A dynamic resource scheduling method, system, device and medium based on optical fiber communication

Abstract

The application discloses a kind of dynamic resource scheduling method, system, equipment and medium based on optical fiber communication, it is related to data processing technical field, comprising: obtaining allocation equipment, and the waiting time of each service, resource demand and channel data are collected in real time;According to the waiting time of each service, resource demand and channel data obtain first dynamic priority;And the waiting time of each service is sequentially arranged in the order from small to big and forms time sequence, and according to time sequence, the adjustment factor corresponding to each service is obtained, and according to the adjustment factor corresponding to each service and first dynamic priority, second dynamic priority is obtained, and the second dynamic priority of each service is sequentially arranged in the order from small to big and forms service sequence;The maximum available resource amount of allocation equipment is obtained, and according to service sequence and maximum available resource amount, dynamic resource scheduling strategy is obtained.The application has the advantages of dynamic priority, accurate identification and good scheduling effect.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a dynamic resource scheduling method, system, device and medium based on optical fiber communication. BACKGROUND

[0002] In optical fiber communication, the fiber-to-the-home base station (FTTRB) as a terminal optical distribution node undertakes the last resource scheduling and quality of service guarantee task in a high-density user environment. However, under the background of explosive growth of intelligent home, 4K / 8K video stream, virtual reality and other businesses, the existing technology usually adopts a static allocation strategy based on fixed priority or a single dimension (such as channel quality, business demand), which faces serious challenges.

[0003] Specifically, there are several core defects in the prior art. The method of the prior art relies on preset rules (such as "first-come-first-served" or "high-demand-first"), which cannot dynamically respond to real-time changes in business load and channel state. When there is a sudden high-bandwidth demand from users (such as multi-person VR collaboration), the fixed strategy may allocate high-demand businesses to low-quality channels due to not considering channel interference strength, causing transmission delay or even interruption. Static allocation has insufficient resource utilization at business peak times, while dynamic fluctuation scenarios have high misallocation rates. At the same time, the method of the prior art usually processes key parameters such as business demand, waiting time, and channel interference in isolation, lacks a coupling optimization mechanism for multi-dimensional parameters, and only sorting by channel quality will sacrifice the fairness of high-demand but slightly high-interference businesses. Simply relying on waiting time may cause low-interference, high-priority businesses (such as emergency medical monitoring) to wait for a long time. This single-dimensional decision makes it difficult to balance system throughput, fairness, and latency, especially in densely populated urban base station coverage areas, where multiple business competitions will cause serious resource contention problems. Furthermore, the method of the prior art simply reduces the allocation by proportion when the total amount of resources is insufficient (such as limited spectrum resources), without establishing a dynamic mapping relationship between interference strength and business demand. More importantly, the adaptability of the method of the prior art is poor. When multiple businesses have similar waiting times, the traditional method cannot identify the competition intensity of business clusters due to not quantifying local time density. If the waiting times of 10 businesses are concentrated in the [2.0s, 2.5s] interval, the static strategy will treat them as ordinary queues rather than high-conflict events, resulting in overly averaged resource allocation, and critical businesses (such as real-time games) cannot obtain sufficient bandwidth, significantly reducing user experience. SUMMARY

[0004] In view of the defects in the prior art, the present application provides a dynamic resource scheduling method, system, device and medium based on optical fiber communication.

[0005] The application discloses a dynamic resource scheduling method based on optical fiber communication, which comprises the following steps: acquiring an allocation device, and collecting the waiting time, resource demand and channel data of each service in the coverage area of the allocation device in real time; acquiring the first dynamic priority corresponding to each service according to the waiting time, resource demand and channel data of each service; arranging the waiting time of each service in the order from small to large and forming a time sequence, acquiring the adjustment factor corresponding to each service according to the time sequence, and acquiring the second dynamic priority according to the adjustment factor and the first dynamic priority corresponding to each service, and arranging the second dynamic priority of each service in the order from small to large and forming a service sequence; acquiring the maximum available resource amount of the allocation device, and acquiring a dynamic resource scheduling strategy according to the service sequence and the maximum available resource amount.

[0006] Optionally, the adjustment factor corresponding to each service according to the time sequence comprises the following steps: acquiring the waiting time of the i-th in the time sequence, acquiring the average interval between the i-th waiting time and its adjacent waiting time as an adjacent average interval, acquiring the j-th service corresponding to the i-th waiting time, and acquiring the adjustment factor of the j-th service according to the adjacent average interval of the i-th waiting time.

[0007] Optionally, the adjustment factor of the j-th service according to the adjacent average interval of the i-th waiting time is expressed as: ; wherein, is the adjustment factor of the j-th service, is the first scaling coefficient, is the i+1-th waiting time in the time sequence, is the i-th waiting time in the time sequence, is the i-1-th waiting time in the time sequence, is the number of services in the coverage area of the allocation device.

[0008] Optionally, the dynamic resource scheduling strategy according to the service sequence and the maximum available resource amount comprises the following steps: judging whether the maximum available resource amount is less than the sum of the resource demands of each service; if not, completing resource scheduling according to the order of the service sequence to meet the resource demands of each service; if yes, acquiring the channel interference index corresponding to each service according to the resource demand and the channel data of each service, acquiring the resource weight of each service according to the channel interference index of each service, acquiring the dynamic resource scheduling amount corresponding to each service according to the resource weight of each service and the maximum available resource amount, and completing resource scheduling according to the dynamic resource scheduling amount corresponding to each service and the order of the service sequence.

[0009] Optionally, the channel interference index corresponding to each service according to the resource demand and the channel data of each service is expressed as:​​ ; wherein, is a channel interference indicator corresponding to the jth service, is a real-time channel interference corresponding to the jth service, is a latency corresponding to the jth service, is a time stage constant, is a resource requirement corresponding to the jth service, is a resource requirement corresponding to the kth service, is a number of services within a coverage area of the allocation device.

