Switch distributed control method and system of power distribution cabinet based on Internet of Things

Abstract

The invention discloses a switch distributed control method and system of a power distribution cabinet based on the Internet of Things. The method comprises the following steps: constructing a multi-level power distribution topology network; deploying a layered distributed control unit; an Internet of Things communication network covering the power distribution cabinets at all levels is established, and point-to-point communication between the control units at all levels is achieved; a hierarchical distributed control decision is designed, wherein a main control unit is designed to be responsible for global load balancing, energy distribution and safety strategy making and issuing, an area control unit is designed to be responsible for area load scheduling, energy optimization and exception handling in an area, and a terminal control unit is designed to be responsible for local load control, energy consumption execution and real-time safety protection; and based on a hierarchical distributed control decision, coordinating each level of control unit to execute a multi-level cooperative control process of load balancing, energy optimization and safety protection. According to the invention, the defects of cooperative control, efficient energy utilization, self-adaptive capability and the like in an existing power distribution system can be overcome.

Description

Technical Field

[0001] This application relates to the field of power distribution control technology, specifically to a distributed control method and system for power distribution cabinet switches based on the Internet of Things. Background Technology

[0002] With the continuous development and expansion of power systems, electricity demand has become increasingly complex and diverse. Traditional power distribution systems typically employ a centralized control architecture, with a central control unit managing all distribution switches. This architecture played a crucial role in the stable operation of the power system for a certain period, promoting orderly power distribution and meeting the relatively simple electricity needs of the time. However, with the changing times, power systems face more challenges and higher requirements, and the limitations of the centralized control architecture have gradually become apparent.

[0003] Centralized control architecture relies on a central control unit to manage and schedule all power distribution switches in a unified manner, achieving power distribution and control through centralized processing and decision-making. However, centralized control architecture has a high risk of single point of failure; once the central control unit fails, the entire power distribution system may be paralyzed. The system response speed is slow, making it difficult to quickly respond to sudden changes in electricity demand. It also has poor scalability, making it difficult to adapt to the continuous expansion of the power system and the increasing complexity of electricity demand.

[0004] In recent years, the application of distributed control technology in power distribution systems has gradually attracted attention. Distributed control distributes control functions among multiple control units, each responsible for managing power distribution switches in a specific area or at a specific level. These control units work collaboratively through a communication network. However, existing distributed control schemes suffer from insufficient collaboration between control units. Each control unit often operates independently, lacking an effective collaborative mechanism and making it difficult to achieve globally optimized control. Their intelligence level is limited; most control units only perform simple switching operations, lacking intelligent decision-making capabilities based on data analysis. The system's adaptability is weak, making it difficult to dynamically adjust control strategies according to changes in the power environment and user needs. Energy utilization efficiency is low, lacking refined management of energy consumption, leading to energy waste. Furthermore, the safety mechanism is imperfect; in abnormal situations, the distributed nodes lack a collaborative response mechanism, affecting system security. Summary of the Invention

[0005] To address the shortcomings in existing power distribution systems regarding collaborative control, intelligent sensing and control, energy efficiency, and adaptive capabilities, this application provides a distributed control method and system for power distribution cabinets based on the Internet of Things (IoT).

[0006] In a first aspect, this application provides a distributed control method for switches in a power distribution cabinet based on the Internet of Things, comprising: A multi-level power distribution topology network is constructed, including: a primary topology layer with primary distribution cabinets, a secondary topology layer with multiple secondary distribution cabinets, and a tertiary topology layer with multiple terminal distribution cabinets; each distribution cabinet integrates a communication gateway and environmental sensors, and is equipped with corresponding topology nodes and power distribution switches; Deploy a hierarchical distributed control unit, including: a main control unit deployed in a primary distribution cabinet, an area control unit deployed in a secondary distribution cabinet, and a terminal control unit deployed in a tertiary distribution cabinet; Establish an Internet of Things (IoT) communication network covering all levels of power distribution cabinets to enable point-to-point communication between control units at all levels; Based on the constructed multi-level power distribution topology network and hierarchical distributed control units, hierarchical distributed control decisions are designed, including: designing a main control unit responsible for global load balancing, energy distribution and security policy formulation and distribution; designing regional control units responsible for regional load scheduling, energy optimization and anomaly handling within their respective regions; and designing terminal control units responsible for local load control, energy consumption execution and real-time security protection. Based on hierarchical distributed control decision-making, a multi-level collaborative control process is established to coordinate the load balancing, energy optimization and safety protection of control units at all levels. This includes a multi-level bottom-up perception and early warning collaborative control process and a multi-level top-down decision-making and scheduling collaborative control process.

[0007] By adopting the above scheme, a multi-level power distribution topology network is constructed, hierarchical distributed control units are deployed, an Internet of Things communication network is established, hierarchical distributed control decisions are designed, and multi-level collaborative control processes are executed. This realizes the distributed control of the power distribution system, dynamically adjusts the power supply strategy according to actual power demand, reduces unnecessary energy consumption, and ensures power safety.

[0008] Preferably, the multi-level collaborative control process, which coordinates the load balancing of control units at all levels based on hierarchical distributed control decisions, includes: The terminal control unit collects terminal load data and environmental data in real time, compares them with a preset dynamic load threshold based on the environmental data, and obtains terminal load over-limit data or predicts terminal over-limit data through deep learning algorithms. The regional control unit summarizes regional terminal load data and analyzes whether the regional terminal load triggers the peer load coordination condition. The peer load coordination condition refers to the ability to coordinate and schedule regional terminal loads to eliminate terminal load over-limit. If the peer load coordination condition is triggered, a greedy algorithm is used to obtain the optimal strategy for regional terminal load coordination to eliminate terminal load over-limit. If the peer load coordination condition is not triggered, the regional terminal load gap is calculated and the regional load demand is updated and reported. Based on the regional load demand and global power supply capacity reported by the regional control unit, the main control unit generates a global load quota allocation strategy using a linear programming algorithm and preset load priorities for each region, and sends it to the regional control unit. Then, the regional control unit decomposes the regional load quota based on preset terminal load priorities and sends it to the terminal control unit. Finally, the terminal control unit adjusts the terminal operating power by controlling the switch of the power distribution cabinet according to the decomposed load quota.

[0009] By adopting the above scheme, the terminal control unit monitors and reports the terminal load status, and the regional control unit analyzes whether the same-level load coordination condition is triggered. When triggered, a greedy algorithm is used to obtain the optimal strategy to eliminate the limit violation. When not triggered, the gap is calculated and reported. The main control unit generates a global load quota allocation strategy based on the reported demand and the global power supply capacity. The regional control unit decomposes and distributes the strategy, and the terminal control unit adjusts the terminal operating power according to the quota. This specifically realizes the balanced distribution of load in the power distribution system and improves the stability and reliability of power supply.

[0010] Preferably, a multi-level collaborative control process based on hierarchical distributed control decision-making, coordinating control units at all levels to complete control command interaction in order to execute energy optimization includes: The system utilizes a terminal control unit to collect real-time energy consumption data from terminals and calculate energy efficiency parameters. These parameters are then compared with preset energy efficiency parameters to obtain abnormal terminal energy efficiency data. Alternatively, a deep learning algorithm can be used to predict terminal energy consumption data and obtain abnormal energy efficiency data. A regional control unit aggregates regional terminal energy consumption data and abnormal energy efficiency data, analyzing whether the regional terminal energy consumption data triggers a peer-to-peer energy consumption coordination condition. This condition refers to the ability to coordinate and compensate for energy consumption data through regional terminal energy consumption to eliminate abnormal terminal energy efficiency. If the peer-to-peer coordination condition is triggered, a multi-objective optimization algorithm is used to obtain the optimal strategy for regional terminal energy consumption coordination to eliminate abnormal terminal energy efficiency. If the condition is not triggered, the regional terminal energy efficiency optimization requirement is calculated, and the regional energy consumption requirement is updated and reported. Combining the daily peak and off-peak electricity prices and regional energy consumption demand, the main control unit uses an energy allocation algorithm to formulate a global energy allocation strategy and distributes it to the regional control unit. Then, the regional control unit uses the global energy allocation strategy and the energy consumption characteristics of the terminals in the region to formulate a corresponding regional terminal energy allocation strategy and distributes it to the terminal control unit. Finally, the terminal control unit uses the corresponding regional terminal energy allocation strategy to adjust the power operation mode of the terminals in different time periods by controlling the switch of the distribution cabinet.

