CN113965504A - Safety reinforcement acceptance method and system for network equipment of transformer substation - Google Patents
Safety reinforcement acceptance method and system for network equipment of transformer substation Download PDFInfo
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Abstract
The invention discloses a transformer substation network equipment safety reinforcement acceptance system, which comprises a network equipment reinforcement acceptance module, a neural network training module, an automatic rectification module and an output module; the network equipment reinforcing acceptance module can acquire specific state information of the network equipment, and encodes the equipment state while outputting an equipment acceptance result; the neural network training module can train the neural network on the information codes, construct a network model which accords with the actual condition of the equipment and generate equipment reinforcing codes; the automatic rectification module decodes the received equipment reinforcing codes, converts the equipment reinforcing codes into trigger commands which can be executed by the network equipment, sends the trigger commands to the network equipment to realize the automatic rectification of the network equipment, simultaneously acquires state information fed back after the automatic rectification and selects the state information to return to the network equipment reinforcing module for cyclic processing or outputs final results to the output module.
Description
Technical Field
The invention relates to the technical field of network equipment safety, in particular to a transformer substation network equipment safety reinforcement acceptance method and a transformer substation network equipment safety reinforcement acceptance system.
Background
With the development of modern intellectualization and the continuous deepened application of the transformer substation, the network security of the transformer substation in China urgently needs to be changed so as to deal with the increasingly complex network security environment. The basic network equipment in the substation is as follows: computers, hubs, switches, bridges, routers, gateways, network interface cards, wireless access points, printers and modems, fiber optic transceivers, fiber optic cables, etc., whether local area networks, metropolitan area networks, or wide area networks, are typically physically comprised of network connection devices and transmission media, such as network cards, hubs, switches, routers, network wires, RJ45 connectors, etc. The network equipment comprises equipment such as a repeater, a network bridge, a router, a gateway, a firewall, a switch and the like, and the equipment needs to be tested and safely checked and accepted aiming at different network equipment firmware.
In the prior art, inspection personnel mainly adopt various tools such as a line sequence tester, a line finder, a professional cable authentication instrument, a notebook computer, a mobile phone and the like to solve problems, and after network equipment is inspected one by one, corresponding commands are input one by one according to inspection objects to complete safety reinforcement inspection and acceptance. Therefore, in the prior art, the reinforcement acceptance of the network equipment mainly depends on manual work, the requirement on the technical level of personnel is high, and the integrity and the specialty of the reinforcement acceptance are greatly influenced by artificial factors; secondly, the acceptance worker needs to bring all the tools, the operation is complicated, and the problems of inaccurate acceptance result, network equipment failure and the like are caused by improper use of the acceptance tool or incorrect input command in the acceptance tool. For reinforcement acceptance of network equipment of a transformer substation, on one hand, not only the integrity and accuracy of the acceptance need to be ensured, but also the safe and reliable operation of a power grid can be ensured while the network equipment is safely reinforced and accepted.
Disclosure of Invention
The invention provides a safety reinforcement acceptance method for network equipment of a transformer substation, which solves the problems of complex operation and misoperation in the existing reinforcement acceptance, and can realize safe and reliable operation of a power grid while ensuring the specialty and integrity of acceptance by utilizing an automatic rectification mode.
The technical scheme of the invention is as follows:
the invention discloses a transformer substation network equipment safety reinforcement acceptance system which comprises a network equipment reinforcement acceptance module, a neural network training module, an automatic correction module and an output module, wherein the network equipment reinforcement acceptance module is used for receiving network equipment safety reinforcement information; the network equipment reinforcing acceptance module is a functional module formed based on an operation instruction set, can convert a network equipment state information operation instruction into a trigger type instruction which can be executed on the network equipment and send the trigger type instruction to the network equipment, obtains a state information output acceptance result fed back by the network equipment, encodes the information and then packages and transmits the information to the neural network training module; the neural network training module can train the neural network on the information codes, construct a network model which accords with the actual condition of the equipment and generate equipment reinforcing codes; the automatic rectification module decodes the received equipment reinforcing codes, converts the equipment reinforcing codes into trigger commands which can be executed by the network equipment, sends the trigger commands to the network equipment to realize the automatic rectification of the network equipment, simultaneously acquires state information fed back after the automatic rectification and selects the state information to return to the network equipment reinforcing module for cyclic processing or outputs final results to the output module.
