CN108173682A - A kind of intelligent industrial control instrument suitable for integrated signal management - Google Patents
A kind of intelligent industrial control instrument suitable for integrated signal management Download PDFInfo
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Abstract
The invention belongs to technical field of chemical production equipment, disclose a kind of intelligent industrial control instrument suitable for integrated signal management, are provided with：Central controller, external storage capacity extension module, digital decoder, address matcher, digital storage, display module, power supply module, system reset module, system maintaining module, manual control module, data interface module and drive control device module；Central controller connects external storage capacity extension module, digital decoder, address matcher, digital storage, display module, power supply module, system reset module, system maintaining module, manual control module, data interface module and drive control device module respectively.The achievable serial bus data of the present invention directly inputs, and multiple can be connected in parallel on a serial data bus, and table is respectively received by the universal serial bus and reads respective data, is voluntarily worked, is applicable in the Industry Control from various different serial bus protocols, multiple functional.
Description
Technical Field
The invention belongs to the field of chemical production equipment, and particularly relates to an intelligent industrial control instrument suitable for integrated signal management.
Background
A digital meter is a meter that displays a measured value digitally. A meter that converts the measurements into digital quantities and displays them in digital form. In industrial measurement, analog quantities such as measured variables or displacements, current, voltage, air pressure and the like are converted into digital quantities (analog-to-digital conversion) through an analog-to-digital converter. The digital meter displays the measured quantity in a digital form, and the reading is visual. Generally comprising: the electric quantity is indicated by a dial and a pointer, and the electromagnetic force-based electric measuring circuit, the analog-to-digital conversion and the digital display are three parts. The existing digital display instrument is mainly designed aiming at analog quantity, pulse quantity and switching value input signals, and along with the development of a control mode, the existing input mode cannot adapt to the development requirement of a digital control system. The existing instruments are classified into two types according to bus interfaces, namely, the existing instruments without digital bus interfaces cannot be directly connected with a bus system for use, and the existing instruments with digital bus interfaces have the input modes of analog quantity input, pulse quantity input and switching quantity input, such as thermocouples, thermal resistors, current signals, voltage signals, square wave pulses and the like. The existing meter communicates with an upper computer such as a computer through a serial bus interface and transmits data to the bus (the computer), but cannot directly receive or read the data from the bus (the computer). Namely, the serial bus interface is a communication output interface, and the meter cannot directly input and read serial bus data. The input mode of the existing instrument is analog quantity input, pulse quantity input and switching value input, such as a thermocouple, a thermal resistor, a current signal, a voltage signal, square wave pulse and the like, and can not be directly input and received serial bus data. When the input data is transmitted in a long distance, the error is large and even distortion occurs, on the premise that a power supply is not considered, each instrument needs at least one two-core shielding cable to transmit an input data signal, when the number of the instruments is large, a large number of cables are needed, not only is the cost of the cables increased, but also a large amount of engineering cost is generated, and meanwhile, the later maintenance is not convenient.
The performance evaluation of the instrument network is one of the challenging subjects in the safety evaluation of the instrument information system, and is the fundamental basis and precondition for the research and implementation of the instrument network. The performance evaluation of the instrument network comprises three aspects of selection of an evaluation index set, a specific evaluation method, a corresponding evaluation system and the like. However, the current performance evaluation of the meter network faces many problems, such as the multi-dynamic meter network environment, the multi-parameter influence factor, the multi-index measurement criteria, and so on. First, meter network performance assessment is influenced by a variety of factors in the meter network environment, and meter networks exhibit multi-dynamic environmental characteristics. The instrument network has the characteristics of high randomness, time-varying property, multi-hop property and the like, and meanwhile, the interaction interference among multiple nodes is very serious. And secondly, the evaluation effect of the instrument network is determined by dynamic environment factors such as dynamic link length change, uneven service distribution, irregular link distribution and the like. In addition, the measurement index parameters for evaluating the efficiency of the instrument network comprise the capacity limit of the instrument network, the information throughput change of the instrument network, the data loss probability estimation of the instrument network, the end-to-end delay, the influence of the queuing length and the like, and also concern about the reconstruction time of the instrument network and the like.
