1. A reversible recovery failure-oriented workshop key manufacturing resource (SIS) identification method is characterized by comprising the following steps:

the method comprises the following steps that firstly, workpieces produced in a production period are automatically associated with production plans, production processes and related manufacturing resources of the workpieces on the basis of an Internet of things RFID technology and a relational SQL database, according to an SIS model in an infectious disease research theory, the total quantity of the manufacturing resources in the production period of a whole workshop is assumed to be constant and is recorded as U, and the manufacturing resources with faults and the manufacturing resources without faults in the manufacturing resources of the discrete workshop are recorded as U: x (t)₀) And Y (t)₀):

Representing X (t) by relational algebra₀) And Y (t)₀) Comprises the following steps:

X(t₀)＝{x₁(t₀),x₂(t₀),x₃(t₀),…,x_j(t₀)}

Y(t₀)＝{y₁(t₀),y₂(t₀),y₃(t₀),…,y_k(t₀)}

wherein:

x_j(t₀) Manufacturing a resource for the jth initial failed start time;

y_k(t₀) Manufacturing resources for the kth initial non-failed time;

automatically associating workpieces, production plans, production processes and corresponding manufacturing resources in a certain production period in a manufacturing workshop based on an Internet of things RFID technology and a relational SQL database, automatically converting the association relationship into a connecting edge in a workshop production manufacturing system network, wherein the weight of the edge is the processing time of a process, and all manufacturing resources of machine tool equipment, a cutter, a clamp, a measuring tool and personnel in the workshop production process are finally mapped into nodes in the manufacturing system network;

step three, according to the grouping result of the step one, setting the probability that the manufacturing resources without faults finally have faults due to the manufacturing resources with faults, the effective action number of the manufacturing resources with faults to the manufacturing resources without faults in unit time, and the ratio of the number of the manufacturing resources with faults to the total number of the manufacturing resources with faults again to the total number of the manufacturing resources with faults, wherein the ratio is respectively as follows: beta, gamma, lambda;

step four, the fault propagation rate between the manufacturing resources with the connection relation is the ratio of the weight of the edge between the two connections to the maximum weight in the whole network, beta_ijThe calculation is as follows:

wherein δ is the contact probability of two manufacturing resources, the contact probability is 1 when there is a connecting edge between the two, and the contact probability is 0 when there is no connecting edge;

step five, solving the change of the number of the manufacturing resource bottlenecks without faults caused by the manufacturing resources with faults initially through an SIS model along with time:

wherein I (t) is the number of the fault-prone manufacturing resource bottleneck occurrence number which changes along with the time;

step six, the importance of the manufacturing resources which have failed initially is marked by weighting the peak value of the number of the bottleneck resources and the time length reaching the peak value:

wherein ZYD (i) is the importance of the manufacturing resources for which group i initially failed,

t (i) is the time when the number of manufacturing resource bottlenecks in the ith group reaches the peak value;

the number of manufacturing resource bottlenecks occurring for the ith group reaches a peak value;

k₁，k₂weights for time to peak and peak;

step seven, changing the grouping condition of the manufacturing resources with faults and the manufacturing resources without faults in the workshop manufacturing resources, recalculating the importance degrees according to the steps one to six, and repeating the steps continuously in sequence until the importance degrees of all possible grouping conditions are obtained;

and step eight, obtaining key manufacturing resource nodes in the discrete workshop manufacturing system according to the magnitude sequence of all the obtained importance degrees.

Background

In the face of competitive pressures from the market, more and more enterprises are focusing their efforts on improvements and optimizations of production systems and processes in order to meet the needs of different customers. However, efficient control of the production system requires manufacturing resources such as personnel, machines, materials, and workpieces related to the production activities, and can be achieved through organic combination and mutual cooperation. The method has the advantages that scientific and systematic analysis and quantitative evaluation and evaluation are carried out on the production system, and the method has positive effects on the aspects of enhancing the stability of a discrete manufacturing workshop, improving the on-time completion rate of workpieces, further improving the economic benefits of enterprises and the like.

The premise of the production system analysis is that the production links of the discrete manufacturing workshop are fully known and accurately modeled, the risk points and key nodes of the production plan in a section of production period of the workshop can be identified by establishing key indexes, and accurate data support is provided for subsequent targeted improvement.

