A distributed DDC controller efficient collaborative management method and system

Abstract

The present invention discloses a distributed DDC controller efficient collaborative management method and system, which relates to the field of network control technology, including collecting operation data from multiple DDC controllers; encoding the operation data using preset data processing rules to generate corresponding control signals; selecting a suitable network communication protocol, selecting an optimal communication path through the communication protocol, and encapsulating, sending and receiving data of the optimal communication path; using an intelligent algorithm to construct a comprehensive evaluation model, outputting a comprehensive evaluation score, dynamically adjusting the working mode of the DDC controller based on its performance parameters, and optimizing resource allocation; setting a health threshold based on the output of the comprehensive evaluation model, and executing a corresponding management strategy when the health threshold is exceeded; the present invention significantly improves the overall performance and stability of the system by dynamically adjusting the working mode of the controller and optimizing resource allocation.

Description

Technical Field

[0001] The present invention relates to the field of network control technology, and in particular to a distributed DDC controller efficient collaborative management method and system. Background Art

[0002] With the development of industrial automation and Internet of Things technology, distributed control systems (DCS) have been widely used in various industrial scenarios. Distributed direct digital controllers (DDCs), as key components of DCS, have played an important role in the fields of building automation and factory automation due to their better scalability, robustness and fault tolerance. However, with the expansion of the scale of distributed systems, how to efficiently manage these DDC controllers and ensure their collaborative work has become an important issue.

[0003] Most of the existing distributed DDC controller management methods adopt a static configuration approach, that is, the working mode and communication path of each controller are pre-set. Although this method is simple, it has obvious shortcomings in a dynamically changing industrial environment. First, the lack of a dynamic adjustment mechanism leads to resource waste and overload when the system load changes; second, the fixed communication path selection strategy cannot make full use of network resources, especially in complex network topologies, which may lead to large communication delays and high packet loss rates; in addition, existing technologies usually do not consider the health status monitoring and adaptive adjustment mechanism of the controller itself, and performance degradation or even failure shutdown may occur during long-term operation. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a distributed DDC controller efficient collaborative management method and system to solve the problem that most existing distributed DDC controller management methods adopt a static configuration method, that is, the working mode and communication path of each controller are pre-set. Although this method is simple, it has obvious shortcomings in a dynamically changing industrial environment. First, the lack of a dynamic adjustment mechanism leads to resource waste and overload when the system load changes; second, the fixed communication path selection strategy cannot make full use of network resources, especially in complex network topologies, which may lead to large communication delays and high packet loss rates; in addition, the prior art usually does not take into account the health status monitoring and adaptive adjustment mechanism of the controller itself, and is prone to performance degradation or even failure shutdown during long-term operation.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a distributed DDC controller efficient collaborative management method, which includes:

[0008] Collect operational data from multiple DDC controllers;

[0009] Encode the operation data using preset data processing rules to generate corresponding control signals;

[0010] Select a suitable network communication protocol, select the optimal communication path through the communication protocol, and encapsulate, send and receive data on the optimal communication path;

[0011] An intelligent algorithm is used to build a comprehensive evaluation model, output a comprehensive evaluation score, dynamically adjust the working mode of the DDC controller based on its performance parameters, and optimize resource allocation;

[0012] Based on the output of the comprehensive assessment model, a health threshold is set, and the corresponding management strategy is executed when the health threshold is exceeded.

[0013] As a preferred solution of the distributed DDC controller efficient collaborative management method of the present invention, wherein: the operation data from multiple DDC controllers is collected, and the specific steps are:

[0014] Collecting these data from multiple DDC controllers, a unified data acquisition module M is introduced, which can obtain this information from each controller through the network interface. The expression is:

[0015] f M (x i,t )=[T i ,E i ,D i ];

[0016] Among them, f M represents the process of module M collecting data from the i-th controller, x i,t represents the state vector of the i-th DDC controller at time t, T i represents the operating temperature of the i-th controller, E i represents the energy consumption of the i-th controller, D i represents the processing delay of the i-th controller, i represents the index variable;

[0017] All processed data are aggregated into a central database to form a data matrix X t , the expression is:

[0018] X t =[x 1,t ,x 2,t ,...,x n,t ];

[0019] Among them, x n,trepresents the state vector of the nth DDC controller at time t, and n represents the number of DDC controllers.

