An analysis method and analysis system for bacterial distribution in a chicken house

Abstract

The application discloses an analysis method and analysis system for bacterial distribution in a chicken house. Firstly, actual bacterial concentration parameters and actual three-dimensional coordinate parameters of each measuring point in a chicken house to be analyzed are acquired, airflow parameters of each measuring point are acquired through an airflow model of the chicken house to be analyzed, three-dimensional coordinate parameters of a prediction point are selected, each measuring point is screened according to a screening function, an effective measuring point set is acquired, and bacterial flux and weight parameters are respectively calculated through a time interpolation method. Finally, prediction bacterial concentration parameters of the prediction point are calculated according to each bacterial flux and each weight parameter, and a bacterial concentration distribution map is finally obtained. The application comprehensively evaluates airflow conditions of each prediction point and influences of the measuring points on the prediction point based on actual measured parameters and an airflow model, estimates bacterial concentration of the prediction point by combining an airflow solidification algorithm and a time interpolation algorithm, and thus rapidly and accurately obtains the bacterial concentration distribution map in the chicken house.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of poultry breeding, in particular to a method and system for analyzing the distribution of bacteria in a chicken coop. BACKGROUND

[0002] In the livestock and poultry breeding environment, monitoring and predicting the distribution of bacteria in the chicken coop is an important link to ensure the health and production safety of livestock and poultry. Bacterial infection is a major health concern in large-scale chicken farms. In the prior art, the distribution of bacteria in the chicken coop is generally monitored / detected by manual inspection or random sampling by a single sensor device. Some chicken coops also choose to set up multiple fixed detection points to measure the bacterial content in different areas of the chicken coop. However, due to the limitations of device cost and operational complexity, the accuracy of the bacterial distribution map obtained is poor, which leads to poor accuracy of bacterial distribution analysis, and cannot meet the actual epidemic prevention and control needs. SUMMARY

[0003] The main purpose of the present application is to provide a method and system for analyzing the distribution of bacteria in a chicken coop, aiming to solve the problem of poor analysis accuracy in the prior art.

[0004] The present application achieves the above-mentioned purpose by the following technical solutions:

[0005] A method for analyzing the distribution of bacteria in a chicken coop, comprising:

[0006] obtaining actual bacterial concentration parameters and actual three-dimensional coordinate parameters of each measurement point in the chicken coop to be analyzed;

[0007] constructing an air flow model of the chicken coop to be analyzed, and obtaining air flow parameters of each measurement point according to the air flow model and the actual three-dimensional coordinate parameters, wherein the air flow parameters include air flow velocity and air flow direction;

[0008] selecting a prediction point P and obtaining a prediction point three-dimensional coordinate parameter;

[0009] setting a screening function according to the air flow model, screening each measurement point according to the screening function, the actual three-dimensional coordinate parameter and the prediction point three-dimensional coordinate parameter, and obtaining an effective measurement point set S;

[0010] calculating the bacterial flux and weight parameter of each effective measurement point to the prediction point by time interpolation method;

[0011] calculating the prediction bacterial concentration parameter of the prediction point according to each bacterial flux and each weight parameter;

[0012] repeating the steps of selecting the prediction point P and obtaining the prediction point three-dimensional coordinate parameter, and obtaining a prediction bacterial concentration parameter set;

[0013] A bacterial concentration distribution map is generated by fitting the actual bacterial concentration parameters and the predicted bacterial concentration parameter set.

[0014] Optionally, an airflow model of the chicken coop to be analyzed is constructed, and the airflow parameters of each measurement point are obtained based on the airflow model and the actual three-dimensional coordinate parameters, including the following steps:

[0015] Obtain the boundary conditions of the air inlet, air outlet, and fitting boundary conditions of several fitting points in the chicken house to be analyzed.

[0016] Establish a three-dimensional model of the chicken coop to be analyzed;

[0017] An airflow model for the chicken house to be analyzed is constructed based on the inlet boundary conditions, outlet boundary conditions, fitting boundary conditions, and three-dimensional model.

[0018] Based on the actual three-dimensional coordinate parameters, the airflow parameters of each measurement point are extracted from the airflow model, wherein the airflow parameters include airflow velocity and airflow direction.

