CN110956365A - Colony total number dynamic risk assessment method of wheat flour supply chain based on hybrid Bayesian network - Google Patents

Colony total number dynamic risk assessment method of wheat flour supply chain based on hybrid Bayesian network Download PDF

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CN110956365A
CN110956365A CN201911095119.0A CN201911095119A CN110956365A CN 110956365 A CN110956365 A CN 110956365A CN 201911095119 A CN201911095119 A CN 201911095119A CN 110956365 A CN110956365 A CN 110956365A
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赵峙尧
王小艺
陈谦
熊科
张新
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Abstract

The invention provides a dynamic risk assessment method for the total number of colonies of a wheat flour supply chain based on a hybrid Bayesian network, and belongs to the technical field of food safety risk assessment. The invention comprises the following steps: selecting key monitoring nodes in a wheat flour supply chain, establishing a Bayesian network model of the wheat flour supply chain, taking the key nodes as discrete modes, switching among the modes representing probabilistic migration of the total number of colonies on the supply chain, describing continuous dynamic behavior of the total number of the colonies through a dynamic growth model of the colonies under each mode, and establishing a hybrid Bayesian network model; establishing a discrete time reasoning evolution mechanism of the hybrid Bayesian network, predicting the total colony number distribution of the wheat flour supply chain based on Monte Carlo simulation, and measuring the total colony number hazard degree according to hazard indexes. The method is suitable for detection and evaluation of food safety in complex environment, improves detection efficiency and risk evaluation accuracy, and solves the problem that process risk is difficult to accurately and quantitatively measure.

Description

Colony total number dynamic risk assessment method of wheat flour supply chain based on hybrid Bayesian network
Technical Field
The invention belongs to the technical field of food safety risk assessment, and particularly relates to a dynamic risk assessment method for the total number of colonies in a wheat flour supply chain based on a hybrid Bayesian network.
Background
Wheat is the second major food crop in the world, and is also the third major crop in china following corn and rice. Since the beginning of the 80's of the 20 th century, the worldwide wheat production has increased significantly, the annual wheat production in China exceeds 1.2 hundred million tons, China has become the world's largest wheat producing country, and wheat is one of the most important staple foods in China. However, in recent years, wheat flour food safety problems have emerged endlessly and have attracted wide attention in various social circles. To ensure the quality of wheat and its by-products, food safety regulations need to cover the whole supply chain, and perform the whole process inspection and supervision of wheat flour quality from raw materials to finished products, rather than just end product detection in the traditional sense. In recent years, the international food safety organization is strongly pushing the application of risk assessment techniques to assess food safety issues.
Risk assessment is a structured scientific process for estimating the probability and severity of risk and the consequent uncertainty to determine the potential harm and associated risk to healthy life from exposure to biological, chemical or physical hazards in food. The current Chinese Food Safety Law (FSL) requires the establishment of a national food safety risk assessment system to assess the risk of hazards in Chinese foods and food additives. However, most of the existing safety risk assessment methods aiming at food hazards study the hazards in the raw materials or products of the supply chain through chemical test methods, and the migration characteristic of the hazards on the complete supply chain from the raw materials to the finished products is not considered; most of the existing safety risk assessment methods for food supply chains only consider management factors such as manpower, logistics, funds and the like, and do not consider risks brought by hazards. It follows that existing research cannot be applied to hazard risk assessment towards the food supply chain. In addition, the food safety risk lacks reasonable measurement indexes, and most of the existing food safety risk assessment methods directly and qualitatively describe the risk by using 'standard exceeding'.
Disclosure of Invention
The invention aims to make up the deficiency of research on risk problems of supply chain hazards, provides a dynamic risk assessment method for the total number of colonies of a wheat flour supply chain based on a hybrid Bayesian network by taking 'risk' as a guide, and provides a new solution and a feasible solution for solving the safety problem of biological hazards of a food supply chain.
The invention provides a dynamic risk assessment method for the total number of colonies of a wheat flour supply chain based on a hybrid Bayesian network, which comprises the following specific steps:
the method comprises the following steps: establishing a hybrid Bayesian network model of colony total number distribution characteristics of a wheat flour supply chain;
and establishing a bacterial colony dynamic growth model which comprises a bacterial colony growth dynamic model, a dynamic parameter model and an environment model. The input of the colony growth kinetic model is time, the output is colony total number, and important parameters in the model are determined by the kinetic parameter model. The input of the dynamic parameter model is temperature and humidity, and the output is lag phase and specific growth rate. The environment model is divided into a temperature model and a humidity model, the input is time, and the output is temperature and humidity respectively.
