Intelligent regulation and control system based on resource environment data

Abstract

The invention discloses an intelligent regulation and control system based on resource environment data, and relates to the field of energy management, and the intelligent regulation and control system comprises the steps: obtaining the energy consumption data of an expressway, and generating an energy regulation and control instruction according to the energy consumption data and a data processing technology; monitoring carbon emission data in the operation process of the expressway, and dynamically adjusting an energy regulation and control instruction based on the carbon emission data and a linkage control technology; monitoring environmental parameters of the expressway, and combining the environmental parameters with a fuzzy reasoning technology to adjust the use state of operation equipment at the expressway; and according to the real-time energy consumption data, the carbon emission data and the environmental parameters, an operation strategy at the highway is optimized, and intelligent energy regulation and control are realized. According to the invention, aiming at a highway system, functions of multiple aspects of energy management, carbon emission monitoring, environment monitoring and the like are integrated, and real-time dynamic monitoring and intelligent regulation and control of energy, carbon emission and environment conditions are realized.

Description

Technical Field

[0001] The present invention relates to the field of energy management, and in particular to an intelligent control system based on resource and environmental data. Background Art

[0002] With the rapid development of science and technology and the continuous progress of society, environmental protection and energy conservation have become hot issues of concern to people. In this context, in order to improve energy utilization efficiency, reduce carbon emissions, and improve environmental conditions, the research and development of intelligent management systems for road traffic (especially highway systems) has become particularly important. These systems not only need to be able to monitor energy consumption and carbon emissions in real time, but also need to be able to intelligently adjust in different situations to achieve optimal energy efficiency and minimum environmental impact.

[0003] The intelligent energy management system (EMS) in the existing technology has the ability to monitor and manage energy consumption in buildings or areas. It mainly collects energy usage data (such as electricity, water, and natural gas consumption) and environmental parameters (such as temperature and humidity) in real time through a distributed sensor network installed in the monitoring area, receives data collected by the sensor network, performs data analysis and processing, and optimizes and adjusts energy usage by controlling electrical appliances or system operations based on the data processing results. According to the analysis results, it provides an interface for users to query data and receive notifications, and allows users to set energy usage strategies.

[0004] However, although the above solutions are helpful in energy management, their main disadvantages are: lack of refined management of the specific environment and conditions of highways, and lack of targeted solutions in carbon emission calculation and environmental monitoring. They cannot fully cover the impact of highways on the environment, often lack the ability to adjust sensitively to environmental changes, and cannot respond to the impact of environmental changes on energy demand in a timely manner, resulting in inefficient energy utilization and energy waste.

[0005] At the same time, traditional environmental monitoring technology relies on fixed monitoring sites, which cannot provide comprehensive coverage, and the data update frequency is low, which makes the environmental data lack real-time and accuracy, making it difficult to provide effective support for energy management and environmental protection. In addition, carbon emissions control is imprecise and there is a lack of effective real-time carbon emissions monitoring and management mechanisms. The reliability and accuracy are insufficient, and thus carbon emissions cannot be effectively controlled and reduced, posing a challenge to environmental protection and sustainable development.

[0006] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0007] In view of the problems in the related technology, the present invention proposes an intelligent control system based on resource and environmental data to overcome the above-mentioned technical problems existing in the existing related technology.

[0008] To this end, the specific technical solution adopted by the present invention is as follows:

[0009] A smart control system based on resource and environmental data, the smart control system comprising:

[0010] Energy management unit, used to obtain energy consumption data on the highway and generate energy control instructions based on the energy consumption data and data processing technology;

[0011] Carbon emission monitoring unit, which is used to monitor carbon emission data during highway operation and dynamically adjust energy control instructions based on carbon emission data and linkage control technology;

[0012] Environmental perception unit, used to monitor environmental parameters on the highway, and combine environmental parameters with fuzzy reasoning technology to adjust the use status of equipment running on the highway;

[0013] The central control unit is used to optimize the operation strategy on the highway according to real-time energy consumption data, carbon emission data and environmental parameters, and realize intelligent energy regulation.

[0014] Preferably, the energy management unit comprises:

[0015] A sensor network deployment module is used to deploy energy information sensors in the highway area and use the energy information sensors to collect energy consumption data of the highway;

[0016] An energy data processing module is used to construct an energy consumption prediction model after performing denoising and filtering on energy consumption data, and output predicted energy consumption based on the energy consumption prediction model;

[0017] A decision-making instruction formulation module is used to determine energy adjustment parameters according to energy forecast consumption and energy use demand, and calculate adjustment control instructions based on the energy adjustment parameters and control algorithms;

[0018] The instruction execution control module is used to use the actuator to receive the adjustment control instruction and regulate the operating state of the energy-using equipment according to the adjustment control instruction.

[0019] Preferably, the environment sensing unit includes:

[0020] An environmental data processing module is used to collect environmental data on the highway through an environmental sensor network and perform pre-processing operations on the environmental data using a denoising processing technique;

[0021] An environmental data fusion module is used to fuse environmental data using Kalman filtering technology, determine environmental parameters based on the fusion results, and evaluate the environmental conditions at the highway based on the environmental parameters;

[0022] A fuzzy rule determination module is used to generate a fuzzy set according to the error between the environmental condition evaluation result and the preset environmental condition value, and determine the fuzzy rule based on the fuzzy set and the actual control demand;

[0023] The operating equipment control module is used to generate fuzzy outputs by performing fuzzy reasoning based on fuzzy rules and fuzzy sets, and convert the fuzzy outputs into actual control quantities to adjust the usage status of the operating equipment on the highway.

[0024] Preferably, the central control unit comprises:

[0025] A data receiving module is used to receive real-time energy usage data, carbon emission data and environmental data collected by energy information sensors, energy consumption sensors and environmental sensors, and generate real-time operation data;

[0026] A comprehensive analysis module is used to perform comprehensive analysis on real-time operation data using data analysis algorithms, and identify abnormal operation conditions and control optimization space on the expressway based on the comprehensive analysis results;

[0027] The strategy formulation module is used to formulate energy scheduling and carbon emission control strategies based on abnormal operation conditions and regulation optimization space to achieve the purpose of dynamic energy allocation;

[0028] The control instruction generation module is used to convert energy scheduling and carbon emission control strategies into control instructions, and dynamically adjust the operating parameters of the energy management unit and the carbon emission monitoring unit based on the control instructions.

[0029] Preferably, a data analysis algorithm is used to perform a comprehensive analysis on the real-time operation data, and based on the comprehensive analysis results, the abnormal operation conditions and control optimization space at the highway are identified, including:

[0030] A Bayesian framework structure and a preset parameter vector are used to generate a linear relationship expression between real-time operation data and abnormal operation conditions at the highway;

[0031] A sparse prior operation is introduced for the preset parameter vector, and the hyperparameters controlling sparsity are optimized by maximizing the marginal approximation function in the sparse prior process;

[0032] Solve the optimal hyperparameters based on the iterative optimization process, and obtain the sparsely completed parameter vector based on the optimal hyperparameters to update the linear relationship expression;

[0033] The real-time operation data is input as the input matrix into the linear relationship expression to obtain the abnormal operation conditions at the highway, and the regulation and optimization space of energy monitoring and carbon emission monitoring is analyzed based on the abnormal operation conditions.