[0011] By adopting the above scheme, the terminal control unit monitors the terminal energy consumption reports, the regional control unit summarizes and analyzes whether the same-level energy consumption coordination conditions are triggered, and if triggered, calculates the optimal strategy to eliminate energy efficiency anomalies. If not triggered, it reports energy consumption demand. The main control unit formulates a global energy allocation strategy based on peak and valley electricity prices and reported demand. The regional control unit further decomposes the strategy and distributes it. The terminal control unit adjusts the terminal power operation mode according to the strategy, which specifically realizes the optimized allocation and utilization of energy. The terminal power can be dynamically adjusted according to different time periods and energy consumption conditions to improve energy utilization efficiency.

[0012] Preferably, a multi-level collaborative control process based on hierarchical distributed control decision-making, coordinating control units at all levels to complete control command interaction in order to execute safety protection, includes: The terminal control unit collects terminal electrical parameters, equipment status parameters, and environmental data in real time. It uses deep learning algorithms to determine whether a fault has occurred and the type and level of the fault. It obtains or predicts terminal faults, their types and levels, and uploads the data to the regional control unit. The regional control unit uses deep learning algorithms to determine whether fault propagation has occurred and the extent of the propagation based on the received terminal faults, their types and levels, and local protection status. If it is determined that a fault has propagated to other areas, the regional control unit continues to perform regional-level data cross-validation and fault location, and uploads the fault location and fault propagation results to the main control unit. Based on fault location and fault propagation results, the main control unit uses a deep learning algorithm to obtain a global safety isolation policy and distributes it to the regional control unit. Based on the fault location and fault propagation results and the global regional safety isolation policy, the regional control unit uses a deep learning algorithm to obtain the corresponding regional terminal safety isolation policy and distributes it to the terminal control unit. Then, the terminal control unit uses the corresponding regional terminal safety isolation policy to adjust whether the terminal is isolated by controlling the switch of the distribution cabinet.

[0013] By adopting the above scheme, the terminal control unit monitors the terminal faults, their types, and levels in real time; the area control unit matches the preset area isolation strategy and estimates whether it will lead to fault propagation, and performs data cross-validation and fault location when the fault propagates, thereby improving the accuracy of fault judgment; the main control unit matches the global safety isolation strategy based on the fault location and propagation results, the area control unit further matches the terminal safety isolation strategy, and the terminal control unit controls the switch to adjust whether the terminal is isolated according to the strategy, effectively isolating the faulty terminal when a fault occurs, and ensuring power safety.

[0014] Preferably, in the multi-level collaborative control process that coordinates the load balancing, energy optimization and safety protection of control units at all levels, it is determined whether there is a conflict between the multi-level collaborative control processes of different dimensions at the same time, and the conflicting multi-level collaborative control processes of different dimensions are identified and obtained; from the obtained multi-level collaborative control processes of different dimensions with conflict, it is determined whether there is a fault level that is obtained or predicted to occur that is a serious fault level. If it is determined to exist, the multi-level collaborative control process for safety protection is executed first. If the condition is not met, the multi-level collaborative control process for each dimension is evaluated according to the corresponding dimension's preset core scoring indicators to obtain the corresponding score for each dimension's multi-level collaborative control process. Specifically, the preset core scoring indicators for the multi-level collaborative control process executing load balancing include: a quantitative indicator of the proportion of the difference between the current terminal load and the terminal load limit falling within different preset ranges; the preset core scoring indicators for the multi-level collaborative control process executing energy optimization include: a quantitative indicator of the economic expenditure range for eliminating current terminal energy efficiency anomalies; and the preset core scoring indicators for the multi-level collaborative control process executing security protection include: a quantitative indicator of the proportion of each fault level. Based on the scores obtained from the multi-level collaborative control processes of different dimensions with conflicting conditions, the priorities of the multi-level collaborative control processes executing load balancing, energy optimization, and security protection are set from high to low, with higher-priority multi-level collaborative control processes being executed first.

[0015] By adopting the above scheme, when the multi-level collaborative control process of load balancing, energy optimization and safety protection is executed in the control units at all levels, it is determined whether there is a conflict between the collaborative control processes of different dimensions and the conflicting processes are identified; when there is a serious fault, the safety protection process is executed first to ensure system safety; when there is no serious fault, each process is evaluated by preset core scoring indicators, and priority is set according to the score and the high priority process is executed first, so as to ensure that the control process can be reasonably arranged under different control requirements.

[0016] Preferred options also include: In the multi-level collaborative control process that coordinates load balancing, energy optimization, and security protection among control units at all levels, when using a regional control unit to execute the control process, if a preset key decision scenario is triggered, including: adjusting preset key terminal load quotas, adjusting preset key terminal energy consumption data, or obtaining preset key terminal security isolation strategies, then a verification command is sent to the upper and lower level control units of the regional control unit. If the verification by the upper level control unit regarding load quota adjustment, terminal energy consumption data adjustment, and preset key terminal isolation does not conflict with the regional load quota, regional terminal energy allocation, and regional security isolation strategies issued by the upper level control unit, and the verification by the lower level control unit regarding load quota adjustment, terminal energy consumption data adjustment, and preset key terminal isolation is conditionally executed by the lower level control unit, then a successful verification is sent back to the regional control unit, and the process involving load quota adjustment, terminal energy consumption data adjustment, and preset key terminal isolation continues to be executed using the regional control unit; otherwise, a verification failure is sent back to the regional control unit, and the process involving load quota adjustment, terminal energy consumption data adjustment, and preset key terminal isolation continues to be re-acquired using the regional control unit.

[0017] By adopting the above scheme, when the regional control unit executes key decisions, the verification by the upper and lower level control units ensures that the decisions do not conflict with the strategies at all levels and are conditionally executable by the lower levels. This improves the accuracy and reliability of the multi-level collaborative control process and avoids control errors caused by decision conflicts.

[0018] Secondly, this application provides a distributed control system for a power distribution cabinet based on the Internet of Things, comprising: The power distribution topology construction module is used to build a multi-level power distribution topology network, including: a primary topology layer with primary distribution cabinets, a secondary topology layer with multiple secondary distribution cabinets, and a tertiary topology layer with multiple terminal distribution cabinets; each distribution cabinet integrates a communication gateway and environmental sensors, and is equipped with corresponding topology nodes and power distribution switches; The control unit deployment module is used to deploy hierarchical distributed control units, including: a main control unit deployed in a primary distribution cabinet, an area control unit deployed in a secondary distribution cabinet, and a terminal control unit deployed in a tertiary distribution cabinet; The communication network establishment module is used to establish an Internet of Things (IoT) communication network covering all levels of power distribution cabinets, enabling point-to-point communication between control units at all levels. The control decision design module is used to design hierarchical distributed control decisions based on the constructed multi-level power distribution topology network and hierarchical distributed control units. This includes: designing the main control unit to be responsible for global load balancing, energy allocation and security policy formulation and distribution; designing the regional control unit to be responsible for regional load scheduling, energy optimization and anomaly handling within the region; and designing the terminal control unit to be responsible for local load control, energy consumption execution and real-time security protection. The collaborative control execution module is used to coordinate the execution of multi-level collaborative control processes for load balancing, energy optimization, and safety protection by control units at all levels based on hierarchical distributed control decisions. This includes: a multi-level bottom-up perception and early warning collaborative control process and a multi-level top-down decision and scheduling collaborative control process.

[0019] By adopting the above scheme, distributed control of the distribution cabinet switches is realized. The control strategy can be dynamically adjusted according to the power environment and demand, enhancing the system's adaptability and intelligence. Multi-level collaborative control ensures power safety and improves energy utilization efficiency.

[0020] Thirdly, this application provides a computer-readable storage medium including a stored computer program, wherein the computer program, when running, controls the device where the computer-readable storage medium is located to perform the method described above.

[0021] Fourthly, this application provides a computer device, the computer device including a memory, a processor and a program stored in the memory and executable thereon, the program being executed by the processor to implement the steps of the method described above.

