Distributed DDC controller collaborative management method in open network

Abstract

The invention discloses a distributed DDC controller collaborative management method and system in an open network, and belongs to the technical field of wireless network security, and the method comprises the steps: building a three-dimensional topological map of a controller according to Wi-Fi channel state information in the open network, carrying out the dynamic trust evaluation, and setting a lightweight communication protocol stack in each distributed DDC controller node, generating a distributed key pool based on an elliptic curve encryption algorithm to implement dynamic key distribution; the distributed DDC controller analyzes the input instruction and switches a communication interface; and performing task pre-allocation, dynamically adjusting and continuously monitoring a task execution state, and starting an arbitration mechanism to perform dynamic adjustment. According to the method, the collaborative operation capability of the distributed DDC controller is optimized, the network security and the resource utilization efficiency are also improved, so that the controller can be more flexible and efficient when coping with a complex network environment, and more reliable use experience and higher security guarantee are brought to a user.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless network security, and in particular relates to a method for collaborative management of distributed DDC controllers in an open network. Background Art

[0002] In today's industrial automation and intelligent control fields, distributed control systems (DCS) are widely used due to their high flexibility and scalability. In recent years, with the rapid development of the Internet of Things (IoT) and smart manufacturing, the architecture of DCS systems has been continuously optimized, achieving more efficient resource management and data flow. Representative technologies, such as the collaborative working model between a central controller and multi-node controllers, enable intelligent adaptation to complex and dynamic environments through real-time data collection and analysis. However, while existing technologies have made significant progress in system response speed, reliability, and data throughput, they still face challenges in trust management and security. In open network environments, in particular, the dynamic changes in trust relationships between nodes often lead to potential security risks, posing significant challenges to the normal operation of the system.

[0003] Current distributed control systems primarily rely on fixed trust models and static key management mechanisms, often using simple methods to assess node trust levels, such as those based on historical node behavior data or simple network intrusion detection algorithms. This approach not only fails to dynamically adapt to changes in the network environment, but also lacks the ability to flexibly respond to abnormal behavior. For example, when a node's behavior data suddenly changes, the existing system's trust assessment mechanism may be unable to respond in a timely manner, causing the node to be mistakenly deemed secure, increasing the risk of network attacks. Furthermore, traditional key distribution methods typically fail to fully utilize the relative trust levels between nodes, making the key management process complex and error-prone. These shortcomings not only affect the collaborative management efficiency of distributed controllers but also, to a certain extent, restrict the overall security of the system in an open network environment. Summary of the Invention

[0004] In view of the above-mentioned existing problems, the technical problems solved by the present invention are: how to reasonably allocate and dynamically adjust tasks based on the capabilities, trust levels and topological distances of nodes to improve the utilization efficiency of network resources and the effectiveness of task execution; in multi-node collaboration, how to effectively detect conflicts and resolve them quickly and accurately through an arbitration mechanism to ensure the stability and reliability of network operation.

[0005] To solve the above technical problems, a collaborative management method for distributed DDC controllers in an open network is proposed, including: A three-dimensional topological map of the controller is constructed based on the Wi-Fi channel status information in the open network, and a dynamic trust assessment is performed based on the node's historical behavior data to output the node trust level. A lightweight communication protocol stack is built into each distributed DDC controller node, which includes three communication interfaces: instant interface, aggregation interface, and collaborative interface. A distributed key pool is generated based on the elliptic curve encryption algorithm, and dynamic key distribution is implemented according to the node trust level. The distributed DDC controller parses input instructions, deploys an event-driven architecture in the protocol stack, and switches communication interfaces according to input instructions. Tasks are pre-assigned based on device capabilities, trust levels, and topological distances, and the task execution status is dynamically adjusted and continuously monitored. An arbitration mechanism is initiated for dynamic adjustments.

[0006] As a preferred embodiment of the method for collaborative management of distributed DDC controllers in an open network described in the present invention, the Wi-Fi channel status information includes: each node continuously scans the CSI data of surrounding APs, extracts signal arrival angle and delay characteristics, and uses the least squares method to solve the three-dimensional coordinates of the node based on the Wi-Fi channel status information to establish a three-dimensional topological map of the controller; The node historical behavior data includes historical response delay and data integrity rate.