[0034] Preferably, energy dispatch and carbon emission control strategies are formulated based on abnormal operation conditions and regulation optimization space to achieve the purpose of dynamic energy allocation, including:

[0035] The state vector space is defined after obtaining the energy usage, carbon emission level and environmental parameters based on the abnormal operation situation, and the action vector space is defined based on the control optimization space to define the control actions that can be taken;

[0036] Based on the action vector and state vector, a reward function is defined to judge the pros and cons of energy scheduling and carbon emission control strategies, and deep neural network approximation technology is used to map the state vector space to the action vector space to generate a strategy function;

[0037] Define the objective function based on the reward function, and use the policy gradient method and the policy function to update the parameters of the deep neural network approximation technology to maximize the objective function;

[0038] According to the maximization objective function, the optimal control action that can be taken is defined in the control optimization space to generate the optimization strategy for energy scheduling and carbon emission control, so as to achieve the purpose of dynamic energy allocation.

[0039] Preferably, the expression for maximizing the objective function is:

[0040] ;

[0041] In the formula, represents the gradient of the objective function J(π) with respect to the parameter θ, logπ(a t ∣s t ;θ) represents the policy function π for the action vector a t In the state vector s t The logarithmic probability under π represents the interior of the state under the policy function π, s t represents the state vector, a t represents the action vector, π represents the policy function, θ represents the neural network parameters, R t′ represents the cumulative reward from time step t to the future, γ represents the discount factor, t′ represents the time period from time step t to the future, and T represents the maximum value of the time step.

[0042] Preferably, the control instruction generation module includes:

[0043] The optimization strategy parsing submodule is used to parse the energy scheduling and carbon emission control optimization strategies into the execution tasks required by the energy management unit and the carbon emission monitoring unit;

[0044] The instruction generation and selection submodule is used to calculate the control signal after selecting the control mode according to the nature of the execution task, and perform digital-to-analog conversion and packaging operations on the control signal to generate a control instruction;

[0045] A control instruction sending submodule is used to encrypt the control instruction and send it to the corresponding unit;

[0046] The execution feedback adjustment module is used to monitor the execution status of control instructions, and initiate corrective instructions when abnormal execution status is found, implement dynamic adjustments to control instructions, and analyze the energy and carbon emissions status after the execution status strategy is adjusted.

[0047] Preferably, after selecting the control mode according to the nature of the task to be executed, calculating the control signal, and performing digital-to-analog conversion and packaging operations on the control signal to generate the control instruction include:

[0048] Select the corresponding control method based on the nature of the task to be performed, set the parameters of the control method, and calculate the specific control signal according to the control method and strategy requirements;

[0049] The control signal is converted into an analog signal by digital-to-analog conversion technology, and after amplifying and filtering the analog signal, the analog signal is converted into a binary coding format;

[0050] According to a preset communication protocol, the binary signal is packaged and processed into a parameterized control instruction, and an error detection code is added to the end of the control instruction;

[0051] A confirmation mechanism is configured for the control instruction. The confirmation mechanism is used to detect whether the feedback control instruction is received and executed by the energy management unit and the carbon emission monitoring unit in the subsequent issuance process, and the control instruction issuance sub-module is adjusted according to the feedback result.

[0052] Preferably, encrypting the control instruction and sending it to the corresponding unit includes:

[0053] Generate an additional verification code based on the control instruction, and use an encryption algorithm to encrypt the control instruction and the additional verification code to generate a key, and distribute the key to the energy management unit and the carbon emission monitoring unit through a secure channel;

[0054] Use real-time communication protocols to issue transmission control instructions, and deploy firewalls at relay nodes in the transmission path to detect the status of instruction transmission;

[0055] After the energy management unit and the carbon emission monitoring unit receive the corresponding encrypted control instructions, the confirmation mechanism is used to feed back instruction reception confirmation information to the instruction generation selection submodule;

[0056] The energy management unit and the carbon emission monitoring unit obtain the control instruction and the additional check code according to the key decryption, recalculate the check code of the decrypted control instruction, compare the additional check code with the check code, and verify the consistency of the control instruction;

[0057] If the check codes are consistent, it means that the control instruction can be executed. If the check codes are inconsistent, the control instruction is rejected and fed back to the central control unit to regenerate the control instruction.

[0058] The beneficial effects of the present invention are:

[0059] 1. The present invention not only integrates multiple functions such as energy management, carbon emission monitoring and environmental monitoring for highway systems, but also realizes real-time dynamic monitoring and intelligent regulation of energy, carbon emissions and environmental conditions by utilizing advanced deep learning algorithms and Internet of Things technologies, in order to achieve higher energy efficiency and environmental protection effects.

[0060] 2. The present invention integrates dynamic environmental perception and intelligent control technology to achieve real-time prediction and adjustment of energy demand, improve energy efficiency and reduce waste. At the same time, it introduces advanced sensor networks and data processing technology to achieve comprehensive and real-time monitoring of environmental conditions, ensure the accuracy and real-time nature of environmental data, and establish a real-time carbon emission monitoring system and supporting management strategies to accurately monitor and control carbon emissions, providing a scientific basis for carbon emission reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0062] Figure 1 is a principle block diagram of an intelligent control system based on resource and environmental data according to an embodiment of the present invention;

[0063] Figure 2 This is an architecture diagram of an energy management unit in a smart control system based on resource and environmental data according to an embodiment of the present invention;

[0064] Figure 3 is a processing flow chart of an energy management unit in a smart control system based on resource and environmental data according to an embodiment of the present invention;

[0065] Figure 4 is a control execution flow chart of an energy management unit in a smart control system based on resource and environmental data according to an embodiment of the present invention;

[0066] Figure 5 is an architectural diagram of a carbon emission monitoring unit in a smart control system based on resource and environmental data according to an embodiment of the present invention;

[0067] Figure 6 is an analysis flow chart of a carbon emission monitoring unit in a smart control system based on resource and environmental data according to an embodiment of the present invention;

[0068] Figure 7 is an architectural diagram of an environment perception unit in a smart control system based on resource environment data according to an embodiment of the present invention;

[0069] Figure 8 is a processing flow chart of an environment perception unit in a smart control system based on resource environment data according to an embodiment of the present invention;

[0070] Fig. 9 It is an architecture diagram of a central control unit in an intelligent control system based on resource and environmental data according to an embodiment of the present invention.

[0071] In the figure:

[0072] 1. Energy management unit; 2. Carbon emission monitoring unit; 3. Environmental perception unit; 4. Central control unit. DETAILED DESCRIPTION

[0073] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments. They can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. By referring to these contents, ordinary technicians in this field should be able to understand other possible implementation methods and advantages of the present invention.

[0074] According to an embodiment of the present invention, a smart control system based on resource and environmental data is provided.

[0075] The present invention will now be further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to an intelligent control system based on resource environment data according to an embodiment of the present invention, the intelligent control system includes:

[0076] The energy management unit 1 is used to obtain energy consumption data on the highway and generate energy control instructions based on the energy consumption data and data processing technology.

[0077] In this embodiment, the energy management unit 1 includes:

[0078] A sensor network deployment module is used to deploy energy information sensors in the highway area and use the energy information sensors to collect energy consumption data of the highway;

[0079] An energy data processing module is used to construct an energy consumption prediction model after performing denoising and filtering on energy consumption data, and output predicted energy consumption based on the energy consumption prediction model;

[0080] A decision-making instruction formulation module is used to determine energy adjustment parameters according to energy forecast consumption and energy use demand, and calculate adjustment control instructions based on the energy adjustment parameters and control algorithms;

[0081] The instruction execution control module is used to use the actuator to receive the adjustment control instruction and regulate the operating state of the energy-using equipment according to the adjustment control instruction.