The invention also discloses a transformer substation network equipment safety reinforcement acceptance method, which is characterized in that the transformer substation network equipment safety reinforcement is carried out by utilizing the safety reinforcement acceptance system, the obtained network equipment state information and the specific automatic correction operation are coded, an automatic reinforcement model is generated based on neural network training, and the automatic correction of the equipment is realized after the neural network output which is finally generated to adapt to the equipment requirement is executed based on the model.
Further, the transformer substation network equipment safety reinforcement acceptance method specifically comprises the following steps:
(1) acquiring specific state information of the network equipment by a network equipment reinforcing acceptance module, outputting an equipment acceptance result, and simultaneously encoding the equipment state to generate an equipment state information encoding data set in a binary state;
(2) a training set is formed by the neural network training module by means of the binary-state equipment state information coding data set generated by the processing of the step (1) and the reinforcing method data set, the neural network is trained in a supervision mode, a network model conforming to the actual situation of the equipment is further obtained, then the binary-state equipment state information coding data set generated by the processing of the step (1) is used as a model input, and equipment reinforcing codes are finally generated;
(3) the automatic rectification module decodes the equipment reinforcing codes output by the neural network training module after receiving the equipment reinforcing codes, and decodes and codes the solution into command codes executable by the equipment;
(4) judging whether the network equipment passing through the automatic rectification module meets the requirements or not, if so, finishing the automatic rectification successfully, and if not, returning to the network equipment reinforcement acceptance module and then performing the circular neural network training again through the steps until a neural network model which is adaptive to the equipment requirements is generated;
(5) and finally, the output module outputs the reinforcement acceptance condition and the automatic correction condition.
Further, the network device consolidation acceptance module in step (1) takes whether the specific state information obtained from the network device exists in the information as a basis for encoding the device state, and the specific rule is as follows: if the specific state information exists in the information, the information is coded into 1, otherwise, the information is coded into 0, and finally a string of 14-bit-long binary state information codes is formed.
Further, the specific state information acquired from the network device by the network device consolidation and acceptance module in the step (1) includes a user and a password, device management, network service, security protection, log and audit.
Further, the neural network training module in the step (2) utilizes a BP neural network, the BP neural network is composed of an input layer, a hidden layer and an output layer, the input layer comprises 14 neurons, the hidden layer is a layer and comprises 10 neurons, and the output layer comprises 5 neurons.
Further, in the step (3), the automatic rectification module decodes the device strengthening code generated by the neural network training module, each bit in the code sequence is 0 or 1 and corresponds to a specific automatic rectification operation, when a certain bit in the code sequence is 0, it indicates that the operation represented by the bit is not executed, otherwise, it needs to be executed.
Further, the automatic rectification operation includes an automatic rectification rule table for specific state information acquired from the network device, and the specific operation includes user and password, device management, network service, security protection, log and audit.
Further, the specific operation in the automatic rectification rule table constitutes a solution library, and after the solution library is encoded, the encoded sequence is fed back to the step (2) to be a reinforcement method data set.
Further, the transformer substation network equipment safety reinforcement acceptance method comprises an online mode and an offline mode, wherein the offline mode is that client software is installed through a terminal tool to perform reinforcement acceptance on the transformer substation network equipment; the online mode is that a client is used on the terminal tool to send a command for requesting reinforcement acceptance to a specified server, and the server executes the reinforcement acceptance command.
Compared with the prior art, the invention has the following beneficial effects: the network equipment of the transformer substation comprises a dispatching data network switch, a dispatching data network router and a station control layer switch. The security reinforcement of the network equipment of the transformer substation is carried out from the aspects of user and password, equipment management, network service, security protection, log and audit, and the requirements are far higher than that of common network equipment; the network operation of the transformer substation cannot be interrupted in real time, and the reinforcement work performed on network equipment cannot have errors, but data such as remote regulation and control cannot be sent upwards, real-time monitoring cannot be performed, transformer substation data cannot be acquired for real-time decision-making, the transformer substation equipment cannot be remotely controlled, and particularly, an unmanned transformer substation cannot be emergently controlled to cause large-area power failure; according to the transformer substation network equipment safety reinforcement acceptance method and the transformer substation network equipment safety reinforcement acceptance system, a machine learning algorithm is used, on one hand, a neural network model is built, and then targeted reinforcement acceptance and automatic correction are achieved, specific problems can be specifically analyzed and solved, the flexibility of software is improved, over-programmed operation is avoided, and meanwhile, the stability of transformer substation network equipment safety reinforcement acceptance is improved; on the other hand can accomplish remote control and check and accept and automatic rectification under the condition that does not influence the operation of transformer substation's network equipment, only need the operator just can carry out the reinforcement of transformer substation's network equipment at the customer end and check and accept and automatic rectification one-key operation, solved the loaded down with trivial details operation, maloperation, automatic rectification that exist among the prior art and realized difficult scheduling problem, not only improved the work efficiency who consolidates and check and accept and alleviateed a ray of staff's work burden, can ensure the operation of electric wire netting safe and reliable moreover.