[0010] Optionally, a first dynamic priority corresponding to each service is obtained according to a service type, a latency, a resource requirement, and channel data of each service, and is represented as: ; wherein, is a first dynamic priority corresponding to the jth service, is a resource requirement corresponding to the jth service, is a resource requirement corresponding to the kth service, is a number of services within a coverage area of the allocation device, is a real-time channel interference corresponding to the jth service, is a second scaling coefficient, is a latency corresponding to the jth service.

[0011] A dynamic resource scheduling system based on fiber-optic communication is also provided, and the system includes: an acquisition module configured to acquire an allocation device and collect, in real time, a service type, a latency, a resource requirement, and channel data of each service within a coverage area of the allocation device; a first data processing module configured to obtain a first dynamic priority corresponding to each service according to the service type, the latency, the resource requirement, and the channel data of each service; a second data processing module configured to arrange the latency of each service in ascending order to form a time sequence, obtain an adjustment factor corresponding to each service according to the time sequence, obtain a second dynamic priority according to the adjustment factor corresponding to each service and the first dynamic priority, and arrange the second dynamic priority of each service in ascending order to form a service sequence; and a resource scheduling module configured to obtain a maximum available resource amount of the allocation device and obtain a dynamic resource scheduling strategy according to the service sequence and the maximum available resource amount.

[0012] Optionally, the resource scheduling module is further configured to: determine whether the maximum available resource amount is less than the sum of the resource requirements of the services; if not, sequentially complete resource scheduling according to the order of the service sequence to meet the resource requirements of the services; if yes, obtain the channel interference indicators corresponding to the services according to the resource requirements of the services and the channel data, obtain the resource weights of the services according to the channel interference indicators of the services, obtain the dynamic resource scheduling amounts corresponding to the services according to the resource weights of the services and the maximum available resource amount, and sequentially complete resource scheduling according to the dynamic resource scheduling amounts corresponding to the services and the order of the service sequence.

[0013] The electronic device is also provided, comprising: a memory, which stores a computer program; a processor, which is configured to execute the computer program in the memory to implement the above-mentioned dynamic resource scheduling method based on optical fiber communication.

[0014] The non-transitory computer readable storage medium is also provided, which stores a computer program, and the program is executed by a processor to implement the above-mentioned dynamic resource scheduling method based on optical fiber communication.

[0015] The beneficial effects of the present application are embodied in:

[0016] In the whole dynamic resource scheduling method based on optical fiber communication, a dynamic priority model is constructed based on the real-time collected waiting time, resource demand and channel interference data, breaking the single-dimensional decision limitation of the existing static strategy, realizing the collaborative optimization of business demand, channel quality and timeliness, avoiding the continuous shelving of key businesses due to the characteristics of low interference channel, and introducing the dynamic product effect of resource demand proportion and real-time channel interference intensity, so that high bandwidth businesses preferentially obtain resource tilt in high interference channel, reducing the continuous occupation of channel caused by retransmission by accelerating the transmission process, improving resource utilization and inhibiting interference diffusion. Further, the adjustment factor based on the waiting time sequence innovatively quantifies the business competition intensity in the local time window, accurately identifies the business cluster conflict through adjacent interval mean detection and dynamic amplification of the exponential adjustment factor, breaks through the limitation of the existing method to the time dimension competition blind area, preferentially allocates excess resources to the business cluster in the high-density period, and avoids the real-time business stall caused by the average scheduling. Further, when the total amount of resources is insufficient, a dynamic mapping mechanism of channel interference index and business demand is constructed, the waiting time, interference intensity and resource demand proportion are nonlinearly coupled to calculate the resource weight, replacing the existing proportional reduction strategy, so that high interference businesses can still obtain adaptive resources to accelerate the release of the channel through the weight proportion when the resources are tight, and low interference long waiting businesses can avoid resource depletion through time factor compensation, realizing the accurate balance of interference suppression and fairness. Finally, the service sequence is modified by the double modification of dynamic priority and adjustment factor, ensuring that the high conflict business cluster and isolated high priority business can obtain the time-optimal resource allocation as needed, eliminating the time sequence misalignment and resource fragmentation problem caused by the existing polling mechanism. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed in the specific embodiments or the prior art description will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, each element or part is not necessarily drawn according to the actual scale.

[0018] Figure 1 A step schematic diagram of the dynamic resource scheduling method based on optical fiber communication of the present application;

[0019] Figure 2 A part of step schematic diagram of S3 in the dynamic resource scheduling method based on optical fiber communication of the present application;

[0020] Figure 3 A part of step schematic diagram of S4 in the dynamic resource scheduling method based on optical fiber communication of the present application;

[0021] Figure 4 A block diagram of an electronic device according to an embodiment of the present application is shown.

[0022] Reference signs:

[0023] 700 - electronic device, 701 - processor, 702 - memory, 703 - multimedia component, 704 - I / O interface, 705 - communication component. DETAILED DESCRIPTION

[0024] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations.

[0025] Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative labor are within the scope of protection of the present application.

[0026] It should be noted that: similar reference signs and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0027] As shown in Figure 1 A dynamic resource scheduling method based on optical fiber communication is provided, comprising:

[0028] S1, acquiring an allocation device, and collecting the service type, waiting time, resource demand and channel data of each service in the coverage area of the allocation device in real time;

[0029] S2, acquiring a first dynamic priority corresponding to each service according to the service type, waiting time, resource demand and channel data of each service;

[0030] S3, arranging the waiting time of each service in order from small to large and forming a time sequence, acquiring an adjustment factor corresponding to each service according to the time sequence, acquiring a second dynamic priority according to the adjustment factor corresponding to each service and the first dynamic priority, and arranging the second dynamic priority of each service in order from small to large and forming a service sequence;

[0031] S4, acquiring the maximum available resource amount of the allocation device, and acquiring a dynamic resource scheduling strategy according to the service sequence and the maximum available resource amount.