[0022] In summary, this application has the following beneficial effects: 1. Construct a multi-level power distribution topology network, deploy hierarchical distributed control units, establish an Internet of Things (IoT) communication network, design hierarchical distributed control decisions, coordinate the execution of multi-level collaborative control processes for load balancing, energy optimization, and safety protection by control units at all levels, realize collaborative control of control units at all levels, realize intelligent sensing, remote control, and data analysis of power distribution switches based on IoT, achieve efficient energy utilization under the premise of ensuring power safety, dynamically adjust quota strategies according to user needs, and improve adaptive capabilities; 2. When the control units at all levels execute the multi-level collaborative control process, the conflict between different processes is effectively handled to ensure that safety is prioritized in the event of a serious fault. At the same time, the priority of each process is reasonably set according to the preset core scoring indicators, so that the system operates more stably and efficiently, ensuring the safe and reliable operation of the power distribution system and the rational use of energy. 3. When coordinating the multi-level collaborative control process of load balancing, energy optimization and safety protection of control units at all levels, the consistency of decisions with the overall strategy and the feasibility of lower-level execution are ensured through verification by upper and lower level control units when the regional control unit makes key decisions, thus avoiding control conflicts. Attached Figure Description

[0023] Figure 1 This is a flowchart of the distributed control method for the switch of a power distribution cabinet based on the Internet of Things, as described in a specific embodiment. Figure 2 This is a diagram showing the equipment deployment of a multi-level power distribution topology network in the distributed control method for the switchgear based on the Internet of Things described in a specific embodiment. Figure 3 This is a topology diagram of the multi-level power distribution topology network in the distributed control method for the switchgear based on the Internet of Things described in a specific embodiment. Figure 4 This is a schematic diagram of the distributed control system for the switchgear based on the Internet of Things described in a specific embodiment. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0025] like Figure 1 As shown in the figure, this application discloses a distributed control method for a distribution cabinet based on the Internet of Things (IoT). Specifically, it includes: constructing a multi-level distribution topology network, deploying hierarchical distributed control units, establishing an IoT communication network, designing hierarchical distributed control decisions, and coordinating the execution of multi-level collaborative control processes by each level of control unit. The multi-level distribution topology network provides the physical foundation for distributed control; the hierarchical distributed control units realize the decentralization and hierarchical nature of control functions; the IoT communication network ensures data interaction between control units; the hierarchical distributed control decisions guide the work of each level of control unit; and the multi-level collaborative control processes ensure the achievement of the overall system objectives, thereby improving the energy efficiency, safety, and adaptability of the distribution system. The following is a further detailed description of this application.

[0026] S1. Construct a multi-level power distribution topology network.

[0027] Specifically, a hierarchical power distribution network structure consisting of a primary topology layer, a secondary topology layer, and a tertiary topology layer is established, forming the physical foundation for distributed control. For example... Figure 2 and Figure 3 As shown, a primary distribution cabinet (box) is deployed in the primary topology layer, serving as the source of the entire topology structure, responsible for receiving power from the upper level and distributing power to the lower level topology layers. Multiple secondary distribution cabinets (boxes) are deployed in the secondary topology layer, each equipped with a topology node and a distribution switch, connecting the primary distribution cabinets (boxes) and the tertiary topology layer. Multiple terminal distribution boxes are deployed in the tertiary topology layer, each equipped with a topology node and a distribution switch, directly connecting to the terminal loads.

[0028] S2, Deploy a hierarchical distributed control unit.

[0029] Specifically, deploying hierarchical distributed control units includes deploying a main control unit in a primary distribution cabinet, area control units in a secondary distribution cabinet, and terminal control units in a tertiary distribution cabinet. A hierarchical relationship is established between control units, allowing higher-level control units to override the decision-making processes of lower-level control units and issue decision commands to them, while lower-level control units can operate independently in the event of a communication interruption.

[0030] The system comprises several control units. The main control unit is responsible for formulating and distributing global control strategies. Specifically, it collects data from control units at all levels to formulate global load balancing, energy allocation, and security strategies, and then distributes these strategies to regional control units. Regional control units are responsible for controlling power distribution switches within specific areas. They possess a degree of autonomous decision-making capability and employ embedded controllers and corresponding control algorithms for local analysis. Specifically, based on the main control unit's strategies and collected terminal data, they perform load scheduling, energy optimization, and anomaly handling within their respective areas, and report relevant data to the main control unit. Terminal control units are responsible for directly controlling the power distribution switches, thereby controlling the power supply status and local protection of terminal loads. Specifically, based on instructions from regional control units and local data, they perform local load control and real-time safety protection to ensure the normal operation of terminal loads. Each level of distribution cabinet (box) integrates intelligent molded case switches, intelligent switches, communication gateways, environmental sensors, and other components, forming local control units.

[0031] S3. Establish an Internet of Things (IoT) communication network covering all levels of power distribution cabinets to achieve point-to-point communication between control units at all levels.

[0032] Specifically, such as Figure 2 As shown, an IoT sensing and communication network covering all levels of distribution cabinets (boxes) is constructed to achieve data collection, transmission, and sharing. Smart switches are deployed in each level of distribution cabinet (box) to achieve real-time acquisition of current, voltage, power, frequency, temperature, and power factor. Environmental sensors are deployed inside the distribution cabinets (boxes) to monitor environmental parameters such as temperature, humidity, and smoke levels. Each level of distribution cabinet (box) is connected to the IoT via a communication gateway to enable real-time data uploading and command distribution.

[0033] A distributed data storage mechanism is established, with pre-defined key power distribution data. This data is stored locally while simultaneously uploaded to the cloud, ensuring data reliability and consistency. Point-to-point communication between control units is implemented, including point-to-point communication between control units at different levels and point-to-point communication between control units at the same level. This communication connects the main control unit, regional control units, and terminal control units to form a data closed loop.

[0034] S4. Based on the constructed multi-level power distribution topology network and hierarchical distributed control unit, design hierarchical distributed control decision.

[0035] Specifically, the main control unit is responsible for global load balancing, energy distribution, and the formulation and distribution of security policies. Regarding global load balancing, the main control unit calculates the optimal load distribution scheme based on the load status of each level of distribution cabinet and the demand of the terminal loads, ensuring load balance across all areas. For energy distribution, the main control unit formulates reasonable energy distribution strategies based on energy supply and energy consumption requirements to improve energy efficiency. Regarding security policy formulation, the main control unit establishes a comprehensive security policy model, monitors and evaluates the system's security status in real time, formulates corresponding security policies, and distributes them to control units at all levels.

[0036] The regional control unit is responsible for regional load scheduling, energy optimization, and anomaly handling within its designated area. Regional load scheduling adjusts the status of power distribution switches based on changes in terminal load within the area to ensure load stability. Energy optimization analyzes energy consumption within the area and implements energy-saving measures to reduce waste. Anomaly handling involves timely response and processing of any abnormal situations occurring within the area.

[0037] The terminal control unit is designed to handle local load control, energy consumption management, and real-time safety protection. Through regional load scheduling schemes and regional energy distribution strategies, it controls the on / off state of power distribution switches to achieve personalized power supply and energy consumption management for the terminal loads. Real-time safety protection monitors the electrical parameters and equipment status of the terminal loads to promptly identify and address potential safety hazards, ensuring the safe operation of the terminal loads.

[0038] S5. Based on hierarchical distributed control decision-making, it coordinates the execution of multi-level collaborative control processes for load balancing, energy optimization, and safety protection by various control units.

[0039] Specifically, based on the control framework provided by the aforementioned hierarchical distributed control decision, control information is exchanged through the coordination of control units at all levels, thereby achieving hierarchical collaborative control in dimensions such as load balancing, energy optimization, and safety protection, thus improving the energy utilization efficiency, safety, and adaptability of the power distribution system.

[0040] Among them, the hierarchical collaborative control process in dimensions such as load balancing, energy optimization and security protection includes steps such as top-down control, bottom-up feedback and peer coordination. Specifically, the hierarchical collaborative control process in each dimension includes: multi-level bottom-up perception and early warning collaborative control process and multi-level top-down decision and scheduling collaborative control process.

