New energy centralized control center data security management method

Abstract

The invention relates to the technical field of data security, in particular to a new energy centralized control center data security management method, which comprises the following steps: acquiring new energy power plant data and network state data; constructing a wake flow propagation model, and evaluating the influence of the wake flow effect on the data transmission path; constructing a data transmission path planning model, and extracting a potential transmission path based on a multi-path unicast network; and taking the minimum communication modulation and wake flow confrontation cost as a target, and extracting an optimal transmission path from the potential transmission paths. The invention aims to solve the technical problems of safety and reliability of data transmission of the new energy power plant and improve the efficiency, safety and reliability of safety management of production data of the new energy power plant.

Description

Technical Field

[0001] The present invention relates to the field of data security technology, and in particular to a data security management method for a new energy centralized control center. Background Art

[0002] The new energy centralized control center refers to a central control system used for centralized management and monitoring of new energy power generation equipment. It is the core component of the new energy power generation system. Through centralized control and management of various power generation devices, it can realize real-time monitoring, scheduling and optimized operation of the entire power generation system. On the one hand, the new energy centralized control center is used to centrally manage power generation equipment distributed in different regions, and realize unified scheduling and operation optimization of equipment through remote monitoring and control; on the other hand, it is used to monitor the operating status and environmental conditions of power generation equipment in real time, and perform intelligent scheduling according to grid demand and user demand to maximize power generation efficiency and power supply reliability; in addition, the new energy centralized control center also needs to collect and analyze the operating data of power generation equipment, and provide decision support and reference for energy management departments through monitoring and evaluation of energy consumption and power generation efficiency, so as to promote the rational use and conservation of energy.

[0003] However, with the continuous expansion of renewable energy power generation and the deepening of digital transformation, today's renewable energy centralized control centers have collected a large amount of data, and are facing increasingly severe data security challenges and technical difficulties; renewable energy centralized control centers need to process massive amounts of data, including real-time equipment operating parameters, environmental monitoring data, market transaction information, and user electricity consumption behavior. The transmission of these data requires not only high speed and low latency, but also network bandwidth and energy consumption costs. Especially in the complex and changeable power plant environment, the external environment will also affect the quality and efficiency of data transmission, which ultimately leads to insufficient data transmission efficiency and high transmission costs in the renewable energy centralized control center; for this reason, we propose a data security management method for the renewable energy centralized control center. Summary of the invention

[0004] In order to solve the technical problems of security and reliability of data transmission in new energy power plants, the purpose of the present invention is to provide a data security management method for a new energy centralized control center. The technical solution adopted is as follows:

[0005] A data security management method for a new energy centralized control center, the method comprising:

[0006] Obtain new energy power plant data and network status data;

[0007] Construct a wake propagation model to evaluate the impact of wake effects on data transmission paths;

[0008] Construct a data transmission path planning model to extract potential transmission paths based on multi-path unicast networks;

[0009] With the goal of minimizing the communication modulation and wake countermeasure costs, the optimal transmission path is extracted from potential transmission paths.

[0010] Furthermore, obtaining new energy power plant data and network status data includes:

[0011] Obtain wind turbine layout data and wind condition timing data in new energy power plants;

[0012] The network topology of the renewable energy power plant is extracted, and the data transmission area of ​​the renewable energy power plant is divided.

[0013] Furthermore, the network topology of the new energy power plant is extracted, and the data transmission area of ​​the new energy power plant is divided into:

[0014] Based on the connectivity and independence between different data transmission sub-areas in the new energy power plant, the optimal division of the data transmission area of ​​the new energy power plant is obtained with the goal of maximizing the connectivity of the data transmission nodes in the data transmission sub-area.

[0015] Furthermore, a wake propagation model is constructed to evaluate the impact of wake effects on the data transmission path, including:

[0016] Based on computational fluid dynamics, the wake effect in the renewable energy power plant is predicted using wind time series data, and the velocity distribution, turbulence intensity and diffusion rate of the wake effect are extracted;

[0017] The data transmission path evaluation results are extracted based on the turbulence intensity and diffusion rate of the wake effect.

[0018] Furthermore, the data transmission path evaluation results extracted based on the turbulence intensity and diffusion rate of the wake effect include:

[0019] Signal path evaluation to extract signal path deflections caused by wake effects.

