A meteorological data transmission method based on quantum key encryption

Abstract

The present application relates to meteorological data transmission, in particular to a kind of meteorological data transmission method based on quantum key encryption, construct minimum comprehensive cost objective function, combine constraint condition and construct data transmission optimization model;Improved anolis carolinensis optimization algorithm is used to solve data transmission optimization model, and the optimal data transmission parameter group is obtained;Global meteorological terminal data acquisition is carried out, quantum key agreement, data encryption, encapsulation transmission, terminal decryption verification are completed based on the optimal data transmission parameter group, and full-link secure transmission is realized;For extreme weather scene, real-time monitoring transmission index change situation, dynamic rescheduling optimization is carried out;The technical scheme provided by the present application can effectively overcome the defects that transmission resources cannot be efficiently and adaptively scheduled in the prior art.

Description

Technical Field

[0001] This invention relates to meteorological data transmission, and more specifically to a meteorological data transmission method based on quantum key encryption. Background Technology

[0002] Meteorological data is the core foundational data for meteorological monitoring, disaster early warning, climate analysis, and disaster prevention and mitigation decision-making. It encompasses multi-source heterogeneous information from ground observations, upper-air sounding, and meteorological satellite remote sensing. The real-time performance and transmission security of this data directly impact the stable operation of meteorological services. Currently, meteorological data is mostly transmitted across regions via public communication links, dedicated wireless channels, and satellite channels. Traditional encrypted transmission methods suffer from security vulnerabilities such as easily cracked keys, channel eavesdropping, and data tampering, making it difficult to meet the high-security transmission requirements of classified meteorological data and critical detection information.

[0003] Quantum key encryption possesses technological advantages such as one-time pad, instant detection of eavesdropping, and theoretical unconditional security, effectively compensating for the security shortcomings of traditional encryption methods. However, existing quantum key encrypted meteorological data transmission schemes have significant flaws. Unreasonable multi-link transmission bandwidth allocation and disordered quantum key distribution time slot allocation easily lead to problems such as link load imbalance and key resource waste. Simultaneously, fixed encryption computing power configurations and lack of equipment power consumption control easily result in key resource waste and link load imbalance, thereby increasing transmission latency and exacerbating equipment energy consumption. Furthermore, in complex meteorological scenarios such as extreme strong convection, heavy rain, and strong electromagnetic interference, transmission stability and anti-interference capabilities are significantly reduced.

[0004] Traditional resource scheduling optimization algorithms often employ fixed search mechanisms, exhibiting poor balancing between global exploration and local exploitation. They are prone to getting trapped in local optima and struggle to coordinate optimization of multiple metrics such as transmission latency, link balance, quantum key utilization, and encryption energy consumption. Furthermore, they cannot dynamically adapt to complex meteorological environments. Therefore, there is an urgent need to design a quantum key encryption-based meteorological data transmission method that combines intelligent optimization algorithms to achieve adaptive scheduling of transmission resources, balancing the security, real-time performance, and operational stability of meteorological data transmission. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] In view of the above-mentioned shortcomings of the existing technology, the present invention provides a meteorological data transmission method based on quantum key encryption, which can effectively overcome the shortcomings of the existing technology in that it is difficult to efficiently and adaptively schedule transmission resources.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides the following technical solution: A method for transmitting meteorological data based on quantum key encryption includes the following steps: S1. Construct a comprehensive cost objective function and build a data transmission optimization model based on constraints; S2. The beaked lizard optimization algorithm is used to solve the data transmission optimization model to obtain the optimal data transmission parameter set; S3. Collect meteorological terminal data across the entire region, and complete quantum key negotiation, data encryption, encapsulation and transmission, and terminal decryption verification based on the optimal data transmission parameter set to achieve secure transmission across the entire link. S4. For extreme weather scenarios, monitor changes in transmission indicators in real time and perform dynamic rescheduling optimization. In the optimization algorithm for the beaked lizard: Real-time assessment of population optimization dispersion, dynamic calculation of survival pressure threshold, and intelligent switching between global exploration and local development phases; In the global exploration phase, based on the phototropic growth algorithm PGA's phototropic sensing biomimetic mechanism, the behavior of the beaked lizard wandering around the periphery of its territory guided by the photosensitive eyes of its cranial eyes is simulated. Based on the light gradient perceived by the cranial eyes, a large-scale migration search is carried out to avoid premature convergence and local optimum locking of the algorithm. During the partial development phase, the nest habitat biomimetic mechanism based on the Artificial Lemming Algorithm (ALA) simulates the fine exploration behavior of the lemur hibernating and foraging around the nest. Based on the nest memory mechanism, it defends its high-quality habitat area and improves the solution accuracy by conducting fine optimization through small-scale exploration and gradual position fine-tuning.