However, the current performance evaluation of the meter network has the problem that the individual performance of the meter network is evaluated from a single angle, and the system has serious one-sidedness, limitation and the like, and cannot reflect the overall performance or the group performance of the system. Therefore, the overall efficiency evaluation of the instrument network describes the countermeasure effect of the instrument network from a multi-dimensional perspective, and the attributes and the mutual relation of each part of the countermeasure effect of the instrument network are described, so that the efficiency evaluation effect of the instrument network is accurately and comprehensively reflected.
In addition, due to the complexity of the instrument network, there are challenges in terms of multi-dynamic instrument network environment, multi-parameter influence factors, multi-index measurement criteria, and the like, so that the evaluation of the instrument network performance is often impossible to select only a single performance index, and a group of performance indexes in multiple layers and multiple aspects is necessary to be selected for performance, thereby forming a performance index system. The index system is composed of a group of related indexes reflecting the attributes of various aspects of the evaluated object, and one index system can only meet the characteristics of one aspect of the evaluated object, so that the purpose of evaluating the efficiency of the evaluated object is determined before the index system is established, and a corresponding index system is established according to a certain principle, so that a reasonable evaluation result can be obtained. The core problem of multi-index comprehensive evaluation is to determine whether an evaluation index system is scientific and reasonable or not, and is directly related to the evaluation quality.
In the prior art, the overall efficiency and the group efficiency of the system cannot be well reflected by aiming at the problems of one-sidedness, limitation and the like in the evaluation process of a single performance index.
The network is a heterogeneous fusion network with various access technologies and multi-level deployment, the topology and the architecture of the network change along with the change of nodes in the network, and the instrument network is easy to generate a large amount of network alarm information and network faults, cannot accurately and timely position the network faults and cannot meet the instrument requirements.
Under the background, the problem of network fault is actively positioned, from the viewpoint of instrument use, the network is optimized, the network can be self-adaptively healed after the network is in fault, and the problem to be solved is that the intelligent level of the network is greatly improved.
In summary, the problems of the prior art are as follows: the existing instrument can not directly input and read serial bus data, has short transmission distance and large error, and can not meet the requirements of manufacturers.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an intelligent industrial control instrument suitable for integrated signal management.
The invention is realized in this way, an intelligent industrial instrument suitable for integrated signal management is provided with:
the device comprises a central controller, an external storage capacity expansion module, a digital decoder, an address matcher, a digital memory, a display module, a power supply module, a system reset module, a system maintenance module, a manual control module, a data interface module and a drive controller module.
The central controller is respectively connected with the external storage capacity expansion module, the digital decoder, the address matcher, the digital memory, the display module, the power supply module, the system reset module, the system maintenance module, the manual control module, the data interface module and the driving controller module.
The central controller is integrated with an instrument network performance evaluation module; the evaluation method of the instrument network performance evaluation module comprises the following steps:
1) setting a key index set;
2) refining the same-layer index and calculating the weight;
3) calculating cross-layer index weight;
the process of initializing the key index set in the step 1) is as follows: in a scenario with N experts participating in system evaluation, the index types given by expert i are:
wherein,Niis a set of multiple indexes, refers to the weight of an index j given by an expert i,the number of the types of the indexes is expressed,indicates the number of index types, M indicates the maximum number of indexes,the N experts form a total set of indices for the system as N' ═ Ni,i＝1,...,N}；
The process of refining the same layer index and calculating the weight in the step 2) comprises the following steps: in a scenario with N experts participating in system evaluation, a common key index type and weights of corresponding indexes are solved according to a total index set of a system formed by the N experts, wherein the total index set is N' ═ { Ni, i ═ 1.,. N }, and the refined index type is denoted as N0Then, thenWhereinRepresents the intersection of index types Ni, i ═ 1, N given by N experts; the weight vector of the key index after refining is calculated by an average method, namelyWhereinRepresents the j th index added to the refined index set N0The weight of the index element in (1);
