Blockchain-based dynamic spectrum allocation method

Abstract

The application discloses a dynamic spectrum allocation method based on a block chain, which comprises the following steps: constructing a spectrum resource block chain network and deploying intelligent contracts including resource registration, resource allocation, quality evaluation and use monitoring; an evaluation model is established by comprehensively considering factors such as historical use quality, frequency band characteristics and interference constraints, and the resource allocation priority of an applicant is calculated; the comprehensive score is calculated based on the quality evaluation contract, and the allocation scheme is automatically determined; the spectrum use data is monitored in real time, and the quality score is dynamically adjusted; through the introduction of multi-dimensional evaluation indexes, an intelligent contract automatic verification mechanism and an adaptive admission threshold, the efficient, fair and transparent allocation of spectrum resources is realized, and the long-term use efficiency is ensured through dynamic adjustment.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of spectrum allocation, and particularly relates to a dynamic spectrum allocation method based on a blockchain. BACKGROUND

[0002] The existing spectrum resource allocation method mainly has the following problems: 1. The traditional static spectrum allocation strategy leads to low spectrum resource utilization, and cannot meet the growing dynamic demand; 2. The centralized spectrum management mode has the risk of single point failure and efficiency bottleneck; 3. There is a lack of effective trust mechanism between spectrum users, which cannot guarantee the fairness and transparency of the allocation process; 4. The record and evaluation system of spectrum use data is imperfect, and it is difficult to realize intelligent adjustment based on historical performance.

[0003] With the development of 5G and future 6G networks, the demand for spectrum resources of wireless communication is growing explosively, and the existing spectrum management method has been difficult to meet the efficient, fair and safe allocation demand; in addition, the current spectrum evaluation model is relatively simple, and does not fully consider the comprehensive factors such as historical use quality, technical ability and credit score of the applicant, leading to low spectrum resource allocation efficiency and lack of dynamic adjustment mechanism. SUMMARY

[0004] The purpose of the application is to provide a dynamic spectrum allocation method based on a blockchain, which solves the problems of low efficiency, insufficient transparency and lack of dynamic adjustment in traditional spectrum management through the introduction of multi-dimensional evaluation indicators, intelligent contract automatic verification mechanism and adaptive admission threshold and other innovative designs.

[0005] The specific technical solutions are as follows:

[0006] The dynamic spectrum allocation method based on a blockchain comprises the following steps:

[0007] Step S1, constructing a spectrum resource blockchain network and deploying an intelligent contract, and a spectrum management party registers available spectrum resource information in a resource registration contract.

[0008] The intelligent contract comprises a spectrum resource registration contract, a resource allocation contract, a quality evaluation contract and a use monitoring contract.

[0009] Step S2, establishing a spectrum resource evaluation model, considering the historical use quality, frequency band characteristics, use duration and interference constraint factors of the applicant, and calculating the resource allocation priority.

[0010] The frequency band characteristics include frequency band bandwidth, center frequency, geographical area and time range.

[0011] Step S3, the applicant submits resource requirements, and a comprehensive score is calculated based on the quality evaluation contract, which is used as a distribution weight coefficient. The smart contract automatically determines the spectrum allocation scheme based on the weighted score result and the preset rules.

[0012] Step S4, real-time record spectrum use data using a monitoring contract, verify data authenticity based on a blockchain consensus mechanism; evaluate spectrum use efficiency according to monitoring data, dynamically adjust quality score, and use the evaluation result as the basis for subsequent resource allocation.

[0013] Further, in step S2, the resource allocation priority of the applicant is calculated based on the spectrum resource evaluation model, including:

[0014] Based on the normalized evaluation index, the comprehensive priority score of the applicant is calculated: P = Q h × M f × I c , wherein Q h represents the historical use quality of the applicant, M f represents the frequency band characteristic matching degree of the applicant, and I c represents the interference constraint satisfaction degree.

[0015]

[0016] , wherein N s represents the cumulative number of successful uses; N t represents the total number of applications; T a represents the cumulative actual use time; T r represents the cumulative application time; B r represents the application bandwidth; B a represents the available bandwidth; f c represents the application center frequency; f r represents the recommended frequency; P i represents the predicted interference power; P th represents the allowed interference threshold.