[0007] As a preferred solution of the method for collaborative management of distributed DDC controllers in an open network described in the present invention, wherein: the output node trust level includes performing a dual-channel trust evaluation based on the node's historical behavior data, wherein the dual channels include a real-time channel and a cumulative channel; Real-time monitoring of instantaneous abnormal behavior is performed through a real-time channel. Trust points are deducted each time an abnormal behavior is detected. At the same time, a sliding window is used through a cumulative channel to calculate the trend indicator of data integrity within a specified time period, and historical indicators are calculated using the specified time period as a window. Calculate a basic trust value by assigning weights to historical indicators, and output a final trust level score based on the basic trust value; When the trust level score triggers a low trust threshold, the isolation procedure of the node and its controller is started, and the node trust level is output and marked on the topology map.

[0008] As a preferred solution of the method for collaborative management of distributed DDC controllers in an open network described in the present invention, the dynamic key distribution includes a hierarchical key system constructed based on an elliptic curve cryptography algorithm, including a root key and a session key, the root key is jointly kept by the highest-trusted node in the topology map, and a threshold signature mechanism is adopted; The session key is dynamically assigned encryption strength based on the trust level. When the node trust level is ≥ the high trust threshold, a 256-bit key and full protocol stack functionality are used. When the low trust threshold ≤ node trust level < high trust threshold, the key validity period is limited and management instructions are disabled; When the node trust level is less than the low trust threshold, only a temporary key is allocated and the session duration is compressed.

[0009] As a preferred embodiment of the method for collaborative management of distributed DDC controllers in an open network according to the present invention, the switching of the communication interface according to the input instruction includes deploying an event-driven architecture in the protocol stack, the distributed DDC controller parsing the input instruction, parsing the instruction header field, extracting instruction features such as urgency, data volume, and number of associated nodes, and standardizing the instruction features; The standardized instruction features are converted into feature vectors. The improved weighted Jaccard algorithm is used to calculate the initial correlation between events. The final correlation result is dynamically corrected based on the trust level. The three communication interfaces of the protocol stack are switched based on the dynamically corrected final correlation result. When the final correlation result is greater than or equal to the preset first correlation threshold, the current controller interface is switched to an immediate interface and the immediate mode is adopted; When the preset second correlation threshold ≤ the final correlation result < the preset first correlation threshold, the current controller is switched to an aggregate interface and the aggregation mode is adopted; When the final correlation result is less than the preset second correlation threshold, the current controller is switched to the collaborative interface and the collaborative mode is adopted.

[0010] As a preferred solution of the method for collaborative management of distributed DDC controllers in an open network described in the present invention, wherein: the task pre-allocation includes implementing task management in a pre-allocation phase and a dynamic adjustment phase; In the pre-allocation phase, the task requirement document is parsed to extract key constraints such as equipment capability requirements, task timeliness, and resource dependencies, and generate an initial allocation plan: The first priority is to match the device capability tag; The second priority is to select nodes whose trust level is greater than or equal to the high trust threshold; The third priority is to allocate the nearest object based on the distance on the three-dimensional topological map; During the dynamic adjustment phase, the task execution status is continuously monitored. When an abnormal conflict is detected, the arbitration mechanism is triggered, and the current task progress, real-time device status, and the latest topology map information are input to execute the arbitration decision, reallocate the execution nodes, and roll back the data.

[0011] As a preferred solution of the method for collaborative management of distributed DDC controllers in an open network described in the present invention, the arbitration mechanism includes calculating the topology sensitivity in the three-dimensional topology map, extracting the trust level of the conflicting device when an abnormal conflict is detected, calculating the impact entropy value of the task, and preferentially selecting the solution with the smallest entropy change; When there are two or more identical minimum values, global arbitration is performed and cross-domain negotiation is initiated. All controllers exchange the trust level and topology sensitivity of the topology map. A weighted voting mechanism is used to select the solution with the largest weight value. The arbitration result is reversely updated to the node through the protocol stack.

[0012] Another object of the present invention is to provide a distributed DDC controller collaborative management system in an open network.