According to expert experience, selecting key nodes for monitoring total number of colonies in each link of a wheat flour supply chain, and establishing a Bayesian network model of the wheat flour supply chain.
Nodes of the Bayesian network are defined as discrete modes, and switching between the modes is modeled by Bayesian conditional probability so as to describe probabilistic migration of colony counts on a supply chain. And describing the continuous dynamic behavior of the colony count growth in each mode through a colony dynamic growth model to obtain a hybrid Bayesian network model of the colony count distribution characteristics of the wheat flour supply chain.
Step two: predicting the colony total number distribution characteristics of the wheat flour supply chain;
establishing a discrete time reasoning evolution mechanism of the hybrid Bayesian network, and dividing the discrete time reasoning evolution mechanism into a continuous process variable evolution part and a discrete switching evolution part; in discrete switch evolution, a discrete set of conditional probability events across key segments of the wheat flour supply chain
Figure BDA0002268091210000021
Middle extraction mode switching probability matrix pi ═ piij]M×MM denotes the total number of discrete modes, πijRepresentation mode qiSwitching to mode qjThe modal probability of the next moment is calculated by combining the modal switching probability and the modal probability of the current moment; the colony count distribution of the wheat flour supply chain is then predicted based on the Monte Carlo simulation.
Step three: and calculating the colony total hazard degree according to the predicted colony total distribution of the wheat flour supply chain.
Compared with the prior art, the invention has the following advantages:
(1) the invention effectively combines qualitative and quantitative risk assessment methods, and carries out quantitative risk assessment aiming at key nodes in the wheat flour supply chain, thereby not only reducing the detection and assessment range and improving the detection efficiency and the assessment efficiency, but also ensuring the accuracy of the detection result and the risk assessment.
(2) According to the invention, the distribution characteristics of the total number of the bacterial colonies in the food supply chain are modeled into the hybrid Bayesian network, a dynamic growth model of the bacterial colonies is introduced, the uncertainty of continuous dynamic growth and discrete transfer of the bacterial colonies is considered, the Bayesian network model and the hybrid system are effectively combined, the food safety detection and evaluation under the complex environment are adapted, and the applicability and the accuracy of the detection and the risk evaluation are improved.
(3) The invention provides a hazard index for measuring the total number of bacterial colonies in a food supply chain, solves the problem that the process risk is difficult to accurately and quantitatively measure, and improves the risk evaluation precision compared with the process variable as a measurement index.
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FIG. 1 is a flow chart of a risk assessment method of the present invention based on the total number of colonies on the hybrid Bayesian network food supply chain;
FIG. 2 is a simplified flow diagram of the wheat flour supply chain according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a Bayesian network structure of a wheat flour supply chain established in an embodiment of the present invention;
FIG. 4 is a diagram showing an example of a hybrid Bayesian network structure for the colony count distribution characteristic of the wheat flour supply chain according to the present invention;
FIG. 5 is a schematic view of a general hazard space;
FIG. 6 is a graph of unit colony growth in a particular mode of an embodiment of the invention;
FIG. 7 is a graph of colony population distribution characteristics predicted based on a hybrid Bayesian network in accordance with an embodiment of the present invention;
FIG. 8 is a graph showing the calculation results of the colony count hazard in the wheat flour supply chain according to the example of the present invention.
Detailed Description
The present invention will be described in further detail and with reference to the accompanying drawings so that those skilled in the art can understand and practice the invention.
As shown in fig. 1, the present invention provides a dynamic risk assessment method for colony count of wheat flour supply chain based on hybrid bayesian network, which includes firstly, establishing a hybrid bayesian network model of colony count distribution characteristics on the wheat flour supply chain, wherein the discrete mode of the model qualitatively considers colony count distribution state under key link in the supply chain; continuous dynamic behaviors of each mode are described by a dynamic growth model of the bacterial colony, and the growth environments of the bacterial colonies in different modes are different; the migration characteristics of the total number of colonies between different modalities satisfy a conditional probability distribution, which is determined by the production process and obeys certain distribution characteristics. Then, probability distribution characteristic prediction of the total number of wheat flour colonies is realized by using a Monte Carlo method. Finally, a risk index is provided for carrying out quantitative risk assessment on the total number of colonies in the wheat flour supply chain. The following is described in detail in three steps.
The method comprises the following steps: and establishing a colony total number distribution characteristic model of the wheat flour supply chain to describe the colony growth condition under the environment to be tested. The method integrates the existing classical colony growth dynamics model, the dynamics parameter model and the parameters, adds the environment model to establish a new complete colony dynamic growth model, fuses the Bayesian network and the hybrid system based on the characteristics of an evaluation object, and provides a new technology for evaluating the colony risk of the wheat flour supply chain by using the hybrid Bayesian network.
