Communication control method, device and oxygen-enriched combustion system

Abstract

This invention belongs to the technical field of enriched-oxygen combustion systems, and particularly relates to a communication control method, device, and system. The communication control method includes acquiring sensor data and converting it into digital signals; classifying and packaging the data according to its data type; filtering high-priority data packets according to set criteria; fragmenting the high-priority data packets and transmitting them after labeling them with fragment numbers; receiving the fragmented data packets and reassembling them into complete data packets according to their numbers; receiving and analyzing the data from the complete data packets to generate optimization instructions or alarm information. This invention achieves a dynamic balance of the overall link load. Solving the latency problem reduces link congestion, thus providing more resource redundancy to combat interference. Solving the interference problem ensures the integrity of the link data, avoiding unnecessary delays or erroneous control due to incorrect data. These two methods complement each other, achieving optimization of both latency and interference issues.

Description

Technical Field

[0001] This invention belongs to the technical field of oxygen-enriched combustion systems, and particularly relates to a communication control method, device, and oxygen-enriched combustion system. Background Technology

[0002] Oxygen-enriched combustion systems are a technology that optimizes the combustion process by increasing the oxygen concentration in the combustion air. Widely used in industries such as copper smelting and steel production, oxygen-enriched combustion significantly increases combustion temperature and thermal efficiency by raising the oxygen concentration to 25%-100%, while reducing fuel consumption and emissions of pollutants such as NOx and CO2. Oxygen-enriched combustion systems are typically equipped with multiple sensors to monitor key parameters in the combustion process in real time, such as oxygen concentration, temperature, pressure, and flue gas composition, ensuring the stability and efficiency of the combustion process. These systems rely on efficient communication and control systems for data transmission and feedback to achieve precise dynamic control.

[0003] Oxygen-enriched combustion requires real-time adjustment of the oxygen-to-fuel ratio. However, due to current communication system latency, real-time data collected by sensors may not be transmitted to the control center quickly, leading to lag in feedback control. This results in incomplete combustion, causing energy waste and increased emissions. For example, with an average transmission delay of 2 seconds between oxygen concentration data collection and the control center, this cumulative effect can reduce combustion efficiency by more than 5% in high-frequency dynamic adjustment scenarios. Furthermore, the complex industrial environment in which oxygen-enriched combustion systems operate makes communication links susceptible to physical, environmental, and electromagnetic interference. Key data such as oxygen concentration and pressure transmitted by sensors may not reach the control center completely due to link interference, leading to inaccurate judgments of the combustion status and the generation of erroneous control commands.

[0004] In actual operation, delay and interference problems often occur simultaneously. In high-frequency control scenarios, this delay-interference superposition effect may cause frequent adjustments to the equipment, increase system instability, and even lead to equipment safety risks. How to solve the above problems by changing the communication control method has become a technical problem that needs to be solved by those skilled in the art. Summary of the Invention

[0005] This invention provides a communication control method, device, and oxygen-enriched combustion system, thereby effectively solving the problems in the prior art.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] Communication control methods, including:

[0008] Collect sensor data from the oxygen-enriched combustion system and convert it into digital signals;

[0009] The data is categorized according to its data type and packaged according to standard protocols.

[0010] High-priority data packets are selected based on the set criteria;

[0011] The high-priority data packets are fragmented and transmitted after being labeled with fragment numbers;

[0012] Receive fragmented data packets and reassemble them into complete data packets according to their numbers;

[0013] The system receives and analyzes the data from the complete data packet, generating optimization instructions or alarm information.

[0014] Furthermore, it also includes:

[0015] A checksum is added to the high-priority data packet before fragmentation processing;

[0016] After the fragmented data packets are reassembled, the error level of the complete data packet is detected based on the checksum.

[0017] Whether to resend the data depends on the degree of error.

[0018] Furthermore, a checksum is added to the high-priority data packets before fragmentation processing, including:

[0019] The high-priority data packets are converted into binary format to obtain the conversion result;

[0020] The FEC algorithm is used to generate redundant check bits for the transformation result.

[0021] The redundant check bits are appended to the end of the conversion result.