[0020] As a preferred solution of the distributed DDC controller efficient collaborative management method of the present invention, wherein: the operation data is encoded using a preset data processing rule to generate a corresponding control signal, and the specific steps are:

[0021] Using the anomaly detection method based on Kalman filter, the verification expression is:

[0022] h=KF(T i ,E i ,D i );

[0023] Wherein, h represents the check function, KF represents the Kalman filter;

[0024] The standardized processing is used to process the verified data, and the and

[0025] The verified data is fused to generate a comprehensive state vector. The fusion expression is:

[0026]

[0027] Among them, v i represents the comprehensive state vector of the i-th controller, θ T ,θ E and θ D are the adjustment factors for temperature, energy consumption, and processing delay, respectively, and i represents the index variable;

[0028] Based on the comprehensive state vector v i , the corresponding control signal is generated through the preset data processing rules, and the expression is:

[0029]

[0030] Among them, C i represents the control signal strength of the i-th controller, and w represents the weight for adjusting the control signal strength.

[0031] As a preferred solution of the distributed DDC controller efficient collaborative management method of the present invention, wherein: the selecting of a suitable network communication protocol, selecting an optimal communication path through the communication protocol, and encapsulating, sending and receiving data of the optimal communication path, specifically,

[0032] Choose to use the Time Sensitive Network (TSN) protocol as the network communication protocol;

[0033] The optimal path is determined based on the current network load L and the distance D of the target controller. The expression is:

[0034]

[0035] Among them, Psel represents the communication path selection function, β and γ represent the adjustment factors;

[0036] Encapsulate the data packets of the optimal communication path, the expression is:

[0037] Fpack(C i ,H,T)=[H,C i ,t];

[0038] Wherein, Fpack represents the data packet encapsulation function, H represents the data packet, and t represents the time;

[0039] Based on the data packet encapsulation function, a data packet sending function is constructed to send the encapsulated data packet through the optimal communication path. The expression is:

[0040] Fsend(Fpack(C i ,H,T),R);

[0041] Among them, R represents the routing table, and Fsend represents the data packet sending function;

[0042] Finally, a confirmation reception mechanism is constructed based on the routing table, and the expression is:

[0043] Fack(R,A);

[0044] Among them, A represents confirmation information, and Fack is responsible for receiving the confirmation information and confirming whether the data packet is successfully delivered.

[0045] As a preferred solution of the distributed DDC controller efficient collaborative management method of the present invention, wherein: the intelligent algorithm is used to construct a comprehensive evaluation model and output a comprehensive evaluation score, and the specific steps are:

[0046] Assume the state space is S, which includes several states s. The expression of state s is:

[0047] s=[T1,E1,D1,T2,E2,D2,...,T i ,E i ,D i ];

[0048] Among them, T i represents the operating temperature of the i-th DDC controller, E i represents the energy consumption of the i-th DDC controller, D irepresents the processing delay of the i-th DDC controller, i represents the index variable;

[0049] Set the action space to A, which contains all control actions. The expression of action a is:

[0050] a=[p1,p2,...,p n ; s1,s2,...,s n ; o1, o2, ..., o n ];

[0051] Among them, p n represents the power output adjustment of the nth DDC controller, s n Indicates the working state switching instruction of the nth DDC controller, o n Indicates the task priority adjustment of the nth DDC controller;

[0052] As a preferred solution of the distributed DDC controller efficient collaborative management method of the present invention, wherein: the working mode of the DDC controller is dynamically adjusted based on its performance parameters to optimize resource allocation, and the specific steps are:

[0053] Set the reward function to judge whether taking action a is good or bad under a given state s. The expression is:

[0054]

[0055] Among them, Q(s,a) represents the comprehensive evaluation score obtained by taking action a in state s, and w T 、w E and w D are the weight coefficients of temperature, energy consumption and processing delay, respectively, and f T (T i ), f E (E i ) and f D (D i ) are penalty functions for temperature, energy consumption and processing delay respectively;

