Multi-equipment cooperative control method, system and equipment for feeding of smelting furnace

Abstract

The invention relates to the technical field of smelter equipment control, in particular to a smelter feeding multi-equipment cooperative control method, system and equipment, and the method comprises the steps: constructing a real-time communication network between smelter feeding equipment, synchronizing the clock signals of the smelter feeding equipment, and scheduling the transmission time sequence of control instructions according to the priority; continuously collecting operation state data of each smelting furnace feeding device according to a real-time communication network, and generating a device operation index according to a preset abnormality judgment rule; when the device operation index indicates that the first device is in an abnormal state, dynamically allocating a control task of the first device to at least one second device based on a current topological structure of the real-time communication network, and adjusting a link connection relationship of the real-time communication network; and according to the instruction interface type of the target equipment, converting the cooperative control instruction into an instruction format adaptive to the target equipment. According to the invention, the problems of multi-device communication delay, high abnormal detection misjudgment rate and low protocol conversion efficiency in a complex environment are effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of furnace equipment control, and particularly to a multi-device collaborative control method, system and equipment for furnace feeding. Background Art

[0002] In high-temperature industrial scenarios, the furnace feeding process involves the collaborative control of multiple devices such as robotic arms, conveyor belts, and sensors. The core lies in achieving precise material delivery and seamless cooperation between devices through efficient communication and dynamic scheduling. Traditional methods usually adopt a centralized control architecture, rely on wired Ethernet or conventional wireless networks to achieve communication between devices, and perform anomaly detection through fixed threshold rules.

[0003] However, with the expansion of furnace scale and the complexity of production environment, the existing technologies have certain defects. Traditional communication networks are prone to signal attenuation and transmission delay under interference, resulting in the out-of-sync of control instructions and device status feedback. Especially in multi-device collaborative scenarios, the action timing deviation between robotic arms and conveyor belts is likely to cause material accumulation or collision. In addition, the difference in instruction formats between heterogeneous devices leads to complex protocol conversion, requiring customized development of interfaces, with a long system integration cycle and poor compatibility. Although there have been improved routing algorithms, there are still problems such as insufficient fault tolerance, weak adaptability to dynamic environments, and low cross-protocol collaboration efficiency, which are difficult to meet the high-real-time and high-reliable furnace feeding control requirements.

[0004] The information disclosed in this background art section is only intended to deepen the understanding of the overall background art of the present disclosure, and should not be regarded as an admission or any form of implication that this information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0005] The present invention provides a multi-device collaborative control method, system and equipment for furnace feeding, which can effectively solve the problems in the background art.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is: A multi-device collaborative control method for furnace feeding, the method comprising: Construct a real-time communication network among furnace feeding devices, synchronize the clock signals of each furnace feeding device, and schedule the transmission timing of control instructions according to priorities; Continuously collect the operation status data of each furnace feeding device according to the real-time communication network, and generate device operation metrics according to preset anomaly determination rules; When the device operation metrics indicate that a first device is in an abnormal state, based on the current topology of the real-time communication network, dynamically allocate the control task of the first device to at least one second device, and adjust the link connection relationship of the real-time communication network; Convert the collaborative control instruction into an instruction format adapted to the target device according to the instruction interface type of the target device, and issue and execute it according to the real-time communication network.

[0007] Furthermore, build a real-time communication network among the furnace feeding devices, including: Divide the furnace feeding devices into multiple communication domains, establish a main link within each communication domain according to the deterministic communication protocol, and establish redundant links between domains based on a multi-hop ad hoc network; Adopt a global clock alignment mechanism based on frequency synchronization within each communication domain to perform dynamic bandwidth reservation for the transmission timing of the control instruction; When the end-to-end delay of the main link exceeds the preset threshold, trigger the redundant link to take over the transmission of the control instruction, and update the routing path based on the network topology connectivity matrix; During the transmission of the control instruction, perform adaptive coding and modulation on the control instruction according to the real-time channel state information, and give priority to ensuring the transmission success rate of the furnace safety interlock instruction.

[0008] Furthermore, establish redundant links between domains through a multi-hop ad hoc network, including: Deploy routing proxy nodes in the border devices of each communication domain, and the routing proxy nodes periodically broadcast detection messages containing the device load rate within the domain and the real-time channel state information; The routing proxy node receives the detection messages of adjacent nodes and constructs a joint path evaluation function, and selects a multi-hop path that meets the real-time constraint of furnace control as the redundant link; When the main link triggers redundant switching, according to the calculation result of the joint path evaluation function, fragment the control instruction and transmit it in parallel through at least two redundant links; During the data transmission process, dynamically adjust the fragmentation strategy according to the real-time channel state information, and preferentially allocate the furnace safety interlock instruction to the redundant link with the highest link quality index.