Drawings
FIG. 1 is a flow chart of a transformer substation network equipment security reinforcement acceptance method of the present invention;
FIG. 2 is a schematic diagram of a security reinforcing and acceptance system of the network equipment of the transformer substation;
FIG. 3 is a schematic diagram of a neural network model structure according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments.
Example 1
Referring to fig. 2, a transformer substation network device security reinforcement acceptance system includes a network device reinforcement acceptance module, a neural network training module, an automatic rectification module and an output module, which are sequentially in communication connection; the network equipment reinforcing acceptance module is a functional module formed based on an operation instruction set, can convert a network equipment state information operation instruction into a trigger type instruction which can be executed on the network equipment and send the trigger type instruction to the network equipment, obtains a state information output acceptance result fed back by the network equipment, encodes the information and then packages and transmits the information to the neural network training module; the specific status information obtained by the network device ruggedization acceptance module from the network device includes but is not limited to the following (1) user and password: whether the user authority realizes the separation of the three authorities and whether the password of the user accords with the rules and the like; (2) equipment management: whether SSH service is started or not, processing of login failure, ACL policy reinforcement acceptance and the like; (3) network service: whether a static route exists or not, whether FTP/HTTP/TELNET service is disabled or not, and the like; (4) safety protection: whether the idle port is closed or not, whether the MAC address is bound or not, whether the NTP service condition limits the login address range or not and the like; (5) logging and auditing: whether a log function, an audit function and the like are started; the network equipment reinforcement acceptance module can encode the information and serve as input of neural network training;
the neural network training module can perform supervised training on the neural network according to the existing equipment state and reinforcement method data set, construct a network model according with the actual condition of the equipment, and then input binary state information codes as the model to generate equipment reinforcement codes; in this embodiment, the neural network training module adopts a BP neural network, the BP neural network is composed of an input layer, a hidden layer and an output layer, the input layer includes 14 neuron hidden layers as one layer and includes 10 neurons, and the output layer includes 5 neurons;
the automatic rectification module decodes the received equipment reinforcing codes, converts the equipment reinforcing codes into trigger commands which can be executed by the network equipment, sends the trigger commands to the network equipment to realize the automatic rectification of the network equipment, simultaneously acquires state information fed back after the automatic rectification and selects the state information to return to the network equipment reinforcing module for cyclic processing or outputs final results to the output module.
Example 2
A safety reinforcement acceptance method for network equipment of a transformer substation is characterized in that a safety reinforcement acceptance system in an embodiment is utilized to carry out safety reinforcement on the network equipment of the transformer substation, acquired state information of the network equipment and specific automatic correction operation are coded, an automatic reinforcement model is generated based on neural network training, and automatic correction of the equipment is realized after neural network output which is finally generated to adapt to equipment requirements based on the model is executed.