[0022] Furthermore, the method for determining the length of the redundancy check bit includes:

[0023] The error level of the previous data transmission is obtained, and the length of the redundancy check bit is adjusted accordingly based on the obtained result.

[0024] Furthermore, the complete data packet is analyzed using a gradient boosting decision tree, which includes:

[0025] A plurality of decision trees, each decision tree including a root node, internal nodes and leaf nodes, are connected in series to form an integrated architecture, wherein each decision tree iterates by fitting the residual of the previous model in turn.

[0026] The loss function guides the optimization of the model.

[0027] Hyperparameter control is used to set at least the number, depth, and learning rate of the decision trees.

[0028] Furthermore, the complete data packet is analyzed using a gradient boosting decision tree, which includes several decision trees;

[0029] The number of decision trees is positively correlated with the length of the redundant check bits.

[0030] Communication control device, including:

[0031] The data acquisition module collects sensor data from the oxygen-enriched combustion system and converts it into digital signals;

[0032] The classification module identifies the data type of the sensor data, classifies it according to the data type, and packages the data according to a standard protocol.

[0033] The filtering module has preset criteria and filters out high-priority data packets according to the preset criteria.

[0034] The data fragmentation module performs fragmentation processing on the high-priority data packets and transmits them after marking the fragmentation numbers;

[0035] The data reassembly module receives fragmented data packets and reassembles them into complete data packets according to their serial numbers.

[0036] The decision module receives and analyzes the data from the complete data packet, and generates optimization instructions or alarm information.

[0037] Furthermore, it also includes a data integrity verification module, which adds a checksum to the high-priority data packet before fragmentation processing, and detects the error level of the complete data packet based on the checksum after the fragmented data packet is reassembled;

[0038] The data fragmentation module maintains the original data transmission according to the degree of error; or, retransmits the erroneous fragments; or, retransmits the fragmentation results of the high-priority data packets.

[0039] Furthermore, the decision-making module integrates a gradient boosting decision tree, which includes:

[0040] A plurality of decision trees, each decision tree including a root node, internal nodes and leaf nodes, are connected in series to form an integrated architecture, wherein each decision tree iterates by fitting the residual of the previous model in turn.

[0041] The loss function guides the optimization of the model.

[0042] Hyperparameter control is used to set at least the number, depth, and learning rate of the decision trees.

[0043] The oxygen-enriched combustion system uses the communication control device described above for data communication.

[0044] The technical solution of this invention can achieve the following technical effects:

[0045] In this invention, dynamic balancing of the overall link load is achieved. Solving the latency problem reduces link congestion, thereby providing more resource redundancy to combat interference. Solving the interference problem ensures the integrity of link data and avoids unnecessary delays or erroneous control caused by erroneous data. The two methods complement each other and optimize both latency and interference problems. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 A flowchart of the communication control method;

[0048] Figure 2 This is an optimization method for the retransmission strategy;

[0049] Figure 3 A flowchart for adding a verification code;

[0050] Figure 4 This is a framework diagram of the communication control device. Detailed Implementation

[0051] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0053] Example 1

[0054] Communication control methods, such as Figure 1 As shown, it includes:

[0055] S1: Collect sensor data from the oxygen-enriched combustion system and convert it into digital signals; specifically, the types of data collected include, but are not limited to, oxygen concentration, temperature, pressure, flow rate, and flue gas composition.

[0056] S2: Classify the data according to its data type and package the data according to standard protocols; in this embodiment, the data can be classified according to its purpose or characteristics, such as real-time sensor data directly related to the combustion state, control commands or their auxiliary parameters generated based on real-time data analysis, and monitoring data of emissions during the combustion process; and the standard protocols referred to include JSON and Protobuf.

[0057] S3: Filter high-priority data packets according to set criteria; by filtering high-priority data, resources can be concentrated to prioritize the processing of data most critical to combustion efficiency and safety; in specific implementation, data priority rules need to be predefined, that is, the set criteria used for filtering. For example, oxygen concentration data has the highest priority because it directly determines the efficiency and stability of the combustion process. Temperature, pressure, and flue gas composition data are secondary and used to assist in judging the combustion status. Low-priority data, such as historical records or non-critical operating parameters, can be delayed in transmission or processed in batches.