[0082] like Figure 2 As shown, it needs to be explained that the main function of the energy management unit 1 is to monitor and manage the energy usage of the highway and realize real-time optimization and regulation of energy.

[0083] Specific energy information sensors mainly include current sensors (used to monitor power usage and record current changes in real time), water flow meters (used to monitor water resource consumption and record water flow data in real time), etc. Current sensors and water flow meters are deployed at key nodes on highways, such as toll stations, power distribution stations, service areas, etc., to ensure that key areas of energy consumption are fully covered and real-time energy consumption data on highways, including power usage and water resource consumption, are collected.

[0084] The collected energy consumption data is pre-processed by denoising and filtering to ensure the accuracy and effectiveness of the data. At the same time, the Kalman filter algorithm is used for data fusion to improve the accuracy of the data.

[0085] At the same time, a linear regression model is used to analyze the impact of various factors on energy consumption, and an energy consumption prediction model is established. Its mathematical formula is: E=β0+β1X1+β2X2+…+β n X n ; Where E is energy consumption, X1, X2, ..., X n is the factor that affects energy consumption, β 0、 β 1、 …, β n is the regression coefficient.

[0086] Time series analysis models are used to predict future energy consumption. The commonly used model is the autoregressive integrated moving average (ARIMA) model, and its mathematical formula is: Where Y t is the energy consumption value at time t, is the error term, and α, δ, and γ are model parameters.

[0087] Based on the data analysis results, the proportional-integral-derivative (PID) control algorithm is used to adjust energy use to ensure real-time optimization of energy use. The mathematical formula of the PID control algorithm is:

[0088] ;

[0089] Where u(t) is the control variable, e(t) is the error (the difference between actual energy use and expected use), and K p , K i and K d are proportional, integral and derivative gains respectively; is the derivative of the error with respect to time, representing the rate of change of the error with time, is the cumulative error from 0 to t, representing the sum of past errors.

[0090] Specific as Figure 3 and Figure 4 As shown, the processing flow of the energy management unit 1 is as follows:

[0091] Step 1: Data collection: Collect highway energy consumption data in real time through the sensor network to ensure the real-time and comprehensiveness of the data.

[0092] Step 2: Data preprocessing: Perform preprocessing such as denoising and filtering on the collected data to improve the accuracy and reliability of the data. For example, use the Kalman filter algorithm to fuse multi-source data to obtain more accurate energy consumption data.

[0093] Step 3: Data analysis: Use linear regression model and time series analysis model to analyze and predict the preprocessed data. The linear regression model is used to analyze the impact of various factors on energy consumption and establish a prediction model for energy consumption; the time series analysis model is used to predict future energy consumption.

[0094] Step 4: Decision making: Based on the data analysis results, formulate energy usage optimization strategies, for example, by analyzing historical data and predicting future energy demand, determine the optimal energy allocation plan and generate specific control instructions.

[0095] Step 5: Execution control: Adjust energy usage according to the established optimization strategy to ensure efficient energy utilization and real-time optimization. For example, use the PID control algorithm to adjust energy allocation in real time according to actual energy usage to reduce energy waste.

[0096] Specifically, the control actuator receives the analysis results and optimization strategies, and uses the PID control algorithm to calculate specific control instructions based on the received data and strategies, and determines the energy usage parameters that need to be adjusted. Based on the calculation results, the control execution unit adjusts the operating status of the energy-using equipment through the actuator to achieve real-time optimization control.

[0097] PID control algorithm detailed description:

[0098] Proportional control (P): Directly adjust the control amount according to the current error. The advantage of proportional control is fast response, but it may cause steady-state error when used alone. Specifically: uP (t) = K p e(t), where e(t) is the error, K p is the proportion, uP (t) is the proportional control amount;

[0099] Integral control (I): Accumulate the error and eliminate the steady-state error by eliminating the accumulated error. The advantage of integral control is that it can eliminate the steady-state error, but the response is slow and may cause oscillation. Specifically:

[0100] ;

[0101] Where e(t) is the error, K i is the proportional integral, uI(t) is the integral control quantity, is the cumulative error from 0 to t, representing the sum of past errors.

[0102] Differential control (D): Controlling the rate of change of the error can improve the response speed and stability of the system. The advantage of differential control is that it can predict the trend of error changes, but it is sensitive to noise, specifically:

[0103] ;

[0104] In the formula, uD(t) is the differential control quantity, K d For the integral, is the derivative of the error with respect to time, representing the rate of change of the error with time.

[0105] Furthermore, through the combined effects of proportional, integral and differential control, the PID control algorithm can achieve precise regulation of energy use and ensure the stability and response speed of the system.

[0106] The carbon emission monitoring unit 2 is used to monitor the carbon emission data during the operation of the highway, and dynamically adjust the energy control instructions based on the carbon emission data and linkage control technology.

[0107] like Figure 5As shown, it needs to be explained that the main function of the carbon emission monitoring unit 2 is to monitor in real time the carbon emissions generated by electricity consumption, fuel use, air conditioning operation, etc. during highway operation, provide detailed carbon emission data, and work with the energy management module to formulate emission reduction strategies.

[0108] The carbon emission data during the operation of highways is obtained through energy consumption sensors, including power sensors (used to monitor the power usage of each node on the highway), fuel sensors (used to monitor the fuel usage of highway maintenance vehicles and power generation equipment), air conditioning sensors (used to monitor the air conditioning usage in highway service areas and control centers), etc. These sensors are installed at key equipment and facilities on highways, such as street lights, monitoring equipment, service areas and control centers.

[0109] The collected data includes power consumption data (power usage of street lighting, monitoring equipment, electric toll collection systems, etc.), fuel consumption data (fuel usage of maintenance vehicles, backup generators, etc.), air conditioning usage data (air conditioning operation status of service areas, control centers, etc.), etc.

[0110] Data preprocessing: The data collected by the sensor is preprocessed by denoising and filtering to ensure the accuracy and reliability of the data, and then the carbon emission calculation model is obtained, as follows:

[0111] Carbon emissions from electricity consumption: YCO2 = Electricity × Emission Factor;

[0112] Among them, YCO2 is the carbon emission of electricity consumption, Electricity is the electricity consumption, and Emission Factor is the emission factor of electricity.

[0113] Carbon emissions from fuel use: TCO2 = Fuel × Fuel Emission Factor;

[0114] Among them, TCO2 is the carbon emission from fuel use, Fuel is the fuel usage, and Fuel Emission Factor is the fuel emission factor.

[0115] Carbon emissions from air conditioning: RCO2 = AC Energy × Emission Factor;

[0116] Among them, RCO2 is the carbon emissions from the use of air conditioning, AC Energy is the energy consumed by air conditioning, and EmissionFactor is the corresponding emission factor.

[0117] At the same time, the linear regression model is used to analyze the impact of different factors on carbon emissions. The specific calculation formula is: L=ο0+ο1M1+ο2M2+...+ο n M n ;

[0118] Among them, L is carbon emissions, M1, M2, ..., M n are influencing factors, ο0, ο1, ..., ο n is the regression coefficient.