[0009] Furthermore, update the routing path based on the network topology connectivity matrix, including: Construct the adjacency relationship of the furnace feeding devices in the multi-hop ad hoc network, map the adjacency relationship between devices to the adjacency matrix weight, and generate a connectivity matrix; According to the network topology change after the redundant link switching is triggered, calculate the node degree and path redundancy coefficient of the connectivity matrix, and screen a candidate routing path set that meets the real-time constraint of furnace control; Perform dynamic evaluation on the candidate routing path set, and select a path with a delay volatility lower than a fixed threshold and a packet loss rate lower than a fixed threshold as the updated routing path; During the transmission process, if it is detected that the rank of the connectivity matrix decreases and exceeds a preset threshold, a topology reconstruction instruction is triggered to recalculate the connectivity matrix.

[0010] Furthermore, a joint path evaluation function is constructed, including: Obtain the link quality index, the end-to-end delay, and the in-domain device load rate, and collect the bandwidth load ratio and the bit error rate of the redundant link in real time; Based on the real-time constraint of the furnace control, perform a weighted summation operation on the link quality index, the end-to-end delay, the in-domain device load rate, the bandwidth load ratio, and the bit error rate; Based on the weight coefficients, establish the joint path evaluation function, and when transmitting the furnace safety interlock instruction, increase the weight coefficient of the link quality index; Dynamically adjust each weight coefficient according to iterative optimization and keep the sum unchanged, and calculate the maximum path evaluation output value, and screen the redundant links that meet the preset performance threshold according to the evaluation result.

[0011] Furthermore, device operation indicators are generated according to preset anomaly determination rules, including: Generate a dynamic anomaly determination threshold based on the furnace environment parameters, where the dynamic anomaly determination threshold includes a static baseline threshold and a dynamic compensation value linked to the furnace environment parameters; Perform a fusion calculation on the multi-dimensional operation parameters of the furnace feeding device, where the fusion calculation adjusts the weight distribution rule based on the dynamic compensation value; Generate a hierarchical anomaly identifier according to the comparison relationship between the result of the fusion calculation and the dynamic anomaly determination threshold; Trigger differentiated device operation indicators based on the hierarchical anomaly identifier, and the device operation indicators include the link connection relationship adjusted according to the anomaly level.

[0012] Furthermore, the determination of the dynamic compensation value includes: Obtain the furnace environment parameters in real time, and the furnace environment parameters include furnace temperature, vibration intensity, and harmful gas concentration; According to the change range of the furnace environment parameters, calculate the dynamic correction coefficients corresponding to each parameter, where the correction coefficients of the furnace temperature and the vibration intensity are generated by mapping through a preset non-linear relationship; Compensate and calibrate the dynamic correction coefficients based on the device performance decay rate to generate the dynamic compensation value.

[0013] Furthermore, convert the collaborative control instruction into an instruction format adapted to the target device, including: Analyze the semantic logic of the collaborative control instruction to generate an instruction action sequence that matches the functions of the target device; According to the instruction interface type of the target device, call the protocol conversion rule library preset in the real-time communication network to convert the instruction action sequence into a syntax structure that can be parsed by the target device; Based on the current topology structure of the real-time communication network, perform conflict detection on the syntax structure. If an instruction path incompatible with the topology structure is detected, trigger the regeneration of the instruction action sequence; According to the dynamic changes of the device operation metrics, adjust the encapsulation format and transmission priority of the syntax structure to generate an instruction data packet that adapts to the load status of the real-time communication network; Based on the priority scheduling result, distribute the instruction data packet to the target device for execution according to the real-time communication network.

[0014] A multi-device collaborative control system for furnace feeding, the system includes: A communication transmission module that constructs a real-time communication network among furnace feeding devices, synchronizes the clock signals of each furnace feeding device, and schedules the transmission timing of control instructions according to priorities; An index generation module that continuously collects the operation status data of each furnace feeding device according to the real-time communication network and generates device operation metrics according to preset abnormal determination rules; A dynamic allocation module that, when the device operation metrics indicate that the first device is in an abnormal state, based on the current topology structure of the real-time communication network, dynamically allocates the control task of the first device to at least one second device and adjusts the link connection relationship of the real-time communication network; An instruction conversion module that converts the collaborative control instruction into an instruction format adapted to the target device according to the instruction interface type of the target device and issues it for execution according to the real-time communication network.

[0015] A multi-device collaborative control device for furnace feeding, which is used to implement the multi-device collaborative control method for furnace feeding described above.

[0016] Through the technical solution of the present invention, the following technical effects can be achieved: The real-time response reliability of furnace feeding control is improved, the device collaboration accuracy and system fault tolerance ability are enhanced, and the integration complexity and cost of heterogeneous devices are reduced through dynamic protocol adaptation.

[0017] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the specific embodiments of this application are specifically cited below. Description of the Drawings

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1 It is a schematic flowchart of the multi-device collaborative control method for furnace feeding; Figure 2 It is a schematic flowchart of constructing a real-time communication network; Figure 3 It is a schematic flowchart of generating device operation indicators; Figure 4 It is a schematic flowchart of instruction format conversion. Detailed implementation manners

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the description 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 of the related listed items.