[0019] Optionally, a sieving function is set according to the airflow model, and each measurement point is sieved according to the sieving function, the actual three-dimensional coordinate parameters, and the predicted three-dimensional coordinate parameters to obtain an effective measurement point set S, including the following steps:

[0020] Based on the actual three-dimensional coordinate parameters and the three-dimensional coordinate parameters of the predicted point, the distance parameter set {d1(P), d2(P), ..., d...} between the predicted point and each measurement point is calculated. i (P)}, where i represents the number of the measurement point;

[0021] Connect the predicted points to each measurement point to obtain a straight propagation path;

[0022] Obtain the airflow direction at each measurement point;

[0023] The set of angles between the airflow direction and the propagation path {θ1(P), θ2(P), ..., θ3(P)} is calculated based on the propagation path straight line and each of the airflow direction parameters. i (P)};

[0024] Based on the distance parameter set, the included angle set, and the sieving function, each measurement point is sieved, and the measurement points that satisfy the sieving function are taken as valid measurement points, and the set of valid measurement points S is output.

[0025] Optionally, the expression for the sieving function is: and , where d max This represents the maximum effective distance set, where i represents the measurement point number, P represents the prediction point, and θ represents the maximum effective distance. i(P) represents an included angle between an airflow direction of the measurement point numbered i and a propagation path from the measurement point numbered i to the prediction point P.

[0026] Optionally, the bacterial flux and the weight parameter of each effective measurement point to the prediction point are calculated respectively by time interpolation method;

[0027] The bacterial concentration is corrected by time interpolation method to obtain a bacterial concentration correction parameter;

[0028] The basic attenuation coefficient and the attenuation rate coefficient are set according to the airflow model to determine an airflow attenuation function;

[0029] The angle attenuation function is set according to the attenuation rate coefficient;

[0030] The propagation time from the measurement point to the prediction point is obtained according to the time interpolation method;

[0031] The weight parameter is calculated according to the airflow attenuation function, the angle attenuation function and the propagation time;

[0032] The bacterial flux is calculated according to the bacterial concentration correction parameter.

[0033] Optionally, the bacterial concentration is corrected by time interpolation method to obtain a bacterial concentration correction parameter, including the following steps:

[0034] The effective average wind speed on the propagation path is calculated by integration;

[0035] The propagation time is determined according to the effective average wind speed and the distance parameter;

[0036] According to the propagation time, the bacterial concentration parameter is corrected by time interpolation method to obtain a bacterial concentration correction parameter.

[0037] Optionally, the calculation expression of the effective average wind speed is

[0038] The expression of the propagation time is ,

[0039] The expression of the bacterial concentration correction parameter is:

[0040] wherein represents a wind speed component consistent with the propagation direction, the sampling time satisfies , t k and t k+1 represent two adjacent sampling times; T represents time, represents the bacterial concentration parameter at the sampling time t k , and represents the bacterial concentration fitting parameter at the sampling time t k+1 .​

[0041] Optionally, the calculation expression of the air flow attenuation function is , the angle attenuation function ; the calculation expression of the weight parameter is , and the calculation expression of the bacterial flux is ; wherein represents the wind speed component of the wind speed of the measurement point numbered i in the propagation direction to the prediction point;

[0042] Optionally, the calculation expression of the predicted bacterial concentration parameter is , wherein represents the predicted bacterial concentration parameter of the prediction point P at time T.

[0043] Correspondingly, the application also discloses an analysis system based on the above analysis method, comprising:

[0044] A parameter acquisition module is configured to acquire actual bacterial concentration parameters and actual three-dimensional coordinate parameters of each measurement point in a chicken house to be analyzed;

[0045] An air flow parameter acquisition module is configured to construct an air flow model of the chicken house to be analyzed, and acquire air flow parameters of each measurement point according to the air flow model and the actual three-dimensional coordinate parameters;

[0046] A prediction point selection module is configured to select a prediction point and acquire a three-dimensional coordinate parameter of the prediction point;

[0047] A screening module is configured to set a screening function according to the air flow model, and screen each measurement point according to the screening function, the actual three-dimensional coordinate parameter and the three-dimensional coordinate parameter of the prediction point, so as to acquire an effective measurement point set S;

[0048] A first calculation module is configured to calculate the bacterial flux and the weight parameter of each effective measurement point to the prediction point by a time interpolation method;

[0049] The predicted bacterial concentration parameter of the prediction point is calculated according to the bacterial flux and the weight parameter;

[0050] The step of selecting the prediction point P and acquiring the three-dimensional coordinate parameter of the prediction point is repeated, and a predicted bacterial concentration parameter set is acquired;

[0051] An image fitting module is configured to generate a bacterial concentration distribution map by fitting the actual bacterial concentration parameters and the predicted bacterial concentration parameter set.