[0013] As a preferred solution of the collaborative management system of distributed DDC controllers in an open network described in the present invention, it is characterized by including a topology map construction module, a key protection module, an instruction parsing module and a task allocation module; The topology map construction module constructs a three-dimensional topology map of the controller based on the Wi-Fi channel status information in the open network, and performs dynamic trust evaluation based on the node's historical behavior data to output the node trust level; The key protection module has a built-in lightweight communication protocol stack in each distributed DDC controller node. The protocol stack includes three communication interfaces: instant interface, aggregation interface, and collaborative interface. It generates a distributed key pool based on the elliptic curve encryption algorithm and implements dynamic key distribution according to the node trust level. The command parsing module and the distributed DDC controller parse the input command, deploy an event-driven architecture in the protocol stack, and switch the communication interface according to the input command; The task allocation module pre-allocates tasks based on device capabilities, trust levels, and topological distances, dynamically adjusts and continuously monitors task execution status, and initiates an arbitration mechanism for dynamic adjustment.

[0014] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the method for collaborative management of distributed DDC controllers in an open network are implemented.

[0015] A computer-readable storage medium stores a computer program thereon, wherein when the computer program is executed by a processor, the computer program implements the steps of the method for collaborative management of distributed DDC controllers in an open network.

[0016] The beneficial effects of this invention are: By collecting Wi-Fi channel state information (CSI) and historical node behavior data, dynamic trust assessment and the construction of a three-dimensional topology map are achieved. This step facilitates the shared location and signal characteristics of nodes through the joint construction of the map, thereby enhancing network reliability. This not only enables rapid response to abnormal behavior, but also further improves network security and stability through real-time and cumulative channel assessment mechanisms, effectively preventing potential threats from malicious nodes. This directly enhances the overall trust and security of the system.

[0017] A built-in lightweight communication protocol stack and dynamic key distribution based on node trust levels enable flexible, efficient, and secure communications. By using encryption strategies of varying strengths for nodes with different trust levels, the security of critical data and the rational use of resources are ensured. For example, high-trust nodes can use stronger encryption to ensure information confidentiality, while low-trust nodes have their permissions restricted, helping to mitigate potential risks and thus improving the overall security and defense capabilities of the network system.

[0018] By parsing input commands and switching communication interfaces, the system automatically adjusts communication modes based on the urgency and data volume of different events. The distributed controller strikes an optimal balance between efficiency and safety, ensuring the real-time nature and responsiveness of commands, especially in emergency situations. This enhances system flexibility, enabling it to autonomously switch operating modes based on actual needs, avoiding resource waste and improving responsiveness to complex events.

[0019] Pre-assigning tasks based on device capabilities, trust levels, and topological distances enables intelligent scheduling of task management. The introduction of an arbitration mechanism allows the system to react quickly when resource conflicts or trust level drops are detected, mitigating potential task execution risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 An overall flow chart of a method for collaborative management of distributed DDC controllers in an open network provided by one embodiment of the present invention.

[0022] Figure 2 A system solution module diagram of a distributed DDC controller collaborative management system in an open network provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0023] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0024] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0025] Example 1, with reference to Figure 1 , which is the first embodiment of the present invention, provides a method for collaborative management of distributed DDC controllers in an open network, comprising: S1: Build a three-dimensional topology map of the controller based on Wi-Fi channel status information and native data in the open network, and perform dynamic trust evaluation based on node historical behavior data to output node trust levels.

[0026] It should be noted that to collect Wi-Fi channel state information (CSI), each node continuously scans the CSI data of surrounding APs and extracts the signal angle of arrival (AoA) and delay characteristics; Nodes measure the CSI parameters of at least three fixed APs and use the least squares method to calculate their three-dimensional coordinates. The three APs with the strongest signals are selected as the coordinate system reference (no pre-set location information is required). All nodes share their own coordinates and measured AP-CSI characteristics. A consistency check eliminates abnormal positioning results (for example, sudden changes in data with a moving speed greater than 5m / s) and establishes a three-dimensional topology map for the controller. This 3D topology map, constructed using Wi-Fi channel state information (CSI), provides real-time information on the location and status of devices in the network, providing a basis for subsequent decision-making.

[0027] Node historical behavior data includes but is not limited to historical response delay, data integrity rate, etc.

