Intelligent switch cabinet control system and control method

Abstract

The invention relates to the field of electric power automation, and discloses an intelligent switch cabinet control system and method, and the system comprises sensing equipment which is used for collecting the voltage, current and temperature multi-dimensional operation parameters of a power grid; the edge calculation module is in communication connection with the sensing equipment and is configured to generate a switch control strategy through dynamic modeling and an optimization algorithm based on the collected operation parameters; a control execution module which is in communication connection with the edge calculation module and is used for receiving the switch control strategy and adjusting the operation state of the switch cabinet; and the cloud platform is in communication connection with the edge computing module and is used for storing operation parameters, fault response records and an optimization control model and providing global data analysis and optimization services. By adopting the technical scheme of combining the sensing equipment and the edge calculation module, real-time acquisition and dynamic modeling of multi-dimensional parameters of the power grid are realized, and the technical effect of quickly capturing the running state change of the power grid is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power automation, and in particular to an intelligent switch cabinet control system and a control method. Background Art

[0002] The field of power automation is widely used in the management of power substations, power dispatching of industrial facilities, and power supply guarantee of large data centers. However, as society's requirements for the continuity and reliability of power systems continue to increase, existing technical solutions have gradually exposed many shortcomings in practical applications. Traditional switchgear control systems mostly rely on timed tasks and simple threshold judgment mechanisms. Although this method can achieve a certain degree of automation, it is too rigid and lacks flexibility when the operating status of the power grid is complex and changeable. These systems usually use fixed time intervals or preset thresholds to trigger control actions, but when faced with sudden failures or new operating modes, it is difficult to identify and adjust in time, resulting in slow operation response.

[0003] In recent years, although some systems have begun to introduce historical data analysis and edge computing technologies, their intelligence level is still limited. These systems rely more on static historical data templates for analysis and lack dynamic modeling and real-time learning capabilities, which makes them unable to handle new fault modes in power grid operation. In addition, such systems often cannot autonomously learn and summarize historical fault modes, and it is difficult to flexibly adapt to the real-time changing power grid status, resulting in high maintenance costs under complex working conditions.

[0004] At the same time, data transmission security is also a weak link in traditional control systems. With the improvement of the level of intelligent power grid, the amount of data communication between devices has increased dramatically. However, the existing system lacks effective protection measures in data encryption, communication protection and eavesdropping detection, and is vulnerable to external attacks or data leakage risks.

[0005] Therefore, the shortcomings of the existing switchgear control system in terms of intelligent analysis capabilities, dynamic adaptability, fault prediction accuracy and data communication security have significantly limited its wider application in modern power grids. How to improve the intelligence level of the switchgear control system so that it can achieve real-time monitoring, fault prediction and rapid response in complex power grid environments while ensuring the security of data transmission has become an important technical issue to be solved.

[0006] Therefore, the present invention proposes an intelligent switch cabinet control system and control method to solve the deficiencies of the prior art. Summary of the invention

[0007] In view of the deficiencies in the prior art, the present invention provides an intelligent switch cabinet control system and control method, which solves the problems of insufficient intelligent analysis capability and dynamic adaptability of the existing switch cabinet control system, difficulty in real-time monitoring of grid status changes, prediction of new faults and rapid response, and weak data communication security.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent switch cabinet control system, comprising: Sensor equipment, used to collect multi-dimensional operating parameters of the power grid, such as voltage, current, and temperature; an edge computing module, in communication with the sensor device, configured to generate a switch control strategy based on the collected operating parameters through dynamic modeling and optimization algorithms; A control execution module, which is in communication with the edge computing module and is used to receive a switch control strategy and adjust the operating state of the switch cabinet; A cloud platform, which is in communication with the edge computing module and is used to store operating parameters, fault response records, and optimization control models, and provide global data analysis and optimization services; The data communication security module is deployed between the edge computing module and the cloud platform and is used for data encryption, eavesdropping detection, and security key distribution to ensure the security and integrity of data transmission.

[0009] Preferably, the sensing device comprises: Voltage sensors are used to monitor voltage changes at key nodes of the switch cabinet in real time; Current transformers, used to obtain the current strength of the circuit and generate safety assessment data; Temperature sensor, used to monitor the temperature distribution of the operating environment inside the switch cabinet.

[0010] Preferably, the edge computing module generates a switch control strategy based on an optimization control algorithm, and the optimization control algorithm associates the grid operation state with the control input through dynamic modeling to minimize the system's operation deviation and control energy consumption.

[0011] Preferably, the data communication security module includes: An encryption module is used to optimize encryption parameters based on the signal-to-noise ratio of the communication channel and improve the anti-interference ability of data transmission; An eavesdropping detection module is used to detect eavesdropping during data transmission through the quantum key distribution protocol; The dynamic key distribution module is used to generate and distribute keys based on dynamic security requirements to ensure the security of data transmission.

[0012] Preferably, the cloud platform analyzes and optimizes historical fault data and real-time operation data through a Bayesian learning model, and dynamically updates fault modes and optimizes control strategies.

[0013] Preferably, the control execution module includes a relay and a circuit breaker, the relay adjusts the contact closing or opening state according to the control signal of the edge computing module, and the circuit breaker is used to quickly isolate the fault area and restore power supply to the non-fault area.

