Automatic addressing method for FFU group control system based on network topology self-discovery

Abstract

The invention discloses an automatic addressing method for an FFU group control system based on network topology self-discovery, which belongs to the technical field of environment control, and comprises the following steps: sending topology detection signals to a plurality of FFU nodes, receiving response signals to construct a global topological graph for describing a connection relationship among the FFU nodes, obtaining regional grouping information to execute a hierarchical address allocation algorithm, and sending the regional grouping information to the FFU nodes; generating a unique address containing region attribution for each FFU node, and acquiring state data from a corresponding target FFU node; and in combination with the obtained environment parameter target, applying a multivariable cooperative control algorithm to calculate cooperative control parameters, sending the cooperative control parameters to the corresponding target FFU nodes, and controlling the corresponding target FFU nodes to adjust the environment parameters. According to the invention, through sending topology detection signals, constructing a global topological graph, executing a hierarchical address allocation algorithm and applying a multivariable cooperative control algorithm, automatic deployment and intelligent regional cooperative control of the FFU system are realized.

Description

Technical Field

[0001] This invention relates to the field of environmental control technology, and in particular to an automatic addressing method for an FFU group control system based on network topology self-discovery. Background Technology

[0002] Fan filter units (FFUs) are the core equipment in cleanrooms that provide clean air. They draw in air through a built-in fan, filter it through a high-efficiency filter, and then deliver it out. In high-level clean environments such as semiconductor manufacturing and biopharmaceuticals, hundreds or even thousands of FFUs are often deployed to work together, forming a large-scale FFU group control system to ensure that environmental parameters such as cleanliness, temperature, humidity, and pressure gradient of the entire space remain stable and meet standards.

[0003] Existing FFU group control systems typically employ a master-slave control architecture, where a master controller communicates and controls multiple subordinate FFU nodes via wired or wireless networks. During system deployment, each FFU node needs to be manually configured with a unique network address, usually by setting a hardware DIP switch or writing it one by one via serial port. The master controller controls the FFUs based on a pre-entered address list, and its control strategies are mostly globally unified adjustments or simple group control, lacking awareness of the physical adjacency relationships between FFU nodes.

[0004] However, the manual address configuration process for large-scale FFU systems is extremely cumbersome, time-consuming, and prone to errors, increasing deployment costs and timelines. Secondly, this static address and network configuration method lacks flexibility; when FFU nodes need to be replaced, added, or removed, manual intervention is required again, preventing the system from self-adapting and dynamically reconfiguring. Finally, because the control system does not understand the physical topology of the FFU network, its control strategy is often extensive, making it difficult to achieve fine-grained coordinated control of key parameters such as pressure differences between different areas, easily leading to inter-area airflow disturbances or non-optimal energy consumption. Summary of the Invention

[0005] To address the above problems, this invention provides an automatic addressing method for FFU group control systems based on network topology self-discovery. By sending topology detection signals, constructing a global topology map, executing a hierarchical address allocation algorithm, and applying a multivariate collaborative control algorithm, the automated deployment and intelligent regional collaborative control of the FFU system are achieved.

[0006] The above objectives can be achieved through the following approach: An automatic addressing method for an FFU (Functional Function Unit) group control system based on network topology self-discovery includes: sending topology detection signals to multiple FFU nodes and receiving response signals containing device identifiers and adjacency parameters; constructing a global topology graph describing the connection relationships between FFU nodes based on the device identifiers and adjacency parameters in the response signals; parsing the global topology graph to obtain regional grouping information, and performing a hierarchical address allocation algorithm based on the regional grouping information to generate a unique address containing regional affiliation for each FFU node; obtaining state data containing wind speed parameters, pressure difference parameters, and energy consumption parameters from the corresponding target FFU node based on the unique address; obtaining environmental parameter targets for defining the system operating state, and combining the state data with the global topology graph, applying a multivariate cooperative control algorithm to calculate cooperative control parameters for balancing the states between regions; and sending the cooperative control parameters to the corresponding target FFU node based on the unique address to control the corresponding target FFU node to adjust the environmental parameters.

[0007] Optionally, constructing a global topology graph describing the connection relationships between FFU nodes includes: extracting device identifiers and adjacency parameters from the response signal; establishing a node connection graph representing FFU nodes and their connection relationships based on the device identifiers and adjacency parameters; dividing the node connection graph using preset grouping rules to generate multiple regional subgraphs; and combining the multiple regional subgraphs to obtain a global topology graph.

[0008] Optionally, generating multiple regional subgraphs includes: obtaining the physical location parameters or signal strength parameters of each FFU node; calculating the physical distance between nodes based on the physical location parameters, and calculating the connection weight between nodes based on the signal strength parameters; and dividing the node connection graph based on the physical distance or the connection weight to form regional subgraphs.

[0009] Optionally, generating a unique address containing region affiliation information includes: parsing the global topology map to obtain region grouping information, and assigning a region address prefix to each region according to the region grouping; assigning a unique suffix address to the FFU node in each region; and performing a combination operation between the region address prefix and the suffix address to generate a unique address.

[0010] Optionally, the calculation of the cooperative control parameters for balancing the states between regions includes: calculating the inter-regional pressure balance parameters and regional energy consumption balance parameters based on the pressure difference parameters, energy consumption parameters and the global topology map in the state data; and using the inter-regional pressure balance parameters and regional energy consumption balance parameters as constraints, generating cooperative control parameters according to the environmental parameter targets.