[0022] Embodiment 1; As Figure 1 shown, the present application provides a multi-device collaborative control method for furnace feeding, and the method includes: S10: Construct a real-time communication network among the furnace feeding devices, synchronize the clock signals of each furnace feeding device, and schedule the transmission timing of control instructions according to the priority; S20: Continuously collect the operation status data of each furnace feeding device according to the real-time communication network, and generate device operation indicators according to the preset abnormal determination rules; S30: When the device operation indicator indicates that the first device is in an abnormal state, based on the current topology structure of the real-time communication network, dynamically allocate the control task of the first device to at least one second device, and adjust the link connection relationship of the real-time communication network; S40: Convert the collaborative control instruction into an instruction format adapted to the target device according to the instruction interface type of the target device, and issue and execute it according to the real-time communication network.

[0023] Specifically, first, a real-time communication network is established among the charging devices of each furnace during the deployment phase to ensure that control instructions and operation data can be transmitted with low latency and high reliability. The real-time communication network is uniformly managed by a central controller, which periodically sends clock synchronization signals to each slave device, enabling all devices to have a consistent time reference, providing a time guarantee for subsequent instruction scheduling and status alignment. During operation, the operation status information of all charging devices, including key electrical parameters and sensor feedback signals, is continuously collected through the real-time communication network. After real-time analysis of this data, the operation status indicators of the devices, such as status labels like normal, abnormal, or standby, are calculated according to preset abnormal determination rules, providing a basis for device status identification and collaborative strategy selection. When a device is determined to be in an abnormal state, based on the current real-time communication network topology structure, the working capabilities and available resources of other devices are quickly evaluated, and a suitable standby device is selected to undertake part or all of the control tasks of the abnormal device. At the same time, the logical connection relationships and control priorities in the network are dynamically updated according to the task adjustment to ensure the coherence and stability of the overall collaboration process. Considering the differences in control interfaces among different devices, an instruction conversion is set up to convert the unified collaborative control instructions into specific formats adapted to the control interfaces of target devices. The converted control instructions are sent to the corresponding devices according to network scheduling rules, ensuring that each device can accurately and efficiently respond to scheduling tasks and achieving the goal of cross-device collaborative control.

[0024] Through the technical solution of the present invention, the real-time response reliability of furnace charging control is improved, the device collaboration accuracy and system fault tolerance ability are enhanced, and the integration complexity and cost of heterogeneous devices are reduced through dynamic protocol adaptation.

[0025] Furthermore, as Figure 2 shown, a real-time communication network among the furnace charging devices is constructed, including: The furnace charging devices are divided into multiple communication domains. Within each communication domain, a main link is established according to the deterministic communication protocol, and redundant links are established among domains based on a multi-hop self-organizing network; Within each communication domain, a global clock alignment mechanism based on frequency synchronization is adopted to dynamically reserve bandwidth for the transmission timing of control instructions; When the end-to-end delay of the main link exceeds a preset threshold, the redundant link is triggered to take over the transmission of control instructions, and the routing path is updated based on the network topology connectivity matrix; During the transmission of control instructions, the control instructions are adaptively encoded and modulated according to the real-time channel state information, and the transmission success rate of the furnace safety interlock instructions is preferentially guaranteed.

[0026] As a preference of the above embodiments, in order to improve communication efficiency and controllability of management, various devices for charging the entire furnace are divided into multiple communication domains. Devices within each communication domain are relatively independent based on physical location or functional logic. A deterministic communication protocol is used to establish a main link within the domain, so as to ensure that control data has a fixed delay boundary and high stability during transmission within the domain. At the same time, redundant links are established between different communication domains through a multi-hop ad-hoc network, and path reconstruction can be automatically performed when a link anomaly or interruption occurs, realizing network fault tolerance capabilities within the global scope; to ensure that control instructions can be delivered within a predetermined time window in the entire communication system, a global clock alignment mechanism based on frequency synchronization is introduced within each communication domain. The global clock alignment mechanism issues a frequency synchronization signal through the master control node, enabling all communication nodes to dynamically plan the required bandwidth resources in advance according to the task scheduling situation on the basis of global clock consistency, and completing the orderly transmission of instructions in their respective time slots, thereby minimizing delays or data loss caused by bandwidth conflicts; during communication, when it is detected that the end-to-end transmission delay of the main link exceeds the set threshold, the redundant link is immediately triggered to take over the communication task of the main link, and routing calculation is performed through a real-time updated network topology connectivity matrix, quickly selecting an alternative path with the best connectivity and the lightest load to complete the forwarding of control instructions, effectively preventing control anomalies caused by link congestion or interruption; during the transmission of control instructions, adaptive coding modulation is performed on the instruction data in combination with the real-time channel state information of each link. By selecting a modulation method and an error correction coding scheme that match the current channel state, it is ensured that the communication link still has good transmission robustness in a dynamic environment. At the same time, for furnace safety interlock instructions involved in the operation of the furnace, such as emergency shutdown and valve linkage, the highest priority processing strategy is given, and its transmission success rate is guaranteed at all levels such as bandwidth allocation, adaptive coding, and link scheduling, thereby enhancing the safety and reliability of the overall system.