Disclosure of Invention

In view of the above problems, the present invention provides a method for identifying SIS manufacturing resources in a workshop for reversible recovery failures, which comprises automatically associating workpieces produced in a production cycle with production plans, production processes and related manufacturing resources through information technology and database technology, assuming that manufacturing resources with failures and manufacturing resources without failures initially occur in discrete workshop manufacturing resources according to an SIS model in the infectious disease research theory, solving the time-dependent change in the number of bottlenecks occurring in other manufacturing resources caused by the initially failed manufacturing resources through the SIS model, marking the importance of the initially failed manufacturing resources through the peak value of the number of bottleneck resources and the weighted result of the time for reaching the peak value, and then changing the grouping condition of the initially failed manufacturing resources and the manufacturing resources without failures in the workshop manufacturing resources, and recalculating the importance, repeating the recalculation until the importance of all possible grouping conditions is obtained, and finally obtaining the key manufacturing resource nodes in the discrete workshop manufacturing system according to the magnitude sequence of all the obtained importance.

In order to achieve the purpose, the invention adopts the technical scheme that:

a reversible recovery failure-oriented workshop key manufacturing resource (SIS) identification method comprises the following steps:

the method comprises the following steps that firstly, workpieces produced in a production period are automatically associated with production plans, production processes and related manufacturing resources of the workpieces on the basis of an Internet of things RFID technology and a relational SQL database, according to an SIS model in an infectious disease research theory, the total quantity of the manufacturing resources in the production period of a whole workshop is assumed to be constant and is recorded as U, and the manufacturing resources with faults and the manufacturing resources without faults in the manufacturing resources of the discrete workshop are recorded as U: x (t)₀) And Y (t)₀):

Representing X (t) by relational algebra₀) And Y (t)₀) Comprises the following steps:

X(t₀)＝{x₁(t₀),x₂(t₀),x₃(t₀),…,x_j(t₀)}

Y(t₀)＝{y₁(t₀),y₂(t₀),y₃(t₀),…,y_k(t₀)}

wherein:

x_j(t₀) Manufacturing a resource for the jth initial failed start time;

y_k(t₀) Manufacturing resources for the kth initial non-failed time;

automatically associating workpieces, production plans, production processes and corresponding manufacturing resources in a certain production period in a manufacturing workshop based on an Internet of things RFID technology and a relational SQL database, automatically converting the association relationship into a connecting edge in a workshop production manufacturing system network, wherein the weight of the edge is the processing time of a process, and all manufacturing resources of machine tool equipment, a cutter, a clamp, a measuring tool and personnel in the workshop production process are finally mapped into nodes in the manufacturing system network;

step three, according to the grouping result of the step one, setting the probability that the manufacturing resources without faults finally have faults due to the manufacturing resources with faults, the effective action number of the manufacturing resources with faults to the manufacturing resources without faults in unit time, and the ratio of the number of the manufacturing resources with faults to the total number of the manufacturing resources with faults again to the total number of the manufacturing resources with faults, wherein the ratio is respectively as follows: beta, gamma, lambda;

step four, the fault propagation rate between the manufacturing resources with the connection relation is the ratio of the weight of the edge between the two connections to the maximum weight in the whole network, beta_ijThe calculation is as follows:

wherein δ is the contact probability of two manufacturing resources, the contact probability is 1 when there is a connecting edge between the two, and the contact probability is 0 when there is no connecting edge;

step five, solving the change of the number of the manufacturing resource bottlenecks without faults caused by the manufacturing resources with faults initially through an SIS model along with time:

wherein I (t) is the number of bottlenecks occurring in the failure-prone manufacturing resource over time.

Step six, the importance of the manufacturing resources which have failed initially is marked by weighting the peak value of the number of the bottleneck resources and the time length reaching the peak value:

wherein ZYD (i) is the importance of the manufacturing resources for which group i initially failed,

t (i) is the time when the number of manufacturing resource bottlenecks in the ith group reaches the peak value;

the number of manufacturing resource bottlenecks occurring for the ith group reaches a peak value;

k₁，k₂weights for time to peak and peak;

step seven, changing the grouping condition of the manufacturing resources with faults and the manufacturing resources without faults in the workshop manufacturing resources, recalculating the importance degrees according to the steps one to six, and repeating the steps continuously in sequence until the importance degrees of all possible grouping conditions are obtained;

and step eight, obtaining key manufacturing resource nodes in the discrete workshop manufacturing system according to the magnitude sequence of all the obtained importance degrees.

