Edge gateway-based electricity room environment regulation and control method and device

Abstract

The invention discloses an electricity room environment regulation and control method and device based on an edge gateway. An intelligent sensing network is constructed by deploying at least one type of environment sensor in an electricity room; an edge intelligent fusion platform is constructed by customizing edge gateway hardware design and establishing a software platform; setting an environment parameter intelligent regulation and control strategy and a self-adaptive regulation and control strategy; and constructing a cloud-side collaborative architecture and augmented reality remote operation and maintenance system, and performing electricity room environment regulation and control in combination with the intelligent sensing network and the edge intelligent fusion platform. According to the technical scheme of the invention, through highly refined design, intelligence, precision and foresight of electricity room environment regulation and control are realized. By integrating the latest edge calculation, big data analysis, machine learning and Internet of Things technologies, an electricity room environment management mode capable of real-time sensing, intelligent analysis, autonomous regulation and control and continuous optimization is constructed.

Description

Technical Field

[0001] The present invention relates to the field of power distribution technology, and in particular to a method and device for controlling an environment of an electric room based on an edge gateway. Background Art

[0002] With the rapid development of science and technology, the operation and maintenance management of the power industry is facing unprecedented challenges and opportunities. The traditional power room environment control method mainly relies on manual inspection and regular maintenance. This method is not only inefficient, but also difficult to detect potential problems in a timely and accurate manner, resulting in frequent equipment failures and affecting the stability and safety of power supply.

[0003] In recent years, the rise of technologies such as the Internet of Things (IoT), edge computing, big data analysis, and machine learning have provided new solutions for power room environmental control. IoT technology enables various devices in the power room to be interconnected and interoperable, realizing real-time data collection and transmission; edge computing technology sinks the data processing and analysis capabilities to the edge of the network, improving the real-time and efficiency of data processing; big data analysis technology can deeply explore the value behind the data and discover hidden laws and trends; machine learning technology gives the system the ability to self-learn and optimize, enabling it to continuously adapt to complex and changing environments.

[0004] However, most of the power room environment control systems on the market are still in the primary stage, with problems such as low intelligence, insufficient control accuracy, and limited prediction ability. Therefore, a highly intelligent, precise and forward-looking power room environment control method is particularly important. Summary of the invention

[0005] The present invention provides a power room environment control method and device based on an edge gateway to achieve highly intelligent, precise and forward-looking power room environment control.

[0006] According to one aspect of the present invention, a power room environment control method based on an edge gateway is provided, comprising:

[0007] Building an intelligent sensing network by deploying at least one type of environmental sensor in the power room;

[0008] Build an edge intelligent fusion platform by customizing edge gateway hardware design and establishing a software platform;

[0009] Set up intelligent control strategies and adaptive control strategies for environmental parameters;

[0010] Build a cloud-edge collaborative architecture and augmented reality remote operation and maintenance system, and combine the intelligent perception network and edge intelligent fusion platform to control the power room environment.

[0011] Optionally, the step of constructing an intelligent sensing network by deploying at least one type of environmental sensor in the power room includes:

[0012] Deploying a temperature and humidity sensor with a measurement error within a preset range in the power room;

[0013] Deploy infrared thermal imaging sensors according to the locations of the equipment in the power room and set temperature thresholds for different areas;

[0014] Deploy a sound recognition sensor in the power room and set an abnormal noise threshold;

[0015] Deploy harmful gas monitoring sensors in the power room whose response time is less than a preset value;

[0016] Constructing a wireless mesh network architecture to establish data communication between the environmental sensors and form the intelligent sensing network.

[0017] Optionally, the establishing of the software platform includes:

[0018] Build an edge operating system based on containerization technology, and integrate the pre-built environmental physical model with the machine learning model to obtain a hybrid model;

[0019] Construct a data preprocessing module and set data cleaning rules, feature extraction rules and normalization processing rules.

[0020] Optionally, the setting of the intelligent control strategy of environmental parameters includes:

[0021] Formulate regulatory strategies at different levels based on environmental prediction models;

[0022] Through fuzzy control algorithm, the control intensity is dynamically adjusted according to the changing trend and rate of environmental parameters;

[0023] Construct an equipment health status evaluation index system, and use time series analysis and machine learning algorithms to predict the health status of the equipment in the power room;

[0024] Set warning thresholds at different levels and formulate corresponding emergency response measures.