[0028] Furthermore, a dual-channel trust evaluation is performed based on the node's historical behavior data, where the dual channels include a real-time channel and a cumulative channel; Among them, the real-time channel monitors instantaneous abnormal behavior (for example, the number of ARP requests per unit time suddenly increases by 50%, and the TCP retransmission rate is greater than 15%). Each time an abnormality is detected, the trust value is deducted by 5 points. At the same time, the trust value recovery mechanism (such as automatic +2 points for no abnormalities per hour) avoids permanent penalties caused by occasional abnormalities.

[0029] The cumulative channel uses a sliding window to calculate the trend indicator of the data integrity rate in the past hour. The historical indicators calculated in a 1-hour window include data integrity rate, response stability, and topology consistency. It should be noted that data integrity rate = (number of successfully verified data packets / total number of received packets) × 100; Response stability = 1-(maximum delay-minimum delay) / average delay; Topological consistency = the inverse of the Euclidean distance between the current coordinate and the historical average coordinate; The trust value is calculated by dynamic trust evaluation based on historical indicators. The basic value is calculated by assigning weights to historical indicators and summing them. The final trust level is The system updates and marks the topology map every 5 minutes. When the trust threshold (60 points) is reached, the node isolation process is initiated, and the node trust level (0-100 points) is output and marked on the topology map. This serves as the basis for subsequent key distribution decisions. Nodes with a trust value below 60 points automatically trigger the topology reconstruction process and isolate the current node from its controller. If the current node's score falls below 60 points more than three times, the current controller is disconnected, effectively preventing malicious nodes from disrupting network stability. Trust assessment is based on historical behavior data, real-time anomaly detection, and accumulated data trends, increasing the system's sensitivity to abnormal behavior and enhancing network security.

[0030] S2: A lightweight communication protocol stack is built into each distributed DDC controller node. The protocol stack includes three communication interfaces: real-time interface, aggregation interface, and collaborative interface. A distributed key pool is generated based on the elliptic curve encryption algorithm, and dynamic key distribution is implemented according to the node trust level.

[0031] Furthermore, dynamic key distribution includes,building a hierarchical key system including root key and session key,based on elliptic curve cryptography (SM2 algorithm); The root key uses a threshold signature (TSS) mechanism with a (3,2) threshold, and the root key is jointly kept by the top three most trusted nodes in the topology map; The session key is dynamically assigned encryption strength based on the trust level, specifically: When the node trust level is ≥ the high trust threshold, 256-bit keys and full protocol stack functions are used; When the low trust threshold ≤ node trust level < high trust threshold, the key validity period is limited (for example, 1 hour, manually set according to the application scenario) and management instructions are disabled; When the node trust level is less than the low trust threshold, only a temporary key is allocated and the session duration is compressed to 30 seconds; the high trust threshold is 80 points and the low trust threshold is 60 points.

[0032] Bind key distribution to topology status. When a new node is added, the neighbor node group key rotation is triggered. When a node is offline for a timeout, its related key shards are automatically invalidated. When the mobile controller position offset exceeds 5 meters, a temporary device fingerprint is generated. The temporary device fingerprint is in the form of a hash (node ID + coordinates + timestamp) to prevent forgery.

[0033] The use of elliptic curve encryption algorithm and dynamic key distribution mechanism ensures the allocation of appropriate keys at different trust levels, improves the security and efficiency of the system, can quickly adapt to environmental changes, reduces the potential attack surface, and protects data transmission within the controller from attack and theft.

[0034] S3: The distributed DDC controller parses the input instructions, deploys an event-driven architecture in the protocol stack, and switches the communication interface according to the input instructions.

[0035] It should be noted that the switching of the communication interface according to the input instruction includes deploying an event-driven architecture in the protocol stack, the distributed DDC controller parsing the input instruction, parsing the instruction header field, extracting instruction features such as urgency, data volume, and number of associated nodes, and normalizing the instruction features. After normalization, the normalized features are converted into feature vectors, and the improved weighted Jaccard algorithm is called to calculate the initial correlation between events:

[0036] in, is the initial correlation, is the current event task feature vector (i.e., the standardized instruction feature), is the historical reference event task feature vector (taken from the mean of similar tasks), is the feature weight (for example, the urgency weight can be 0.5, the data volume weight can be 0.3, and the number of associated nodes weight can be 0.2); The average time taken for historical event tasks on the current node. It is the maximum allowed time for similar event tasks preset by the system, n is the number of different event tasks, and i is the variable index; It should be noted that the urgency weight (0.5) is the highest priority for timeliness of instructions in industrial control scenarios (for example, emergency shutdown instructions require a millisecond-level response), and the 50% weight reflects the emphasis on safety, which is in line with the IEC 62443 standard's weight allocation convention for key instructions; the data volume weight (0.3) affects bandwidth usage, but non-real-time requirements are lower than urgency, and the 30% weight balances resource usage and efficiency; the node number weight (0.2) is the number of associated nodes that reflects the complexity of the task, and the 20% weight avoids excessive distraction.