[0119] The ARIMA model is used to predict carbon emissions, specifically: t =a+bC t-1 +fd t-1 +d t Where W t is the carbon emission at time t, d t is the error term, and a, b, and f are model parameters.

[0120] Specifically, based on the analysis results and in coordination with the energy management unit 1, a feedback control algorithm is used to dynamically adjust energy use to reduce carbon emissions, wherein the feedback control algorithm is:

[0121] ;

[0122] In the formula, G(t) is the control strategy, L k is the expected carbon emissions, L is the actual carbon emissions, K p , K i and K d are the proportional, integral and derivative gains respectively.

[0123] Based on the results of data analysis, energy use strategies are adjusted, such as optimizing power dispatch, improving fuel efficiency, improving air conditioning use plans, etc., and real-time monitoring and feedback adjustments are made to ensure that carbon emissions remain within the set target range.

[0124] Specific as Figure 6 As shown, the processing flow of the carbon emission monitoring unit 2 is as follows:

[0125] Step 1: Data collection: Energy consumption sensors are used to collect data on electricity, fuel, and air conditioning operation in real time.

[0126] Step 2: Data cleaning: Perform denoising, filtering and other cleaning processes on the collected data to ensure the accuracy of the data.

[0127] Step 3: Data analysis: Use the carbon emission calculation model for electricity, fuel and air conditioning to analyze and calculate the data.

[0128] Step 4: Report generation: Generate detailed carbon emission reports to support decision-making. These reports include carbon emission data for each energy use link, helping managers identify high-emission links and take corresponding optimization measures.

[0129] Step 5: Linked control: Based on carbon emission data, adjust the energy management strategy to achieve dynamic regulation, and ensure that the overall carbon emissions meet environmental protection requirements by working in collaboration with the energy management module.

[0130] The environmental sensing unit 3 is used to monitor the environmental parameters at the highway and adjust the use status of the running equipment at the highway by combining the environmental parameters with fuzzy reasoning technology.

[0131] In this embodiment, the environment sensing unit 3 includes:

[0132] An environmental data processing module is used to collect environmental data on the highway through an environmental sensor network and perform pre-processing operations on the environmental data using a denoising processing technique;

[0133] An environmental data fusion module is used to fuse environmental data using Kalman filtering technology, determine environmental parameters based on the fusion results, and evaluate the environmental conditions at the highway based on the environmental parameters;

[0134] A fuzzy rule determination module is used to generate a fuzzy set according to the error between the environmental condition evaluation result and the preset environmental condition value, and determine the fuzzy rule based on the fuzzy set and the actual control demand;

[0135] The operating equipment control module is used to generate fuzzy outputs by performing fuzzy reasoning based on fuzzy rules and fuzzy sets, and convert the fuzzy outputs into actual control quantities to adjust the usage status of the operating equipment on the highway.

[0136] Specific as Figure 7 As shown, it needs to be explained that the environmental sensor network includes temperature and humidity sensors (to detect the temperature and humidity of the surrounding environment) and air quality sensors (to detect the concentration of pollutants such as PM2.5, PM10, CO2, NO2, SO2 in the air), etc. The sensors are installed along the expressway and its surrounding areas, including tunnels, bridges, toll stations, service areas and other locations to ensure the comprehensiveness and representativeness of the data. The sensor network is used to collect environmental parameter data in real time, including temperature, humidity, PM2.5 concentration, PM10 concentration, CO2 concentration, NO2 concentration, SO2 concentration, etc.

[0137] The collected data is pre-processed by denoising and filtering to ensure the accuracy and stability of the data. Common methods include moving average filtering, median filtering, etc. At the same time, Kalman filtering is used to achieve multi-source data fusion to improve the reliability and accuracy of the data. The specific mathematical formula of Kalman filtering is as follows:

[0138] ;

[0139] In the formula, is the estimated value of the state at the current time k, K k is the Kalman gain, Z k is the observation value at the current moment k, and H is the observation matrix. is the estimated value of the state at the current time k-1.

[0140] Through Kalman filtering, data from different sensors can be effectively integrated to obtain more accurate environmental parameter estimates. The specific Kalman filter is a recursive estimation algorithm based on the state space model, which can optimally estimate the dynamic system in a noisy environment. Its basic steps include:

[0141] (1) Prediction steps:

[0142] ;

[0143] ;

[0144] (2) Update steps:

[0145] ;

[0146] ;

[0147] ;

[0148] In the formula, is the predicted state estimate, is the predicted covariance matrix, K k is the Kalman gain, is the updated state estimate, P k∣k is the updated covariance matrix, F is the state transfer matrix, B is the control input matrix, Q is the process noise covariance matrix, R is the observation noise covariance matrix, and I is the identity matrix.

[0149] Through the above steps, Kalman filtering can continuously estimate and update environmental parameters and provide reliable environmental data support.

[0150] At the same time, the fuzzy control algorithm is used to adjust the energy use and carbon emission control strategies according to environmental data. The fuzzy control algorithm can handle complex and nonlinear systems. By defining fuzzy rules, it can achieve precise control of the system. Its mathematical formula is: p=s(v, Δv), where p is the control output, v is the error, Δv is the error change rate, and s is the fuzzy rule.

[0151] Through the fuzzy control algorithm, the system's operating parameters can be dynamically adjusted according to environmental changes to optimize energy use and carbon emission control.

[0152] It should be explained that fuzzy control is a control method based on fuzzy logic that can handle complex and uncertain systems. Its basic steps include:

[0153] Fuzzification: Fuzzify the input variables (error v and error change rate Δv), that is, map them into fuzzy sets.

[0154] Fuzzy rules: According to the actual control requirements of the system, a series of fuzzy rules are formulated, such as:

[0155] If v is positive and Δv is positive, then p should be positive.

[0156] If v is negative and large, and Δv is negative and large, then p should be negative and large.

[0157] Reasoning: Based on fuzzy rules and fuzzy input, fuzzy reasoning is performed to obtain fuzzy output.

[0158] Defuzzification: Convert the fuzzy output into the actual control quantity p.

[0159] Through the fuzzy control algorithm, dynamic regulation of environmental parameters can be achieved, improving the intelligence level and control accuracy of the system.

[0160] Specific as Figure 8 As shown, the processing flow of the environment perception unit 3 is as follows:

[0161] Step 1: Data collection: Collect environmental data around the highway in real time through the environmental sensor network to ensure the real-time and comprehensiveness of the data.

[0162] Step 2: Data fusion: Use Kalman filter algorithm to fuse multi-source data to obtain accurate environmental parameters. Kalman filter can effectively eliminate noise and improve data reliability.

[0163] Step 3: Environmental analysis: Conduct a comprehensive analysis based on the integrated environmental data to evaluate the environmental conditions. The analysis content includes temperature change trends, humidity levels, air quality conditions, etc.

[0164] Step 4: Strategy optimization: Combine the results of environmental analysis to optimize energy use and carbon emission control strategies. By analyzing the impact of different environmental parameters on energy consumption and emissions, develop the optimal regulation strategy.

[0165] Step 5: Feedback control: According to the optimized strategy, the system operation is adjusted through the feedback control unit to achieve intelligent management. Specific control measures include adjusting lighting intensity, ventilation system operation mode, vehicle emission control, etc.