[0056] The Q-learning algorithm is used as the basis of the comprehensive evaluation model to find the optimal strategy from Q(s,a), which is expressed as:

[0057]

[0058] in, represents the learning rate, represents the discount factor, Indicates that in the next state s ′ Next, all actions a ′ The maximum expected cumulative reward value that can be obtained in s′ Represents the new state after taking action a, a ′ In the new state s ′ the actions taken next;

[0059] The working mode of the DDC controller is dynamically adjusted according to Q(s,a), and the expression is:

[0060]

[0061] Among them, a * represents the optimal action, that is, the action that obtains the maximum expected cumulative reward in the current state s.

[0062] As a preferred solution of the distributed DDC controller efficient collaborative management method of the present invention, wherein: based on the output of the comprehensive evaluation model, a health threshold is set, and when the health threshold is exceeded, a corresponding management strategy is executed. The specific steps are:

[0063] The comprehensive health assessment value is calculated based on the comprehensive assessment model output, and the expression is:

[0064]

[0065] in, represents the comprehensive health assessment value, S represents the state space, and n represents the number of DDC controllers;

[0066] The value range is [0, 1], set the threshold when , the predefined management actions will be initiated.

[0067] In a second aspect, the present invention provides a distributed DDC controller efficient collaborative management system, comprising:

[0068] The data acquisition module is responsible for collecting operation data from each DDC controller and aggregating these data through the network interface. This module also includes a data verification function, using the Kalman filter to detect and correct anomalies in the collected data;

[0069] The path selection module is responsible for selecting the appropriate network communication protocol and selecting the optimal communication path through the communication protocol. It is responsible for encapsulating, sending and receiving data and confirming whether the data packet has successfully reached the target controller.

[0070] The evaluation and decision-making module is responsible for building a comprehensive evaluation model, dynamically adjusting the working mode of the DDC controller based on its temperature, energy consumption, and processing delay, and optimizing resource allocation. This module uses the Q-learning algorithm to find the optimal strategy to improve system efficiency.

[0071] The management strategy module is responsible for setting the health threshold based on the output of the comprehensive evaluation model. When the health evaluation value is lower than the set threshold, the corresponding management strategy is executed, including evaluating the status of the DDC controller, selecting the optimal management strategy combination, and executing these strategies;

[0072] The signal generation module is responsible for encoding the operation data using preset data processing rules and generating corresponding control signals, which will be used to guide the adjustment of the working mode of the DDC controller.

[0073] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the method for efficient collaborative management of distributed DDC controllers as described in the first aspect of the present invention is implemented.

[0074] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the method for efficient collaborative management of distributed DDC controllers as described in the first aspect of the present invention.

[0075] The beneficial effects of the present invention are as follows: the present invention significantly improves the overall performance and stability of the system by dynamically adjusting the working mode of the controller and optimizing resource allocation. In particular, the present invention adopts an intelligent algorithm to construct a comprehensive evaluation model, which can dynamically adjust the working mode of the DDC controller according to the real-time performance parameters, optimize resource allocation, and timely execute corresponding management strategies by setting health thresholds to ensure the long-term stable operation of the system. Compared with the traditional static management method, the method of the present invention not only improves the flexibility and response speed of the system, but also enhances the system's adaptability and fault tolerance, and has significant technical advantages. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0077] Figure 1 This is a flow chart of the efficient collaborative management method of distributed DDC controllers in Example 1.

[0078] Figure 2 This is a flow chart of the efficient collaborative management system of distributed DDC controllers in Example 1. DETAILED DESCRIPTION

[0079] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0080] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and 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.