[0052] Compared with the prior art, the application has the following beneficial effects:

[0053] ​The application firstly acquires actual bacteria concentration parameters and actual three-dimensional coordinate parameters of each measuring point in the chicken house to be analyzed, and acquires airflow parameters of each measuring point through an airflow model of the chicken house to be analyzed, then selects a prediction point and acquires three-dimensional coordinate parameters of the prediction point, sets a screening function according to the airflow model, screens each measuring point according to the screening function, the actual three-dimensional coordinate parameters and the three-dimensional coordinate parameters of the prediction point to acquire an effective measuring point set S, and calculates bacteria flux and weight parameters of each effective measuring point to the prediction point through a time interpolation method respectively; finally, the application calculates a prediction bacteria concentration parameter of the prediction point according to each bacteria flux and each weight parameter; the above steps are repeated to acquire prediction bacteria concentration parameters of multiple prediction points, and the actual measured actual bacteria concentration parameters and the calculated prediction bacteria concentration parameter set are fitted to generate a bacteria concentration distribution map.

[0054] Compared with the prior art, the application arranges multiple measuring points in the chicken house to be analyzed, and acquires actual bacteria concentration parameters and actual three-dimensional coordinate parameters of the measuring points actually detected; airflow is one of main factors causing changes in bacteria distribution in a closed chicken house, and the construction of an airflow model of the chicken house to be analyzed can restore the airflow form inside the chicken house as much as possible, and then acquire basic parameters for calculation.

[0055] In the airflow movement process, airflow from one point to another point gradually attenuates with distance, so when the distance is too large, the influence between two points can be ignored, the setting of the screening function can screen out the measuring points that will not affect the prediction point, reduce the calculation amount, and also eliminate data interference, which is beneficial to improve the analysis accuracy.

[0056] Secondly, each effective measuring point has a wind speed component in the propagation direction thereof and the prediction point, the wind speed component will drive part of the airflow containing bacteria to move to the prediction point, the airflow is assumed to be uniform in bacteria concentration, based on the technical idea of airflow solidification, the wind speed component will deliver a certain amount of gas to the prediction point in a unit time and unit diffusion area, and the bacteria concentration of the prediction point can be estimated by collecting the gas delivered by multiple measuring points, all measuring point parameters that can affect the bacteria concentration of the prediction point are considered in the above method, and the airflow model is combined, so that the analysis accuracy is improved.

[0057] Finally, since the wind speed components of different measuring points are different, and the distances from different measuring points to the prediction point are also different, the order of the airflow reaching the prediction point is different, and for this, the application estimates by time interpolation method, that is, if the propagation time of the airflow from the measuring point to the prediction point is t, the airflow received by the prediction point at T time is actually the airflow at T-t time, and the bacterial concentration parameter at this time can be obtained by combining the time difference algorithm with the bacterial concentration values measured by the measuring point before and after, thereby reducing the influence of time on the estimation and improving the analysis accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0058] Figure 1 A flow chart of an analysis method for bacterial distribution in a henhouse provided in Embodiment 1 of the application;

[0059] Figure 2 A calculation principle diagram of the included angle;

[0060] Figure 3 An actual bacterial concentration parameter distribution histogram;

[0061] Figure 4 A structure diagram of an analysis system provided in Embodiment 2 of the application;

[0062] The object, functional features and advantages of the application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0063] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative efforts belong to the protection scope of the application.

[0064] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the application are only used to explain the relative positional relationship, movement condition, etc. between components in a certain posture (as shown in the drawings), and if the certain posture changes, the directional indications also change accordingly.

[0065] In the present application, unless otherwise explicitly specified and limited, the terms "connection", "fixation" and the like should be understood in a broad sense, for example, "fixation" can be fixed connection, or detachable connection, or integral; can be mechanical connection, or electrical connection; can be directly connected, or indirectly connected through an intermediate medium, can be the internal communication of two elements or the interaction relationship of two elements, unless otherwise explicitly limited. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0066] In addition, if the present application has a description of "first", "second" and the like in the embodiments, the description of "first", "second" and the like is only for the purpose of description, and cannot be understood as indicating or implying the relative importance of the indicated technical features or implicitly indicating the number of the indicated technical features. Therefore, the features limited by "first", "second" can explicitly or implicitly include at least one of the features. In addition, the meaning of "and / or" appearing throughout the text includes three parallel schemes. For example, "robot coordinate system and / or m" includes robot coordinate system scheme, or m scheme, or robot coordinate system and m scheme. In addition, the technical solutions of each embodiment can be combined with each other, but it must be based on the realization of ordinary skilled in the art, when the combination of technical solutions appears contradictory or unachievable, it should be considered that the combination of technical solutions does not exist, nor within the scope of protection required by the present application.

