Communication control method and device and eutrophic combustion system

Abstract

The invention belongs to the technical field of eutrophic combustion systems, and particularly relates to a communication control method and device and an eutrophic combustion system.The communication control method comprises the steps that sensor data are collected and converted into digital signals; classifying and packaging according to data types; screening out high-priority data packets according to a set standard; carrying out fragmentation processing on the high-priority data packet, and transmitting the high-priority data packet after labeling fragmentation labels; receiving the fragmented data packets, and reassembling the fragmented data packets into a complete data packet according to the number; and receiving and analyzing the data of the complete data packet, and generating an optimization instruction or alarm information. According to the invention, the dynamic balance of the overall link load is realized, the solution of the delay problem reduces the link congestion, thereby providing more resource redundancy for the counterbalance of the interference problem, the solution of the interference problem ensures the completeness of the link data, the unnecessary delay or error control caused by error data is avoided, and the user experience is improved. The two means complement each other, and optimization of delay and interference dual problems is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of nutrient-rich combustion systems, and in particular relates to a communication control method, device and nutrient-rich combustion system. Background Art

[0002] Oxygen-enriched combustion system is a technology that optimizes the combustion process by increasing the oxygen concentration in the combustion air. It is widely used in industrial fields such as copper smelting and steel production. By increasing the oxygen concentration to 25%-100%, oxygen-enriched combustion can significantly increase the combustion temperature and improve thermal efficiency, while reducing fuel consumption and pollutants such as NOx and CO2 emissions. Oxygen-enriched combustion systems are usually equipped with a variety of sensors for real-time monitoring of key parameters in the combustion process, such as oxygen concentration, temperature, pressure and flue gas composition, to ensure the stability and efficiency of the combustion state. The above system form relies on an efficient communication control system for data transmission and feedback to achieve precise dynamic control.

[0003] The nutrient-rich combustion process requires real-time adjustment of the oxygen-fuel supply ratio. However, due to the delay of the current communication system, the real-time data collected by the sensor may not be transmitted quickly to the control center, resulting in a lag in feedback control, which causes incomplete combustion in the combustion process, resulting in energy waste and increased emissions. Taking the average transmission delay of 2 seconds from oxygen concentration data collection to the control center as an example, in the scenario of high-frequency dynamic adjustment, this cumulative effect may cause the combustion efficiency to decrease by more than 5%. In addition, due to the complex industrial environment in which the nutrient-rich combustion system is located, the communication link is easily affected by physical, environmental and electromagnetic interference. The key data such as oxygen concentration and pressure transmitted by the sensor cannot reach the control center completely due to link interference, which will cause the system to make inaccurate judgments on the combustion state and generate erroneous control instructions.

[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 equipment, increase system instability, and even lead to safety risks to equipment. How to solve the above problems by changing the communication control method has become a technical problem that technicians in this field need to solve. Summary of the invention

[0005] The present invention provides a communication control method, device and nutrient-rich combustion system, thereby effectively solving the problems in the background technology.

[0006] To achieve the above object, the technical solution of the present invention is as follows: A communication control method, comprising: Collect sensor data of the nutrient-rich combustion system and convert it into digital signals; Classify data according to data type and package the data according to standard protocols; Filter out high-priority packets based on set criteria; Slice the high priority data packet and transmit it after marking the slice numbers; Receive fragmented data packets and reassemble them into complete data packets according to the numbers; Receive and analyze the data of the complete data packet, and generate optimization instructions or alarm information.

[0007] Furthermore, it also includes: Adding a checksum to the high priority data packet before fragmentation processing; After the fragmented data packets are reassembled, detecting the error level of the complete data packet according to the check code; Whether the data should be retransmitted is determined according to the error level.

[0008] Further, adding a checksum to the high priority data packet before fragmentation processing includes: Convert the high priority data packet into binary format to obtain a conversion result; Generate redundant check bits for the conversion result by using an FEC algorithm; The redundant check bit is appended to the end of the conversion result.