[0081] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0082] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a distributed DDC controller efficient collaborative management method, comprising the following steps:

[0083] S1 collects operation data from multiple DDC controllers;

[0084] Collecting these data from multiple DDC controllers, a unified data acquisition module M is introduced, which can obtain this information from each controller through the network interface. The expression is:

[0085] f M (x i,t )=[T i ,E i ,D i ];

[0086] Among them, f M represents the process of module M collecting data from the i-th controller, x i,t represents the state vector of the i-th DDC controller at time t, T i represents the operating temperature of the i-th controller, E i represents the energy consumption of the i-th controller, D i represents the processing delay of the i-th controller, i represents the index variable;

[0087] All processed data are aggregated into a central database to form a data matrix X t , the expression is:

[0088] X t =[x 1,t ,x 2,t ,...,xn,t ];

[0089] Among them, x n,t represents the state vector of the nth DDC controller at time t, and n represents the number of DDC controllers;

[0090] Using time t as the identifier of the data, the frequency of data update is measured by the time difference Δt, and the function g(Δt) is constructed based on the current system load and network bandwidth. The expression is:

[0091]

[0092] Where L represents the current system load, B represents the network bandwidth, α represents the adjustment factor, and log represents the logarithmic function;

[0093] In order to ensure that the collected data can accurately reflect the current status of each DDC controller, the data is verified.

[0094] S2 encodes the operation data using preset data processing rules to generate corresponding control signals;

[0095] The anomaly detection method based on Kalman filter is used, which can effectively identify and correct data deviations caused by sensor noise and network transmission errors. The verification expression is:

[0096] h=KF(T i ,E i ,D i );

[0097] Among them, h represents the verification function, which verifies and corrects the collected data to ensure data quality, and KF represents the Kalman filter;

[0098] The standardized processing is used to process the verified data, and the and

[0099] The verified data is fused to generate a comprehensive state vector. The fusion expression is:

[0100]

[0101] Among them, v i represents the comprehensive state vector of the i-th controller, θ T ,θ E and θ D are the adjustment factors for temperature, energy consumption, and processing delay, respectively, and i represents the index variable;

[0102] Based on the comprehensive state vector v o, the corresponding control signal is generated through the preset data processing rules, and the expression is:

[0103]

[0104] Among them, C i represents the control signal strength of the i-th controller, and w represents the weight for adjusting the control signal strength.

[0105] S3 selects a suitable network communication protocol, selects the optimal communication path through the communication protocol, and encapsulates, sends, and receives data on the optimal communication path;

[0106] The time-sensitive network (TSN) protocol is selected as the network communication protocol because it supports deterministic data transmission and can meet the strict real-time requirements in industrial automation environments. The TSN protocol can realize time-triggered data transmission on Ethernet, ensuring that the control signal can reach the target controller on time, improving the response speed and stability of the system.

[0107] The optimal path is determined based on the current network load L and the distance D of the target controller. The expression is:

[0108]

[0109] Among them, Psel represents the communication path selection function, the value range of Psel is a positive real number, the smaller the value of Psel, the better the path, β and γ represent adjustment factors, which are used to balance the impact of network load and distance on path selection;

[0110] Encapsulate the data packets of the optimal communication path, the expression is:

[0111] Fpack(C i ,H,T)=[H,C i ,t];

[0112] Among them, Fpack represents the data packet encapsulation function, H represents the data packet, including the source address and the destination address, and t represents the time;

[0113] Based on the data packet encapsulation function, a data packet sending function is constructed to send the encapsulated data packet through the optimal communication path. The expression is:

[0114] Fsend(Fpack(C i ,H,T),R);

[0115] Among them, R represents the routing table, and Fsend represents the data packet sending function;

[0116] Finally, a confirmation reception mechanism is built based on the routing table to confirm whether the data packet has successfully reached the target controller. The expression is:

[0117] Fack(R,A);

[0118] Among them, A represents confirmation information, and Fack is responsible for receiving the confirmation information and confirming whether the data packet is successfully delivered;

[0119] Fpack(C i ,H,T) is a data packet, including the control signal C i and related information;

[0120] The value fields of Fsend and Fack are Boolean values, indicating whether the data packet is successfully sent and received, respectively.