the step of calculating the cross-layer index weight in the step 3) is as follows: the high layer realizes the weighted calculation of the index weight relative to the adjacent low layer, and the higher layer further realizes the weighted calculation of the index weight relative to the high layer; the specific calculation formula isWhereinIs the true weight of the high-level indicator,a weight determined for the higher layer based on an expert system, λ j being a quantity related to the set of lower layer indicators that determines the higher layer indicators,
the central controller is also connected with the instrument network performance evaluation module through an integrated fault positioning early warning module; the fault location early warning of the fault location early warning module comprises the following steps:
establishing a mapping relation between the fault alarm information and the fault between networks through correlation analysis, constructing a fault positioning model, and positioning the network fault through a BP neural network; the network fault location specifically comprises the following processes:
firstly, an m-dimensional alarm vector Q is obtainedn＝(s1,s2,s3…sm) And n-dimensional fault vector On＝(p1,p2,p3…pm) Simultaneously inputting the data through a plurality of network nodes, so that the system has a parallel structure and parallel processing capability and dynamically processes the input in real time;
secondly, the BP network gives a value in a designated range to each connected weight value, and designates a threshold value for each neuron node;
thirdly, the group input alarm sample machine target result is provided for the network, and the connection weight of the neural network node, the threshold value and the input and output values of each hidden layer unit are calculated;
then, the layer errors are positive: calculating the error of the output layer unit by using the target vector and the actual output value of the network, correcting the connection weight and the threshold value by combining the output of each unit of the hidden layer, and performing reverse error propagation correction;
and finally, training the sample vector and training the heterogeneous network system until the complete sample is trained, and inputting the operation and maintenance fault alarm information into the trained BP network to carry out network fault positioning.
Further, the mapping to the heterogeneous network through the wireless network parameters to obtain the positioned network fault, in the process, each network node is used as an intelligent agent to participate in the network fault positioning process, and the specific process is as follows:
selecting a Q learning method to establish a parameter system, establishing and maintaining a two-dimensional Q value table, wherein the first dimension is used for representing all possible states, and the second dimension represents possible actions taken by the network node intelligent agent; the element Q (S, a) of each Q value table corresponds to the Q value at which the agent takes action a in state S; the general criteria for a user to select an action are: the network node intelligent agent selects actions according to a certain probability based on the Q value table of each action in a given state, and the larger the Q value of one action is, the larger the probability of selection is; when a network fault occurs, firstly, a network node intelligent agent determines a heterogeneous network wireless parameter to be optimized according to correlation analysis and logistic regression analysis of network alarm information in operation and maintenance data, a new state S is constructed, and after the state S is constructed, each Q value corresponding to S is calculated;
then, the network node agent according to Q value with a certain probability P
Selecting an optimization strategy, namely action a;
finally, the system terminal updates the state S and the value of the selected action in the Q value table according to the network node agent return value and the existing Q value, wherein,
r (x, α) is E { R | s, a },pi is the selected strategy α is the learning factor.
Further, the power supply module comprises a triode V, a diode D, a chip IC and a chip IC, wherein a pin 1 of the chip IC is connected with a resistor R, an anode of the resistor R, a diode D and a power VCC, a pin 2 of the chip IC is connected with a capacitor C, a resistor R, a fixed end of a potentiometer RP, a cathode of a storage battery E, an anode of the diode D and a pin 1 of the chip IC, an anode of the storage battery E is connected with a switch S, the other end of the switch S is connected with the resistor R, a fixed end of the potentiometer RP, an anode of the diode D and an emitting electrode of the triode V, a base electrode of the triode V is connected with the other end of the resistor R and the resistor R, a collector connecting point R of the triode V and the other end of the resistor R, the other end of the resistor R is connected with a cathode of, the sliding end of a potentiometer RP1 is connected with a pin 2 of a chip IC1, the other end of a resistor R2 is connected with the other fixed end of a potentiometer RP2, the sliding end of the potentiometer RP2 is connected with a pin 6 of the chip IC1, the cathode of a diode D2 is connected with the other end of a capacitor C3, the other end of a capacitor C4, a pin 3 of a chip IC1 and a core controller, the other end of the resistor R7 is connected with a pin 8 of a chip IC1, a pin 4 of a chip IC1 is connected with the other end of the capacitor C2 and the cathode of a diode D4, a pin 5 of a chip IC1 is connected with the other end of a capacitor C1, a pin 7 of a chip IC1 is connected with the cathode of a diode D3, the anode of a diode D3 is connected with the other end of a resistor R6, the model of the chip IC 1.
