47 results about "Chaotic map" patented technology

The invention provides a multi-feature information hidden document integrity verification and tampering positioning method, and relates to the field of network security and digital document protection. The multi-feature information hidden document integrity verification and tampering positioning method comprises the following steps: S1, document multi-dimensional feature extraction and encryption: based on an original Word document, adopting an SHA-3 algorithm to calculate a full-text hash value as a content feature, and extracting document object model features such as paragraph hierarchy, table structure and the like through a DOM tree analyzer; after the two types of features are combined into a feature matrix, a dynamic secret key is generated through Logistic chaotic mapping for XOR encryption, and an encrypted document feature matrix is generated. A dynamic key is generated by combining Logistic chaotic mapping encryption through a multi-dimensional extraction technology fusing document content hash values and structural features, so that the anti-cracking capability of a feature matrix is remarkably improved, the confidentiality of document features in open network transmission is ensured, the block information retention rate is simulated and predicted by adopting Monte Carlo, and the watermark embedding weight is dynamically allocated.

The invention relates to the technical field of power system control, in particular to a distributed power supply parameter adaptive control method, system and device based on an improved grey wolf algorithm and a medium. Obtaining distributed power supply parameters to construct a VSG model; establishing an objective function by taking the rotational inertia and the damping coefficient as optimization variables and setting stability constraints; adopting an improved grey wolf algorithm comprising a Tent chaotic mapping initialization population, a nonlinear convergence factor, an adaptive weight strategy and a Levy flight mechanism to optimize parameters; and the rotational inertia and the damping coefficient are dynamically adjusted according to the angular velocity change rate to realize parameter adaptive control. The problems that a traditional grey wolf algorithm is prone to falling into local optimum, VSG parameter optimization lacks systematicness, and fixed parameters are difficult to adapt to changing working conditions are solved, parameter optimization precision is improved, coordinated optimization of rotational inertia and damping coefficients is achieved, the system can intelligently adjust control parameters according to the disturbance degree, system power and frequency oscillation is effectively restrained, and the system performance is improved. And the grid connection stability and dynamic response performance of the distributed power supply are improved.

The invention discloses a renewable energy scheduling control method based on electricity-carbon-green evidence market coupling, and relates to the technical field of renewable energy control, and the method comprises the following steps: predicting wind and light output fluctuation intervals based on a neural network, and classifying the wind and light output fluctuation intervals; constructing a first simulator based on chaotic mapping, and constructing a second simulator based on quantum machine learning; controlling to switch the first simulator and the second simulator according to a classification result, and outputting a wind-light output predicted value; and controlling energy storage output according to the wind-solar output predicted value and a preset electricity-carbon interaction model. The method is used for solving the problem of insufficient simulator adaptability in different wind and light output fluctuation scenes and insufficient energy storage control capability in an electricity, carbon and green evidence cooperative scene.

The invention relates to a computer-based image encryption method using a Bessel map with high chaotic performance, which is developed to fill the gaps between a high performance chaotic map and a simple encryption scheme.

The invention relates to the technical field of path planning, and provides a multi-strategy collaborative enhancement path planning method and navigation equipment. The path planning method comprises the following steps: acquiring an environment map comprising a starting position and a target position; initializing DBO algorithm parameters; initializing a dung beetle population based on Bernoulli chaotic mapping; calculating the fitness value of each dung beetle; determining the boundary of each dung beetle, and continuously iteratively updating the individual optimal fitness value and the global optimal fitness value of each dung beetle; and outputting the optimal path of the unmanned aerial vehicle until the number t of iterations is greater than the preset number T. According to the method, the initial solution is generated by introducing the chaos mapping based on Bernoulli distribution, so that the diversity and the ergodicity of the initial population are enhanced, premature convergence is effectively avoided, and the global search capability of the algorithm is improved.

The invention relates to the field of image encryption, in particular to a chaos image encryption method based on DNA coding, which comprises the following steps: S1, calculating a hash value of a plaintext grayscale image by using a hash algorithm, and generating an initial key based on the hash value, S2, iterating a chaos sequence by using the initial key as an initial value and a control parameter of an iteration variable of two-dimensional chaos mapping 2D-CSMM, performing segmentation and quantization processing on the chaotic sequence to obtain a quantization matrix; performing reduction XOR operation on the quantization matrix to obtain a bit matrix; performing scrambling processing on a plaintext grayscale image based on the quantization matrix and the bit matrix to obtain a ciphertext image; s5, performing DNA diffusion processing on the ciphertext image based on the quantization matrix and the bit matrix to obtain an encrypted image; s6, decrypting the encrypted image according to the inverse process of image encryption to obtain a decrypted image; according to the encryption method provided by the invention, the security of image encryption is greatly improved, and the security performance and the encryption effect are good.