[0037] Dynamic correction based on trust level:

[0038] in, The final correlation result is is the score of the node trust level, is the channel quality compensation value calculated based on the packet error rate; it should be noted that 0.8 is the set benchmark value, corresponding to the lowest trusted node with a high trust threshold of 80 points, ensuring that even if the node just meets the standard (80 points), it still retains 80% of the basic relevance weight to avoid frequent mode switching caused by trust fluctuations; 0.2 is the slope, and for every increase of 1 point in trust score, the coefficient increases by 0.01 (0.2 / 20), so that the 100-point node receives the full correction (0.8+0.2=1.0), which simplifies the calculation and reflects the principle of high-trust node decision-making priority.

[0039] According to the results of dynamic correction, the three communication interfaces of the protocol stack are switched. Among them, the event modes corresponding to the instant interface, aggregation interface and collaborative interface are instant mode, aggregation mode and collaborative mode respectively; When the final correlation result is greater than or equal to the preset first correlation threshold, the current controller interface is switched to an immediate interface and the immediate mode is adopted; When the preset second correlation threshold ≤ the final correlation result < the preset first correlation threshold, the current controller is switched to an aggregate interface and the aggregation mode is adopted; When the final correlation result is less than the preset second correlation threshold, the current controller is switched to the collaborative interface and the collaborative mode is adopted; if the calculated interface to be switched is consistent with the current interface, no conversion is required and it remains unchanged; the preset first correlation threshold and the preset second correlation threshold are preset by the back-end control personnel; Specifically, the immediate mode is set to preempt dedicated time slots, disable data compression, and enable end-to-end confirmation; The aggregation mode is set to enable differential encoding and perform batch signature verification; The collaborative mode is set to establish a temporary storage pool, enable memory sharing and delayed confirmation mechanism.

[0040] Dynamically adjust the interface based on the urgency of the event and the amount of data to ensure that the system can respond to important instructions as quickly as possible, improving the overall operating efficiency of the system. Automatically identify and optimize event relationships through the weighted Jaccard algorithm, which helps reduce conflicts and risky operations in system operation and improve decision-making quality. The correlation results are combined with the node trust level to dynamically correct protocol switching, ensuring that high-trust nodes execute tasks first, ensuring the safety and reliability of tasks.

[0041] S4: Pre-allocate tasks based on device capabilities, trust levels, and topological distances, dynamically adjust and continuously monitor task execution status, and initiate an arbitration mechanism for dynamic adjustments.

[0042] Furthermore, task management in the pre-allocation stage and the dynamic adjustment stage is implemented; In the pre-allocation phase, the task requirement document is parsed to extract key constraints such as equipment capability requirements (such as accuracy and power), task timeliness (such as deadline windows), and resource dependencies, and generate an initial allocation plan: The first priority is to directly match the device capability tag; The second priority is to select nodes with a trust level ≥ a high trust threshold; The third priority is to allocate the nearest object based on the distance on the three-dimensional topological map; During the dynamic adjustment phase, the task execution status is continuously monitored. When the following conflicts are detected: resource overrun (for example, CPU > 90% for 10 seconds), communication delay exceeding the threshold (for example, communication delay exceeding 200ms), or trust level degradation exceeding the warning line, the arbitration mechanism is triggered. Input parameters include the current task progress, real-time device status, and the latest topology map information, and the arbitration decision is executed.