The invention has the beneficial effects that:

1) key manufacturing resources may be determined for a discrete manufacturing plant in a reversible recovery manufacturing resource failure environment.

2) The importance of the key manufacturing resource can be quantitatively described by the peak-to-peak time and the weighting of the peak value of the bottleneck resource quantity.

3) The influence of the connection relationship between the manufacturing resources on the propagation of the reversible recovery fault can be accurately described.

4) Other key manufacturing resources needing attention in production management in a manufacturing workshop can be combed through sequencing of fault propagation speeds, a plan is made in advance, and the flexibility of a production organization is improved.

Drawings

FIG. 1 is a classification of manufacturing resources for a manufacturing plant.

FIG. 2 is a shop manufacturing resource network building framework.

FIG. 3 is a value of the probability of contact between different manufacturing resources.

FIG. 4 is a flow diagram of key manufacturing resource node identification.

Detailed Description

A reversible recovery failure-oriented workshop key manufacturing resource (SIS) identification method comprises the following steps:

referring to fig. 1, based on the internet of things RFID technology and a relational SQL database, workpieces produced in a production period are automatically associated with their production plans, production processes, and related manufacturing resources, according to an SIS model in the infectious disease research theory, assuming that the total amount of manufacturing resources in the production period of the whole workshop remains unchanged, and the total amount is denoted as U, and the initial faulty manufacturing resources and non-faulty manufacturing resources in the discrete workshop manufacturing resources are denoted as: x (t)₀) And Y (t)₀)。

Representing X (t) by relational algebra₀) And Y (t)₀) Comprises the following steps:

X(t₀)＝{x₁(t₀),x₂(t₀),x₃(t₀),…,x_j(t₀)}

Y(t₀)＝{y₁(t₀),y₂(t₀),y₃(t₀),…,y_k(t₀)}

wherein:

x_j(t₀) Manufacturing a resource for the jth initial failed start time;

y_k(t₀) Manufacturing resources for the kth initial non-failed time;

step two, referring to fig. 2, based on the internet of things RFID technology and a relational SQL database, automatically associating workpieces, production plans, production processes and corresponding manufacturing resources in a certain production period in a manufacturing workshop, automatically converting the association relationship into a connecting edge in a workshop production manufacturing system network, wherein the weight of the edge is the processing time of a process, and finally mapping all manufacturing resources such as machine tool equipment, a cutter, a clamp, a measuring tool, personnel and the like in the workshop production process into nodes in the manufacturing system network;

step three, according to the grouping result of the step one, setting the probability that the manufacturing resources without faults finally have faults due to the manufacturing resources with faults, the effective action number of the manufacturing resources with faults to the manufacturing resources without faults in unit time, and the ratio of the number of the manufacturing resources with faults to the total number of the manufacturing resources with faults again to the total number of the manufacturing resources with faults, wherein the ratio is respectively as follows: beta, gamma, lambda;

step four, referring to fig. 3, the fault propagation rate between the manufacturing resources having the connection relationship is the ratio of the weight of the edge between the two connections to the maximum weight in the whole network, β_ijThe calculation is as follows:

wherein δ is the contact probability of two manufacturing resources, the contact probability is 1 when there is a connecting edge between the two, and the contact probability is 0 when there is no connecting edge;

step five, solving the change of the number of the manufacturing resource bottlenecks without faults caused by the manufacturing resources with faults initially through an SIS model along with time:

wherein I (t) is the number of bottlenecks occurring in the failure-prone manufacturing resource over time.

And step six, marking the importance of the manufacturing resources which have failed initially by using the weighting result of the peak value of the number of the bottleneck resources and the time reaching the peak value.

Wherein ZYD (i) is the importance of the manufacturing resources for which group i initially failed,

t (i) is the time when the number of manufacturing resource bottlenecks in the ith group reaches the peak value;

the number of manufacturing resource bottlenecks occurring for the ith group reaches a peak value;

k₁，k₂weights for time to peak and peak;

step seven, referring to fig. 4, changing the grouping condition of the manufacturing resources with faults and the manufacturing resources without faults in the workshop manufacturing resources, recalculating the importance according to the steps one to six, and repeating the steps in sequence until the importance of all possible grouping conditions is obtained;

and step eight, obtaining key manufacturing resource nodes in the discrete workshop manufacturing system according to the magnitude sequence of all the obtained importance degrees.