[0009] Preferably, in S1, a minimum comprehensive cost objective function is constructed, and a data transmission optimization model is constructed in conjunction with constraints, including: S11. Taking into account transmission delay, link balance, quantum key utilization, and encryption energy consumption, construct an objective function for data transmission optimization; S12. Determine the constraints for data transmission optimization; S13. Combine the objective function and constraints of data transmission optimization to construct a data transmission optimization model.

[0010] Preferably, in S11, a data transmission optimization objective function is constructed by comprehensively considering transmission delay, link balance, quantum key utilization, and encryption energy consumption, including: Construct the objective function F(X) that minimizes the overall cost: ; Where X is the solution vector, containing multi-link transmission bandwidth, quantum key distribution time slot, power consumption of data encryption transmission equipment, and meteorological terminal data return rate, and T d The end-to-end encrypted data transmission delay coefficient. This is the load imbalance coefficient for the transmission link. For quantum key utilization, P e The power consumption coefficient of quantum encryption and data transmission devices. , , , All are weighting coefficients, and .

[0011] Preferably, the constraints for determining data transmission optimization in S12 include: 1) Encrypted transmission delay constraint T d :T d ≤T max T max This represents the upper limit of the end-to-end encrypted data transmission delay coefficient. 2) Quantum key utilization constraints : ; 3) Equipment power consumption constraint P e :P e ≤P lim P lim Rated power consumption factor for quantum encryption and data transmission devices; 4) Transmission bandwidth constraint B k : 0≤B k ≤B max B k Allocate bandwidth for the k-th transmission link, B max This represents the maximum transmission bandwidth for a single link.

[0012] Preferably, in S2, the beaked lizard optimization algorithm is used to solve the data transmission optimization model to obtain the optimal data transmission parameter set. The beaked lizard optimization algorithm includes: S21. Randomly generate an initial population in the search space. The position of each beaked lizard in the population corresponds to a solution vector, and initialize the algorithm parameters. S22. Real-time assessment of population optimization dispersion, dynamic calculation of survival pressure threshold, and intelligent switching between global exploration / local development phases; S23. In the global exploration phase, simulate the territory periphery roaming behavior guided by the skull-top eye of the beaked lizard. Based on the light gradient perceived by the skull-top eye, carry out a large-scale migration search to avoid premature convergence and local optimum locking of the algorithm, and then enter S25. S24. In the local development stage, simulate the fine exploration behavior of the lizard hibernating and foraging around the nest. Based on the nest memory mechanism, defend the high-quality habitat area. Through small-scale exploration and gradual position fine-tuning, fine optimization is carried out to improve the solution accuracy and proceed to S25. S25. Use the objective function of data transmission optimization to evaluate all beaked lizards in the current population, calculate the corresponding fitness value, and record and update the historical best solution; S26. Determine whether the iteration termination condition is met. If the iteration termination condition is not met, return to S22. Otherwise, use the historical optimal solution as the optimal data transmission parameter set.

[0013] Preferably, in S22, the population optimization dispersion is evaluated in real time, the survival pressure threshold is dynamically calculated, and the global exploration / local development phase is intelligently switched, including: S221. Calculate the population fitness dispersion at the t-th iteration. :

[0014] in, Let i be the position of the i-th beaked lizard in the t-th iteration. The corresponding fitness value, The global optimal solution at the t-th iteration The corresponding fitness value, where N is the number of beaked lizards in the current population; S222. Calculate the survival pressure threshold at the t-th iteration. : ; in, , These are the basic and minimum survival pressure thresholds, respectively. The maximum population fitness dispersion is T, and the maximum number of iterations is T. S223, Set the survival pressure threshold at the t-th iteration. Compare with the random number r1, if If so, proceed to S23 to execute the global exploration phase; if Then proceed to S24 to execute the partial development phase; Where r1 is a random number uniformly distributed in the range [0,1].