Further, the central controller is an STC89C52 series single-chip microcomputer.
Further, the manual control module is a matrix keyboard.
Further, the display module is an LCD display screen.
Furthermore, the system reset module control system can perform initialization operation, and disorder of the system after misoperation is prevented.
Further, the external storage capacity expansion module is an SDRAM chip.
The invention can realize direct input of serial bus data, can connect in parallel a plurality of serial data buses, and the meter respectively receives and reads respective data through the serial buses, works by itself, is suitable for industrial control of various serial bus protocols, has complete functions, can display data of various serial bus protocols, and transmit and output analog quantity and switching quantity, and simultaneously adopts a power supply mode of an uninterrupted power supply, thereby avoiding the problem of interrupted work caused by power failure or insufficient power, and a system maintenance module carries out system maintenance, thereby ensuring the normal operation of the system.
According to the method, through three steps of setting a key index set, refining the same-layer index, calculating the weight and calculating the cross-layer index weight, the performance indexes are selected from multiple layers and multiple aspects to evaluate the overall performance of the network, so that the problems of one-sidedness, limitation and the like in the evaluation process aiming at a single performance index in the prior art are effectively solved, and the overall performance and the group performance of the system are better reflected; the influence of the bottom layer index on the upper layer index and the importance degree of the bottom layer index and the same layer index are comprehensively considered when the key index is selected, so that the evaluation is more accurate and the actual network requirements are better met; the index set is greatly reduced to key indexes, theoretical analysis and operation on a specific evaluation process are simplified, real-time performance is higher, and evaluation on the overall efficiency of the network is more accurate. Providing assurance for the application of the meter.
The invention realizes the network self-healing by mining and analyzing the operation and maintenance data of the instrument network, positioning the network fault and then optimizing the network parameters. The method has the advantages that the faults of the instrument network are intelligently positioned, the network faults in the transmission signals are accurately judged, the Q learning method is adopted for self-healing of the network and self-optimization of the network, the network performance is optimized in a fine-grained mode, the network quality is improved, the system performance is improved, and high-efficiency, safe and stable network operation is guaranteed. Providing assurance for the use of the meter.
Drawings
FIG. 1 is a schematic structural diagram of an intelligent industrial control instrument suitable for integrated signal management according to an embodiment of the present invention;
FIG. 2 is a circuit diagram of a power module provided by an embodiment of the invention;
in the figure: 1. a central controller; 2. an external storage capacity expansion module; 3. a power supply module; 4. a data interface; 5. a manual control module; 6. a drive module; 7. a system reset module; 8. a system maintenance module; 9. a display module; 10. a digital storage; 11. an address matcher; 12. a digital decoder.
Detailed Description
In order to further understand the contents, features and effects of the present invention, the following embodiments are illustrated and described in detail with reference to the accompanying drawings.
The structure of the present invention will be described in detail below with reference to the accompanying drawings.
As shown in fig. 1, an intelligent industrial control instrument suitable for integrated signal management according to an embodiment of the present invention includes: the system comprises a central controller 1, an external storage capacity expansion module 2, a power supply module 3, a data interface 4, a manual control module 5, a driving module 6, a system reset module 7, a system maintenance module 8, a display module 9, a digital memory 10, an address matcher 11 and a digital decoder 12.
The central controller 1 is respectively connected with an external storage capacity expansion module 2, a digital decoder 12, an address matcher 11, a digital memory 10, a display module 9, a power supply module 3, a system reset module 7, a system maintenance module 8, a manual control module 5, a data interface module 4 and a drive controller module 6.