[0025] Optionally, the environmental prediction model is constructed in the following manner:

[0026] Through the fusion analysis of meteorological data and sensor data, social media data and IoT device status through deep learning algorithms, the prediction accuracy and timeliness can be improved;

[0027] Dynamically adjust the weights of different data sources in the environmental prediction model according to the importance and real-time nature of the data;

[0028] Reinforcement learning or adaptive neural network technology is used to enable the environmental prediction model to automatically adjust model parameters and structure according to historical prediction errors.

[0029] Optionally, the fuzzy control algorithm is used to dynamically adjust the control intensity according to the change trend and rate of the environmental parameters, including:

[0030] Define the fuzzy controller, set the input variables as the environmental parameter change amount and environmental parameter change rate, and the output variable as the control intensity;

[0031] Defining fuzzy sets for the input variables and output variables, designing membership functions, and mapping input values ​​to the fuzzy sets;

[0032] Establish fuzzy rules to describe the relationship between environmental parameter changes and regulation intensity;

[0033] According to the current environmental parameter change amount and change rate, the membership degree of the environmental parameter on the respective fuzzy set is determined.

[0034] By applying the rules in the fuzzy rule base and through fuzzy logic operations, the fuzzy set of output variables obtained by fuzzy reasoning is converted into the control intensity value.

[0035] Optionally, the construction of a cloud-edge collaborative architecture and an augmented reality remote operation and maintenance system includes:

[0036] Design data transmission protocols for cloud-edge collaboration, and deploy big data analysis platforms and machine learning training platforms on the cloud;

[0037] Setting up an AR remote operation and maintenance platform, and displaying the real-time video stream and enhanced information of the power room through the AR remote operation and maintenance platform;

[0038] Prompt maintenance personnel to locate problems through AR annotation and voice guidance;

[0039] Calibration nodes are deployed in the intelligent sensing network, and calibration operations are triggered regularly through parameters of preset calibration logic.

[0040] According to another aspect of the present invention, a power room environment control device based on an edge gateway is provided, comprising:

[0041] A first construction unit is used to construct an intelligent sensing network by deploying at least one type of environmental sensor in the power room;

[0042] The second building unit is used to build an edge intelligent fusion platform by customizing edge gateway hardware design and establishing a software platform;

[0043] A strategy setting unit, used to set an intelligent control strategy and an adaptive control strategy for environmental parameters;

[0044] The third construction unit is used to build a cloud-edge collaborative architecture and augmented reality remote operation and maintenance system, and combine the intelligent perception network and edge intelligent fusion platform to control the power room environment.

[0045] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0046] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the power room environment control method based on the edge gateway as described in any embodiment of the present invention.

[0047] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the power room environment control method based on an edge gateway as described in any embodiment of the present invention when executed.

[0048] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program, and the computer program, when executed by a processor, implements the power room environment control method based on an edge gateway as described in any embodiment of the present invention.

[0049] The technical solution of the embodiment of the present invention realizes intelligent, precise and forward-looking power room environment control through highly detailed design. By integrating the latest edge computing, big data analysis, machine learning and Internet of Things technologies, a power room environment management method capable of real-time perception, intelligent analysis, autonomous control and continuous optimization is constructed.

[0050] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 It is a flow chart of a power room environment control method based on an edge gateway provided in Embodiment 1 of the present invention;

[0053] Figure 2It is a structural schematic diagram of a power room environment control device based on an edge gateway provided in Embodiment 2 of the present invention;

[0054] Figure 3 It is a structural schematic diagram of an electronic device for implementing the power room environment control method based on an edge gateway according to an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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 should fall within the scope of protection of the present invention.

[0056] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0057] Embodiment 1

[0058] Figure 1 This is a flow chart of a power room environment control method based on an edge gateway provided in the first embodiment of the present invention. This embodiment is applicable to the case of intelligent analysis and autonomous control of the power grid environment. The method can be executed by a power room environment control device based on an edge gateway. The power room environment control device based on an edge gateway can be implemented in the form of hardware and / or software. The power room environment control device based on an edge gateway can be configured in an electronic device. Figure 1 As shown, the method includes:

[0059] S110: Building an intelligent sensing network by deploying at least one type of environmental sensor in the power room.

[0060] In the embodiment of the present invention, S110 includes:

[0061] Deploying a temperature and humidity sensor with a measurement error within a preset range in the power room;

[0062] Deploy infrared thermal imaging sensors according to the locations of the equipment in the power room and set temperature thresholds for different areas;

[0063] Deploy a sound recognition sensor in the power room and set an abnormal noise threshold;

[0064] Deploy harmful gas monitoring sensors in the power room whose response time is less than a preset value;

[0065] Constructing a wireless mesh network architecture to establish data communication between the environmental sensors and form the intelligent sensing network.