[0015] Preferably, in S23, during the global exploration phase, the roaming behavior of the beaked lizard guided by the light-sensing eyes on its skull periphery is simulated. A large-scale migration search is conducted based on the illumination gradient perceived by the skull-sensing eyes to avoid premature convergence and local optima locking in the algorithm, including: S231. Calculate the adaptive roaming step size coefficient at the t-th iteration. Dynamic adjustment of the movement speed of the beaked lizard under simulated changes in ambient light intensity: ; in, , These are the minimum and maximum adaptive roaming step size coefficients, respectively; S232. Calculate the scalar eye photomodulation factor at the t-th iteration. Quantifying the inhibitory effect of ambient light intensity on roaming activity: ; Where I(t) is the normalized ambient light intensity coefficient at the t-th iteration, I max The maximum normalized ambient light intensity coefficient is fixed at 1; S233. Update the position during the global exploration phase according to the following formula: ; in, , Let be the j-th dimension position of the i-th beaked lizard at the t-th and t+1-th iterations, respectively. For the random individual at the t-th iteration The j-th dimension position, The global optimal solution at the t-th iteration The j-th dimension position, Let j be the position of the i-th beaked lizard in the j-th iteration. The partial derivative of the real-time ambient light intensity is used to simulate the magnitude and direction of changes in the intensity of light around the location perceived by the cranial eye of the beaked lizard, providing directional guidance for roaming in low-light areas outside its territory.

[0016] Preferably, in S24, during the local development phase, the fine-tuning exploration behavior of the beaked lizard lurking and foraging around its nest is simulated. Based on the nest memory mechanism, it defends its high-quality habitat area. Through small-scale, incremental exploration and gradual positional fine-tuning, it performs fine optimization to improve the solution accuracy, including: S241. Calculate the adaptive fine trial coefficient of the i-th beaked lizard in the t-th iteration. The simulation dynamically adjusts the precision of the probe based on the quality of the living environment: ; in, Basic trial coefficient, Let be the historical best fitness value of the i-th beaked lizard at the t-th iteration. The fitness difference normalization coefficient; S242. Update the location during the local development phase according to the following formula: ; in, Let be the historical best position in dimension j for the i-th beaked lizard at iteration t, which is the historical best nest position in dimension j for the i-th beaked lizard at iteration t. To test the perturbation coefficient, r² is a standard normally distributed random number. This is a nest memory decay factor, which weakens the strong constraint of old nests in the later stages of iteration and avoids algorithm stagnation.

[0017] Preferably, in S3, full-domain meteorological terminal data collection is performed, and quantum key negotiation, data encryption, encapsulation and transmission, and terminal decryption verification are completed based on the optimal data transmission parameter set to achieve secure transmission across the entire link, including: S31. Deploy meteorological terminals including ground meteorological monitoring stations, radiosondes, and meteorological satellites to collect structured and unstructured meteorological data in real time, including temperature, humidity, air pressure, wind speed, and meteorological radar echoes. S32. Allocate quantum key distribution time slots according to the optimal data transmission parameter set. The two communicating parties complete real-time synchronous negotiation of quantum keys through the quantum channel to realize dynamic key update of one-time pad. S33. The transmitting end uses quantum key distribution to encrypt the raw meteorological data in groups, and combines the optimized allocation of link transmission bandwidth to complete data encapsulation and time slot queuing for transmission; S34. During data transmission, the balanced transmission link load is relied upon to avoid link congestion and disconnection under extreme weather conditions. S35. The receiving end uses a synchronous quantum key to complete data decryption and integrity verification, and then uploads the data to the meteorological business platform after verification. S36, real-time transmission latency, link balance, quantum key utilization, and encryption energy consumption, periodically triggers secondary optimization of the data transmission optimization model to achieve adaptive adjustment of meteorological data transmission.

[0018] Preferably, in S4, for extreme weather scenarios, changes in transmission indicators are monitored in real time, and dynamic rescheduling optimization is performed, including: S41. Under extreme weather conditions, monitor link error rate, quantum key synchronization success rate and transmission delay fluctuation in real time; S42. When any transmission index exceeds the preset threshold, the beaked lizard optimization algorithm is automatically restarted to quickly update the optimal data transmission parameter set and dynamically adapt to complex weather environments.

