A resource scheduling method, device and equipment based on a smart gateway wireless CCO module

Abstract

The embodiment of the present disclosure provides a resource scheduling method, device and equipment based on an intelligent gateway wireless CCO module, which comprises the following steps: determining predicted device data based on historical terminal transmission data of a terminal device; determining basic frequency spectrum scheduling information according to the predicted device data and a data acquisition type; determining a to-be-processed scheduling attribute based on the basic frequency spectrum scheduling information and the predicted device data; when the to-be-processed scheduling attribute does not meet a preset scheduling attribute, processing the basic frequency spectrum scheduling information based on a frequency spectrum resource optimization function to obtain an updated to-be-processed scheduling attribute; and taking the basic frequency spectrum scheduling information obtained when the updated to-be-processed scheduling attribute meets the preset scheduling attribute as target frequency spectrum scheduling information. The technical scheme of the embodiment of the present disclosure realizes the determination of the target frequency spectrum scheduling information based on the predicted device data and the frequency spectrum resource optimization function, realizes the iterative optimization of the basic frequency spectrum scheduling information, and improves the scheduling efficiency.

Description

TECHNICAL FIELD

[0001] The embodiment of the present disclosure relates to the technical field of power Internet of Things, in particular to a resource scheduling method, device and equipment based on a wireless CCO module of an intelligent gateway. BACKGROUND

[0002] With the development of scientific and technological level, the power Internet of Things can connect various types of power terminal devices, and through the intelligent gateway, the data of the terminal devices obtained can be protocol-converted and network-interconnected, so that the data of the terminal devices can be uploaded to the cloud server. The wireless CCO module of the intelligent gateway, as a centralized control and optimization module of the intelligent gateway, can schedule and allocate network resources to ensure the transmission quality of different traffic types of data.

[0003] The method currently used for scheduling and allocating network resources is mainly as follows: the wireless CCO module of the intelligent gateway receives a data reporting request of a terminal device, determines the spectrum resource or communication channel expected by each terminal device according to the data reporting request, polls whether the communication channel is available according to the receiving order of the data reporting request, delays a period of time for detection again if the channel is occupied, and allocates the spectrum resource to the corresponding terminal device after the channel is idle. However, this method only schedules resources according to the receiving order of the data reporting request, and has the problems of being unable to ensure the real-time performance of data transmission and low scheduling efficiency. SUMMARY

[0004] The embodiment of the present disclosure provides a resource scheduling method and device based on a wireless CCO module of an intelligent gateway, which determines target spectrum scheduling information based on predicted device data and a spectrum resource optimization function, iteratively optimizes the basic spectrum scheduling information, and improves the scheduling efficiency.

[0005] In a first aspect, the embodiment of the present disclosure provides a resource scheduling method based on a wireless CCO module of an intelligent gateway, characterized in that the method comprises:

[0006] For at least one edge-end collaborative region, based on historical terminal transmission data of at least one terminal device in the edge-end collaborative region within a first preset time length, determine predicted device data of the at least one terminal device at at least one predicted time within a predicted time length, wherein the first preset time length is a preset time length before the current time, and the predicted device data includes predicted bandwidth resources, predicted transmission data volume, and the number of devices of the at least one terminal device;

[0007] According to the predicted device data of the at least one terminal device and a pre-set data collection type, determine the basic spectrum scheduling information of a device set belonging to the same data collection type, wherein the device set includes at least one terminal device;

[0008] determine a to-be-processed scheduling attribute corresponding to the basic frequency spectrum scheduling information based on the basic frequency spectrum scheduling information corresponding to the data collection type and the predicted device data;

[0009] process the basic frequency spectrum scheduling information based on a spectrum resource optimization function to obtain an updated to-be-processed scheduling attribute, and obtain, as target frequency spectrum scheduling information, the basic frequency spectrum scheduling information obtained when the updated to-be-processed scheduling attribute satisfies the preset scheduling attribute, when the to-be-processed scheduling attribute does not satisfy a preset scheduling attribute.