[0027] Furthermore, redundant links are established between domains through a multi-hop ad-hoc network, including: Deploy routing proxy nodes in the boundary devices of each communication domain. The routing proxy nodes periodically broadcast probe messages containing the load rate of devices within the domain and real-time channel state information; The routing proxy nodes receive the probe messages of adjacent nodes and construct a joint path evaluation function, and select a multi-hop path that meets the real-time constraint of furnace control as the redundant link; When the main link triggers redundant switching, according to the calculation result of the joint path evaluation function, the control instructions are fragmented and transmitted in parallel through at least two redundant links; During data transmission, the fragmentation strategy is dynamically adjusted according to the real-time channel state information, and furnace safety interlock instructions are preferentially allocated to the redundant link with the highest link quality index.

[0028] As an optimization of the above embodiments, in order to enhance the communication reliability and latency robustness of the furnace feeding system in the scenario of multi-device collaborative control, a redundant link construction and dynamic scheduling mechanism based on a multi-hop ad hoc network is proposed. When the normal communication link is unavailable or the delay fluctuates greatly, the mechanism can automatically switch to a highly reliable redundant path, and through fragment transmission, link quality awareness, and scheduling optimization, ensure the real-time transmission of critical control instructions. During the deployment phase, it is preferable to divide the feeding devices in the communication topology into several communication domains, and configure routing proxy nodes in the boundary devices of each domain. Each routing proxy node independently monitors the channel and evaluates the communication. Its tasks are not limited to forwarding, but also participate in the construction of inter-domain routing and policy formulation. The routing proxy node broadcasts probe messages in a periodic manner. The probe messages at least include information such as the load rate of the current node, the average communication latency within the current domain, and link status parameters. To reduce the occupancy of the broadcast bandwidth, a compression coding and incremental update mechanism can be adopted, and only update the key indicators when the status changes significantly. After all neighboring routing proxy nodes receive the probe messages, they fuse the local status with the status of neighboring nodes, and use a weighted path evaluation model to calculate the path scores of available multi-hop paths. The path scores can comprehensively consider multiple factors such as latency, packet loss rate, link load, and available bandwidth to ensure that the selected path meets the requirements of the furnace control for communication real-time and stability. When the end-to-end delay monitored by the primary link exceeds the set threshold or abnormal packet loss occurs, the routing proxy node automatically triggers the redundant link switching process through control scheduling. The control instruction data packet is split into several logical fragments at this stage, and parallel transmission scheduling is performed according to the path scores of each redundant path to achieve the rapid delivery of the core control signal. To improve the robustness of the instruction transmission, during the transmission process, continuously sense the real-time channel status of each link. When some redundant paths show trends of jitter or interruption, the control scheduling automatically adjusts the fragment ratio or aborts some paths, and instead compensates through links with better status. For control instructions related to furnace safety interlocks, preferentially allocate them to the redundant path with the highest link quality index, and enable an acknowledgment mechanism to ensure their reliable delivery. To further enhance the adaptive ability of the system, a prediction mechanism can also be combined to analyze the trend of the link status, make decisions on path migration or resource reservation in advance, and thus improve the control continuity and reliability in the dynamic and complex industrial field environment.

[0029] Furthermore, updating the routing path based on the network topology connectivity matrix includes: Construct the adjacency relationship of the furnace feeding devices in the multi-hop ad hoc network, map the adjacency relationship between devices to the adjacency matrix weights, and generate the connectivity matrix; According to the network topology changes after the redundant link switching is triggered, calculate the node degrees and path redundancy coefficients of the connectivity matrix, and screen the candidate routing path set that meets the real-time constraints of the furnace control; Dynamically evaluate the candidate routing path set, and select the path with a delay volatility lower than a fixed threshold and a packet loss rate lower than a fixed threshold as the updated routing path; During the transmission process, if it is detected that the rank of the connectivity matrix decreases and exceeds the preset threshold, a topology reconstruction instruction is triggered to recalculate the connectivity matrix.

[0030] As an optimization of the above embodiment, to achieve high-reliability path management and dynamic optimization of the furnace feeding equipment in a multi-hop self-organizing network environment, means such as adjacency relationship modeling, real-time topology monitoring, and path quality evaluation are combined to achieve fast adaptive re-routing when the communication link changes or degrades, and improve the collaborative control performance in dynamic scenarios; in the initialization stage, first collect the adjacency relationship information between all furnace feeding equipment, including physical connection status, inter-node communication capabilities, and transmission distance data, etc., and convert these adjacency relationships into a weighted adjacency matrix. The weights in the weighted adjacency matrix are set according to actual link parameters, such as communication success rate, link bandwidth, or stability index, and further construct a network connectivity matrix through graph theory methods to quantify the global communication connectivity between device nodes; during operation, once it is detected that the performance of the main link deteriorates and triggers a redundant link switch, the routing reconstruction is automatically started, and the change of the current network topology structure is re-analyzed. At this time, calculate the degree of each node and the redundancy coefficient of each path using the topology data, and combine the connectivity matrix to screen out a set of candidate routing path sets that still maintain logical communication connectivity. These candidate routing paths need to meet the basic requirements for real-time performance and fault tolerance to ensure the stable availability of redundant communication; after selecting the candidate routing path set, dynamically monitor and evaluate the key performance indicators of each path. During the evaluation process, focus on core parameters such as end-to-end delay fluctuation, packet loss rate, and bandwidth utilization rate of the path, and perform rolling calculations. If the delay volatility and packet loss rate of a certain path are both lower than the preset threshold within a certain evaluation window, it will be included in the current optimal routing path, and the routing table is updated for actual data forwarding; in addition, to ensure the topology self-healing ability even when the network structure changes violently, a real-time monitoring mechanism for the rank of the connectivity matrix is introduced, and the rank value of the current connectivity matrix is calculated periodically to reflect the effective dimension of the overall network structure. Once this rank value decreases and exceeds the set safety threshold, it indicates that the network may have serious breaks or path degradation. At this time, the topology reconstruction process is automatically triggered, which will rescan the reachability between nodes and generate a new adjacency matrix and connectivity matrix, thus completing the overall reconstruction of path planning and system routing.