The invention provides a chaos enhanced credible authentication method based on a vehicle-mounted gateway, and relates to the technical field of credible authentication, and the method comprises the following steps: generating a chaos sequence by using improved Tent chaos mapping; dynamically disturbing a round constant CK of an SM4 encryption algorithm by using the chaos sequence to generate a confused round key; synchronously updating a session key between the vehicle-mounted gateway and the ECU based on a cellular automaton and a chaotic mapping mechanism; identity authentication is executed through a bidirectional challenge-response mechanism; the vehicle-mounted gateway sends an encrypted challenge frame containing a random number R1 to the ECU; after decryption, the ECU generates a response frame by using a SipHash function and returns the response frame, and meanwhile, sends a reverse challenge frame containing a random number R2; and the vehicle-mounted gateway verifies the response frame and then generates a reverse response frame, and the two parties update the session key after the ECU passes the verification. According to the method, the anti-attack capability can be remarkably improved, the method can efficiently adapt to an embedded environment, and meanwhile the system reliability can be effectively enhanced.

The invention relates to a scheduling optimization method and device of a micro-grid, scheduling equipment and a storage medium, and belongs to the technical field of energy scheduling, and the scheduling optimization method of the micro-grid comprises the steps: obtaining historical power generation data and historical load demand data of a target micro-grid, constructing an initial time sequence based on the historical power generation data and the historical load demand data; predicting a trend term and a seasonal term of the target time sequence based on the first prediction model and the initial time sequence, predicting a residual term of the target time sequence based on the second prediction model and the initial time sequence, and constructing the target time sequence based on the trend term, the seasonal term and the residual term of the target time sequence; constructing an initial population based on chaotic mapping, and correcting the initial population based on the operation parameters of the target microgrid; and determining a scheduling optimization strategy of the target microgrid based on the corrected initial population and the target time sequence. According to the invention, the energy utilization rate and economy of the micro-grid can be improved.

The invention relates to the technical field of image encryption, in particular to a block compressed sensing-combined chaotic image encryption method, which comprises the following steps of: S10, acquiring a plaintext grayscale image, calculating a hash value of the plaintext grayscale image by using an SHA-512 algorithm and generating an initial secret key; s20, constructing a measurement matrix required by block compressed sensing and a chaos sequence and a matrix required by an image encryption algorithm by using the chaos sequence iterated by the two-dimensional composite chaos mapping and the initial secret key; s30, performing block processing on the plaintext grayscale image P, measuring each image block by using the measurement matrix, and combining the processed blocks into a new matrix; s40, the new matrix is subjected to scrambling operation based on DNA and S-box, and a ciphertext is obtained; s50, performing diffusion operation on the ciphertext to obtain a final encrypted image CI; s60, decrypting the encrypted image according to an inverse process of image encryption, and reconstructing the decrypted image by using an SL0 algorithm to obtain a final reconstructed image; the algorithm has good safety performance.

The invention discloses a gear service life prediction method and system based on big data. The method comprises the steps of data acquisition, data set division, gear service life prediction model establishment, parameter optimization and gear service life prediction. The invention belongs to the field of data processing, and particularly relates to a gear service life prediction method and system based on big data, according to the scheme, a dynamic weight updating mechanism based on prediction residual errors is introduced, and high sensitivity to abnormal conditions is ensured; a smooth weight adjustment function is introduced for the conditions of abnormally short life and abnormally long life, so that the risk of missed judgment is reduced; adding prediction uncertainty penalty into the abnormal region loss item; the accuracy of gear life prediction is improved; through chaotic mapping initialization, multi-stage iteration and double-strategy updating, adaptive evolution rate and difference measurement are carried out; the method ensures that the requirement of efficient response of industrial monitoring is met in a complex industrial environment with more noise interference, and the effect of predicting the service life of the gear is improved.