[0014] Preferably, the cloud platform performs group analysis on the power grid status based on the stored power grid operation data through a dynamic clustering algorithm to identify potential abnormal operation modes.

[0015] Preferably, the edge computing module dynamically allocates encryption and monitoring resources through a resource optimization control algorithm to meet security requirements while minimizing the occupancy of computing resources.

[0016] Preferably, the data communication security module adjusts bandwidth, signal-to-noise ratio and encryption parameters based on a communication channel capacity optimization algorithm to achieve a balance between efficient and secure data transmission.

[0017] The present invention also provides an intelligent switch cabinet control method, comprising the following steps: Collecting the operating parameters of the power grid through sensor equipment; The edge computing module dynamically models and optimizes the operating parameters to generate the optimal switch control strategy; The control execution module adjusts the switch cabinet operation status according to the switch control strategy, including isolating the fault area and restoring power supply to the non-fault area; The data communication security module encrypts the operating parameters and control instructions to ensure the security of data transmission; The cloud platform stores operating data and optimizes control strategies through historical data analysis.

[0018] The present invention provides an intelligent switch cabinet control system and control method, which have the following beneficial effects: 1. The present invention adopts a technical solution combining sensor equipment with edge computing modules to achieve real-time collection and dynamic modeling of multi-dimensional parameters of the power grid, and achieves the technical effect of quickly capturing changes in the operating status of the power grid. Compared with the control method that relies on fixed thresholds and time intervals in the prior art, it solves the problems of insufficient monitoring accuracy and delayed response.

[0019] 2. The present invention achieves the technical effect of accurately predicting and quickly responding to power grid faults through the technical solution of collaborative optimization of cloud platform and edge computing module, combined with historical data analysis and real-time strategy generation. Compared with the fault handling method of the prior art that relies on static rules, it effectively solves the problem of poor prediction accuracy and long response time for new faults under complex working conditions.

[0020] 3. The present invention adopts a technical solution combining dynamic key distribution and eavesdropping detection in the data communication security module to ensure the security and integrity of the data transmission process and achieve the technical effect of effectively preventing data leakage. Compared with the existing solutions with weak data communication protection measures, the risk of data transmission being easily tampered or eavesdropped is solved.

[0021] 4. The present invention improves the operating efficiency and maintenance convenience of the system through the distributed cloud platform architecture and the technical solution of optimizing the allocation of edge computing resources, and achieves the technical effect of reducing maintenance costs. Compared with the traditional solution that relies on manual intervention and single regulation in the prior art, it solves the problem of high system maintenance cost and low efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a system architecture diagram of the present invention; Figure 2 It is a schematic diagram of the structure of the sensor device of the present invention; Figure 3 It is a structural schematic diagram of a data communication security module of the present invention; Figure 4 It is a flow chart of the steps of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings of the present invention specification to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] See also Figure 1-Figure 3 , an embodiment of the present invention provides an intelligent switch cabinet control system, comprising: Sensor equipment, used to collect multi-dimensional operating parameters of the power grid, such as voltage, current, and temperature; Specifically, the sensor equipment of the intelligent switch cabinet control system is the core data acquisition module of the system, which is used to collect multi-dimensional parameters of the power grid operation status, including voltage, current and temperature. This module directly affects the accuracy and response speed of subsequent modeling analysis and control strategies. In general, sensor equipment needs to have high precision, high sensitivity and strong anti-interference ability to adapt to the complex power grid operation environment. The sensor equipment not only provides real-time monitoring data, but also improves data quality through preliminary processing and abnormal data elimination, providing accurate input for the edge computing module.

[0025] As an option, the design of the sensor equipment needs to take into account the sampling frequency, signal stability and equipment durability. Especially in a highly volatile power grid environment, ensuring the continuity and reliability of data collection is key.

[0026] In this embodiment, the sensing equipment consists of a voltage sensor, a current transformer and a temperature sensor. They are distributed at key nodes of the switch cabinet and are used to collect key parameters of power grid operation in real time. The following is a specific technical implementation method: Voltage sensors use high-precision sensing technology based on the Hall effect, which can sense changes in AC or DC voltage and cover the measurement range from low voltage to high voltage (such as 220V to 10kV). In general, voltage sensors use proportional conversion circuits to reduce high voltage signals to safe low voltage signals for subsequent processing.

[0027] The voltage sensor uses the Hall effect principle to detect voltage by using the interaction between current and magnetic field. Its output signal can be expressed as:

[0028] in: It is the low voltage signal output by the sensor; is the original high voltage signal input; is the proportionality factor, which depends on the design parameters and measurement range of the sensor; is the offset, which is used to correct the zero drift of the sensor.

[0029] Anti-interference design, specifically, in order to improve the anti-interference performance of the voltage sensor, a low-pass filter is added in this embodiment to weaken high-frequency electromagnetic interference. The cutoff frequency of the filter can be calculated by the following formula:

[0030] in: is the cutoff frequency of the filter; is the resistance value of the filter circuit; is the capacitance value of the filter circuit.

[0031] In a possible implementation, the design frequency range of the filter is selected to be 1 kHz to 10 kHz to cover the main noise frequency band of the power grid.