[0020] Furthermore, the turbulence intensity and diffusion rate based on the wake effect extract the data transmission path evaluation results and also include:

[0021] Signal attenuation calculation, estimating signal attenuation based on wake turbulence intensity and signal frequency;

[0022] Multipath effect analysis, identifying the signal reflection, refraction and diffraction paths caused by wake turbulence, using ray tracing to simulate the multipath effect, and extracting the signal delay and phase change caused by the multipath effect.

[0023] Furthermore, a data transmission path planning model is constructed to extract potential transmission paths based on a multi-path unicast network, including:

[0024] The wake sensitivity coefficient of each edge is calculated based on the signal attenuation and the signal delay and phase change caused by the multipath effect;

[0025] Potential transmission paths are extracted based on wake sensitivity coefficient thresholds.

[0026] Further, extracting potential transmission paths based on wake sensitivity coefficient thresholds includes:

[0027] A non-dominated sorting genetic algorithm is used to extract a set of potential transmission paths according to the wake sensitivity coefficient and signal path deflection.

[0028] Furthermore, with the goal of minimizing the communication modulation and wake countermeasure costs, the optimal transmission path is extracted from the potential transmission paths, including:

[0029] According to the modulation rate of the potential transmission path before and after the wake effect, the communication modulation cost of the potential transmission path caused by the wake effect is calculated;

[0030] The tail flow countermeasure cost caused by increasing the forward error correction coding strength of the potential transmission path is calculated based on the coding overhead and decoding delay;

[0031] With the goal of minimizing the communication modulation cost and the wake confrontation cost, the particle swarm algorithm is used to solve the optimal transmission path.

[0032] Furthermore, the method also includes: presetting a security level coefficient, and adaptively selecting an encryption algorithm and encryption strength according to a characteristic vector of a network environment and a data scenario.

[0033] The present invention has the following beneficial effects:

[0034] The data security management method for the new energy centralized control center provided in the present application considers the data transmission stability under the wake effect, evaluates the influence of the wake effect on the data transmission path, and then constructs a data transmission path planning model, extracts potential transmission paths, and finally extracts the optimal transmission path by minimizing the communication modulation and wake confrontation costs; while taking into account the influence of the wake effect on data transmission, the method realizes efficient and reliable data communication by further optimizing the data transmission path, and can also effectively reduce the data transmission cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are 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.

[0036] Figure 1A schematic diagram of a flow chart of a data security management method for a new energy centralized control center provided by an embodiment of the present invention;

[0037] Figure 2 A schematic diagram of a flow chart of data transmission path evaluation provided by one embodiment of the present invention;

[0038] Figure 3 A schematic diagram of a flow chart of extracting an optimal transmission path provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the data security management method of a new energy centralized control center proposed by the present invention, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0040] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0041] The specific scheme of the data security management method of a new energy centralized control center provided by the present invention is described in detail below with reference to the accompanying drawings.

[0042] The inventors have found in actual work that in new energy power plants, due to the constant changes in wind speed and wind direction, the wind turbine wake effect will also change accordingly. Therefore, the selection of data transmission paths should have the ability to dynamically adjust to adapt to real-time changes in environmental conditions. This application is based on the fact that the signal transmission path between the wireless signal tower and the wind turbine in the wind farm is affected by the wake effect, and proposes a data security management method for a new energy centralized control center.

[0043] See also Figure 1 , which shows a method flow chart of a data security management method for a new energy centralized control center provided by an embodiment of the present invention, the method comprising:

[0044] Step S100: Acquire new energy power plant data and network status data;

[0045] Step S100 specifically includes:

[0046] Step S110: Obtaining wind turbine layout data and wind condition time series data in the new energy power plant;

[0047] Specifically, the wind turbine layout data at least includes the layout data such as the location, model, and capacity of the wind turbines in the wind farm; the wind condition time series data at least includes wind speed, wind direction, turbulence intensity, etc., all of which are collected in real time through the sensor network or directly retrieved from the historical database; wherein the wind turbine layout data can be expressed as L = {(x i ,y i ),T i ,C i}|i=1,2,…,N,where (x i ,y i ) represents the position coordinates of the i-th fan; T i The model of the i-th fan; C i The capacity of the i-th wind turbine; N represents the total number of wind turbines; For wind condition time series data, the wind speed and wind direction change over time are mainly considered, which is the basic input of computational fluid dynamics (CFD) simulation and is used to predict wake effects;