[0011] Optionally, the calculation of inter-regional pressure balance parameters and regional energy consumption balance parameters includes: based on the regional grouping information in the global topology map, performing aggregate calculations on the pressure difference parameters in each region to obtain the regional average pressure difference, and calculating the regional average pressure difference difference between adjacent regions to quantify the inter-regional pressure balance parameters; based on the regional grouping information in the global topology map, performing aggregate calculations on the energy consumption parameters in each region to obtain the regional average energy consumption, and calculating the deviation between the regional average energy consumption and the system average energy consumption to quantify the regional energy consumption balance parameters.

[0012] Optionally, the step of sending cooperative control parameters to the corresponding target FFU node based on the unique address to control the corresponding target FFU node to adjust environmental parameters includes: encapsulating the cooperative control parameters into a control instruction data frame containing the unique address of the target FFU node; sending the control instruction data frame to the corresponding target FFU node based on the unique address to control the corresponding target FFU node to adjust environmental parameters and update the status data; and obtaining the updated status data from the corresponding target FFU node based on the unique address to update the cooperative control parameters.

[0013] Optionally, the method further includes: dividing the communication time into downlink time slices for sending the control command data frames and uplink time slices for the target FFU node to send the status data; and alternately scheduling the downlink time slices and the uplink time slices in each communication cycle.

[0014] Optionally, the method further includes: periodically broadcasting a preset topology maintenance signal to all FFU nodes in the global topology graph; receiving confirmation responses from each FFU node and identifying node change information indicating node joining or leaving; and performing incremental updates on the global topology graph and the unique addresses of the FFU nodes based on the node change information.

[0015] Based on the same inventive concept, this invention also provides an automatic addressing system for an FFU group control system based on network topology self-discovery. The system includes: a node detection module, used to send topology detection signals to multiple FFU nodes and receive response signals containing device identifiers and adjacency parameters; a topology graph generation module, used to construct a global topology graph describing the connection relationships between FFU nodes based on the device identifiers and adjacency parameters in the response signals; a node address generation module, used to parse the global topology graph to obtain regional grouping information and execute a hierarchical address allocation algorithm based on the regional grouping information to generate a unique address containing regional affiliation for each FFU node; a node data acquisition module, used to acquire state data containing wind speed parameters, pressure difference parameters, and energy consumption parameters from the corresponding target FFU node based on the unique address; a control parameter generation module, used to acquire environmental parameter targets for defining the system's operating state, and combine the state data with the global topology graph, applying a multivariate collaborative control algorithm to calculate collaborative control parameters for balancing the states between regions; and a node control module, used to send the collaborative control parameters to the corresponding target FFU node based on the unique address, controlling the corresponding target FFU node to adjust the environmental parameters.

[0016] Compared with the prior art, the present invention has the following advantages: 1. This invention achieves a high degree of automation in the deployment and maintenance of FFU group control systems. Through network topology self-discovery, automatic area division, and hierarchical address allocation, the system can eliminate the complex process of large-scale manual configuration and debugging, enabling plug-and-play devices; when the network structure changes, the system can self-sensitize and incrementally update topology and address information, greatly reducing initial deployment costs and subsequent maintenance difficulties, and improving the system's scalability and flexibility; 2. This invention constructs a highly efficient and precise collaborative control mechanism. Based on automatically generated unique addresses containing regional affiliation information, the system can perform efficient regional management and communication; by introducing inter-regional pressure balance and energy consumption balance as dynamic constraints, and applying a multivariate collaborative control algorithm, the control strategy is no longer an isolated individual adjustment, but an intelligent decision oriented towards regional balance and global optimization, significantly improving the accuracy of environmental control and the operational stability of the system; 3. This invention optimizes the energy efficiency and operational health of the entire FFU group control system. By balancing and regulating regional energy consumption, it avoids situations where some areas operate at high loads for extended periods while others experience excessively low loads, thus balancing equipment wear and extending the overall service life. Simultaneously, precise and coordinated control of inter-regional air pressure effectively prevents the risk of cross-contamination caused by pressure gradient imbalances, achieving overall optimization of system energy consumption while ensuring cleanliness requirements.

[0017] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating an automatic addressing method for an FFU group control system based on network topology self-discovery, according to an embodiment of the present invention.

[0020] Figure 2 This is a schematic diagram of the structure of an automatic addressing system for an FFU group control system based on network topology self-discovery, according to an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Reference Figure 1 One embodiment of the present invention proposes an automatic addressing method for an FFU group control system based on network topology self-discovery. By sending topology detection signals, constructing a global topology map, executing a hierarchical address allocation algorithm, and applying a multivariate collaborative control algorithm, the method realizes the automated deployment and intelligent regional collaborative control of the FFU system.

[0023] The method described in this embodiment specifically includes: Send topology probe signals to multiple FFU nodes and receive response signals containing device identifiers and adjacency parameters; Based on the device identifier and adjacency parameters in the response signal, a global topology graph describing the connection relationship between FFU nodes is constructed; The global topology map is parsed to obtain region grouping information, and a hierarchical address allocation algorithm is executed based on the region grouping information to generate a unique address containing the region affiliation for each FFU node; Based on the unique address, obtain status data containing wind speed parameters, pressure difference parameters, and energy consumption parameters from the corresponding target FFU node; Obtain the target environmental parameters used to define the system's operating state, and combine the state data with the global topology map. Apply a multivariate cooperative control algorithm to calculate the cooperative control parameters used to balance the states between regions. Based on the unique address, cooperative control parameters are sent to the corresponding target FFU node to control the target FFU node to adjust environmental parameters.