[0041] In the bottom-up perception and early warning collaborative control process, the terminal control unit collects information such as terminal load data, environmental data, electrical parameters, and equipment status parameters in real time, and uploads this information to the area control unit. The area control unit summarizes and analyzes this information and uploads the analyzed information to the main control unit. Based on the information uploaded by the area control unit, the main control unit performs a global assessment and decision-making, and formulates corresponding control strategies.

[0042] In the top-down decision-making and scheduling collaborative control process, the main control unit distributes the formulated control strategy to the regional control units. Based on the main control unit's strategy, the regional control units formulate control schemes for their respective regions and distribute them to the terminal control units. The terminal control units then execute the corresponding control operations according to the instructions of the regional control units, thereby controlling the terminal load.

[0043] In one specific embodiment, considering that different control logics and optimization algorithms need to be adapted for different types of collaborative control (such as the real-time requirements of load balancing, the economic requirements of energy optimization, and the reliability requirements of security protection), appropriate perception and early warning algorithms, and decision-making and scheduling algorithms are adopted from the three dimensions of load, energy, and security to further refine the control logic, coordinate the execution of multi-level collaborative control processes of corresponding dimensions by each level of control unit, and achieve more optimized collaborative control; the method includes: First, the core objectives of load coordination control are global load balancing, regional load matching, and terminal load stability. Based on hierarchical distributed control decisions, the multi-level coordinated control process that coordinates the execution of load balancing by various control units specifically includes: Bottom-up collaborative control process (sensing and early warning): The terminal control unit collects real-time terminal load data (e.g., load type, current power, adjustable performance) and environmental data (e.g., temperature, humidity). The collected terminal load data is compared with a preset dynamic load threshold based on the environmental data. This dynamic load threshold is dynamically adjusted based on environmental data and can be learned from historical data or set using expert experience. For example, if B2's current power is 35kW, the load threshold is 30kW when the ambient temperature exceeds 27°C. Based on the comparison results, terminal load over-limit data is obtained. Furthermore, to further prevent overload situations, a deep learning algorithm (e.g., LSTM algorithm) embedded in the terminal control unit learns from historical terminal load data to predict terminal load trends and predict over-limit data. The terminal load data containing the acquired or predicted over-limit data is then uploaded to the area control unit.

[0044] The regional control unit aggregates regional terminal load data and analyzes it to determine whether the regional terminal load triggers the peer-to-peer load coordination condition. The peer-to-peer load coordination condition refers to the ability to coordinate and schedule regional terminal loads to eliminate terminal load exceeding limits. Specifically, it determines the current regional terminal load exceeding the limit and the load redundancy of other terminals in adjacent regions. It then determines whether the load redundancy of other terminals in adjacent regions can offset the current regional terminal load exceeding the limit. If it can offset the current regional terminal load exceeding the limit, then peer-to-peer load coordination is considered triggered. Considering that there are multiple coordination and scheduling methods for peer-to-peer load coordination, an algorithm embedded in the regional control unit is used to obtain the optimal coordination and scheduling decision. This embodiment uses a greedy algorithm.

[0045] When the peer-to-peer load coordination condition is triggered, a greedy algorithm is used to obtain the optimal strategy for regional terminal load coordination to eliminate terminal load exceeding limits. Specifically, this includes: initializing candidate regions, i.e., filtering regions directly connected to this region with communication latency less than 100ns based on topology; defining a greedy index, sorting them according to (available redundant power / transmission loss (which can be estimated based on transmission distance and historical transmission loss)); iterative selection, prioritizing requesting load support from the region with the highest greedy index. If the corresponding region's resources are insufficient, the process moves to the next region until the value required to eliminate terminal load exceeding limits is met or all candidate regions are traversed; the iteration terminates; if the peer-to-peer load coordination condition is not triggered, the regional terminal load gap is calculated and the regional load demand is updated and reported; the regional terminal load gap refers to the difference between the current regional terminal load exceeding limit value and the load redundancy (sum) of other terminals in adjacent regions.

[0046] Top-down collaborative control process (decision-making and scheduling): Based on the regional load demand and global power supply capacity reported by the regional control units, the algorithm embedded in the main control unit is used to complete the global load balancing allocation to generate a global load allocation decision. Specifically, this includes: generating a global load quota allocation strategy through a linear programming algorithm and preset load priorities for each region, and then distributing it to the regional control units; for example, designing a linear programming algorithm with a weighted combination of minimum global load fluctuation and minimum cross-regional transmission loss, as shown in the following formula: In the formula, The actual power supply quota for regional terminals; The average quota for all terminals in the region. The power loss rate for transmitting electricity from region i to region j is a fixed parameter based on the topology; For power transfer from region i to region j, These are the weighting coefficients; the constraints include: in, Maximum power supply capacity; The minimum necessary load and maximum carrying capacity for the corresponding area; The load demand reported by the region; among which, Includes load gaps, and when solving If no solution is found, meaning the sum of the uploaded regional load demands exceeds the maximum power supply capacity, then the load demands of lower-priority regions are reduced according to the preset load priorities of each region to ultimately solve for the regional load quota. The regional control unit then decomposes the regional load quota based on the preset terminal load priorities and distributes it to the terminal control unit. Specifically, based on the characteristics of each terminal load (e.g., terminal load priority) and the load demands of each terminal load in the region, sequential matching is performed. While ensuring the basic power supply for each terminal is met, redundant loads are allocated according to priority, thus decomposing and obtaining the load quota for each terminal. The terminal control unit then adjusts the terminal operating power by controlling the power distribution cabinet switches according to the decomposed load quota. For example, if the power of the B2 air conditioner is reduced from 40kW to 20kW, the air conditioner switch is adjusted to 50% power.

[0047] Second, energy collaborative control focuses on economy and efficiency. Specifically, it is based on hierarchical distributed control decision-making and coordinates the multi-level collaborative control process of load balancing at all levels of control units. This process includes: Bottom-up collaborative control process (sensing and early warning): The terminal control unit collects real-time energy consumption data from the terminals and calculates energy efficiency parameters (active power / reactive power). These parameters are compared with preset energy efficiency parameters (which can be dynamically adjusted according to the service life of the terminal equipment in the region) to obtain abnormal terminal energy efficiency data, i.e., terminals with low energy efficiency. In addition, to further avoid abnormal terminal energy efficiency, the terminal control unit uses a deep learning algorithm (such as LSTM algorithm) embedded in the terminal control unit to learn from historical terminal energy consumption data, predict the changing trend of terminal energy consumption load, predict and obtain terminal energy consumption and abnormal energy efficiency data, and upload the terminal energy consumption data containing the obtained or predicted abnormal terminal energy efficiency data to the regional control unit.

[0048] The regional control unit aggregates regional terminal energy consumption data and energy efficiency anomaly data, and analyzes and determines whether the regional terminal energy consumption data triggers the same-level energy consumption coordination condition. The same-level energy consumption coordination condition refers to the ability to coordinate and compensate for energy consumption data at the regional terminals to eliminate terminal energy efficiency anomalies. This involves determining the active / reactive power compensation values ​​corresponding to the current regional terminal energy efficiency optimization requirements, as well as the redundant active / reactive power compensation values ​​of other terminals in adjacent regions. It is then determined whether the power resource redundancy of other terminals in adjacent regions can offset the current regional terminal energy efficiency anomaly, i.e., meet the active / reactive power compensation values ​​corresponding to the energy efficiency optimization requirements. If it can offset the active / reactive power compensation values ​​corresponding to the current regional terminal energy efficiency optimization requirements, then same-level energy consumption coordination is considered triggered. For example, if region B has low energy efficiency during peak hours (power factor 0.85), and adjacent region A has redundant reactive power compensation resources (15kvar), these are sent to region B to increase B's power factor to 0.9, thus meeting the energy efficiency requirements.