[0032] The current transformer uses the principle of electromagnetic induction to convert the original high current signal into a low current signal, which is convenient for safe processing and accurate measurement. Its core components include primary winding, secondary winding and core structure.

[0033] The output signal of the current transformer can be expressed as:

[0034] in: is the current signal output by the secondary winding; It is the original input high current signal; is the turns ratio, which is the ratio of the number of turns in the primary winding to the number of turns in the secondary winding.

[0035] Overload protection design, generally, in order to prevent overload caused by input current exceeding the rated range of the device, a current limiting protection device is designed in this embodiment. When it is detected that the input current exceeds the rated range, the protection mechanism is automatically triggered to block the input signal to avoid damage to the device.

[0036] Linear calibration, in actual measurement, in order to improve the linearity of the output signal, the current transformer uses a shunt resistor to convert the output current into a voltage. The specific conversion formula is:

[0037] in: is the voltage signal on the shunt resistor; is the resistance of the shunt resistor.

[0038] The temperature sensor is used to detect the temperature distribution of the internal environment of the switch cabinet, and its measurement range is usually -40° C. to 150° C. In this embodiment, the temperature sensor includes two types: contact thermocouple and non-contact infrared temperature measuring device.

[0039] For non-contact infrared temperature measurement devices, their working principle is based on the Stefan-Boltzmann law, which calculates the temperature by measuring the infrared radiation intensity on the surface of an object. The calculation formula is as follows:

[0040] in: is the surface temperature of the object (in K); is the infrared radiation power; is the Stefan-Boltzmann constant, approximately ; is the radiating surface area of ​​the object.

[0041] In one possible implementation, the temperature sensor transmits measurement data through a wireless communication module (such as LoRa) to reduce complex wiring requirements and improve the deployment flexibility of the device.

[0042] Real-time anomaly detection and elimination, in order to ensure data quality, the sensor equipment is equipped with an abnormal data detection and elimination module, which uses a sliding window algorithm to calculate the data mean and variance in real time. The conditions for outlier elimination are:

[0043] in: is the current data value; is the mean of the sliding window; is the standard deviation of the sliding window.

[0044] Low power design, in some embodiments, the sensor device adopts a low power design, and reduces energy consumption by dynamically adjusting the sampling frequency. For example, the sampling frequency of the voltage and current sensors is 1kHz when the grid is running stably, and it is increased to 10kHz in the fault detection mode.

[0045] Through the above design, the sensing device realizes high-precision, multi-dimensional real-time acquisition of key parameters such as grid voltage, current, and temperature. At the same time, combined with filtering, anomaly detection, and low-power design, the reliability and adaptability of data acquisition are further improved, providing high-quality data input for subsequent modeling analysis and control optimization. The edge computing module is connected to the sensing device in communication and is configured to generate a switch control strategy based on the collected operating parameters through dynamic modeling and optimization algorithms; Specifically, the edge computing module is one of the core components of the intelligent switch cabinet control system. It directly receives real-time data from sensor devices and generates the optimal control strategy for the power grid operation status through efficient dynamic modeling and optimization control algorithms. The collaborative relationship with the sensor device is data input, and the collaborative relationship with the control execution module is strategy output. In general, this module needs to improve computing efficiency while ensuring the accuracy of the calculation results to meet the dynamic control requirements in a complex power grid environment.

[0046] As an option, the edge computing module can combine multiple modeling methods, optimization algorithms and adaptive strategy adjustment technologies to further enhance the ability to respond to sudden failures and complex working conditions.

[0047] In this embodiment, the edge computing module mainly includes a dynamic modeling module, an optimization control module, and a strategy generation module. These submodules achieve efficient operation through parallel computing and collaborative optimization. The following is a detailed technical description: Dynamic modeling module,The dynamic modeling module is used to construct a mathematical model of the power grid state and describe the dynamic changes of the power grid operation parameters over time.

[0048] Mathematical model description: In general, the power grid state exhibits strong coupling and nonlinear characteristics. This embodiment adopts a nonlinear dynamic system for modeling, and its form is:

[0049] in: is the time variable; is the state variable vector of the power grid, including voltage, current, load, etc.; is the control input vector, which represents the effect of switchgear operation on the grid state; is the state transfer function, which represents the nonlinear relationship between the state variable and the control input; is the noise interference term, which is used to simulate the impact of the external environment on the power grid state.

[0050] Selection of state transfer function, as an implementation method, the state transfer function It can take the following polynomial form:

[0051] in: , are the dimensions of state variables and control inputs, respectively; is the coefficient describing the interaction between the state variable and the control input; is the coefficient of the time-dependent term; is the order of the time-dependent polynomial.

[0052] Parameter estimation method, the parameters in the state transfer function can be obtained through historical data training. Specifically, this embodiment uses the least squares method to estimate the parameters:

[0053] in: is the observed state variable vector; is the characteristic matrix containing state variables, control inputs and time terms; is the parameter vector to be estimated; is the optimal parameter vector.,Optimization control module,The optimization control module is based on the output of the,dynamic modeling module and generates a control strategy through an optimization,algorithm, with the goal of minimizing the system operation deviation and,control cost.

[0054] In this embodiment, the objective function of the optimization control is defined as:

[0055] in: is the system comprehensive cost function; is the state deviation cost function; is the control cost function; To optimize the time window.