[0067] Embodiment 1:

[0068] Reference Figures 1 to 3 As an optional embodiment of the present application, a method for analyzing the distribution of bacteria in a chicken house is disclosed, comprising the following steps:

[0069] S1, obtaining the actual bacteria concentration parameters and actual three-dimensional coordinate parameters of each measurement point in the chicken house to be analyzed;

[0070] First, select several points in the chicken house to be analyzed as measurement points, preferably, each measurement point is selected in the space 0.5-0.7m away from the ground, which does not belong to the activity space of the chicken, which is beneficial to avoid the influence of chicken activity on air flow; At the same time, the above-mentioned area is close to the activity range of the chicken, which can more truly reflect the bacteria concentration in the activity area of the chicken;

[0071] It should be noted that the method described in the present application is suitable for large indoor chicken free-range farms;

[0072] After the measurement points are selected, set bacteria concentration sensors, such as air microorganism samplers, in each measurement point;

[0073] After the equipment is set up, the actual bacterial concentration parameters C(1, t1), C(2, t1), ..., C(i, t1), ..., C(i, t1), can be obtained by setting the sampling period for each measurement point. u ), C(2, t u ), ..., C(i, t) u ); where i represents the number of each measurement point, t u This indicates the sampling time;

[0074] Simultaneously, by establishing a three-dimensional coordinate system based on the architectural drawings of the chicken coop, the actual three-dimensional coordinate parameters (x1, y1, z1), (x2, y2, z2), ..., (x...) of each measurement point can be obtained. i y i , z i ); reference Figure 3 This application provides a bar chart showing the distribution of actual bacterial concentration parameters at different locations within a chicken house at a certain moment.

[0075] S2. Construct an airflow model of the chicken house to be analyzed, and obtain the airflow parameters of each measurement point based on the airflow model and the actual three-dimensional coordinate parameters, where i represents the measurement point number;

[0076] S21. Obtain the boundary conditions of the air inlet, the air outlet, and the fitting boundary conditions of several fitting points of the chicken house to be analyzed.

[0077] Wind speed and direction detection devices are installed at all air inlets of the chicken house to be analyzed, and the same detection devices are also installed at all air outlets. The air inlet boundary conditions and air outlet boundary conditions at different times are obtained through the above detection devices, wherein the air inlet boundary conditions and air outlet boundary conditions include wind speed and wind direction.

[0078] Since the chicken coop has a large space, in order to further improve the accuracy of the calculation, several fitting points are selected at different holes in the chicken coop. The number of fitting points is determined according to the actual situation. It should be noted that some measurement points can be used as fitting points.

[0079] Subsequently, wind speed and wind direction detection devices were set up at each fitting point, and the three-dimensional coordinates of each fitting point were obtained. The detected fitting boundary conditions were then matched with the three-dimensional coordinates.

[0080] S22. Establish a three-dimensional model of the chicken coop to be analyzed;

[0081] Based on the three-dimensional dimensions of the chicken coop to be analyzed, a full-size three-dimensional model of the chicken coop is constructed using architectural software;

[0082] S23. Construct an airflow model for the chicken house to be analyzed based on the inlet boundary conditions, outlet boundary conditions, fitting boundary conditions, and three-dimensional model.

[0083] The air inlet boundary conditions, air outlet boundary conditions, fitting boundary conditions, and three-dimensional model obtained in steps S21 and S22 are used to construct an airflow model of the chicken house to be analyzed using CFD software.

[0084] S24. Extract the airflow parameters of each measurement point from the airflow model according to the actual three-dimensional coordinate parameters, wherein the airflow parameters include airflow velocity and airflow direction.

[0085] By combining the three-dimensional coordinates of each measurement point, points are selected in the airflow model to obtain the airflow parameters at each point. The airflow parameters include airflow velocity and airflow direction.

[0086] S3. Select prediction point P and obtain the three-dimensional coordinate parameters of the prediction point;

[0087] Randomly select points within the airflow model of the chicken coop to be analyzed, and use these selected points as prediction points P. Simultaneously, obtain the three-dimensional coordinate parameters (x, y, y) of the prediction points. P y P , z P );

[0088] S4. Set a sieving function according to the airflow model, and sieve each measurement point according to the sieving function, the actual three-dimensional coordinate parameters and the predicted three-dimensional coordinate parameters to obtain an effective measurement point set S;

[0089] S41. Calculate the distance parameter set {d1(P), d2(P), ..., d...} between the predicted point and each measurement point based on the actual three-dimensional coordinate parameters and the predicted point's three-dimensional coordinate parameters. i (P)}, where i represents the number of the measurement point;

[0090] Obtain the actual coordinate parameters (x1, y1, z1), (x2, y2, z2), ..., (x...) of each measurement point. i y i , z i ) and the predicted point's three-dimensional coordinate parameters (x P y P , z P );

[0091] The distance parameters between the predicted point and each measurement point are calculated according to the Euclidean distance formula, and the calculated distance parameters are aggregated to form a distance parameter set {d1(P), d2(P), ..., d...}. i (P)}, where i represents the number of the measurement point;

[0092] The expression for the distance calculation formula is as follows: ;d i(P) represents the distance between the measurement point numbered i and the prediction point.