[0058] S4: Fragment high-priority data packets and transmit them after labeling them with fragment numbers; transmitting data packets in fragments allows for identification and retransmission of fragments even if some fragments are lost, improving data integrity. Attaching a unique number and data packet identifier to each fragment ensures that the receiving end can correctly identify the order and origin of the fragments.

[0059] S5: Receives fragmented data packets and reassembles them into complete data packets according to their numbers; providing accurate input for subsequent analysis and control command generation;

[0060] S6: Receives and analyzes the complete data packet, generating optimization instructions or alarm messages. Optimization instructions are used to control the execution unit of the oxygen-enriched combustion system to adjust the oxygen-to-fuel supply ratio.

[0061] In the operating scenario of an oxygen-enriched combustion system, both latency and interference are closely related to the transmission efficiency and stability of the communication link. Specifically, latency stems from excessively long transmission times, while interference arises from compromised data integrity and accuracy during transmission. To address these bottlenecks in the transmission link, this embodiment reduces the amount of data transmitted through priority filtering, thereby lowering the overall transmission pressure on the link. This simultaneously benefits both latency and interference reduction. Data fragmentation and labeling not only enhance anti-interference capabilities but also further shorten latency through a more efficient transmission mechanism. These two methods work synergistically to reduce the link burden, both lowering the transmission time of critical data and improving the link's anti-interference capabilities.

[0062] In this embodiment, priority filtering prioritizes the transmission of critical data packets, reducing the occupation of transmission resources by secondary data and directly reducing data transmission latency. With reduced data traffic, the amount of retransmission data required by the fragmentation mechanism is also reduced, making link resources more abundant. This further reduces the additional latency caused by retransmission. With the two methods working together, the integrity and timeliness of the transmission of high-priority critical data are guaranteed first, and the combined impact of latency and interference is controlled to the lowest level.

[0063] By implementing the above technical solutions, a dynamic balance of the overall link load is achieved. Solving the latency problem reduces link congestion, thereby providing more resource redundancy to combat interference. Solving the interference problem ensures the integrity of link data and avoids unnecessary delays or erroneous control caused by erroneous data. The two methods complement each other and optimize both latency and interference problems.

[0064] As a preferred embodiment of the above embodiments, and as an optimization of the retransmission strategy, such as Figure 2 As shown, the communication control method also includes:

[0065] A1: Add a checksum to high-priority data packets before fragmentation. Specifically, a checksum algorithm, such as CRC checksum or FEC encoding, can be used to generate a total checksum for all data in the high-priority data packets. By adding the total checksum, a basis for subsequent error detection and processing can be provided, which helps to quickly detect anomalies in the data packets.

[0066] A2: After the fragmented data packets are reassembled, the error level of the complete data packet is detected based on the check code. During the process, the receiving end reassembles the data packets according to the fragment number order. Missing or incorrect fragments can be recorded in a statistical table or other statistical methods can be used. The check code of the reassembled complete data packet is recalculated, and the calculation result is compared with the check code attached by the sending end. When there is a complete match, it means that the data packet is complete and correct. When there is a partial match, it means that the data packet has different degrees of error or loss.

[0067] As a specific method for obtaining the degree of error, the number of faulty or lost fragments can be counted first, and the degree of error can be calculated using the following formula:

[0068] Error severity = (Number of faulty or lost fragments / Total number of fragments) × 100%

[0069] A3: Determine whether to resend the data based on the severity of the error. Specifically, choose one of the following operations to perform:

[0070] Instead of retransmitting, the complete assembled data packet is used directly for analysis. In practice, this operation is performed when the error level is low, for example, when the error level calculated using the above formula is less than 5%, it is considered to be low. This operation directly ignores small-range errors and uses the current data packet for combustion status analysis and optimization control. In scenarios where the system allows for a certain degree of error, such as small fluctuations in oxygen concentration, resource consumption can be reduced.