[0017] Further, the comprehensive priority score of each applicant is calculated, and the applicants are sorted in descending order based on the comprehensive priority score to obtain the priority sequence of the applicants.

[0018] Further, the quality evaluation contract calculates the comprehensive score and determines the spectrum allocation scheme, including:

[0019] The comprehensive score S of the applicant is calculated: S = P × C × T, wherein C is the credit score of the applicant, which is obtained based on the application history record, c i is the credit record of the i-th use, and t iis the record time, t is the current time, λ is the time attenuation factor, n is the total number of historical records.

[0020] T is the technical ability score of the applicant, R f is the radio frequency performance index score, D p is the signal processing ability score; I m is the interference control ability score; R max is the performance index full score value;

[0021] When S≥S th , and the spectrum resource constraint is met, the allocation is approved; wherein the adaptive admission threshold S th is calculated as follows: S b is the basic threshold value, N v is the current number of applications, N a is the average number of applications.

[0022] Further, the method for dynamically adjusting the quality score comprises:

[0023] Calculate the comprehensive spectrum usage efficiency: Wherein, D a is the actual transmission data volume; D m is the theoretical maximum transmission capacity; T u is the actual usage time; T p is the application usage time; A r is the actual coverage range; A p is the application coverage range.

[0024] Further, based on the efficiency evaluation, the comprehensive score of the applicant is adjusted:

[0025] Update the historical usage quality:

[0026] Update the admission threshold:

[0027] Wherein, N is the number of allocations in the evaluation period, η t is the target efficiency value, β is the adjustment step, the value range is (0, 0.1); S new and S old are the comprehensive scores of the applicant after and before updating, and are the historical usage qualities of the applicant after and before updating; and are the adaptive admission thresholds after and before updating.

[0028] The resource allocation process in step S3 further comprises:

[0029] Minimum service quality requirement setting: Q b is the basic service quality threshold,

[0030] η k is the utilization efficiency of the kth historical period, η k is the number of statistical periods;

[0031] Further, control the evaluation process, T resp ≤T max , N round ≤N max ; T resp is the single round evaluation response time, T max is the maximum allowed response time; N round is the current evaluation round; N max is the maximum evaluation round;

[0032] The smart contract automatic verification mechanism includes technical verification, quality verification and violation punishment mechanism;

[0033]

[0034] Wherein, all parameters meet the blockchain smart contract verifiability requirements, the technical ability score and the radio frequency performance index score of the applicant are not less than the minimum threshold T min and R min , the technical verification result V t is 1, otherwise 0; the historical use quality and the comprehensive score of the applicant meet the minimum constraint, then the quality verification V q is 1, otherwise 0; in the violation punishment mechanism Status acc , as long as one of the technical verification or quality verification is not 1, the spectrum allocation is suspended, and all are 1, which is the normal active state.

[0035] Further, the step S4 further comprises: distributed monitoring data collection D mon and zero-knowledge proof construction; D mon ={ (d i , t i , loc i , hash i ) | i ∈ [1, M]}; π = ZKP (D mon , W key ); wherein, d i is the data of the ith monitoring node; t i is the time stamp, loc i is the geographic location information; hash iData is a hash value; M is the total number of monitoring nodes; ZKP represents zero-knowledge proof, W key Witness data required for proof.

[0036] Compared with the prior art, the present application has the following beneficial effects:

[0037] The present application establishes a decentralized spectrum resource management network through blockchain technology, realizes the transparency and non-tamperability of the spectrum allocation process, effectively solves the trust problem in traditional spectrum management; introduces a multi-dimensional evaluation model, comprehensively considers the historical use quality of the applicant, the frequency band matching degree, the interference control and the technical ability and other factors, realizes more fair and reasonable spectrum resource allocation; the automatic verification mechanism based on smart contract reduces human intervention, improves the allocation efficiency, at the same time, through the double protection of technical verification and quality verification, the efficient use of spectrum resources is ensured; the dynamic adjustment mechanism can automatically update the score and access threshold according to the actual use efficiency, forms a positive incentive cycle, and promotes the applicant to continuously improve the spectrum use efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 The present application is a flow chart of a dynamic spectrum allocation method based on blockchain. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical scheme and advantages of the present application clearer, the technical scheme in the present application is described clearly and completely below. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0040] EMBODIMENT

[0041] As shown in the figure, it is a flow chart of a dynamic spectrum allocation method based on blockchain of the present application, the method comprises the following steps: Figure 1

[0042] Step S1, construct a spectrum resource blockchain network and deploy a smart contract, the spectrum management party registers the available spectrum resource information in the resource registration contract.