The invention belongs to the technical field of DDoS attack prediction, and provides a DDoS attack prediction method and system based on a chaotic mapping echo state network. The method comprises the following steps: S10, determining an optimization objective function of a harmony search algorithm as a variance value predicted by an echo state network, and initializing related parameters of the harmony search algorithm; s20, generating an initial weight matrix by using the Logistic-tent chaotic mapping; s30, updating the harmony memory bank, selecting a weight matrix with the optimal fitness from the updated harmony memory bank, and constructing and training an ESN prediction model; and S40, processing the network flow sequence data detected in real time by using the trained ESN prediction model so as to perform DDoS attack prediction. According to the method, the echo state network DDoS attack prediction model based on chaotic mapping is adopted, the time efficiency is improved, and the DDoS attack situation can be effectively predicted in real time.

The invention provides a chaos image encryption method based on improved Josephus transformation and knight cruise joint scrambling, which comprises the following steps: firstly, generating a chaos pseudo-random integer sequence which is subjected to integer processing by adopting a memristor chaos system, then dividing a pixel matrix into a plurality of small cell pixel matrixes, in each cell pixel matrix, a counting starting point, a counting period and a counting direction are respectively controlled by using a chaos pseudo-random integer sequence, and block scrambling is carried out by using an improved Josephus scrambling algorithm. Then, each cell pixel matrix subjected to improved Josephus block scrambling is regarded as a checkerboard point, and a chaos pseudo-random integer sequence is utilized to control the cruise direction of the rider; the improved knight cruise algorithm and the improved Josephus scrambling algorithm are used for scrambling the image, the correlation of adjacent pixels of the scrambled image can be effectively reduced, and the global encryption effect of the image is improved; and the pixels are diffused by using the chaotic pseudo-random integer sequence which is generated by the memristor chaotic system and is subjected to integer processing, so that the security of the encrypted image is improved.

The invention discloses a hyperchaotic image encryption method and system based on dual memristor cascade coupling, a medium and equipment. The method comprises the following steps: S1, constructing a hyperchaotic system based on dual memristor cascade coupling; s2, acquiring a plaintext image, performing hash processing on the plaintext image to obtain a key, and converting the key through a preset mathematical model to obtain an initial value of the hyperchaotic system; s3, iteratively operating the hyper-chaotic system based on the initial value of the hyper-chaotic system to generate a first chaotic sequence, a second chaotic sequence and a third chaotic sequence; and S4, performing encryption operation on the plaintext image by using the first chaotic sequence, the second chaotic sequence and the third chaotic sequence to obtain a ciphertext image. According to the invention, a hyperchaotic system is constructed based on double memristor cascade coupling, a chaotic sequence with high pseudo-randomness is generated, scrambling-diffusion-scrambling structure encryption is carried out on a plaintext image through the chaotic sequence, and then a ciphertext image with high security, high randomness and high anti-noise capability is generated.

This paper proposes a chaotic image encryption method based on the combined scrambling of the improved Josephus transform and the Knight's Tour. First, a chaotic pseudo-random integer sequence is generated and integerized using a memristive chaotic system. Then, the pixel matrix is ​​divided into several small cell pixel matrices. Within each cell pixel matrix, the chaotic pseudo-random integer sequence is used to control the counting starting point, counting period, and counting direction, respectively. Block scrambling is performed using the improved Josephus scrambling algorithm. Subsequently, each cell pixel matrix after the improved Josephus block scrambling is treated as a checkerboard point, and the direction of the Knight's Tour is controlled using the chaotic pseudo-random integer sequence. The image is scrambled using the improved Knight's Tour algorithm and the improved Josephus scrambling algorithm, effectively reducing the correlation between adjacent pixels in the scrambled image and improving the global encryption effect of the image. Finally, the chaotic pseudo-random integer sequence generated and integerized by the memristive chaotic system is used to diffuse the pixels, enhancing the security of the encrypted image.