[0024] Specifically, this method solves the cumbersome manual address configuration and topology maintenance problems in large-scale FFU group control systems, achieving plug-and-play functionality and self-organization. By constructing and utilizing a global topology map, the system gains a deep understanding of the physical layout of the entire clean environment, enabling refined and collaborative management based on regions, rather than controlling individual devices in isolation. This collaborative control strategy can proactively balance pressure differences and energy consumption between regions while meeting overall environmental parameter targets, effectively preventing local environmental anomalies and optimizing the overall operational energy efficiency of the system. Ultimately, this method significantly improves the automation level, control accuracy, operational stability, and management efficiency of FFU group control systems, enabling the system to adapt to complex environments more intelligently and efficiently.

[0025] Optionally, constructing a global topology graph describing the connection relationships between FFU nodes includes: Extract the device identifier and adjacency parameters from the response signal; Based on the device identifier and the adjacency parameters, a node connection graph representing FFU nodes and their connection relationships is established; The node connection graph is divided using preset grouping rules to generate multiple regional subgraphs, and the multiple regional subgraphs are combined to obtain a global topology graph.

[0026] Specifically, firstly, each received response signal data frame is parsed to extract the device identifier of the FFU node that sent the signal and its adjacency parameters. The device identifier is a unique hardware address, such as a MAC address, fixed at the factory for each FFU node, used to uniquely identify the node in the network. The adjacency parameters are a list containing the set of device identifiers of other FFU nodes that the FFU node can directly communicate with or perceive during the topology probing phase. After data extraction, the system begins to build a preliminary node connection graph. This graph is an undirected graph structure, where each FFU node is represented by a vertex and labeled with its device identifier. If two FFU nodes, such as node A and node B, find each other's device identifier in their respective adjacency parameter lists, an edge is established between the two vertices representing node A and node B, indicating a direct physical or logical connection between them. After traversing the adjacency parameters of all nodes, a node connection graph depicting the physical connections of the entire FFU network is constructed. Subsequently, a preset grouping rule is applied to divide the node connection graph into regions to generate multiple regional subgraphs. Finally, these sub-maps containing area affiliation information are logically combined to form the final global topology map. This global topology map not only includes all FFU nodes and their direct connections, but more importantly, each node is assigned an area identifier, reflecting its group affiliation within the entire cleanroom or large-scale deployment environment.

[0027] Optionally, generating multiple regional sub-maps includes: Obtain the physical location parameters or signal strength parameters of each FFU node; The physical distance between nodes is calculated based on the physical location parameters, and the connection weight between nodes is calculated based on the signal strength parameters. Based on the physical distance or the connection weight, the node connection graph is divided into regional subgraphs.

[0028] Specifically, to divide the node connectivity graph into multiple subgraphs reflecting physical or logical clustering, it is first necessary to obtain the physical location parameters or signal strength parameters of each FFU node. Physical location parameters typically refer to the coordinates of the FFU node in the three-dimensional space of the cleanroom; these coordinates are pre-configured and stored in each node during system installation and deployment. Signal strength parameters are the Received Signal Strength Indicator (RSSI) value measured by each FFU node during the network topology discovery phase when listening to topology probe signals sent by neighboring nodes. After obtaining these parameters, the system selects the appropriate calculation path based on the type of available parameters. If physical location parameters are used, the system calculates the physical distance between any two FFU nodes. For example, the physical distance between node i and node j. It can be calculated using the following formula: , in, Represents the physical distance between node i and node j; , , ) is the physical location parameter of FFU node i; , , ) represents the physical location parameters of FFU node j. These parameters are obtained by the system during the initialization phase. If signal strength is used, the system will calculate the connection weights between nodes based on this parameter. Connection weight is an indicator that measures the quality of the communication link and logical proximity between nodes. Generally, higher signal strength indicates closer distance between nodes or a better communication environment, and therefore a higher connection weight should be assigned. Connection Weight Signal strength parameter The system uses a function to map the original signal strength values ​​to positively vectorized weights, ensuring that stronger signals correspond to weights that better reflect tighter connections. After calculating the physical distance or connection weights between all node pairs, the system assigns these metrics as attributes of the edges to the node connectivity graph. Subsequently, graph clustering or community detection algorithms are applied to partition this weighted node connectivity graph. For example, when partitioning based on physical distance, clustering algorithms such as K-Means or DBSCAN can be used to group nodes that are close to each other in spatial coordinates into the same cluster. When partitioning based on connection weights, community detection algorithms designed to maximize modularity, such as the Louvain algorithm, can be used, effectively dividing the network into communities where the sum of their internal connection weights is much greater than the sum of their connection weights with the rest of the network. After the algorithm completes, the node connectivity graph is divided into several node sets, and each node set and its internal connections together constitute a subgraph.

