow body weight change is analyzed according to the target cow body weight comprehensive index. If the health condition of the target cow body weight change is abnormal, the identity information of the target cow is reported to the management personnel for early warning.

[0066] Specifically, the calculation formula of the target cow body weight comprehensive index is μ = ln(1 + η1*Δx + η2*Δy), wherein Δx represents a set target cow body weight growth health index proportion factor, and Δy represents a set target cow body weight growth index proportion factor.

[0067] In another specific embodiment, the analysis of the health condition of the target cow body weight change is as follows: by extracting the cow body weight comprehensive set numerical range in the cloud database, the target cow body weight comprehensive index is compared with the cow body weight comprehensive set numerical range, if the target cow body weight comprehensive index is within the cow body weight comprehensive set numerical range, no early warning signal is sent, if the target cow body weight comprehensive index is not within the cow body weight comprehensive set numerical range, an early warning signal is sent, the identity information of the target cow is sent to the management personnel in the form of a short message through a background system connection, and manual intervention is prompted.

[0068] The embodiment of the present application carries out targeted analysis on the body weight growth health index and the body weight growth compliance index of the dairy cow respectively, and then calculates the comprehensive index of the target dairy cow body weight, combines and analyzes the body weight change of the dairy cow and the body weight change of other dairy cows to obtain the comprehensive body weight change of the dairy cow, effectively provides scientific and reasonable basis for the health monitoring and management of the dairy cow, solves the problem of not comprehensive analysis of the health condition of the dairy cow, avoids the phenomenon that the dairy cow grows unhealthily due to the weak supervision of the dairy cow, and to a certain extent, reduces the hidden danger of the disease of the dairy cow, is beneficial to collect more high-quality milk, and realizes higher breeding economic benefits.

[0069] The above is only an example and description of the concept of the present application, and those skilled in the art can make various modifications or supplements or replace with similar ways, as long as the concept of the present application is not deviated or the scope defined by the present application is not exceeded, which belongs to the protection scope of the present application.

Claims

1. A dairy cow automatic weighing and recognition system, characterized in that, The system comprises: A target cow guiding module for guiding the target cow to a cow identity recognition waiting area through a cow guiding device; A target cow automatic identification module for identifying the identity of the target cow when the target cow reaches the cow identity recognition waiting area, the identity recognition comprising ear identification and body posture identification, and the pneumatic door being opened after successful identification; A pneumatic door closing module for detecting the distance between the tail of the target cow and the pneumatic door, analyzing a safety distance coefficient, and executing a closing instruction accordingly; A target cow dynamic weighing module for monitoring the weighing information of the target cow within a set time period when the pneumatic door is closed, the weighing information comprising body weight at each unit time point and posture action images, processing the real-time body weight data of the target cow, and transmitting the data to a cloud database; The cloud database is used for receiving the real-time body weight data of the target cow, storing the body weight data of each cow within a set period, standard ear tag information of each cow, standard body posture information of each cow, standard weighing posture action images of the cow, and a set numerical range of the comprehensive index of the body weight of the cow; A cow period body weight data extraction module for extracting the body weight of the target cow on each day within a set period from the cloud database, and analyzing the body weight growth health index and the body weight growth compliance index of the target cow; A cow period body weight data analysis module for combining the body weight growth health index and the body weight growth compliance index of the target cow to obtain the comprehensive index of the body weight of the target cow, analyzing the health condition of the body weight change of the target cow, and reporting the identity information of the target cow to the management personnel for early warning if the health condition of the body weight change of the target cow is abnormal.

2. The automatic weighing and identification system for dairy cows according to claim 1, characterized in that: The specific analysis method for identifying the identity of the target cow is as follows: An electronic ear identifier is arranged on the pneumatic door of the weighing machine to identify the electronic ear tag worn by the target cow, and the ear tag information of the target cow is obtained, including the breed, code and age of the cow. The ear tag information of the target cow is matched with the standard ear tag information of each cow in the cloud database. If the ear tag information of the target cow is consistent with the standard ear tag information of a certain cow in the cloud database, the identity information of the cow is taken as the identity information of the target cow. If the ear tag information of the target cow is inconsistent with the standard ear tag information of each cow in the cloud database, the body posture identity recognition authentication of the target cow is performed.

3. The automatic weighing and identification system for dairy cows according to claim 2, characterized in that: The specific analysis method for the body posture identity recognition authentication of the target cow is as follows: An electronic camera is arranged on the pneumatic door of the weighing machine to shoot a video of the whole appearance of the target cow, and a whole appearance image of the target cow is obtained by digital video sampling technology, so as to obtain the body state information of the target cow, the body state information including a contour volume and a body surface pattern image, and the number of black regions in the body surface pattern image of the target cow is counted and the number of white regions Standard body state information of each cow in the cloud database is extracted to obtain a standard contour volume and the number of standard black regions and the number of standard white regions in the body surface pattern image of each cow, and the body state matching degree of the target cow and each cow is analyzed, and the calculation formula is: wherein represents the contour volume of the target cow, represents the contour volume of the qth cow, represents the number of standard black regions in the body surface pattern image of the qth cow, represents the number of standard white regions in the body surface pattern image of the qth cow, , , , respectively represent the body state matching proportion weight corresponding to the set contour volume, the number of black regions in the body surface pattern image and the number of white regions, respectively represent the allowable difference of the set contour volume, the number of black regions in the body surface pattern image and the number of white regions, represents the set body state matching index, and e represents a natural constant. The body state matching degrees of the target cow and each cow are arranged in order of size, and the identity information of the cow with the highest body state matching degree of the target cow is taken as the identity information of the target cow.