[0066] Select a temperature and humidity sensor with high precision and low drift characteristics to ensure that the measurement error is within ±0.5%RH and ±0.1℃.

[0067] Deploy infrared thermal imaging sensors to cover all key equipment in the power room, and set temperature thresholds for different areas, such as ≤70°C for the transformer area.

[0068] Introduce sound recognition sensors to monitor equipment operating noise and set abnormal noise thresholds. If the noise exceeds 85dB, an alarm will be triggered.

[0069] Harmful gas monitoring sensors must have fast response capabilities, such as the SF6 sensor response time ≤ 30 seconds.

[0070] The wireless Mesh network architecture is adopted to ensure the reliability and stability of data communication between sensors.

[0071] Deploy sensor calibration nodes and automatically or manually trigger the calibration process regularly to reduce the impact of environmental interference on data accuracy.

[0072] In addition, sensor calibration nodes can be deployed to automatically or manually trigger the calibration process regularly to ensure the accuracy of sensor data. For the automatic calibration process, the calibration node automatically sends a calibration signal or request to the sensor to be calibrated according to the preset calibration cycle (such as daily, weekly or monthly) and calibration standard (such as a known baseline value or reference signal). After receiving the calibration signal, the sensor performs the calibration action and returns the calibration result to the calibration node. The calibration node evaluates the returned calibration result to determine whether the sensor meets the calibration requirements. If the calibration is successful, the calibration parameters of the sensor are updated; if the calibration fails, the error information is recorded and an alarm is triggered. For the manual calibration process, the operation and maintenance personnel manually trigger the calibration process of the calibration node through the operation interface or remote command. The calibration node sends a calibration request or signal to the sensor to be calibrated, and the sensor performs a manual calibration action (such as adjusting the zero point, full scale, etc.). During the calibration process, the operation and maintenance personnel can verify the calibration effect by observing the sensor output or using standard equipment. After the calibration is completed, manually confirm whether the calibration result meets the requirements. If successful, the calibration parameters and calibration time are recorded. If failed, recalibration or troubleshooting is performed.

[0073] S120, builds an edge intelligent fusion platform through customized edge gateway hardware design and establishment of software platform.

[0074] High-performance FPGA chips are selected and combined with ARM processors to achieve efficient coordination of data processing and algorithm calculations.

[0075] Design dedicated AI acceleration modules to perform hardware acceleration on environment prediction and fault prediction algorithms.

[0076] Integrated large-capacity storage module for caching historical data and model parameters.

[0077] In an embodiment of the present invention, a software platform is established, including:

[0078] Build an edge operating system based on containerization technology, and integrate the pre-built environmental physical model with the machine learning model to obtain a hybrid model;

[0079] Construct a data preprocessing module and set data cleaning rules, feature extraction rules and normalization processing rules.

[0080] Using containerization technology, applications and their dependencies are packaged into an independent container. The container shares the kernel with the host system at runtime, but is isolated from each other in terms of file system, process space, etc. This isolation ensures that each application runs in its independent environment without interference from other applications, while improving the portability and deployment efficiency of the application. Based on containerization technology, the edge operating system can quickly deploy and update application services to adapt to the changing needs of power room environmental control. For example, when the environmental monitoring application needs to be updated, only the corresponding container needs to be updated without complex reconfiguration of the entire edge gateway system.

[0081] The edge operating system is responsible for managing various resources on the edge gateway, including hardware resources (such as processors, memory, storage, etc.) and software resources (such as applications, drivers, etc.). At the same time, the edge operating system also has the function of managing the life cycle of containers, such as starting, stopping, pausing, and resuming operations, which facilitates the management and maintenance of applications.

[0082] A hybrid model (such as LSTM + physical equation) is used to achieve fusion. As a powerful recurrent neural network, LSTM (Long Short-Term Memory Network) is good at processing time series data and can learn the complex laws of environmental parameters changing over time. Physical equations are based on the physical principles in the power room environment, such as heat conduction equations, fluid mechanics equations, etc., to accurately describe the physical processes in the environment. For example, in temperature prediction, physical equations can provide basic temperature change trends based on factors such as equipment power and heat dissipation structure, while LSTM can learn nonlinear changes and sudden interference factors in historical data, thereby improving prediction accuracy.