[0048] Step S120: extract the network topology of the new energy power plant and divide the data transmission area of ​​the new energy power plant; specifically, in this embodiment, an adjacency matrix is ​​used to describe the network topology, where each node represents a device or data point, and the edge represents the connection relationship between devices. Then, based on the connectivity and geographical layout of the network, the network in the entire new energy power plant can be divided into several sub-areas, which can be expressed as R k represents the kth region; represents the i-th node in the k-th region, and m represents the total number of nodes in the region;

[0049] Step S120 specifically includes:

[0050] Step S121: Based on the connectivity and independence between different data transmission sub-areas in the new energy power plant, the optimal division of the data transmission area of ​​the new energy power plant is obtained with the goal of maximizing the connectivity of the data transmission nodes in the data transmission sub-areas. In this embodiment, in order to optimize the division of the sub-areas, the expression of the objective function introduced is:

[0051]

[0052] In the formula, w ij Represents the weights of the i-th node and the j-th node; Used to maximize the connectivity between nodes in the sub-region; is used to penalize the connection between sub-regions, and λ is a weight factor for adjusting the balance between the two items; the objective function takes into account the connectivity within the sub-region and the independence between the sub-regions; through the minimum value of the objective function, an approximately optimal sub-region division scheme can be found, and algorithms such as genetic algorithms, simulated annealing, and spectral clustering can be used to solve the optimal division of data transmission areas in new energy power plants; in this embodiment, by maximizing the connectivity of nodes within the sub-region, the delay of data transmission can be reduced, and the efficiency and reliability of data transmission can be improved. At the same time, the independence between sub-regions helps to limit the impact range of network failures or attacks, and improve the robustness and security of the overall network. Ultimately, reasonable sub-region division helps to optimize resource allocation, reduce cross-regional data transmission, and reduce network congestion and energy consumption.

[0053] Step S200: constructing a wake propagation model to evaluate the influence of the wake effect on the data transmission path;

[0054] Step S200 specifically includes:

[0055] Step S210: Based on computational fluid dynamics, the wake effect in the new energy power plant is predicted using the wind condition time series data, and the velocity distribution, turbulence intensity and diffusion rate of the wake effect are extracted; specifically, CFD (computational fluid dynamics) software, such as ANSYS Fluent or OpenFOAM, is used to establish a fluid mechanics model of the wind farm based on the wind condition time series data (wind speed, wind direction, turbulence intensity) in the wind farm and the geometric information of the wind turbine. The model considers the disturbance of the wind turbine to the airflow and predicts the velocity distribution, turbulence intensity and diffusion rate of the wake effect;

[0056] See also Figure 2 As shown, step S220: extracting the data transmission path evaluation result based on the turbulence intensity and diffusion rate of the wake effect; specifically, extracting the influence of the wake effect on the signal transmission path, signal attenuation and multipath effect, including:

[0057] Step S221: signal path evaluation, extracting the signal path deflection caused by the wake effect; specifically, simulating the propagation of the signal in the wake by using ray tracing technology or an electromagnetic wave propagation model, and evaluating the deformation and offset of the signal path caused by the wake effect;

[0058] On the one hand, the propagation path of the signal in space can be represented by a curve s(t), where s(t) is a three-dimensional vector function that represents the position of the signal at time t. The signal path deflection caused by the wake effect can be described by a second-order nonlinear differential equation, which is expressed as follows:

[0059]

[0060] Where F(s,u,t) is the wake effect force function, which depends on the signal position s, fluid velocity u and time t, represents the acceleration of the signal path in the wake, and is a mathematical abstraction of the influence of the wake effect on the signal propagation path;

[0061] On the other hand, the signal path deflection ΔP can be calculated by integrating the deviation between the actual signal path p(s) and the ideal straight line path l(s), and its expression is:

[0062] ΔP=∫L(x i )|p(s)-l(s)|ds

[0063] In the formula, L(x i ) is the path x i , p(s) is the actual path of the signal, l(s) is the ideal straight path; ΔP is used to quantify the deviation between the signal path and the ideal straight path, reflecting the degree of distortion of the signal path by the wake effect;

[0064] In step S221, through ray tracing or electromagnetic wave propagation model, combined with the wake effect force function, the signal propagation path in a complex environment can be accurately evaluated, and the impact of the wake effect on the signal path can be evaluated, which can guide the planning of the signal transmission path, avoid or reduce the wake dense area, and improve the stability and reliability of signal transmission; at the same time, accurately evaluating the degree of signal path deflection is helpful to design compensation mechanisms, such as adjusting the antenna direction or using a more powerful signal transmission power to overcome the signal loss caused by the wake effect.