[0093] S42, connecting the prediction point with each measurement point respectively to obtain a propagation path straight line;

[0094] Referring to Figure 2 , connecting the prediction point with each measurement point respectively by a straight line, and the straight line is the propagation path straight line between the prediction point and each measurement point;

[0095] S43, obtaining the air flow direction of each measurement point;

[0096] S44, calculating a set of angles {θ1(P), θ2(P),..., θ i (P)} between the air flow direction and the propagation path according to the propagation path straight line and the air flow direction parameter of each measurement point;

[0097] Referring to Figure 2 , first, a plane L is generated, and the propagation path straight line between the prediction point and a measurement point and the unit vector according to the wind speed of the measurement point are located on the plane L;

[0098] The angle between the propagation path straight line and the unit vector bracket is calculated, and the angle is the angle between the air flow direction and the propagation path calculated according to the propagation path straight line and the air flow direction parameter of each measurement point;

[0099] Similarly, the angles with other measurement points can be obtained, and a set of angles is obtained.

[0100] S45, screening each measurement point according to the distance parameter set, the angle set and the screening function, taking the measurement point satisfying the screening function as an effective measurement point, and outputting an effective measurement point set S.

[0101] The screening function is set, and the expression of the screening function is and , wherein d max represents the maximum effective distance, i represents the measurement point number, P represents the prediction point, represents the angle between the air flow direction of the measurement point numbered i and the propagation path between the measurement point numbered i and the prediction point P.

[0102] It should be noted that the maximum effective distance d max is generally determined according to actual measurement or actual experience. During the propagation of the air flow, the propagation speed will gradually decrease, and the influence on the prediction point will also gradually decrease until there is no influence, so the measurement point with no influence or small influence on the prediction point is excluded by the maximum effective distance d max ;

[0103] Meanwhile, when the angle between the wind direction and the propagation path is greater than 90°, the wind direction is opposite to the propagation path, and the wind direction has no effect on the prediction point, and vice versa, when the angle is less than 90°, the wind direction is the same as the propagation path, so the part of the measurement point with incorrect wind direction is excluded;

[0104] Through twice screening of the measurement points by the distance and the airflow direction, the associated and unassociated measurement points are excluded, so as to reduce the complexity of the calculation, especially in the case of setting a large number of measurement points, the calculation efficiency can be effectively improved, and the calculation accuracy can be ensured at the same time;

[0105] S5, the bacterial flux and the weight parameter of each effective measurement point to the prediction point are calculated by the time interpolation method;

[0106] S51, the bacterial concentration is corrected by the time interpolation method to obtain a bacterial concentration correction parameter;

[0107] S511, the effective average wind speed on the propagation path is calculated by integration.

[0108] The calculation expression of the effective average wind speed is , wherein represents the wind speed component consistent with the propagation direction, that is, the airflow of the measurement point numbered i in the propagation direction of the prediction point;

[0109] S512, the propagation time is determined according to the effective average wind speed and the distance parameter;

[0110] The expression of the propagation time is ; wherein d i (P) represents the distance between the measurement point numbered i and the prediction point;

[0111] S513, the bacterial concentration parameter is corrected by the time interpolation method according to the propagation time to obtain a bacterial concentration correction parameter;

[0112] The expression of the bacterial concentration correction parameter is: ,

[0113] , wherein represents the wind speed component consistent with the propagation direction, and the sampling time satisfies , t k and t k+1 represent two adjacent sampling times; T represents the time, represents the bacterial concentration parameter at the sampling time t k , represents the bacterial concentration fitting parameter at the sampling time t k+1 ;

[0114] The technical principle of the above-mentioned bacteria concentration correction parameter is described by taking the measurement point i and the prediction point as an example;

[0115] The time required for the airflow to flow from the measurement point i to the observation point is the propagation time calculated in step S512. Assuming that the bacteria concentration in the airflow does not change during the intercalation process, the bacteria concentration parameter received by the prediction point at time T is the same as the bacteria concentration parameter of the measurement point at time T-t ip ;

[0116] The bacteria concentration parameter of the measurement point at time T-t ip is highly correlated with the parameters of the two closest sampling time points before and after it, that is, the bacteria concentration of each measurement point changes linearly, thereby obtaining the above-mentioned bacteria concentration correction parameter.