[0009] Furthermore, the method for determining the length of the redundant check bit includes: The error degree of the last data transmission is obtained, and the length of the redundant check bits is positively correlated with the obtained result.

[0010] Further, the complete data packet is analyzed using a gradient boosting decision tree, and the gradient boosting decision tree includes: A plurality of decision trees, each of which includes a root node, an internal node, and a leaf node, wherein the plurality of decision trees are connected in series to form an integrated architecture, wherein each of the decision trees sequentially fits the residual of a previous model to implement iteration; A loss function, guiding the optimization of the model; Hyperparameter controls, at least for setting the number, depth and learning rate of the decision trees.

[0011] Further, the complete data packet is analyzed using a gradient boosting decision tree, wherein the gradient boosting decision tree includes a plurality of decision trees; The number of decision trees is positively correlated with the length of the redundant check bits.

[0012] A communication control device, comprising: Data acquisition module, which collects sensor data of the nutrient-rich combustion system and converts it into digital signals; A classification module, which identifies the data type of the sensor data, classifies the data according to the data type, and packages the data according to a standard protocol; A screening module, which is preset with a set standard and screens out high priority data packets according to the set standard; A data fragmentation module is used to fragment the high priority data packet and transmit it after marking the fragment numbers; The data reassembly module receives the fragmented data packets and reassembles them into complete data packets according to the numbers; The decision module receives and analyzes the data of the complete data packet and generates optimization instructions or alarm information.

[0013] Furthermore, it also includes a data integrity check module, which adds a check code to the high priority data packet before fragmentation processing, and detects the error degree of the complete data packet according to the check code after the fragmented data packet is reassembled; The data fragmentation module maintains the original data transmission according to the error degree; or resends the error fragment; or resends the fragmentation result of the high priority data packet.

[0014] Furthermore, the decision module is integrated with a gradient boosting decision tree, and the gradient boosting decision tree includes: A plurality of decision trees, each of which includes a root node, an internal node, and a leaf node, wherein the plurality of decision trees are connected in series to form an integrated architecture, wherein each of the decision trees sequentially fits the residual of a previous model to implement iteration; A loss function, guiding the optimization of the model; Hyperparameter controls, at least for setting the number, depth and learning rate of the decision trees.

[0015] The nutrient-rich combustion system uses the communication control device as described above to perform data communication.

[0016] The technical solution of the present invention can achieve the following technical effects: In the present invention, a dynamic balance of the overall link burden is achieved, and the solution to the delay problem reduces link congestion, thereby providing more resource redundancy for countering the interference problem. The solution to the interference problem ensures the integrity of the link data and avoids unnecessary delays or erroneous control due to erroneous data. The two means complement each other and achieve the optimization of the dual problems of delay and interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 is a flow chart of a communication control method; Figure 2 It is an optimization method for retransmission strategy; Figure 3 Flow chart for adding verification code; Figure 4 A framework diagram of a communication control device. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0021] Embodiment 1 Communication control methods, such as Figure 1 As shown, including: S1: Collect sensor data of the nutrient-rich combustion system and convert it into digital signals; specifically, the collected data types include but are not limited to oxygen concentration, temperature, pressure, flow rate, flue gas composition, etc.; S2: Classify according to data type and package the data according to standard protocols; in this embodiment, the data can be classified according to their uses or characteristics, such as real-time sensor data directly related to the combustion state, control instructions or their auxiliary parameters generated based on real-time data analysis, and monitoring data of combustion process emissions; and the standard protocols referred to include JSON and Protobuf; S3: Filter out high-priority data packets according to set standards; by filtering high-priority data, resources can be concentrated to prioritize the data that is most critical to combustion efficiency and safety; in specific implementation, data priority rules, i.e., set standards for screening, need to be defined in advance, for example: oxygen concentration data has the highest priority because it directly determines the efficiency and stability of the combustion process, followed by temperature, pressure, and flue gas composition data, which are used to assist in determining the combustion state, and low-priority data, such as historical records or non-critical operating parameters, can be delayed or processed in batches; S4: Fragment high-priority data packets and transmit them after marking the fragment numbers. After the data packets are fragmented and transmitted, even if some fragments are lost, they can be identified and retransmitted by numbering to improve data integrity. A unique number and data packet identifier is attached to each fragment to ensure that the receiving end can correctly identify the order and source of the fragments. S5: Receive fragmented data packets and reassemble them into complete data packets according to the numbers; provide accurate input for subsequent analysis and control instruction generation; S6: Receive the data of the complete data packet and analyze it to generate optimization instructions or alarm information. The optimization instructions are used to control the execution unit of the nutrient-rich combustion system to adjust the oxygen and fuel supply ratio.