[0031] Furthermore, construct a joint path evaluation function, including: Obtain the link quality index, end-to-end delay, and in-domain device load rate, and collect the bandwidth load ratio and bit error rate of the redundant link in real time; Based on the real-time constraints of furnace control, perform a weighted summation operation on link quality indicators, end-to-end delay, device load rate within the domain, bandwidth load ratio, and bit error rate; Establish a joint path evaluation function based on weight coefficients. When transmitting the furnace safety interlock instruction, increase the weight coefficient of the link quality indicator; Dynamically adjust each weight coefficient according to iterative optimization while keeping the sum unchanged, calculate the maximum path evaluation output value, and screen redundant links that meet the preset performance threshold according to the evaluation results.

[0032] As a preference for the above embodiment, to achieve intelligent routing decision-making of the furnace feeding equipment in a multi-hop redundant communication network, a strategy for constructing a joint path evaluation function is proposed, which is used to dynamically determine whether each candidate redundant link meets the real-time and reliability requirements of furnace control, so as to achieve optimal scheduling during the path selection process; first, various key network state parameters are collected in real time, including link quality indicators, end-to-end transmission delay, processing load rate of each device within the current communication domain, bandwidth load ratio of redundant links, and bit error rate. These data are periodically collected by the routing proxy nodes on the boundary devices and summarized and processed by the network controller; according to the real-time control requirements of the furnace feeding system, the above five types of indicators are weighted and summarized, and each indicator is assigned an adjustable weight coefficient, which together constitute the parameter input of the joint path evaluation function. The core of the evaluation function is a weighted summation model, in which the link quality indicator and end-to-end delay have the greatest impact on communication real-time, so they are usually given higher weights in the initial state; when it is detected that the current task type is the transmission of the furnace safety interlock instruction, the weight distribution strategy in the joint path evaluation function will be automatically adjusted. At this time, the weight coefficient of the link quality indicator is preferentially increased to enhance the preference for high-reliability paths. This weight dynamic adjustment mechanism can improve the transmission success rate of key control instructions on redundant links; during operation, an iterative optimization mechanism is used to dynamically adjust each weight coefficient. After each path evaluation, the weight distribution is optimized and updated according to the feedback path performance results (such as whether the control time delay limit is met, whether packet loss occurs, etc.). By searching for the weight combination scheme under the premise of keeping the sum of each weight coefficient unchanged, the output value of the joint path evaluation function is maximized; when a new path evaluation result is generated, the sum is compared with the preset performance threshold. Only when the evaluation score of a certain path exceeds the threshold and remains stable within a certain time window, this path is selected as the current effective redundant transmission channel.

[0033] Furthermore, as Figure 3 shown, generate device operation indicators according to the preset abnormal determination rules, including: Generate a dynamic anomaly determination threshold based on furnace environment parameters, where the dynamic anomaly determination threshold includes a static baseline threshold and a dynamic compensation value linked to the furnace environment parameters; Perform a fusion calculation on the multi-dimensional operating parameters of the furnace feeding equipment, where the fusion calculation adjusts the weight distribution rule based on the dynamic compensation value; Generate a hierarchical anomaly identifier based on the comparison relationship between the result of the fusion calculation and the dynamic anomaly determination threshold; Trigger differentiated equipment operating metrics based on the hierarchical anomaly identifier, where the equipment operating metrics include the link connection relationship adjusted according to the anomaly level.