[0049] Based on the triggering of peer-level coordination conditions, energy coordination, compared to load matching, places greater emphasis on energy optimization. Therefore, a multi-objective optimization algorithm is chosen to obtain the optimal strategy for regional terminal energy consumption coordination to eliminate terminal energy efficiency anomalies. This includes defining an objective function, including minimizing cross-regional line loss. , where i represents the neighboring regions of the current region. This represents the compensation value transmitted from region i to the current region. Let i be the line loss rate from region i to the current region; minimize the total collaborative cost: , Compensation for energy consumption per unit; maximizing power supply stability: , To obtain the weighted comprehensive objective function for the current regional voltage after compensation, the decision variables (compensation combinations for terminals in different adjacent regions) and constraints (compensation to meet the current regional deficit) are determined. The algorithm solves the problem, outputs and filters the Pareto solution set, and obtains the optimal strategy for regional terminal energy consumption coordination. If the same-level coordination condition is not triggered, the regional terminal energy efficiency optimization requirement is calculated and the regional energy consumption requirement is updated and reported. The regional terminal energy efficiency optimization requirement refers to the difference between the active / reactive power compensation value corresponding to the current regional terminal energy efficiency optimization requirement and the active / reactive power redundancy (sum) of other terminals in adjacent regions.

[0050] Top-down collaborative control process (decision-making and scheduling): Combining daily peak and off-peak electricity prices and regional energy consumption demand, the main control unit formulates a global energy allocation strategy using an energy allocation algorithm and distributes it to regional control units. The global energy allocation strategy is constructed using a dual-objective optimization model, including: defining the objective function, which includes: minimizing the global electricity cost. , The power allocation for region i during time period t. The electricity price for time period t; Maximize the overall energy efficiency index: , , Let be the energy efficiency parameter of region i during time period t. For the line loss rate of i in time period t, a weighted objective function is obtained; constraints are defined, including: safety constraints (e.g., the total global power allocation is not greater than the power supply capacity and the power allocation of a single area is not greater than the corresponding limit power), demand constraints (e.g., the power allocation of a single area is not less than the rigid energy consumption demand), and energy efficiency constraints (e.g., the energy efficiency parameter of a single area is not less than the preset energy efficiency parameter, and the cross-regional transmission line loss rate is not less than the preset line loss rate); decision variables are defined, including: the power allocation of each area in time period t, and the active / reactive power compensation values ​​of each area in time period t; finally, the global energy allocation strategy is solved.

[0051] The regional control unit then formulates a corresponding regional terminal energy allocation strategy based on the global energy allocation strategy and the terminal energy consumption characteristics within the region, and distributes it to the terminal control unit. Specifically, this includes: breaking down the global energy allocation strategy into quotas, allocating the total regional quota by time period (peak and off-peak), and reserving 10% redundancy; breaking down power quotas according to energy efficiency targets; determining and tagging the terminal energy consumption characteristics within the region, such as: non-adjustable critical terminals (e.g., machine tools), adjustable important terminals (air conditioning), adjustable general terminals (lighting), and energy efficiency auxiliary terminals (reactive power compensation); based on these tags, setting peak-hour allocation strategies includes: full allocation to non-adjustable critical terminals, basic power allocation (ensuring basic operation) and redundant allocation (redundant allocation according to the preset adjustment benefit priority of each terminal), suspension or reduction of allocation for adjustable general terminals when peak-hour quotas are tight, and deployment of energy efficiency auxiliary terminals as needed; and off-peak allocation strategies include: basic power allocation to non-adjustable critical terminals, full allocation to adjustable important terminals, supplementary allocation to adjustable general terminals, and deployment of energy efficiency auxiliary terminals as needed.

[0052] Then, the terminal control unit uses the corresponding regional terminal energy distribution strategy to adjust the terminal's power operation mode at different times by controlling the switch of the power distribution cabinet.

[0053] Third, the security collaborative control, with rapid response, precise isolation, and global protection as its core, is based on hierarchical distributed control decision-making. It coordinates control units at all levels to complete control command interaction, and the multi-level collaborative control process for executing security protection includes: Bottom-up collaborative control process (perception and early warning): The terminal control unit collects terminal electrical parameters, equipment status parameters and environmental data in real time. The embedded deep learning algorithm determines whether a fault has occurred and the type and level of the fault. The terminal faults that have occurred are obtained, as well as their types and levels. In order to further enhance safety protection, the deep learning algorithm is used to predict the terminal faults that have occurred and their types and levels. The obtained or predicted terminal faults and their types and levels are uploaded to the area control unit. When the fault level is severe, local protection is activated and the local protection status is uploaded to the area control unit.

[0054] The regional control unit continues to utilize the received terminal faults, fault types and levels, and local protection status to determine whether fault propagation has occurred and the extent of fault propagation through deep learning algorithms. Specifically, an embedded deep learning algorithm can be selected to learn historical terminal faults, fault types and levels, local protection status, and the occurrence and extent of fault propagation under corresponding conditions to obtain a fault propagation and severity judgment model, ultimately determining whether fault propagation has occurred and its extent. If it is determined that fault propagation has occurred and spread to other areas, the regional control unit continues to request terminal electrical parameters, equipment status parameters, and environmental data from other propagation areas for regional-level data cross-validation and fault location. Specifically, an embedded deep learning algorithm can also be selected to learn the terminal electrical parameters, equipment status parameters, and environmental data corresponding to the current area and the propagation areas, along with corresponding historical fault location and fault propagation results, to generate a fault location and fault propagation result judgment model, thereby obtaining the fault location and fault propagation results and uploading them to the main control unit.

[0055] Top-down collaborative control process (decision-making and scheduling): Based on fault location and fault propagation results, the main control unit uses a deep learning algorithm to obtain a global safety isolation strategy and distribute it to the regional control unit. Specifically, by learning historical terminal fault location and fault propagation results, as well as the global safety isolation strategy given by experts under the corresponding conditions, a global safety isolation strategy acquisition model is generated, and then the corresponding global safety isolation strategy is obtained and distributed to the regional control unit.

[0056] Based on the fault location and fault propagation results and the global regional security isolation strategy, the regional control unit uses a deep learning algorithm to obtain the corresponding regional terminal security isolation strategy and distributes it to the terminal control unit. Similarly, by learning the historical terminal fault location and fault propagation results, the global security isolation strategy, and the corresponding regional terminal security isolation strategy given by experts under the corresponding conditions, a regional terminal security isolation strategy acquisition model is generated, and then the corresponding regional terminal security isolation strategy is obtained and distributed to the regional control unit.

[0057] Then, the terminal control unit adjusts whether the terminal is isolated by controlling the switch of the power distribution cabinet according to the corresponding area terminal safety isolation strategy.

[0058] In a specific embodiment, considering that multi-level collaborative control processes at the same time often result in conflicts in the control of power distribution switches corresponding to the same terminal, and considering that controlling switches solely according to the priority order of collaborative control across different dimensions may lead to excessive safety redundancy or unnecessary losses, the method for conflict optimization includes: In the multi-level collaborative control process that coordinates the load balancing, energy optimization and safety protection of control units at all levels, it is necessary to determine whether there are conflicts in the multi-level collaborative control processes of different dimensions at the same time, and to identify and obtain the multi-level collaborative control processes of different dimensions that have conflicts. Specifically, conflict identification is achieved by comparing whether the switches of the power distribution cabinet corresponding to the multi-level collaborative control terminals of different dimensions conflict, such as whether the same switch is required to be turned on and off by different control strategies at the same time, and to determine whether there is a conflict.

[0059] If a fault level is determined to be a serious fault level in the multi-level collaborative control process of different dimensions with conflicting information, then the multi-level collaborative control process of safety protection will be prioritized and executed.

[0060] If the problem is determined to be nonexistent, then there is no serious security issue. In this case, the multi-level collaborative control process for each dimension is evaluated according to the corresponding core scoring indicators, and a score is obtained for each multi-level collaborative control process. Based on the scores obtained from the multi-level collaborative control processes of different dimensions that have conflicts, the priority of the multi-level collaborative control process for executing load balancing, the multi-level collaborative control process for executing energy optimization, and the multi-level collaborative control process for executing security protection is set from high to low, and the multi-level collaborative control process with the highest priority is executed first.

