In-loop simulation method and system for industrial control protocol security
By using a loop simulation method and system to automatically identify and classify devices, perform protocol simulation and vulnerability feature extraction, and combine the Ollam model to identify unknown vulnerabilities, the problem of low efficiency in vulnerability feature extraction and poor identification rate of unknown vulnerabilities in power IoT industrial control systems is solved. This achieves a closed loop of detection and protection, and improves the security of power industrial control systems.
Patent Information
- Application Number
- CN202511794716.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-01
- Publication Date
- 2026-02-03
AI Technical Summary
Existing technologies in power IoT industrial control systems suffer from low efficiency in vulnerability feature extraction, poor identification rate of unknown vulnerabilities, limited attack surface coverage, lack of multi-dimensional detection methods, manual intervention required for equipment classification, lack of linkage between the detection environment and the production environment, lack of a closed-loop mechanism for vulnerability verification and remediation, and inability to translate detection results into actual protection capabilities.
We employ an in-loop simulation method and system that secures industrial control protocols. By connecting devices through multiple physical interfaces, we automatically identify and input them into a unified asset database, perform protocol simulation and vulnerability feature extraction, combine the Ollam model to identify unknown vulnerabilities, conduct multi-dimensional attack simulation and defense strategy verification, and establish a closed-loop mechanism that links the penetration testing platform with the production environment.
It achieves automatic extraction of vulnerability features and intelligent prediction of unknown vulnerabilities, improving the identification rate of unknown vulnerabilities, automatically constructs a unified equipment asset database, realizes a closed loop of detection and protection, ensures that detection results are directly transformed into production protection measures, and improves the security of power industrial control systems.
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Figure CN121462301A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of power Internet of Things (IoT) industrial control protocol security detection and in-loop simulation technology, specifically involving an in-loop simulation method and system for industrial control protocol security. Background Technology
[0002] Full-scenario industrial control security in the power Internet of Things (IoT) refers to the construction of an integrated security protection system covering equipment management, protocol detection, interface protection, and operation and maintenance support, addressing the unique characteristics of the power sector, such as equipment heterogeneity, protocol specificity, and network complexity. This system aims to resist network attacks, uncover potential vulnerabilities, and ensure the stable operation of the power grid, and has become a technical consensus in this field.
[0003] However, existing technologies have the following shortcomings: they rely on manually configured rule bases, resulting in low efficiency in vulnerability feature extraction and poor identification rate of unknown vulnerabilities; their attack surface coverage is limited, often supporting only single vulnerability scanning and lacking multi-dimensional detection methods such as traffic replay and attack injection, making it difficult to fully expose risks; power industrial control systems have a wide variety of heterogeneous equipment types and models, requiring existing technologies to manually classify and match equipment information, making it impossible to automatically build a unified asset database, which increases the difficulty of subsequent detection and vulnerability localization; they only stay at the "vulnerability discovery" stage, and the detection environment is not linked to the power production environment; they lack a closed-loop mechanism for vulnerability verification and remediation, and have not established a defense baseline adapted to power business, resulting in detection results that cannot be translated into actual protection capabilities.
[0004] Therefore, a new method is urgently needed. Summary of the Invention
[0005] The purpose of this invention is to provide an in-loop simulation method and system for industrial control protocol security. This method enables automatic extraction of vulnerability features and prediction of attack paths, effectively improving the identification rate of unknown vulnerabilities. It can automatically identify, classify, and match equipment information, and build a unified equipment asset database without manual intervention.