[0056] Specifically, the state deviation cost function can be expressed as:

[0057] in: is the bias weight coefficient; is the target value of the state variable; is the number of state variables.

[0058] The control cost function is:

[0059] in: is the weight coefficient of the control input; The number of control inputs.

[0060] Optimization method,The solution to the optimization problem adopts the combination of variational principle and gradient descent method. The specific steps include: According to the variational principle, the partial derivative of the objective function with respect to the control input is derived, and the necessary conditions for optimization are obtained.

[0061] Use gradient descent to iteratively update the control input: in: For the The control input for the iteration; is the learning rate; is the gradient of the objective function to the control input. Strategy generation module,The strategy generation module converts the optimization results into control instructions for the switch cabinet and passes them to the control execution module.

[0062] Instruction formatting, optimization control The continuous signal output needs to be converted into a discrete switch instruction. As an option, the present embodiment uses a piecewise linear interpolation method to discretize the continuous signal.

[0063] Redundancy check, during data transmission, the control instruction may be interfered by noise. In this embodiment, the strategy generation module uses a cyclic redundancy check (CRC) algorithm to check the control instruction to ensure the accuracy of data transmission.

[0064] Delay optimization,To reduce the delay of control instructions, the strategy generation module adopts a multi-threaded architecture,,allowing modeling, optimization and instruction generation to run in parallel.,Through dynamic priority scheduling, it ensures that control instructions of key nodes are,transmitted first.

[0065] In some embodiments, the edge computing module also integrates a fault pattern recognition function. When the similarity between real-time data and historical fault patterns exceeds a set threshold, the module triggers a quick response mode and directly calls a preset fault handling strategy.

[0066] In some other embodiments, the edge computing module supports interaction with an external power grid management system (EMS). Through two-way data transmission, the module can combine local optimization results with global scheduling strategies to achieve multi-level collaborative optimization.

[0067] Through the modular design of dynamic modeling, optimization control and strategy generation, the edge computing module in this embodiment can operate efficiently in a complex power grid environment. Its unique parameter estimation, objective function design and parallel processing technology provide intelligent and real-time guarantees for the switch cabinet control system. At the same time, by introducing fault identification and global scheduling support functions, the adaptability and application scope of the module are further expanded.

[0068] A control execution module, which is in communication with the edge computing module and is used to receive a switch control strategy and adjust the operating state of the switch cabinet; Specifically, the control execution module, as the core execution unit of the intelligent switch cabinet control system, is responsible for converting the optimization control strategy generated by the edge computing module into specific hardware operations. It mainly completes the fault isolation, power supply restoration and circuit protection functions of the power grid by adjusting the status of relays and circuit breakers. In general, the control execution module needs to have the characteristics of fast response, high precision and anti-interference to meet the dynamic adjustment requirements under complex working conditions.

[0069] As an option, the design of the control execution module needs to support multiple operation modes, including automated fault handling, emergency power off, and remote control. At the same time, to ensure the safety and reliability of operation, the module should provide a real-time status feedback mechanism and combine auxiliary electronic components to monitor and adjust key parameters.

[0070] The control execution module in this embodiment is composed of relays, circuit breakers and various auxiliary electronic components, which can efficiently perform power grid regulation tasks. The following is the specific technical implementation of each part: Implementation of relays, relays are the basic components of the control execution module, used to control the on and off state of the circuit. Its design focuses on response speed, contact stability and power consumption optimization.

[0071] The relay drives the coil by receiving the control signal, thereby changing the contact state. The relationship between the magnetic field generated by the electromagnetic coil and the input signal can be expressed as:

[0072] in: is the magnetic induction intensity in the coil; is the vacuum permeability; is the number of coil turns; is the input current; is the magnetic path length.

[0073] The open and closed state of the contacts is determined by the magnetic field strength. Decide when When the trigger threshold is exceeded, the contacts are closed, otherwise the contacts are open.

[0074] Anti-jitter design, in order to prevent the signal error caused by jitter of the relay contacts during the switching process, an anti-jitter circuit is added in this embodiment. The anti-jitter circuit uses an RC filter to smooth the signal, and its filtering time constant can be expressed as:

[0075] in: is the filtering time constant; is the resistance value; is the capacitance value.

[0076] Through reasonable choice and The value can ensure the stable switching of the relay contacts and avoid erroneous operation caused by signal jitter.

[0077] Energy consumption optimization: When the relay coil is continuously powered on, the power consumption will increase. In this embodiment, the pulse width modulation (PWM) technology is used to reduce energy consumption. The coil current is adjusted by the following formula:

[0078] in: is the average current of the coil, is the duty cycle of PWM; is the maximum current of the coil.

[0079] Implementation of circuit breakers. Circuit breakers are mainly used for overload protection and fault isolation of power grids. Their design needs to comprehensively consider breaking capacity, action time and arc suppression effect.

[0080] The breaking characteristics of the circuit breaker. The circuit breaker needs to quickly cut off the current under overload or short circuit conditions. The breaking time is determined by the following formula:

[0081] in: For the break time; is the fault current; is the rated current of the circuit breaker; The basic break time.