This invention belongs to the field of digital image encryption technology and discloses a selective encryption method for chaotic images based on facial recognition and key optimization. The method includes the following steps: Based on an existing chaotic mapping system model, an exponential term and a modulo function are added to optimize the system model's performance, establishing a three-dimensional amplitude-limited coupled hyperchaotic mapping system model; based on the three-dimensional amplitude-limited coupled hyperchaotic mapping system model, a color image face encryption scheme is designed. This invention employs the aforementioned selective encryption method for chaotic images based on facial recognition and key optimization to establish a three-dimensional amplitude-limited coupled hyperchaotic mapping system model, selectively encrypting facial regions, resulting in a wide range of chaos and high complexity; it adopts a face recognition encryption scheme based on the DeepFace model to effectively improve encryption speed; and it uses the PSO optimization algorithm to effectively resist known plaintext attacks, offering significant advantages in computational efficiency and encryption quality compared to traditional encryption methods.

The invention relates to the technical field of image encryption, in particular to a chaotic image encryption method based on multi-cavity attractor features, which comprises the following steps: constructing a TDHNN model by using an activation function and a toroidal surface parameter equation; an MC-TDHNN model based on a TDHNN model is constructed by using a multi-stage step state extension method; and generating a chaos sequence by using the MC-TDHNN model, and performing scrambling operation and diffusion operation on a plaintext image by using the chaos sequence to realize plaintext image encryption. The method solves the problem that the conventional encryption means is not tightly combined with the chaotic system, the inherent randomness of the chaotic system is not fully utilized, and the encryption effect is limited.

Privacy-Aware CBIR Computing Device with Chaotic Map Encryption and Optimized Clustering in Cloud Environments

The invention belongs to the technical field of rocket guidance, and provides a carrier rocket trajectory optimization method based on an improved DE-SQP algorithm to solve the technical problem that the expected purpose cannot be achieved when only a heuristic algorithm is adopted to solve the problem of carrier rocket ascending section trajectory optimization under complex constraints at present. Comprising the following steps: S1, initializing parameters; s2, based on the step S1, performing population initialization by adopting a Chebyshev chaotic mapping fusion reverse learning strategy to obtain an initial population; s3, performing updating iteration on the initial population obtained in the step S2; s4, when an algorithm termination criterion is met, stopping the updating iteration process in the step S3, and outputting an individual with the highest fitness in the current population as an optimal solution; and S5, inputting the optimal solution obtained in the step S4 as an initial value into an SQP algorithm for solving, and outputting an optimal trajectory. The method provided by the invention has strong global optimization and local accurate search capabilities, and can effectively solve the problem of trajectory optimization of the carrier rocket in the rising stage.

The present invention discloses a method, system, terminal and medium for salt field evaporation calculation based on a hybrid strategy SNS, including: initializing HSNS and objective function parameters; initializing a user group based on an improved Circle chaotic map; calculating the objective function values of the viewpoints held by each user in the user group to obtain the objective function values of each user; randomly selecting a decision model based on the viewpoints held by each user and updating the viewpoints of each user; generating a new user group based on the updated user group and improved lens imaging learning; updating the global optimal positions of the users in the new user group based on a simulated annealing mechanism; comparing whether the difference between the updated optimal position and the previous optimal position meets the set requirements, or whether the number of iterations reaches the maximum value until the conditions are met. The present invention can effectively improve the search efficiency of the algorithm, improve the optimization speed of the algorithm, increase the algorithm efficiency; increase the probability that the algorithm jumps out of the local optimum and finds the global optimum.

The invention discloses a chaotic image encryption method, decryption method and encryption system, and the encryption method comprises the steps: S1, carrying out the discrete wavelet transformation of an original image, obtaining a low-frequency coefficient A2 and a high-frequency coefficient A1, carrying out the linear mapping of A1 and A2 to 0-255, and obtaining a high-frequency sequence B1 and a low-frequency sequence B2; s2, using S-STM chaotic mapping to generate a chaotic sequence S1 with the same length as the low-frequency coefficient and the high-frequency coefficient, and using S1 to perform scrambling based on index sorting on B1 and B2 to obtain scrambling sequences C1 and C2; s3, mapping the chaos sequence S1 to 0-255 to obtain a chaos sequence S2, and performing XOR diffusion operation on the chaos sequence S2, C1 and C2 to obtain a diffusion high-frequency sequence D1 and a diffusion low-frequency sequence D2; s4, performing compressed sensing on the D1 in sequence to obtain a compressed matrix F1; s5, performing elliptic curve encryption on the F1 to obtain elliptic curve encrypted data G1; s6, performing DNA coding and DNA operation on the D2 in sequence to obtain DNA encrypted data F2; and S7, the G1 and the F2 are combined to generate a ciphertext image P1.