[0166] The central control unit 4 is used to optimize the operation strategy at the highway according to the real-time energy consumption data, carbon emission data and environmental parameters, and realize intelligent energy regulation.

[0167] It needs to be explained that the central control unit 4 is the core of the entire system and is responsible for receiving and processing data from the energy management unit 1, the carbon emission monitoring unit 2 and the environmental perception unit 3. The central control unit 4 adopts advanced data processing technology and intelligent algorithms to conduct comprehensive analysis of the data, formulate energy use, carbon emission control and environmental optimization strategies, and execute control operations through each unit to realize intelligent management of the system.

[0168] The central control unit 4 is connected to the energy management unit 1, the carbon emission monitoring unit 2 and the environmental perception unit 3 through a high-speed data communication network to ensure real-time transmission and processing of data. The central control unit 4 also provides data query, strategy setting and system monitoring functions through the user interface module.

[0169] The data communication network uses high-speed Ethernet or wireless network to ensure stable and reliable data transmission between each module and the central control unit 4. The user interface module is connected to the central control system through a display screen and input devices for user operation and monitoring.

[0170] like Fig. 9 As shown, in this embodiment, the central control unit 4 includes:

[0171] The data receiving module is used to receive real-time energy usage data, carbon emission data and environmental data collected by energy information sensors, energy consumption sensors and environmental sensors, and generate real-time operation data.

[0172] A comprehensive analysis module is used to perform comprehensive analysis on real-time operation data using data analysis algorithms, and identify abnormal operation conditions and control optimization space on the expressway based on the comprehensive analysis results;

[0173] The strategy formulation module is used to formulate energy scheduling and carbon emission control strategies based on abnormal operation conditions and regulation optimization space to achieve the purpose of dynamic energy allocation;

[0174] The control instruction generation module is used to convert energy scheduling and carbon emission control strategies into control instructions, and dynamically adjust the operating parameters of the energy management unit and the carbon emission monitoring unit based on the control instructions.

[0175] In this embodiment, when a data analysis algorithm is used to perform a comprehensive analysis on the real-time operation data, and abnormal operation conditions and control optimization space at the highway are identified based on the comprehensive analysis results, a Bayesian framework structure and a preset parameter vector can be used to generate a linear relationship expression between the real-time operation data and the abnormal operation conditions at the highway; a sparse prior operation is introduced to the preset parameter vector, and in the sparse prior process, the hyperparameters controlling sparsity are optimized by maximizing the marginal approximate function; the optimal hyperparameters are solved based on an iterative optimization process, and the sparse completed parameter vector is obtained based on the optimal hyperparameters to update the linear relationship expression; the real-time operation data is input as an input matrix into the linear relationship expression to obtain abnormal operation conditions at the highway, and the control optimization space of energy monitoring and carbon emission monitoring is analyzed based on the abnormal operation conditions.

[0176] It should be explained that the comprehensive analysis module can use a variety of data analysis algorithms (such as regression analysis, time series analysis, Kalman filtering, etc.) to conduct comprehensive analysis of data, identify abnormal situations and optimize space, and introduce sparse Bayesian Learning (SBL) for data analysis and strategy formulation. Compared with traditional regression analysis and Kalman filtering, this method is more flexible and can effectively process high-dimensional data and sparse data, and has stronger predictive ability and model interpretability.

[0177] SBL is a machine learning method based on the Bayesian framework. By introducing sparse priors, the model can automatically select important features, thereby improving the generalization ability and interpretability of the model. In the energy-carbon-ring system, SBL can be used to extract key features from a large amount of environmental, energy, and carbon emission data, identify anomalies, and optimize strategies. The specific steps are as follows:

[0178] Step 1: Assume input data X∈R n ×p and output data y∈R n There is a linear relationship between them: y=Xη+χ; where η∈R n is the parameter vector of the model, χ is the noise term that follows the normal distribution, χ~N(0,σ 2 I).

[0179] Step 2: Sparse prior: Introduce a sparse prior to the parameter vector η so that the model can automatically select the most important features:

[0180] ;

[0181] In the formula, α={α1, α2,…,α p} is a hyperparameter for controlling sparsity, p is the number of hyperparameters; η j represents the number of j-th regression coefficients, with a mean of 0 and a variance of The normal distribution of .

[0182] Step 3: Marginal likelihood function: Optimize the hyperparameter α by maximizing the marginal likelihood function:

[0183] ;

[0184] ;

[0185] In the formula, Denotes a given hyperparameter α and noise variance σ 2 In the case of , the marginal likelihood of the target variable y is, represents the normal distribution, with mean μ and covariance matrix Σ, representing the probability distribution of variable y, X represents the input feature matrix, T represents the maximum time step, σ 2 represents the noise variance, is the intensity of the observed noise, I represents the unit matrix, represents the noise variance σ 2 The impact on observation.

[0186] Step 4: Parameter estimation: Find the optimal α and σ through iterative optimization 2 , thus obtaining a sparse parameter vector η.

[0187] In this embodiment, when formulating energy scheduling and carbon emission control strategies based on abnormal operating conditions and regulation optimization space to achieve the purpose of dynamic energy allocation, the state vector space can be defined after obtaining energy usage, carbon emission levels and environmental parameters based on abnormal operating conditions, and the action vector space can be defined according to the regulation optimization space definition of the regulation actions that can be taken; the reward function is defined based on the action vector and the state vector to judge the pros and cons of the energy scheduling and carbon emission control strategies, and the state vector space is mapped to the action vector space using the deep neural network approximation technology to generate the policy function; the objective function is defined based on the reward function, and the policy gradient method and the policy function are used to update the parameters of the deep neural network approximation technology to obtain the maximized objective function; according to the maximized objective function, the optimal regulation action that can be taken in the regulation optimization space definition is found to generate the optimization strategy for energy scheduling and carbon emission control, so as to achieve the purpose of dynamic energy allocation.

[0188] It should be explained that in the process of policy formulation, energy use and carbon emission control strategies are automatically formulated according to the analysis results to optimize energy distribution and use. Specifically, deep reinforcement learning (DRL) can be introduced. It learns the optimal energy scheduling strategy and carbon emission control strategy by constantly interacting with the environment. This algorithm is particularly suitable for dealing with decision-making problems in complex dynamic systems, such as highway energy management and carbon emission control. At the same time, deep reinforcement learning combines the advantages of deep learning and reinforcement learning. Through neural network approximation of state-action value function or policy function, it can find the optimal strategy in complex high-dimensional state space. In the energy-carbon-ring system, the DRL algorithm can dynamically optimize the distribution and use of energy to minimize carbon emissions and maximize energy efficiency. The specific content and algorithm steps are as follows:

[0189] Step 1: State space (S): Based on the current energy usage, carbon emission level, environmental parameters, etc., define the state vector as: S t =[E t , C t , P t , A t ], where E t is the current energy consumption, C t is the carbon emission level, P t is the energy price, A t are environmental parameters (such as temperature, humidity, etc.).

[0190] Step 2, Action Space (A): The set of actions that can be taken includes adjusting energy distribution, starting or stopping equipment, switching energy types, etc. The action vector is defined as: a t =[△E t , △C t 、M t ], where △E t is the adjusted energy distribution, △C t is the adjusted carbon emissions, M t Is the device operating mode.