[0029] Optionally, generating a unique address containing regional affiliation information includes: The global topology map is parsed to obtain region grouping information, and a region address prefix is ​​assigned to each region according to the region grouping; Each FFU node within a region is assigned a unique suffix address within that region. The region address prefix and the suffix address are combined to generate a unique address.

[0030] Specifically, the system first parses the global topology map to identify the various independent sub-maps within it; this process is known as obtaining region grouping information. The system assigns a unique region address prefix to each identified sub-map. This assignment can be an ordered, automated numbering process; for example, the system assigns incrementally increasing numerical identifiers as region address prefixes based on the order in which the sub-maps were discovered or according to a preset physical location order. Then, the focus shifts to the internal structure of individual regions. For any given region, the system traverses all FFU nodes in that sub-map and assigns each node a unique suffix address within that region. Again, this can be a simple counting process, sequentially numbering each node within the region starting from 1 until all nodes have been assigned a suffix address. This suffix address only needs to be unique within its own region. Finally, the system combines the region address prefix and suffix address into a final unique address through a combination operation. This combination operation is typically implemented using bitwise operations to create a structured, hierarchical address. For example, a unique address can be generated as follows: , in, This represents the unique address of the final generated FFU node; This is the region address prefix of the region to which the node belongs, obtained from the aforementioned region allocation process; It is the suffix address assigned to the node within its region, obtained through the node allocation process within the region; This is a preset constant representing the number of binary bits occupied by the suffix address. Its value depends on the maximum number of FFU nodes that can be accommodated in a single region. For example, if... A value of 8 indicates that each region can support a maximum of 256 nodes; It is a binary left shift operator that shifts the binary representation of the region address prefix P by L bits to the higher bits, making room for the subsequent suffix address; It is a binary OR operator that calculates the prefix and suffix addresses of the shifted region address. The binary representations are combined to form a complete, hierarchical, unique address. Each address generated in this way has its high-order bits representing the area it belongs to and its low-order bits representing its identity within that area, thus ensuring the uniqueness of the address throughout the entire FFU group control system.

[0031] Optionally, the calculation of the cooperative control parameters for balancing the states between regions includes: Based on the pressure difference parameters, energy consumption parameters and global topology map in the state data, the inter-regional pressure balance parameters and regional energy consumption balance parameters are calculated. Using the inter-regional air pressure balance parameters and regional energy consumption balance parameters as constraints, collaborative control parameters are generated based on the environmental parameter targets.

[0032] Optionally, the calculated inter-regional pressure balance parameters and regional energy consumption balance parameters include: Based on the regional grouping information in the global topology map, the pressure difference parameters in each region are aggregated to obtain the regional average pressure difference, and the regional average pressure difference between adjacent regions is calculated to quantify the inter-regional pressure balance parameters. Based on the regional grouping information in the global topology map, the energy consumption parameters in each region are aggregated to obtain the regional average energy consumption, and the deviation between the regional average energy consumption and the system average energy consumption is calculated to quantify the regional energy consumption balance parameters.

[0033] Specifically, the calculation of inter-regional pressure balance parameters and regional energy consumption balance parameters involves two parallel quantization processes based on the constructed global topology map and real-time acquired state data. First, to calculate the inter-regional pressure balance parameters, the system utilizes the regional grouping information in the global topology map to operate on each divided region. For any region R, the system traverses all FFU nodes within that region, collects their pressure difference parameters, and obtains the regional average pressure difference through aggregation. The aggregation operation is typically an arithmetic mean, calculated using the following formula: , in, It is the regional average pressure difference in region R; The differential pressure parameter represents the i-th FFU node within region R, and this value is obtained in real time from the node status data; This is the total number of FFU nodes within region R, information obtained from the global topology map. After calculating the average pressure differential across all regions, the system again refers to the global topology map to determine adjacent region pairs. For each pair of adjacent regions, for example, region R... and region The system calculates the regional average pressure difference between them, i.e. By combining all the pressure differences between these adjacent regions, the final inter-regional pressure balance parameters can be quantified. This parameter visually reflects the pressure gradient distribution at the boundaries of each region within the system as a set of numerical values. Simultaneously, the system executes a process to calculate regional energy balance parameters. Similar to the pressure difference calculation, the system first aggregates the energy consumption parameters of all FFU nodes within each region R based on regional grouping information to obtain the regional average energy consumption. Next, the system calculates the overall average energy consumption of the entire FFU group control system, which is achieved by summing the energy consumption parameters of all FFU nodes and dividing by the total number of FFU nodes in the system. Finally, the deviation between the average energy consumption of each region and the overall system average energy consumption is calculated. , in, This is the energy consumption deviation in region R; It is the regional average energy consumption of region R; This represents the system's average energy consumption. Both values ​​are derived by aggregating energy consumption parameters obtained from node status data. The energy consumption deviation across all regions is also considered. By combining these parameters, the regional energy consumption balance parameters can be quantified. It characterizes the degree to which the energy consumption of each region deviates from the overall average level of the system.