[0043] It should be noted that the arbitration mechanism includes calculating the topology sensitivity in the three-dimensional topology map. When a conflict is detected, the trust level of the conflicting device is extracted, the impact entropy value of each strategy is calculated, and the solution with the smallest entropy change is preferentially selected. When multiple identical minimum values exist, global arbitration is performed and cross-domain negotiation is initiated. Each domain controller exchanges the trust distribution and topology sensitivity of the topology map. A weighted voting mechanism is used, where the voting weight is the inverse of the domain's topology sensitivity multiplied by the average node trust level. The solution with the largest weight is selected, and the arbitration result is used to reversely update the access control policy of each node through the protocol stack. The topology sensitivity in the three-dimensional topology map is calculated based on the sum of the Euclidean distance of nodes and the weighted score of the node strength in the three-dimensional topology map.

[0044] By analyzing task requirements and performing intelligent pre-allocation based on node capabilities, trust levels, and distances, the success rate of tasks and resource utilization efficiency can be improved, a continuous monitoring and arbitration mechanism can be implemented, and timely adjustments can be made when resources are exceeded or trust is reduced, thereby enhancing the stability and reliability of the entire controller collaboration. Cross-domain negotiation and information sharing between different controllers are allowed, and weighted voting is used to ensure the selection of the best decision, thereby enhancing the integration and freedom of the entire controller collaboration.

[0045] Embodiment 2, the second embodiment of the present invention, is different from the previous embodiment in that: If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0046] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0047] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, and then editing, interpreting, or processing in another suitable manner as necessary, and then storing it in a computer memory.

[0048] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0049] Example 3, reference Figure 2 , which is the third embodiment of the present invention, provides a distributed DDC controller collaborative management system in an open network, including a topology map construction module, a key protection module, an instruction parsing module, and a task allocation module; The topology map construction module builds a three-dimensional topology map of the controller based on the Wi-Fi channel status information in the open network, and performs dynamic trust evaluation based on the node's historical behavior data to output the node trust level; The key protection module has a built-in lightweight communication protocol stack in each distributed DDC controller node. The protocol stack includes three communication interfaces: instant interface, aggregation interface, and collaborative interface. It generates a distributed key pool based on the elliptic curve encryption algorithm and implements dynamic key distribution according to the node trust level. Instruction parsing module, the distributed DDC controller parses input instructions, deploys an event-driven architecture in the protocol stack, and switches the communication interface according to the input instructions; The task allocation module pre-allocates tasks based on device capabilities, trust levels, and topological distances, dynamically adjusts and continuously monitors task execution status, and initiates an arbitration mechanism for dynamic adjustments.

[0050] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for collaborative management of distributed DDC controllers in an open network, characterized by: include, Build a three-dimensional topology map of the controller based on Wi-Fi channel status information in the open network, and perform dynamic trust evaluation based on node historical behavior data to output node trust levels; A lightweight communication protocol stack is built into each distributed DDC controller node. The protocol stack includes three communication interfaces: instant interface, aggregation interface, and collaborative interface. A distributed key pool is generated based on the elliptic curve encryption algorithm, and dynamic key distribution is implemented based on the node trust level. The distributed DDC controller parses input commands, deploys an event-driven architecture in the protocol stack, and switches the communication interface based on the input commands; Pre-allocate tasks based on device capabilities, trust levels, and topological distances, dynamically adjust and continuously monitor task execution status, and initiate an arbitration mechanism for dynamic adjustments.

2. The method for collaborative management of distributed DDC controllers in an open network according to claim 1, wherein: The Wi-Fi channel status information includes: each node continuously scans the CSI data of surrounding APs, extracts the signal arrival angle and delay characteristics, uses the least squares method to solve the node's three-dimensional coordinates based on the Wi-Fi channel status information, and establishes a three-dimensional topological map of the controller; The node historical behavior data includes historical response delay and data integrity rate.

3. The method for collaborative management of distributed DDC controllers in an open network according to claim 2, wherein: The output node trust level includes performing a dual-channel trust evaluation based on the node's historical behavior data, wherein the dual channels include a real-time channel and a cumulative channel; Real-time monitoring of instantaneous abnormal behavior is performed through a real-time channel. Trust points are deducted each time an abnormal behavior is detected. At the same time, a sliding window is used through a cumulative channel to calculate the trend indicator of data integrity rate within a specified time period, and historical indicators are calculated using the specified time period as a window. Calculate a basic trust value by assigning weights to historical indicators, and output a final trust level score based on the basic trust value; When the trust level score triggers a low trust threshold, the isolation procedure of the node and its controller is started, and the node trust level is output and marked on the topology map.