The central controller is integrated with an instrument network performance evaluation module; the evaluation method of the instrument network performance evaluation module comprises the following steps:
1) setting a key index set;
2) refining the same-layer index and calculating the weight;
3) calculating cross-layer index weight;
the process of initializing the key index set in the step 1) is as follows: in a scenario with N experts participating in system evaluation, the index types given by expert i are:
wherein,Niis a set of multiple indexes, refers to the weight of an index j given by an expert i,the number of the types of the indexes is expressed,number of index typesM represents the maximum index number,the N experts form a total set of indices for the system as N' ═ Ni,i＝1,...,N}；
The process of refining the same layer index and calculating the weight in the step 2) comprises the following steps: in a scenario with N experts participating in system evaluation, a common key index type and weights of corresponding indexes are solved according to a total index set of a system formed by the N experts, wherein the total index set is N' ═ { Ni, i ═ 1.,. N }, and the refined index type is denoted as N0Then, thenWhereinRepresents the intersection of index types Ni, i ═ 1, N given by N experts; the weight vector of the key index after refining is calculated by an average method, namelyWhereinRepresents the j th index added to the refined index set N0The weight of the index element in (1);
the step of calculating the cross-layer index weight in the step 3) is as follows: the high layer realizes the weighted calculation of the index weight relative to the adjacent low layer, and the higher layer further realizes the weighted calculation of the index weight relative to the high layer; the specific calculation formula isWhereinIs the true weight of the high-level indicator,a weight determined for the higher layer based on an expert system, λ j being a quantity related to the set of lower layer indicators that determines the higher layer indicators,
the central controller is also connected with the instrument network performance evaluation module through an integrated fault positioning early warning module; the fault location early warning of the fault location early warning module comprises the following steps:
establishing a mapping relation between the fault alarm information and the fault between networks through correlation analysis, constructing a fault positioning model, and positioning the network fault through a BP neural network; the network fault location specifically comprises the following processes:
firstly, an m-dimensional alarm vector Q is obtainedn＝(s1,s2,s3…sm) And n-dimensional fault vector On＝(p1,p2,p3…pm) Simultaneously inputting the data through a plurality of network nodes, so that the system has a parallel structure and parallel processing capability and dynamically processes the input in real time;
secondly, the BP network gives a value in a designated range to each connected weight value, and designates a threshold value for each neuron node;
thirdly, the group input alarm sample machine target result is provided for the network, and the connection weight of the neural network node, the threshold value and the input and output values of each hidden layer unit are calculated;
then, the layer errors are positive: calculating the error of the output layer unit by using the target vector and the actual output value of the network, correcting the connection weight and the threshold value by combining the output of each unit of the hidden layer, and performing reverse error propagation correction;
and finally, training the sample vector and training the heterogeneous network system until the complete sample is trained, and inputting the operation and maintenance fault alarm information into the trained BP network to carry out network fault positioning.
Further, the mapping to the heterogeneous network through the wireless network parameters to obtain the positioned network fault, in the process, each network node is used as an intelligent agent to participate in the network fault positioning process, and the specific process is as follows:
selecting a Q learning method to establish a parameter system, establishing and maintaining a two-dimensional Q value table, wherein the first dimension is used for representing all possible states, and the second dimension represents possible actions taken by the network node intelligent agent; the element Q (S, a) of each Q value table corresponds to the Q value at which the agent takes action a in state S; the general criteria for a user to select an action are: the network node intelligent agent selects actions according to a certain probability based on the Q value table of each action in a given state, and the larger the Q value of one action is, the larger the probability of selection is; when a network fault occurs, firstly, a network node intelligent agent determines a heterogeneous network wireless parameter to be optimized according to correlation analysis and logistic regression analysis of network alarm information in operation and maintenance data, a new state S is constructed, and after the state S is constructed, each Q value corresponding to S is calculated;
then, the network node agent according to Q value with a certain probability P
Selecting an optimization strategy, namely action a;
finally, the system terminal updates the state S and the value of the selected action in the Q value table according to the network node agent return value and the existing Q value, wherein,
r (x, α) is E { R | s, a },pi is the selected strategy α is the learning factor.