Step 101, firstly, a bacterial colony dynamic growth model is established, wherein the bacterial colony dynamic growth model comprises a bacterial colony growth dynamic model, a dynamic parameter model and an environment model. The input of the colony growth kinetic model is time, the output is colony total number, and important parameters in the model are determined by the kinetic parameter model. The input of the dynamic parameter model is temperature and humidity, and the output is lag phase and specific growth rate. The environment model is divided into a temperature model and a humidity model, the input is time, and the output is temperature and humidity respectively.
The established colony dynamic growth model is as follows:
Figure BDA0002268091210000031
ln(1/λ)＝C0+C1·bw+C2·bw 2+C3·T+C4·T2+C5·T·bw(2)
μ(T,aw)＝μopt·τ(T)·ρ(aw) (3)
Figure BDA0002268091210000032
Figure BDA0002268091210000033
T(t)＝fT(T(t-1))+ωT(6)
Figure BDA0002268091210000034
formula (1) represents a colony growth kinetic model for describing the growth condition of colonies under a specific environment, wherein N (t) represents the total number of colonies at the time t, and N0Indicates the initial value of the total number of colonies, A indicates the maximum value of the total number of colonies, and μ indicates the number of bacteriaColony counts are compared to growth rate, lambda represents the growth lag phase of colony counts, and t represents time. Equation (2) represents a kinetic parametric model of the parameter λ, consisting of temperature T and humidity awDeciding, for better hyperbolic fitting effect, that
Figure BDA0002268091210000035
bwAs an intermediate parameter, C0-C5Are fitting parameters. Equation (3) represents a kinetic parametric model of the parameter μ, also represented by temperature T and humidity awDetermination of μoptExpressing the colony population ratio to the growth rate optimum value, and τ (T) representing the temperature factor model, as shown in equation (4), ρ (a)w) The humidity factor model is expressed as shown in equation (5). T ismin、TmaxRespectively representing the minimum and maximum values of the temperature growth interval, ToptRepresents the temperature optimum for microbial growth. a isw,minMinimum boundary value of humidity growth interval, aw,optRepresents the humidity optimum for microbial growth. Equations (6) and (7) represent the temperature and humidity environment models, respectively, fTRepresenting the temperature dynamic variation function, omegaTIs the system noise of the temperature environment model,
Figure BDA0002268091210000041
a function representing the dynamic variation of the humidity,
Figure BDA0002268091210000042
is the system noise of the humidity environment model.
Expressing the colony total ratio growth rate and the growth lag phase at the time t as mu (t) and lambda (t), respectively, establishing a process variable vector x and a parameter vector theta of a colony dynamic growth model, as follows:
x＝(N(t),μ(t),λ(t))T
θ＝(C1,C2,C3,C4,C5,Tmin,Tmax,Topt,aw,min,aw,opt)T
assuming that the above model parameter vector θ is constant, the dynamic growth model of the colony for a specific environment is represented by equation (8):
Figure BDA0002268091210000043
wherein f (x, theta) is a dynamic colony growth model consisting of the formulas (1) to (7). The above models in combination can describe the growth of colonies under a specific dynamic environment.
Step 102, secondly, establishing a Bayesian network model of the wheat flour supply chain. And qualitatively selecting key nodes for monitoring the total number of colonies from each link of the wheat flour supply chain according to an expert experience method, and constructing a Bayesian network model based on the key nodes to describe the migration rule of the total number of the colonies in each link. The bayesian network model will serve as the basic framework for carrying out a risk assessment of the total number of colonies on the wheat flour supply chain.
According to expert experience, M key links of colony total number distribution characteristics in the wheat flour supply chain are selected in a setting mode to form a simplified wheat flour supply chain Bayesian network model, as shown in figure 2, for example, the key links in the whole wheat flour supply chain are as follows: the method comprises a raw wheat storage and cleaning link 1, a wheat wetting and cleaning link 2, a grinding and screening link, a packaging link, a finished product storage link and the like, wherein the wheat wetting link relates to a water consumption link, and the packaging link relates to a packaging material link. Expressing the selected key links as node sets
Figure BDA0002268091210000049
Therein node EjRepresents j is 1,2, …, M, and the relation between each key ring is defined by discrete conditional probability event pijRepresenting, and composing event sets
Figure BDA0002268091210000044
i, j ═ 1,2, …, M; collection
Figure BDA0002268091210000045
And
Figure BDA0002268091210000046
combined establishing Bayesian network modelModel (III)
Figure BDA0002268091210000047
Event pijRepresenting events, sets of events, from the ith link to the jth link
Figure BDA0002268091210000048
An event in (1) generally occurs in an adjacent link. Numbering the links shown in FIG. 2, a Bayesian network structure of the wheat flour supply chain can be established as shown in FIG. 3, where a node represents a key link in the wheat flour supply chain, e.g., E1Representing a coarse wheat storage link, E4Represents the wheat wetting link.