[0019] (III) Beneficial Effects

[0020] Compared with existing technologies, the meteorological data transmission method based on quantum key encryption provided by this invention has the following advantages: 1) Optimize resource allocation and improve overall utilization rate The Beaked Lizard optimization algorithm is used to perform multi-dimensional collaborative optimization of multi-link transmission bandwidth, quantum key distribution time slots, power consumption of data encryption transmission equipment, and data return rate of meteorological terminals. It breaks through the limitations of the disordered and rigid allocation of traditional resources. The algorithm accurately matches the meteorological data transmission needs by intelligently switching between global exploration and local development. It avoids the waste of quantum keys and idle link bandwidth, and reasonably manages equipment power consumption. It achieves efficient coordination of quantum keys, communication links, and computing resources, and greatly improves the comprehensive utilization rate of various transmission resources. 2) Dynamic adaptation and scheduling improve transmission efficiency. Leveraging the adaptive optimization capability of the Beaked Lizard optimization algorithm and combining it with a dynamic rescheduling optimization mechanism for extreme weather scenarios, the algorithm enables real-time optimization and adjustment of data transmission parameters. The Beaked Lizard optimization algorithm can quickly respond to fluctuations in link error rate, quantum key synchronization success rate, and transmission latency, avoiding link congestion and disconnection under extreme weather conditions. At the same time, it reduces transmission latency and balances link load through fine optimization, reduces the waiting time for encrypted data transmission, improves the efficiency of the entire process of meteorological data collection, encryption, transmission, and decryption, and ensures data real-time performance. 3) Strengthen end-to-end protection and enhance security capabilities. Employing a quantum key distribution system with a "one-time pad" encryption mode, it fundamentally resists security threats such as channel eavesdropping and data tampering. Compared with traditional encryption transmission modes, it can significantly improve the confidentiality and integrity of meteorological data transmission. At the same time, by monitoring changes in transmission indicators in real time and dynamically updating the optimal data transmission parameter set, it constructs a full-link security system of "encryption protection - real-time monitoring - dynamic feedback". It can quickly adapt to complex meteorological scenarios such as extreme strong convection, rainstorms, and strong electromagnetic interference, effectively avoiding risks such as key failure and data leakage, and providing reliable security for the transmission of confidential meteorological data and critical detection information. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0022] Figure 1 This is a schematic diagram of the process of the present invention; Figure 2 This is a schematic diagram illustrating the process of solving the data transmission optimization model using the beaked lizard optimization algorithm in this invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0024] The core of this invention lies in addressing the challenges of balancing multiple objectives in quantum key encrypted meteorological data transmission, including transmission delay, link balance, quantum key utilization, and encryption energy consumption. Traditional resource scheduling optimization algorithms suffer from rigid search patterns, susceptibility to local optima, and difficulty adapting to extreme weather scenarios. To address these issues, a quantum key encrypted meteorological data transmission method based on the Tuatara Optimization Algorithm (TOA) is designed. This method constructs a survival pressure adaptive switching mechanism driven by population adaptation dispersion, simulating the tuatara's skull-eye photosensitive roaming and nest-hibernating foraging behavior. It dynamically adjusts the algorithm's global exploration and local development to obtain the optimal data transmission parameter set, achieving secure, efficient, and stable transmission of meteorological data across the entire link.

[0025] In this invention, the core improvements to the beaked lizard optimization algorithm include: Real-time assessment of population optimization dispersion, dynamic calculation of survival pressure threshold, and intelligent switching between global exploration and local development phases; In the global exploration phase, based on the phototropic growth algorithm PGA's phototropic sensing biomimetic mechanism, the behavior of the beaked lizard wandering around the periphery of its territory guided by the photosensitive eyes of its cranial eyes is simulated. Based on the light gradient perceived by the cranial eyes, a large-scale migration search is carried out to avoid premature convergence and local optimum locking of the algorithm. During the partial development phase, the nest habitat biomimetic mechanism based on the Artificial Lemming Algorithm (ALA) simulates the fine exploration behavior of the lemur hibernating and foraging around the nest. Based on the nest memory mechanism, it defends its high-quality habitat area and improves the solution accuracy by conducting fine optimization through small-scale exploration and gradual position fine-tuning.

[0026] The following describes the specific process of the meteorological data transmission method based on quantum key encryption provided by this invention, using a concrete example (e.g.) Figure 1 (as shown) and technical effects.

[0027] S1. Construct a comprehensive cost objective function, and build a data transmission optimization model based on constraints, including: S11. Taking into account transmission delay, link balance, quantum key utilization, and encryption energy consumption, construct an objective function for data transmission optimization, including: Construct the objective function F(X) that minimizes the overall cost: ; Where X is the solution vector, containing multi-link transmission bandwidth, quantum key distribution time slot, power consumption of data encryption transmission equipment, and meteorological terminal data return rate, and T d The end-to-end encrypted data transmission delay coefficient. This is the load imbalance coefficient for the transmission link. For quantum key utilization, P e The power consumption coefficient of quantum encryption and data transmission devices. , , , All are weighting coefficients, and ; S12. Determine the constraints for data transmission optimization, including: 1) Encrypted transmission delay constraint T d :T d ≤T max T max This represents the upper limit of the end-to-end encrypted data transmission delay coefficient. 2) Quantum key utilization constraints : ; 3) Equipment power consumption constraint P e :P e ≤P lim P lim Rated power consumption factor for quantum encryption and data transmission devices; 4) Transmission bandwidth constraint B k : 0≤B k ≤B max B k Allocate bandwidth for the k-th transmission link, B max This represents the maximum transmission bandwidth of a single link. S13. Combine the objective function and constraints of data transmission optimization to construct a data transmission optimization model.