[0032] In this embodiment, it should be noted that in S1, the initial stage first needs to determine the base station device as the resource allocation core node and its physical coverage range. The coverage radius of the base station is affected by the deployment environment (such as the building structure of dense urban area, the distribution of indoor obstacles) and the signal transmission characteristics (such as optical signal attenuation, multipath effect), and needs to be dynamically calibrated by real-time monitoring of signal strength and user location. For example, in a residential area with dense smart home devices, the effective coverage radius of the base station may be shortened due to wall obstruction, at which point the signal path needs to be optimized in combination with the indoor fiber distribution node to ensure that edge users can access. In addition, the resource scheduling capability of the base station is directly related to the density of users within its coverage radius, for example, when there are a large number of VR collaborative terminals and 8K video stream devices within the coverage range of the base station, the geographical distribution of each service terminal within the coverage radius needs to be accurately identified to avoid channel congestion caused by user overload in local areas.

[0033] Further, multi-dimensional service parameters need to be collected in real time to support dynamic decision-making. Different types of services determine the resource requirements of the service, for example, the dual requirements of low latency and high bandwidth for virtual reality services, the strong dependence of intelligent security devices on stability, and the special requirements of emergency medical monitoring on priority. The waiting time parameter is used to quantify the service queue state, for example, multi-person VR collaboration requires a much lower waiting time threshold than ordinary file download due to the requirement of real-time interaction. The collection of resource requirements (representing bandwidth requirements) needs to be dynamically adapted in combination with the type of service, such as dynamically adjusting the bandwidth baseline for 4K video stream according to the resolution. Channel data is obtained by real-time scanning of the real-time channel interference of each sub-channel (obtained by bit error rate and signal-to-noise ratio).

[0034] In S2, the calculation of the first dynamic priority realizes the comprehensive evaluation of the service state by fusing multi-dimensional parameters. First, a dynamic weight model is constructed by combining the waiting time, resource demand and channel interference intensity of the service: the longer the waiting time, the higher the risk of service backlog, and the priority weight needs to be increased through an exponential decay function to avoid service timeout caused by long queuing (for example, if the waiting time of multi-person VR collaboration exceeds the threshold, its priority will be significantly increased to ensure real-time interaction); the resource demand proportion reflects the dependence of the service on bandwidth, and high-demand services (such as 8K video streaming) need to be allocated higher basic weights, and if high-demand services are in a high-interference channel, their weights will be increased, because if services in a high-interference channel occupy resources for a long time, data retransmission will be caused due to poor signal quality, further exacerbating channel congestion, and by amplifying their priority, the resource scheduling of such services can be accelerated, and their residence time in the high-interference channel can be shortened. For example, when the multi-person VR collaboration service causes a decrease in data transmission efficiency due to channel interference, its weight is increased by dynamic priority to make it have priority to obtain high-bandwidth resources, complete transmission quickly to release channel pressure, and avoid chain interference caused by retransmission. Among them, the channel interference intensity is dynamically modified by real-time monitoring of the priority, for example, if the smart home control signal has a high bit error rate due to co-channel interference, even if its resource demand is low, its priority will still be moderately increased to shorten its time in the high-interference channel, thereby optimizing the overall channel utilization.

[0035] Further, when an emergency medical monitoring service coexists with an ordinary video download service: the former, although having low resource demand, has a priority gradually exceeding the latter over time due to increasing waiting time and unstable channel quality. At the same time, in a high-interference environment, even if the service demand is the same, the priority of the service in the high-interference channel will be significantly higher than that of the service in the low-interference channel. For example, if multiple 4K video terminals in a smart home have high channel interference due to device density, the system will preferentially allocate resources to the terminal with the strongest interference to make it complete transmission quickly and exit the competition, thereby reducing the overall interference level. At the same time, the waiting time dynamically modifies the priority through an exponential decay function, ensuring that services that have been waiting for a long time (such as emergency medical monitoring) can gradually increase their priority over time even if they are in a low-interference channel, avoiding being completely preempted by high-interference services. This mechanism not only solves the resource efficiency problem of high-interference channels, but also takes into account the fairness requirements of different service types.

[0036] In S3, the priority is dynamically corrected by analyzing the density of waiting time to solve the potential conflict of competing resources in the service cluster. First, the services are arranged in time sequence in ascending order of waiting time, and then the average value of the interval between adjacent waiting times of each service is calculated to quantify the competition intensity of the services in the local time window. For example, when the waiting times of multiple smart home devices (such as security cameras and environmental sensors) are concentrated in similar time periods, the average value of the adjacent interval is small, indicating that there is high-density service competition in this period. At this time, the priority of these services is amplified by an exponential adjustment factor, and resources are preferentially allocated to alleviate local congestion. For example, if multiple VR terminals cause the waiting times to be densely distributed in the interval of 1.2 seconds to 1.5 seconds due to the simultaneous start of collaborative applications, the adjustment factor will identify this time cluster as a high-conflict event, and the second dynamic priority of this group of services will be increased to make them have priority over the isolated queued services to obtain scheduling, avoiding the VR screen freezing caused by average allocation.

[0037] Further, the coupling mechanism of the adjustment factor and the first dynamic priority realizes the dual optimization of time and space dimensions. When the waiting time of a high-demand service (such as 8K video streaming) is located in a dense area, its original high first priority will be further amplified by the adjustment factor, forming a superposition effect. For example, in the smart medical scenario, multiple remote surgery tracking devices and patient monitors are simultaneously connected to the base station, and if their waiting times are concentrated in the interval of 0.8 seconds to 1.0 seconds, even if the channel interference of the surgery tracking device is high, the second priority of the surgery tracking device will be enhanced by the adjustment factor, so that it can obtain stable channel resources in priority, ensuring the real-time transmission of surgery instructions. At the same time, for services that have been waiting for a long time but are in a low-competition period (such as firmware upgrade tasks in the early morning), even if their first priority is low, they will still maintain a reasonable scheduling order according to the time sequence distribution, preventing burst services from completely occupying the regular service window. This mechanism effectively solves the problem that traditional methods cannot identify the competition of service clusters in the time dimension.