And 103, establishing a colony total number distribution characteristic hybrid Bayesian network model of the wheat flour supply chain.
Nodes of the Bayesian network are defined as discrete modes, and switching between the modes is modeled by Bayesian conditional probability so as to describe probabilistic migration of colony counts on a supply chain. And describing the continuous dynamic behavior of the colony count growth in each mode through a colony dynamic growth model to obtain a hybrid Bayesian network model of the colony count distribution characteristics of the wheat flour supply chain.
The invention takes a Bayesian network model of a wheat flour supply chain as a framework, introduces a microorganism dynamic growth model, considers the continuous dynamic growth of microorganisms in each node and the discrete transfer of microorganisms caused by discrete conditional probability events, and establishes a hybrid Bayesian network model aiming at the total number distribution characteristics of colonies
Figure BDA0002268091210000051
Hybrid Bayesian network model with established colony total number distribution characteristics
Figure BDA0002268091210000052
The following were used:
Figure BDA0002268091210000053
wherein,
Figure BDA0002268091210000054
middle element qjIs node EjM represents the total number of discrete modes, one mode for each node.
For the
Figure BDA0002268091210000055
Dynamic growth mode capable of being divided into bacterial colonies
Figure BDA0002268091210000056
And static growth mode of colony
Figure BDA0002268091210000057
Dynamic growth mode in colony
Figure BDA0002268091210000058
Then, the bacterial colony grows by itself according to the growth rule shown in the formulas (1) to (7); in the static growth mode of colony
Figure BDA0002268091210000059
Then, colonies hardly grew by themselves. The switching between modalities is modeled by a bayesian network as follows:
Figure BDA00022680912100000510
wherein t is sampling time;
Figure BDA00022680912100000511
represents node EjMode q at time tjBy modal probability hjRepresents, satisfies
Figure BDA00022680912100000512
Figure BDA00022680912100000513
Represents node EiModality q at time t-1i(t-1) conversion to node EjMode q at time tj(t) probability of switching from mode to mode piijRepresents, satisfies
Figure BDA00022680912100000514
i＝1,2,…,M。
For the
Figure BDA00022680912100000515
There is a discrete-time continuous (variable) dynamic behavior:
Figure BDA00022680912100000516
wherein x (t) and y (t) represent the process equation and the measurement equation, respectively, of a process variable vector x, FjIs a system equation for
Figure BDA00022680912100000517
Fj(x (t-1)) can be obtained by discretizing equations (1) - (7) in time; for the
Figure BDA00022680912100000518
Then there is Fj(x(t-1))＝x(t-1)。
Figure BDA00022680912100000519
Is of mode qjThe process noise of (a) is generated,
Figure BDA00022680912100000520
in the form of a normal distribution of the signals,
Figure BDA00022680912100000521
is of mode qjThe noise covariance matrix of the process equation,
Figure BDA00022680912100000522
is of mode qjThe noise-driven term of the process equation.
Figure BDA00022680912100000523
Is of mode qjClothes for measuring noiseFrom a normal distribution
Figure BDA00022680912100000524
Is of mode qjA noise covariance matrix of the measurement equation,
Figure BDA00022680912100000525
is of mode qjThe noise-driven term of the measurement equation. CjIs a matrix of measurement equation coefficients.
Thus, a colony total number distribution characteristic hybrid Bayesian network model of the wheat flour supply chain is established. In the example of the present invention, M is 9, and the total number of colonies of the established wheat flour supply chain is mixed with the structure of the bayesian network model, as shown in fig. 4.
Step two: the colony count distribution characteristics of the wheat flour supply chain are predicted.
Since the wheat flour supply chain is a spatio-temporal sequence chain, the distribution characteristics and the risk degree of aspergillus flavus vary with time and space on the chain. Therefore, the established hybrid Bayesian network model has deductive characteristics in both time and space dimensions, and is mainly reflected in discrete modal evolution and microorganism continuous process variable evolution based on Bayesian theory. The hybrid Bayesian network inference evolution has randomness, and is mainly reflected in dynamic random change of a storage environment and migration probability change caused by uncertain fluctuation of performance of each production link. In order to solve the problems, the invention utilizes a Monte Carlo method and combines a probability statistical rule to obtain the probability distribution characteristics of the total number of each colony on different time-space points.