[0117] It should be noted that, represents the bacteria concentration parameter at time t k , which is historical data that has been obtained and can be directly adjusted;

[0118] The parameter at time t k+1 may not be collected due to not reaching the time point, and at this time, the bacteria concentration fitting parameter at time t k+1 is used as a substitute;

[0119] Specifically, if no sudden situation such as disinfection occurs, the bacteria concentration fitting parameter at time t may be used as a substitute;The above-mentioned calculation method is also applicable to the case where the data amount is small, such as when the device is started;

[0120] Another calculation method is to generate a bacteria concentration change trend graph of the measurement point i by fitting a linear function when the data accumulates to a certain amount, and then substitute the bacteria concentration fitting parameter at time t k+1 ;

[0121] Due to the differences in distance and airflow velocity, the order in which the airflow of different measurement points reaches the prediction point is different. By using the above-mentioned time interpolation method, the bacteria concentration of the arriving airflow can be corrected, thereby eliminating the calculation error caused by the above-mentioned time difference, and further improving the accuracy of the analysis of the bacteria concentration of the prediction point;

[0122] S52, setting a basic attenuation coefficient and an attenuation rate coefficient according to the airflow model to determine an airflow attenuation function;

[0123] The staff sets the basic attenuation coefficient a0 and the airflow rate attenuation coefficient k according to the airflow model; the calculation expression of the airflow attenuation function is ;

[0124] S53, setting an angle attenuation function according to the attenuation rate coefficient;

[0125] The calculation expression of the angle attenuation function is ,

[0126] S54, obtaining the propagation time from the measurement point to the prediction point according to the time interpolation method;

[0127] obtaining the propagation time obtained in the calling step S512;

[0128] S55, calculating a weight parameter according to the airflow attenuation function, the angle attenuation function and the propagation time;

[0129] The calculation expression of the weight parameter is ; in the above weight parameter calculation formula, the application simultaneously introduces airflow attenuation, angle attenuation and time, that is, simultaneously considers the differences in distance, wind direction and time, so as to as objectively as possible comprehensively evaluate the influence of the observation point i on the prediction point P, and then classify the influences of various observation points on the prediction point P, more objectively restore the real situation of the prediction point, and improve the analysis accuracy.

[0130] S56, calculating a bacterial flux according to the bacterial concentration correction parameter;

[0131] The calculation expression of the bacterial flux is , wherein represents the wind speed component of the wind speed of the measurement point numbered i in the propagation direction to the prediction point;

[0132] It should be noted that the bacterial flux refers to the total amount of bacteria transported to the prediction point per unit time and per unit diffusion area;

[0133] In the case that the external airflow environment is relatively stable, the total amount of gas transported from the measurement point i to the prediction point per unit time and per unit diffusion area is certain, and can be calculated through the technical idea of airflow solidification;

[0134] S6, calculating a prediction bacterial concentration parameter of the prediction point according to each bacterial flux and each weight parameter;

[0135] The calculation expression of the prediction bacterial concentration parameter is , wherein represents the prediction bacterial concentration parameter of the prediction point P at T moment;

[0136] In the calculation formula of the bacterial concentration parameter, the transport fluxes of all effective measurement points are further corrected through the weight parameters, that is, the influences of the airflow speed attenuation, angle attenuation and time and other main factors on the bacterial flux are considered through the weight parameters, so as to further improve the accuracy of the prediction bacterial concentration parameter calculation.

[0137] The denominator part is the total amount of gas transported to the prediction point from each measurement point;

[0138] S7, repeat the steps of selecting the prediction point P and obtaining the three-dimensional coordinate parameters of the prediction point, and obtaining the prediction bacterial concentration parameter set;

[0139] When the prediction bacterial concentration parameter of a prediction point P is calculated, a prediction point P is randomly selected again, and the above steps are repeated. Finally, all the calculated prediction bacterial concentration parameters are collected to obtain the prediction bacterial concentration parameter set.

[0140] S8, fitting to generate a bacterial concentration distribution map according to the actual bacterial concentration parameter set and the prediction bacterial concentration parameter set.

[0141] First, a fitting three-dimensional coordinate system is constructed, and points are taken in the three-dimensional coordinate system according to the actual three-dimensional coordinates of each measurement point and the three-dimensional coordinates of each prediction point. Then, different colors are used to mark the vertical range of each measurement point and each prediction point, that is, different colors are used to mark the bacterial concentration in different vertical ranges. For example, the deeper the color, the greater the bacterial concentration.

[0142] Then, the bacterial concentration parameters of each measurement point and each prediction point are marked with corresponding colors. Finally, a simple color distribution map is generated between each measurement point and each prediction point through fitting calculation, so as to obtain the bacterial concentration distribution map.