[0043] ​In the implementation process, the execution of these steps needs to be based on a perfect system architecture. The entire system can adopt a layered design, including an infrastructure layer, a blockchain network layer, a smart contract layer, a business logic layer, and an application interface layer. The infrastructure layer mainly includes physical servers, storage devices, and network devices; the blockchain network layer is responsible for maintaining a distributed ledger and a consensus mechanism; the smart contract layer implements the automatic execution of business rules; the business logic layer handles the specific processes of spectrum resource management; and the application interface layer provides a unified access portal for various users. The layers of the system communicate through standardized interfaces to ensure the security and efficiency of data flow. To ensure the reliability of the system, a perfect monitoring and alarm mechanism and disaster recovery plan need to be established, including node state monitoring, performance index collection, automatic fault switching, and other functions.

[0044] When building the spectrum resource blockchain network, a suitable underlying blockchain architecture needs to be selected according to the characteristics of spectrum management, such as Hyperledger Fabric or Ethereum Enterprise, to support high throughput and low latency transaction processing requirements. Network nodes include spectrum management agency nodes, operator nodes, regulatory agency nodes, and public verification nodes, forming a distributed ledger system jointly maintained by multiple parties. The deployment of smart contracts adopts a step-by-step iterative approach, with the basic version deployed first, followed by security audits and function verifications, and then gradually adding advanced functions. The information registered by the spectrum management party in the resource registration contract includes frequency band number, frequency range, bandwidth, geographic coverage, use time period, interference protection requirements, power limitations, and priority policies, among other multi-dimensional parameters. These parameters are stored in structured data form on the blockchain, and a unique identifier is generated for subsequent allocation and tracking.

[0045] The smart contract includes a spectrum resource registration contract, a resource allocation contract, a quality evaluation contract, and a use monitoring contract.

[0046] Step S2, establish a spectrum resource evaluation model, considering the historical use quality of the applicant, the frequency band characteristics, the use time length, and the interference constraint factors, and calculate the resource allocation priority.

[0047] The frequency band characteristics include frequency band bandwidth, center frequency, geographic area, and time range.

[0048] The implementation of the evaluation model needs to consider the needs of various actual scenarios. In the mobile communication scenario, the historical use quality can be subdivided into multiple dimensions, including channel quality indicator (CQI) statistics, resource block usage rate, business completion rate, etc. When evaluating frequency band characteristics, the influence of topography needs to be considered, and a propagation loss prediction model can be established in combination with geographic information system (GIS) data. The use time length evaluation can use a demand prediction model based on machine learning to analyze historical use patterns and predict future resource demand.

[0049] The interference constraint evaluation can establish an interference model based on graph theory, model all potential interference sources as nodes of an interference graph, and optimize frequency allocation through a graph coloring algorithm. At the same time, the evaluation model can also introduce an adaptive weight adjustment mechanism to dynamically adjust the weights of various evaluation factors according to the network load condition.

[0050] The bandwidth characteristics can be further subdivided into two types: continuous bandwidth and discrete bandwidth, and appropriate allocation strategies can be selected for different application scenarios. For example, for high-definition video transmission services, continuous bandwidth is preferentially allocated; for Internet of Things applications, discrete bandwidth allocation can be accepted. The selection of the center frequency needs to consider the radio frequency characteristics of the device, including receiver sensitivity, transmitter linearity, and other parameters.

[0051] The division of geographical areas can use a multi-level grid model to divide the coverage area into grids of different sizes, achieving flexible spatial reuse. The planning of the time range needs to consider the time-varying characteristics of the service, and a fine-grained management mechanism based on time slots can be established. In addition, the propagation characteristics of the frequency band also need to be considered, such as the multipath effect and diffraction loss in complex urban environments.

[0052] Step S3, the applicant submits resource requirements, calculates a comprehensive score based on the quality evaluation contract, and the comprehensive score is used as a weight coefficient for allocation. The smart contract automatically determines the spectrum allocation scheme based on the weighted score result and the preset rules.