[0022] In the working scenario of the nutrient-rich combustion system, both delay and interference are actually closely related to the transmission efficiency and stability of the communication link. Specifically, the delay problem stems from the long link transmission time, while the interference problem stems from the loss of data integrity and accuracy during the transmission process. In view of the bottleneck of the above-mentioned transmission link, this embodiment reduces the amount of transmitted data through priority screening, and reduces the transmission pressure of the link as a whole, thereby benefiting from both delay and interference. Data fragmentation and labeling not only enhance the anti-interference ability, but also further shorten the delay through a more efficient transmission mechanism. The two means form a positive superposition in reducing the link burden, which not only reduces the transmission time of key data, but also improves the anti-interference ability of the link.

[0023] In this embodiment, priority filtering gives priority to the transmission of key data packets, reducing the occupation of transmission resources by secondary data, which directly reduces the delay of data transmission. When the data flow is reduced, the amount of retransmitted data required by the fragmentation mechanism is also reduced, making link resources more sufficient, thereby further reducing the additional delay caused by retransmission. After the two methods work together, the transmission integrity and timeliness of high-priority key data are prioritized, and the combined effects of delay and interference are controlled to the lowest level.

[0024] Through the implementation of the above technical solutions, a dynamic balance of the overall link burden is achieved. The solution to the delay problem reduces link congestion, thereby providing more resource redundancy for countering the interference problem. The solution to the interference problem ensures the integrity of the link data and avoids unnecessary delays or erroneous control due to erroneous data. The two means complement each other and achieve the optimization of the dual problems of delay and interference.

[0025] As a preferred embodiment of the above, as an optimization method of the retransmission strategy, such as Figure 2 As shown, the communication control method also includes: A1: Add a checksum to the high-priority data packet before fragmentation. Specifically, a checksum algorithm, such as CRC checksum or FEC coding, can be used to generate an overall checksum for all data in the high-priority data packet. By adding the overall checksum, a basis is provided for subsequent error detection and processing, which helps to quickly discover anomalies in the data packet. A2: After the fragmented data packets are reassembled, the error degree of the complete data packet is detected according to the checksum. During the implementation process, the receiving end reassembles the data packet according to the fragment number sequence. The missing fragments or erroneous fragments can be recorded in a statistical table or other statistical methods are used. The checksum is recalculated for the reassembled complete data packet, and the calculation result is compared with the checksum attached by the sending end. If they match completely, it means that the data packet is complete and correct. If they do not match completely, it means that the data packet has different degrees of error or loss. As a specific method for obtaining the error degree, the number of errors or lost fragments can be counted first, and the error degree can be calculated using the following formula: Error degree = number of erroneous or lost shards / total number of shards × 100%; A3: Determine whether to resend the data based on the error level. Specifically, select one of the following operations: Instead of resending, the assembled complete data packet is directly used for analysis. In specific implementation, this operation is performed when the error level is low, for example, when the error level calculated by the above formula is less than 5%, the error level is low. This operation directly ignores small-range errors and uses the current data packet for combustion state analysis and optimization control. In scenarios where the system allows a certain error, such as a small fluctuation in oxygen concentration, resource consumption can be reduced. Or, resend the erroneous fragment; also based on the standard of the above operation, this operation can be performed when the error degree calculated by the above formula is within the range of 5%-20%, the number of lost or erroneous fragments is limited, and the data packet can be repaired by partial retransmission; specifically, the receiving end generates a retransmission request according to the erroneous fragment number, and sends the lost or erroneous fragment number to the sending end. After receiving the request, the sending end only resends the erroneous fragment. This operation avoids retransmission of the complete data packet, saves transmission bandwidth, and improves data transmission efficiency by partially repairing errors; Or, resend the fragmentation result of the high-priority data packet; also based on the standard of the above operation, this operation can be performed when the error degree calculated by the above formula is greater than 20%. In this case, the local repair cost is often too high. After the receiving end determines that the number of fragmentation errors exceeds the threshold, it sends a request to resend the complete data packet. The sending end re-segments and sends all fragments. This operation can ensure the integrity of the data packet, avoid data distortion or control instruction errors caused by a large number of fragmentation errors, and provide reliable data transmission guarantee in severe interference environments.