[0083] The fused model can more accurately predict the changing trend of the power room environment and provide a more reliable basis for intelligent control. For example, when predicting the temperature change of the equipment, not only physical factors such as the current operating power and heat dissipation of the equipment can be considered, but also seasonal changes in historical temperature data, equipment aging and other factors can be combined to predict abnormal temperature rise in advance, so as to take timely control measures, such as adjusting ventilation or starting refrigeration equipment, to improve the stability and safety of equipment operation, and achieve energy-saving optimization.

[0084] Clean the data collected by the intelligent sensing network to remove invalid data such as outliers and missing values. For example, when the temperature and humidity sensor produces abnormally high or low measurement values ​​due to electromagnetic interference and other reasons, the data cleaning function can identify and remove these abnormal data to ensure the data quality for subsequent analysis and model training. At the same time, for missing values ​​in the data, interpolation methods (such as linear interpolation, mean interpolation, etc.) can be used to supplement them to ensure data integrity.

[0085] Extract key features that can reflect the power room environment and equipment status from the raw data. For example, extract spectral features (such as main frequency, frequency doubling, etc.) and time domain features (such as peak value, root mean square value, etc.) from vibration data, and extract temperature change trends (such as heating rate, cooling rate, etc.), temperature fluctuation range and other features from temperature data. Then normalize the extracted features, map the values ​​of different features to a specific interval (such as [0,1] or [-1,1]), eliminate the dimensional differences between different features, and make the data more suitable for subsequent model training and analysis. For example, unify the values ​​of vibration amplitude and temperature to the same order of magnitude to avoid over-emphasis or neglect of certain features due to different feature dimensions in model training, and improve the accuracy and generalization ability of the model.

[0086] S130: Setting an intelligent control strategy and an adaptive control strategy for environmental parameters.

[0087] In an embodiment of the present invention, setting an environmental parameter intelligent control strategy includes:

[0088] Formulate regulatory strategies at different levels based on environmental prediction models;

[0089] Through fuzzy control algorithm, the control intensity is dynamically adjusted according to the changing trend and rate of environmental parameters;

[0090] Construct an equipment health status evaluation index system, and use time series analysis and machine learning algorithms to predict the health status of the equipment in the power room;

[0091] Set warning thresholds at different levels and formulate corresponding emergency response measures.

[0092] Based on the environmental prediction model, a multi-level control strategy is formulated, such as primary control (adjusting ventilation volume), intermediate control (starting refrigeration equipment), and advanced control (adjusting equipment load).

[0093] The fuzzy control algorithm is introduced to dynamically adjust the control intensity according to the changing trend and rate of environmental parameters.

[0094] Build an equipment health status assessment indicator system, including temperature, vibration, current and other dimensions.

[0095] Use time series analysis and machine learning algorithms (such as random forest and SVM) to predict the health status of equipment.

[0096] Set different levels of warning thresholds, such as yellow warning (minor abnormality), orange warning (moderate abnormality), and red warning (serious abnormality), and formulate corresponding emergency response measures.

[0097] In the embodiment of the present invention, the environment prediction model is constructed in the following manner:

[0098] Through the fusion analysis of meteorological data and sensor data, social media data and IoT device status through deep learning algorithms, the prediction accuracy and timeliness can be improved;

[0099] Dynamically adjust the weights of different data sources in the environmental prediction model according to the importance and real-time nature of the data;

[0100] Reinforcement learning or adaptive neural network technology is used to enable the environmental prediction model to automatically adjust model parameters and structure according to historical prediction errors.

[0101] The environmental prediction model integrates multi-source heterogeneous data, such as meteorological data (temperature, humidity, air pressure, etc.), sensor data (temperature and humidity in the power room, equipment temperature, concentration of harmful gases, etc.), social media data (people activities in the surrounding area, electricity demand trends, etc.) and IoT device status (operating status of connected devices, load conditions, etc.). These data are fused and analyzed through deep learning algorithms (such as convolutional neural networks, recurrent neural networks, etc.). For example, convolutional neural networks are used to extract features of spatial correlation in meteorological data, and recurrent neural networks are used to learn the time series features of sensor data, so as to more comprehensively capture the factors affecting the power room environment.

[0102] The following is a possible implementation of a control strategy:

[0103] Primary control (intelligent ventilation control):

[0104] High-precision sensors (temperature and humidity sensors, human body pyroelectric induction sensors, etc.) are used to monitor indoor and outdoor environmental parameters in real time, and AI algorithms (such as neural network algorithms) are used to predict the trend of indoor environmental changes. For example, by analyzing the activity patterns of personnel and the current indoor temperature and humidity conditions, the impact of personnel activities on the environment in the future can be predicted, and ventilation strategies can be adjusted in advance.