Referring to fig. 1, the transformer substation network device security reinforcement acceptance method specifically includes the following steps:
(1) acquiring specific state information of the network equipment by a network equipment reinforcing acceptance module, outputting an equipment acceptance result, and simultaneously encoding the equipment state to generate an equipment state information encoding data set in a binary state; the specific status information obtained by the network device ruggedization acceptance module from the network device includes but is not limited to the following (1) user and password: whether the user authority realizes the separation of the three authorities and whether the password of the user accords with the rules and the like; (2) equipment management: whether SSH service is started or not, processing of login failure, ACL policy reinforcement acceptance and the like; (3) network service: whether a static route exists or not, whether FTP/HTTP/TELNET service is disabled or not, and the like; (4) safety protection: whether the idle port is closed or not, whether the MAC address is bound or not, whether the NTP service condition limits the login address range or not and the like; (5) logging and auditing: whether a log function, an audit function and the like are started; the network equipment reinforcing and checking module is used as a basis for coding the equipment state according to whether the specific state information acquired from the network equipment exists in the information, and the specific rule is as follows: if the specific state information exists in the information, the encoding is 1, otherwise, the encoding is 0, and finally a string of 14-bit-length binary state information encoding is formed;
(2) a training set is formed by a neural network training module by means of the binary-state equipment state information coding data set generated by the processing of the step (1) and a reinforcing method data set, the neural network is trained in a supervision mode, a network model conforming to the actual situation of the equipment is further obtained, then the binary-state equipment state information code generated by the processing of the step (1) is used as model input, and finally equipment reinforcing code is generated; in this embodiment, the neural network training module adopts a BP neural network, the BP neural network is composed of an input layer, a hidden layer and an output layer, the input layer includes 14 neurons represented by an x sequence, the hidden layer is one layer and includes 10 neurons, the output layer includes 5 neurons represented by a y sequence, and the neural network model structure is shown in fig. 3;
the activation function of the hidden layer is a Sigmoid function:
the activation function of the output layer adopts a stage function:
x represents the total input value received by the neuron into the hidden layer; the neural network training module finally generates a string of device reinforcing codes with the length of 5 bits,
(3) the automatic rectification module decodes the equipment reinforcing codes output by the neural network training module after receiving the equipment reinforcing codes, and decodes and codes the solution into command codes executable by the equipment; each bit in the equipment reinforced coding sequence is 0 or 1 and corresponds to a specific automatic rectification operation, when a certain bit in the coding sequence is 0, the operation represented by the bit is not executed, otherwise, the operation is executed; the automatic rectification operation comprises an automatic rectification rule table aiming at specific state information acquired from network equipment, and the specific operation comprises a user and a password (the user which does not realize the separation of three rights automatically rectifies and meets the requirement, the password does not accord with the automatic rectification password again of the rule, local authentication is started, an authentication mode is set, and the like), equipment management (an access control list is configured, login failure and overtime processing are carried out, an opened SSH service is closed, and the like), network service (an existing static route is closed, the opened TELNET/FTP/HTTP service is closed, and the like), safety protection (a port in an idle state is closed, an MAC address is bound with a corresponding IP, an NTP server address is set, a login address range is set, and the like), and log and audit (SNMP and log audit are configured, and the like); the concrete operation in the automatic rectification rule table forms a solution library, after the solution library is coded, the coded sequence is fed back to the step (2) to be a reinforcement method data set, the coding and the existing equipment state information code form a training set of a neural network, the neural network training is carried out again to construct a network model, and the cycle is carried out;
(4) judging whether the network equipment passing through the automatic rectification module meets the requirements or not, if so, finishing the automatic rectification successfully, and if not, returning to the network equipment reinforcement acceptance module and then performing the circular neural network training again through the steps until a neural network model which is adaptive to the equipment requirements is generated;
(5) and finally, the output module outputs the reinforcement acceptance condition and the automatic correction condition.
Further, the transformer substation network equipment safety reinforcement acceptance method comprises an online mode and an offline mode, wherein the offline mode is that client software is installed through a terminal tool to perform reinforcement acceptance on the transformer substation network equipment; the online mode is that a client is used on the terminal tool to send a command for requesting reinforcement acceptance to a specified server, and the server executes the reinforcement acceptance command.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (10)
1. The utility model provides a transformer substation's network equipment safety is consolidated and is checked acceptance system which characterized in that: the system comprises a network equipment reinforcement acceptance module, a neural network training module, an automatic rectification module and an output module; the network equipment reinforcing acceptance module is a functional module formed based on an operation instruction set, can convert a network equipment state information operation instruction into a trigger type instruction which can be executed on the network equipment and send the trigger type instruction to the network equipment, obtains a state information output acceptance result fed back by the network equipment, encodes the information and then packages and transmits the information to the neural network training module; the neural network training module can train the neural network on the information codes, construct a network model which accords with the actual condition of the equipment and generate equipment reinforcing codes; the automatic rectification module decodes the received equipment reinforcing codes, converts the equipment reinforcing codes into trigger commands which can be executed by the network equipment, sends the trigger commands to the network equipment to realize the automatic rectification of the network equipment, simultaneously acquires state information fed back after the automatic rectification and selects the state information to return to the network equipment reinforcing module for cyclic processing or outputs final results to the output module.