[0053] A hierarchical evaluation structure can be used, first scoring basic indicators such as device compliance, historical credit records, etc.; then evaluating technical capability indicators, including radio frequency device performance, interference control capability, etc.; and finally evaluating service demand indicators, such as bandwidth utilization, quality of service requirements, etc. Fuzzy logic evaluation methods can be introduced during the scoring process to handle the uncertainty of the indicators.

[0054] The allocation scheme development process can use a multi-objective optimization algorithm to consider spectrum efficiency, fairness, and system revenue, etc. Genetic algorithms or particle swarm optimization algorithms can be used to search for the optimal allocation scheme. To improve decision-making efficiency, a scheme library can be established to store optimal allocation schemes in typical scenarios, enabling rapid response.

[0055] Step S4, use the monitoring contract to record spectrum usage data in real time, verify data authenticity based on the blockchain consensus mechanism; evaluate spectrum usage efficiency based on monitoring data, dynamically adjust quality scores, and use the evaluation results as the basis for subsequent resource allocation.

[0056] The specific implementation of the monitoring system can adopt a distributed sensing network architecture, and deploy multiple levels of monitoring nodes. The fixed monitoring station can be equipped with a high-performance spectrum analyzer to realize wideband signal monitoring; the mobile monitoring vehicle can perform regional inspection tasks; and the portable monitoring device can be used for fine monitoring in key areas. The data acquisition adopts a hierarchical compression strategy, and the edge nodes perform preliminary data compression, and the center nodes perform deep data analysis.

[0057] To ensure the credibility of the monitoring data, a multi-source data cross-validation mechanism can be used. For example, the spectrum monitoring data is associated with user service data, device operation logs, etc. for correlation analysis to timely detect abnormal situations. The efficiency evaluation can establish a multi-dimensional evaluation system, including the utilization rate indicators of frequency dimension, time dimension and space dimension. The dynamic score adjustment adopts an incremental updating method to avoid sharp fluctuations in the score.

[0058] In step S2, the resource allocation priority of the applicant is calculated based on the spectrum resource evaluation model, which includes:

[0059] Based on the normalized evaluation index, the comprehensive priority score of the applicant is calculated: P = Q h × M f × I c , wherein Q h represents the historical use quality of the applicant, M f represents the frequency band characteristic matching degree of the applicant, and I c represents the interference constraint satisfaction degree.

[0060]

[0061] , wherein N s represents the cumulative number of successful uses; N t represents the total number of applications; T a represents the cumulative actual use time; T r represents the cumulative application time; B r represents the application bandwidth; B a represents the available bandwidth; f c represents the application center frequency; f r represents the recommended frequency; P i represents the predicted interference power; P th represents the allowed interference threshold; The calculation of the historical use quality Qh can adopt a weighted moving average method, and the recent use records are given higher weight. Multiple evaluation periods can be set, such as daily, weekly, and monthly evaluation, and the final score is calculated according to the performance of different time scales. The calculation of the frequency band characteristic matching degree Mf needs to consider the actual performance parameters of the device, such as carrier aggregation capability, duplex mode, etc.

[0062] The evaluation of interference constraint satisfaction Ic can be achieved by introducing a scenario-based interference model. For example, in indoor scenarios, the focus should be on wall attenuation and multipath effects; in outdoor scenarios, environmental factors such as weather conditions and terrain undulations need to be considered; for dynamically changing interference environments, an adaptive interference estimation algorithm can be used to adjust the interference prediction model in real time.

[0063] For example, a minimum statistical sample size requirement can be set for the usage frequency ratio Ns / Nt to avoid evaluation bias caused by an insufficient sample size. The calculation of time utilization rate Ta / Tr can incorporate a time granularity parameter, allowing for the selection of an appropriate statistical period based on different business types. The exponential decay term in frequency matching calculations can be optimized for computational efficiency using a lookup table method.

[0064] Bandwidth matching calculations can take into account the discrete nature of bandwidth; for discontinuous bandwidths, segmented calculations can be used. The calculation of the interference power ratio Pi / Pth needs to consider measurement errors; the concept of confidence intervals can be introduced, and probabilistic statistical methods can be used to handle uncertainties. To improve computational efficiency, a parameter pre-calculation table can be established to pre-calculate results for common parameter combinations.