[0071] Alternatively, the erroneous fragments can be retransmitted. Also based on the standards described above, this operation can be performed when the error rate, calculated using the above formula, is within the range of 5%-20%. Since the number of lost or erroneous fragments is limited, data packets can be repaired through partial retransmission. Specifically, the receiving end generates a retransmission request based on the erroneous fragment number and sends the lost or erroneous fragment number to the sending end. Upon receiving the request, the sending end only retransmits the erroneous fragments. This operation avoids retransmitting complete data packets, saves transmission bandwidth, and improves data transmission efficiency by partially repairing errors.

[0072] Alternatively, the fragmentation results of high-priority data packets can be retransmitted. Also based on the above operating criteria, this operation can be performed when the error rate calculated using the above formula is greater than 20%. In such cases, local repair is often too costly. After the receiving end determines that the number of fragmentation errors exceeds the threshold, it sends a request to retransmit the complete data packet. The sending end then re-processes the fragmentation and retransmits all fragments. This operation ensures the integrity of the data packet, avoids data distortion or control command errors caused by a large number of fragmentation errors, and provides reliable data transmission guarantees in severely interfered environments.

[0073] In this preferred solution, a complete chain based on checksum detection and error severity assessment is established, and a retransmission strategy is dynamically adjusted to achieve a balance between data integrity and transmission efficiency. This not only enables flexible responses to different levels of interference but also minimizes communication latency and resource waste.

[0074] As a preferred embodiment of the above, such as Figure 3 As shown, a checksum is added to high-priority data packets before fragmentation, including:

[0075] B1: Convert high-priority data packets into binary format to obtain the conversion result; the original format of the data packets is usually human-readable text format, such as JSON, XML, etc., but the redundancy check algorithm can only be applied to binary data. This step can provide a foundation for subsequent check code generation, provide standardized binary input for the encoding algorithm, and enable the check code generation to be applied accurately to each bit of the data packet.

[0076] B2: Generate redundant check bits for the conversion result using the FEC algorithm; in the specific implementation process, the conversion result is used as the input of FEC, and the redundant check bits can be generated using the RS algorithm. The length of the generated redundant bits can depend on the error correction capability setting of FEC.

[0077] B3: Add the redundancy check bit to the end of the conversion result. After adding the redundancy check bit, the data packet can directly enter the fragmentation and transmission stage. Regardless of whether the link is stable, the receiving end can use the redundancy bit for verification and error correction.

[0078] Compared to traditional checksums, FEC can not only detect errors but also automatically correct them within a certain range, avoiding retransmissions. In environments with high interference or high packet loss rates, data packets can still maintain integrity through redundant check bits. FEC's automatic error correction capability reduces round-trip latency during data transmission. It is particularly suitable for the real-time requirements of high-priority data and can quickly recover complete data when links are limited. Depending on the transmission environment and data priority, the FEC algorithm can adjust the number of redundant bits, thereby achieving a balance between error correction capability and transmission efficiency.

[0079] As a preferred embodiment of the above, in order to increase real-time performance, the method for determining the length of the redundant check bit includes: obtaining the error level of the previous data transmission and adjusting the length of the redundant check bit in a positive correlation with the obtained result.

[0080] As a specific implementation of this preferred solution:

[0081] When the error level is low, taking the example of less than 5% in the above embodiment, the redundancy check bits are minimized and used only for detection. For example, the minimum error correction capability of Reed-Solomon encoding is used, such as 2 bytes of redundancy check. This method can reduce the data packet size and improve transmission efficiency.

[0082] When the error rate is moderate, taking the range of 5%-20% in the above embodiment as an example, the redundant check bits are increased to detect and repair an appropriate amount of errors. For example, 4 bytes of redundant check are added for every 8 bytes of data. This method provides higher error correction capability, balances the increase in redundancy with transmission stability, and avoids retransmission.

[0083] When the error length is high and the error rate is greater than 20% in the above embodiment, the redundancy check bits are maximized. For example, 8 bytes of redundancy check are added for every 8 bytes of data, so as to ensure that the receiving end can repair the data packet under extremely unstable link conditions as much as possible.