[0105] Adaptive air volume control: Adopt variable frequency fan technology to achieve adaptive adjustment of ventilation volume according to indoor temperature and humidity, CO2 concentration and outdoor meteorological conditions (temperature, humidity, wind speed, etc.). For example, when the indoor temperature rises and the CO2 concentration exceeds the standard, combined with the low outdoor temperature and suitable humidity, increase the ventilation volume, introduce outdoor fresh air, reduce indoor temperature and CO2 concentration, improve indoor air quality, and accurately control the air volume according to actual needs to avoid energy waste.

[0106] Intermediate control (intelligent cooling strategy):

[0107] Different cooling modes are set according to the ambient temperature and humidity, such as dehumidification mode (starting when the humidity is higher than the set threshold and the temperature is within a certain range, improving comfort by reducing air humidity), energy-saving mode (reducing cooling power and reducing energy consumption when temperature fluctuations are small and there are fewer people), etc., and automatically switch according to the environmental prediction results. For example, in the rainy season when the humidity is high and the temperature is moderate, it automatically switches to dehumidification mode to avoid energy waste caused by excessive cooling; when it is predicted that the ambient temperature will not change much in the future, it switches to energy-saving mode in advance.

[0108] By monitoring the operating status of refrigeration equipment (such as compressor current, evaporator temperature, etc.) and energy consumption data, machine learning algorithms (such as support vector machines, decision trees, etc.) are used to predict potential failures. For example, the fluctuation of compressor current and the trend of energy consumption are analyzed. When abnormalities are found, maintenance is arranged in advance, worn parts are replaced or equipment parameters are adjusted to reduce environmental fluctuations caused by equipment failures and ensure stable refrigeration effects.

[0109] Advanced control (dynamic load management and optimization):

[0110] Using cloud computing and big data analysis technology, the load of all devices in the system (such as servers, communication equipment, power equipment, etc.) is monitored and predicted in real time. Through load balancing algorithms (such as weighted polling algorithm, minimum connection number algorithm, etc.), tasks and resources are automatically allocated to ensure load balance of each device, avoid overloading of some devices while other devices are idle, improve the overall operation efficiency of the system, and reduce energy consumption. For example, in the power room of the data center, computing tasks are dynamically allocated based on load indicators such as the server's CPU usage rate and memory occupancy rate, so that the load of each server is kept within a reasonable range.

[0111] According to the prediction results of the environmental prediction model for the system load, when it is predicted that the system load will increase significantly (such as during business peak periods or when the ambient temperature rises sharply, resulting in an increase in equipment heat dissipation needs), the backup equipment is automatically started or the equipment power is increased to ensure that the system can meet the demand; when the load decreases (such as during business slack periods or when the ambient temperature is suitable), the number of running devices is reduced or the power is reduced to achieve flexible allocation and efficient use of resources and reduce operating costs.

[0112] In the embodiment of the present invention, the fuzzy control algorithm is used to dynamically adjust the control intensity according to the change trend and rate of the environmental parameters, including:

[0113] Define the fuzzy controller, set the input variables as the environmental parameter change amount and environmental parameter change rate, and the output variable as the control intensity;

[0114] Defining fuzzy sets for the input variables and output variables, designing membership functions, and mapping input values ​​to the fuzzy sets;

[0115] Establish fuzzy rules to describe the relationship between environmental parameter changes and regulation intensity;

[0116] According to the current environmental parameter change amount and change rate, the membership degree of the environmental parameter on the respective fuzzy set is determined.

[0117] By applying the rules in the fuzzy rule base and through fuzzy logic operations, the fuzzy set of output variables obtained by fuzzy reasoning is converted into the control intensity value.

[0118] Input variables: environmental parameter changes (such as temperature difference ΔT, humidity difference ΔH, etc.), reflecting the deviation between the current environmental parameters and the set values; environmental parameter change rates (such as temperature change rate dT / dt, humidity change rate dH / dt, etc.), reflecting the speed of change of environmental parameters. These input variables can fully describe the changes in environmental conditions and provide a basis for control decisions.

[0119] Output variables: The intensity of regulation (such as ventilation volume adjustment, refrigeration equipment power adjustment, etc.) directly determines the intensity of regulation of the system. By adjusting the output variables, precise control of the power room environmental parameters can be achieved.

