CN115981192A - Industrial network based cooperative control and prejudgment method - Google Patents
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Abstract
The invention belongs to the field of industrial networks, relates to a data processing technology, is used for solving the problem that the existing industrial network cooperative control method cannot carry out prejudgment analysis on network faults, and particularly relates to a method based on industrial network cooperative control and prejudgment, which comprises the following steps: and (3) carrying out data transmission analysis on the industrial network: marking a data transmission network of an industrial network as an analysis object, setting an analysis period for the analysis object, acquiring memory data, throughput data and bandwidth data of the analysis object in the analysis period, carrying out numerical calculation to obtain a salient coefficient, and marking the analysis object as a backbone object or an analysis object according to the numerical value of the salient coefficient; the method carries out data transmission analysis on the industrial networks, carries out comprehensive analysis on network data transmission parameters in an analysis period, carries out quantitative analysis on each industrial network, matches the backbone network with the branch network, and analyzes the fault characteristics of the backbone network and the branch network.
Description
Technical Field
The invention belongs to the field of industrial networks, relates to a data processing technology, and particularly relates to a cooperative control and prejudgment method based on an industrial network.
Background
The industrial network is a novel infrastructure, an application mode and an industrial ecology deeply integrated by a new generation of information communication technology and industrial economy, and a brand new manufacturing and service system covering a whole industrial chain and a whole value chain is constructed by comprehensively connecting people, machines, objects, systems and the like, so that a realization approach is provided for the digitalization, networking and intelligent development of industry and even industry, and the industrial network is an important basic stone of the fourth industrial revolution;
however, the existing industrial network cooperative control method is difficult to perform comprehensive analysis according to the transmission relationship and the operation state between the networks, so that the network fault cannot be pre-judged and analyzed, and the problem of low industrial network fault processing efficiency is caused.
Disclosure of Invention
The invention aims to provide an industrial network cooperative control and prejudgment method, which is used for solving the problem that the conventional industrial network cooperative control method cannot carry out prejudgment analysis on network faults.
The technical problems to be solved by the invention are as follows: how to provide an industrial network-based cooperative control and prejudgment method capable of prejudging and analyzing network faults.
The purpose of the invention can be realized by the following technical scheme:
the cooperative control and prejudgment method based on the industrial network comprises the following steps:
the method comprises the following steps: and (3) carrying out data transmission analysis on the industrial network: marking a data transmission network of an industrial network as an analysis object, setting an analysis period for the analysis object, acquiring memory data, throughput data and bandwidth data of the analysis object in the analysis period, carrying out numerical calculation to obtain a salient coefficient, and marking the analysis object as a backbone object or an analysis object according to the numerical value of the salient coefficient;
step two: performing cooperative management analysis on the backbone object and the branch object: carrying out data transmission detection on an analysis object in an analysis period: acquiring rate data, time delay data and packet loss data of an analysis object, carrying out numerical calculation to obtain a detection coefficient of the analysis object, and marking the analysis object as a normal transmission object or an abnormal transmission object according to the numerical value of the detection coefficient;
step three: matching the backbone object marked as the transmission abnormal object with the branch object, judging whether the matched backbone object and the branch object have a data transmission relation or not, and marking the branch object as a related object or a parallel object of the backbone object according to a judgment result;
step four: performing network fault pre-judgment analysis on the backbone object and the associated object: the operation time of the industrial network in a unit day is divided into a plurality of operation time periods, all abnormal transmission objects in the operation time periods are marked, backbone objects in the marked abnormal transmission objects are extracted, the dominant values of the associated objects are obtained, a dominant set is established, and early warning protection is performed on network faults through the dominant set.
As a preferred embodiment of the present invention, in the step one, the memory data is a total memory value of transmission data of the analysis object in the analysis period, and the throughput data is an average throughput value of the analysis object in the analysis period; the bandwidth data is an average of the bandwidth amounts of the analysis objects in the analysis period.