[0034] Specifically, after obtaining the inter-regional pressure balance parameters and regional energy consumption balance parameters, the system uses them as constraints for a multivariable cooperative control algorithm. This means that while seeking to achieve the main environmental parameter objectives, the control algorithm must simultaneously ensure that the inter-regional pressure difference remains within a preset healthy range and that the energy consumption load of each region tends to be balanced. Multivariable cooperative control algorithms, such as model predictive control or constrained optimization algorithms, will establish a comprehensive optimization objective function. This function aims to minimize the deviation between the current system state and the environmental parameter objectives, while incorporating deviations from the ideal pressure balance and energy consumption balance states as penalty terms into the cost. Objective Function It can be represented as: , in, It is the overall control cost that needs to be minimized; It is the error between the current system state and the target environmental parameters; It is a quantified value of the inter-regional pressure balance parameter; These are the quantified values ​​of regional energy consumption balance parameters. All three variables are calculated from real-time collected status data and preset targets. , , These are preset weighting coefficients used to adjust the degree of emphasis placed on the primary objective, air pressure balance, and energy consumption balance. , , This is a penalty function, such as a squared function, used to convert the error or deviation into a non-negative cost value. The control algorithm can solve for this... Minimize the operation instructions of each FFU node, such as adjusting wind speed, and finally generate a set of coordinated control parameters. This set of parameters is the set of instructions that can achieve the goal in an optimal way and satisfy all constraints.

[0035] Optionally, the step of sending cooperative control parameters to the corresponding target FFU node based on the unique address, and controlling the corresponding target FFU node to adjust environmental parameters, includes: The collaborative control parameters are encapsulated into a control instruction data frame containing the unique address of the target FFU node; Based on the unique address, the control command data frame is sent to the corresponding target FFU node to control the corresponding target FFU node to adjust the environmental parameters and update the status data; Based on the unique address, updated status data is obtained from the corresponding target FFU node to update the collaborative control parameters.

[0036] Specifically, the process of sending coordinated control parameters to the target FFU node and controlling its adjustment is a precise closed-loop control operation. After the control parameter generation module calculates the coordinated control parameters for each FFU node, the node control module initiates the command issuance process. First, the coordinated control parameters for a specific target FFU node, such as a specific wind speed adjustment value, along with the unique address of that target FFU node, are encapsulated according to a predefined communication protocol format to generate a structured control command data frame. In the data structure of this data frame, the header key field is the unique address of the target node, while the data payload contains the specific coordinated control parameters. Subsequently, this control command data frame is broadcast or requested to the FFU network through its physical communication interface. Every FFU node on the network continuously listens to the communication link. When an FFU node receives a data frame, it first parses the header of the data frame, extracts the target address, and compares it with its own stored unique address. Only when the two match perfectly will the FFU node confirm that this is a command sent to itself and continue processing. Next, the target FFU node parses the payload portion of the data frame, extracts the cooperative control parameters, and converts them into actual operating commands for its own actuators, such as the wind turbine drive, thereby adjusting its own operating state, such as wind speed. This adjustment process immediately causes changes in its environmental parameters. Sensors inside the FFU node measure the adjusted wind speed, differential pressure, and energy consumption parameters in real time and update these new values ​​in its locally stored state data. To form a complete closed-loop control, after issuing control commands, the system will again send a data request to the target FFU node based on the same unique address, or wait for the node to actively report its updated state data within a preset reporting cycle. After obtaining this latest state data reflecting the control effect, it will feed it back to the control parameter generation module. In the next control cycle, this module will use this updated data as new input, re-compare and calculate with the target environmental parameters, thereby generating a new round of more adaptive cooperative control parameters. The continuous cycle of command issuance, state adjustment, data feedback, and parameter updates constitutes the system's dynamic feedback adjustment mechanism.

[0037] Optionally, the method further includes: The communication time is divided into downlink time slices for sending the control command data frames and uplink time slices for the target FFU node to send the status data. The downlink time slice and the uplink time slice are alternately scheduled within each communication cycle.

[0038] Specifically, the continuous communication time is first divided into fixed-length communication cycles. Within each communication cycle, time is further precisely divided into two logical time slots with different functions: a downlink time slot and an uplink time slot. The downlink time slot is strictly defined as a period dedicated to sending data to the target FFU node. During this time slot, the system has exclusive access to the communication bus and can broadcast or send one or more control command data frames to the target FFU node in the network without conflict. All FFU nodes are in receive mode during this period, passively listening to and receiving commands sent to them. When the preset downlink time slot ends, the communication state immediately switches to the uplink time slot. The uplink time slot is a period reserved specifically for the target FFU node to report status data. During this period, the system switches to receive mode, and one or more target FFU nodes gain the right to send data. To avoid conflicts caused by multiple FFU nodes sending data simultaneously, the uplink time slot can be further subdivided into smaller time slots, and the system can allocate a fixed reporting time slot to each FFU node based on its unique address. By strictly and cyclically alternating downlink and uplink time slices within each communication cycle, the system establishes a clear and predictable communication rhythm. Within a cycle, the instruction is first issued, and then the execution result is received within the same cycle, thus forming an efficient communication loop.

[0039] Optionally, the method further includes: A preset topology maintenance signal is periodically broadcast to all FFU nodes in the global topology graph; Receive confirmation responses from each FFU node and identify node change information indicating whether a node has joined or left. Based on the node change information, perform incremental updates on the global topology map and the unique addresses of the FFU nodes.