[0028] S2. The beaked lizard optimization algorithm is used to solve the data transmission optimization model to obtain the optimal data transmission parameter set, such as... Figure 2 As shown, it includes: S21. Randomly generate an initial population in the search space. The position of each beaked lizard in the population corresponds to a solution vector, and initialize the algorithm parameters. S22. Real-time assessment of population optimization dispersion, dynamic calculation of survival pressure threshold, and intelligent switching between global exploration / local development phases; S23. In the global exploration phase, simulate the territory periphery roaming behavior guided by the skull-top eye of the beaked lizard. Based on the light gradient perceived by the skull-top eye, carry out a large-scale migration search to avoid premature convergence and local optimum locking of the algorithm, and then enter S25. S24. In the local development stage, simulate the fine exploration behavior of the lizard hibernating and foraging around the nest. Based on the nest memory mechanism, defend the high-quality habitat area. Through small-scale exploration and gradual position fine-tuning, fine optimization is carried out to improve the solution accuracy and proceed to S25. S25. Use the objective function of data transmission optimization to evaluate all beaked lizards in the current population, calculate the corresponding fitness value, and record and update the historical best solution; S26. Determine whether the iteration termination condition is met. If the iteration termination condition is not met, return to S22. Otherwise, use the historical optimal solution as the optimal data transmission parameter set.

[0029] Specifically, S22 assesses the population's optimization dispersion in real time, dynamically calculates the survival pressure threshold, and intelligently switches between global exploration and local development phases, including: S221. Calculate the population fitness dispersion at the t-th iteration. :

[0030] in, Let i be the position of the i-th beaked lizard in the t-th iteration. The corresponding fitness value, The global optimal solution at the t-th iteration The corresponding fitness value, where N is the number of beaked lizards in the current population; S222. Calculate the survival pressure threshold at the t-th iteration. : ; in, , These are the basic and minimum survival pressure thresholds, respectively. The maximum population fitness dispersion is T, and the maximum number of iterations is T. S223, Set the survival pressure threshold at the t-th iteration. Compare with the random number r1, if If so, proceed to S23 to execute the global exploration phase; if Then proceed to S24 to execute the partial development phase; Where r1 is a random number uniformly distributed within the range [0,1]. Specifically, in S23, during the global exploration phase, the roaming behavior of the beaked lizard guided by the light-sensing cranial eye is simulated. A large-scale migration search is conducted based on the light gradient perceived by the cranial eye to avoid premature convergence and local optimum locking, including: S231. Calculate the adaptive roaming step size coefficient at the t-th iteration. Dynamic adjustment of the movement speed of the beaked lizard under simulated changes in ambient light intensity: ; in, , These are the minimum and maximum adaptive roaming step size coefficients, respectively; S232. Calculate the scalar eye photomodulation factor at the t-th iteration. Quantifying the inhibitory effect of ambient light intensity on roaming activity: ; Where I(t) is the normalized ambient light intensity coefficient at the t-th iteration, I max The maximum normalized ambient light intensity coefficient is fixed at 1; S233. Update the position during the global exploration phase according to the following formula: ; in, , Let be the j-th dimension position of the i-th beaked lizard at the t-th and t+1-th iterations, respectively. For the random individual at the t-th iteration The j-th dimension position, The global optimal solution at the t-th iteration The j-th dimension position, Let j be the position of the i-th beaked lizard in the j-th iteration. The partial derivative of the real-time ambient light intensity is used to simulate the magnitude and direction of changes in the intensity of light around the location perceived by the cranial eye of the beaked lizard, providing directional guidance for roaming in low-light areas outside its territory.

[0031] Specifically, in S24, during the local development phase, the fine-tuning probing behavior of the beaked lizard lurking and foraging around its nest is simulated. Based on the nest memory mechanism, it defends its high-quality habitat area. Through small-scale, incremental probing and gradual positional fine-tuning, it performs fine optimization to improve the solution accuracy, including: S241. Calculate the adaptive fine trial coefficient of the i-th beaked lizard in the t-th iteration. The simulation dynamically adjusts the precision of the probe based on the quality of the living environment: ; in, Basic trial coefficient, Let be the historical best fitness value of the i-th beaked lizard at the t-th iteration. The fitness difference normalization coefficient; S242. Update the location during the local development phase according to the following formula: ; in, Let be the historical best position in dimension j for the i-th beaked lizard at iteration t, which is the historical best nest position in dimension j for the i-th beaked lizard at iteration t. To test the perturbation coefficient, r² is a standard normally distributed random number. This is a nest memory decay factor, which weakens the strong constraint of old nests in the later stages of iteration and avoids algorithm stagnation.