As a preferred embodiment of the present invention, in the step one, the specific process of marking the analysis object as a transmission normal object or a transmission abnormal object includes: acquiring a protrusion threshold value through a storage module, and comparing a protrusion coefficient of an analysis object with the protrusion threshold value:
if the salient coefficient is smaller than the salient threshold value, marking the corresponding analysis object as a branch object;
if the salient coefficient is larger than or equal to the salient threshold value, marking the corresponding analysis object as a backbone object;
and the cooperative control platform receives the backbone object and the branch object and then sends the backbone object and the branch object to the cooperative analysis module.
As a preferred embodiment of the present invention, in step two, the rate data is a rate at which the analysis object transmits data on the digital channel when performing data transmission detection, the delay data is a time required for the analysis object to transmit data from one end of the network to the other end when performing data transmission detection, and the packet loss data is a packet loss rate when performing data transmission detection on the analysis object.
As a preferred embodiment of the present invention, in step two, the specific process of marking the analysis object as a transmission normal object or a transmission abnormal object includes: acquiring a detection threshold value through a storage module, and comparing a detection coefficient of an analysis object with the detection threshold value:
if the detection coefficient is smaller than the detection threshold, judging that the data transmission detection result of the corresponding analysis object is unqualified, and marking the corresponding analysis object as a transmission abnormal object;
if the detection coefficient is larger than or equal to the detection threshold, judging that the data transmission detection result of the corresponding analysis object is qualified, and marking the corresponding analysis object as a normal transmission object.
As a preferred embodiment of the present invention, in step three, the specific process of marking the branch object as an associated object or a parallel object of the backbone object includes: judging whether the matched backbone object and the branch object have a data transmission relation or not:
if yes, marking the branch object as a related object of the backbone object;
if not, marking the branch object as a parallel object of the backbone object;
and the cooperative control platform receives the backbone object and the associated object and then sends the backbone object and the associated object to the prejudgment analysis module.
As a preferred embodiment of the present invention, in step four, the obtaining of the dominant value of the associated object includes: marking all abnormal transmission objects in the operation time period, extracting the backbone objects in the marked abnormal transmission objects, and judging whether the abnormal transmission objects have the associated objects of the marked backbone objects:
if so, marking the corresponding running time period as an explicit value of the associated object;
and if not, marking the corresponding running time period as an implicit value of the associated object.
As a preferred embodiment of the present invention, in step four, the specific process of performing early warning protection on the network failure includes: when the data transmission detection result of the backbone object is unqualified, extracting the current operation time period, marking the corresponding associated object as an early warning object when the dominant set contains the same elements as the current operation time period, sending the corresponding associated object and the early warning signal to the cooperative control platform, and sending the associated object and the early warning signal to a mobile phone terminal of a manager after the cooperative control platform receives the associated object and the early warning signal.
The invention has the following beneficial effects:
1. the industrial network can be subjected to data transmission analysis through the transmission analysis module, the mass analysis is carried out on each industrial network through comprehensive analysis of network data transmission parameters in an analysis period, so that the industrial networks are classified according to the mass analysis result, the transmission relation between the networks can be deeply analyzed after classification, the backbone network and the branch network are matched, and the fault characteristics of the backbone network and the branch network are analyzed, so that the network fault prediction function is realized;
2. the cooperative management analysis can be carried out on the backbone object and the branch object through the cooperative analysis module, and whether the data transmission process of the analysis object is qualified or not is judged through data transmission detection, so that the explicit-implicit relation between the backbone object and the branch object is screened from the data transmission process to the network fault moment, and the fault characteristics of the backbone object and the branch object are fed back in combination with the data transmission relation between the backbone object and the branch object;
3. the network fault pre-judgment analysis can be carried out on the backbone object and the associated object through the pre-judgment analysis module, the time period of the network fault is marked by recording the time when the backbone object has the network fault, the visibility value of the associated object of the backbone object in the operation time period can be further judged, the visibility rule of the associated object in the operation time period is fed back, and therefore the network fault of the associated object is predicted according to the visibility set.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a block diagram of a system according to a first embodiment of the present invention;
FIG. 2 is a flowchart of a method according to a second embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Industrial networks are not simple applications of the internet in industry, but have a much richer connotation and extension. The system takes a network as a basis, a platform as a center, data as an element and safety as a guarantee, is not only an infrastructure for industrial digitization, networking and intelligent transformation, but also an application mode for deep integration of internet, big data, artificial intelligence and entity economy, and is also a new state and a new industry, and the form, the supply chain and the industry chain of an enterprise are reshaped.