[0143] Example 2:

[0144] Referring to the drawings accompanying the specification Figure 4 , this embodiment discloses an analysis system for bacterial distribution in a chicken coop, which comprises a parameter acquisition module, an airflow parameter acquisition module, and a prediction point selection module. The parameter acquisition module, the airflow parameter acquisition module, and the prediction point selection module are independent of each other, and their output ends are connected to a screening module to transmit the obtained parameters to the screening module. The output end of the screening module is connected to a first calculation module, which is used to obtain a prediction bacterial concentration parameter set. The output end of the first calculation module is provided with an image fitting module, which is used to generate a bacterial concentration distribution map according to the actual bacterial concentration parameter set and the prediction bacterial concentration parameter set.

[0145] Compared with the prior art, the present application arranges multiple measurement points in the chicken coop to be analyzed, and detects the actual bacterial concentration parameters and the actual three-dimensional coordinate parameters of the measurement points. Airflow is one of the main factors causing changes in bacterial distribution in a closed chicken coop. By constructing an airflow model of the chicken coop to be analyzed, the internal airflow pattern can be restored as much as possible, and the basic parameters for calculation can be obtained.

[0146] In the process of air flow movement, the air flow from one point to another point gradually attenuates with distance, so when the distance is too large, the influence between two points can be ignored, by setting the screening function, the part of the measurement points which will not constitute the influence of the prediction point can be screened out, which can reduce the amount of calculation and also can eliminate data interference, which is conducive to improving the accuracy of analysis;

[0147] Secondly, each effective measurement point has a wind speed component in its propagation direction with the prediction point, the above-mentioned wind speed component will drive part of the air flow containing bacteria to move to the prediction point, assuming that the above-mentioned air flow is uniform in bacterial concentration, based on the technical idea of air flow solidification, the above-mentioned wind speed component will transport a certain amount of gas to the prediction point per unit time per unit diffusion area, and the bacterial concentration of the prediction point can be estimated by collecting the gas transported by multiple measurement points, all measurement point parameters that may affect the bacterial concentration of the prediction point are considered in the above-mentioned method, and the air flow model is combined, thereby improving the accuracy of analysis;

[0148] Finally, because the wind speed components of different measurement points are different, and the distances from different measurement points to the prediction point are also different, therefore, there is a certain difference in the order of air flow reaching the prediction point, for this, the present application estimates by time interpolation method, that is, if the propagation time of air flow from the measurement point to the prediction point is t, then the air flow received by the prediction point at T time is actually the air flow at T-t time, by combining the time difference algorithm with the bacterial concentration values measured by the measurement point before and after, the bacterial concentration parameter at this time can be estimated, thereby reducing the influence of time on estimation and improving the accuracy of analysis.

[0149] The above is only the preferred embodiment of the present application, and does not limit the patent scope of the present application, any equivalent structure or equivalent process transformation using the content of the specification and drawings, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for analysis of bacterial distribution within a chicken house, characterized in that, The method comprises the following steps: acquiring actual bacteria concentration parameters and actual three-dimensional coordinate parameters of each measuring point in the chicken house to be analyzed; constructing an air flow model of the chicken house to be analyzed, and acquiring air flow parameters of each measuring point according to the air flow model and the actual three-dimensional coordinate parameters, wherein the air flow parameters comprise air flow velocity and air flow direction; selecting a prediction point P and acquiring three-dimensional coordinate parameters of the prediction point; setting a screening function according to the air flow model; According to the actual three-dimensional coordinate parameters and the predicted point three-dimensional coordinate parameters, a distance parameter set {d1(P), d2(P),..., di(P)} of the predicted point and each measuring point is calculated, where i represents the number of the measuring point. i (P)} is calculated, where i represents the number of the measuring point. connecting the prediction point with each measuring point respectively to acquire a straight line of a propagation path; acquiring the air flow direction of each measuring point; According to the propagation path straight line and each of the airflow directions, a set of angles {θ1(P), θ2(P),..., θ i (P)} between the airflow directions and the propagation paths is calculated. i (P) represents an angle between the airflow direction of the measurement point numbered i and the propagation path from the measurement point numbered i to the prediction point P. screening each measuring point according to the distance parameter set, the angle set and the screening function, taking the measuring points meeting the screening function as effective measuring points, and outputting an effective measuring point set S; calculating the bacteria flux and weight parameters of each effective measuring point to the prediction point by the time interpolation method, specifically comprising: calculating the effective average wind speed on the propagation path by integration; determining the propagation time according to the effective average wind speed and the distance parameter; correcting the bacteria concentration parameter by the time interpolation method according to the propagation time to acquire a bacteria concentration correction parameter; setting a basic attenuation coefficient and an attenuation rate coefficient according to the air flow model to determine an air flow attenuation function; setting an angle attenuation function according to the attenuation rate coefficient; acquiring the propagation time from the measuring point to the prediction point according to the time interpolation method; calculating the weight parameter according to the air flow attenuation function, the angle attenuation function and the propagation time; The bacterial flux is calculated according to the bacterial concentration correction parameter; the calculation expression of the bacterial flux is , wherein Vxi represents the wind speed component in the propagation direction to the prediction point of the wind speed at the measurement point numbered i, d i (P) represents the distance parameter of the prediction point from each measurement point numbered i, represents the bacterial concentration correction parameter; calculating the prediction bacteria concentration parameter of the prediction point according to the bacteria flux and the weight parameter; repeating the steps of selecting the prediction point P and acquiring the three-dimensional coordinate parameters of the prediction point to acquire a prediction bacteria concentration parameter set; fitting and generating a bacteria concentration distribution map according to the actual bacteria concentration parameters and the prediction bacteria concentration parameter set.