[0006] To achieve the above objectives, this invention provides an in-loop simulation method and system for industrial control protocol security, comprising the following steps: S1. Connect the electrical equipment under test through multiple physical interfaces of the electrical application access module, start the equipment fingerprint recognition program, collect the basic hardware characteristics and power-specific characteristics of the equipment, and enter the equipment information into the unified asset database based on the equipment matching degree calculation model to obtain the basic data of the equipment under test. S2. Based on the device under test (DUT) basic data in S1, protocol simulations are performed for wireless links, network links, and serial links respectively; the consistency of the protocol simulation is calculated by comparing the historical link data packet feature sets with the real link data packet feature sets. And ensure that it is higher than a preset first threshold, outputting full simulation communication data packets, protocol interaction timing, and simulation scenario parameters; the first threshold is not lower than 95%; The wireless links include WIFI, 4G / 5G, NB-IoT, and MQTT protocol links; the network interface links include application layer and control layer protocol links; the serial port links include MODBUS, Scheduling 101 / 103 protocol, and DL / T645 protocol links. S3. Preprocess the simulated communication data in S2 to generate input data that conforms to the Ollama model format requirements; load the Ollama model to perform vulnerability feature extraction, and optimize attack path prediction by combining protocol type information; obtain the unknown vulnerability identification rate by calculating the ratio of the number of identified unknown vulnerabilities to the total number of potential unknown vulnerabilities; when the identification rate is higher than a preset second threshold, trigger an active warning and generate a detection report; the second threshold is not lower than 85%. S4. Launch multi-dimensional attacks based on the detection report of S3, evaluate the attack effect based on the attack success rate, conduct attack and defense exercises to verify the protection strategy, evaluate the defense effectiveness based on the vulnerability repair rate, and generate attack and defense strategies and vulnerability repair solutions. S5, based on the attack and defense strategies and vulnerability remediation solutions of S4, interconnects the penetration testing and defense platform with production site equipment through hierarchical VPN networking, and deploys vulnerability remediation solutions; it formulates a layered defense baseline based on the protection effectiveness formula and monitors it through the security situation awareness platform.
[0007] Preferably, in S1, the multiple physical interfaces include network ports, CAN, RS485, and RS232 interfaces; the basic hardware features include device model, firmware version, and hardware interface type; and the power-specific features include supported power protocol types and service function modules. Preferably, in S1, the device matching degree calculation model is implemented using the following formula: ; In the formula, Device matching score is used to quantify the overall similarity between the device to be identified and the feature template of standard devices; For macro-level weighting coefficients, ; Sub-feature weights; Based on basic hardware characteristics, Features specific to the power industry; The number of basic hardware features, The number of electricity-specific features; For the feature template of the corresponding equipment in the asset library; Type coefficient weights; Adjustment coefficient for equipment type.
[0008] Preferably, in S2, the formula for calculating the consistency of the simulation protocol is: ; In the formula, This represents the degree of consistency in protocol simulation. For historical link data packet feature set, The feature set is the real link data packet feature set, where the protocol simulation consistency is preferred. .
[0009] Preferably, the simulation of the wireless link described in S2 involves configuring a cloud-edge collaborative wireless simulation node to simulate different bandwidth and latency scenarios, and injecting data packets that conform to the power Internet of Things communication protocol to generate simulated traffic. The simulation of the network port link simulates the request-response interaction process between master and slave devices by loading the power service protocol stack, and records the communication timing and data format. The simulation of the serial port link involves matching the baud rate and parity parameters of the serial port of the device under test, sending instruction packets that conform to the power serial port protocol specification, and capturing the device's response data.
[0010] Preferably, in S3, the formula for the unknown vulnerability identification rate is expressed as: ; In the formula, For the unknown vulnerability identification rate, The number of unknown vulnerabilities identified. This represents the total number of potential unknown vulnerabilities in the simulation environment. Among them, the optimal unknown vulnerability identification rate is to trigger an active warning when R≥85%, and generate a detection report containing the vulnerability location and impact range.
[0011] Preferably, S3 also includes: The reasoning process and intermediate results of the Ollama model are recorded through a log system that is isolated from the production environment and has full audit trail capabilities. Combined with vulnerability information from an external threat intelligence platform, the vulnerability results identified by the model are verified a second time. The location of the vulnerability corresponds to a specific simulated link in S2, such as a wireless link, a network port link, or a serial port link. The assessment of the scope of impact is associated with at least one simulation scenario parameter in S2, such as network bandwidth or latency.