Example one
As shown in fig. 1, the cooperative control and anticipation system based on the industrial network includes a cooperative control platform, and the cooperative control platform is communicatively connected with a transmission analysis module, a cooperative analysis module, an anticipation analysis module, and a storage module.
The transmission analysis module is used for carrying out data transmission analysis on the industrial network: the method comprises the steps of marking a data transmission network of an industrial network as an analysis object i, i =1,2, \8230, wherein n and n are positive integers, setting an analysis period for the analysis object i, and acquiring memory data NCi, throughput data TTi and bandwidth data DKi of the analysis object i in the analysis period, wherein the memory data NCi is the total memory value of the transmission data of the analysis object i in the analysis period, the throughput data TTi is the average throughput value of the analysis object i in the analysis period, and the network throughput refers to the maximum rate which can be accepted by equipment under the condition of no frame loss, and the test method comprises the following steps: sending a certain number of frames at a certain rate in the test, calculating the frames transmitted by the equipment to be tested, and if the number of the sent frames is equal to that of the received frames, increasing the sending rate and retesting; if the received frame is less than the sending frame, reducing the sending rate for retesting until a final result is obtained; the bandwidth data DKi is an average value of bandwidth quantity of an analysis object i in an analysis period, the bandwidth is also called bandwidth, and refers to data quantity which can be transmitted in a fixed time, namely, the capacity of transmitting data in a transmission pipeline, in a digital device, the bandwidth is usually expressed by bps, namely, the number of bits which can be transmitted per second, in an analog device, the bandwidth is usually expressed by transmission period per second or hertz (Hz), a salient coefficient TCi of the analysis object i in the analysis period is obtained by a formula TCi = α 1 × nci + α 2 × tti + α 3 × DKi, the salient coefficient is a value which reflects the magnitude of the analysis object during data transmission, and the larger the value of the salient coefficient is, the larger the quantity of the analysis object during data transmission is indicated; wherein alpha 1, alpha 2 and alpha 3 are all proportionality coefficients, and alpha 1 is more than alpha 2 and more than alpha 3 is more than 1; obtaining the protrusion threshold TCmax through the storage module, and comparing the protrusion coefficient TCi of the analysis object i with the protrusion threshold TCmax: if the protrusion coefficient TCi is smaller than the protrusion threshold TCmax, marking the corresponding analysis object as a branch object; if the protrusion coefficient TCi is greater than or equal to the protrusion threshold TCmax, marking the corresponding analysis object as a backbone object; the backbone object and the branch object are sent to a cooperative control platform, and the cooperative control platform sends the backbone object and the branch object to a cooperative analysis module after receiving the backbone object and the branch object; the method comprises the steps of carrying out data transmission analysis on the industrial networks, carrying out comprehensive analysis on network data transmission parameters in an analysis period, carrying out quantum analysis on each industrial network, classifying the industrial networks according to a quantum analysis result, carrying out deep analysis on transmission relations between the networks after classification, matching a backbone network and a branch network, and analyzing fault characteristics of the backbone network and the branch network, so that a network fault prediction function is realized.