[0032] In the technical solution of this application, the Beaked Lizard optimization algorithm is used to perform multi-dimensional collaborative optimization of multi-link transmission bandwidth, quantum key distribution time slot, power consumption of data encryption transmission equipment, and data return rate of meteorological terminals. This breaks through the limitations of the disordered and fixed allocation of traditional resources. The algorithm accurately matches the meteorological data transmission needs by intelligently switching between global exploration and local development. This avoids both the waste of quantum keys and idle link bandwidth, and also reasonably manages the power consumption of equipment. It achieves efficient coordination of quantum keys, communication links, and computing resources, and greatly improves the comprehensive utilization rate of various transmission resources.

[0033] S3. Conduct full-domain meteorological terminal data collection, and complete quantum key negotiation, data encryption, encapsulation and transmission, and terminal decryption verification based on the optimal data transmission parameter set to achieve secure transmission across the entire link, including: S31. Deploy meteorological terminals including ground meteorological monitoring stations, radiosondes, and meteorological satellites to collect structured and unstructured meteorological data in real time, including temperature, humidity, air pressure, wind speed, and meteorological radar echoes. S32. Allocate quantum key distribution time slots according to the optimal data transmission parameter set. The two communicating parties complete real-time synchronous negotiation of quantum keys through the quantum channel to realize dynamic key update of one-time pad. S33. The transmitting end uses quantum key distribution to encrypt the raw meteorological data in groups, and combines the optimized allocation of link transmission bandwidth to complete data encapsulation and time slot queuing for transmission; S34. During data transmission, the balanced transmission link load is relied upon to avoid link congestion and disconnection under extreme weather conditions. S35. The receiving end uses a synchronous quantum key to complete data decryption and integrity verification, and then uploads the data to the meteorological business platform after verification. S36, real-time transmission latency, link balance, quantum key utilization, and encryption energy consumption, periodically triggers secondary optimization of the data transmission optimization model to achieve adaptive adjustment of meteorological data transmission.

[0034] S4. For extreme weather scenarios, monitor changes in transmission indicators in real time and perform dynamic rescheduling optimization, including: S41. Under extreme weather conditions, monitor link error rate, quantum key synchronization success rate and transmission delay fluctuation in real time;

[0035] S42. When any transmission index exceeds the preset threshold, the beaked lizard optimization algorithm is automatically restarted to quickly update the optimal data transmission parameter set and dynamically adapt to complex weather environments.

[0036] The technical solution of this application relies on the adaptive optimization capability of the beaked lizard optimization algorithm and combines it with the dynamic rescheduling optimization mechanism for extreme weather scenarios to achieve real-time optimization and adjustment of data transmission parameters. The beaked lizard optimization algorithm can quickly respond to link error rate, quantum key synchronization success rate and transmission latency fluctuations, avoid link congestion and disconnection under extreme weather conditions, and at the same time reduce transmission latency and balance link load through fine optimization, reduce the waiting time for data encryption transmission, improve the efficiency of the entire process of meteorological data from collection, encryption to transmission and decryption, and ensure data real-time performance.

[0037] Meanwhile, the quantum key "one-time pad" encryption mode fundamentally resists security threats such as channel eavesdropping and data tampering, significantly improving the confidentiality and integrity of meteorological data transmission compared to traditional encryption transmission modes. By monitoring changes in transmission indicators in real time and dynamically updating the optimal data transmission parameter set, a full-link security system of "encryption protection - real-time monitoring - dynamic feedback" is constructed. This system can quickly adapt to complex meteorological scenarios such as extreme strong convection, rainstorms, and strong electromagnetic interference, effectively avoiding risks such as key failure and data leakage, and providing reliable security for the transmission of classified meteorological data and critical detection information.

[0038] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for transmitting meteorological data based on quantum key encryption, characterized in that: Includes the following steps: S1. Construct a comprehensive cost objective function and build a data transmission optimization model based on constraints; S2. The beaked lizard optimization algorithm is used to solve the data transmission optimization model to obtain the optimal data transmission parameter set; S3. Collect meteorological terminal data across the entire region, and complete quantum key negotiation, data encryption, encapsulation and transmission, and terminal decryption verification based on the optimal data transmission parameter set to achieve secure transmission across the entire link. S4. For extreme weather scenarios, monitor changes in transmission indicators in real time and perform dynamic rescheduling optimization. The optimization algorithm for the beaked lizard includes: Real-time assessment of population optimization dispersion, dynamic calculation of survival pressure threshold, and intelligent switching between global exploration and local development phases; In the global exploration phase, based on the phototropic growth algorithm PGA's phototropic sensing biomimetic mechanism, the behavior of the beaked lizard wandering around the periphery of its territory guided by the photosensitive eyes of its cranial eyes is simulated. Based on the light gradient perceived by the cranial eyes, a large-scale migration search is carried out to avoid premature convergence and local optimum locking of the algorithm. During the partial development phase, the nest habitat biomimetic mechanism based on the Artificial Lemming Algorithm (ALA) simulates the fine exploration behavior of the lemur hibernating and foraging around the nest. Based on the nest memory mechanism, it defends its high-quality habitat area and improves the solution accuracy by conducting fine optimization through small-scale exploration and gradual position fine-tuning.