4. The automatic weighing and identification system for dairy cows according to claim 1, characterized in that: The specific analysis method for the safety distance coefficient is as follows: A1: through the weighing machine door set up laser radar, target dairy cow tail laser irradiation, record the signal emission time and signal receiving time interval t, according to the formula The distance between the target dairy cow tail and the pneumatic door is obtained, wherein v represents the speed of light. A2: analysis of the safety distance coefficient, the formula is: Wherein represents the set of cow tail and pneumatic door safety distance; A3: When the safety distance coefficient reaches the set safety distance coefficient, the pneumatic door executes the closing instruction. When the safety distance coefficient does not reach the set safety distance coefficient, the target cow is driven by the internal driving device of the weighing machine, and the above steps are repeated until the pneumatic door executes the closing instruction.

5. The automatic weighing and identification system for dairy cows according to claim 1, characterized in that: The monitoring target cow's weight information in a set time period is specifically analyzed as follows: B1: measuring the weight of the target cow at each unit time point in the set time period through a weight sensor arranged in a cow weighing area to obtain the weight of the target cow at each unit time point, denoted as , j represents the number of the jth unit time point, . B2: Through the electronic camera set by the cow weighing area, the posture action image of the target cow at each unit time point in the set time period is captured to obtain the posture action image of the target cow at each unit time point, the posture action image of the target cow at each unit time point is compared with the standard cow weighing posture action image in the cloud database, if the posture action image of the target cow at a certain unit time point is consistent with the standard cow weighing posture action image in the cloud database, the body weight at the unit time point is retained, if the posture action image of the target cow at a certain unit time point is inconsistent with the standard cow weighing posture action image in the cloud database, the body weight at the unit time point is excluded, the retained body weight of the target cow at each unit time point in the set time period is counted, which is recorded as the retained body weight of the target cow at each unit time point , i represents the number of the i th retained unit time point, .

6. The automatic weighing and identification system for dairy cows according to claim 5, characterized in that: The specific analysis method for the real-time body weight data of the target cow is as follows: The real-time body weight data of the target dairy cow is obtained by screening the maximum value and the minimum value of the body weight of each retained unit time point of the target dairy cow according to the formula wherein represents the maximum value of the body weight of each retained unit time point of the target dairy cow, represents the minimum value of the body weight of each retained unit time point of the target dairy cow, and g represents the total number of retained unit time points in the set time period.

7. The automatic weighing and identification system for dairy cows according to claim 1, characterized in that: The specific analysis method for the body weight growth health index of the target cow is as follows: By extracting the body weight of the target cow in the cloud database on each day within the set period , f represents the number of the fth day, , according to the formula , the body weight growth rate of the target cow on each day within the set period is obtained, wherein represents the body weight of the target cow on the f-1th day within the set period, the number of days of positive growth of the body weight of the target cow is counted from the body weight growth rate of the target cow on each day within the set period , and the number of days of negative growth of the body weight of the target cow , so as to analyze the growth and health index of the target cow , and the calculation formula is: , wherein represents the proportion weight of the growth and health index corresponding to the set number of days of positive growth of the body weight of the target cow, represents the proportion weight of the growth and health index corresponding to the set number of days of negative growth of the body weight of the target cow, represents the set proportion weight factor of the growth and health index, and p represents the total number of days of measuring the body weight within the set period.

8. The automatic weighing and identification system for dairy cows according to claim 7, characterized in that: The specific analysis method for the body weight growth compliance index of the target cow is as follows: By extracting the standard ear tag information of each cow in the cloud database and the weight data of each cow in the set period, including the weight of each day, according to the breed and age of the target cow ear tag information, each cow of the same breed and age as the target cow is screened and recorded as each similar cow, and the weight of each similar cow in the set period is compared , z represents the number of the zth similar cow, , the average daily weight gain of each similar cow is obtained, and the calculation formula is: , wherein represents the weight of the zth similar cow on the f-1th day in the set period, so as to analyze the target cow weight growth in accordance with the exponential , and the calculation formula is: , wherein k represents the total number of similar cows.

9. The automatic weighing and identification system for dairy cows according to claim 8, characterized in that: The target cow weight comprehensive index, a calculation formula of which is: Wherein Indicates a set target cow weight growth health index proportion factor, Indicates a set target cow weight growth compliance index proportion factor.

10. The automatic weighing and identification system for dairy cows according to claim 9, characterized in that: The specific analysis method for the health condition of the body weight change of the target cow is as follows: The target cow weight comprehensive index is compared with the cow weight comprehensive index setting value range by extracting the cow weight comprehensive index setting value range in the cloud database, if the target cow weight comprehensive index is in the cow weight comprehensive index setting value range, no early warning signal is sent, if the target cow weight comprehensive index is not in the cow weight comprehensive index setting value range, an early warning signal is sent, the identity information of the target cow is sent to the management personnel in the form of short message through the background system connection, and manual intervention is prompted.

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