[0038] In S4, first, fine resource allocation is realized by combining the priority order of the service sequence and the resource weight ratio. When the maximum available resource quantity is sufficient to cover all service demands, the priority order of the service sequence is strictly followed, and the resource is allocated in full from the service with the highest second dynamic priority, ensuring that high-priority services (such as real-time game sessions) have priority to obtain complete bandwidth and complete scheduling quickly. For example, in the smart home scenario, if the VR collaboration service jumps to the top of the service sequence due to the adjustment factor, it will be allocated all the required bandwidth within 0.5 seconds, and then the low-priority services such as security cameras will be processed in order, thereby ensuring the real-time performance of critical services. This mechanism avoids the timing misalignment problem introduced by traditional polling mechanisms through millisecond-level fast scheduling.

[0039] Further, when the total amount of resources is insufficient, the resource weight is dynamically allocated according to the channel interference index on the basis of maintaining the service sequence order. Each service obtains resource allocation in proportion to the weight, rather than simple reduction or over-allocation. For example, when multiple 4K video terminals compete for limited spectrum in a dense urban area, high-interference services will obtain a larger proportion of resource weight due to their high channel interference index, but the actual allocation amount of all terminals is still strictly constrained by the weight. Assuming that a certain VR terminal weight accounts for 30%, the allocation is allocated 30% of the total resources rather than over-supply, ensuring that other services (such as medical monitoring) can still obtain basic resources according to the weight. At the same time, the weight calculation and resource allocation of all services are completed in a very short time, high-priority services are started first, and low-priority services follow in sequence, which not only maintains the fairness of the scheduling order, but also suppresses the sustained impact of high-priority services when the maximum available resource amount is insufficient through weight mapping.

[0040] In summary, in the entire dynamic resource scheduling method based on optical fiber communication, a dynamic priority model is constructed based on real-time collected waiting time, resource demand and channel interference data, breaking the single-dimensional decision limit of existing static strategies, realizing the collaborative optimization of service demand, channel quality and timeliness, while avoiding the continuous shelving of key services (such as emergency medical monitoring) due to low-interference channel characteristics, and introducing the dynamic product effect of resource demand proportion and real-time channel interference intensity, so that high-bandwidth services (such as 8K video streaming) can preferentially obtain resource tilt in high-interference channels, reducing the channel continuous occupation caused by retransmission by accelerating the transmission process, which not only improves resource utilization but also suppresses interference diffusion. Further, the adjustment factor based on the waiting time sequence innovatively quantifies the service competition intensity in the local time window, accurately identifies service cluster conflicts (such as intensive concurrent VR collaboration requests) through adjacent interval mean detection and dynamic amplification of the exponential adjustment factor, breaking through the limitations of existing methods in the time dimension competition blind area, and preferentially allocating excess resources to high-density service clusters in the time period, avoiding the real-time service lag caused by average scheduling. Further, when the total amount of resources is insufficient, a dynamic mapping mechanism of channel interference index and service demand is constructed, and the waiting time, interference intensity and resource demand proportion are nonlinearly coupled to calculate the resource weight, replacing the existing proportional reduction strategy, so that high-interference services can still obtain adaptive resources to accelerate the release of the channel when the resource is tight, while low-interference long-waiting services can avoid resource depletion through time factor compensation, achieving accurate balance between interference suppression and fairness. Finally, the service sequence is modified by the dual modification of dynamic priority and adjustment factor, ensuring that high-conflict service clusters (such as multiple AR devices in smart factories) and isolated high-priority services (such as remote surgery tracking) can obtain time-optimal resource allocation as needed, eliminating the time sequence misalignment and resource fragmentation problems caused by the existing polling mechanism.

[0041] As Figure 2In one embodiment, as shown, S3 includes obtaining an adjustment factor corresponding to each service according to the time sequence.

[0042] S31, obtaining the i-th waiting time in the time sequence, obtaining the average of the interval between the i-th waiting time and its adjacent waiting time as the adjacent average interval;

[0043] S32, obtaining the j-th service corresponding to the i-th waiting time, and obtaining the adjustment factor of the j-th service according to the adjacent average interval of the i-th waiting time.

[0044] In this embodiment, it should be noted that in S31, the competition intensity of the service cluster is identified by quantifying the density of adjacent waiting times to optimize resource allocation. The waiting time of each service is placed in a global time sequence, and the average of the interval between its previous and subsequent service waiting times is calculated to form an adjacent average interval index. This index reflects the degree of service accumulation within a local time window: when the waiting times of multiple services are densely distributed within a narrow interval (such as multiple security cameras triggering events simultaneously in a smart home scenario), the adjacent interval is significantly reduced, indicating high concurrent resource contention in that time period. For example, if multiple VR collaboration terminals cause the waiting time to concentrate in the 0.5-0.8 second interval due to simultaneous user application startup, the competition intensity of this time cluster is more than 3 times that of isolated services by calculating the adjacent interval average, thereby triggering the subsequent priority adjustment mechanism.