2. The analysis method according to claim 1, characterized in that, The method of constructing an air flow model of the chicken house to be analyzed and acquiring air flow parameters of each measuring point according to the air flow model and the actual three-dimensional coordinate parameters comprises the following steps: acquiring an inlet boundary condition, an outlet boundary condition and fitting boundary conditions of a plurality of fitting points of the chicken house to be analyzed; establishing a three-dimensional model of the chicken house to be analyzed; constructing an air flow model of the chicken house to be analyzed according to the inlet boundary condition, the outlet boundary condition, the fitting boundary conditions and the three-dimensional model; extracting the airflow parameters of each measuring point from the airflow model according to the respective actual three-dimensional coordinate parameters where i represents the number of the measuring point.

3. The analysis method of claim 1, wherein, The expression of the screening function is and where d max represents the set maximum effective distance, i represents the measurement point number, P represents the prediction point, θ i (P) represents the angle between the airflow direction of the measurement point numbered i and the propagation path from the measurement point numbered i to the prediction point P; d i (P) represents the distance parameter of the prediction point and each measurement point numbered i.

4. The analysis method of claim 1, wherein, The calculation expression of the effective average wind speed is The expression of the propagation time is d i (P) represents the distance parameter of the prediction point and each measurement point numbered i, and the expression of the bacterial concentration correction parameter is: , wherein represents the wind speed component consistent with the propagation direction, the sampling time satisfying , t k and t k+1 represent two adjacent sampling times; T represents the time, represents the bacterial concentration parameter at the sampling time t k , represents the bacterial concentration fitting parameter at the sampling time t k+1 .

5. The analysis method of claim 1, wherein The calculation expression of the airflow attenuation function is , wherein a0 represents a basic attenuation coefficient, k represents an attenuation rate coefficient, d i (P) represents the distance parameter between the prediction point and each measurement point numbered i; the calculation expression of the angle attenuation function is , wherein k represents an attenuation rate coefficient, represents the cosine value of the included angle between the airflow direction of the measurement point numbered i and the propagation path from the measurement point numbered i to the prediction point P. The calculation expression of the weight parameter is , d i (P) represents the distance parameter of the prediction point and each measurement point numbered i, T represents the time, t ip represents the propagation time.

6. The analysis method of claim 1, wherein, The calculation expression of the predicted bacterial concentration parameter is wherein denotes the predicted bacterial concentration parameter at the prediction point P at time T, wherein denotes the wind speed component of the wind speed of the measurement point with the number i in the propagation direction to the prediction point, denotes the bacterial flux, denotes the weight parameter.

7. An analysis system based on the analysis method according to any one of claims 1 to 6, characterized in that The method comprises the following steps: a parameter acquisition module, configured to acquire actual bacteria concentration parameters and actual three-dimensional coordinate parameters of each measuring point in the chicken house to be analyzed; The air flow parameter acquisition module is configured to construct an air flow model of the chicken house to be analyzed, and acquire air flow parameters of each measuring point according to the air flow model and the actual three-dimensional coordinate parameters. ; a prediction point selection module, configured to select a prediction point and acquire three-dimensional coordinate parameters of the prediction point; a screening module, configured to set a screening function according to an air flow model, screen each measuring point according to the screening function, the actual three-dimensional coordinate parameters and the three-dimensional coordinate parameters of the prediction point, and acquire an effective measuring point set S; a first calculation module, configured to calculate the bacteria flux and weight parameters of each effective measuring point to the prediction point by the time interpolation method; calculate the prediction bacteria concentration parameter of the prediction point according to the bacteria flux and the weight parameter; repeat the steps of selecting the prediction point P and acquiring the three-dimensional coordinate parameters of the prediction point to acquire a prediction bacteria concentration parameter set; an image fitting module, configured to fit and generate a bacteria concentration distribution map according to the actual bacteria concentration parameters and the prediction bacteria concentration parameter set.

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