[0082] In a possible implementation, the circuit breaker has a built-in electronic control module to detect the magnitude of the current in real time. When the current exceeds a set threshold, the circuit breaker immediately triggers a disconnection action.

[0083] Arc suppression technology, during the high current breaking process, a high temperature arc may be generated between the contacts. In this embodiment, the arc is suppressed by combining magnetic field confinement and cooling materials. The arc energy is calculated by the following formula: in: is the arc energy; is the arc voltage; is the arc current; is the arc duration.

[0084] By optimizing the contact design and selecting high heat-resistant materials, the loss of equipment caused by arc can be significantly reduced.

[0085] Auxiliary electronic components include signal amplifiers, status monitoring modules and protection circuits, which are used to improve the accuracy of control signals and the safety of equipment operation.

[0086] Signal amplifier, which is used to amplify the control signal output by the edge computing module to ensure that it can stably drive the relay and circuit breaker. The gain of the amplifier can be expressed as:

[0087] in: is the voltage gain; is the output voltage after amplification; is the input voltage.

[0088] The status monitoring module monitors the operating status of relays and circuit breakers in real time and sends feedback data to the edge computing module. As an option, the monitoring module uses optocoupler isolation technology to avoid high-voltage signals interfering with low-voltage control circuits.

[0089] Protection circuit: In this embodiment, a surge protection circuit is designed to prevent transient high voltage from damaging the components of the control execution module. The protection circuit uses a varistor (MOV), and its trigger voltage is calculated by the following formula:

[0090] in: is the trigger voltage of the varistor; is the nominal voltage; is the voltage tolerance. The operation flow of the control execution module is as follows: Receive the control signal from the edge computing module and perform signal amplification and anti-interference processing.

[0091] Adjust the working status of relays and circuit breakers according to control signals, including switching circuits and isolating faults.

[0092] Monitor the operating status of the equipment in real time and transmit feedback data back to the edge computing module.

[0093] After the fault is eliminated, the relay and circuit breaker are automatically restored to their original states to ensure the normal operation of the power grid.

[0094] In some embodiments, the control execution module may also support multi-mode operation. For example, when the power grid detects a high load operation state, the module may switch to an energy-saving mode to reduce energy consumption by optimizing the contact operation frequency.

[0095] In another implementation, the module adds direct communication with the cloud platform. When the local control signal fails, the module can obtain the backup control strategy through the cloud to ensure the continuous and stable operation of the power grid.

[0096] The control execution module in this embodiment realizes efficient grid state regulation and fault response through the organic combination of relays, circuit breakers and auxiliary electronic components. The module's anti-interference design, arc suppression technology and surge protection function significantly improve the reliability and safety of the system. At the same time, the module's multi-mode operation and cloud collaboration capabilities provide strong support for grid operation under complex working conditions.

[0097] A cloud platform, which is in communication with the edge computing module and is used to store operating parameters, fault response records, and optimization control models, and provide global data analysis and optimization services; Specifically, the cloud platform is one of the core components of the intelligent switchgear control system, and is responsible for global data storage, analysis, and optimization. It provides continuous support for edge computing modules and control execution modules through efficient distributed architecture and intelligent algorithms, ensuring the stable operation of the system in a complex power grid environment. In general, the cloud platform needs to have efficient data processing capabilities and high security, especially when the power grid status is changeable or there are sudden failures.

[0098] As an option, the design of the cloud platform not only supports real-time data storage and analysis, but also enables dynamic model training and global optimization, providing theoretical support and data guarantee for the efficient operation of the power grid.

[0099] The cloud platform in this embodiment is composed of a data storage module, a data analysis module and a secure communication module. The following are detailed technical contents and implementation methods: Data storage module,The data storage module is the basic part of the cloud platform, responsible for storing real-time data from sensor devices and edge computing modules, as well as historical system operation data. Generally, this module adopts a distributed database architecture to improve storage efficiency and reliability.

[0100] Distributed storage design, in this embodiment, the data storage module adopts a master-slave replication strategy to achieve high availability. The master node is responsible for processing data write operations, and the slave node synchronously stores data to provide read services. Data consistency is achieved through a two-phase commit protocol (2PC), and its synchronization delay can be expressed by the following formula:

[0101] in: Delay for data synchronization; is the network bandwidth; For the The size of each data shard; The total number of data shards.

[0102] To optimize storage efficiency, the cloud platform separates data into two categories: hot data and cold data. Hot data is the key operating parameters monitored in real time and stored in the cache; cold data is the historical records stored in the disk or cloud storage service. The switching of hot and cold data is triggered by the following conditions:

[0103] in: is the time span of the data; The time threshold is set.

[0104] In a possible implementation, the storage time window of hot data is set to 24 hours, and data exceeding this time is automatically classified as cold data.

[0105] Data analysis module,The data analysis module is the core of the cloud platform, and its main functions include model training, pattern recognition and fault prediction.

[0106] Model training, the cloud platform dynamically trains historical data through the Bayesian learning model to update the power grid failure mode and optimization strategy. The goal of the Bayesian model is to calculate the posterior distribution of the parameters, the formula is:

[0107] in: For parameters The posterior probability of For data The likelihood function of ; For parameters The prior distribution of For data The marginal distribution of .