The cooperative analysis module is used for performing cooperative management analysis on the backbone object and the branch object: carrying out data transmission detection on an analysis object in an analysis period: obtaining rate data SL, time delay data SY and packet loss data DB of an analysis object, wherein the rate data SL is the rate of transmitting data on a digital channel when the analysis object carries out data transmission detection, the time delay data SY is the time required for transmitting data from one end to the other end of a network when the analysis object carries out data transmission detection, the packet loss data DB is the packet loss rate when the analysis object carries out data transmission detection, the network packet loss rate is the ratio of the lost part of a data packet to the total number of the transmitted data packets, the data is transmitted by being divided into data packets in the network, each data packet is provided with a frame for representing data information and providing a data route, and the transmission of the data packet in a general medium is that a small part of the data packet is lost due to the overlarge distance between two terminals, and most of the data packet reaches a target terminal; obtaining a detection coefficient JC of the analysis object through a formula JC = (beta 1 × SL)/(beta 2 × SY + beta 3 × SB), wherein the detection coefficient is a numerical value reflecting the normal degree of network transmission of the analysis object, and the smaller the numerical value of the detection coefficient is, the higher the normal degree of network transmission of the corresponding analysis object is; wherein beta 1, beta 2 and beta 3 are proportionality coefficients, and beta 1 is more than beta 2 and more than beta 3 is more than 1; acquiring a detection threshold value JCmin through a storage module, and comparing a detection coefficient JC of an analysis object with the detection threshold value JCmin: if the detection coefficient JC is smaller than the detection threshold JCmin, judging that the data transmission detection result of the corresponding analysis object is unqualified, and marking the corresponding analysis object as a transmission abnormal object; if the detection coefficient JC is greater than or equal to a detection threshold JCmin, judging that the data transmission detection result of the corresponding analysis object is qualified, and marking the corresponding analysis object as a normal transmission object; matching the backbone object marked as the transmission abnormal object with the branch object, and judging whether the matched backbone object and the branch object have a data transmission relation: if yes, marking the branch object as a related object of the backbone object; if not, marking the branch object as a parallel object of the backbone object; the backbone object and the associated object are sent to a cooperative control platform, and the cooperative control platform sends the backbone object and the associated object to a pre-judging analysis module after receiving the backbone object and the associated object; and performing cooperative management analysis on the backbone object and the branch object, and judging whether the data transmission process of the analysis object is qualified or not through data transmission detection, so as to screen the explicit-implicit relationship between the backbone object and the branch object from the time of network failure, and feeding back the failure characteristics of the backbone object and the branch object by combining the data transmission relationship between the backbone object and the branch object.
The prejudgment analysis module is used for carrying out network fault prejudgment analysis on the backbone object and the associated object: dividing the operation time of the industrial network in a unit day into a plurality of operation time periods, marking all abnormal transmission objects in the operation time periods, extracting backbone objects in the marked abnormal transmission objects, and judging whether related objects of the marked backbone objects exist in the abnormal transmission objects: if so, marking the corresponding running time period as an explicit value of the associated object; if not, marking the corresponding running time period as a recessive value of the associated object; extracting all dominant values of the associated objects in an analysis period and establishing a dominant set; when the data transmission detection result of the backbone object is unqualified, extracting the current operation time period, marking the corresponding associated object as an early warning object when the dominant set contains the same elements as the current operation time period, sending the corresponding associated object and the early warning signal to a cooperative control platform, and sending the associated object and the early warning signal to a mobile phone terminal of a manager after the cooperative control platform receives the associated object and the early warning signal; the network fault pre-judging analysis is carried out on the backbone object and the associated object, the time period of the network fault is marked by recording the time when the backbone object has the network fault, the visibility value of the associated object of the backbone object in the operation time period can be judged, the visibility rule of the associated object in the operation time period is fed back, and therefore the network fault of the associated object is predicted according to the visibility set.
Example two
As shown in fig. 2, the cooperative control and anticipation method based on the industrial network includes the following steps:
the method comprises the following steps: and (3) carrying out data transmission analysis on the industrial network: marking a data transmission network of an industrial network as an analysis object, setting an analysis period for the analysis object, acquiring memory data, throughput data and bandwidth data of the analysis object in the analysis period, carrying out numerical calculation to obtain a salient coefficient, marking the analysis object as a backbone object or an analysis object according to the numerical value of the salient coefficient, carrying out deep analysis on the transmission relationship between the network and the network, matching the backbone network with a branch network, and analyzing the fault characteristics of the backbone network and the branch network, thereby realizing the network fault prediction function;
step two: performing cooperative management analysis on the backbone object and the branch object: carrying out data transmission detection on an analysis object in an analysis period: acquiring rate data, time delay data and packet loss data of an analysis object, carrying out numerical calculation to obtain a detection coefficient of the analysis object, and marking the analysis object as a normal transmission object or an abnormal transmission object according to the numerical value of the detection coefficient;
step three: matching the backbone object marked as the abnormal transmission object with the branch object, judging whether the matched backbone object and the branch object have a data transmission relation, marking the branch object as a related object or a parallel object of the backbone object according to a judgment result, carrying out deep analysis on the transmission relation between the network and the network, matching the backbone network with the branch network, and analyzing the fault characteristics of the backbone network and the branch network, thereby realizing the network fault prediction function;
step four: performing network fault pre-judgment analysis on the backbone object and the associated object: the method comprises the steps of dividing the operation time of the industrial network in a unit day into a plurality of operation time periods, marking all abnormal transmission objects in the operation time periods, extracting backbone objects in the marked abnormal transmission objects, obtaining dominant values of associated objects, establishing a dominant set, carrying out early warning protection on network faults through the dominant set, feeding back the explicit and implicit rules of the associated objects in the operation time periods, and predicting the network faults of the associated objects according to the dominant set.