[0040] Specifically, to ensure the FFU group control system can adapt to dynamic changes in the number of nodes and connection relationships during operation, the system implements a periodic topology maintenance and self-healing mechanism. This mechanism broadcasts a preset topology maintenance signal to all known FFU nodes recorded in the global topology graph at fixed time intervals, such as every few minutes. This signal is equivalent to a network-wide roll call, and also serves as a beacon for new nodes joining the network. After the broadcast, a preset waiting window is entered to receive responses. All normally functioning FFU nodes, upon receiving the signal, immediately reply with an acknowledgment response containing their own device identifier. By comparing the list of received acknowledgment responses with the list of nodes in the global topology graph, node change information can be identified. Specifically, if a node recorded in the global topology graph fails to send an acknowledgment response by the end of the waiting window, the system marks it as a detached node. Conversely, if a response from an unknown device identifier is received, the system identifies it as a newly joined node. Based on the identified node change information, the system then performs an incremental update operation, rather than a complete reconstruction. For detached nodes, the system removes the vertex representing the node and all its connected edges from the global topology graph, reclaims its previously occupied unique address, and returns it to the address pool for future use. For newly added nodes, the system adds them as new vertices to the global topology graph, determines their connectivity with existing nodes by analyzing the adjacency parameters that may be included in their initial response, and establishes corresponding edges in the graph. Subsequently, based on the new node's position in the topology graph, the system assigns it to a suitable region and allocates a new unique address containing region affiliation information from the available address pool of that region. This update process only applies to nodes that have changed and their adjacency relationships, maintaining the stability of the rest of the network.

[0041] Based on the same inventive concept, such as Figure 2 As shown, the present invention also provides an automatic addressing system for an FFU group control system based on network topology self-discovery, the system comprising: The node detection module is used to send topology detection signals to multiple FFU nodes and receive response signals containing device identifiers and adjacency parameters. The topology graph generation module is used to construct a global topology graph describing the connection relationships between FFU nodes based on the device identifiers and adjacency parameters in the response signal. The node address generation module is used to parse the global topology map to obtain region grouping information, and execute a hierarchical address allocation algorithm based on the region grouping information to generate a unique address containing the region affiliation for each FFU node; The node data acquisition module is used to acquire status data, including wind speed parameters, pressure difference parameters, and energy consumption parameters, from the corresponding target FFU node based on the unique address. The control parameter generation module is used to obtain environmental parameter targets for defining the system's operating state, and, in conjunction with the state data and the global topology map, to calculate the cooperative control parameters for balancing the states between regions using a multivariable cooperative control algorithm. The node control module is used to send collaborative control parameters to the corresponding target FFU node based on the unique address, and control the corresponding target FFU node to adjust environmental parameters.

[0042] To verify the feasibility of this invention in practice, it was applied to a cleanroom in an SMT assembly line. To ensure a high level of cleanliness and environmental stability during manufacturing, this cleanroom deployed over 100 FFU (Fan Filter Units) distributed across several key process areas, including the photolithography, etching, and cleaning zones. Under traditional management methods, configuring addresses, mapping topologies, and controlling the partitions of these thousands of FFUs requires significant manual labor and makes dynamic collaborative optimization between areas difficult. This semiconductor manufacturing company aims to use the method of this invention to achieve plug-and-play functionality, automatic addressing, and intelligent collaborative control of the FFU system.

[0043] To verify the feasibility of this invention in practice, it was applied to the cleanroom of an SMT assembly line in a large electronics factory. This cleanroom, to ensure a high level of cleanliness and environmental stability during the manufacturing of precision electronic components, deployed more than one DC brushless FFU (Fan Filter Unit) device. Under traditional management methods, address configuration, topology mapping, and zoning control of these FFUs require significant manual labor and are difficult to achieve dynamic collaborative optimization between areas. The company adopted a one-stop solution for motor drive and intelligent control to achieve plug-and-play functionality, automatic addressing, and intelligent collaborative control based on high-performance vector control for the FFU system.

[0044] In this embodiment, after the FFU group control system in the cleanroom is started, the intelligent gateway, model BR-GW-01, which serves as the core of the system, sends topology detection signals to all FFU nodes in the network through its node detection module. Each FFU node is equipped with a high-performance vector controller for brushless motors. Upon receiving the signal, it sends back a response signal containing its own unique device identifier (MAC address) and adjacency parameters, i.e., a list of neighboring nodes identified based on the received signal strength. Based on these response signals, the topology graph generation module first constructs an original node connection graph containing more than 100 vertices (FFUs) and their connections. Subsequently, based on the physical location parameters preset during deployment, the system applies the DBSCAN clustering algorithm to divide the node connection graph, automatically dividing the physically concentrated FFUs into multiple regional subgraphs such as solder paste printing areas, high-speed surface mount areas, and reflow soldering areas. Finally, these subgraphs are combined into a global topology graph with regional grouping information, realizing the automatic address encoding unique to the group control system and ensuring a zero mis-address rate.

[0045] After the global topology map is constructed, the node address generation module begins to perform hierarchical address allocation. The system assigns region address prefixes, such as 0x01, 0x02, and 0x03, to the solder paste printing area, high-speed surface mount area, and reflow soldering area, respectively. Next, each FFU node within a region is assigned a unique suffix address within that region. For example, the 5th FFU node in the solder paste printing area has a region prefix P of 0x01 and a suffix address S of 0x05. Assuming the suffix address occupies 8 bits (L=8), the system performs combination operations... A unique address 0x0105 is generated for it. The entire addressing process is completed automatically within minutes after the system starts.