[0010] In a second aspect, an embodiment of the present application further provides a resource scheduling device based on an intelligent gateway wireless CCO module, characterized by comprising:

[0011] a predicted device data determination module configured to determine, for at least one edge-terminal collaborative region, predicted device data of at least one terminal device at at least one predicted time within a predicted time length based on historical terminal transmission data of the at least one terminal device within a first preset time length, wherein the first preset time length is a preset time length before a current time, and the predicted device data comprises predicted bandwidth resources, a predicted transmission data amount, and a device quantity of the at least one terminal device;

[0012] a basic frequency spectrum scheduling information determination module configured to determine, according to the predicted device data of the at least one terminal device and a preset data collection type, basic frequency spectrum scheduling information of a device set belonging to the same data collection type, wherein the device set comprises at least one terminal device;

[0013] a to-be-processed scheduling attribute determination module configured to determine a to-be-processed scheduling attribute corresponding to the basic frequency spectrum scheduling information based on the basic frequency spectrum scheduling information corresponding to the data collection type and the predicted device data;

[0014] a target frequency spectrum scheduling information determination module configured to process the basic frequency spectrum scheduling information based on a spectrum resource optimization function to obtain an updated to-be-processed scheduling attribute, and obtain, as target frequency spectrum scheduling information, the basic frequency spectrum scheduling information obtained when the updated to-be-processed scheduling attribute satisfies the preset scheduling attribute, when the to-be-processed scheduling attribute does not satisfy a preset scheduling attribute.

[0015] In a third aspect, an embodiment of the present application further provides an electronic device, comprising:

[0016] one or more processors;

[0017] a storage device configured to store one or more programs,

[0018] When the one or more programs are executed by the one or more processors, the one or more processors implement the resource scheduling method based on the intelligent gateway wireless CCO module as any of the embodiments of the application.

[0019] In a fourth aspect, the embodiments of the application further provide a storage medium containing computer executable instructions for executing the resource scheduling method based on the intelligent gateway wireless CCO module as any of the embodiments of the application when executed by a computer processor.

[0020] The technical scheme of the embodiments of the present disclosure is that, for at least one edge-terminal collaborative region, based on historical terminal transmission data of at least one terminal device in the edge-terminal collaborative region within a first preset time length, the predicted device data of the at least one terminal device at at least one predicted moment within a predicted time length is determined, wherein the first preset time length is a preset time length before the current moment, and the predicted device data includes predicted bandwidth resources, predicted transmission data volume, and the number of devices of the at least one terminal device; then, based on the predicted device data of the at least one terminal device and a pre-set data collection type, the basic spectrum scheduling information of a device set belonging to the same data collection type is determined, wherein the device set includes the at least one terminal device; further, based on the basic spectrum scheduling information corresponding to the data collection type and the predicted device data, the corresponding to-be-processed scheduling attribute of the basic spectrum scheduling information is determined; finally, under the condition that the to-be-processed scheduling attribute does not satisfy a preset scheduling attribute, the basic spectrum scheduling information is processed based on a spectrum resource optimization function to obtain an updated to-be-processed scheduling attribute, and the basic spectrum scheduling information obtained when the updated to-be-processed scheduling attribute satisfies the preset scheduling attribute is taken as target spectrum scheduling information, solving the problem of low scheduling efficiency in the prior art when the network resource is scheduled based on the intelligent gateway wireless CCO module, that is, the network resource is scheduled according to the receiving order of the data reporting request. The embodiments of the present application adopt the predicted device data and the spectrum resource optimization function to determine the target spectrum scheduling information, realize the iterative optimization of the basic spectrum scheduling information, improve the scheduling efficiency, and achieve the effects of improving the spectrum resource utilization rate and ensuring the real-time data transmission. BRIEF DESCRIPTION OF DRAWINGS

[0021] The above and other features, advantages, and aspects of the present disclosure will become more apparent as various embodiments of the present disclosure are described in conjunction with the accompanying drawings, in which like reference numbers represent like elements throughout the drawings. It should be noted that the drawings are schematic and elements do not necessarily appear to scale.

[0022] Figure 1 is a flowchart of a resource scheduling method based on an intelligent gateway wireless CCO module provided by the embodiments of the present disclosure;

[0023] Figure 2 FIG. 1 is a schematic diagram of an edge coordination zone provided by an embodiment of the present disclosure;

[0024] Figure 3 FIG. 4 is a schematic diagram of a transmission sequence provided by an embodiment of the present disclosure;

[0025] Figure 4 FIG. 5 is a schematic diagram of time slot allocation provided by an embodiment of the present disclosure;

[0026] Figure 5 FIG. 6 is a structural schematic diagram of a resource scheduling device based on a smart gateway wireless CCO module provided by an embodiment of the present disclosure;

[0027] Figure 6 FIG. 7 is a structural schematic diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0028] The present application will be further described below in conjunction with the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application. In addition, it should be noted that, for the convenience of description, only the parts related to the present application are shown in the drawings, but not all the structures.