Further, the power supply module 3 comprises a triode V, a diode D, a chip IC and a chip IC, wherein a pin 1 of the chip IC is connected with a resistor R, an anode of the resistor R, a diode D and a power VCC, a pin 2 of the chip IC is connected with a capacitor C, a resistor R, a fixed end of a potentiometer RP, a cathode of a storage battery E, an anode of the diode D and a pin 1 of the chip IC, an anode of the storage battery E is connected with a switch S, the other end of the switch S is connected with the resistor R, a fixed end of the potentiometer RP, an anode of the diode D and an emitting electrode of the triode V, a base of the triode V is connected with the other end of the resistor R and the other end of the collector connecting point R of the triode V and the other end of the resistor R, the other end of the resistor R is connected with a cathode of the, the sliding end of a potentiometer RP1 is connected with a pin 2 of a chip IC1, the other end of a resistor R2 is connected with the other fixed end of a potentiometer RP2, the sliding end of the potentiometer RP2 is connected with a pin 6 of the chip IC1, the cathode of a diode D2 is connected with the other end of a capacitor C3, the other end of a capacitor C4, a pin 3 of a chip IC1 and a core controller, the other end of the resistor R7 is connected with a pin 8 of a chip IC1, a pin 4 of a chip IC1 is connected with the other end of the capacitor C2 and the cathode of a diode D4, a pin 5 of a chip IC1 is connected with the other end of a capacitor C1, a pin 7 of a chip IC1 is connected with the cathode of a diode D3, the anode of a diode D3 is connected with the other end of a resistor R6, the model of the chip IC 1.
Further, the central controller is an STC89C52 series single-chip microcomputer.
Further, the manual control module is a matrix keyboard.
Further, the display module is an LCD display screen.
Furthermore, the system reset module control system can perform initialization operation, and disorder of the system after misoperation is prevented.
Further, the external storage capacity expansion module is an SDRAM chip.
The working principle of the invention is as follows: the resistor R1 and the potentiometer RP1 in FIG. 2 are used for detecting the lower limit value of the voltage of the storage battery; r2 and RP2 are used to detect the upper limit value. When the battery voltage is lower than the lower limit value, 555 is set, the discharge tube in the IC1 is cut off, and at the moment, no current flows through the diode D2. R4 is the bias resistor of transistor V1. Transistor V1 is saturated and conducting, the voltage of the source VCC charges the battery E through resistor R4 and transistor V1. When the voltage of the storage battery E rises to a set upper limit value, namely the potential of a pin 6 of the chip IC1 is higher than a threshold level, the chip IC1 resets, a discharge tube in the IC1 is in a conducting state, and the triode V1 is cut off. The charging is stopped. When power is unexpectedly cut off, the voltage of the pin 3 of the chip IC2 is reduced from +5V to about 4.8V. The diode D2 is conducted, the storage battery E supplies power to the control center through the D2, uninterrupted power supply is guaranteed, the instrument is subjected to system self-test after being started, the instrument enters a system parameter setting state after self-test, a user performs parameter setting on a keyboard, and the instrument automatically enters a working state after setting is completed. The bus type instrument receives the data and decodes the data to separate an address signal and a data signal, and when the address signal is inconsistent with the internal storage address, the data is represented as invalid data, and the instrument automatically returns to a data receiving state. When the address data is consistent with the internal storage address, the data is effective data, the data is transmitted to the digital memory for storage, digital display is carried out at the same time, and analog quantity and switching quantity are output according to requirements.
The invention can also output the data of the computer through the serial data port, convert the data into the standard serial bus data through the code converter, and then transmit to each bus type instrument through the serial bus, the bus type instrument displays the data directly, and output the external control according to the requirement. The original industrial instrument displays the received analog signal on site and transmits the analog signal to a control center through a serial bus. In order to display the display data of the original industrial instrument by the instrument at other positions, the serial bus interface of the bus instrument is connected to the original serial bus, and the baud rate, address, protocol and other parameters of the instrument are set to directly display the data. The capacity of the single chip microcomputer is expanded through the external storage capacity expansion module, system initialization operation is conducted through the system reset module, the problem of system disorder caused by manual misoperation is avoided, and regular maintenance is conducted on the system through the system maintenance module, so that normal work of the system is guaranteed.