The present invention relates to the technical field of information transmission. More specifically, it relates to a dynamic diffusion chaotic image encryption and decryption method and system based on Raspberry Pi and NTRU algorithm. The key points of its technical solution are as follows: S1. Generate a random initial sequence based on the size of the initial image; S2. Substitute the random initial sequence into a three-dimensional digital domain chaotic system to generate a chaotic matrix; S3. Encrypt the chaotic matrix to obtain a ciphertext image; S4. Input the ciphertext image into a decryption algorithm to obtain a plaintext image. The present invention combines the excellent chaos and pseudo-randomness of the high-dimensional digital domain chaotic system to design a chaotic image encryption algorithm with dynamic characteristics, improving the security of the image encryption algorithm.

The present invention discloses a method for enhancing the dynamic characteristics of a chaotic encryption system based on an echo state network. The method employs a method of constructing an enhanced 3-D HICM cascade chaotic map by combining two chaotic cascade maps to generate a chaotic sequence with improved performance. The chaotic sequence is input into an echo state network (ESN) for training to obtain an output sequence. The ESN output sequence is processed to obtain a key group, which is used to sequentially encrypt bits, constellation diagrams, and subcarrier information. The trained ESN is used to obtain the same key group, and after receiving a signal at a receiving end, the signal is decrypted. The present invention can generate a chaotic sequence with improved performance, increase the key space of the chaotic sequence, and improve the complexity of the chaotic system. The adopted ESN can effectively simplify the network structure compared to other traditional neural network structures, reducing the computational complexity and training time of network training.

The invention relates to the technical field of communication, in particular to a method for generating an optimal wide-interval frequency hopping sequence based on a genetic algorithm, which comprises the following steps of: generating an initial population by utilizing Bernoulli chaotic mapping; calculating the maximum Hamming autocorrelation function value of individuals in the initial population or the excellent population obtained by the last iteration, and calculating the fitness of the individuals; based on the fitness of the individuals, the probability that the individuals are selected as a parent population is calculated, and the individuals meeting the requirements are selected as the parent population based on the probability; two individuals are randomly selected from the parent population each time, and the individual with the lowest Hamming autocorrelation is directly added into the excellent population obtained through the iteration; or after recombination operation and / or mutation operation are / is carried out on the selected two individuals, the individual with the lowest Hamming autocorrelation is added into the excellent population obtained through iteration of this time. According to the method, the genetic algorithm is adopted to generate the optimal wide-interval frequency hopping sequence, the characteristics of global search and self-adaptive optimization can be fully utilized, and quick optimization is carried out in a large search space.

The invention relates to an efficient image encryption method based on a sine transform chaotic system, which belongs to the field of image encryption and comprises the following steps: S1: designing a novel two-dimensional chaotic structure according to the good nonlinear characteristics of a Sine function, using several classic one-dimensional chaotic maps as subsystems to generate a two-dimensional chaotic system, and using a chaotic sequence generated by the two-dimensional chaotic system for an encryption algorithm; S2: using a SHA-512 hash value of a plaintext image to generate an initial value and a control parameter of the chaotic system; S3: performing row scrambling and column scrambling on a plaintext image pixel matrix based on the chaotic sequence generated by the chaotic system and a Knuth-Durstenfeld shuffling algorithm; S4: performing a diffusion operation on a matrix obtained after the scrambling operation and a random matrix generated by the chaotic sequence, and then performing a self-diffusion operation on the obtained diffusion matrix to obtain a ciphertext image; S5: image decryption, and decrypting the encrypted image according to the inverse operations of diffusion and scrambling.

A bearing fault detection method based on an improved sparrow search algorithm is proposed. In the population initialization stage, an improved Chebyshev chaotic map is introduced to increase the diversity of the population and improve the ergodicity of individual sparrows, so that the population can be distributed as evenly as possible in the search space, achieving better population initialization effect. An inertia weight is added to improve the update position of the discoverer to reduce the probability of falling into the local optimum too early. An adaptive selective mutation strategy is proposed to enable the sparrows to escape from the local optimum, avoiding the problem of falling into the local optimal solution that is prone to occur in existing technologies.