[0084] In this preferred scheme, the length of the redundant check bit is dynamically adjusted according to the error level of the previous transmission, so as to achieve a balance between transmission efficiency and reliability. When the link quality is good, unnecessary redundant checks are avoided and the transmission efficiency is improved. When the link quality is poor, the check bit is increased to ensure data integrity. The dynamic adjustment of the redundant check bit enables the system to better adapt to high interference environments and reduce data loss and retransmission.

[0085] As a preferred embodiment of the above, a gradient boosting decision tree is used to analyze the complete data packet. The gradient boosting decision tree includes:

[0086] Several decision trees, each consisting of a root node, internal nodes, and leaf nodes, are connected in series to form an integrated architecture, where each decision tree iterates by fitting the residual of the previous model in turn.

[0087] The loss function guides model optimization;

[0088] Hyperparameter control is used to set at least the number, depth, and learning rate of decision trees.

[0089] In the above preferred scheme, the decision tree is the basic unit of the gradient boosting decision tree, used to decompose complex problems into several sub-problems. Prediction is achieved through recursive feature splitting. Each decision tree fits the residual of the previous model in turn, which can gradually reduce the prediction error. The loss function in the gradient boosting decision tree is used to guide the optimization of the model. By minimizing the gradient of the loss function, such as mean squared error or log loss, each tree is built in the direction of reducing error. Hyperparameter control, as a key regulation term of the gradient boosting decision tree, can effectively control the model capacity, limit the complexity of a single tree, prevent overfitting, and balance the optimization speed and model performance by adjusting the contribution weight of each tree.

[0090] As another optimization approach, gradient boosting decision trees are used to analyze complete data packets. A gradient boosting decision tree consists of several decision trees; the number of decision trees is positively correlated with the length of the redundancy check bit. As the length of the redundancy check bit increases, the number of decision trees used for analysis increases synchronously to compensate for the impact of reduced data integrity on analysis accuracy when the transmission environment deteriorates, thus achieving coordinated optimization of transmission and analysis.

[0091] In this first embodiment, a method for dynamically adjusting the length of the redundancy check bit and a method for dynamically adjusting the number of decision trees in the gradient boosting decision tree based on the length of the redundancy check bit are provided. When the two methods are used simultaneously, the following advantages can be achieved:

[0092] By optimizing the linkage between transmission and analysis, a synergistic effect is achieved in ensuring data integrity and improving model analysis accuracy. This allows for dynamic response to changes in the transmission environment and adaptation to complex industrial scenarios. The collaboration between transmission and analysis reduces the impact of erroneous data, enhancing overall system performance. This optimization approach allows for the optimization of resource allocation based on actual needs, avoiding excessive redundancy or computational burden, thereby ensuring the efficient and stable operation of the oxygen-enriched combustion system and reducing energy consumption and emissions.

[0093] Example 2

[0094] Communication control devices, such as Figure 4 As shown, it includes:

[0095] The data acquisition module collects sensor data from the oxygen-enriched combustion system and converts it into digital signals;

[0096] The classification module identifies the data type of sensor data, classifies it according to the data type, and packages the data according to standard protocols.

[0097] The filtering module has preset criteria, which are used to filter high-priority data packets.

[0098] The data fragmentation module fragments high-priority data packets and transmits them after labeling them with fragment numbers;

[0099] The data reassembly module receives fragmented data packets and reassembles them into complete data packets according to their serial numbers.

[0100] The decision-making module receives and analyzes the data from the complete data packet, generating optimization instructions or alarm information.

[0101] As a preferred embodiment, the communication control device further includes a data integrity verification module, which adds a check code to high-priority data packets before fragmentation processing, and detects the error level of the complete data packets based on the check code after the fragmented data packets are reassembled.

[0102] The data fragmentation module can either maintain the original data transmission based on the degree of error, or resend the erroneous fragments, or resend the fragmentation results of high-priority data packets.

[0103] In this embodiment, preferably, the decision module integrates a gradient boosting decision tree, which includes:

[0104] Several decision trees, each consisting of a root node, internal nodes, and leaf nodes, are connected in series to form an integrated architecture, where each decision tree iterates by fitting the residual of the previous model in turn.