[0026] In this preferred solution, a balance between data integrity and transmission efficiency is achieved by dynamically adjusting the retransmission strategy based on a complete chain of check code detection and error degree assessment; it can not only respond flexibly to different interference levels, but also minimize communication delays and resource waste.

[0027] As a preferred embodiment of the above, Figure 3 As shown in the figure, a checksum is added to the high priority data packet before fragmentation, including: B1: Convert the high-priority data packet into binary format to obtain the conversion result; the original format of the data packet is usually a human-readable text format, such as JSON, XML, etc., but the redundancy check algorithm can only act on binary data. This step can provide a basis for the subsequent check code generation and provide standardized binary input for the encoding algorithm, so that the check code generation can accurately act on each bit of the data packet; B2: Generate redundant check bits for the conversion result through the FEC algorithm; in the specific implementation process, the conversion result is used as the input of the FEC, and the redundant check bits can be generated through the RS algorithm. The length of the generated redundant bits can depend on the error correction capability setting of the FEC; B3: Add redundant check bits to the end of the conversion result. After adding the redundant check bits, the data packet can directly enter the fragmentation processing and transmission stage. Regardless of whether the link is stable, the receiving end can use the redundant bits for verification and error correction.

[0028] Compared with traditional detection check codes, FEC can not only detect errors, but also automatically repair errors within a certain range to avoid retransmission operations. In environments with high interference or high packet loss rates, data packets can still ensure integrity through redundant check bits. FEC's automatic error correction capability reduces round-trip delays during data transmission; it is particularly suitable for the real-time requirements of high-priority data and can quickly restore complete data when the link is restricted; according to the transmission environment and data priority, the FEC algorithm can adjust the number of redundant bits to achieve a balance between error correction capability and transmission efficiency.

[0029] As a preferred embodiment of the above, in the process of determining the length of the redundant check bit, in order to increase real-time performance, the method for determining the length of the redundant check bit includes: obtaining the error degree of the last data transmission, and adjusting the length of the redundant check bit in a positive correlation according to the obtained result.

[0030] As a specific implementation of this preferred solution: When the error degree is a low error rate, also taking the above embodiment as an example, the redundant check bits are minimized and used only for detection, for example, the minimum error correction capability of Reed-Solomon coding is used, such as 2-byte redundant check. This method can reduce the size of the data packet and improve the transmission efficiency; When the error level is a medium error rate, taking the range of 5%-20% in the above embodiment as an example, redundant check bits are increased to detect and repair appropriate errors. For example, 4 bytes of redundant check bits are added for every 8 bytes of data. This method provides higher error correction capability, balances increased redundancy with transmission stability, and avoids retransmission. When the error length is a high error rate, for example, greater than 20% in the above embodiment, the redundant check bits are maximized, for example, 8 bytes of redundant check are added for every 8 bytes of data, thereby ensuring that the receiving end can repair the data packet when the link is extremely unstable.