[0012] Preferably, the attack success rate formula in S4 is: ; In the formula, The total number of attack types. For the first The severity coefficient of this type of attack For the first The number of successful attacks of this type For the first Total number of attacks of this type; The formula for the vulnerability repair rate is: ; In the formula, The total number of vulnerability levels. For the first The weight of a vulnerability level 1 For the first The number of Class 1 vulnerabilities patched For the first The total number of Class A vulnerabilities.
[0013] This invention also provides an in-loop simulation system for industrial control protocol security, comprising: The device access module utilizes the network port, CAN, RS485, and RS232 interfaces of the electrical application access module to physically connect devices such as power sensors and PLCs to the circuit. It is configured to connect to the electrical device under test through multiple physical interfaces and perform device fingerprinting to build a unified asset database; The multi-link protocol simulation module is communicatively connected to the device access construction module and is configured to perform protocol simulation for different link types based on asset database data. The intelligent security detection module is communicatively connected to the multi-link protocol simulation module and is configured to analyze simulation data and perform vulnerability detection and early warning. The penetration testing module is communicatively connected to the intelligent security detection module and is configured to conduct attack and defense drills based on the detection results and evaluate the protection effect. The protection closed-loop management module is communicatively connected to the penetration attack and defense exercise module and is configured to deploy protection measures and monitor the production environment through a secure network connection.
[0014] Therefore, the present invention employs the above-mentioned secure in-loop simulation method and system for industrial control protocols. Compared with the prior art, the technical solution of the present invention has the following beneficial effects: (1) Integrate the Ollama large model to realize automatic extraction of vulnerability features and intelligent prediction of multi-step attack paths, thereby improving the identification rate of unknown vulnerabilities; at the same time, integrate the penetration testing toolchain to cover multi-dimensional attack surfaces, thereby achieving the upgrade from passive detection to proactive warning. (2) Through the “heterogeneous device access + fingerprint recognition” technology of the electrical application access module, the device identification, classification and information matching are completed automatically. A unified device asset database can be built without manual intervention, laying a precise data foundation for detection and vulnerability location. (3) Relying on the penetration attack and defense platform to link the power production environment, establish a closed loop of “vulnerability discovery-verification-repair”; at the same time, formulate security testing specifications and defense baselines covering typical power businesses, and directly transform the test results into implementable production protection measures to achieve integrated testing and protection.
[0015] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description
[0016] Figure 1 The diagram shows the overall network architecture of the loop detection system in an embodiment of the loop simulation method and system for industrial control protocol security of the present invention. Detailed Implementation
[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Unless otherwise defined, the technical or scientific terms used in the present invention should have the ordinary meaning understood by those skilled in the art.
[0018] Example 1 like Figure 1 As shown, the in-loop simulation method and system for industrial control protocol security of the present invention includes the following steps: S1. Using the network port, CAN, RS485, RS232, and other interfaces of the electrical application access module, physically connect the power sensors, PLC, and other devices under test to the system; start the device fingerprint recognition program to collect the basic hardware characteristics of the devices. and electricity-specific characteristics Among them, basic hardware features Including device model, firmware version, hardware interface type, and power-specific characteristics. This includes supported power protocol types and business function modules.
[0019] Simultaneously, obtain the feature template of the corresponding device in the asset database. Based on the collected features, the matching degree between the current device and the pre-stored device feature templates in the asset database is calculated using a device matching degree calculation model. The device matching degree Calculated using the following formula: ; In the formula, Device matching score is used to quantify the overall similarity between the device to be identified and the feature template of standard devices; For macro-level weighting coefficients, take... ; Sub-feature weights; Based on basic hardware characteristics, Features specific to the power industry; The number of basic hardware features, The number of electricity-specific features; For the feature template of the corresponding equipment in the asset library; Type coefficient weights; Adjustment coefficient for equipment type.
[0020] When calculated At that time, the equipment information is automatically entered into the unified asset database, and the accurate asset matching of power industrial control equipment is completed and the basic data of the equipment under test is obtained. S2. Based on the basic data of the device under test in S1, perform high-fidelity protocol simulations for wireless, network port and serial port links respectively.