[0108] In one possible implementation, the cloud platform uses online Bayesian updating technology, that is, each time new data is received When , update the model parameters The posterior distribution of :

[0109] in: For in time Given data at any time In the case of The posterior probability indicates that the model parameters The probability distribution of is used to reflect the correction effect of the latest data on the model parameters; For data The likelihood function indicates that the hypothesis parameter is When, data The observation probability is used to measure the ability of the model to generate the current data under given parameters; For in time Given data at any time In the case of The posterior probability represents the updated distribution of model parameters based on the data at the previous moment, which is the core of recursive calculation in dynamic Bayesian learning. For data The marginal probability distribution of , which indicates the overall possibility of the current data.

[0110] Pattern recognition: The pattern recognition function uses clustering algorithms to group and analyze power grid operation data to identify potential abnormal patterns. The commonly used optimization goal is to minimize the variance of the data within the group:

[0111] in: is the number of clusters; For the clusters; For data points; For the The centroid of the clusters.

[0112] Specifically, this embodiment uses a density-based clustering algorithm (such as DBSCAN) to automatically discover abnormal points in the data and mark them as isolated points.

[0113] Fault prediction, the data analysis module combines historical data and real-time data to predict the power grid operation trend through linear regression and time series models. The prediction error is calculated by the following formula:

[0114] in: is the prediction error; is the number of data points; is the true value; is the predicted value.

[0115] Secure communication module,The secure communication module is responsible for the security of data interaction between the cloud platform and other modules, which is mainly achieved through quantum key distribution (QKD) and data encryption transmission.

[0116] Quantum key distribution, in this embodiment, the cloud platform uses the BB84 protocol to generate encryption keys, and its core principle is based on the non-replicability of quantum states. The specific steps include: The sender generates a random quantum state sequence and transmits it to the receiver through a quantum channel.

[0117] The receiver uses a random measurement basis to measure the quantum state and discloses the information of the measurement basis.

[0118] The two parties generate a shared key by comparing quantum states that are consistent with the measurement basis.

[0119] The efficiency of key generation is affected by the quantum bit error rate (QBER), and the security key rate is calculated by the following formula:

[0120] in: is the security key rate; is the original key rate; is the entropy function of the quantum bit error rate.

[0121] Data encryption transmission, in order to further improve the security of data transmission, the secure communication module adopts a transmission mechanism based on a symmetric encryption algorithm. Encryption strength It can be expressed as:

[0122] in: is the entropy of the key; is the mutual information between the attacker and the system.

[0123] The specific operation process of the cloud platform is as follows: Receive real-time data from sensor devices and edge computing modules and store them in a distributed database; Use data analysis modules to process historical data and real-time data, including failure mode extraction, trend prediction and model update; Feedback the analysis results to the edge computing module to optimize the control strategy; Through quantum key distribution and encrypted transmission, the security and integrity of data interaction are ensured. Specifically, when the cloud platform detects an abnormal pattern, it will give priority to sending an alarm signal to the edge computing module and provide corresponding optimization suggestions.

[0124] In some embodiments, the cloud platform also supports cross-regional data sharing and collaborative optimization. For example, in a distributed power grid scenario, multiple cloud platforms can share model parameters through collaborative training, thereby improving overall optimization performance.

[0125] In other embodiments, the cloud platform combines edge computing functions to shift some data analysis tasks to edge devices to reduce the computing load of the central server.

[0126] The cloud platform in this embodiment achieves efficient management of power grid operation data through distributed storage, intelligent analysis and secure communication technology. Its dynamic model training, pattern recognition and quantum key distribution functions provide a solid guarantee for the intelligence and reliability of the system. At the same time, combined with cross-regional collaboration and edge sinking functions, the cloud platform can adapt to the diverse needs of complex power grid environments.

[0127] A data communication security module is deployed between the edge computing module and the cloud platform and is used for data encryption, eavesdropping detection, and security key distribution to ensure the security and integrity of data transmission; Specifically, the data communication security module is the core security unit of the intelligent switch cabinet control system, focusing on protecting the security of data transmission between modules to avoid data leakage, tampering or loss. In general, this module needs to ensure the integrity, confidentiality and anti-attack of data transmission through multi-layer encryption technology, eavesdropping detection mechanism and dynamic key distribution function. In order to adapt to the complex power grid communication environment, its design needs to take into account both real-time and high efficiency, and have the ability to dynamically adjust and adapt security strategies.

[0128] As an option, this module forms a complete secure communication link by collaborating with the cloud platform, edge computing module and control execution module, and realizes absolutely secure key generation and distribution functions based on quantum key distribution.

[0129] The data communication security module in this embodiment is composed of an encryption module, an eavesdropping detection module, and a dynamic key distribution module, and combines physical and mathematical techniques to improve security. The following is a detailed technical implementation: Encryption module, the encryption module is the basic component of the data communication security module, which is mainly used to encrypt and decrypt the transmitted data to prevent the leakage of sensitive information during the transmission process.

[0130] The encryption and decryption process, the encryption module uses a symmetric encryption algorithm, especially the Advanced Encryption Standard (AES). The encryption process can be described as:

[0131] in: is the ciphertext, indicating the encrypted data; It is plain text, indicating the original data that needs to be transmitted; Key-based The encryption function.