[0120] Fuzzy sets and membership functions: Define fuzzy sets for each input and output variable, such as "negative large" (NB), "negative medium" (NM), "negative small" (NS), "zero" (ZE), "positive small" (PS), "positive medium" (PM), "positive large" (PB), etc. Design membership functions (such as triangular, trapezoidal or Gaussian membership functions) to map the exact input values ​​to the fuzzy sets. For example, for the temperature difference ΔT, when ΔT is -5℃, according to the membership function, its membership in the "negative large" fuzzy set is 0.8, and its membership in the "negative medium" fuzzy set is 0.2, which describes the degree to which the temperature difference belongs to different fuzzy states.

[0121] Based on expert experience or experimental data, a series of fuzzy rules are established to describe the relationship between environmental parameter changes and control intensity. For example, the rule "If the temperature difference ΔT is 'positive' and the temperature change rate dT / dt is 'positive', then the control intensity should be 'positive'" means that when the temperature is much higher than the set value and the temperature rises very quickly, the power of the refrigeration equipment or the ventilation volume should be greatly increased to quickly reduce the temperature.

[0122] According to the current environmental parameter change amount and change rate, their membership in their respective fuzzy sets is determined. Then, the rules in the fuzzy rule base are applied to calculate the fuzzy set membership of the output variable through fuzzy logic operations (such as taking the smaller, taking the larger, algebraic product, etc.). For example, the current temperature difference ΔT has a membership of 0.6 in the "positive and large" fuzzy set, and the temperature change rate dT / dt has a membership of 0.7 in the "positive and large" fuzzy set. According to the above rules, the regulation intensity has a membership of 0.6 in the "positive and large" fuzzy set through taking the smaller operation.

[0123] The output variable fuzzy set obtained by fuzzy reasoning is converted into an accurate control intensity value. Common methods include the maximum membership method, the centroid method (weighted average method), the median method, etc. For example, when using the centroid method, the membership of each element in the output fuzzy set is multiplied by its corresponding control intensity value, then summed, and then divided by the total membership to obtain the final control intensity value. For example, if the output fuzzy set is {(PB, 0.3), (PM, 0.5), (PS, 0.2)}, the corresponding control intensity values ​​are 10, 5, and 2, respectively. The calculated accurate control intensity value is (10×0.3+5×0.5+2×0.2) / (0.3+0.5+0.2)=5.3, and the ventilation volume or refrigeration equipment power is adjusted according to this value.

[0124] The control intensity value obtained by defuzzification is applied to the actual system to adjust the ventilation volume, refrigeration equipment power, etc., to achieve fine control of environmental parameters. For example, according to the calculated ventilation volume adjustment amount, the speed of the variable frequency fan is controlled to increase or decrease the ventilation volume.

[0125] In practical applications, the parameters of the fuzzy controller (such as membership function, fuzzy rules, etc.) are continuously adjusted and optimized according to the system response (such as the actual changes in environmental parameters) and the effects of environmental changes. For example, if it is found that the ambient temperature fluctuates greatly after being regulated according to the current fuzzy rules, it may be that the fuzzy rules are not reasonable enough or the membership function is not set accurately. By adjusting the rules or function parameters, the control accuracy and stability can be improved. Intelligent optimization algorithms such as genetic algorithms and particle swarm optimization can be used to automatically adjust the fuzzy controller parameters to achieve a more efficient optimization process, so that the control strategy is more adaptable to the complex changes in the power room environment.

[0126] S140. Build a cloud-edge collaborative architecture and augmented reality remote operation and maintenance system, and combine the intelligent perception network and edge intelligent fusion platform to control the power room environment.

[0127] In an embodiment of the present invention, a cloud-edge collaborative architecture and an augmented reality remote operation and maintenance system are constructed, including:

[0128] Design data transmission protocols for cloud-edge collaboration, and deploy big data analysis platforms and machine learning training platforms on the cloud;

[0129] Setting up an AR remote operation and maintenance platform, and displaying the real-time video stream and enhanced information of the power room through the AR remote operation and maintenance platform;

[0130] Prompt maintenance personnel to locate problems through AR annotation and voice guidance;

[0131] Calibration nodes are deployed in the intelligent sensing network, and calibration operations are triggered regularly through parameters of preset calibration logic.