Step 201, establishing a discrete time inference evolution mechanism of the hybrid Bayesian network. The Bayesian evidence theory and the hybrid system evolution are combined and applied to a discrete time reasoning evolution mechanism of the hybrid Bayesian network, and the distribution characteristics of the later time sequence evaluation object can be predicted through the known detection data at the specific time point.
Given a process variable evolution time horizon [0, K ]]K is a positive integer and represents the total number of sampling time; initial miscellaneous state s of process variable vector0＝(q0,x0)，q0Denotes the initial modality, x0Representing the initial process variable vector, one takes on three dimensions
Figure BDA0002268091210000061
Space(s)
Figure BDA0002268091210000062
Random process { s (t) ═ (q (t), x (t))), t ∈ [0, K]Is the execution of the hybrid bayesian network model,
Figure BDA0002268091210000063
representing a set of accepted real numbers. The execution of the hybrid Bayesian network model of the colony count of the wheat flour supply chain is mainly divided into two parts of continuous process variable evolution and discrete switching evolution.
(1) Continuous process variable evolution of colony counts of wheat flour supply chain is determined by corresponding continuous variation model for each mode for which
Figure BDA0002268091210000064
The medium continuous process variable evolution is determined by equation (11).
In addition, for
Figure BDA0002268091210000065
The continuous process variable evolution of (2) needs modeling of the modal environment, and the modeling is as follows:
Figure BDA0002268091210000066
Figure BDA0002268091210000067
wherein, Tqj(t) represents the mode qjThe temperature at the time of the next t,
Figure BDA0002268091210000068
is of mode qjThe set temperature of (a) is set,
Figure BDA0002268091210000069
representation mode qjSystem noise of temperature model, normal distribution
Figure BDA00022680912100000610
A noise covariance matrix that is a modal-specific temperature model;
Figure BDA00022680912100000611
representation mode qjThe humidity at the time of the next t,
Figure BDA00022680912100000612
is of mode qjThe set humidity of (a) is set,
Figure BDA00022680912100000613
representation mode qjSystematic noise of humidity model, compliance
Figure BDA00022680912100000614
A noise covariance matrix that is a particular modal process equation.
And the total number variable of the colonies in the dynamic growth modes under different environments continuously evolves according to a specific colony dynamic growth model.
(2) Discrete switch evolution of colony counts for wheat flour supply chains, satisfying Bayesian networks, from discrete conditional probability event sets
Figure BDA00022680912100000615
Mid-extraction mode switching probability matrix
Figure BDA00022680912100000616
At time t-1, the modal probabilities are as shown in equation (14):
Figure BDA00022680912100000617
wherein,
Figure BDA00022680912100000618
representing the j-th node as the mode q at the moment t-1jBy modal probability hj(t-1).
At time t, mode qjThe probability of (c) is:
Figure BDA0002268091210000071
wherein h isi(t-1) represents the mode q at the time t-1iThe probability of (c).
(3) The hybrid Bayesian network evolution discrete time execution algorithm comprises the following steps;
step a, setting t to 0 and q (0) to q0,x(0)＝x0；
Step b, if t is less than K, obtaining the expression according to the formulas (14) to (15)
Figure BDA0002268091210000072
Representing the mode at the moment t + 1;
and c, if q (t +1) ═ q (t), acquiring according to the formula (11)
Figure BDA0002268091210000073
Otherwise, according to the switching probability matrix
Figure BDA0002268091210000074
Obtaining
Figure BDA0002268091210000075
As the initial value of the next mode;
step d, t is increased by 1;
and e, if t is equal to K, ending the algorithm.
Step 202, colony count distribution prediction for wheat flour supply chain based on Monte Carlo simulation.
In the step, the hybrid Bayesian network evolution process is simulated for a plurality of times through Monte Carlo simulation, the noise influence can be weakened by combining with the probability statistical rule, more accurate probability distribution of the total number of the bacterial colonies on different time points is obtained, and the uncertainty problem of continuous dynamic growth and discrete transfer of the bacterial colonies is solved. The specific flow steps of colony total number distribution prediction of the wheat flour supply chain based on Monte Carlo simulation in the step are as follows:
2021. setting initial values of model parameters to theta0The number of Monte Carlo simulation particles is I; each particle represents a set of colony counts and model parameters;
2022. at a given time range [0, K]Based on the total number of colonies and the initial value (x) of the model parameter0,θ0) The d-th particle is assigned with an initial value (x)0 (d),θ0 (d)) The following are:
x0 (d)＝x0,θ0 (d)＝θ0(8)
2023. and for the moment t, based on the colony count and the model parameter value at the moment t-1, performing single-step prediction by using an evolution discrete time execution algorithm of the colony count distribution characteristic hybrid Bayesian network of the wheat flour supply chain to obtain a colony count predicted value at the moment t.