[0021] For wireless links (such as WIFI, 4G / 5G, NB-IoT, MQTT protocols), based on the wireless communication capabilities of devices in the asset database, cloud-edge collaborative wireless simulation nodes are configured to simulate different bandwidth and latency scenarios, inject typical wireless communication data packets of the power Internet of Things to generate simulated traffic, and obtain simulated communication data and simulation scenario parameters. For network interface links (such as application layer and control layer protocols), retrieve the power business protocol stacks (such as electricity consumption acquisition protocol and dispatch control protocol) supported by the devices in the asset database, start the network interface protocol simulator, simulate the master-slave device request-response interaction process and record the communication timing and data format; For serial port links (such as MODBUS, scheduling 101 / 103 protocol, DL / T645 protocol), based on the serial port attributes of the devices in the asset database (such as baud rate, parity bit, protocol type), enable the serial port simulation module, send command packets that conform to the power serial port protocol specification and capture device response data; The high consistency between the simulation environment and the real power industrial control scenario is verified by using a protocol simulation consistency formula, which is expressed as follows: ; In the formula, This represents the consistency value of the protocol simulation. For historical link data packet feature set, The feature set of real link data packets; consistency verification requirements To ensure the simulation environment has high fidelity; S3. Preprocess the simulation communication data in S2, including deduplication, normalization and vectorization, and key field annotation. Simultaneously, combine the protocol interaction information from S2 to annotate key fields in the data packets, generating the input data for the Ollama model. ; Load the pre-trained vulnerability feature extraction model and input the pre-processed... Vulnerability feature extraction is performed, a process that can be represented as extracting features from input data. Mapping to vulnerability feature vectors: ; In the formula, For feature extraction function, The extracted vulnerability feature vector; By combining the protocol type information of S2 (such as "MODBUS protocol" and "Schedule 103 protocol version"), the attack path prediction logic is optimized. To ensure the auditability and analyzability of the process, a log system with complete audit trail capabilities, isolated from the production environment, is used to record the model inference process and intermediate results. This system is also correlated with S2 simulation scenario parameters to analyze the impact range of vulnerabilities in that scenario. Furthermore, an external threat intelligence platform is used to obtain known vulnerability information related to the current protocol type, which is then used to assist in verifying and correlating the vulnerability results identified by the model. Finally, the identification effectiveness is quantified using an unknown vulnerability identification rate formula, expressed as: ; In the formula, For the identification rate of unknown vulnerabilities; Number of unknown vulnerabilities identified; This represents the total number of potential unknown vulnerabilities in the simulation environment. when When a proactive alert is triggered, a detection report is automatically generated. This report includes at least the location and scope of the vulnerability, thus upgrading from passive detection to proactive alert. S4. Based on the detection report from S3, conduct attack behavior simulation, defense capability verification, and data statistical analysis, specifically as follows: Attack behavior simulation involves launching multi-dimensional attacks, such as fuzzy attacks and CPU load attacks, on the attack platform, targeting different attack types (e.g., scheduling protocol attacks, wireless link attacks, serial port command attacks, etc.). The vulnerability exploitation process is simulated through the vulnerability system of the penetration testing platform, and an attack severity coefficient is introduced to optimize the attack success rate calculation. The formula is: ; In the formula, The total number of attack types. For the first The severity coefficient of this type of attack, and the scheduling protocol attack. Common information leakage attacks ; For the first The number of successful attacks of this type; For the first Total number of attacks of this type; Conduct personnel training and live-fire attack and defense exercises to verify the intelligent protection strategy. Introduce vulnerability level weights (high-risk, medium-risk, low-risk) to optimize vulnerability remediation rate calculations. The formula is: ; In the formula, The total number of vulnerability levels. For the first The weight of level 1 vulnerabilities, and the weight of high-risk vulnerabilities. Medium-risk vulnerabilities Low-risk vulnerabilities ; For the first The number of Class A vulnerabilities patched; For the first The total number of level 1 vulnerabilities; Generate multi-dimensional reports including attack behavior analysis, asset inventory, and vulnerability verification, based on and Optimize attack and defense strategies and vulnerability remediation solutions; S5, based on S4's attack and defense strategies and vulnerability remediation solutions, conducts tiered production environment linkage, integrates defense baselines with industry standards, and implements closed-loop operation and maintenance throughout the entire lifecycle, specifically: Configure a hierarchical VPN network with a core control layer, business application layer, and terminal equipment layer to interconnect the penetration attack and defense platform with intelligent remote control devices, power consumption acquisition systems, PLCs, and other equipment in the production site according to their security levels.