[0065] Step S222: Signal attenuation calculation: estimate the signal attenuation based on the wake turbulence intensity and signal frequency. In a wind farm environment, the wake effect will increase the turbulence intensity, thereby affecting the path and intensity of signal propagation. The calculation expression of the signal attenuation is:

[0066] α(f,σ)=α0+βσ 2 f 2

[0067] In the formula, α(f,σ) represents the signal attenuation under the signal frequency f and turbulence intensity σ; α0 represents the preset basic attenuation coefficient; β represents the preset correlation coefficient with the signal frequency f and turbulence intensity σ; this formula allows the accurate evaluation of the signal attenuation under different signal frequencies and turbulence intensities, which is crucial for optimizing the signal transmission path and adjusting the signal strength in complex environments; by predicting the signal attenuation, appropriate measures (such as adjusting the transmission power, using stronger coding, or changing the frequency) can be taken to compensate for the attenuation, thereby improving the reliability of the communication link;

[0068] Step S223: Multipath effect analysis, identifying the signal reflection, refraction and diffraction paths caused by wake turbulence, using ray tracing to simulate the multipath effect, extracting the signal delay and phase change caused by the multipath effect. Ray tracing can simulate the propagation path of the signal in a complex environment and identify all possible propagation paths, including direct paths and reflection paths. The calculation expression of the signal delay and phase change caused by the multipath effect is:

[0069]

[0070] Where, d re Represents the reflection path length; d dir represents the direct path length; c represents the speed of light; f represents the signal frequency; Δt represents the time delay; Indicates phase difference; by identifying the multipath effect, appropriate signal processing technology (such as equalizer or spread spectrum technology) can be used to offset or utilize the multipath effect and improve the signal reception quality;

[0071] Step S300: constructing a data transmission path planning model, and extracting potential transmission paths based on a multi-path unicast network;

[0072] Step S300 specifically includes:

[0073] Step S310: Calculate the wake sensitivity coefficient of each edge according to the signal attenuation and the signal delay and phase change caused by the multipath effect; for example, there are M nodes in the network, and the network topology is represented by the adjacency matrix A, where A ij represents the connection weight from node i to node j. In this embodiment, the wake sensitivity coefficient W is introduced ij , which indicates the degree of influence by the wake, is expressed as:

[0074]

[0075] In the formula, α ij Indicates the signal attenuation; Δt ij Indicates signal delay; Represents the phase change of the signal; the function g(.) can be defined according to the specific situation, for example, it can be a linear combination of these factors:

[0076]

[0077] Where w1, w2, and w3 are all weight factors, which are used to adjust the influence of signal attenuation, signal delay, and phase change on the wake sensitivity coefficient. By calculating the wake sensitivity coefficient of each edge, the influence of wake effect on signal transmission can be more accurately evaluated, so as to avoid or reduce the passage through wake sensitive areas in path planning, improve the stability and reliability of signal transmission, and help to prioritize the allocation of resources to paths with lower wake sensitivity when communication resources are limited, thereby reducing the influence of signal attenuation and multipath effects.

[0078] Step S320: extracting potential transmission paths based on a wake sensitivity coefficient threshold; optionally, defining a wake sensitivity coefficient threshold W th Any edge with a wake sensitivity coefficient higher than this threshold will be considered unsuitable for data transmission; remove all edges with a wake sensitivity coefficient exceeding W th The edges of the network are removed, and connected subgraphs are generated based on the remaining edges. Each path in these subgraphs is a potential transmission path. G(V,E) is the original network topology graph, where V is the node set and E is the edge set. All the wake sensitivity coefficients greater than W are removed. th After the edges are added, a new edge set E is generated. ′ , where E ′ =e∈E|W ij ≤W th , based on E ′ , generate a connected subgraph G ′ (V ′ ,E ′ ), where V ′ YesE ′ All nodes covered;

[0079] Step S320 specifically includes:

[0080] Step S321: Using a non-dominated sorting genetic algorithm, extract a set of potential transmission paths according to the wake sensitivity coefficient and the signal path deflection; specifically, we have m potential signal transmission paths, each potential transmission path x i (i=1,2,…m,) Suppose there are two objective functions f1x i and f2x i ; First, a certain number of potential signal transmission paths are randomly generated as the initial population. These paths may come from all possible combinations of network topology, but they mainly focus on the paths with wake sensitivity coefficients below the threshold W th Then, for each potential transmission path, the wake sensitivity coefficient f1x is calculated. i =W ij (x i ), and the signal path deflection degree f2x i =ΔP(xi ), considering the non-dominated relationship: potential transmission path x i If on any target due to x j , and not inferior to x in another objective j ,Right now And f2x i ≤f2x j , then it is represented as the potential transmission path x i Nondominated x j (denoted as x i <x j ); We divide the population into multiple levels according to the non-dominated relationship. The first level includes all individuals that are not dominated by other individuals. In each level, the crowding distance between individuals is calculated, that is, the size of the space around the individual in the target space; the next generation of individuals is selected according to the non-dominated level and crowding degree, and individuals with low levels are given priority. In the same level, individuals are selected according to the crowding degree. Finally, crossover and mutation operations are applied to generate new individuals. The above steps are repeated until the preset number of iterations is met or other stopping criteria are reached; in this embodiment, through the NSGA-II algorithm, we can obtain a set of potential transmission paths containing a variety of different wake sensitivities and signal path deflections. These paths provide the best trade-off between different goals. NS The GA-II algorithm can simultaneously optimize the two objectives of wake sensitivity and signal path deflection and find an effective trade-off between the two, which helps to select the best signal transmission path in a complex network environment; by considering the wake sensitivity coefficient, the signal attenuation and delay caused by the wake effect can be reduced, thereby improving the stability and reliability of data transmission; through the non-dominated sorting and congestion selection mechanism, the algorithm can maintain the diversity and rapid convergence of the population during the search process and avoid falling into the local optimal solution. The final generated set of potential transmission paths provides decision makers with multiple alternatives, each of which achieves a different balance between wake sensitivity and signal path deflection, making it easy to choose according to actual needs and constraints.

[0081] Step S400: extracting the optimal transmission path from potential transmission paths with the goal of minimizing communication modulation and wake countermeasure costs;

[0082] See also Figure 3 As shown, step S400 specifically includes:

[0083] Step S410: Calculate the communication modulation cost of the potential transmission path caused by the wake effect according to the modulation rate of the potential transmission path before and after the wake effect, and the expression is:

[0084]

[0085] In the formula, C m (P) represents the communication modulation cost of transmission path P; represents the initial modulation rate of edge e without wake influence; represents the final modulation rate of edge e under the influence of wake; E e represents the energy consumption of edge e; in this embodiment, for each edge e, its modulation rate before and after the wake impact is measured or predicted, and the relative change of the modulation rate is calculated, that is, The modulation rate change is multiplied by the energy consumption of the edge to obtain the communication modulation cost of a single edge. The communication modulation costs of all edges are summed up to obtain the total communication modulation cost of the entire path, reducing the signal distortion caused by the tail flow, thereby reducing the retransmission probability and system delay.

[0086] Step S420: Calculate the tail flow countermeasure cost caused by increasing the forward error correction coding strength of the potential transmission path according to the coding overhead and the decoding delay, and the expression is:

[0087]

[0088] In the formula, C a (P) represents the wake resistance cost of the transmission path P; q e represents the additional bit rate of FEC encoding of edge e; l e represents the data length of the transmission of edge e; this formula is determined by determining the additional bit rate q of the FEC encoding on each edge e , and measure or calculate the transmission data length l of each edge e ,After calculating the wake confrontation cost of each edge, the wake confrontation cost of all edges is summarized to obtain the total wake confrontation cost of the entire path, which enhances the robustness of data transmission and improves data integrity and reliability under harsh conditions.