The invention can realize direct input of serial bus data, can connect in parallel a plurality of serial data buses, and the meter respectively receives and reads respective data through the serial buses, works by itself, is suitable for industrial control of various serial bus protocols, has complete functions, can display data of various serial bus protocols, and transmit and output analog quantity and switching quantity, and simultaneously adopts a power supply mode of an uninterrupted power supply, thereby avoiding the problem of interrupted work caused by power failure or insufficient power, and a system maintenance module carries out system maintenance, thereby ensuring the normal operation of the system.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and all simple modifications, equivalent changes and modifications made to the above embodiment according to the technical spirit of the present invention are within the scope of the technical solution of the present invention.
Claims (7)
1. The utility model provides an intelligent industrial control instrument suitable for signal management integrates, a serial communication port, intelligent industrial control instrument suitable for signal management integrates is provided with:
the system comprises a central controller, an external storage capacity expansion module, a digital decoder, an address matcher, a digital memory, a display module, a power supply module, a system reset module, a system maintenance module, a manual control module, a data interface module and a drive controller module;
the central controller is respectively connected with the external storage capacity expansion module, the digital decoder, the address matcher, the digital memory, the display module, the power supply module, the system reset module, the system maintenance module, the manual control module, the data interface module and the driving controller module.
The central controller is integrated with an instrument network performance evaluation module; the evaluation method of the instrument network performance evaluation module comprises the following steps:
1) setting a key index set;
2) refining the same-layer index and calculating the weight;
3) calculating cross-layer index weight;
the process of initializing the key index set in the step 1) is as follows: in a scenario with N experts participating in system evaluation, the index types given by expert i are:
wherein,Niis a set of multiple indexes, refers to the weight of an index j given by an expert i,the number of the types of the indexes is expressed,indicates the number of index types, M indicates the maximum number of indexes,the N experts form a total set of indices for the system as N' ═ Ni,i＝1,...,N}；
The process of refining the same layer index and calculating the weight in the step 2) comprises the following steps: in a scenario with N experts participating in system evaluation, a common key index type and weights of corresponding indexes are solved according to a total index set of a system formed by the N experts, wherein the total index set is N' ═ { Ni, i ═ 1.., N }, and the refined key index type and the weights of the corresponding indexes are obtainedThe index type of (2) is denoted as N0Then, thenWhereinRepresents the intersection of index types Ni, i ═ 1, N given by N experts; the weight vector of the key index after refining is calculated by an average method, namelyWhereinRepresents the j th index added to the refined index set N0The weight of the index element in (1);
the step of calculating the cross-layer index weight in the step 3) is as follows: the high layer realizes the weighted calculation of the index weight relative to the adjacent low layer, and the higher layer further realizes the weighted calculation of the index weight relative to the high layer; the specific calculation formula isWhereinIs the true weight of the high-level indicator,a weight determined for the higher layer based on an expert system, λ j being a quantity related to the set of lower layer indicators that determines the higher layer indicators,
the central controller is also connected with the instrument network performance evaluation module through an integrated fault positioning early warning module; the fault location early warning of the fault location early warning module comprises the following steps:
establishing a mapping relation between the fault alarm information and the fault between networks through correlation analysis, constructing a fault positioning model, and positioning the network fault through a BP neural network; the network fault location specifically comprises the following processes:
firstly, an m-dimensional alarm vector Q is obtainedn＝(s1,s2,s3…sm) And n-dimensional fault vector On＝(p1,p2,p3…pm) Simultaneously inputting the data through a plurality of network nodes, so that the system has a parallel structure and parallel processing capability and dynamically processes the input in real time;
secondly, the BP network gives a value in a designated range to each connected weight value, and designates a threshold value for each neuron node;
thirdly, the group input alarm sample machine target result is provided for the network, and the connection weight of the neural network node, the threshold value and the input and output values of each hidden layer unit are calculated;
then, the layer errors are positive: calculating the error of the output layer unit by using the target vector and the actual output value of the network, correcting the connection weight and the threshold value by combining the output of each unit of the hidden layer, and performing reverse error propagation correction;
and finally, training the sample vector and training the heterogeneous network system until the complete sample is trained, and inputting the operation and maintenance fault alarm information into the trained BP network to carry out network fault positioning.