4. The method for collaborative management of distributed DDC controllers in an open network according to claim 3, wherein: The dynamic key distribution includes a hierarchical key system based on the elliptic curve cryptography algorithm, including a root key and a session key. The root key is jointly kept by the highest-trusted node in the topology map and adopts a threshold signature mechanism. The session key is dynamically assigned encryption strength based on the trust level. When the node trust level is ≥ the high trust threshold, a 256-bit key and full protocol stack functionality are used. When the low trust threshold ≤ node trust level < high trust threshold, the key validity period is limited and management instructions are disabled; When the node trust level is less than the low trust threshold, only a temporary key is allocated and the session duration is compressed.

5. The method for collaborative management of distributed DDC controllers in an open network according to claim 4, wherein: The switching of the communication interface according to the input instruction includes deploying an event-driven architecture in the protocol stack, the distributed DDC controller parsing the input instruction, parsing the instruction header field, extracting instruction features such as urgency, data volume, and number of associated nodes, and standardizing the instruction features; The standardized instruction features are converted into feature vectors. The improved weighted Jaccard algorithm is used to calculate the initial correlation between events. The final correlation result is dynamically corrected based on the trust level. The three communication interfaces of the protocol stack are switched based on the dynamically corrected final correlation result. When the final correlation result is greater than or equal to the preset first correlation threshold, the current controller interface is switched to an immediate interface and the immediate mode is adopted; When the preset second correlation threshold ≤ the final correlation result < the preset first correlation threshold, the current controller is switched to an aggregate interface and the aggregation mode is adopted; When the final correlation result is less than the preset second correlation threshold, the current controller is switched to the collaborative interface and the collaborative mode is adopted.

6. The method for collaborative management of distributed DDC controllers in an open network according to claim 5, wherein: The task pre-allocation includes implementing task management in the pre-allocation phase and the dynamic adjustment phase; In the pre-allocation phase, the task requirement document is parsed to extract key constraints such as equipment capability requirements, task timeliness, and resource dependencies, and generate an initial allocation plan: The first priority is to match the device capability tag; The second priority is to select nodes whose trust level is greater than or equal to the high trust threshold; The third priority is to allocate the nearest object based on the distance on the three-dimensional topological map; During the dynamic adjustment phase, the task execution status is continuously monitored. When an abnormal conflict is detected, the arbitration mechanism is triggered, and the current task progress, real-time device status, and the latest topology map information are input to execute the arbitration decision, reallocate the execution nodes, and roll back the data.

7. The method for collaborative management of distributed DDC controllers in an open network according to claim 6, wherein: The arbitration mechanism includes calculating the topology sensitivity in the three-dimensional topology map, extracting the trust level of the conflicting device when an abnormal conflict is detected, calculating the impact entropy value of the task, and giving priority to the solution with the smallest entropy change; When there are two or more identical minimum values, global arbitration is performed and cross-domain negotiation is initiated. All controllers exchange the trust level and topology sensitivity of the topology map. A weighted voting mechanism is used to select the solution with the largest weight value. The arbitration result is reversely updated to the node through the protocol stack.

8. A system using the method for collaborative management of distributed DDC controllers in an open network according to any one of claims 1 to 7, characterized in that: It includes topology map construction module, key protection module, instruction parsing module and task allocation module; The topology map construction module constructs a three-dimensional topology map of the controller based on the Wi-Fi channel status information in the open network, and performs dynamic trust evaluation based on the node's historical behavior data to output the node trust level; The key protection module has a built-in lightweight communication protocol stack in each distributed DDC controller node. The protocol stack includes three communication interfaces: instant interface, aggregation interface, and collaborative interface. It generates a distributed key pool based on the elliptic curve encryption algorithm and implements dynamic key distribution according to the node trust level. The command parsing module and the distributed DDC controller parse the input command, deploy an event-driven architecture in the protocol stack, and switch the communication interface according to the input command; The task allocation module pre-allocates tasks based on device capabilities, trust levels, and topological distances, dynamically adjusts and continuously monitors task execution status, and initiates an arbitration mechanism for dynamic adjustment.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a method for collaborative management of distributed DDC controllers in an open network according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for collaborative management of distributed DDC controllers in an open network according to any one of claims 1 to 7 are implemented.

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