[0121] S4 uses intelligent algorithms to build a comprehensive evaluation model, outputs a comprehensive evaluation score, dynamically adjusts the working mode of the DDC controller based on its performance parameters, and optimizes resource allocation;

[0122] Assume that the state space is S, which contains all the states of the DDC controller. The state space S includes several states s, and the expression of state s is:

[0123] s=[T1,E1,D1,T2,E2,D2,...,T i ,E i ,D i ];

[0124] Among them, T i represents the operating temperature of the i-th DDC controller, E i represents the energy consumption of the i-th DDC controller, D i represents the processing delay of the i-th DDC controller, i represents the index variable;

[0125] Set the action space to A, which contains all control actions. The expression of action a is:

[0126] a=[p1,p2,...,p n ; s1,s2,...,s n ; o1, o2, ..., o n ];

[0127] Among them, p n represents the power output adjustment of the nth DDC controller, s n Indicates the working state switching instruction of the nth DDC controller. The working states include energy-saving mode and high-performance mode. n Indicates the task priority adjustment of the nth DDC controller;

[0128] Set the reward function to judge whether taking action a is good or bad under a given state s. The expression is:

[0129]

[0130] Among them, Q(s,a) represents the comprehensive evaluation score obtained by taking action a in state s, and w T 、w E and w D are the weight coefficients of temperature, energy consumption and processing delay, respectively, and f T (T i ), f E (E i ) and f D (D i ) are penalty functions for temperature, energy consumption and processing delay respectively;

[0131] The Q-learning algorithm is used as the basis of the comprehensive evaluation model to find the optimal strategy from Q(s,a), which is expressed as:

[0132]

[0133] in, represents the learning rate, represents the discount factor, Indicates that in the next state s ′ Next, all actions a ′ The maximum expected cumulative reward value that can be obtained in s ′ It represents the new state after taking action a, that is, the next state the system enters after executing the current action, a ′ In the new state s ′ the actions taken next;

[0134] The working mode of the DDC controller is dynamically adjusted according to Q(s,a), and the expression is:

[0135]

[0136] Among them, a * represents the optimal action, that is, the action that obtains the maximum expected cumulative reward in the current state s;

[0137] Temperature penalty function f T (T i ), the expression is:

[0138] f T (T i )=exp(-θ T ·|T i -T ideal |);

[0139] Where exp represents the exponential function, θT Represents the temperature adjustment factor, which is used to adjust the impact of temperature deviation on the reward function. A larger θ T The smaller the value of θ, the more sensitive the function is to temperature deviation. T The value will reduce this sensitivity, T ideal Indicates ideal temperature;

[0140] Energy consumption penalty function f E (E i ), the expression is:

[0141] f E (E i )=exp(-θ E ·(E i -E target ));

[0142] Among them, θ E Represents the energy consumption adjustment factor, which is used to adjust E i With E target The difference between E (E i ), the larger the θ E The value means that the penalty for deviation from the target value is greater, and vice versa, the penalty is smaller. target Indicates the target energy consumption value;

[0143] The penalty function f for processing delay D (D i ), the expression is:

[0144] f D (D i )=exp(-θ D ·(D i -D acceptable ));

[0145] Among them, θ D Indicates the processing delay adjustment factor, which is used to adjust D i With D acceptable The difference between D (D i ), the larger the θ D The value means that the penalty for deviation from the target value is greater, and vice versa, the penalty is smaller. acceptable represents the ideal processing delay;

[0146] Working mode, including power output adjustment, working state switching and task priority adjustment.

[0147] S5 sets health thresholds based on the output of the comprehensive assessment model and executes corresponding management strategies when the health thresholds are exceeded;

[0148] The comprehensive health assessment value is calculated based on the comprehensive assessment model output, and the expression is:

[0149]

[0150] in, represents the comprehensive health assessment value, which indicates the overall health status of all DDC controllers in the entire network, S represents the state space, and n represents the number of DDC controllers;

[0151] The value range is [0, 1], set the threshold when When the predefined management measures are initiated;

[0152] when When the health assessment value drops, the following steps will be initiated to quickly assess the status of all DDC controllers and identify the main factors that cause the health assessment value to drop;

[0153] Select the best combination of management strategies based on the evaluation results;

[0154] Execute selected management strategies, such as adjusting power output, switching operating states, or re-assigning task priorities;

[0155] After executing the policy, continue to monitor changes in system health assessment values;

[0156] If the system health assessment value has not recovered to If the above is the case, repeat this step until the system returns to a normal and healthy state.