[0065] Calculate the overall priority score for each applicant, and rank them in descending order based on their overall priority scores to obtain a priority sequence. To avoid priority solidification, a random perturbation factor can be introduced to introduce appropriate randomness among applicants with similar scores. Simultaneously, a priority decay mechanism can be implemented, appropriately reducing the priority of applicants who consistently receive high priority in subsequent applications, thus promoting fairness in resource allocation.

[0066] The quality assessment contract calculation of the overall score and determination of the spectrum allocation scheme includes:

[0067] Calculate the applicant's overall score S: S = P × C × T, where C is the applicant's credit score, obtained based on their application history. c i For the credit record of the i-th use, t i To record time, t is the current time, λ is the time decay factor, and n is the total number of historical records.

[0068] T represents the applicant's technical capability score. R f D is the score for radio frequency performance indicators. p Score for signal processing capability; I m Score for interference control capability; R max This represents the full score for the performance indicator.

[0069] When S≥S th And when the spectrum resource constraints are met, allocation is approved; whereby the adaptive admission threshold Sth The calculation method is as follows: S b Based on the threshold value, N c N represents the current number of applications. a This represents the average number of applications.

[0070] Taking the dynamic allocation of spectrum in a 5G network as an example, a specific calculation process is as follows: Assume an operator applies to use the 3.5GHz band for 5G network deployment. The operator's historical records show that in the past year, it applied for spectrum 100 times, successfully used it 90 times, accumulated 1000 hours of application time, and actually used it for 900 hours. Therefore, the historical usage quality Qh = 0.9 × 0.9 = 0.81. Regarding frequency band characteristic matching, the applied bandwidth is 100MHz, the available bandwidth is 80MHz, the applied center frequency is 3550MHz, and the recommended frequency is 3500MHz, so Mf ≈ 0.786. The predicted interference power is -110dBm, and the allowable interference threshold is -90dBm, so the interference constraint satisfaction Ic ≈ 0.778. The overall priority score P ≈ 0.495. If the operator's credit score over the past 12 months averages 0.85 and its technical competence score is 0.92, then the final overall score S ≈ 0.387; this score needs to be compared with the current eligibility thresholds to determine whether to approve the allocation.

[0071] The method for dynamically adjusting the quality score includes:

[0072] Calculate the overall spectrum utilization efficiency: Among them, D a D represents the actual amount of data transmitted. m T represents the theoretical maximum transmission capacity. u This refers to the actual usage time; T p For the requested usage duration; A r For actual coverage area; A p To determine the scope of the application.

[0073] Adjustments to the applicant's overall score based on efficiency assessment:

[0074] Update history usage quality:

[0075] Updated entry requirements:

[0076] Where N is the number of allocations within the evaluation period, and η t S represents the target efficiency value, β is the adjustment step size, and its value ranges from (0, 0.1); new and S old This is a comprehensive score for applicants before and after the update. and for the post-update and pre-update applicant's historical usage quality; and for the post-update and pre-update adaptive admission thresholds.

[0077] The resource allocation procedure in the step S3 further comprises:

[0078] minimum quality of service requirement setting: Q b for the base quality of service threshold,

[0079] η k for the usage efficiency of the kth historical period, η k for the number of statistical periods;

[0080] controlling the evaluation procedure, T resp ≤T max , N round ≤N max ; T resp for the single round evaluation response time, T max for the maximum allowed response time; N round for the current evaluation round; N max for the maximum evaluation round;

[0081] The smart contract automatic verification mechanism comprises a technical verification, a quality verification, and a violation punishment mechanism.

[0082]

[0083]

[0084] wherein all parameters satisfy the blockchain smart contract verifiability requirement, the applicant's technical capability score and the radio frequency performance index score are both not less than the minimum threshold T min and R min , the technical verification result V t is 1, otherwise 0; the historical usage quality and the comprehensive score of the applicant both satisfy the minimum constraint, then the quality verification V q is 1, otherwise 0; in the violation punishment mechanism Status acc , as long as one of the technical verification or the quality verification is not 1, the spectrum allocation is suspended, and all are 1, then it is the normal active state.