[0132] The decryption process is: in: : Key-based The decryption function.

[0133] To improve encryption efficiency, this module dynamically adjusts the size of the encryption block and key length The size of the encrypted block is defined by the following formula:

[0134] in: : Encryption block size; : key length; : Data transmission rate.

[0135] Noise and interference processing, when transmitting data, the noise in the channel may affect the data integrity. In order to reduce noise interference, the encryption module introduces error correction coding (such as Hamming code) for data verification, and its verification relationship can be expressed as:

[0136] in: Generate a matrix; : The encoded data vector.

[0137] Eavesdropping detection module,The eavesdropping detection module uses quantum key distribution (QKD) technology to detect in real time whether the communication channel is attacked.

[0138] Quantum key distribution principle: Quantum key distribution is based on the non-replicability of quantum states to achieve secure key distribution. In this embodiment, the BB84 protocol is adopted, and its workflow includes the following steps: Quantum state generation: The sender randomly generates a sequence of quantum bits, each of which corresponds to horizontal polarization or vertical polarization; Quantum state measurement: The receiver randomly selects a measurement basis (such as a standard basis or a diagonal basis) to measure the quantum state; Base consistency comparison: Through an open channel, the sender and receiver compare the consistency of the measurement base and retain the data of the consistent parts; Key generation: Post-process the retained data to generate a shared key.

[0139] Eavesdropping introduces a quantum bit error rate (QBER), which is calculated as: in: is the quantum bit error rate; is the number of bit errors; is the total number of bits.

[0140] when Exceeding the set threshold When the eavesdropping detection module determines that there is an attack on the communication channel, it triggers a security alarm.

[0141] Dynamic key distribution module, the dynamic key distribution module generates and distributes dynamic keys in combination with the system operation status to enhance the security and flexibility of data transmission.

[0142] Key generation mechanism. In this embodiment, the key is generated by the SHA-256 hash algorithm. The key generation formula is:

[0143] in: is the generated key; is the SHA-256 hash function; is the seed data; It is a timestamp used to ensure the uniqueness and timeliness of the key. Dynamic update strategy: The dynamic key distribution module regularly updates the key according to the security requirements of the communication environment. The update formula is:

[0144] in: is the new key; is the key used last time; Is the current timestamp.

[0145] Dynamic adjustment of key length, key length According to the security level of the communication channel Dynamic Adjustment:

[0146] in: is the base key length; is the safety factor; Channel security level.

[0147] The operation process of the data communication security module is as follows: Encrypted data transmission: In the encryption module, all data between the edge computing module and the cloud platform are encrypted to generate ciphertext; Eavesdropping detection: The eavesdropping detection module monitors the quantum channel in real time and calculates the quantum bit error rate , determine whether the channel is safe; Key update: The dynamic key distribution module generates new keys according to system requirements and distributes them to relevant modules; Exception handling: If eavesdropping is detected, data transmission will be interrupted immediately, the key update mechanism will be triggered, and a warning signal will be issued.

[0148] In some embodiments, the data communication security module supports multi-channel transmission, that is, data is dispersed into multiple channels for transmission to further reduce the risk of a single channel being eavesdropped.

[0149] In another implementation, the module integrates Zero Trust Architecture to enhance the overall security of the communication process through dynamic authentication and access control technology.

[0150] The data communication security module in this embodiment achieves high security and reliability of data transmission within the system through symmetric encryption, eavesdropping detection and dynamic key distribution functions. Its quantum key distribution technology provides strong physical security protection, while dynamic key update and multi-channel transmission strategy further enhance the flexibility and anti-attack capability of the module, providing a solid security guarantee for the stable operation of the power grid.

[0151] See also Figure 4 The present invention also provides an intelligent switch cabinet control method, comprising the following steps: S1. Collecting the operating parameters of the power grid through sensor equipment; Specifically, this step completes the real-time collection of power grid operating parameters through intelligent sensing equipment. Sensing equipment is installed at key nodes of the power grid, including voltage sensors, current transformers, and temperature sensors. Each sensor captures important data related to the state of the power grid according to its specific function. For example, the voltage sensor is responsible for real-time monitoring of the voltage fluctuations of the power grid, the current transformer accurately measures the current intensity in the circuit, and the temperature sensor monitors the heat distribution inside the switch cabinet to prevent failures caused by overheating. In general, these devices have high sensitivity, fast response, and anti-interference capabilities, which can ensure the accuracy and continuity of data collection.

[0152] S2. The edge computing module dynamically models and optimizes the operating parameters to generate the optimal switch control strategy; specifically, after completing the data collection, the operating parameters are transmitted to the edge computing module for further processing. The edge computing module analyzes the current state of the power grid through dynamic modeling, and generates a control strategy based on historical data and optimization algorithms. The model can identify potential risks in the power grid, such as voltage anomalies or overload conditions, and calculate the optimal switch control strategy based on this. As an option, the module can also adjust the optimization parameters based on real-time data to make the control strategy more in line with the actual working conditions, thereby improving the execution efficiency of the strategy and the stability of the power grid operation.