[0089] Step S430: With the goal of minimizing the communication modulation cost and the wake countermeasure cost, the particle swarm algorithm is used to find the optimal path among the potential transmission paths, and the expression of the total cost function that minimizes the communication modulation cost and the wake countermeasure cost is:

[0090] C(P)=w m C m (P)+w a C a (P)

[0091] Where C(P) represents the cost of the transmission path P; w m represents the communication modulation cost weight; w a represents the wake turbulence combating cost weight; C m (P) represents the communication modulation cost of transmission path P; C a (P) represents the wake resistance cost of the transmission path P; C m(P) may involve switching costs between different modulation schemes (such as QPSK, 16-QAM, 64-QAM, etc.), as well as additional overhead caused by the change of modulation scheme; C a (P) usually involves the use of forward error correction (FEC) and other technologies to enhance the signal to resist signal attenuation or distortion caused by atmospheric turbulence or physical obstacles; forward error correction (FEC) coding is a coding technology that adds redundant information at the sending end, which allows the receiving end to correct errors that occur during data transmission without requesting retransmission. In a communication environment with wake effects, FEC coding can enhance the robustness of the signal and reduce data errors caused by signal distortion; in an environment with significant wake effects, the anti-interference ability of the signal can be improved by increasing the redundancy ratio of FEC coding, which means adding more checksum information to each data packet so that the receiving end can correct more error bits. However, this also means that more bandwidth is required to transmit the same amount of data, so it is a strategy that trades off between transmission efficiency and data reliability; in actual applications, the communication system will dynamically adjust the modulation mode and FEC coding strength according to the real-time strength of the wake effect. For example, when the wake effect is detected to be weak, the system can choose to use high-order modulation and lower FEC coding strength to obtain a higher data transmission rate. On the contrary, when the wake effect is strong, the system will switch to low-order modulation and increase the FEC coding strength to ensure reliable data transmission. This adaptive communication strategy can ensure that the communication link can maintain optimal performance under different environmental conditions, which not only ensures the reliability of data transmission, but also maximizes the transmission efficiency. This is especially important for scenarios such as new energy centralized control centers that require stable and efficient data transmission.

[0092] The process of particle swarm algorithm is as follows:

[0093] Initialize the particle swarm: particles represent potential transmission paths, and their positions are determined by the order of nodes on the path; each particle has a speed and position. During initialization, the particle position is randomly generated and the speed is set to zero or a small random number; then the total cost of the path P corresponding to each particle is calculated, and the fitness function is the inverse of the total cost, that is, the lower the cost, the higher the fitness; then the update rule follows the basic principle of PSO, that is, the particles move towards their own optimal position and personal optimal position, while considering the optimal position of the group; repeatedly update the particle position and speed until the preset number of iterations is reached or other stop conditions are met; after each iteration, record the current optimal path; step S430 improves the efficiency and reliability of data transmission in the new energy centralized control center by applying the particle swarm algorithm to the comprehensive optimization of communication modulation cost and wake countermeasure cost; by optimizing path selection, the system can avoid excessive use of expensive communication modulation or wake countermeasure technology, thereby reducing overall operating costs; on the other hand, the selected path can better resist external interference, reduce data packet loss due to signal attenuation or distortion, and improve transmission reliability and stability.

[0094] Furthermore, the method further comprises:

[0095] Step S500: preset the security level coefficient, and adaptively select the encryption algorithm and encryption strength according to the characteristic vector of the network environment and data scenario; Step S600 aims to dynamically adjust the encryption algorithm and strength according to the characteristics of the network environment and data scenario to adapt to the changing security needs; the specific implementation can be divided into the following key steps:

[0096] First, a security level function needs to be defined. This function receives the feature vector of the network environment and data context as input and outputs a value representing the recommended security level in the current context. This function may be built based on machine learning models, such as support vector machines (SVMs), neural networks, or decision trees. The purpose of training these models is to predict security requirements in specific contexts.

[0097] Construction of feature vector: The feature vector should contain multiple dimensions that can reflect the security of the network environment and data context, such as network traffic patterns, data sensitivity, historical attack events, geographic location, time, and other factors; these features need to be standardized or normalized to ensure that they contribute fairly to the security level function;

[0098] Definition of encryption algorithms and strength levels: Define a series of encryption algorithms {E1,E,...,E n} and the corresponding intensity levels {I1,I2,...,I n}, the strength level can be the key length, number of iterations, or parameter configuration of a specific algorithm, which directly affects the strength and resource consumption of encryption;

[0099] Based on the output of the security level function, a mapping function is used to select the encryption algorithm and strength that best suits the current situation. The goal of the mapping function is to find the strength level closest to the current security level, and its expression is:

[0100] f(S(x)=argmin i∈{1,2…,n} |S(x)-I i |, that is, the selected intensity level I i should make |S(x)-I i | has the smallest value, that is, it is closest to the security level in the current situation; in actual operation, the security level function and the mapping function should be regularly re-evaluated and adjusted to reflect changes in the network environment and data situation to ensure that the encryption strategy is always optimal; in this embodiment, adaptive selection of encryption algorithm and strength can avoid excessive use of computing resources in situations with lower security requirements, while also ensuring that the encryption strength is sufficient in high-risk situations; dynamic adjustment of encryption strategies can better cope with the ever-changing threat environment and improve the security of data transmission and storage; the automated selection mechanism reduces the need for manual intervention, reduces the possibility of configuration errors, and simplifies the management of security policies.