2. The meteorological data transmission method based on quantum key encryption according to claim 1, characterized in that: In S1, a minimum comprehensive cost objective function is constructed, and a data transmission optimization model is built in conjunction with constraints, including: S11. Taking into account transmission delay, link balance, quantum key utilization, and encryption energy consumption, construct an objective function for data transmission optimization; S12. Determine the constraints for data transmission optimization; S13. Combine the objective function and constraints of data transmission optimization to construct a data transmission optimization model.

3. The meteorological data transmission method based on quantum key encryption according to claim 2, characterized in that: S11 comprehensively considers transmission latency, link balance, quantum key utilization, and encryption energy consumption to construct an objective function for data transmission optimization, including: Construct the objective function F(X) that minimizes the overall cost: ； Where X is the solution vector, containing multi-link transmission bandwidth, quantum key distribution time slot, power consumption of data encryption transmission equipment, and meteorological terminal data return rate, and T d The end-to-end encrypted data transmission delay coefficient. This is the load imbalance coefficient for the transmission link. For quantum key utilization, P e The power consumption coefficient of quantum encryption and data transmission devices. , , , All are weighting coefficients, and .

4. The meteorological data transmission method based on quantum key encryption according to claim 3, characterized in that: The constraints for data transmission optimization are determined in S12, including: 1) Encrypted transmission delay constraint T d :T d ≤T max T max This represents the upper limit of the end-to-end encrypted data transmission delay coefficient. 2) Quantum key utilization constraints : ; 3) Equipment power consumption constraint P e :P e ≤P lim P lim Rated power consumption factor for quantum encryption and data transmission devices; 4) Transmission bandwidth constraint B k : 0≤B k ≤B max B k Allocate bandwidth for the k-th transmission link, B max This represents the maximum transmission bandwidth for a single link.

5. The meteorological data transmission method based on quantum key encryption according to claim 1, characterized in that: In S2, the beaked lizard optimization algorithm is used to solve the data transmission optimization model, obtaining the optimal data transmission parameter set, including: S21. Randomly generate an initial population in the search space. The position of each beaked lizard in the population corresponds to a solution vector, and initialize the algorithm parameters. S22. Real-time assessment of population optimization dispersion, dynamic calculation of survival pressure threshold, and intelligent switching between global exploration / local development phases; S23. In the global exploration phase, simulate the territory periphery roaming behavior guided by the skull-top eye of the beaked lizard. Based on the light gradient perceived by the skull-top eye, carry out a large-scale migration search to avoid premature convergence and local optimum locking of the algorithm, and then enter S25. S24. In the local development stage, simulate the fine exploration behavior of the lizard hibernating and foraging around the nest. Based on the nest memory mechanism, defend the high-quality habitat area. Through small-scale exploration and gradual position fine-tuning, fine optimization is carried out to improve the solution accuracy and proceed to S25. S25. Use the objective function of data transmission optimization to evaluate all beaked lizards in the current population, calculate the corresponding fitness value, and record and update the historical best solution; S26. Determine whether the iteration termination condition is met. If the iteration termination condition is not met, return to S22. Otherwise, use the historical optimal solution as the optimal data transmission parameter set.

6. The meteorological data transmission method based on quantum key encryption according to claim 5, characterized in that: In S22, the dispersion of population optimization is evaluated in real time, the survival pressure threshold is dynamically calculated, and the global exploration / local development phase is intelligently switched, including: S221. Calculate the population fitness dispersion at the t-th iteration. : ， in, Let i be the position of the i-th beaked lizard in the t-th iteration. The corresponding fitness value, The global optimal solution at the t-th iteration The corresponding fitness value, where N is the number of beaked lizards in the current population; S222. Calculate the survival pressure threshold at the t-th iteration. : ； in, , These are the basic and minimum survival pressure thresholds, respectively. The maximum population fitness dispersion is T, and the maximum number of iterations is T. S223, Set the survival pressure threshold at the t-th iteration. Compare with the random number r1, if If so, proceed to S23 to execute the global exploration phase; if Then proceed to S24 to execute the partial development phase; Where r1 is a random number uniformly distributed in the range [0,1].