2024. If t is less than K, t is increased by 1, the step 2023 is performed, otherwise, the step 2025 is performed.
2025. If d is less than I, d is increased by 1, the step 2022 is performed, otherwise, the step 2026 is performed.
2026. To pair
Figure BDA0002268091210000076
Calculating the probability density function P (x) of the total number of coloniest|(x0,θ0))；
Figure BDA0002268091210000077
Wherein, P (x)t|(x0,θ0) Is based on (x)0,θ0) The total number of colonies probability density function of (1), δ represents the dirac function.
Step three: and calculating the colony total harmfulness of the wheat flour supply chain.
Generally, a food safety risk quantitative evaluation method directly utilizes process variables (such as heavy metal content, pesticide residue content and the like) as quantitative indexes to evaluate the dynamic risk degree of food, and the result is easily influenced by external noise, so that the evaluation is not accurate. Therefore, the invention provides a hazard index for quantitative risk assessment of food supply chain hazards, which can consider uncertainty in the risk assessment and improve the assessment accuracy.
For a dynamic system, assume an n-dimensional space in which its state variables lie
Figure BDA0002268091210000081
Divisible into a secure space
Figure BDA0002268091210000082
And hazardous spaces
Figure BDA0002268091210000083
Figure BDA0002268091210000084
For a certain time t, the system's hazard r (t) is:
Figure BDA0002268091210000085
wherein R (t) is that the dynamic system stays in the hazard space at the time of t
Figure BDA0002268091210000086
The probability of the inner.
And for a promiscuous Bayesian network model, promiscuous states
Figure BDA0002268091210000087
Representing a process variable miscellaneous state space. For discrete modes
Figure BDA0002268091210000088
In that
Figure BDA0002268091210000089
Spatially, can be divided into safety spaces
Figure BDA00022680912100000810
And a hazardous space
Figure BDA00022680912100000811
Figure BDA00022680912100000812
At qjIn the state, for a given time t, the degree of harmfulness of the system
Figure BDA00022680912100000813
Can be expressed as:
Figure BDA00022680912100000814
for the total number of colonies in the wheat flour supply chain, the degree of harmfulness refers to the possibility that the content exceeds the standard at a specific moment in different links, as shown in fig. 5, wherein Ω: risk set represents the modal hazard space settings.
In the embodiment of the invention, the mode q is selected9Next, a predicted time length N is setTSetting the standard exceeding threshold of the total number of bacterial colonies
Figure BDA00022680912100000815
And carrying out N on the harmfulness of the total number of the bacterial coloniessSubsampling estimates to predict changes in the risk level of colony population.
Example (b):
the method of the present invention was used to perform a dynamic risk assessment of the total number of colonies in the wheat flour supply chain as shown in FIG. 2.
The method comprises the following steps: and establishing a colony total number hybrid Bayesian network model of the wheat flour supply chain.
Considering the distribution characteristics of the colony counts of the wheat flour supply chain in each link, defining each mode of the colony count hybrid Bayesian network model
Figure BDA00022680912100000816
Respectively represents the fungus dropping of each key link in the supply chainAnd (4) the distribution characteristic state of the total number is fallen. According to the qualitative division of the supply chain environment,
Figure BDA00022680912100000817
and other modalities belong to
Figure BDA00022680912100000818
The dynamic model of colony count in each mode has a process equation and a measurement equation. For each modal process equation, it can be obtained from equation (11).
For process noise and noise-driven arrays in the process equation, there is the following equation (20):
Figure BDA00022680912100000819
total number of colonies each modality corresponds to the same measurement equation. The process variables can all be measured directly, i.e. C is taken asjI, then the measurement equation is y (t) x (t) + Γvv (t), for the process noise and noise driving matrix in the measurement equation, there is equation (21) as follows:
Figure BDA00022680912100000820
wherein I represents a measurement coefficient identity matrix. Gamma-shapedw、Qw、Γv、QvAre all set known matrices.