[0061] Among them, the preset core scoring indicators for the multi-level collaborative control process of load balancing include: a quantitative indicator of the proportion of the difference between the current terminal load and the terminal load limit within different preset ranges of difference, such as: the proportion of the difference between 1kW-10kW, 10kW-100kW and greater than 100kW is 2:7:1. The higher the proportion of 1kW-10kW, the higher the quantitative indicator score (80), and the greater the current load coordination and control demand. The higher the proportion of greater than 100kW, the lower the quantitative indicator score (30), and the smaller the current load coordination and control demand. In addition, besides the above scoring indicators, they can also be set from the perspective of the scope of influence, such as: a quantitative indicator of the priority proportion of the terminal load with the highest proportion among the different preset ranges of difference between the current terminal load rate and the terminal load rate limit; for example: the highest proportion is the terminal load with a difference of 10kW. For terminals with a power consumption of -100kW or greater than 100kW, determine the priority ratio of these terminals (e.g., high priority for critical terminals, medium priority for general terminals, and low priority for non-critical terminals). The higher the proportion of high priority terminals, the lower the quantitative indicator score (50), and the smaller the current load coordination and control requirement. The higher the proportion of low priority terminals, the higher the quantitative indicator score (90), and the larger the current load coordination and control requirement. The terminals with the highest proportion are those with a power consumption difference of 1kW-10kW. Determine the priority ratio of these terminals. The higher the proportion of high priority terminals, the higher the quantitative indicator score (90), and the larger the current load coordination and control requirement. The higher the proportion of low priority terminals, the lower the quantitative indicator score (50), and the smaller the current load coordination and control requirement. Then, obtain all the scoring indicators and perform weighted calculations to execute the preset core score of the multi-level collaborative control process for load balancing.

[0062] The preset core scoring indicators for the multi-level collaborative control process for energy optimization include: a quantitative indicator of the economic expenditure range corresponding to the economic expenditure for eliminating the current terminal energy efficiency anomaly, that is, quantifying the economic expenditure range corresponding to the compensation of active / reactive power for eliminating the current terminal energy efficiency anomaly. The larger the range of different economic expenditures, the higher the difficulty of energy optimization, and the lower the quantitative indicator score. Correspondingly, in addition to the economic expenditure perspective, a quantitative indicator of the energy consumption level range corresponding to the current terminal energy efficiency anomaly can also be designed. That is, quantifying the energy consumption level range corresponding to the compensation of active / reactive power for eliminating the current terminal energy efficiency anomaly. The larger the range of different energy consumption levels, the higher the energy optimization demand, and the higher the quantitative indicator score. Then, all scoring indicators are obtained and weighted to calculate the preset core scoring indicators for the multi-level collaborative control process for energy optimization.

[0063] The preset core scoring indicators for the multi-level collaborative control process for implementing security protection include: a quantitative indicator of the proportion of fault levels, which determines the proportion of the current fault level as moderate or severe, with different proportions corresponding to different quantitative scores; a higher proportion of severe faults corresponds to a higher quantitative score. A quantitative indicator of the number of terminals affected by security isolation strategies can also be set, such as quantifying the range of isolated terminals involved in the security isolation strategy within a preset range of isolated terminals. The larger the range of different preset isolated terminals, the higher the security protection requirement and the higher the quantitative indicator score. All scoring indicators are then weighted and calculated to determine the preset core scoring indicators for the multi-level collaborative control process for implementing security protection.

[0064] In a specific embodiment, to ensure that the operation of the regional control unit does not conflict with the strategy issued by the superior and complies with the conditional execution of the subordinate, the accuracy and reliability of the multi-level collaborative control process are improved, and the effective implementation of load balancing, energy optimization, and safety protection control is guaranteed; the method further includes: In the multi-level collaborative control process that coordinates the load balancing, energy optimization and safety protection of control units at all levels, when the control process is executed by the regional control unit, if a preset key decision scenario is triggered, a verification command is sent to the upper and lower level control units of the regional control unit. If the verification by the upper and lower level control units is successful, the regional control unit is allowed to continue to execute the collaborative control process; if the verification by the upper and lower level control units fails, the regional control unit is allowed to be re-executed to execute the collaborative control process.

[0065] Specifically, the preset key decision scenarios include: adjusting preset key terminal load quota scenarios (i.e., involving the adjustment of key terminal load quota), adjusting preset key terminal energy consumption data scenarios (i.e., involving the adjustment of key terminal energy consumption), and obtaining preset key terminal security isolation strategies scenarios (i.e., involving key terminals performing security isolation operations).

[0066] Specifically, the conditions for successful verification using upper and lower level control units include: the upper level control unit's verification involving load quota adjustment, terminal energy consumption data adjustment, and isolation of preset key terminals does not conflict with the regional load quota, regional terminal energy allocation, and regional security isolation policies issued by the upper level control unit; and the lower level control unit's verification involving load quota adjustment, terminal energy consumption data adjustment, and isolation of preset key terminals is conditionally executed, i.e., the load quota adjustment does not exceed the preset load threshold, the terminal energy consumption adjustment does not exceed the operating energy consumption threshold, and key terminals are allowed to be isolated. When the conditions for successful verification by upper and lower level control units are met, a successful verification is sent back to the regional control unit, and the process involving load quota adjustment, terminal energy consumption data adjustment, and isolation of preset key terminals continues to be executed using the regional control unit. Otherwise, a failed verification is sent back to the regional control unit, and the process involving load quota adjustment, terminal energy consumption data adjustment, and isolation of preset key terminals continues to be re-acquired using the regional control unit.

[0067] In a specific embodiment, to better address different power consumption scenarios and emergencies, and to ensure the stable operation of the power distribution system, when a preset applicable scenario for corresponding dimension-based jump-level control is triggered, a jump-level control process can be executed to replace the original multi-level collaborative control process, improving the flexibility and timeliness of control. The method further includes: In the multi-level collaborative control process that coordinates the load balancing, energy optimization, and safety protection of control units at all levels, if it is determined that a pre-set applicable scenario for triggering jump control in the corresponding dimension of load balancing, energy optimization, and safety protection is triggered, including: a pre-set applicable scenario for jump control from level one to level three, and a pre-set applicable scenario for jump control from level three to level one, then a jump control process that coordinates the load balancing, energy optimization, and safety protection of control units at all levels is selected to replace the original multi-level collaborative control process, including: a bottom-up perception and early warning jump control process and a top-down decision-making and scheduling jump control process.

[0068] Specifically, the pre-defined applicable scenarios for triggering load balancing tiered control in corresponding dimensions are as follows. Among these, the pre-defined applicable scenarios for tiered control at levels one to three in the corresponding dimensions include: extreme load scenarios exceeding the processing capacity of the regional control unit, i.e., a regional terminal load instantaneously exceeding its quota by 90%. In such cases, a bottom-up perception and early warning tiered control process is adopted, specifically including: real-time collection of terminal load data using the terminal control unit; aggregation of regional terminal load data using the regional control unit, analysis and judgment of whether the regional terminal load instantaneously exceeds its quota by 80%; if the regional terminal load instantaneously exceeds its quota by 90%, no same-level load coordination judgment is performed, and the regional load demand is directly updated and reported based on the regional terminal load; subsequently, the top-down collaborative control process in the original multi-level collaborative control process of load balancing is executed.

[0069] Among them, the three-level to one-level jump control of the corresponding dimension is preset applicable scenarios, such as: preset terminal load scenario, that is, it is necessary to control according to the preset terminal load of global balance. In this case, a top-down decision and scheduling jump control process is adopted, which specifically includes: using the main control unit to directly generate the preset terminal load allocation strategy and send it to the terminal control unit; then using the terminal control unit to adjust the terminal operating power by controlling the switch of the power distribution cabinet according to the decomposed load quota.

[0070] Specifically, for the preset applicable scenarios of tiered control in the corresponding dimension for triggering energy optimization, including the preset applicable scenarios of tiered control from level one to level three in the corresponding dimension, such as extreme energy consumption scenarios that exceed the processing capacity of the regional control unit, i.e., the energy efficiency optimization demand of the regional terminal exceeds 90% of the power supply, a bottom-up sensing and early warning tiered control process is adopted, which specifically includes: The terminal control unit collects real-time energy consumption data from the terminals and calculates energy efficiency parameters. The regional control unit aggregates regional terminal energy consumption data and energy efficiency parameters. When the energy efficiency optimization demand corresponding to the elimination of abnormal regional terminal energy efficiency data exceeds 90% of the power supply capacity, the regional terminal energy efficiency optimization demand is calculated, the regional energy consumption demand is updated, and reported to the main control unit, replacing the upload to the regional control unit for the same-level energy consumption coordination condition trigger judgment. Subsequently, the top-down collaborative control process in the original multi-level collaborative control process of energy optimization is executed.