[0045] In S32, the adjustment factor is dynamically generated based on the adjacent average interval, and the first dynamic priority is directly multiplied by the adjustment factor to equal the second dynamic priority, achieving time and space dimension enhancement of service priority. The adjustment factor design follows the principle of "the smaller the interval, the greater the priority increase". When a service is detected to be in a high-density time cluster (such as multiple AR devices simultaneously uploading inspection data in an industrial Internet of Things scenario), the adjustment factor value is automatically increased. For example, a device maintenance instruction in a smart factory overlaps with multiple sensor data reporting times, and its adjacent interval average drops to a very low level, and the adjustment factor increases the second dynamic priority of this service by 40%, making it a priority over low-density services in scattered queues to obtain resource scheduling. This mechanism effectively solves the real-time service lag problem caused by the inability to identify time dimension competition in traditional methods in high-concurrency scenarios such as concert live streaming and multi-person VR conferences.

[0046] In one embodiment, S32 includes obtaining the adjustment factor of the j-th service according to the adjacent average interval of the i-th waiting time, which is represented as:

[0047] , , ; wherein,

[0048] is the adjustment factor for the jth service, is the first scaling coefficient, is the i+1th latency in the time series, is the ith latency in the time series, is the i-1th latency in the time series, is the number of services within the allocated device coverage area.

[0049] In the present embodiment, it is noted that the latency of the ith service in the time series is calculated with the average absolute difference of its interval with the previous latency and the next latency , i.e. This value reflects the congestion level of the time window where the service is located, the smaller the interval, the more intensive the adjacent service latencies, and the more intense the resource competition. Further, by the exponential term , the average interval is mapped to the gain coefficient of the adjustment factor; where if the interval tends to 0, it represents a high-density service cluster, the exponential term tends to 1, and the adjustment factor = 2, the priority increase is the largest; if the interval increases, the services are dispersed, the exponential term rapidly decays to 0 as increases, and the adjustment factor tends to 1, and the priority has no additional increase; where the scaling coefficient controls the sensitivity of the adjustment factor to the change of the interval, the larger, the more significant the gain of small intervals, and it is suitable for high concurrency scenarios. Further, the final adjustment factor is 1 + the exponential term, which ensures that the baseline priority is 1, and only dynamically increases the priority when a service cluster occurs.

[0050] In summary, the service cluster competition identification can be realized; specifically, the traditional static strategy cannot perceive the time dimension service density difference (such as 10 service waiting times concentrated in 2.0 to 2.5 seconds), leading to resource allocation averaging. Through adjacent interval mean calculation, the system can quantify the competition intensity of the local time window: for example, the dense interval interval 0.1 seconds: the adjustment factor is significantly improved (such as 1.5-2.0 times), marked as a high conflict event; for example, the sparse interval interval 2.0 seconds: the adjustment factor is close to 1, and is considered as ordinary queuing. Further, dynamic priority correction is also realized, the adjustment factor and the first dynamic priority are multiplied based on service demand, waiting time, and channel interference, forming a space-time double optimization; among them, high demand + high density services such as multi-person VR collaboration, the first priority is already high, and after superimposing the adjustment factor, it is further amplified, and resources are preferentially allocated; and low demand but high density services such as smart home sensor clusters: through compensatory improvement of the adjustment factor, resources are avoided from being occupied by isolated high demand services. Further, inhibition of average allocation is also realized, in the service cluster scenario, the adjustment factor makes the priority of the intensive period of business float as a whole, avoiding the uniform allocation logic in the prior art, and ensuring that critical services obtain excess resources in the competition window.

[0051] For example, assuming that there are 5 services in the coverage range of a base station, the waiting times are arranged in ascending order as T=[2.0s, 2.1s, 2.2s, 2.3s, 5.0s]; among them, the first 4 services are concentrated in the 2.0-2.3 second interval high density cluster, and the 5th service is isolated in the 5.0 second low density. Set =10, the adjustment factor of the 3rd service =2.2s is: 1.3679; directly multiplying the first dynamic priority by the adjustment factor equals the second dynamic priority, and the priority of the service is improved by 36.79%.

[0052] The 5th service is isolated =5s, and according to the expression, it is obtained , and after calculation, the adjustment factor is: ≈1; therefore, the adjustment factor is not improved, and the priority is maintained at the baseline.

[0053] As shown in Figure 3 , in an embodiment, the S4 includes obtaining a dynamic resource scheduling strategy according to a service sequence and a maximum available resource quantity.

[0054] S41, judging whether the maximum available resource quantity is less than the sum of resource demands of the services;

[0055] S42, if not, sequentially completing resource scheduling according to the order of the service sequence to meet the resource demands of the services;

[0056] S43, if yes, acquiring the channel interference indicators corresponding to each service according to the resource requirements and channel data of each service, acquiring the resource weights of each service according to the channel interference indicators of each service, acquiring the dynamic resource scheduling amounts corresponding to each service according to the resource weights of each service and the maximum available resource amount, and sequentially completing resource scheduling according to the dynamic resource scheduling amounts corresponding to each service and the order of the service sequence.

[0057] In the present embodiment, it should be noted that in S41, the global strategy of resource allocation is determined by comparing the maximum available resource amount of the base station with the total demand. If the resource is sufficient (total demand ≤ maximum resource amount), the "full allocation mode" of S42 is triggered: strictly follow the priority order of the service sequence, and start from the second dynamic priority highest service to allocate all the required resources in turn. For example, in the smart home scenario, when the VR collaboration service is at the top of the service sequence, the base station allocs the complete bandwidth to it first, and then processes the low-priority security camera and other services. This mode avoids the delay of high-priority services caused by the sequence disorder in the traditional polling mechanism through millisecond-level fast scheduling (such as completing within 0.5 seconds), and is especially suitable for VR interaction, remote surgery and other scenes with high real-time requirements, ensuring that critical services are completed without waiting.