[0191] Step 3. Reward function (R): Define the reward function R t , used to evaluate the pros and cons of the current strategy, the reward function can be designed as the weighted sum of energy efficiency and carbon emission control: R t = -αE t -βC t , where α and β are weight coefficients, reflecting the different levels of concern for energy consumption and carbon emissions.

[0192] Step 4: Strategy (π): Strategy π (S t) is a mapping function from state space to action space, which is approximated by a deep neural network and is expressed as: t =π(S t ; θ), where θ is the parameter of the neural network.

[0193] Step 5. Objective function: The goal of the DRL algorithm is to find the optimal strategy by maximizing the cumulative reward. The expected cumulative reward is expressed as:

[0194] ;

[0195] Step 5: Gradient update: Use the policy gradient method to update the parameters θ of the neural network to maximize the objective function J(π):

[0196] ;

[0197] In the formula, represents the gradient of the objective function J(π) with respect to the parameter θ, logπ(a t ∣s t ;θ) represents the policy function π for the action vector a t In the state vector s t The logarithmic probability under π represents the interior of the state under the policy function π, s t represents the state vector, a t represents the action vector, π represents the policy function, θ represents the neural network parameters, R t′ represents the cumulative reward from time step t to the future, γ represents the discount factor, t′ represents the time period from time step t to the future, and T represents the maximum value of the time step.

[0198] In this embodiment, the DRL algorithm can dynamically formulate optimal energy usage and carbon emission control strategies by continuously exploring and utilizing the state space and action space. For example, when energy prices rise or carbon emissions approach the upper limit, the system can minimize costs and carbon emissions by adjusting energy allocation or changing equipment operation modes. In addition, the DRL algorithm can adapt to environmental changes and quickly adjust strategies when the environment suddenly changes (such as extreme weather or energy crises) to achieve dynamic optimization of energy and carbon emissions.

[0199] In this embodiment, the control instruction generation module includes:

[0200] The optimization strategy parsing submodule is used to parse the energy scheduling and carbon emission control optimization strategies into the execution tasks required by the energy management unit and the carbon emission monitoring unit;

[0201] The instruction generation and selection submodule is used to calculate the control signal after selecting the control mode according to the nature of the execution task, and perform digital-to-analog conversion and packaging operations on the control signal to generate a control instruction;

[0202] A control instruction sending submodule is used to encrypt the control instruction and send it to the corresponding unit;

[0203] The execution feedback adjustment module is used to monitor the execution status of control instructions, and initiate corrective instructions when abnormal execution status is found, implement dynamic adjustments to control instructions, and analyze the energy and carbon emissions status after the execution status strategy is adjusted.

[0204] It should be explained that the formulated strategy is converted into specific control instructions through the control instruction generation module, and the operating parameters of the energy management unit 1 and the carbon emission monitoring unit 2 are adjusted to realize dynamic regulation and intelligent management of the system.

[0205] In this embodiment, after the control mode is selected according to the nature of the execution task, the control signal is calculated, and the control signal is subjected to digital-to-analog conversion and packaging operations to generate a control instruction. The corresponding control mode can be selected based on the nature of the execution task, and the parameters of the control mode can be set, and the specific control signal can be calculated according to the control mode and strategy requirements; the control signal is converted into an analog signal through digital-to-analog conversion technology, and after amplifying and filtering the analog signal, the analog signal is converted into a binary encoding format; according to a pre-set communication protocol, the binary signal is packaged and processed to be converted into a parameterized control instruction, and an error detection code is attached to the end of the control instruction; a confirmation mechanism is configured for the control instruction, and the confirmation mechanism is used to detect whether the feedback control instruction is received and executed by the energy management unit 1 and the carbon emission monitoring unit 2 in the subsequent issuance process, and the control instruction issuance sub-module is adjusted according to the feedback result.

[0206] Specifically, the overall energy use and carbon emission control strategy is decomposed into specific instructions that can be executed by each module. According to the results generated by the strategy (such as adjusting the energy allocation, adjusting the equipment working mode, etc.), the macro strategy is converted into specific tasks that each module needs to perform, and the complex strategy is further decomposed into subtasks that can be executed by a single control instruction. For example, the strategy may require reducing carbon emissions, which can be decomposed into adjusting the input power of the energy management unit 1, controlling the threshold setting of the carbon emission monitoring unit 2, etc.

[0207] Select a suitable control model for each specific control task to ensure the accurate execution of instructions. Select a suitable control model according to the nature of the task (such as linear control, nonlinear control, optimal control, etc.). For example, for temperature control, you can use the PID control model; for energy distribution, you may need to use the optimal control based on the prediction model. Set the parameters of the selected control model to meet the current system operating status and strategy requirements, for example, adjust the proportional, integral and differential coefficients of the PID controller.

[0208] The decomposed tasks and corresponding control models are used to generate specific control instructions. According to the control model and strategy requirements, specific control signals are calculated. These control signals may include voltage, current, switch status, threshold adjustment, etc.

[0209] Convert control signals into instruction formats that can be understood by each module, for example, convert voltage regulation signals into analog signals, or convert switch operation instructions into binary format.

[0210] In the process of control signal calculation, specific control signals are calculated based on carbon emission data, external energy usage demand and emission targets, and previously set feedback control models (such as PID controllers). These signals are generated based on the gap between the current carbon emissions and the target values, and may include: voltage adjustment (control signals for adjusting power consumption may involve adjusting voltage to optimize energy efficiency), current control (calculating the required current adjustment signal based on different equipment load conditions), switch state adjustment (turning on or off certain equipment (such as backup generators) to meet different energy needs), threshold adjustment (setting new operating thresholds for various sensors and actuators to ensure that they operate within the optimal range), etc.

[0211] For control signals that require voltage or current adjustment, the digital signal can be converted into an analog signal through a DAC (Digital-to-Analog Converter). For example, the calculated voltage adjustment signal is converted into a corresponding voltage value, which is suitable for driving the analog input of power equipment. In some cases, the analog signal may need to be amplified or filtered to ensure that the signal quality meets the required standards. Amplifiers and filters can be used to enhance the amplitude of the signal or remove noise.

[0212] For switch operations or other discrete control signals, they are converted into binary coding format, which is widely used in digital control systems to ensure the accuracy and reliability of instructions. At the same time, according to specific communication protocols (such as Modbus, CAN, RS-485, etc.), the binary signals are encapsulated to meet the communication standards. This ensures that the signals are correctly decoded and executed when transmitted on the network.

[0213] For the threshold setting signal, it is converted into a parameterized instruction format. For example, the operating threshold of the temperature sensor is set to a certain numerical range, and an instruction containing the parameters of this range is generated and sent to the corresponding sensor or controller. In some intelligent devices, the instruction format may need to be adjusted according to the adaptive capability of the device. For example, some devices can automatically adjust the input format to adapt to different operating conditions, and then adjust the instruction format according to the feedback from the device.

[0214] The converted signal is packaged and processed according to the communication protocol. The packaged content may include: signal type identifier, data field, check code, start and end flags, etc. If the communication protocol involves multi-level encapsulation (such as the data link layer and network layer in the TCP / IP protocol), it will be encapsulated layer by layer to ensure the integrity and correctness of the instruction during the transmission process. At the same time, an error detection code (such as CRC code) is attached to the end of the instruction to detect errors that may occur during the transmission process, and a confirmation mechanism is configured. When the instruction is received and executed by the target unit, the confirmation mechanism will send a confirmation signal back to the central control unit 4. If the confirmation signal is not received or an error occurs, the instruction will be resent to ensure the reliability of the operation.