[0022] Differentiated remediation solutions are deployed for different device types, specifically: For core control devices such as PLCs, a hot patch rollback mechanism is used to deploy S4 vulnerability remediation solutions; for terminal devices such as sensors and meters, protocol parameters are updated through remote configuration.
[0023] The actual protective effectiveness of the protection scheme in the production environment after deployment can be quantified using the following formula: ; In the formula, This represents the number of attacks successfully intercepted. The total number of attacks suffered by the production environment; To assess the effectiveness of protection, this is used to quantify the protective capabilities of defensive measures for the production environment.
[0024] Based on the quantitative results, a layered defense baseline is established, a list is configured at the device access layer, and based on the protocol simulation results of S2, communication lists and encryption requirements for power-specific protocols such as scheduling 101 / 103 and DL / T645 are formulated; according to the attack and defense strategy of S4, targeted intrusion detection rules are deployed.
[0025] Through the security situation awareness platform, attack attempts, successful defenses, and vulnerability recurrences in the production environment are collected in real time, and dynamic reports on the effectiveness of protection are generated regularly. When the recurrence rate of a certain type of patched vulnerability exceeds a preset threshold (such as 5%), the S4 penetration testing platform is triggered to perform secondary verification and optimize the patching plan. The defense baseline and attack and defense strategies are updated periodically in conjunction with power industry vulnerability intelligence, such as the CVE power equipment special database, to ensure synchronization with technology iteration.
[0026] Therefore, the present invention adopts the above-mentioned industrial control protocol secure in-loop simulation method and system. This method realizes automatic extraction of vulnerability features and attack path prediction, effectively improving the identification rate of unknown vulnerabilities; it can automatically identify, classify devices and match information, and build a unified device asset library without manual intervention.
[0027] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0028] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.
Claims
1. A secure in-loop simulation method for industrial control protocols, characterized in that, Includes the following steps: S1. Connect the electrical equipment under test through multiple physical interfaces of the electrical application access module, start the equipment fingerprint recognition program, collect the basic hardware characteristics and power-specific characteristics of the equipment, and enter the equipment information into the unified asset database based on the equipment matching degree calculation model to obtain the basic data of the equipment under test. S2. Based on the basic data of the device under test in S1, perform protocol simulations for wireless links, network links, and serial links respectively; calculate the consistency of protocol simulation by comparing the feature sets of historical link data packets with the feature sets of real link data packets, and ensure that it is higher than a preset first threshold; output the full set of simulated communication data packets, protocol interaction timing, and simulation scenario parameters; the first threshold is not lower than 95%. The wireless links include WIFI, 4G / 5G, NB-IoT, and MQTT protocol links; the network interface links include application layer and control layer protocol links; the serial port links include MODBUS, Scheduling 101 / 103 protocol, and DL / T645 protocol links. S3. Preprocess the simulated communication data in S2 to generate input data that conforms to the Ollama model format requirements; load the Ollama model to perform vulnerability feature extraction, and optimize attack path prediction by combining protocol type information; obtain the unknown vulnerability identification rate by calculating the ratio of the number of identified unknown vulnerabilities to the total number of potential unknown vulnerabilities; when the identification rate is higher than a preset second threshold, trigger an active warning and generate a detection report; the second threshold is not lower than 85%. S4. Launch multi-dimensional attacks based on the detection report of S3, evaluate the attack effect based on the attack success rate, conduct attack and defense exercises to verify the protection strategy, evaluate the defense effectiveness based on the vulnerability repair rate, and generate attack and defense strategies and vulnerability repair solutions. S5, based on the attack and defense strategies and vulnerability remediation solutions of S4, interconnects the penetration testing and defense platform with production site equipment through hierarchical VPN networking, and deploys vulnerability remediation solutions; it formulates a layered defense baseline based on the protection effectiveness formula and monitors it through the security situation awareness platform.