[0058] In S42, the full allocation mechanism when the resource is sufficient solves the problem of low resource utilization of the static strategy. The traditional method may allocate high-bandwidth services to low-quality channels due to fixed rules (such as "high demand first") at service peak, resulting in retransmission and resource waste. The present scheme, through the dynamic priority ordering of the service sequence, allocates high-quality channel resources to high-demand services (such as 8K video stream) in high-interference channels first, and combines the waiting time weight to ensure that long-queued services (such as medical monitoring) are not missed. For example, when multiple 4K video terminals are in high-interference channels but the service sequence priorities are different, complete bandwidth is allocated to each service in order to avoid interference superposition through channel switching, which meets the real-time requirement and improves the channel multiplexing efficiency.

[0059] In S43, the "weight proportion allocation mode" is started when the resources are insufficient, and the resource weight is dynamically calculated through the channel interference index. This index integrates real-time interference strength, waiting time and resource demand proportion, so that high interference services can obtain higher weight even if the resource demand (such as bandwidth demand) is low. For example, when multiple AR devices in the intelligent factory cause a sharp increase in channel interference due to mechanical shielding, they are allocated a higher proportion of resources than their demand, speeding up their transmission to release the channel in advance; at the same time, low-interference long-waiting services (such as firmware upgrades) obtain basic resources through time factor compensation to avoid complete starvation. When allocating resources, strictly follow the service sequence order and divide the resources according to the weight: if a VR service weight accounts for 30%, allocate 30% of the total resources instead of over-provisioning to ensure that other services obtain the remaining resources according to the weight. This mechanism maintains the fairness of multi-service competition while suppressing the continuous occupation of high-interference services, reducing the mis-scheduling rate by more than 50% compared with the traditional proportion reduction strategy.

[0060] In one embodiment, the channel interference index corresponding to each service is obtained according to the resource demand and channel data of each service in S43, which is represented as:

[0061] ; wherein,

[0062] is the channel interference index corresponding to the jth service, is the real-time channel interference corresponding to the jth service, is the waiting time corresponding to the jth service, is the time stage constant, is the resource demand corresponding to the jth service, is the resource demand corresponding to the kth service, is the number of services in the coverage area of the allocation device.

[0063] In this embodiment, it should be noted that the real-time channel interference has an amplification effect; wherein, when allocating high-demand services in high-interference channels, the prior art does not consider the increase in retransmission rate caused by interference, resulting in resource waste (such as multiple retransmissions of 8K video streams in low-quality channels), and directly reflects the channel quality (such as calculated through bit error rate or signal-to-noise ratio), the higher the interference strength, the larger the value, and it is used as a multiplication factor, so that high-interference services naturally obtain a higher interference index base. For example, a VR service has an interference index = 5 on a channel with an interference index = 1. In summary, this can force the system to prioritize resource allocation for high-interference services, shorten their occupation time in poor-quality channels, and reduce the overall network retransmission rate.

[0064] Further, For dynamic compensation of latency weight, in the static strategy of the prior art, low-interference but long-latency services such as medical monitoring are easily continuously occupied by high-interference services, resulting in service timeout. Specifically, in the dynamic compensation of latency weight, is a time phase constant (such as 1 second), used to normalize the influence of latency; when increases, linear amplification, for example = 2s is = 0.5 times; thus, compensation gain is provided for services with long latency, avoiding their being indefinitely delayed due to good channel quality but insufficient priority.

[0065] Further, For normalized control of resource demand proportion, the proportional reduction strategy of the prior art may allocate excessive resources to high-demand services in a high-interference scenario, exacerbating channel congestion. Specifically, the ratio of the demand of a single service to the total demand is taken as a weight coefficient to limit the absolute resource proportion of high-demand services. This avoids high-demand services from occupying an absolute dominant position in interference index calculation, ensuring that low-demand but high-priority services such as control signals obtain basic resource guarantee.

[0066] In summary, single-dimensional deviation is suppressed, and the product form rather than weighted summation ensures that the three parameters are indispensable. If the latency of a high-interference service is short and the demand is low, its interference index may be lower than that of a low-interference but long-latency high-demand service, achieving multi-dimensional balance. Further, dynamic adaptation to service scenarios is also achieved; high- values quickly increase priority and are preferentially scheduled to release the channel; over time, growth gradually compensates for the priority of low-interference services; resource proportion automatically adapts to changes in total demand, preventing resource mismatch caused by static quotas.

[0067] For example, assume that there are two services in the coverage range of a base station: service 1 is VR collaboration: = 60 dB, = 2s, = 50 Mbps; and service 2 is file download: = 90 dB, = 1s, = 50 Mbps; the total demand = 100 Mbps, and it is assumed that = 1s. Finally, the channel interference index corresponding to the first service is calculated as: 90, and the channel interference index corresponding to the second service is calculated as: 90. Then the total channel interference index is 180, and the resource weight of service 1 and the resource weight of service 2 are both 50%.

[0068] Effect description: Although the bandwidth requirements of service 1 and service 2 are the same, service 1 obtains 50% of the resources through long waiting time, and service 2 obtains the same resources through high real-time channel interference.

[0069] In one embodiment, S2, the first dynamic priority corresponding to each service is obtained according to the service type, waiting time, resource requirement and channel data of each service, which is expressed as:

[0070] ; wherein,

[0071] is the first dynamic priority corresponding to the jth service, is the resource requirement corresponding to the jth service, is the resource requirement corresponding to the kth service, is the number of services in the coverage area of the allocation device, is the real-time channel interference corresponding to the jth service, is the second scaling coefficient, is the waiting time corresponding to the jth service.

[0072] In this embodiment, it should be noted that for , there are the same parts in the expression in S43, and the functions are also roughly the same; wherein is the normalization control of the resource requirement ratio, which solves the problem that the prior art does not consider the pressure of the overall system caused by the resource occupation of high demand services when allocating high demand services, which easily leads to long-term occupation of channels by low-priority high-demand services; wherein is the priority amplification of real-time channel interference, which solves the problem of resource waste caused by not considering retransmission triggered by interference when the prior art allocates high demand services to low quality channels.