[0215] In this embodiment, when the control instruction is encrypted and sent to the corresponding unit, an additional verification code can be generated based on the control instruction, and the control instruction and the additional verification code are encrypted using an encryption algorithm to generate a key, and the key is distributed to the energy management unit 1 and the carbon emission monitoring unit 2 through a secure channel; a real-time communication protocol is used to send transmission control instructions, and a firewall is deployed at the relay node in the transmission path to detect the status of instruction transmission; after the energy management unit 1 and the carbon emission monitoring unit 2 receive the corresponding encrypted control instruction, the confirmation mechanism is used to feedback the instruction reception confirmation information to the instruction generation selection submodule; the energy management unit 1 and the carbon emission monitoring unit 2 decrypt the control instruction and the additional verification code according to the key, and recalculate the verification code of the decrypted control instruction, compare the additional verification code with the verification code, and verify the consistency of the control instruction; if the verification code is consistent, it means that the control instruction is executable, if the verification code is inconsistent, the control instruction is refused to be executed and fed back to the central control unit to regenerate the control instruction.

[0216] It needs to be explained that the generated control instructions are passed to the corresponding execution units and their execution is monitored. Specifically, a pair of keys (public key and private key) are generated and distributed to relevant control modules through secure channels. The public key is used for encryption and the private key is used for decryption. After the control signal is converted into the instruction format, the instructions are encrypted using the selected encryption algorithm. Even if the encrypted instructions are intercepted during transmission, they cannot be deciphered. At the same time, the keys need to be updated regularly to prevent key leakage and ensure that each module obtains the updated keys in a timely manner.

[0217] It also uses communication protocols suitable for real-time requirements such as TCP / IP or UDP to ensure the accurate transmission of instructions, monitor the status of data transmission in real time, detect whether there is abnormal traffic or potential attacks, and take appropriate countermeasures when problems are found. At the same time, firewalls are deployed at relay nodes on the transmission path to further protect the security of data transmission.

[0218] Specifically, before the instruction is encrypted, a verification code is generated and attached to the encrypted data. The verification code will be used for integrity verification after transmission. After the target execution unit receives the encrypted instruction, it uses the private key to decrypt it to obtain the original control instruction and the verification code, and then recalculates the verification code of the decrypted instruction and compares it with the verification code attached to the transmission. If the verification codes are consistent, it means that the instruction has not been tampered with and can be executed; otherwise, the execution is refused and feedback is given to the central control unit 4. If the consistency verification fails, the execution unit should immediately send a warning message to the central control unit 4 and request to resend the instruction. The control strategy can be dynamically adjusted according to the feedback.

[0219] At the same time, it can also implement a real-time dynamic key update mechanism to ensure that the key is not outdated and effectively prevent malicious attacks, establish a sound instruction retransmission strategy, and automatically retransmit and ensure successful execution when network transmission is interrupted or instruction execution fails.

[0220] During the instruction transmission process, the control instructions are transmitted to the corresponding execution units through high-speed data communication networks (such as Ethernet and wireless networks). This process needs to ensure the integrity and timeliness of the data.

[0221] After executing the instruction, the execution unit returns the feedback data to the central control unit 4 for monitoring the execution and adjusting the strategy. For example, if the energy management unit 1 does not adjust the power as expected, it will immediately recalculate and issue a new instruction to ensure that the instruction is executed correctly and make necessary adjustments based on the feedback information.

[0222] At the same time, the status of the execution unit is monitored in real time to ensure that the instructions are executed correctly. If an abnormality is detected or the instruction fails to achieve the expected effect, the system will automatically issue a corrective instruction and dynamically adjust the strategy based on the execution results and feedback information. For example, if the carbon emission monitoring unit 2 feedback shows that carbon emissions are still exceeding the standard, energy allocation may be further reduced or the operating frequency of the equipment may be increased.

[0223] Analyze the feedback data from the execution unit to confirm whether various strategic goals have been achieved. For example, analyze whether the energy usage data and carbon emission data are in line with expectations. If the execution effect is not ideal, the optimization algorithm will be triggered for secondary regulation to regenerate and issue control instructions to ensure that the strategic goals are ultimately achieved.

[0224] Through the organic combination and intelligent control of the above-mentioned units, the intelligent control system based on resource and environmental data of this embodiment can significantly improve the energy utilization efficiency of highways, reduce carbon emissions, improve environmental protection performance, and achieve sustainable development.

[0225] Therefore, this embodiment demonstrates the connection relationship between the energy management unit 1, the carbon emission monitoring unit 2 and the environmental perception unit 3 and the central control unit 4. Through the close integration of multiple functional units, comprehensive monitoring and intelligent regulation of highway energy use, carbon emissions and environmental impacts are achieved. Each unit operates independently to complete specific tasks, and performs data aggregation, processing and decision-making through the central control unit 4 to ensure efficient, stable and intelligent operation of the system.

[0226] In summary, with the help of the above technical solutions of the present invention, the present invention forms an intelligent control system based on resource and environmental data by integrating three major units: energy management, carbon emission monitoring and environmental perception, and realizes real-time monitoring and intelligent management of highway energy use, carbon emissions and environmental conditions. Combining the innovative points of each unit and the coordinated operation of the central control unit, it has shown significant advantages in solving the defects of the prior art. Through real-time monitoring and data analysis by the energy management unit 1, linear regression model and time series analysis are used to predict and optimize energy use, which significantly improves energy utilization efficiency and reduces waste. Through the PID control algorithm, the system can make adaptive adjustments according to real-time data to ensure energy. The carbon emission monitoring unit 2 uses advanced carbon emission sensors and data analysis models to monitor the carbon emissions of highways in real time and accurately, calculates carbon emissions using emission factor models, and works together with the energy management unit 1 to achieve dynamic regulation of carbon emissions through feedback control algorithms, effectively reducing carbon emissions and contributing to environmental protection and sustainable development. Finally, the environmental perception unit 3 uses a variety of environmental sensors to collect temperature, humidity, air quality and other data in real time, and uses the Kalman filter algorithm to fuse multi-source data to ensure the accuracy and reliability of environmental data. It combines fuzzy control algorithms to conduct a comprehensive analysis of environmental parameters, optimize energy use and carbon emission control strategies, and improve the intelligence level of the system.

[0227] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent control system based on resource and environmental data, characterized in that: The intelligent control system includes: Energy management unit, used to obtain energy consumption data on the highway and generate energy control instructions based on the energy consumption data and data processing technology; Carbon emission monitoring unit, which is used to monitor carbon emission data during highway operation and dynamically adjust energy control instructions based on carbon emission data and linkage control technology; Environmental perception unit, used to monitor environmental parameters on the highway, and combine environmental parameters with fuzzy reasoning technology to adjust the use status of equipment running on the highway; The central control unit is used to optimize the operation strategy on the highway according to real-time energy consumption data, carbon emission data and environmental parameters, and realize intelligent energy regulation.