[0046] After addressing is completed, the node data acquisition module, based on the generated unique address, begins periodically acquiring status data from each FFU node. This data includes not only wind speed and energy consumption parameters directly reported by the FFU's internal controller, but also environmental parameters collected by externally supplied high-precision differential pressure sensors and temperature and humidity sensors. All data is aggregated to the intelligent gateway. The control parameter generation module acquires the overall environmental parameter targets for the cleanroom, such as maintaining a positive pressure difference of 3 Pa between the solder paste printing area and the high-speed surface mount area (to prevent dust from spreading from the surface mount area to the more rigorous printing area), and combines this with the goal of optimizing the total system energy consumption. At a certain moment, the system data shows that the actual average pressure difference between the solder paste printing area and the high-speed surface mount area is as high as 8.2 Pa, and the regional average energy consumption of the solder paste printing area is much higher than the system average energy consumption. At this point, the control parameter generation module uses the inter-regional air pressure balance parameters and regional energy consumption balance parameters as constraints and applies a multivariate collaborative control algorithm for calculation. The algorithm ultimately calculates the collaborative control parameters: appropriately reduce the airflow speed of some FFUs in the solder paste printing area, while slightly increasing the airflow speed of some FFUs in the high-speed placement area.

[0047] Finally, the node control module encapsulates these coordinated control parameters into control command data frames with the unique address of the target FFU and sends them to the target FFU via downlink time slices. Upon receiving the command, the FFU adjusts its wind speed and reports the updated status data in subsequent uplink time slices. Through this closed-loop feedback mechanism, the system continuously optimizes control commands, ensuring that the status of each area approaches the target value.

[0048] During operation, an engineer replaced a faulty FFU in the reflow soldering area. After connecting a new FFU to power and communication lines, the system detected the new node through periodic topology maintenance signals and automatically completed the topology update and address allocation for the node within 30 seconds. The entire process required no manual intervention, fully demonstrating the system's plug-and-play characteristics.

[0049] To verify the beneficial effects of the present invention, the performance of the system in terms of automatic addressing, cooperative control and dynamic maintenance was recorded and analyzed.

[0050] Table 1 Comparison of Efficiency between Automatic Addressing and Manual Configuration of FFU System Table 2 Data on the Effect of Regional State Coordination Control Table 3 Network Dynamic Maintenance Response Data Table As can be seen from the data in Tables 1 to 3 above, this invention demonstrates significant advantages in the automated management and intelligent control of FFU group control systems. Table 1 clearly shows that, compared to the traditional large-scale FFU network configuration work that requires several engineers to spend nearly two weeks to complete, the automatic addressing method of this invention completed the topology discovery, area division, and address allocation for all 1024 FFUs in just 2.5 minutes, without any manual intervention. This greatly shortens the system deployment and debugging cycle, reducing labor costs and the risk of errors. Table 2 data verifies the effectiveness of the collaborative control strategy of this invention. Before control, the pressure difference between the solder paste printing area and the high-speed placement area was as high as 8.2 Pa, significantly deviating from the target value of 3.0 Pa, and the energy consumption of the solder paste printing area was excessively high, with uneven energy distribution across the area. After the coordinated control of this invention, the inter-regional pressure difference was precisely adjusted to 3.2 Pa, very close to the target value. Simultaneously, through coordinated adjustment of the FFU wind speeds in the two regions, the average energy consumption of the solder paste printing area decreased significantly, while the energy consumption of the high-speed surface mount area was appropriately increased, resulting in a more balanced load between the two regions. This achieved a reduction in total system energy consumption while meeting the pressure difference requirements. This demonstrates the dual value of this method in maintaining environmental stability and improving energy efficiency. Table 3 shows the system's robustness and self-healing capabilities. Whether a node fails and goes offline or a new node is plug-and-play, the system can automatically complete network discovery, identification, and reconstruction in a very short time, ensuring the real-time accuracy of the global topology and address allocation. This rapid dynamic maintenance capability guarantees the long-term stable operation of the system, eliminating complex post-maintenance work and truly realizing the system's self-management.

[0051] It should be noted that the formulas described above, through the principle of dimensional consistency and mathematical standardization methods (such as normalization, dimensionless parameter conversion, or unit system unification), can translate physical quantities with different properties into unitless standard values ​​or parameters that can be superimposed in the same dimension. This eliminates the interference of different dimensions on the computational logic, allowing the formulas to retain the original data distribution characteristics while possessing mathematical rationality and adaptability to objective laws. These are conventional technical methods and will not be elaborated further. The electrical connections between the various units described above do not necessarily represent direct or indirect connections; any indirect connection method is applicable to the embodiments of this invention as long as it achieves the purpose of this invention. The above descriptions are merely exemplary embodiments of this invention and should not be construed as limiting the scope of this invention.

[0052] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. An automatic addressing method for an FFU group control system based on network topology self-discovery, characterized in that, The method includes: Send topology probe signals to multiple FFU nodes and receive response signals containing device identifiers and adjacency parameters; Based on the device identifier and adjacency parameters in the response signal, a global topology graph describing the connection relationship between FFU nodes is constructed; The global topology map is parsed to obtain region grouping information, and a hierarchical address allocation algorithm is executed based on the region grouping information to generate a unique address containing the region affiliation for each FFU node; Based on the unique address, obtain status data containing wind speed parameters, pressure difference parameters, and energy consumption parameters from the corresponding target FFU node; Obtain the target environmental parameters used to define the system's operating state, and combine the state data with the global topology map. Apply a multivariate cooperative control algorithm to calculate the cooperative control parameters used to balance the states between regions. Based on the unique address, cooperative control parameters are sent to the corresponding target FFU node to control the target FFU node to adjust environmental parameters.