[0105] The loss function guides model optimization;

[0106] Hyperparameter control is used to set at least the number, depth, and learning rate of decision trees.

[0107] The technical effects achieved in this embodiment are the same as those in Embodiment 1 above, and will not be repeated here.

[0108] Example 3

[0109] The oxygen-enriched combustion system uses a communication control device as described in Embodiment 1 for data communication. The technical effects achieved in this embodiment are the same as those in Embodiment 1 above, and will not be repeated here.

[0110] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A communication control method, characterized in that, include: Sensor data from the oxygen-enriched combustion system is collected and converted into digital signals; the data is classified according to its data type and packaged according to a standard protocol; high-priority data packets are selected based on set criteria; the high-priority data packets are fragmented and transmitted after being labeled with fragment numbers; Receive fragmented data packets and reassemble them into complete data packets according to their numbers; Receive and analyze the data from the complete data packet to generate optimization instructions or alarm information; It also includes: adding a checksum to the high-priority data packet before fragmentation; after the fragmented data packet is reassembled, detecting the error level of the complete data packet based on the checksum; determining whether the data should be retransmitted based on the error level, and performing one of the following operations: Instead of retransmitting, the complete assembled data packet is used directly for analysis; or, the erroneous fragment is retransmitted; or, the fragmentation results of the high-priority data packet are retransmitted. Adding a checksum to the high-priority data packet before fragmentation includes: converting the high-priority data packet into binary format to obtain a conversion result; generating a redundant checksum bit for the conversion result using the FEC algorithm; and appending the redundant checksum bit to the end of the conversion result. The method for determining the length of the redundant checksum bit includes: The error level of the previous data transmission is obtained, and the length of the redundancy check bit is adjusted accordingly based on the obtained result.

2. The communication control method according to claim 1, characterized in that, The complete data packet is analyzed using a gradient boosting decision tree, which includes: A plurality of decision trees, each decision tree including a root node, internal nodes and leaf nodes, are connected in series to form an integrated architecture, wherein each decision tree iterates by fitting the residual of the previous model in turn. The loss function guides the optimization of the model. Hyperparameter control is used to set at least the number, depth, and learning rate of the decision trees.

3. The communication control method according to claim 1, characterized in that, The complete data packet is analyzed using a gradient boosting decision tree, which includes several decision trees; The number of decision trees is positively correlated with the length of the redundant check bits.

4. A communication control device, employing the communication control method as described in claim 1, characterized in that, include: The data acquisition module collects sensor data from the oxygen-enriched combustion system and converts it into digital signals; The classification module identifies the data type of the sensor data, classifies it according to the data type, and packages the data according to a standard protocol. The filtering module has preset criteria and filters out high-priority data packets according to the preset criteria. The data fragmentation module performs fragmentation processing on the high-priority data packets and transmits them after marking the fragmentation numbers; The data reassembly module receives fragmented data packets and reassembles them into complete data packets according to their serial numbers. The decision module receives and analyzes the data from the complete data packet, and generates optimization instructions or alarm information.

5. The communication control device according to claim 4, characterized in that, It also includes a data integrity verification module, which adds a check code to the high-priority data packet before fragmentation processing, and detects the error level of the complete data packet based on the check code after the fragmented data packet is reassembled. The data sharding module maintains the original data transmission according to the degree of error. Alternatively, resend the erroneous fragment; or resend the fragmentation result of the high-priority data packet.

6. The communication control device according to claim 4, characterized in that, The decision module integrates a gradient boosting decision tree, which includes: A plurality of decision trees, each decision tree including a root node, internal nodes and leaf nodes, are connected in series to form an integrated architecture, wherein each decision tree iterates by fitting the residual of the previous model in turn. The loss function guides the optimization of the model. Hyperparameter control is used to set at least the number, depth, and learning rate of the decision trees.

7. An oxygen-enriched combustion system, characterized in that, Data communication is performed using the communication control device as described in any one of claims 4 to 6.

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