[0073] Further, is the nonlinear correction of the waiting time logical function; when tends to 0, the function value approaches 0.5, and the priority grows slowly, avoiding excessive occupation of short waiting services; when exceeds a threshold such as is greater than 10, the value of tends to 1, and the priority is significantly improved. is the second scaling coefficient, which controls the slope of the function, the greater the priority of the long waiting service rises faster, which can balance the fairness of short waiting services and the timeliness of long waiting services, and avoid unlimited delay of critical services such as medical monitoring.

[0074] In summary, the single dimension deviation is suppressed, and the high demand service is in the low interference channel and short waiting time, and its priority can be lower than the low demand but high interference and long waiting service, realizing multi-dimensional fairness. Further, the service scene is dynamically adapted, the priority is quickly improved, and the channel pressure is released by priority scheduling; over time, the growth gradually improves the competitiveness of low interference services; the resource proportion automatically adapts to the total demand change, avoiding resource mismatch caused by static quota.

[0075] For example, assuming that there are two services in the coverage range of the base station: service 1 is VR cooperation: = 60 dB, = 2 s, = 50 Mbps; service 2 is file download: = 90 dB, = 1 s, = 50 Mbps; the total demand = 100 Mbps, and it is assumed that = 1. Finally, the first dynamic priority corresponding to the first service is calculated by bringing into the expression: 26.4, and the channel interference index corresponding to the second service is: 32.85. Although the bandwidth demands of service 1 and service 2 are the same, the priority of service 2 is higher than that of service 1, and resource allocation is obtained in priority through high demand, high interference and reasonable waiting time. If the real-time channel interference of service 1 increases to 80 dB, then = 35.2, which is higher than that of service 2.

[0076] A dynamic resource scheduling system based on optical fiber communication is also provided, and the system comprises:

[0077] An acquisition module is configured to acquire an allocation device and collect, in real time, the service type, waiting time, resource demand and channel data of each service in the coverage area of the allocation device;

[0078] A first data processing module is configured to obtain a first dynamic priority corresponding to each service according to the service type, waiting time, resource demand and channel data of each service;

[0079] A second data processing module is configured to arrange the waiting time of each service in order from small to large to form a time sequence, obtain an adjustment factor corresponding to each service according to the time sequence, obtain a second dynamic priority according to the adjustment factor corresponding to each service and the first dynamic priority, and arrange the second dynamic priority of each service in order from small to large to form a service sequence;

[0080] ​The resource scheduling module is configured to acquire the maximum available resource amount of the allocation device, and acquire a dynamic resource scheduling strategy according to the service sequence and the maximum available resource amount.

[0081] In one embodiment, the resource scheduling module is further configured to: determine whether the maximum available resource amount is less than the sum of the resource requirements of the services; if not, sequentially complete resource scheduling according to the order of the service sequence to meet the resource requirements of the services; and if yes, acquire a channel interference index corresponding to each service according to the resource requirement of each service and the channel data, acquire a resource weight of each service according to the channel interference index of each service, acquire a dynamic resource scheduling amount corresponding to each service according to the resource weight of each service and the maximum available resource amount, and sequentially complete resource scheduling according to the dynamic resource scheduling amount corresponding to each service and the order of the service sequence.

[0082] In the embodiment, it should be noted that, as to the above-mentioned dynamic resource scheduling system based on fiber communication, the specific manner of performing operations has been described in detail in the embodiments of the dynamic resource scheduling method based on fiber communication, and will not be described in detail here.

[0083] Figure 4 is a block diagram of an electronic device for a dynamic resource scheduling method based on fiber communication according to an exemplary embodiment. As shown in Figure 4 the electronic device 700 can include a processor 701, a memory 702. The electronic device 700 can also include one or more of a multimedia component 703, an I / O interface 704 (input / output interface), and a communication component 705.

[0084] The processor 701 is configured to control overall operations of the electronic device 700 to complete all or part of the steps of the above-described dynamic resource scheduling method based on fiber communication. The memory 702 is configured to store various types of data to support operations of the electronic device 700, which can include, for example, instructions for operating any application or method on the electronic device 700, and application-related data, such as contact data, sent and received messages, pictures, audio, video, and the like. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk. The multimedia component 703 can include a screen and an audio component. The screen can be, for example, a touch screen, and the audio component is configured to output and / or input audio signals. For example, the audio component can include a microphone configured to receive external audio signals. The received audio signals can be further stored in the memory 702 or transmitted through the communication component 705. The audio component also includes at least one speaker configured to output audio signals. The I / O interface 704 provides an interface between the processor 701 and other interface modules, which can be a keyboard, a mouse, a button, and the like. The buttons can be virtual buttons or physical buttons. The communication component 705 is configured to perform wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, near field communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC or other 5G, and the like, or a combination of one or more of them, is not limited herein. Therefore, the corresponding communication component 705 can include a Wi-Fi module, a Bluetooth module, an NFC module, and the like.

[0085] In an exemplary embodiment, the electronic device 700 can be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors, or other electronic elements for performing the above-described dynamic resource scheduling method based on fiber communication.

[0086] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the above-described dynamic resource scheduling method based on fiber communication. For example, the computer-readable storage medium can be the above-described memory 702 including program instructions, which can be executed by the processor 701 of the electronic device 700 to complete the above-described dynamic resource scheduling method based on fiber communication.

[0087] In another exemplary embodiment, a computer program product is also provided, which contains a computer program capable of being executed by a programmable device, and the computer program has code portions for executing the above-described dynamic resource scheduling method based on fiber communication when executed by the programmable device.

[0088] The preferred embodiments of the present disclosure are described in detail above with reference to the accompanying drawings, but the present disclosure is not limited to the specific details in the above-described embodiments. Within the technical concept scope of the present disclosure, various simple modifications can be made to the technical solutions of the present disclosure, and these simple modifications all belong to the protection scope of the present disclosure.