[0034] As a preference of the above embodiments, first, the environment parameters at the furnace site are monitored in real time, including but not limited to temperature, humidity, air flow disturbance frequency, and radiation intensity around the equipment, etc. By setting a static baseline threshold and introducing a dynamic compensation mechanism associated with the environment parameters, a dynamic anomaly determination threshold is generated. The dynamic compensation mechanism can adaptively adjust the original determination threshold according to the strength of the environmental disturbance, forming a determination benchmark with on-site sensitivity, thereby improving the accuracy and timeliness of anomaly recognition; in terms of obtaining operating parameters, multi-dimensional sampling of the operating states of each furnace feeding equipment is performed. The collected parameters include equipment response delay, power fluctuation, temperature rise rate, sensor consistency error, etc. These parameters are subjected to a fusion calculation, and through the weight adjustment rule based on the current dynamic compensation value, different calculation priorities are automatically assigned to each indicator. The fusion calculation not only enhances the collaborative correlation judgment ability between indicators but also can adjust the decision-making sensitivity in real time according to the degree of environmental fluctuation; when the result of the fusion calculation is compared with the dynamic anomaly determination threshold, a hierarchical anomaly identifier is generated. The anomaly level can be divided into multiple levels, such as: normal, warning, mild anomaly, severe anomaly, etc., and different levels correspond to different response strategies; after generating the anomaly identifier, differentiated equipment operating metrics will be automatically triggered according to the anomaly level. One key response action is to dynamically reconstruct the network link connection relationship. For example, when a certain equipment is in a mild anomaly state, only its communication priority is lowered; while in a severe anomaly state, its main link will be interrupted and forced to switch to an alternative path to avoid the abnormal node affecting the stability of the overall system control chain.

[0035] Furthermore, the determination of the dynamic compensation value includes: Obtain the furnace environment parameters in real time, where the furnace environment parameters include furnace temperature, vibration intensity, and harmful gas concentration; Calculate the dynamic correction coefficients corresponding to each parameter according to the change range of the furnace environment parameters, where the correction coefficients of the furnace temperature and vibration intensity are generated by mapping through a preset non-linear relationship; Based on the equipment performance degradation rate, compensate and calibrate the dynamic correction coefficients to generate the dynamic compensation value.

[0036] Preferably, in the above embodiments, key environmental parameters in the furnace area are continuously collected through the deployed environmental monitoring nodes, including furnace temperature, equipment vibration intensity, and harmful gas concentration, etc. The collection frequency of each parameter matches the rhythm of the furnace state change to ensure that fine-grained data fluctuation trends can be captured; after obtaining the raw data, the dynamic correction coefficients are calculated respectively according to the instantaneous change amplitude and short-term average change rate of each parameter. Among them, the correction coefficients of furnace temperature and vibration intensity are not simply linearly superimposed, but are generated based on a preset non-linear mapping model. The non-linear mapping model can be formed through experimental calibration or historical data training, reflecting the weight differences and non-uniform response characteristics of different parameters on the equipment operation state. For example, a small increase in temperature near the critical point may lead to more severe accumulation of equipment thermal stress, so a higher non-linear slope is set for the correction curve in this interval; to prevent misjudgment results caused by environmental anomalies from being amplified, before generating the dynamic compensation value, the equipment performance decay rate also needs to be introduced as an adjustment factor. The equipment performance decay rate is automatically updated by the system regularly according to the equipment aging state, maintenance cycle, and failure rate records, reflecting the current tolerance of the equipment to external environmental interference. When the equipment enters the decline stage, even if the environmental fluctuation is small, its response may become more vulnerable, so the influence of the correction coefficient needs to be amplified; conversely, if the equipment is relatively new, the dynamic correction weight is appropriately reduced to reduce unnecessary alarms or response triggers; the correction coefficients are compensated and calibrated, and are fused according to the set rules to generate a dynamic compensation value. The dynamic compensation value will be an important part of the dynamic anomaly determination threshold, and will adjust the sensitivity and tolerance interval of various equipment operation indicators in real time.

[0037] Furthermore, as Figure 4 shown, the collaborative control instruction is converted into an instruction format adapted to the target device, including: Analyze the semantic logic of the collaborative control instruction to generate an instruction action sequence that matches the function of the target device; According to the instruction interface type of the target device, call the protocol conversion rule library preset in the real-time communication network to convert the instruction action sequence into a syntax structure that can be parsed by the target device; Based on the current topology structure of the real-time communication network, perform conflict detection on the syntax structure. If an instruction path that is incompatible with the topology structure is detected, trigger the regeneration of the instruction action sequence; According to the dynamic changes of the equipment operation indicators, adjust the encapsulation format and transmission priority of the syntax structure to generate an instruction data packet adapted to the load status of the real-time communication network; Based on the priority scheduling result, distribute the instruction data packet to the target device for execution according to the real-time communication network.