[0157] This embodiment also provides a distributed DDC controller efficient collaborative management system, including:

[0158] The data acquisition module is responsible for collecting operation data from each DDC controller and aggregating these data through the network interface. This module also includes a data verification function, using the Kalman filter to detect and correct anomalies in the collected data;

[0159] The path selection module is responsible for selecting the appropriate network communication protocol and selecting the optimal communication path through the communication protocol. It is responsible for encapsulating, sending and receiving data and confirming whether the data packet has successfully reached the target controller.

[0160] The evaluation and decision-making module is responsible for building a comprehensive evaluation model, dynamically adjusting the working mode of the DDC controller based on its temperature, energy consumption, and processing delay, and optimizing resource allocation. This module uses the Q-learning algorithm to find the optimal strategy to improve system efficiency.

[0161] The management strategy module is responsible for setting the health threshold based on the output of the comprehensive evaluation model. When the health evaluation value is lower than the set threshold, the corresponding management strategy is executed, including evaluating the status of the DDC controller, selecting the optimal management strategy combination, and executing these strategies;

[0162] The signal generation module is responsible for encoding the operation data using preset data processing rules and generating corresponding control signals, which will be used to guide the adjustment of the working mode of the DDC controller.

[0163] This embodiment also provides a computer device, which is suitable for the case of an efficient collaborative management method for distributed DDC controllers, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the efficient collaborative management method for distributed DDC controllers proposed in the above embodiment.

[0164] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0165] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for realizing efficient collaborative management of distributed DDC controllers as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0166] In summary, the present invention significantly improves the overall performance and stability of the system by dynamically adjusting the working mode of the controller and optimizing resource allocation. In particular, the present invention adopts an intelligent algorithm to construct a comprehensive evaluation model, which can dynamically adjust the working mode of the DDC controller according to the real-time performance parameters, optimize resource allocation, and timely execute corresponding management strategies by setting health thresholds to ensure the long-term stable operation of the system. Compared with the traditional static management method, the method of the present invention not only improves the flexibility and response speed of the system, but also enhances the system's adaptability and fault tolerance, and has significant technical advantages.

[0167] Example 2, referring to Table 1, is the second example of the present invention. To further verify the technical solution of the present invention, experimental simulation data of a distributed DDC controller efficient collaborative management method is provided.

[0168] Experimental equipment and environment settings: The experimental environment contains 10 DDC controllers, numbered C1 to C10, simulating different industrial automation application scenarios; each controller is equipped with a temperature sensor, an energy consumption metering module, and a processing delay monitoring module; the network environment uses Ethernet connection with a bandwidth of 100Mbps, and the current system load is 60%.

[0169] Data acquisition module configuration: Select a unified data acquisition module to obtain data from each controller through the network interface and record the operating temperature, energy consumption and processing delay of the controller.

[0170] The data collection frequency was set to once every 5 seconds, that is, Δt=5Δt=5 seconds; the collection time was 2 hours, and a total of 720 data were collected.

[0171] Data standardization and verification: Use Kalman filter to verify the collected data to eliminate sensor noise and network transmission errors.

[0172] The temperature is standardized to range from 20°C to 40°C.

[0173] The energy consumption is standardized in the range of 100W to 500W.

[0174] Processing delays are standardized to range from 5ms to 50ms.

[0175] Data fusion and control signal generation: The standardized data is fused to generate a comprehensive state vector v i The adjustment factor is set to θ T =0.5,θ E =0.6,θ D =0.7; Generate control signal C i , the weight factor w is set to 10.

[0176] Communication path selection and data transmission: Select TSN protocol as the network communication protocol to ensure the real-time and deterministic data transmission;

[0177] The optimal path is determined based on the current network load and the distance to the target controller. The path selection function is: Among them, β=0.8, γ=0.9;

[0178] The control signals are encapsulated, sent and received, and a confirmation mechanism is used to ensure the successful delivery of the data packets.

[0179] Comprehensive evaluation model construction and dynamic adjustment: construct the state space S and action space A, set the reward function Q(s,a), and use the Q-learning algorithm to find the optimal strategy;

[0180] Setting health thresholds When the comprehensive health assessment Lower than When the management strategy is started.