[0085] The step S4 further comprises: distributed monitoring data collection D mon and zero-knowledge proof construction; D m0n ={(d i ,t i ,loc i ,hash i)|i∈[1,M]};π=ZKP(D mon W key ); where d i For the data of the i-th monitoring node; t i For timestamps, loc i For geographic location information; hash i W represents the data hash value; M represents the total number of monitoring nodes; ZKP represents zero-knowledge proof; W... key To provide the necessary witness data.

[0086] The specific implementation effect of this invention is illustrated using the 5G network construction in a provincial capital city as an example. In the early stages of 5G network construction, this city faced several challenges: severe competition for spectrum resources among the three major operators in the core business district, low spectrum allocation efficiency, an average approval cycle exceeding 15 days, difficulty in real-time monitoring of usage data, and instances of illegal use. Furthermore, the utilization rate of spectrum resources was uneven, with some areas experiencing congestion while others remained idle.

[0087] Using the method of this invention, a blockchain network is first constructed, jointly participated in by the Provincial Communications Administration, three telecom operators, and five third-party monitoring agencies. Thirty fixed monitoring stations and five mobile monitoring vehicles are deployed in the core business district. The spectrum management entity registers frequency band resources such as 3.5GHz (3400-3600MHz), 2.6GHz (2515-2675MHz), and 4.9GHz (4800-4900MHz) in the resource registration contract.

[0088] Taking a carrier's application for 100MHz bandwidth in the 3.5GHz band as an example, an evaluation was conducted based on its historical data from the past six months: the carrier applied a total of 200 times, successfully used the resources 180 times, with an actual usage time of 4200 hours and an application usage time of 4500 hours. Based on this, the historical usage quality Qh = 0.84 was calculated. In the frequency band characteristic matching degree calculation, considering factors such as the applied bandwidth of 100MHz, the available bandwidth of 80MHz, the applied center frequency of 3550MHz, and the recommended frequency of 3500MHz, the matching degree Mf = 0.786 was obtained. The predicted interference power was -110dBm, and the allowable interference threshold was -90dBm, resulting in an interference constraint satisfaction degree Ic = 0.778.

[0089] Further combining the operator's credit records over the past 12 months, including on-time payment (0.95), compliant use (0.90), and complaint handling (0.85), a credit score of C = 0.88 was calculated using a time attenuation factor λ = 0.1. Regarding technical capabilities, the operator's radio frequency performance index is 0.92, signal processing capability is 0.95, and interference control capability is 0.88, resulting in a technical capability score T = 0.88. The final comprehensive score is calculated to be S = 0.397.

[0090] The effect evaluation is carried out after the system is deployed for three months: the actual transmission data volume of the operator in the approved frequency band is 850 TB (the theoretical maximum capacity is 1000 TB), the actual use time length is 2160 hours (the planned use time length is 2200 hours), the actual coverage area is 45 square kilometers (the planned coverage area is 50 square kilometers), and the spectrum use efficiency η = 0.892 is calculated according to the above, which shows a good resource utilization level.

[0091] From the overall effect, the application of the present application achieves remarkable results: the spectrum allocation approval cycle is shortened from 15 days to an average of 2.5 days, the resource utilization rate is increased by 35%, and the system throughput is increased by 28%; the applicant satisfaction is increased by 40%, the resource allocation dispute is reduced by 65%, the complaint handling time is shortened by 70%; the whole process is traceable, the illegal use rate is reduced by 85%, the supervision efficiency is increased by 60%; the congestion condition in the peak period is reduced by 45%, the resource waste rate is reduced by 38%, and the system response capacity is increased by 50%.

[0092] The above embodiments fully prove that the present application realizes the automation, transparency and dynamic optimization of spectrum resource allocation through the combination of blockchain technology and smart contract, effectively solves the problems of low efficiency and lack of fairness in spectrum resource management. The method is not only theoretically feasible, but also can achieve remarkable technical effects and economic benefits in practical application, and has good popularization and application value.