Based on the industrial network cooperative control and prejudgment method, during working, the data transmission analysis is carried out on the industrial network: marking a data transmission network of an industrial network as an analysis object, setting an analysis period for the analysis object, acquiring memory data, throughput data and bandwidth data of the analysis object in the analysis period, carrying out numerical calculation to obtain a salient coefficient, marking the analysis object as a backbone object or an analysis object according to the numerical value of the salient coefficient, carrying out deep analysis on the transmission relationship between the network and the network, matching the backbone network with a branch network, and analyzing the fault characteristics of the backbone network and the branch network, thereby realizing the network fault prediction function; performing cooperative management analysis on the backbone object and the branch object: carrying out data transmission detection on an analysis object in an analysis period: acquiring rate data, time delay data and packet loss data of an analysis object, carrying out numerical calculation to obtain a detection coefficient of the analysis object, and marking the analysis object as a normal transmission object or an abnormal transmission object according to the numerical value of the detection coefficient; matching the backbone object marked as the abnormal transmission object with the branch object, judging whether the matched backbone object and the branch object have a data transmission relation, marking the branch object as a related object or a parallel object of the backbone object according to a judgment result, carrying out deep analysis on the transmission relation between the network and the network, matching the backbone network with the branch network, and analyzing the fault characteristics of the backbone network and the branch network, thereby realizing the network fault prediction function; performing network fault pre-judging analysis on the backbone object and the associated object: the method comprises the steps of dividing the operation time of the industrial network in a unit day into a plurality of operation time periods, marking all abnormal transmission objects in the operation time periods, extracting backbone objects in the marked abnormal transmission objects, obtaining dominant values of associated objects, establishing a dominant set, carrying out early warning protection on network faults through the dominant set, feeding back the explicit and implicit rules of the associated objects in the operation time periods, and predicting the network faults of the associated objects according to the dominant set.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
The formulas are all obtained by acquiring a large amount of data and performing software simulation, and a formula close to a true value is selected, and coefficients in the formulas are set by a person skilled in the art according to actual conditions; such as: formula TCi = α 1 × nci + α 2 × tti + α 3 × dki; collecting multiple groups of sample data by the technicians in the field and setting a corresponding salient coefficient for each group of sample data; substituting the set salient coefficients and the acquired sample data into formulas, forming a ternary linear equation set by any three formulas, screening the calculated coefficients and taking the mean value to obtain values of alpha 1, alpha 2 and alpha 3 which are 5.32, 3.25 and 2.17 respectively;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and regarding the size of the coefficient, the corresponding salient coefficient is preliminarily set for each group of sample data by a person skilled in the art according to the number of the sample data; it is sufficient if the proportional relationship between the parameter and the quantized value is not affected, for example, the coefficient is proportional to the value of the throughput data.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (8)
1. The cooperative control and prejudgment method based on the industrial network is characterized by comprising the following steps of:
the method comprises the following steps: and (3) carrying out data transmission analysis on the industrial network: marking a data transmission network of an industrial network as an analysis object, setting an analysis period for the analysis object, acquiring memory data, throughput data and bandwidth data of the analysis object in the analysis period, carrying out numerical calculation to obtain a salient coefficient, and marking the analysis object as a backbone object or an analysis object according to the numerical value of the salient coefficient;
step two: performing cooperative management analysis on the backbone object and the branch object: carrying out data transmission detection on an analysis object in an analysis period: acquiring rate data, time delay data and packet loss data of an analysis object, carrying out numerical calculation to obtain a detection coefficient of the analysis object, and marking the analysis object as a normal transmission object or an abnormal transmission object according to the numerical value of the detection coefficient;
step three: matching the backbone object marked as the transmission abnormal object with the branch object, judging whether the matched backbone object and the branch object have a data transmission relation or not, and marking the branch object as a related object or a parallel object of the backbone object according to a judgment result;
step four: performing network fault pre-judgment analysis on the backbone object and the associated object: the operation time of the industrial network in a unit day is divided into a plurality of operation time periods, all abnormal transmission objects in the operation time periods are marked, backbone objects in the marked abnormal transmission objects are extracted, the dominant values of the associated objects are obtained, a dominant set is established, and early warning protection is performed on network faults through the dominant set.