[0181] The experimental data are shown in Table 1:

[0182] Table 1 Experimental record table

[0183]

[0184] From the above table, we can see that the operating temperature, energy consumption and processing delay of different controllers are different, resulting in different comprehensive health assessment values; the specific analysis is as follows:

[0185] Controller C1: operating temperature is 25°C, power consumption is 150W, processing delay is 10ms, and comprehensive health assessment value is 0.92, indicating that the C1 controller performs well in all indicators and is close to the ideal state;

[0186] Controller C2: operating temperature is 35°C, power consumption is 300W, processing delay is 20ms, and comprehensive health assessment value is 0.78; compared with C1, C2 has larger deviations in temperature and power consumption, but is still within a reasonable range;

[0187] Controller C3: operating temperature is 22°C, power consumption is 120W, processing delay is 8ms, and comprehensive health assessment value is 0.95; C3 controller performs excellently, and all indicators are close to optimal;

[0188] Controller C4: operating temperature is 38°C, power consumption is 450W, processing delay is 35ms, and the comprehensive health assessment value is only 0.65; this indicates that the C4 controller has problems in temperature, power consumption, and processing delay, and needs to be adjusted;

[0189] Controller C5: operating temperature is 28°C, power consumption is 200W, processing delay is 15ms, and comprehensive health assessment value is 0.83; the performance of the C5 controller is average, but still within the acceptable range;

[0190] In summary, the present invention significantly improves the overall performance and stability of the distributed DDC controller system by introducing a dynamic adjustment mechanism, optimizing resource allocation, enhancing robustness, and improving real-time performance and reliability.

[0191] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the 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 distributed DDC controller efficient collaborative management method, characterized by: include, Collect operational data from multiple DDC controllers; Encode the operation data using preset data processing rules to generate corresponding control signals; Select a suitable network communication protocol, select the optimal communication path through the communication protocol, and encapsulate, send and receive data on the optimal communication path; An intelligent algorithm is used to build a comprehensive evaluation model, output a comprehensive evaluation score, dynamically adjust the working mode of the DDC controller based on its performance parameters, and optimize resource allocation; Based on the output of the comprehensive assessment model, set health thresholds and execute corresponding management strategies when the health thresholds are exceeded; The specific steps of collecting the operation data from multiple DDC controllers are as follows: Collecting these data from multiple DDC controllers, a unified data acquisition module M is introduced, which can obtain this information from each controller through the network interface. The expression is: ； in, Indicates that module M starts from The process of collecting data by a controller, Indicates A DDC controller is at The state vector of Indicates The operating temperature of the controller, Indicates The energy consumption of each controller, Indicates The processing delay of each controller, Represents an index variable; All processed data are aggregated into a central database to form a data matrix , the expression is: ； in, Indicates A DDC controller is at The state vector of Indicates the number of DDC controllers.

2. The distributed DDC controller efficient collaborative management method according to claim 1, characterized in that: The specific steps of encoding the operation data using the preset data processing rules to generate the corresponding control signal are as follows: Using the anomaly detection method based on Kalman filter, the verification expression is: ； in, represents the verification function, represents the Kalman filter; The standardized processing is used to process the verified data, and the , and ; The verified data is fused to generate a comprehensive state vector. The fusion expression is: ； in, Indicates The combined state vector of the controllers is, , and are the adjustment factors for temperature, energy consumption and processing delay, Represents an index variable; Based on the comprehensive state vector , the corresponding control signal is generated through the preset data processing rules, and the expression is: ； in, Indicates The control signal strength of each controller, Indicates the weight for adjusting the control signal strength.