2. The intelligent industrial control instrument suitable for integrated signal management as claimed in claim 1, wherein the mapping to heterogeneous network through wireless network parameters to obtain the located network fault is performed in a process that each network node participates as an intelligent agent in a network fault locating process, and the specific process is as follows:
selecting a Q learning method to establish a parameter system, establishing and maintaining a two-dimensional Q value table, wherein the first dimension is used for representing all possible states, and the second dimension represents possible actions taken by the network node intelligent agent; the element Q (S, a) of each Q value table corresponds to the Q value at which the agent takes action a in state S; the general criteria for a user to select an action are: the network node intelligent agent selects actions according to a certain probability based on the Q value table of each action in a given state, and the larger the Q value of one action is, the larger the probability of selection is; when a network fault occurs, firstly, a network node intelligent agent determines a heterogeneous network wireless parameter to be optimized according to correlation analysis and logistic regression analysis of network alarm information in operation and maintenance data, a new state S is constructed, and after the state S is constructed, each Q value corresponding to S is calculated;
then, the network node agent according to Q value with a certain probability P
Selecting an optimization strategy, namely action a;
finally, the system terminal updates the state S and the value of the selected action in the Q value table according to the network node agent return value and the existing Q value, wherein,
r (x, α) is E { R | s, a },pi is the selected strategy α is the learning factor.
3. The intelligent industrial instrument suitable for integrated signal management as claimed in claim 1, wherein the power supply module includes a transistor V1, a diode D1, a chip IC1 and a chip IC2, pin 1 of the chip IC2 is connected to a resistor R2, an anode of the diode D2 and a power source VCC, pin 2 of the chip IC2 is connected to a capacitor C2, a resistor R2, one fixed end of a potentiometer RP2, a cathode of a battery E, an anode of the diode D2 and pin 1 of the chip IC2, an anode of the battery E is connected to a switch S2, the other end of the switch S2 is connected to the resistor R2, one fixed end of the potentiometer RP2, an anode of the diode D2 and an emitter of the transistor V2, a base of the transistor V2 is connected to the other end of the resistor R2 and the other end of the resistor R2, a collector of the transistor V2 is connected to a cathode of the diode R2, the other end of the resistor R1 is connected with the other fixed end of the potentiometer RP1, the sliding end of the potentiometer RP1 is connected with a pin 2 of the chip IC1, the other end of the resistor R2 is connected with the other fixed end of the potentiometer RP2, the sliding end of the potentiometer RP2 is connected with a pin 6 of the chip IC1, the cathode of the diode D2 is connected with the other end of the capacitor C3, the other end of the capacitor C4, the pin 3 of the chip IC1 and the core controller, the other end of the resistor R7 is connected with the pin 8 of the chip IC1, the pin 4 of the chip IC1 is connected with the other end of the capacitor C2 and the cathode of the diode D4, the pin 5 of the chip IC1 is connected with the other end of the capacitor C1, the pin 7 of the chip IC1 is connected with the cathode of the diode D3, the anode of the diode D3 is connected with the other end of the resistor R6, the.
4. The intelligent industrial control instrument adapted for integrated signal management of claim 1, wherein the manual control module is a matrix keyboard.
5. The intelligent industrial instrument adapted for integrated signal management of claim 1, wherein said display module is an LCD display.
6. The intelligent industrial control instrument adapted for integrated signal management as claimed in claim 1, wherein the system reset module controls the system to perform initialization operation.
7. The intelligent industrial control instrument adapted for integrated signal management as claimed in claim 1, wherein the external storage capacity expansion module is an SDRAM chip.
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| CN112699420A (en) * | 2020-12-31 | 2021-04-23 | 广州万协通信息技术有限公司 | Active shielding protection device and method for security chip |
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