[0071] Among them, the corresponding dimension of the three-level to one-level jump control is preset to apply to scenarios such as: power grid curtailment, directly cutting off non-critical terminals; the top-down decision-making and scheduling jump control process specifically includes: combining the peak and valley electricity prices of the day and the energy consumption demand of key terminals in the region, using the main control unit to formulate a global energy allocation strategy through energy allocation algorithms, and sending it to the terminal control unit; then using the terminal control unit to adjust the power operation mode of the terminal in each time period by controlling the switch of the distribution cabinet according to the corresponding regional terminal energy allocation strategy.

[0072] Specifically, for the preset applicable scenarios of the corresponding dimension of safety protection jump-level control, including the preset applicable scenarios of the first to third level jump-level control of the corresponding dimension, such as: the fault type is extremely severe; a bottom-up perception and early warning jump-level control process is adopted, which specifically includes: using the terminal control unit to collect terminal electrical parameters, equipment status parameters and environmental data in real time, using deep learning algorithms to determine whether a fault has occurred and the fault type and level, obtaining or predicting the terminal fault and the fault type and level, and if the fault type is extremely severe and the upload to the regional control unit times out, local protection is activated; using deep learning algorithms to determine whether the fault has spread and the degree of fault spread, and uploading the fault location and fault spread results to the main control unit; subsequently, the top-down collaborative control process in the original multi-level collaborative control process of safety protection is executed.

[0073] Among them, the corresponding dimension of the three-level to one-level jump control is preset to apply to the following scenarios: when a fault is detected in the area control unit, the top-down decision-making and scheduling jump control process specifically includes: using the main control unit to obtain the terminal safety isolation policy based on the fault location and fault propagation results through deep learning algorithms and sending it to the terminal control unit; then using the terminal control unit to adjust whether the terminal is isolated by controlling the switch of the power distribution cabinet according to the corresponding area terminal safety isolation policy.

[0074] like Figure 4 As shown, this embodiment discloses a distributed control system for a power distribution cabinet based on the Internet of Things, including: The power distribution topology construction module 101 is used to construct a multi-level power distribution topology network, including: a primary topology layer with primary distribution cabinets, a secondary topology layer with multiple secondary distribution cabinets, and a tertiary topology layer with multiple terminal distribution cabinets; each distribution cabinet integrates a communication gateway and environmental sensors, and is equipped with corresponding topology nodes and power distribution switches. The control unit deployment module 102 is used to deploy hierarchical distributed control units, including: a main control unit deployed in a primary distribution cabinet, an area control unit deployed in a secondary distribution cabinet, and a terminal control unit deployed in a tertiary distribution cabinet; The communication network establishment module 103 is used to establish an Internet of Things communication network covering all levels of power distribution cabinets to realize point-to-point communication between control units at all levels; The control decision design module 104 is used to design hierarchical distributed control decisions based on the constructed multi-level power distribution topology network and hierarchical distributed control units, including: designing the main control unit to be responsible for global load balancing, energy distribution and security policy formulation and distribution; designing the regional control unit to be responsible for regional load scheduling, energy optimization and anomaly handling within the region; and designing the terminal control unit to be responsible for local load control, energy consumption execution and real-time security protection. The collaborative control execution module 105 is used to coordinate the execution of multi-level collaborative control processes for load balancing, energy optimization and safety protection by control units at all levels based on hierarchical distributed control decisions. These processes include: a multi-level bottom-up perception and early warning collaborative control process and a multi-level top-down decision and scheduling collaborative control process.

[0075] A specific embodiment further includes: The collaborative control execution optimization module 106 is used to coordinate the execution of multi-level collaborative control processes for load balancing, energy optimization, and safety protection by control units at all levels. It determines whether conflicts exist between different dimensions of multi-level collaborative control processes at the same time, identifies and acquires conflicting multi-level collaborative control processes of different dimensions, and identifies whether any acquired or predicted fault levels are classified as severe faults. If a severe fault level is identified, the multi-level collaborative control process for safety protection is executed first. If no severe fault level is identified, each multi-level collaborative control process is evaluated according to the corresponding preset core scoring indicators, and a score is acquired for each multi-level collaborative control process. Based on the scores acquired from the conflicting multi-level collaborative control processes, the priority of executing the multi-level collaborative control processes for load balancing, energy optimization, and safety protection is set from high to low, with the highest priority multi-level collaborative control process being executed first.

[0076] The collaborative control execution optimization module 106 is further configured to, in the multi-level collaborative control process of coordinating load balancing, energy optimization, and security protection of control units at all levels, when using the regional control unit to execute the control process, determine if a preset key decision scenario is triggered, and then send a verification command to the upper and lower level control units of the regional control unit. If the verification by the upper level control unit regarding load quota adjustment, terminal energy consumption data adjustment, and preset key terminal isolation does not conflict with the regional load quota, regional terminal energy allocation, and regional security isolation strategy issued by the upper level control unit, and the verification by the lower level control unit regarding load quota adjustment, terminal energy consumption data adjustment, and preset key terminal isolation is conditionally executed by the lower level control unit, then a successful verification is sent back to the regional control unit, and the process involving load quota adjustment, terminal energy consumption data adjustment, and preset key terminal isolation is continued to be executed using the regional control unit; otherwise, a verification failure is sent back to the regional control unit, and the process involving load quota adjustment, terminal energy consumption data adjustment, and preset key terminal isolation is re-acquired using the regional control unit.

[0077] This application also discloses a computer-readable storage medium.

[0078] Specifically, the computer-readable storage medium stores a computer program that can be loaded by a processor and executed, such as the distributed control method for switching of the power distribution cabinet based on the Internet of Things described above. The computer-readable storage medium includes, for example, various media that can store program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0079] This application also discloses a computer device.

[0080] Specifically, the computer device includes a memory and a processor. The memory stores a computer program that can be loaded by the processor and executed to perform the aforementioned distributed control method for the switchgear based on the Internet of Things.

[0081] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A distributed control method for switches in a power distribution cabinet based on the Internet of Things, characterized in that, include: A multi-level power distribution topology network is constructed, including: a primary topology layer with primary distribution cabinets, a secondary topology layer with multiple secondary distribution cabinets, and a tertiary topology layer with multiple terminal distribution cabinets; each distribution cabinet integrates a communication gateway and environmental sensors, and is equipped with corresponding topology nodes and power distribution switches; Deploy a hierarchical distributed control unit, including: a main control unit deployed in a primary distribution cabinet, an area control unit deployed in a secondary distribution cabinet, and a terminal control unit deployed in a tertiary distribution cabinet; Establish an Internet of Things (IoT) communication network covering all levels of power distribution cabinets to enable point-to-point communication between control units at all levels; Based on the constructed multi-level power distribution topology network and hierarchical distributed control units, hierarchical distributed control decisions are designed, including: designing a main control unit responsible for global load balancing, energy distribution and security policy formulation and distribution; designing regional control units responsible for regional load scheduling, energy optimization and anomaly handling within their respective regions; and designing terminal control units responsible for local load control, energy consumption execution and real-time security protection. Based on hierarchical distributed control decision-making, a multi-level collaborative control process is established to coordinate the load balancing, energy optimization and safety protection of control units at all levels. This includes a multi-level bottom-up perception and early warning collaborative control process and a multi-level top-down decision-making and scheduling collaborative control process.

2. The distributed control method for switches in a distribution cabinet based on the Internet of Things as described in claim 1, characterized in that, Based on hierarchical distributed control decision-making, the multi-level collaborative control process that coordinates the load balancing of control units at all levels includes: The terminal control unit collects terminal load data and environmental data in real time, compares them with a preset dynamic load threshold based on the environmental data, and obtains terminal load over-limit data or predicts terminal over-limit data through deep learning algorithms. The regional control unit summarizes regional terminal load data and analyzes whether the regional terminal load triggers the peer load coordination condition. The peer load coordination condition refers to the ability to coordinate and schedule regional terminal loads to eliminate terminal load over-limit. If the peer load coordination condition is triggered, a greedy algorithm is used to obtain the optimal strategy for regional terminal load coordination to eliminate terminal load over-limit. If the peer load coordination condition is not triggered, the regional terminal load gap is calculated and the regional load demand is updated and reported. Based on the regional load demand and global power supply capacity reported by the regional control unit, the main control unit generates a global load quota allocation strategy using a linear programming algorithm and preset load priorities for each region, and sends it to the regional control unit. Then, the regional control unit decomposes the regional load quota based on preset terminal load priorities and sends it to the terminal control unit. Finally, the terminal control unit adjusts the terminal operating power by controlling the switch of the power distribution cabinet according to the decomposed load quota.