Based on this, the modal switching probability distribution characteristics are set as follows:
Figure BDA0002268091210000091
wherein, trunc (0, Uniform (a)1,b1) Represents a modal switching probability pi11Is characterized by taking a constant interval [ a ]1,b1]The medium random value is used as a switching probability value; trunc (0, Uniform (a)2,b2) Represents a modal switching probability pi33Is characterized by taking a constant interval [ a ]2,b2]The medium random value is used as a switching probability value; trunc (0, Uniform (a)3,b3) Represents a modal switching probability pi44Is characterized by taking a constant interval [ a ]3,b3]The medium random value is used as a switching probability value; trunc (0, Uniform (a)4,b4) Represents a modal switching probability pi55Is characterized by taking a constant interval [ a ]4,b4]The medium random value is used as a switching probability value; trunc (0, Uniform (a)5,b5) Represents a modal switching probability pi77Is characterized by taking a constant interval [ a ]5,b5]The medium random value is used as a switching probability value.
The colony count hybrid bayesian network model parameters in this example are shown in table 1:
TABLE 1 colony count hybrid Bayesian network model parameters
Figure BDA0002268091210000092
Step two: predicting colony total number distribution characteristics of a wheat flour supply chain;
the colony count distribution characteristics were predicted according to formulas (12) to (17). Wherein the evolution diagram and distribution characteristics of the total number of colonies per unit time interval are shown in FIGS. 6 and 7.
Step three: calculating the total colony number harmfulness of the wheat flour supply chain;
calculating q according to equation (19)9The total colony number hazard degree at a specific moment under the mode is shown in fig. 8, and the result shows that the hazard degree can effectively reflect the total colony number hazard evolution of the wheat flour supply chain. The harm degree is increased along with the increase of time, and the change trend of the harm degree can obviously reflect the harm evolution of the total number of bacterial colonies of the wheat flour supply chain.
Through the embodiment, the method can be applied to food safety management of the total number of the bacterial colonies in a wheat flour supply chain, the harm degree of the total number of the bacterial colonies in a later time sequence can be deduced through detected prior data, and the prior data are visually fed back to an operator for corresponding problem handling, so that quality monitoring is carried out on the wheat flour in each link from production to sale.

Claims (6)

1. A dynamic risk assessment method for colony count of a wheat flour supply chain based on a hybrid Bayesian network is characterized by comprising the following steps:
the method comprises the following steps: establishing a hybrid Bayesian network model of colony total number distribution characteristics of a wheat flour supply chain;
establishing a dynamic colony growth model by combining a colony growth dynamic model, a dynamic parameter model and an environment model, wherein the dynamic colony growth model is expressed as f (x, theta), x is a process variable vector and consists of the total number of colonies, the specific growth rate of the total number of colonies and a growth lag phase, and theta is a parameter vector; selecting key nodes for monitoring the total number of colonies from all links of a wheat flour supply chain according to expert experience, and constructing a Bayesian network of the wheat flour supply chain; setting each node in the Bayesian network as discrete mode, modeling the switching between modes by Bayesian conditional probability, describing the colony total growth continuous dynamic behavior under each discrete mode by a colony dynamic growth model, obtaining the hybrid Bayesian network model of the colony total distribution characteristics of the wheat flour supply chain, and expressing as
Figure FDA0002268091200000011
Representative node EjM represents the total number of discrete modes of the population count distribution characteristic of (a); the promiscuous state of the promiscuous bayesian network model is denoted as s ═ q, x,
Figure FDA0002268091200000012
step two: predicting colony total number distribution characteristics of a wheat flour supply chain;
firstly, establishing a discrete time reasoning evolution mechanism of a hybrid Bayesian network model of the total number of colonies in a wheat flour supply chain, wherein the discrete time reasoning evolution mechanism is divided into a continuous process variable evolution part and a discrete switching evolution part; in discrete switch evolution, a discrete set of conditional probability events across key segments of the wheat flour supply chain
Figure FDA0002268091200000013
Middle extraction mode switching probability matrix pi ═ piij]M×M，πijRepresentation mode qiSwitching to mode qjThe modal probability of the next moment is calculated by combining the modal switching probability and the modal probability of the current moment; then, predicting the colony count distribution of the wheat flour supply chain based on Monte Carlo simulation;
step three: and evaluating the damage degree of the total colonies according to the predicted distribution of the total colonies of the wheat flour supply chain.