The present application relates to the field of image encryption, and particularly relates to a chaotic image encryption method based on DNA coding, S1: a hash value of a plaintext grayscale image is calculated by using a hash algorithm, and an initial secret key is generated based on the hash value, S2: the initial secret key is used as an initial value and a control parameter of an iteration variable of a two-dimensional chaotic mapping 2D-CSMM, a chaotic sequence is iterated out, the chaotic sequence is subjected to segmentation and quantization processing to obtain a quantization matrix, the quantization matrix is subjected to a reduction exclusive-OR operation respectively to obtain a bit matrix, and a plaintext grayscale image is subjected to a scrambling processing based on the quantization matrix and the bit matrix to obtain a ciphertext image, S5: the ciphertext image is subjected to a DNA diffusion processing based on the quantization matrix and the bit matrix to obtain an encrypted image, and S6: the encrypted image is decrypted according to an inverse process of image encryption to obtain a decrypted image, the encryption method provided by the present application greatly improves the security of image encryption, and has good security performance and encryption effect.

The invention discloses a multi-image encryption method based on three-dimensional memristor chaotic mapping, and the method comprises the steps: enabling an original plaintext to generate a new plaintext image sequence through an XOR operation, and generating an encryption key through a hash function, a mode of combining cyclic displacement and random scrambling is adopted in the scrambling stage; a bidirectional diffusion method is adopted in the diffusion stage, so that each ciphertext value is associated with the key stream and the previous ciphertext value; the provided encryption algorithm can effectively guarantee safe transmission of multiple images, has high anti-attack capability and encryption performance, and is suitable for encryption transmission of large-scale data. A new security solution is provided in the field of image encryption, and the method is especially suitable for application scenarios where a large amount of image data needs to be transmitted, and has wide engineering application value.

The invention discloses a photovoltaic system fault diagnosis method based on Aasso-IKOA-XGBoost, and the method comprises the steps: extracting a waveform time-frequency characteristic component through wavelet analysis, and extracting a plurality of types of characteristic components of fault data through employing a self-adaptive Lasso method; an IKOA algorithm fusing Logistic, Sine and Tent chaotic mapping is provided, so that a better initial solution distribution, parameter setting and position updating mode is generated; and the IKOA algorithm is combined with the XGBoost, an IKOA-XGBoost fault diagnosis model is established, and the fault of the photovoltaic system is diagnosed. According to the method, the diagnosis efficiency, accuracy and intelligent operation and maintenance level of the photovoltaic system can be effectively improved, and reliable, efficient and sustainable operation of the system is ensured.

The invention discloses an inertial sensor calibration method in a while drilling measurement system, comprising: introducing a Tent chaotic map to generate an initial population, an adaptive factor and a logarithmic function, statically improving a traditional northern goshawk optimization algorithm, thereby statically calibrating an error parameter of the inertial sensor; loading the static calibration error parameter value as initial prior knowledge, simulating an explosion phenomenon, and dynamically improving the traditional northern goshawk optimization algorithm, thereby dynamically calibrating and updating the error parameter of the inertial sensor when drilling is stopped at a working measuring point of the while drilling measurement system; the invention respectively designs a static calibration step of the inertial sensor and a dynamic calibration step of the inertial sensor, avoids the cumbersome steps of the traditional calibration scheme of the inertial sensor, and proposes static improvement and dynamic improvement for the traditional northern goshawk optimization algorithm, thereby improving the speed and accuracy of the error calibration.

The invention provides a proton exchange membrane fuel cell energy management method based on an improved Newton-Raphson fuzzy algorithm, and the method comprises the steps: building a topological structure of a hybrid power system, and constructing a mathematical model; initializing a membership function, and performing fuzzy population coding on the membership function to obtain a to-be-optimized variable of the membership function; using chaotic mapping to initialize population positions, calculating fitness values of each population, sorting the fitness values, and calibrating an optimal position and a worst position; performing population selection optimization by using an NRBO algorithm, enabling the population to avoid a local optimal solution, updating an optimal position, and obtaining a population optimal solution as an optimized optimization variable; and decoding the membership function by using the optimized optimization variable, and performing fuzzy logic reasoning and defuzzification to obtain the optimal output power. The hydrogen consumption of the fuel cell system is reduced, and the economical efficiency of the system is improved; the output power fluctuation of the fuel cell is reduced, the energy utilization efficiency of the fuel cell system is improved, and the service life of the fuel cell is prolonged.