3. The distributed control method for switches in a distribution cabinet based on the Internet of Things as described in claim 2, characterized in that, Based on hierarchical distributed control decision-making, the multi-level collaborative control process that coordinates control units at all levels to complete control command interaction and execute energy optimization includes: The system utilizes a terminal control unit to collect real-time energy consumption data from terminals and calculate energy efficiency parameters. These parameters are then compared with preset energy efficiency parameters to obtain abnormal terminal energy efficiency data. Alternatively, a deep learning algorithm can be used to predict terminal energy consumption data and obtain abnormal energy efficiency data. A regional control unit aggregates regional terminal energy consumption data and abnormal energy efficiency data, analyzing whether the regional terminal energy consumption data triggers a peer-to-peer energy consumption coordination condition. This condition refers to the ability to coordinate and compensate for energy consumption data through regional terminal energy consumption to eliminate abnormal terminal energy efficiency. If the peer-to-peer coordination condition is triggered, a multi-objective optimization algorithm is used to obtain the optimal strategy for regional terminal energy consumption coordination to eliminate abnormal terminal energy efficiency. If the condition is not triggered, the regional terminal energy efficiency optimization requirement is calculated, and the regional energy consumption requirement is updated and reported. Combining the daily peak and off-peak electricity prices and regional energy consumption demand, the main control unit uses an energy allocation algorithm to formulate a global energy allocation strategy and distributes it to the regional control unit. Then, the regional control unit uses the global energy allocation strategy and the energy consumption characteristics of the terminals in the region to formulate a corresponding regional terminal energy allocation strategy and distributes it to the terminal control unit. Finally, the terminal control unit uses the corresponding regional terminal energy allocation strategy to adjust the power operation mode of the terminals in different time periods by controlling the switch of the distribution cabinet.

4. The distributed control method for switches in a distribution cabinet based on the Internet of Things according to claim 3, characterized in that, Based on hierarchical distributed control decision-making, the multi-level collaborative control process that coordinates control units at all levels to complete control command interaction in order to execute safety protection includes: The terminal control unit collects terminal electrical parameters, equipment status parameters, and environmental data in real time. It uses deep learning algorithms to determine whether a fault has occurred and the type and severity of the fault. It acquires or predicts terminal faults, their types and severity, and uploads the data to the regional control unit. When the fault severity is determined to be severe, local protection is activated. Based on the received terminal faults, their types and severity, and the local protection status, the regional control unit uses deep learning algorithms to determine whether fault propagation has occurred and the extent of the propagation. If it is determined that the fault has propagated to other areas, the regional control unit continues to perform regional-level data cross-validation and fault location, and uploads the fault location and propagation results to the main control unit. Based on fault location and fault propagation results, the main control unit uses a deep learning algorithm to obtain a global safety isolation policy and distributes it to the regional control unit. Based on the fault location and fault propagation results and the global regional safety isolation policy, the regional control unit uses a deep learning algorithm to obtain the corresponding regional terminal safety isolation policy and distributes it to the terminal control unit. Then, the terminal control unit uses the corresponding regional terminal safety isolation policy to adjust whether the terminal is isolated by controlling the switch of the distribution cabinet.

5. The distributed control method for switches in a distribution cabinet based on the Internet of Things according to claim 4, characterized in that, In the multi-level collaborative control process that coordinates the load balancing, energy optimization and safety protection of control units at all levels, it is determined whether there are conflicts in the multi-level collaborative control processes of different dimensions at the same time, and the conflicting multi-level collaborative control processes of different dimensions are identified and obtained; based on the conflicting multi-level collaborative control processes of different dimensions, it is determined whether there is a fault level that is obtained or predicted to occur as a serious fault level. If it is determined that there is, the multi-level collaborative control process for safety protection is executed first. If the condition is not met, the multi-level collaborative control process for each dimension is evaluated according to the corresponding dimension's preset core scoring indicators to obtain the corresponding score for each dimension's multi-level collaborative control process. Specifically, the preset core scoring indicators for the multi-level collaborative control process executing load balancing include: a quantitative indicator of the proportion of the difference between the current terminal load and the terminal load limit falling within different preset ranges; the preset core scoring indicators for the multi-level collaborative control process executing energy optimization include: a quantitative indicator of the economic expenditure range for eliminating current terminal energy efficiency anomalies; and the preset core scoring indicators for the multi-level collaborative control process executing security protection include: a quantitative indicator of the proportion of each fault level. Based on the scores obtained from the multi-level collaborative control processes of different dimensions with conflicting conditions, the priorities of the multi-level collaborative control processes executing load balancing, energy optimization, and security protection are set from high to low, with higher-priority multi-level collaborative control processes being executed first.

6. The distributed control method for switches in a distribution cabinet based on the Internet of Things as described in claim 5, characterized in that, Also includes: In the multi-level collaborative control process that coordinates load balancing, energy optimization, and security protection among control units at all levels, when using a regional control unit to execute the control process, if a preset key decision scenario is triggered, including: adjusting preset key terminal load quotas, adjusting preset key terminal energy consumption data, or obtaining preset key terminal security isolation strategies, then a verification command is sent to the upper and lower level control units of the regional control unit. If the verification by the upper level control unit regarding load quota adjustment, terminal energy consumption data adjustment, and preset key terminal isolation does not conflict with the regional load quota, regional terminal energy allocation, and regional security isolation strategies issued by the upper level control unit, and the verification by the lower level control unit regarding load quota adjustment, terminal energy consumption data adjustment, and preset key terminal isolation is conditionally executed by the lower level control unit, then a successful verification is sent back to the regional control unit, and the process involving load quota adjustment, terminal energy consumption data adjustment, and preset key terminal isolation continues to be executed using the regional control unit; otherwise, a verification failure is sent back to the regional control unit, and the process involving load quota adjustment, terminal energy consumption data adjustment, and preset key terminal isolation continues to be re-acquired using the regional control unit.

7. A distributed control system for a power distribution cabinet based on the Internet of Things, characterized in that, include: The power distribution topology construction module is used to build a multi-level power distribution topology network, including: a primary topology layer with primary distribution cabinets, a secondary topology layer with multiple secondary distribution cabinets, and a tertiary topology layer with multiple terminal distribution cabinets; each distribution cabinet integrates a communication gateway and environmental sensors, and is equipped with corresponding topology nodes and power distribution switches; The control unit deployment module is used to deploy hierarchical distributed control units, including: a main control unit deployed in a primary distribution cabinet, an area control unit deployed in a secondary distribution cabinet, and a terminal control unit deployed in a tertiary distribution cabinet; The communication network establishment module is used to establish an Internet of Things (IoT) communication network covering all levels of power distribution cabinets, enabling point-to-point communication between control units at all levels. The control decision design module is used to design hierarchical distributed control decisions based on the constructed multi-level power distribution topology network and hierarchical distributed control units. This includes: designing the main control unit to be responsible for global load balancing, energy allocation and security policy formulation and distribution; designing the regional control unit to be responsible for regional load scheduling, energy optimization and anomaly handling within the region; and designing the terminal control unit to be responsible for local load control, energy consumption execution and real-time security protection. The collaborative control execution module is used to coordinate the execution of multi-level collaborative control processes for load balancing, energy optimization, and safety protection by control units at all levels based on hierarchical distributed control decisions. This includes: a multi-level bottom-up perception and early warning collaborative control process and a multi-level top-down decision and scheduling collaborative control process.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform the method as described in any one of claims 1 to 6.

9. A computer device, characterized in that, The computer device includes a memory, a processor, and a program stored in and executable on the memory, the program being executed by the processor to implement the steps of the method as described in any one of claims 1 to 6.

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