[0038] As a preference of the above embodiments, first, a collaborative control instruction is received. The collaborative control instruction is usually constructed with a unified description model and is device-independent. After the instruction is input, its semantic content is deeply parsed to extract elements such as target control actions, execution timings, and constraint logics. During the parsing process, a device function dictionary is matched to automatically generate an action sequence corresponding to the capabilities of the target device, ensuring that the generated actions semantically satisfy the control logic of the target device. Subsequently, according to the communication interface type of the target device, a matching item is retrieved from the protocol conversion rule library of the real-time communication network, and through the invocation of the corresponding conversion, the action sequence is recoded into the instruction syntax structure supported by the target device, and a data packet recognizable by the device is formatted. The protocol conversion rule library supports a hot-plug mechanism, facilitating the quick access of subsequent newly added devices. After the instruction format conversion is completed, it is also necessary to perform a round of conflict detection in combination with the current topology of the communication network. The conflict detection mechanism can identify problems such as routing disconnection and bandwidth conflicts caused by topological changes. If it is found that the path defined in the syntax structure is incompatible with the current topology structure, a reconstruction process is triggered to logically rearrange the original action sequence or degrade the policy to adapt to the new network path. In addition, when generating the final instruction data packet, the operating metrics of each target device, such as processing load, response latency, historical failure rate, etc., are also referred to for adaptive adjustment of the instruction encapsulation format. For example, during the peak network load period, the communication pressure is reduced by simplifying the packet header, enabling differential compression, etc., and the priority of critical tasks (such as safety control instructions) is increased. During the instruction encapsulation stage, the redundancy coding parameters can also be dynamically set according to the link quality to improve the transmission success rate in an unstable network environment. Finally, the instruction data packet enters the communication channel according to the priority scheduling strategy and is distributed to the corresponding target device through the real-time communication network. After receiving and parsing the instruction, the target device executes the control task according to the established action sequence, ensuring the efficient and stable operation of the entire furnace feeding system under multi-device conditions.

[0039] Embodiment 2; Based on the same inventive concept as the multi-device collaborative control method for furnace feeding in the foregoing embodiments, the present invention also provides a multi-device collaborative control system for furnace feeding. The system includes: A communication transmission module that constructs a real-time communication network among the furnace feeding devices, synchronizes the clock signals of each furnace feeding device, and schedules the transmission timing of the control instructions according to the priority. An index generation module that continuously collects the operating status data of each furnace feeding device according to the real-time communication network and generates device operating metrics according to the preset abnormal determination rules. A dynamic allocation module that, when the device operating metrics indicate that the first device is in an abnormal state, dynamically allocates the control task of the first device to at least one second device based on the current topology of the real-time communication network and adjusts the link connection relationship of the real-time communication network. The instruction conversion module converts the collaborative control instructions into an instruction format adapted to the target device according to the instruction interface type of the target device, and issues and executes them according to the real-time communication network.

[0040] The above adjustment system in the present invention can effectively implement the multi-device collaborative control method for furnace feeding, and the technical effects it can achieve are as described in the above embodiments, which will not be elaborated here.

[0041] Embodiment III; Based on the same inventive concept as the multi-device collaborative control method for furnace feeding in the foregoing embodiments, the present invention also provides a multi-device collaborative control device for furnace feeding, which is used to implement the multi-device collaborative control method for furnace feeding.

[0042] The above device in the present invention can effectively implement the multi-device collaborative control method for furnace feeding, and the technical effects it can achieve are as described in the above embodiments, which will not be elaborated here.

[0043] Although the present application has been described in combination with specific features and their embodiments, it is obvious that various modifications and combinations can be made without departing from the spirit and scope of the present application. Accordingly, this specification and the drawings are merely exemplary descriptions of the present application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. The collaborative control method for multiple devices in the furnace feeding, characterized in that, The method includes: Constructing a real-time communication network among the furnace feeding devices, synchronizing the clock signals of each of the furnace feeding devices, and scheduling the transmission timing of control instructions according to priorities; Continuously collecting the operation status data of each of the furnace feeding devices according to the real-time communication network, and generating device operation metrics according to a preset abnormal determination rule; When the device operation metrics indicate that a first device is in an abnormal state, based on the current topological structure of the real-time communication network, dynamically allocating the control task of the first device to at least one second device, and adjusting the link connection relationship of the real-time communication network; Converting the cooperative control instruction into an instruction format adapted to the target device according to the instruction interface type of the target device, and sending it for execution according to the real-time communication network; Constructing a real-time communication network among the furnace feeding devices, including: Dividing the furnace feeding devices into multiple communication domains, establishing a main link according to a deterministic communication protocol within each communication domain, and establishing redundant links between domains based on a multi-hop ad hoc network; Adopting a global clock alignment mechanism based on frequency synchronization within each communication domain to perform dynamic bandwidth reservation for the transmission timing of the control instructions; When the end-to-end delay of the main link exceeds a preset threshold, triggering the redundant link to take over the transmission of the control instructions, and updating the routing path based on the network topology connectivity matrix; During the transmission of the control instructions, adaptively encoding and modulating the control instructions according to real-time channel state information, and preferentially guaranteeing the transmission success rate of the furnace safety interlock instructions.

2. The collaborative control method for multiple devices in the furnace feeding according to claim 1, characterized in that, Establishing redundant links between domains through a multi-hop ad hoc network, including: Deploying routing proxy nodes in the boundary devices of each communication domain, and the routing proxy nodes periodically broadcast detection messages including the device load rate within the domain and the real-time channel state information; The routing proxy node receives the detection messages of adjacent nodes and constructs a joint path evaluation function, and selects a multi-hop path that meets the real-time constraint of furnace control as the redundant link; When the main link triggers a redundant switch, dividing the control instructions into pieces according to the calculation result of the joint path evaluation function and transmitting them in parallel through at least two redundant links; During the data transmission process, dynamically adjusting the fragmentation strategy according to the real-time channel state information, and preferentially allocating the furnace safety interlock instructions to the redundant link with the highest link quality index.