[0132] In order to ensure efficient transmission and synchronization of data between the edge and the cloud, a special data transmission protocol is designed. The protocol uses asynchronous communication to reduce data transmission waiting time and improve transmission efficiency. For example, when the edge gateway collects new power room environment data, it can send the data immediately without waiting for the cloud response. At the same time, the cloud can process the received data at the appropriate time to avoid data blocking. The protocol also has a data compression function to reduce the amount of data transmission and reduce network bandwidth usage. For example, for a large amount of historical temperature and humidity data, compression processing is performed before transmission, and decompression and restoration of the data is performed after receiving it in the cloud.

[0133] The big data analysis platform and machine learning training platform are deployed in the cloud. The big data analysis platform can process a large amount of power room environmental data, including long-term historical data and real-time collected data. Through data mining algorithms (such as cluster analysis, association rule mining, etc.), hidden patterns and rules in the data are discovered. For example, the changing patterns of environmental parameters in the power room in different seasons and time periods are analyzed to provide a basis for optimizing equipment operation. The machine learning training platform uses the powerful computing resources in the cloud to train and optimize environmental prediction models, equipment failure prediction models, etc. For example, a deep learning algorithm is used to learn a large amount of equipment operation status data and failure cases to continuously improve the accuracy and generalization ability of the model.

[0134] The edge gateway communicates with the cloud through a secure encrypted channel to ensure the security of data transmission. The SSL / TLS protocol is used to encrypt the transmitted data to prevent the data from being stolen or tampered with during transmission. At the same time, an identity authentication mechanism is established to ensure that only legitimate edge gateways and cloud services can communicate. For example, when the edge gateway connects to the cloud, it needs to provide a pre-configured digital certificate for identity authentication, and the cloud service also reversely authenticates the edge gateway. Only after the identity authentication of both parties is passed can a secure communication connection be established.

[0135] Develop an AR remote operation and maintenance APP to provide a convenient remote operation interface for operation and maintenance personnel. Through mobile phones or AR glasses and other devices, operation and maintenance personnel can view the real-time video stream and enhanced information of the power room. For example, when viewing the equipment, the APP can overlay the real-time operating parameters of the equipment (such as temperature, current, speed, etc.), historical data trend charts, and three-dimensional models of the equipment on the video screen, so that the operation and maintenance personnel can intuitively understand the status of the equipment. At the same time, the remote fault diagnosis function is realized. When the equipment fails, the APP uses image recognition technology (such as target detection algorithm) to automatically identify the faulty equipment, and through AR annotation (such as marking the faulty part with a red box in the video screen) and voice guidance (such as voice prompts of possible fault causes and solutions), the operation and maintenance personnel can quickly locate the problem and provide solutions.

[0136] The solution of the embodiment of the present invention proposes an intelligent control and predictive optimization solution for the power room environment based on the edge gateway. The solution realizes the all-round and intelligent management of the power room environment by building an intelligent perception network, developing an edge intelligent fusion platform, implementing intelligent control and predictive optimization, building a cloud-edge collaborative architecture, and promoting innovative applications and continuous optimization. The solution not only improves the efficiency and safety of power room operation and maintenance, but also reduces the occurrence of equipment failures through predictive maintenance, reduces operation and maintenance costs, and provides strong support for the sustainable development of the power industry.

[0137] Embodiment 2

[0138] Figure 2 Schematic diagram of the structure of a power room environment control device based on an edge gateway provided in the second embodiment of the present invention. Figure 2 As shown, the device comprises:

[0139] A first construction unit 210 is used to construct an intelligent sensing network by deploying at least one type of environmental sensor in the power room;

[0140] The second construction unit 220 is used to construct an edge intelligent fusion platform by customizing edge gateway hardware design and establishing a software platform;

[0141] A strategy setting unit 230, used to set an intelligent control strategy and an adaptive control strategy for environmental parameters;

[0142] The third construction unit 240 is used to build a cloud-edge collaborative architecture and augmented reality remote operation and maintenance system, and combine the intelligent perception network and edge intelligent fusion platform to perform power room environment control.

[0143] The power room environment control device based on edge gateway provided in the embodiment of the present invention can execute the power room environment control method based on edge gateway provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0144] Embodiment 3

[0145] Figure 3 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0146] like Figure 3 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0147] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0148] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the power room environment control method based on the edge gateway.

[0149] In some embodiments, the power room environment control method based on the edge gateway can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the power room environment control method based on the edge gateway described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the power room environment control method based on the edge gateway in any other appropriate manner (for example, by means of firmware).