2. The cooperative control and anticipation method based on the industrial network according to claim 1, wherein in the first step, the memory data is a total memory value of transmission data of the analysis object in the analysis period, and the throughput data is an average throughput value of the analysis object in the analysis period; the bandwidth data is an average of the bandwidth amounts of the analysis objects in the analysis period.
3. The industrial network cooperative control and anticipation method as claimed in claim 2, wherein in the first step, the specific process of marking the analysis object as a normal transmission object or an abnormal transmission object comprises: acquiring a protrusion threshold value through a storage module, and comparing a protrusion coefficient of an analysis object with the protrusion threshold value:
if the salient coefficient is smaller than the salient threshold value, marking the corresponding analysis object as a branch object;
if the salient coefficient is larger than or equal to the salient threshold value, marking the corresponding analysis object as a backbone object;
and the cooperative control platform receives the backbone object and the branch object and then sends the backbone object and the branch object to the cooperative analysis module.
4. The cooperative control and anticipation method based on the industrial network according to claim 1, wherein in the second step, the rate data is a rate of data transmission on a digital channel when the analysis object performs data transmission detection, the time delay data is a time required for the analysis object to transmit data from one end to the other end of the network when performing data transmission detection, and the packet loss data is a packet loss rate when the analysis object performs data transmission detection.
5. The industrial network based cooperative control and anticipation method according to claim 4, wherein in the second step, the specific process of marking the analysis object as a normal transmission object or an abnormal transmission object includes: obtaining a detection threshold value through a storage module, and comparing a detection coefficient of an analysis object with the detection threshold value:
if the detection coefficient is smaller than the detection threshold, judging that the data transmission detection result of the corresponding analysis object is unqualified, and marking the corresponding analysis object as a transmission abnormal object;
if the detection coefficient is larger than or equal to the detection threshold, judging that the data transmission detection result of the corresponding analysis object is qualified, and marking the corresponding analysis object as a normal transmission object.
6. The industrial network based cooperative control and anticipation method of claim 1, wherein in step three, the specific process of marking the branch object as an associated object or a parallel object of the backbone object comprises: judging whether the matched backbone object and the branch object have a data transmission relation or not:
if yes, marking the branch object as a related object of the backbone object;
if not, marking the branch object as a parallel object of the backbone object;
and the cooperative control platform receives the backbone object and the associated object and then sends the backbone object and the associated object to the prejudgment analysis module.
7. The industrial network based cooperative control and anticipation method according to claim 6, wherein in step four, the obtaining process of the dominant value of the associated object comprises: marking all abnormal transmission objects in the operation time period, extracting the backbone objects in the marked abnormal transmission objects, and judging whether the abnormal transmission objects have the associated objects of the marked backbone objects:
if so, marking the corresponding running time period as an explicit value of the associated object;
and if not, marking the corresponding running time period as an implicit value of the associated object.
8. The industrial network based cooperative control and prejudgment method according to claim 7, wherein in the fourth step, the specific process of performing early warning protection on the network fault comprises: when the data transmission detection result of the backbone object is unqualified, extracting the current operation time period, marking the corresponding associated object as an early warning object when the dominant set contains the same elements as the current operation time period, sending the corresponding associated object and the early warning signal to the cooperative control platform, and sending the associated object and the early warning signal to the mobile phone terminal of a manager after the cooperative control platform receives the associated object and the early warning signal.
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