2. The on-loop simulation method for industrial control protocol security according to claim 1, characterized in that, In S1, the device matching degree calculation model is implemented through the following formula: ； In the formula, Device matching score is used to quantify the overall similarity between the device to be identified and the feature template of standard devices; For macro-level weighting coefficients, ; Sub-feature weights; Based on basic hardware characteristics, Features specific to the power industry; The number of basic hardware features, The number of electricity-specific features; For the feature template of the corresponding equipment in the asset library; Type coefficient weights; Adjustment coefficient for equipment type.
3. The on-loop simulation method for industrial control protocol security according to claim 1, characterized in that, In S1, the basic hardware features include device model, firmware version, and hardware interface type; the power-specific features include supported power protocol types and business function modules.
4. The on-loop simulation method for industrial control protocol security according to claim 1, characterized in that, In S2, the formula for calculating the simulation consistency of the protocol is: ； In the formula, This represents the degree of consistency in protocol simulation. For historical link data packet feature set, This is a feature set of real link data packets.
5. The on-loop simulation method for industrial control protocol security according to claim 1, characterized in that, In S2, the simulation of the wireless link involves configuring a cloud-edge collaborative wireless simulation node to simulate different bandwidth and latency scenarios, and injecting data packets that conform to the power Internet of Things communication protocol to generate simulated traffic. The simulation of the network port link simulates the request-response interaction process between master and slave devices by loading the power service protocol stack, and records the communication timing and data format. The simulation of the serial port link involves matching the baud rate and parity parameters of the serial port of the device under test, sending instruction packets that conform to the power serial port protocol specification, and capturing the device's response data.
6. The on-loop simulation method for industrial control protocol security according to claim 1, characterized in that, S3 also includes: The reasoning process and intermediate results of the Ollama model are recorded through a log system that is isolated from the production environment and has full audit trail capabilities. Combined with vulnerability information from an external threat intelligence platform, the vulnerability results identified by the model are verified a second time. The location of the vulnerability corresponds to a specific simulated link in S2, such as a wireless link, a network port link, or a serial port link. The assessment of the scope of impact is associated with at least one simulation scenario parameter in S2, such as network bandwidth or latency.
7. The on-loop simulation method for industrial control protocol security according to claim 1, characterized in that, The attack success rate formula in S4 is: ； In the formula, The total number of attack types. For the first The severity coefficient of this type of attack; For the first The number of successful attacks of this type; For the first Total number of attacks of this type; The formula for the vulnerability repair rate is: ； In the formula, The total number of vulnerability levels. For the first The weight of a vulnerability at level 1; For the first The number of Class A vulnerabilities patched; For the first The total number of Class A vulnerabilities.
8. A secure in-the-loop simulation system for industrial control protocols, applied to the secure in-the-loop simulation method for industrial control protocols as described in any one of claims 1-7, characterized in that, include: The device access module utilizes the network port, CAN, RS485, and RS232 interfaces of the electrical application access module to physically connect the device under test. It is configured to connect to the electrical device under test through multiple physical interfaces and perform device fingerprinting to build a unified asset database; The multi-link protocol simulation module is communicatively connected to the device access construction module and is configured to perform protocol simulation for different link types based on asset database data. The intelligent security detection module is communicatively connected to the multi-link protocol simulation module and is configured to analyze simulation data and perform vulnerability detection and early warning. The penetration testing module is communicatively connected to the intelligent security detection module and is configured to conduct attack and defense drills based on the detection results and evaluate the protection effect. The protection closed-loop management module is communicatively connected to the penetration attack and defense exercise module and is configured to deploy protection measures and monitor the production environment through a secure network connection.
9. A computer device, characterized in that, include: A processor and a memory, the processor storing a computer program, the processor executing the computer program to implement the method as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when run on a computer, causes the computer to perform the method as described in any one of claims 1-7.