3. The distributed DDC controller efficient collaborative management method according to claim 2, characterized in that: The selecting of a suitable network communication protocol, selecting an optimal communication path through the communication protocol, and encapsulating, sending and receiving data of the optimal communication path, specifically, Choose to use the Time Sensitive Network (TSN) protocol as the network communication protocol; The optimal path is determined based on the current network load L and the distance D of the target controller. The expression is: ； in, represents the communication path selection function, and represents the regulating factor; Encapsulate the data packets of the optimal communication path, the expression is: ； in, Represents the data packet encapsulation function, Indicates data packets, Indicates the moment; Based on the data packet encapsulation function, a data packet sending function is constructed to send the encapsulated data packet through the optimal communication path. The expression is: ； in, Represents the routing table, Represents the data packet sending function; Finally, a confirmation acceptance mechanism is constructed based on the routing table, and the expression is: ； in, Indicates confirmation information. Responsible for receiving confirmation information and confirming whether the data packet was successfully delivered.

4. The distributed DDC controller efficient collaborative management method according to claim 3, characterized in that: The intelligent algorithm is used to construct a comprehensive evaluation model and output a comprehensive evaluation score. The specific steps are: Assume the state space is S, which includes several states s. The expression of state s is: ； in, Indicates The operating temperature of a DDC controller, Indicates The energy consumption of a DDC controller, Indicates The processing delay of a DDC controller, Represents an index variable; Set the action space to A, which contains all control actions. The expression of action a is: ； in, Indicates The power output adjustment of a DDC controller, Indicates A DDC controller working state switching instruction, Indicates Adjust the task priority of each DDC controller.

5. The distributed DDC controller efficient collaborative management method according to claim 4, characterized in that: The specific steps of dynamically adjusting the working mode of the DDC controller based on its performance parameters and optimizing resource allocation are as follows: Set the reward function to judge the given state Take action The quality of is expressed as: ； in, Indicates in status Take action The comprehensive evaluation score obtained is , and are the weight coefficients of temperature, energy consumption and processing delay, respectively. , and are the penalty functions for temperature, energy consumption, and processing delay, respectively; The Q-learning algorithm is used as the basis of the comprehensive evaluation model. Find the optimal strategy, the expression is: ； in, represents the learning rate, represents the discount factor, Indicates that in the next state Next, all actions The maximum expected cumulative reward value that can be obtained in Indicates taking action The new state after In the new state the actions taken next; according to Dynamically adjust the working mode of the DDC controller, the expression is: ； in, represents the optimal action, that is, in the current state The action that obtains the maximum expected cumulative reward.

6. The distributed DDC controller efficient collaborative management method according to claim 5, characterized in that: The output of the comprehensive assessment model is based on setting a health threshold, and when the health threshold is exceeded, a corresponding management strategy is executed. The specific steps are: The comprehensive health assessment value is calculated based on the comprehensive assessment model output, and the expression is: ； in, represents the comprehensive health assessment value, S represents the state space, Indicates the number of DDC controllers; The value range is [0,1], set the threshold ,when < , predefined management actions are initiated.

7. A distributed DDC controller efficient collaborative management system, based on the distributed DDC controller efficient collaborative management method according to any one of claims 1 to 6, characterized in that: include, The data acquisition module is responsible for collecting operation data from each DDC controller and aggregating these data through the network interface. This module also includes a data verification function, using the Kalman filter to detect and correct anomalies in the collected data; The path selection module is responsible for selecting the appropriate network communication protocol and selecting the optimal communication path through the communication protocol. It is responsible for encapsulating, sending and receiving data and confirming whether the data packet has successfully reached the target controller. The evaluation and decision-making module is responsible for building a comprehensive evaluation model, dynamically adjusting the working mode of the DDC controller based on its temperature, energy consumption, and processing delay, and optimizing resource allocation. This module uses the Q-learning algorithm to find the optimal strategy to improve system efficiency. The management strategy module is responsible for setting the health threshold based on the output of the comprehensive evaluation model. When the health evaluation value is lower than the set threshold, the corresponding management strategy is executed, including evaluating the status of the DDC controller, selecting the optimal management strategy combination, and executing these strategies; The signal generation module is responsible for encoding the operation data using preset data processing rules and generating corresponding control signals, which will be used to guide the adjustment of the working mode of the DDC controller.

8. 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 the distributed DDC controller efficient collaborative management method described in any one of claims 1 to 6 are implemented.

9. 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 the distributed DDC controller efficient collaborative management method described in any one of claims 1 to 6 are implemented.

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