[0150] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0151] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0152] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0153] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0154] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0155] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0156] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0157] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A power room environment control method based on edge gateway, characterized in that: include: Building an intelligent sensing network by deploying at least one type of environmental sensor in the power room; Build an edge intelligent fusion platform through customized edge gateway hardware design and software platform establishment; Set up intelligent control strategies and adaptive control strategies for environmental parameters; Build a cloud-edge collaborative architecture and augmented reality remote operation and maintenance system, and combine the intelligent perception network and edge intelligent fusion platform to control the power room environment.

2. The method according to claim 1, characterized in that The method of constructing an intelligent sensing network by deploying at least one type of environmental sensor in the power room includes: Deploy a temperature and humidity sensor with a measurement error within a preset range in the power room; Deploy infrared thermal imaging sensors according to the locations of the equipment in the power room and set temperature thresholds for different areas; Deploy a sound recognition sensor in the power room and set an abnormal noise threshold; Deploy harmful gas monitoring sensors in the power room whose response time is less than a preset value; Constructing a wireless mesh network architecture to establish data communication between the environmental sensors and form the intelligent sensing network.

3. The method according to claim 2, characterized in that The software platform is established, including: Build an edge operating system based on containerization technology, and integrate the pre-built environmental physical model with the machine learning model to obtain a hybrid model; Construct a data preprocessing module and set data cleaning rules, feature extraction rules and normalization processing rules.

4. The method according to claim 1, characterized in that: The setting of the intelligent control strategy of environmental parameters includes: Formulate regulatory strategies at different levels based on environmental prediction models; Through fuzzy control algorithm, the control intensity is dynamically adjusted according to the changing trend and rate of environmental parameters; Construct an equipment health status evaluation index system, and use time series analysis and machine learning algorithms to predict the health status of the equipment in the power room; Set warning thresholds of different levels and formulate corresponding emergency response measures.

5. The method according to claim 4, characterized in that The environmental prediction model is constructed in the following way: Through the fusion analysis of meteorological data and sensor data, social media data and IoT device status through deep learning algorithms, the prediction accuracy and timeliness can be improved; Dynamically adjust the weights of different data sources in the environmental prediction model according to the importance and real-time nature of the data; Reinforcement learning or adaptive neural network technology is used to enable the environmental prediction model to automatically adjust model parameters and structure according to historical prediction errors.

6. The method according to claim 4, characterized in that The fuzzy control algorithm is used to dynamically adjust the control intensity according to the change trend and rate of environmental parameters, including: Define the fuzzy controller, set the input variables as the environmental parameter change amount and environmental parameter change rate, and the output variable as the control intensity; Defining fuzzy sets for the input variables and output variables, designing membership functions, and mapping input values ​​to the fuzzy sets; Establish fuzzy rules to describe the relationship between environmental parameter changes and regulation intensity; According to the current environmental parameter change amount and change rate, the membership degree of the environmental parameter on the respective fuzzy set is determined. By applying the rules in the fuzzy rule base and through fuzzy logic operations, the fuzzy set of output variables obtained by fuzzy reasoning is converted into the control intensity value.

7. According to the method of claim 1, the step of constructing a cloud-edge collaborative architecture and an augmented reality remote operation and maintenance system comprises: Design data transmission protocols for cloud-edge collaboration, and deploy big data analysis platforms and machine learning training platforms on the cloud; Setting up an AR remote operation and maintenance platform, and displaying the real-time video stream and enhanced information of the power room through the AR remote operation and maintenance platform; Prompt maintenance personnel to locate problems through AR annotation and voice guidance; Calibration nodes are deployed in the intelligent sensing network, and calibration operations are triggered regularly through parameters of preset calibration logic.

8. A power room environment control device based on an edge gateway, characterized in that: include: A first construction unit is used to construct an intelligent sensing network by deploying at least one type of environmental sensor in the power room; The second building unit is used to build an edge intelligent fusion platform by customizing edge gateway hardware design and establishing a software platform; A strategy setting unit, used to set an intelligent control strategy and an adaptive control strategy for environmental parameters; The third construction unit is used to build a cloud-edge collaborative architecture and augmented reality remote operation and maintenance system, and combine the intelligent perception network and edge intelligent fusion platform to control the power room environment.

9. An electronic device, characterized in that: The electronic device comprises: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the power room environment control method based on the edge gateway as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the power room environment control method based on an edge gateway as described in any one of claims 1 to 7 when executed.

11. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the computer program implements the power room environment control method based on the edge gateway according to any one of claims 1 to 7.

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