7. The meteorological data transmission method based on quantum key encryption according to claim 6, characterized in that: In S23, during the global exploration phase, the behavior of the beaked lizard, guided by light-sensing cranial eyes, is simulated to roam the periphery of its territory. A large-scale migration search is conducted based on the illumination gradient perceived by the cranial eyes, avoiding premature convergence and local optima locking, including: S231. Calculate the adaptive roaming step size coefficient at the t-th iteration. Dynamic adjustment of the movement speed of the beaked lizard under simulated changes in ambient light intensity: ； in, , These are the minimum and maximum adaptive roaming step size coefficients, respectively; S232. Calculate the scalar eye photomodulation factor at the t-th iteration. Quantifying the inhibitory effect of ambient light intensity on roaming activity: ； Where I(t) is the normalized ambient light intensity coefficient at the t-th iteration, I max The maximum normalized ambient light intensity coefficient is fixed at 1; S233. Update the position during the global exploration phase according to the following formula: ； in, , Let be the j-th dimension position of the i-th beaked lizard at the t-th and t+1-th iterations, respectively. For the random individual at the t-th iteration The j-th dimension position, The global optimal solution at the t-th iteration The j-th dimension position, Let j be the position of the i-th beaked lizard in the j-th iteration. The partial derivative of the real-time ambient light intensity is used to simulate the magnitude and direction of changes in the intensity of light around the location perceived by the cranial eye of the beaked lizard, providing directional guidance for roaming in low-light areas outside its territory.

8. The meteorological data transmission method based on quantum key encryption according to claim 7, characterized in that: In S24, during the local development phase, the fine-tuning exploration behavior of the beaked lizard, which forages and lies dormant around its nest, is simulated. Based on the nest memory mechanism, it defends its prime habitat area. Through small-scale, incremental exploration and gradual positional adjustments, it performs fine-tuning to improve the accuracy of the solution, including: S241. Calculate the adaptive fine trial coefficient of the i-th beaked lizard in the t-th iteration. The simulation dynamically adjusts the precision of the probe based on the quality of the living environment: ； in, Basic trial coefficient, Let be the historical best fitness value of the i-th beaked lizard at the t-th iteration. The fitness difference normalization coefficient; S242. Update the location during the local development phase according to the following formula: ； in, Let be the historical best position in dimension j for the i-th beaked lizard at iteration t, which is the historical best nest position in dimension j for the i-th beaked lizard at iteration t. To test the perturbation coefficient, r² is a standard normally distributed random number. This is a nest memory decay factor, which weakens the strong constraint of old nests in the later stages of iteration and avoids algorithm stagnation.

9. The meteorological data transmission method based on quantum key encryption according to claim 1, characterized in that: S3 performs full-domain meteorological terminal data collection, and completes quantum key negotiation, data encryption, encapsulation and transmission, and terminal decryption verification based on the optimal data transmission parameter set to achieve end-to-end secure transmission, including: S31. Deploy meteorological terminals including ground meteorological monitoring stations, radiosondes, and meteorological satellites to collect structured and unstructured meteorological data in real time, including temperature, humidity, air pressure, wind speed, and meteorological radar echoes. S32. Allocate quantum key distribution time slots according to the optimal data transmission parameter set. The two communicating parties complete real-time synchronous negotiation of quantum keys through the quantum channel to realize dynamic key update of one-time pad. S33. The transmitting end uses quantum key distribution to encrypt the raw meteorological data in groups, and combines the optimized allocation of link transmission bandwidth to complete data encapsulation and time slot queuing for transmission; S34. During data transmission, the balanced transmission link load is relied upon to avoid link congestion and disconnection under extreme weather conditions. S35. The receiving end uses a synchronous quantum key to complete data decryption and integrity verification, and then uploads the data to the meteorological business platform after verification. S36, real-time transmission latency, link balance, quantum key utilization, and encryption energy consumption, periodically triggers secondary optimization of the data transmission optimization model to achieve adaptive adjustment of meteorological data transmission.

10. The meteorological data transmission method based on quantum key encryption according to claim 1, characterized in that: S4 performs dynamic rescheduling optimization in real time to monitor changes in transmission indicators for extreme weather scenarios, including: S41. Under extreme weather conditions, monitor link error rate, quantum key synchronization success rate and transmission delay fluctuation in real time; S42. When any transmission index exceeds the preset threshold, the beaked lizard optimization algorithm is automatically restarted to quickly update the optimal data transmission parameter set and dynamically adapt to complex weather environments.

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