2. A transformer substation network equipment safety reinforcement acceptance method is characterized by comprising the following steps: the safety reinforcement acceptance system of the claim 1 is used for carrying out safety reinforcement acceptance of the network equipment of the transformer substation, the obtained state information of the network equipment and the specific automatic rectification operation are coded, an automatic reinforcement model is generated based on neural network training, and the automatic rectification of the equipment is realized after the neural network output which is finally generated and adapted to the equipment requirement is executed based on the model.
3. The transformer substation network equipment safety reinforcement acceptance method according to claim 2, which specifically comprises the following steps:
(1) acquiring specific state information of the network equipment by a network equipment reinforcing acceptance module, outputting an equipment acceptance result, and simultaneously encoding the equipment state to generate an equipment state information encoding data set in a binary state;
(2) a training set is formed by the neural network training module by means of the binary-state equipment state information coding data set generated by the processing of the step (1) and the reinforcing method data set, the neural network is trained in a supervision mode, a network model conforming to the actual situation of the equipment is further obtained, then the binary-state equipment state information coding data set generated by the processing of the step (1) is used as a model input, and equipment reinforcing codes are finally generated;
(3) the automatic rectification module decodes the equipment reinforcing codes output by the neural network training module after receiving the equipment reinforcing codes, and decodes and codes the solution into command codes executable by the equipment;
(4) judging whether the network equipment passing through the automatic rectification module meets the requirements or not, if so, finishing the automatic rectification successfully, and if not, returning to the network equipment reinforcement acceptance module and then performing the circular neural network training again through the steps until a neural network model which is adaptive to the equipment requirements is generated;
(5) and finally, the output module outputs the reinforcement acceptance condition and the automatic correction condition.
4. The transformer substation network equipment safety reinforcement acceptance method according to claim 3, characterized in that: the network device consolidation acceptance check module in the step (1) takes whether the specific state information acquired from the network device exists in the information as a basis for coding the device state, and the specific rule is as follows: if the specific state information exists in the information, the information is coded into 1, otherwise, the information is coded into 0, and finally a string of 14-bit-long binary state information codes is formed.
5. The transformer substation network equipment safety reinforcement acceptance method according to claim 3, characterized in that: the specific state information acquired from the network equipment by the network equipment consolidation acceptance module in the step (1) comprises a user and a password, equipment management, network service, safety protection, log and audit.
6. The transformer substation network equipment safety reinforcement acceptance method according to claim 3, characterized in that: the neural network training module in the step (2) utilizes a BP neural network, the BP neural network is composed of an input layer, a hidden layer and an output layer, the input layer comprises 14 neurons, the hidden layer is a layer and comprises 10 neurons, and the output layer comprises 5 neurons.
7. The transformer substation network equipment safety reinforcement acceptance method according to claim 3, characterized in that: the automatic rectification module in the step (3) decodes the device strengthening code generated by the neural network training module, each bit in the code sequence is 0 or 1 and corresponds to a specific automatic rectification operation, when a certain bit in the code sequence is 0, the operation represented by the bit is not executed, otherwise, the operation is executed.
8. The transformer substation network equipment safety reinforcement acceptance method of claim 7, characterized in that: the automatic rectification operation comprises an automatic rectification rule table aiming at specific state information acquired from the network equipment, and the specific operation comprises user and password, equipment management, network service, safety protection, log and audit.
9. The transformer substation network equipment safety reinforcement acceptance method according to claim 8, characterized in that: and (3) the specific operation in the automatic rectification rule table forms a solution library, and after the solution library is coded, the coded sequence is fed back to the step (2) to be a reinforcement method data set.
10. The substation network equipment security reinforcement acceptance method of any one of claims 3 to 9, wherein: the safety reinforcement acceptance method of the network equipment of the transformer substation comprises an online mode and an offline mode, wherein the offline mode is to install client software through a terminal tool and perform reinforcement acceptance on the network equipment of the transformer substation; the online mode is that a client is used on the terminal tool to send a command for requesting reinforcement acceptance to a specified server, and the server executes the reinforcement acceptance command.
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