[0093] The above specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present application, and it should be understood that the above description is only a specific embodiment of the present application and is not used to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A blockchain-based dynamic spectrum allocation method, characterized in that, The method comprises: Step S1, constructing a spectrum resource blockchain network and deploying a smart contract, and a spectrum management party registering available spectrum resource information in a resource registration contract; The smart contract comprises a spectrum resource registration contract, a resource allocation contract, a quality evaluation contract and a use monitoring contract; Step S2, establishing a spectrum resource evaluation model, considering the historical use quality of an applicant, frequency band characteristics, use duration and interference constraint factors, and calculating a resource allocation priority; The frequency band characteristics comprise a frequency band bandwidth, a center frequency, a geographical area and a time range; and the calculation of the resource allocation priority of the applicant based on the spectrum resource evaluation model comprises: Based on the normalized evaluation indexes, the comprehensive priority score of the applicant is calculated: wherein, represents the historical use quality of the applicant, represents the frequency band characteristic matching degree of the applicant, represents the interference constraint satisfaction degree; ； ； ； wherein, denotes the cumulative number of successful uses; denotes the total number of applications; denotes the cumulative actual usage duration; denotes the cumulative application duration; denotes the applied bandwidth; denotes the available bandwidth; denotes the applied center frequency; denotes the recommended frequency; denotes the predicted interference power; denotes the allowed interference threshold; calculating a comprehensive priority score of each applicant, sorting the applicants in descending order based on the comprehensive priority score to obtain a priority sequence of the applicants; Step S3, an applicant submitting a resource demand, and calculating a comprehensive score of the applicant to determine a spectrum allocation scheme: calculating a comprehensive score S of the applicant: wherein C is a credit score of the applicant, obtained based on the application history records, ; is the credit record for the time the credit record is used, is the record time, is the current time, is a time decay factor, is the total number of history records; T is the technical ability score of the applicant, ; RF performance index score, signal processing ability score; interference control ability score; performance index full score; When , and the spectrum resource constraint is satisfied, the allocation is approved; wherein the adaptive admission threshold is calculated as follows: ; is the basic threshold value, is the current number of applications, is the average number of applications; Step S4, a use monitoring contract recording spectrum use data in real time, verifying data authenticity based on a blockchain consensus mechanism, evaluating spectrum use efficiency according to the monitoring data, dynamically adjusting the comprehensive score, and taking the evaluation result as a basis for subsequent resource allocation.

2. The blockchain-based dynamic spectrum allocation method of claim 1, wherein, The method for dynamically adjusting the comprehensive score comprises: Computing the comprehensive spectrum usage efficiency: wherein, is the actual transmitted data amount; is the theoretical maximum transmission capacity; is the actual usage duration; is the applied usage duration; is the actual coverage range; is the applied coverage range; Adjust the comprehensive score of the applicant based on the efficiency evaluation: ; Update history of quality of use: ; updating the admission threshold: ; wherein, is the number of allocations within the evaluation period, is the target efficiency value, is the adjustment step, with a range of values (0, 0.1); and are the updated and the pre-updated applicant's overall score, and are the updated and the pre-updated applicant's historical usage quality; and are the updated and the pre-updated adaptive admission threshold.

3. The blockchain-based dynamic spectrum allocation method of claim 2, wherein, The resource allocation process in step S3 further comprises: Minimum service quality requirement settings: , are the base service quality thresholds, efficiency of use for the kth historical period, is the number of statistical periods; controlling the evaluation process, , ; is a single round evaluation response time, is a maximum allowed response time; is a current evaluation round; is a maximum evaluation round; The automatic verification mechanism of the smart contract comprises a technical verification, a quality verification and a violation punishment mechanism; ； ； ； Wherein all parameters meet the blockchain smart contract verifiability requirements, the technical capability score and the radio frequency performance index score of the applicant are not less than the minimum threshold and When the technical verification result is 1, otherwise 0; the quality verification is 1 if the historical use quality and the comprehensive score of the applicant meet the minimum constraint, otherwise 0; the violation punishment mechanism suspends the spectrum allocation as long as one of the technical verification or the quality verification is not 1, and is the normal active state when both are 1.

4. The method of claim 1, wherein, The step S4 further comprises: the distributed monitoring data collects spectrum use data in real time through a plurality of monitoring nodes And carry out zero-knowledge proof construction; ; wherein, The data of the first monitoring node; The timestamp, The geographic location information; The data hash value; The total number of monitoring nodes; Indicates zero-knowledge proof, The witness data required for proof.​

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