2. The method according to claim 1, wherein in step one, each mode q is a mode qjUnder a dynamic growth mode of the bacterial colony, self-growing according to the dynamic growth model of the bacterial colony;
the switching between the setup modalities is as follows:
Figure FDA0002268091200000014
Figure FDA0002268091200000015
wherein, t is the sampling time,
Figure FDA0002268091200000016
representation mode qjProbability h at time tjAnd satisfy
Figure FDA0002268091200000017
Figure FDA0002268091200000018
Representing the mode q at time t-1iConversion to modality q at time tjProbability of (2), probability of modal switching piijSatisfy the requirement of
Figure FDA0002268091200000019
For the
Figure FDA00022680912000000110
All have discrete-time continuous dynamic behavior, expressed as follows:
Figure FDA00022680912000000111
Figure FDA00022680912000000112
where x (t) and y (t) represent the process and measurement equations, respectively, for a process variable vector x, where x is expressed as (n (t), μ (t), λ (t))TN (t) represents the total number of colonies at time t, μ (t) represents the ratio of the total number of colonies at time t to the growth rate, and λ (t) represents the growth lag phase of the total number of colonies at time t; fjSystem equation representing a dynamic growth model of colonies, Fj(x (t-1)) is obtained by discretizing the system equation in time, Fj(x(t-1))＝x(t-1)；
Figure FDA0002268091200000021
Is of mode qjThe process noise of (a) is generated,
Figure FDA0002268091200000022
is of mode qjA noise-driven term of the process equation;
Figure FDA0002268091200000023
is of mode qjThe noise of the measurement of (2) is,
Figure FDA0002268091200000024
is of mode qjMeasuring a noise-driven term of an equation; cjIs a coefficient matrix of the measurement equation.
3. A method according to claim 1 or 2, characterized in that said method is carried out in a single stepIn the second step, continuous process variable evolution of the hybrid Bayesian network model of the colony total number of the wheat flour supply chain is determined by the corresponding continuous change model of each discrete mode, and the mode q is matchedjIn the continuous process variable evolution, the model environment needs to be modeled as follows:
Figure FDA0002268091200000025
Figure FDA0002268091200000026
wherein,
Figure FDA0002268091200000027
representation mode qjThe temperature at the time of the next t,
Figure FDA0002268091200000028
is of mode qjThe set temperature of (a) is set,
Figure FDA0002268091200000029
representation mode qjSystem noise of the temperature model;
Figure FDA00022680912000000210
representation mode qjThe humidity at the time of the next t,
Figure FDA00022680912000000211
is of mode qjThe set humidity of (a) is set,
Figure FDA00022680912000000212
representation mode qjSystem noise of the humidity model.
4. The method as claimed in claim 1 or 2, wherein in the second step, the hybrid Bayesian network model of colony counts of wheat flour supply chain is adoptedDiscrete switching evolution, set at t-1 as mode qiHas a probability of hi(t-1), then at time t, the mode qjProbability h ofj(t) is:
Figure FDA00022680912000000213
wherein,
Figure FDA00022680912000000214
a hybrid bayesian network model characterizing the colony count distribution in the wheat flour supply chain.
5. The method as claimed in claim 1, wherein the step two, the colony count distribution prediction of the wheat flour supply chain based on Monte Carlo simulation comprises the following steps:
step 2021, setting initial values θ of model parameters0The number of Monte Carlo simulated particles I; each particle represents a set of colony counts and model parameters;
step 2022, at a given time range [0, K ]]Based on the total number of colonies and the initial value (x) of the model parameter0,θ0) The d-th particle is assigned with an initial value (x)0 (d),θ0 (d)) The following are:
x0 (d)＝x0,θ0 (d)＝θ0
step 2023, for the time t, based on the total number of colonies and the model parameter at the time t-1, performing single-step prediction by using the discrete switching evolution of the hybrid bayesian network of the total number of colonies of the wheat flour supply chain to obtain a predicted value of the total number of colonies at the time t;
step 2024, if t is less than K, t is increased by 1, the step 2023 is returned, otherwise, the step 2025 is performed;
step 2025, if d is less than I, increasing d by 1, returning to step 2022, otherwise, performing step 2026;
step 2026, for
Figure FDA00022680912000000215
Calculating the probability density function P (x) of the total number of coloniest|(x0,θ0))；
Figure FDA00022680912000000216
Where δ represents the dirac function.
6. The method of claim 1, wherein the step three comprises the steps of:
for discrete modes
Figure FDA0002268091200000031
In n-dimensional space
Figure FDA0002268091200000032
Upper, is divided into a safe space
Figure FDA0002268091200000033
And a hazardous space
Figure FDA0002268091200000034
In mode qjThe degree of harm of the total number of colonies at time t is expressed as
Figure FDA0002268091200000035
Wherein x (t) represents the process variable vector at time t;
setting the over-standard threshold of the total number of the bacterial colonies for the selected modes
Figure FDA0002268091200000036
And setting the prediction duration, and predicting the probability that the content exceeds the standard at a specific moment in a corresponding link in the wheat flour supply chain by sampling and estimating the harm degree of the total number of the colonies for multiple times.
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