3. The multi-device collaborative control method for furnace feeding according to claim 1, characterized in that, Updating the routing path based on the network topology connectivity matrix, including: Constructing the adjacency relationship of the furnace feeding devices in the multi-hop ad hoc network, mapping the adjacency relationship between devices to the weights of the adjacency matrix, and generating a connectivity matrix; According to the network topology change after the redundant link switch is triggered, calculating the node degree and path redundancy coefficient of the connectivity matrix, and screening a candidate routing path set that meets the real-time constraint of furnace control; Performing dynamic evaluation on the candidate routing path set, and selecting a path with a delay volatility lower than a fixed threshold and a packet loss rate lower than a fixed threshold as the updated routing path; During the transmission process, if it is detected that the rank of the connectivity matrix decreases and exceeds a preset threshold, a topology reconstruction instruction is triggered to recalculate the connectivity matrix.

4. The collaborative control method for multiple devices in the furnace feeding according to claim 2, wherein Construct a joint path evaluation function, including: Obtain the link quality index, the end-to-end delay, and the in-domain device load rate, and collect the bandwidth load ratio and the bit error rate of the redundant link in real time; Based on the real-time constraint of the furnace control, perform a weighted summation operation on the link quality index, the end-to-end delay, the in-domain device load rate, the bandwidth load ratio, and the bit error rate; Establish the joint path evaluation function based on the weight coefficients. When transmitting the furnace safety interlock instruction, increase the weight coefficient of the link quality index; Dynamically adjust each weight coefficient according to iterative optimization and keep the sum unchanged, and calculate the maximum path evaluation output value. Select the redundant links that meet the preset performance threshold according to the evaluation results.

5. The multi-device collaborative control method for furnace feeding according to claim 1, characterized in that Generate device operation indicators according to the preset anomaly determination rules, including: Generate a dynamic anomaly determination threshold based on the furnace environment parameters, where the dynamic anomaly determination threshold includes a static baseline threshold and a dynamic compensation value linked to the furnace environment parameters; Perform a fusion calculation on the multi-dimensional operation parameters of the furnace feeding equipment, where the fusion calculation adjusts the weight distribution rule based on the dynamic compensation value; Generate a hierarchical anomaly identifier according to the comparison relationship between the result of the fusion calculation and the dynamic anomaly determination threshold; Trigger different device operation indicators based on the hierarchical anomaly identifier, and the device operation indicators include the link connection relationship adjusted according to the anomaly level.

6. The multi-device collaborative control method for furnace feeding according to claim 5, characterized in that The determination of the dynamic compensation value includes: Obtain the furnace environment parameters in real time, and the furnace environment parameters include furnace temperature, vibration intensity, and harmful gas concentration; Calculate the dynamic correction coefficient corresponding to each parameter according to the change range of the furnace environment parameters, where the correction coefficients of the furnace temperature and the vibration intensity are generated by mapping through a preset non-linear relationship; Compensate and calibrate the dynamic correction coefficient based on the device performance decay rate to generate the dynamic compensation value.

7. The multi-device collaborative control method for the furnace feeding according to claim 1, characterized in that Convert the cooperative control instruction into an instruction format adapted to the target device, including: Analyze the semantic logic of the cooperative control instruction to generate an instruction action sequence that matches the function of the target device; According to the instruction interface type of the target device, call the protocol conversion rule library preset in the real-time communication network to convert the instruction action sequence into a syntax structure that can be parsed by the target device; Based on the current topology structure of the real-time communication network, perform a conflict detection on the syntax structure. If an instruction path that is incompatible with the topology structure is detected, trigger the regeneration of the instruction action sequence; Adjust the encapsulation format and transmission priority of the syntax structure according to the dynamic change of the device operation indicator, and generate an instruction data packet adapted to the load state of the real-time communication network; Based on the priority scheduling result, distribute the instruction data packet to the target device for execution according to the real-time communication network.

8. Multi-device collaborative control system for furnace feeding, characterized in that Adopt the multi-device cooperative control method for furnace feeding as described in claim 1, and the system includes: A communication transmission module that constructs a real-time communication network among the furnace feeding devices, synchronizes the clock signals of each furnace feeding device, and schedules the transmission timing of control instructions according to priorities; An index generation module that continuously collects the operation status data of each furnace feeding device based on the real-time communication network and generates device operation indexes according to preset abnormal determination rules; A dynamic allocation module that, when the device operation index indicates that the first device is in an abnormal state, dynamically allocates the control task of the first device to at least one second device based on the current topology of the real-time communication network and adjusts the link connection relationship of the real-time communication network; An instruction conversion module that converts the collaborative control instruction into an instruction format adapted to the target device according to the instruction interface type of the target device and issues it for execution according to the real-time communication network.

9. The multi-device collaborative control device for furnace feeding, characterized in that, It is used to implement the multi-device collaborative control method for furnace feeding as described in claims 1-7.

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