[0153] S3, the control execution module adjusts the switch cabinet operation status according to the switch control strategy, including isolating the fault area and restoring power supply to the non-fault area; Specifically, after the control execution module receives the control strategy generated by the edge computing module, it adjusts the operating status of the switch cabinet through relays and circuit breakers. When a fault occurs in the power grid, the module quickly performs an isolation operation to disconnect the affected area from the power grid to prevent the fault from expanding. At the same time, for areas not affected by the fault, the module will immediately restore power supply to ensure the continuity of power grid operation. In general, the control execution module has fast response capabilities and high reliability, and can complete the switching of switch states in milliseconds to meet the needs of complex power grid environments.

[0154] S4, data communication security module encrypts operating parameters and control instructions to ensure the security of data transmission; Specifically, during the transmission of data and control instructions, the data communication security module encrypts them to prevent sensitive information from being tampered with or stolen during transmission. Through multi-layer security mechanisms, including dynamic key distribution and eavesdropping detection, the module can monitor the security status of the communication channel in real time. Once a potential attack or abnormal behavior is detected, the module will immediately interrupt data transmission and regenerate security keys. In general, the module combines quantum key distribution technology to provide the highest level of physical security to ensure the security and integrity of data interaction.

[0155] S5. The cloud platform stores operating data and optimizes control strategies through historical data analysis.

[0156] Specifically, the operation data is centrally stored and managed in the cloud platform. The cloud platform uses historical data analysis to mine fault modes, optimize operation models, and dynamically update the control strategy of the edge computing module. This feedback mechanism can improve the system's adaptability to new faults and complex working conditions. In addition, the cloud platform supports large-scale parallel computing and distributed storage to ensure the efficiency and reliability of data processing. In general, the cloud platform also provides a visual interface to facilitate users to understand the real-time status and operation trends of the power grid.

[0157] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent switch cabinet control system, characterized in that: include: Sensor equipment, used to collect multi-dimensional operating parameters of the power grid, such as voltage, current, and temperature; an edge computing module, in communication with the sensor device, configured to generate a switch control strategy based on the collected operating parameters through dynamic modeling and optimization algorithms; A control execution module, which is in communication with the edge computing module and is used to receive a switch control strategy and adjust the operating state of the switch cabinet; A cloud platform, which is in communication with the edge computing module and is used to store operating parameters, fault response records, and optimization control models, and provide global data analysis and optimization services; The data communication security module is deployed between the edge computing module and the cloud platform and is used for data encryption, eavesdropping detection, and security key distribution to ensure the security and integrity of data transmission.

2. The intelligent switch cabinet control system according to claim 1, characterized in that: The sensing device comprises: Voltage sensors are used to monitor voltage changes at key nodes of the switch cabinet in real time; Current transformers, used to obtain the current strength of the circuit and generate safety assessment data; Temperature sensor, used to monitor the temperature distribution of the operating environment inside the switch cabinet.

3. The intelligent switch cabinet control system according to claim 1, characterized in that: The edge computing module generates a switch control strategy based on an optimization control algorithm. The optimization control algorithm associates the grid operation status with the control input through dynamic modeling to minimize the system's operation deviation and control energy consumption.

4. The intelligent switch cabinet control system according to claim 1, characterized in that: The data communication security module comprises: An encryption module is used to optimize encryption parameters based on the signal-to-noise ratio of the communication channel and improve the anti-interference ability of data transmission; An eavesdropping detection module is used to detect eavesdropping during data transmission through the quantum key distribution protocol; The dynamic key distribution module is used to generate and distribute keys based on dynamic security requirements to ensure the security of data transmission.

5. The intelligent switch cabinet control system according to claim 1, characterized in that: The cloud platform analyzes and optimizes historical fault data and real-time operation data through a Bayesian learning model, dynamically updates fault modes and optimizes control strategies.

6. The intelligent switch cabinet control system according to claim 1, characterized in that: The control execution module includes a relay and a circuit breaker. The relay adjusts the contact closing or opening state according to the control signal of the edge computing module. The circuit breaker is used to quickly isolate the fault area and restore power supply to the non-fault area.

7. The intelligent switch cabinet control system according to claim 1, characterized in that: The cloud platform performs group analysis on the power grid status based on the stored power grid operation data through a dynamic clustering algorithm to identify potential abnormal operation modes.

8. The intelligent switch cabinet control system according to claim 1, characterized in that: The edge computing module dynamically allocates encryption and monitoring resources through a resource optimization control algorithm to meet security requirements while minimizing the occupancy of computing resources.

9. The intelligent switch cabinet control system according to claim 1, characterized in that: The data communication security module adjusts bandwidth, signal-to-noise ratio and encryption parameters based on the communication channel capacity optimization algorithm to achieve a balance between efficient and secure data transmission.

10. An intelligent switch cabinet control method, applied to an intelligent switch cabinet control system according to any one of claims 1 to 9, characterized in that: The following steps are involved: Collecting the operating parameters of the power grid through sensor equipment; The edge computing module dynamically models and optimizes the operating parameters to generate the optimal switch control strategy; The control execution module adjusts the switch cabinet operation status according to the switch control strategy, including isolating the fault area and restoring power supply to the non-fault area; The data communication security module encrypts the operating parameters and control instructions to ensure the security of data transmission; The cloud platform stores operating data and optimizes control strategies through historical data analysis.

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