2. According to claim 1, a smart control system based on resource and environmental data is characterized in that: The energy management unit comprises: A sensor network deployment module is used to deploy energy information sensors in the highway area and use the energy information sensors to collect energy consumption data of the highway; An energy data processing module is used to construct an energy consumption prediction model after performing denoising and filtering on energy consumption data, and output predicted energy consumption based on the energy consumption prediction model; A decision-making instruction formulation module is used to determine energy adjustment parameters according to energy forecast consumption and energy use demand, and calculate adjustment control instructions based on the energy adjustment parameters and control algorithms; The instruction execution control module is used to use the actuator to receive the adjustment control instruction and regulate the operating state of the energy-using equipment according to the adjustment control instruction.

3. The intelligent control system based on resource and environmental data according to claim 1 is characterized in that: The environment sensing unit comprises: An environmental data processing module is used to collect environmental data on the highway through an environmental sensor network and perform pre-processing operations on the environmental data using a denoising processing technique; An environmental data fusion module is used to fuse environmental data using Kalman filtering technology, determine environmental parameters based on the fusion results, and evaluate the environmental conditions at the highway based on the environmental parameters; A fuzzy rule determination module is used to generate a fuzzy set according to the error between the environmental condition evaluation result and the preset environmental condition value, and determine the fuzzy rule based on the fuzzy set and the actual control demand; The operating equipment control module is used to generate fuzzy outputs by performing fuzzy reasoning based on fuzzy rules and fuzzy sets, and convert the fuzzy outputs into actual control quantities to adjust the usage status of the operating equipment on the highway.

4. The intelligent control system based on resource and environmental data according to claim 1 is characterized in that: The central control unit comprises: A data receiving module is used to receive real-time energy usage data, carbon emission data and environmental data collected by energy information sensors, energy consumption sensors and environmental sensors, and generate real-time operation data; A comprehensive analysis module is used to perform comprehensive analysis on real-time operation data using data analysis algorithms, and identify abnormal operation conditions and control optimization space on the expressway based on the comprehensive analysis results; The strategy formulation module is used to formulate energy scheduling and carbon emission control strategies based on abnormal operation conditions and regulation optimization space to achieve the purpose of dynamic energy allocation; The control instruction generation module is used to convert energy scheduling and carbon emission control strategies into control instructions, and dynamically adjust the operating parameters of the energy management unit and the carbon emission monitoring unit based on the control instructions.

5. The intelligent control system based on resource and environmental data according to claim 4 is characterized in that: The method of using a data analysis algorithm to perform a comprehensive analysis on the real-time operation data and identifying abnormal operation conditions and control optimization space on the highway based on the comprehensive analysis results includes: A Bayesian framework structure and a preset parameter vector are used to generate a linear relationship expression between real-time operation data and abnormal operation conditions at the highway; A sparse prior operation is introduced for the preset parameter vector, and the hyperparameters controlling sparsity are optimized by maximizing the marginal approximation function in the sparse prior process; Solve the optimal hyperparameters based on the iterative optimization process, and obtain the sparsely completed parameter vector based on the optimal hyperparameters to update the linear relationship expression; The real-time operation data is input as the input matrix into the linear relationship expression to obtain the abnormal operation conditions at the highway, and the regulation and optimization space of energy monitoring and carbon emission monitoring is analyzed based on the abnormal operation conditions.

6. The intelligent control system based on resource and environmental data according to claim 5 is characterized in that: The energy dispatch and carbon emission control strategies are formulated based on abnormal operation conditions and regulation optimization space to achieve the purpose of dynamic energy allocation, including: The state vector space is defined after obtaining the energy usage, carbon emission level and environmental parameters based on the abnormal operation situation, and the action vector space is defined based on the control optimization space to define the control actions that can be taken; Based on the action vector and state vector, a reward function is defined to judge the pros and cons of energy scheduling and carbon emission control strategies, and deep neural network approximation technology is used to map the state vector space to the action vector space to generate a strategy function; Define the objective function based on the reward function, and use the policy gradient method and the policy function to update the parameters of the deep neural network approximation technology to maximize the objective function; According to the maximization objective function, the optimal control action that can be taken is defined in the control optimization space to generate the optimization strategy for energy scheduling and carbon emission control, so as to achieve the purpose of dynamic energy allocation.

7. The intelligent control system based on resource and environmental data according to claim 6 is characterized in that: The expression of the maximized objective function is: ； In the formula, represents the gradient of the objective function J(π) with respect to the parameter θ, logπ(a t ∣s t ;θ) represents the policy function π for the action vector a t In the state vector s t The logarithmic probability under π represents the interior of the state under the policy function π, s t represents the state vector, a t represents the action vector, π represents the policy function, θ represents the neural network parameters, R t′ represents the cumulative reward from time step t to the future, γ represents the discount factor, t′ represents the time period from time step t to the future, and T represents the maximum value of the time step.

8. The intelligent control system based on resource and environmental data according to claim 7 is characterized in that: The control instruction generation module comprises: The optimization strategy parsing submodule is used to parse the energy scheduling and carbon emission control optimization strategies into the execution tasks required by the energy management unit and the carbon emission monitoring unit; The instruction generation and selection submodule is used to calculate the control signal after selecting the control mode according to the nature of the execution task, and perform digital-to-analog conversion and packaging operations on the control signal to generate a control instruction; A control instruction sending submodule is used to encrypt the control instruction and send it to the corresponding unit; The execution feedback adjustment module is used to monitor the execution status of control instructions, and initiate corrective instructions when abnormal execution status is found, implement dynamic adjustments to control instructions, and analyze the energy and carbon emissions status after the execution status strategy is adjusted.

9. The intelligent control system based on resource and environmental data according to claim 8, characterized in that: The step of calculating the control signal after selecting the control mode according to the nature of the task to be executed, and performing digital-to-analog conversion and packaging operations on the control signal to generate a control instruction includes: Select the corresponding control method based on the nature of the task to be performed, set the parameters of the control method, and calculate the specific control signal according to the control method and strategy requirements; The control signal is converted into an analog signal by digital-to-analog conversion technology, and after amplifying and filtering the analog signal, the analog signal is converted into a binary coding format; According to a preset communication protocol, the binary signal is packaged and processed into a parameterized control instruction, and an error detection code is added to the end of the control instruction; A confirmation mechanism is configured for the control instruction. The confirmation mechanism is used to detect whether the feedback control instruction is received and executed by the energy management unit and the carbon emission monitoring unit in the subsequent issuance process, and the control instruction issuance sub-module is adjusted according to the feedback result.

10. The intelligent control system based on resource and environmental data according to claim 9, characterized in that: The step of performing encryption processing on the control instruction and sending it to the corresponding unit comprises: Generate an additional verification code based on the control instruction, and use an encryption algorithm to encrypt the control instruction and the additional verification code to generate a key, and distribute the key to the energy management unit and the carbon emission monitoring unit through a secure channel; Use real-time communication protocols to issue transmission control instructions, and deploy firewalls at relay nodes in the transmission path to detect the status of instruction transmission; After the energy management unit and the carbon emission monitoring unit receive the corresponding encrypted control instructions, the confirmation mechanism is used to feed back instruction reception confirmation information to the instruction generation selection submodule; The energy management unit and the carbon emission monitoring unit obtain the control instruction and the additional check code according to the key decryption, recalculate the check code of the decrypted control instruction, compare the additional check code with the check code, and verify the consistency of the control instruction; If the check codes are consistent, it means that the control instruction is executable. If the check codes are inconsistent, the control instruction is rejected and fed back to the central control unit to regenerate the control instruction.

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