[0031] In this preferred solution, the redundant check bit length is dynamically adjusted according to the error level of the previous transmission to achieve a balance between transmission efficiency and reliability. When the link quality is good, unnecessary redundant checks are avoided to improve transmission efficiency. When the link quality is poor, check bits are added to ensure data integrity. The dynamic adjustment of redundant check bits enables the system to better adapt to high-interference environments and reduce data loss and retransmission.

[0032] As a preferred embodiment of the above, a gradient boosting decision tree is used to analyze the complete data packet, and the gradient boosting decision tree includes: Several decision trees, each decision tree includes a root node, internal nodes, and leaf nodes. Several decision trees are connected in series to form an integrated architecture, in which each decision tree fits the residual of the previous model in turn to achieve iteration; Loss function, guiding model optimization; Hyperparameter controls, at least for setting the number, depth, and learning rate of decision trees.

[0033] In the above preferred scheme, the decision tree is the basic unit of the gradient boosting decision tree, which is used to decompose complex problems into several sub-problems, and realize prediction by recursively splitting features. Each decision tree fits the residual of the previous model in turn, which can gradually reduce the prediction error; the loss function is used to guide the optimization of the model in the gradient boosting decision tree. By minimizing the gradient of the loss function, such as mean square error, logarithmic loss, etc., each tree is constructed in the direction of reducing error; hyperparameter control, as a key control item 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.

[0034] As another optimization direction, the gradient boosting decision tree is used to analyze the complete data packet. The gradient boosting decision tree includes several decision trees, among which the number of decision trees is positively correlated with the length of the redundant check bits. As the length of the redundant check bits 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, and achieve coordinated optimization of transmission and analysis.

[0035] In the first embodiment, a method for dynamically adjusting the length of redundant check bits and a method for dynamically adjusting the number of decision trees in a gradient boosting decision tree based on the length of redundant check bits are provided. When the two methods are used simultaneously, the following advantages can be achieved: Through the linkage optimization of transmission and analysis, the synergy of data integrity protection and model analysis accuracy improvement is achieved, which can dynamically respond to changes in the transmission environment and adapt to complex industrial scenarios; transmission and analysis work together to reduce the impact of erroneous data and improve the overall performance of the system. Under this optimization method, resource allocation can be optimized according to actual needs to avoid excessive redundancy or computing burden, thereby ensuring the efficient and stable operation of the oxygen-enriched combustion system and reducing energy consumption and emissions.

[0036] Embodiment 2 Communication control devices, such as Figure 4 As shown, including: Data acquisition module, which collects sensor data of the nutrient-rich combustion system and converts it into digital signals; The classification module identifies the data type of sensor data, classifies it according to the data type, and packages the data according to the standard protocol; The screening module has preset criteria and screens out high-priority data packets according to the set criteria; The data fragmentation module fragments high-priority data packets and transmits them after marking the fragment numbers; The data reassembly module receives the fragmented data packets and reassembles them into complete data packets according to the numbers; The decision module receives and analyzes the data of the complete data packet and generates optimization instructions or alarm information.

[0037] As a preferred embodiment of the present invention, the communication control device further comprises a data integrity check module, which adds a check code to the high priority data packet before fragmentation processing, and detects the error degree of the complete data packet according to the check code after the fragmented data packet is reassembled; The data fragmentation module maintains the original data transmission according to the error degree; or resends the error fragment; or resends the fragmentation result of the high priority data packet.

[0038] In this embodiment, preferably, the decision module integrates a gradient boosting decision tree, and the gradient boosting decision tree includes: Several decision trees, each decision tree includes a root node, internal nodes, and leaf nodes. Several decision trees are connected in series to form an integrated architecture, in which each decision tree fits the residual of the previous model in turn to achieve iteration; Loss function, guiding model optimization; Hyperparameter controls, at least for setting the number, depth, and learning rate of decision trees.