[0089] In addition, it should be noted that each specific technical feature described in the above-described specific embodiments can be combined in any appropriate manner without contradiction. In order to avoid unnecessary repetition, various possible combinations are not described again by the present disclosure.

[0090] In addition, any combination of various different embodiments of the present disclosure can also be made, as long as it does not deviate from the idea of the present disclosure, and it should also be considered as disclosed by the present disclosure.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can still be modified, or some or all of the technical features can be replaced by equivalent replacements; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should be covered in the scope of the claims and the description of the present application.

Claims

1. A dynamic resource scheduling method based on optical fiber communication, characterized in that, The method comprises the following steps: acquiring an allocation device, and collecting, in real time, the waiting time, resource requirement and channel data of each service in the coverage area of the allocation device; acquiring a first dynamic priority corresponding to each service according to the waiting time, resource requirement and channel data of each service; arranging the waiting time of each service in ascending order to form a time sequence, acquiring an adjustment factor corresponding to each service according to the time sequence, acquiring a second dynamic priority according to the adjustment factor corresponding to each service and the first dynamic priority, and arranging the second dynamic priority of each service in ascending order to form a service sequence; Wherein, the adjusting factor corresponding to each service is obtained according to the time sequence, comprising: obtaining the i th waiting time in the time sequence, obtaining the average value of the interval between the i th waiting time and its adjacent waiting time as the adjacent average interval; obtaining the j th service corresponding to the i th waiting time, and obtaining the adjusting factor of the j th service according to the adjacent average interval of the i th waiting time; the adjusting factor of the j th service is expressed as: , , ; wherein, is the adjusting factor of the j th service, is the first scaling coefficient, is the i+1 th waiting time in the time sequence, is the i th waiting time in the time sequence, is the i-1 th waiting time in the time sequence, is the number of services in the coverage area of the distribution device; acquiring the maximum available resource amount of the allocation device, and acquiring a dynamic resource scheduling strategy according to the service sequence and the maximum available resource amount.

2. The method of claim 1, wherein, The step of acquiring the dynamic resource scheduling strategy according to the service sequence and the maximum available resource amount comprises the following steps: determining whether the maximum available resource amount is less than the sum of the resource requirements of each service; if not, completing resource scheduling according to the order of the service sequence to meet the resource requirements of each service; if yes, acquiring a channel interference index corresponding to each service according to the resource requirement and channel data of each service, acquiring a resource weight of each service according to the channel interference index of each service, acquiring a dynamic resource scheduling amount corresponding to each service according to the resource weight of each service and the maximum available resource amount, and completing resource scheduling according to the dynamic resource scheduling amount corresponding to each service and the order of the service sequence.

3. The method of claim 2, wherein, The step of acquiring the channel interference index corresponding to each service according to the resource requirement and channel data of each service is expressed as: ; wherein, a channel interference indicator corresponding to the jth service, a real-time channel interference corresponding to the jth service, a latency corresponding to the jth service, a time phase constant, a resource requirement corresponding to the jth service, a resource requirement corresponding to the kth service, a number of services within the allocation device coverage area.

4. The method of claim 1, wherein, The step of acquiring the first dynamic priority corresponding to each service according to the service type, waiting time, resource requirement and channel data of each service is expressed as: ; wherein, a first dynamic priority corresponding to the jth service, a resource requirement corresponding to the jth service, a resource requirement corresponding to the kth service, a number of services within a coverage area of the allocation device, a real-time channel interference corresponding to the jth service, a second scaling factor, a latency corresponding to the jth service.

5. A dynamic resource scheduling system based on optical fiber communication, characterized in that, The system is used to implement the dynamic resource scheduling method based on fiber-optic communication in any one of claims 1 to 4, and the system comprises: an acquisition module, configured to acquire an allocation device, and collect, in real time, the service type, waiting time, resource requirement and channel data of each service in the coverage area of the allocation device; a first data processing module, configured to acquire a first dynamic priority corresponding to each service according to the service type, waiting time, resource requirement and channel data of each service; a second data processing module, configured to arrange the waiting time of each service in ascending order to form a time sequence, acquire an adjustment factor corresponding to each service according to the time sequence, acquire a second dynamic priority according to the adjustment factor corresponding to each service and the first dynamic priority, and arrange the second dynamic priority of each service in ascending order to form a service sequence; a resource scheduling module, configured to acquire the maximum available resource amount of the allocation device, and acquire a dynamic resource scheduling strategy according to the service sequence and the maximum available resource amount.

6. The dynamic resource scheduling system based on optical fiber communication according to claim 5, wherein, The resource scheduling module is further configured to: determine whether the maximum available resource amount is less than the sum of the resource requirements of each service; if not, complete resource scheduling according to the order of the service sequence to meet the resource requirements of each service; if yes, acquire a channel interference index corresponding to each service according to the resource requirement and channel data of each service, acquire a resource weight of each service according to the channel interference index of each service, acquire a dynamic resource scheduling amount corresponding to each service according to the resource weight of each service and the maximum available resource amount, and complete resource scheduling according to the dynamic resource scheduling amount corresponding to each service and the order of the service sequence. If yes, a channel interference index corresponding to each service is obtained according to resource requirements and channel data of each service, a resource weight of each service is obtained according to the channel interference index of each service, a dynamic resource scheduling quantity corresponding to each service is obtained according to the resource weight of each service and a maximum available resource quantity, and resource scheduling is sequentially completed according to the dynamic resource scheduling quantity corresponding to each service and an order of a service sequence.

7. An electronic device, comprising: The application relates to a dynamic resource scheduling method based on optical fiber communication. The application relates to a dynamic resource scheduling method based on optical fiber communication. The application relates to a dynamic resource scheduling method based on optical fiber communication.

8. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The application relates to a dynamic resource scheduling method based on optical fiber communication.

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