2. The automatic addressing method for an FFU group control system based on network topology self-discovery as described in claim 1, characterized in that, The construction of the global topology graph describing the connection relationships between FFU nodes includes: Extract the device identifier and adjacency parameters from the response signal; Based on the device identifier and the adjacency parameters, a node connection graph representing FFU nodes and their connection relationships is established; The node connection graph is divided using preset grouping rules to generate multiple regional subgraphs, and the multiple regional subgraphs are combined to obtain a global topology graph.

3. The automatic addressing method for an FFU group control system based on network topology self-discovery as described in claim 2, characterized in that, The generation of multiple region sub-maps includes: Obtain the physical location parameters or signal strength parameters of each FFU node; The physical distance between nodes is calculated based on the physical location parameters, and the connection weight between nodes is calculated based on the signal strength parameters. Based on the physical distance or the connection weight, the node connection graph is divided into regional subgraphs.

4. The automatic addressing method for an FFU group control system based on network topology self-discovery as described in claim 2, characterized in that, The generation of a unique address containing region affiliation information includes: The global topology map is parsed to obtain region grouping information, and a region address prefix is ​​assigned to each region according to the region grouping; Assign a unique suffix address to each FFU node within the corresponding region; The region address prefix and the suffix address are combined to generate a unique address.

5. The automatic addressing method for an FFU group control system based on network topology self-discovery as described in claim 4, characterized in that, The calculated cooperative control parameters for balancing the states between regions include: Based on the pressure difference parameters, energy consumption parameters and global topology map in the state data, the inter-regional pressure balance parameters and regional energy consumption balance parameters are calculated. Using the inter-regional air pressure balance parameters and regional energy consumption balance parameters as constraints, collaborative control parameters are generated based on the environmental parameter targets.

6. The automatic addressing method for an FFU group control system based on network topology self-discovery as described in claim 5, characterized in that, The calculated inter-regional pressure balance parameters and regional energy consumption balance parameters include: Based on the regional grouping information in the global topology map, the pressure difference parameters in each region are aggregated to obtain the regional average pressure difference, and the regional average pressure difference between adjacent regions is calculated to quantify the inter-regional pressure balance parameters. Based on the regional grouping information in the global topology map, the energy consumption parameters in each region are aggregated to obtain the regional average energy consumption, and the deviation between the regional average energy consumption and the system average energy consumption is calculated to quantify the regional energy consumption balance parameters.

7. The automatic addressing method for an FFU group control system based on network topology self-discovery as described in claim 5, characterized in that, The step of sending collaborative control parameters to the corresponding target FFU node based on the unique address, and controlling the corresponding target FFU node to adjust environmental parameters, includes: The collaborative control parameters are encapsulated into a control instruction data frame containing the unique address of the target FFU node; Based on the unique address, the control command data frame is sent to the corresponding target FFU node to control the corresponding target FFU node to adjust the environmental parameters and update the status data; Based on the unique address, updated status data is obtained from the corresponding target FFU node to update the collaborative control parameters.

8. The automatic addressing method for an FFU group control system based on network topology self-discovery as described in claim 7, characterized in that, The method further includes: The communication time is divided into downlink time slices for sending the control command data frames and uplink time slices for the target FFU node to send the status data. The downlink time slice and the uplink time slice are alternately scheduled within each communication cycle.

9. The automatic addressing method for an FFU group control system based on network topology self-discovery according to claim 1, characterized in that, The method further includes: A preset topology maintenance signal is periodically broadcast to all FFU nodes in the global topology graph; Receive confirmation responses from each FFU node and identify node change information indicating whether a node has joined or left. Based on the node change information, perform incremental updates on the global topology map and the unique addresses of the FFU nodes.

10. An automatic addressing system for an FFU group control system based on network topology self-discovery, employing the automatic addressing method for an FFU group control system based on network topology self-discovery as described in any one of claims 1-9, characterized in that, The system includes: The node detection module is used to send topology detection signals to multiple FFU nodes and receive response signals containing device identifiers and adjacency parameters. The topology graph generation module is used to construct a global topology graph describing the connection relationships between FFU nodes based on the device identifiers and adjacency parameters in the response signal. The node address generation module is used to parse the global topology map to obtain region grouping information, and execute a hierarchical address allocation algorithm based on the region grouping information to generate a unique address containing the region affiliation for each FFU node; The node data acquisition module is used to acquire status data, including wind speed parameters, pressure difference parameters, and energy consumption parameters, from the corresponding target FFU node based on the unique address. The control parameter generation module is used to obtain environmental parameter targets for defining the system's operating state, and, in conjunction with the state data and the global topology map, to calculate the cooperative control parameters for balancing the states between regions using a multivariable cooperative control algorithm. The node control module is used to send collaborative control parameters to the corresponding target FFU node based on the unique address, and control the corresponding target FFU node to adjust environmental parameters.

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