[0039] The technical effect achieved by this embodiment is the same as that of the above-mentioned embodiment 1, and will not be repeated here.

[0040] Embodiment 3 The nutrient-rich combustion system uses the communication control device of Example 1 for data communication. The technical effects achieved by this embodiment are the same as those of the above-mentioned Example 1, and will not be described in detail here.

[0041] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A communication control method, characterized in that: include: Collect sensor data of the nutrient-rich combustion system and convert it into digital signals; Classify data according to data type and package the data according to standard protocols; Filter out high-priority packets based on set criteria; Slice the high priority data packet and transmit it after marking the slice numbers; Receive fragmented data packets and reassemble them into complete data packets according to the numbers; Receive and analyze the data of the complete data packet, and generate optimization instructions or alarm information.

2. The communication control method according to claim 1, characterized in that: Also includes: Adding a checksum to the high priority data packet before fragmentation processing; After the fragmented data packets are reassembled, detecting the error level of the complete data packet according to the check code; Whether the data should be retransmitted is determined according to the error level.

3. The communication control method according to claim 2, characterized in that: Adding a checksum to the high priority data packet before fragmentation processing includes: Convert the high priority data packet into binary format to obtain a conversion result; Generate redundant check bits for the conversion result by using an FEC algorithm; The redundant check bit is appended to the end of the conversion result.

4. The communication control method according to claim 3, characterized in that: The method for determining the length of the redundant check bit comprises: The error degree of the last data transmission is obtained, and the length of the redundant check bits is positively correlated with the obtained result.

5. The communication control method according to claim 1, characterized in that: The complete data packet is analyzed using a gradient boosting decision tree, and the gradient boosting decision tree includes: A plurality of decision trees, each of which includes a root node, an internal node, and a leaf node, wherein the plurality of decision trees are connected in series to form an integrated architecture, wherein each of the decision trees sequentially fits the residual of a previous model to implement iteration; A loss function, guiding the optimization of the model; Hyperparameter controls, at least for setting the number, depth and learning rate of the decision trees.

6. The communication control method according to claim 3 or 4, characterized in that: The complete data packet is analyzed using a gradient boosting decision tree, wherein the gradient boosting decision tree includes a plurality of decision trees; The number of decision trees is positively correlated with the length of the redundant check bits.

7. A communication control device, characterized in that: include: Data acquisition module, which collects sensor data of the nutrient-rich combustion system and converts it into digital signals; A classification module, which identifies the data type of the sensor data, classifies the data according to the data type, and packages the data according to a standard protocol; A screening module, which is preset with a set standard and screens out high priority data packets according to the set standard; A data fragmentation module is used to fragment the high priority data packet and transmit it after marking the fragment numbers; The data reassembly module receives the fragmented data packets and reassembles them into complete data packets according to the numbers; The decision module receives and analyzes the data of the complete data packet and generates optimization instructions or alarm information.

8. The communication control device according to claim 7, characterized in that: It also includes a data integrity check module, which adds a check code to the high priority data packet before fragmentation processing, and detects the error degree of the complete data packet according to the check code after the fragmented data packet is reassembled; The data slicing module maintains the original data transmission according to the error degree; Or, resend the erroneous fragment; or, resend the fragmentation result of the high priority data packet.

9. The communication control device according to claim 7, characterized in that: The decision module is integrated with a gradient boosting decision tree, and the gradient boosting decision tree includes: A plurality of decision trees, each of which includes a root node, an internal node, and a leaf node, wherein the plurality of decision trees are connected in series to form an integrated architecture, wherein each of the decision trees sequentially fits the residual of a previous model to implement iteration; A loss function, guiding the optimization of the model; Hyperparameter controls, at least for setting the number, depth and learning rate of the decision trees.

10. A nutrient-rich combustion system, characterized in that: Data communication is performed using the communication control device as described in any one of claims 7 to 9.

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