[0101] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0102] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A data security management method for a new energy centralized control center, characterized in that: The method comprises: Obtain new energy power plant data and network status data; Construct a wake propagation model to evaluate the impact of wake effects on data transmission paths; Construct a data transmission path planning model to extract potential transmission paths based on multi-path unicast networks; With the goal of minimizing the communication modulation and wake countermeasure costs, the optimal transmission path is extracted from potential transmission paths.

2. The data security management method of the new energy centralized control center according to claim 1, characterized in that: Acquisition of new energy power plant data and network status data includes: Obtain wind turbine layout data and wind condition timing data in new energy power plants; The network topology of the renewable energy power plant is extracted, and the data transmission area of ​​the renewable energy power plant is divided.

3. The data security management method of the new energy centralized control center according to claim 2, characterized in that: Extract the network topology of the new energy power plant and divide the data transmission area of ​​the new energy power plant, including: Based on the connectivity and independence between different data transmission sub-areas in the new energy power plant, the optimal division of the data transmission area of ​​the new energy power plant is obtained with the goal of maximizing the connectivity of the data transmission nodes in the data transmission sub-area.

4. The data security management method of the new energy centralized control center according to claim 1, characterized in that: Constructing a wake propagation model to evaluate the impact of wake effects on data transmission paths includes: Based on computational fluid dynamics, the wake effect in the renewable energy power plant is predicted using wind time series data, and the velocity distribution, turbulence intensity and diffusion rate of the wake effect are extracted; The data transmission path evaluation results are extracted based on the turbulence intensity and diffusion rate of the wake effect.

5. The data security management method of the new energy centralized control center according to claim 4, characterized in that: The evaluation results of the data transmission path extracted based on the turbulence intensity and diffusion rate of the wake effect include: Signal path evaluation to extract signal path deflections caused by wake effects.

6. The data security management method of the new energy centralized control center according to claim 5, characterized in that: The turbulence intensity and diffusion rate based on the wake effect are used to extract the data transmission path evaluation results, and the following are also included: Signal attenuation calculation, estimating signal attenuation based on wake turbulence intensity and signal frequency; Multipath effect analysis, identifying the signal reflection, refraction and diffraction paths caused by wake turbulence, using ray tracing to simulate the multipath effect, and extracting the signal delay and phase change caused by the multipath effect.

7. The data security management method of the new energy centralized control center according to claim 6, characterized in that: Construct a data transmission path planning model and extract potential transmission paths based on multi-path unicast networks, including: The wake sensitivity coefficient of each edge is calculated based on the signal attenuation and the signal delay and phase change caused by the multipath effect; Potential transmission paths are extracted based on wake sensitivity coefficient thresholds.

8. The data security management method of the new energy centralized control center according to claim 7, characterized in that: Extraction of potential transmission paths based on wake sensitivity coefficient thresholds includes: A non-dominated sorting genetic algorithm is used to extract a set of potential transmission paths according to the wake sensitivity coefficient and signal path deflection.

9. The data security management method for a new energy centralized control center according to any one of claims 1 to 8, characterized in that: With the goal of minimizing the communication modulation and wake countermeasure costs, the optimal transmission path is extracted from the potential transmission paths, including: According to the modulation rate of the potential transmission path before and after the wake effect, the communication modulation cost of the potential transmission path caused by the wake effect is calculated; Calculate the tail flow countermeasure cost caused by increasing forward error correction coding strength for potential transmission paths based on coding overhead and decoding delay; With the goal of minimizing the communication modulation cost and the wake countermeasure cost, the particle swarm algorithm is used to solve the optimal transmission path.

10. The data security management method of the new energy centralized control center according to claim 9, characterized in that: The method also includes: presetting a security level coefficient, and adaptively selecting an encryption algorithm and encryption strength according to a characteristic vector of a network environment and a data scenario.

Images