[0029] Embodiment One

[0030] Before introducing the technical solutions provided by the embodiments of the present disclosure, the application scenarios can be exemplarily described. The technical solutions provided by the embodiments of the present disclosure can be applied in any scenario of determining target spectrum scheduling information according to predicted device data and spectrum resource optimization function. Optionally, the target spectrum scheduling information can be spectrum scheduling information obtained based on a smart gateway wireless CCO module.

[0031] Figure 1 FIG. 3 is a flow schematic diagram of a resource scheduling method based on a smart gateway wireless CCO module provided by an embodiment of the present disclosure. The embodiments of the present disclosure are applicable to the case of determining target spectrum scheduling information of a terminal device. The method can be executed by a resource scheduling device based on a smart gateway wireless CCO module. The device can be implemented in the form of software and / or hardware. The hardware can be an electronic device such as a server. The electronic device can execute the resource scheduling method based on a smart gateway wireless CCO module provided by the present technical solution.

[0032] As shown in FIG. 4, the method comprises the following steps. Figure 1

[0033] ​S110, for at least one edge-terminal collaborative region, based on the historical terminal transmission data of at least one terminal device in the edge-terminal collaborative region within a first preset time length, determining the predicted device data of at least one terminal device at at least one predicted time within a predicted time length, wherein the first preset time length is a preset time length before the current time, and the predicted device data includes predicted bandwidth resources, predicted transmission data volume, and the number of devices of at least one terminal device.

[0034] As shown in Figure 2 All terminal devices performing data transmission with a certain edge gateway and the set of the edge gateway are called an edge-terminal collaborative region. The edge-terminal collaborative region is a field based on geographical location division, and a field can include multiple terminal devices. The edge gateway accesses all terminal devices in the field to the network and uses the CCO module in the edge gateway to perform resource scheduling, so that the sensing terminal can upload data to the cloud server through each node. The edge-terminal collaborative region can enable data to be fully calculated or processed on the "edge side" close to the terminal device, thereby reducing the dependence on remote cloud servers. The edge gateway refers to a key node or device deployed at the edge of the network, connecting local devices (such as terminal devices) and external networks (such as cloud servers). Unlike traditional gateways that are only responsible for uploading data, edge gateways often have more powerful computing and storage capabilities, enabling local data processing, analysis, filtering, and security management, and enabling real-time response and local management of massive Internet of Things devices. The CCO module can be deployed in the edge gateway and is usually responsible for the control and orchestration functions of the cloud server. The CCO module can manage and coordinate multiple edge gateways and devices thereunder, ensuring the efficiency of data flow and the scalability of the system. The edge gateway can collect and process data from terminal devices and then upload important data to the cloud server. The CCO module can receive data from terminal devices, perform further analysis, storage, and decision-making, and can issue instructions to the edge gateway, thereby enabling management of terminal devices. For example, the CCO module in the edge gateway can allocate network resources for terminal devices in the power system to enable safe and reliable data transmission by sensing terminals. It is suitable for various scenarios in the power industry, such as power generation, power transmission, power transformation, power distribution, power consumption, dispatching, new energy, water storage, and energy storage.

[0035] It should be noted that the terminal device refers to a device in the network that directly interacts with the user or the environment. These devices are usually the end of the network. There are many types of terminal devices, including sensors (such as temperature sensors, humidity sensors), actuators (such as motors, light controls), wearable devices (such as smart watches, health monitors), smartphones, tablets, computers, smart home devices (such as smart speakers, smart door locks), etc. Moreover, many terminal devices have data acquisition capabilities, can sense environmental changes, and can transmit data to edge gateways or clouds for further processing and analysis.

[0036] The first preset time period can be one day or one month before the current time, etc. The historical terminal transmission data refers to the historical data generated and stored by the terminal device within the first preset time period, and then uploaded to the upper system (such as edge gateway, cloud server or CCO module) in a certain way (batch or time period). The historical terminal transmission data can include the terminal configuration, demand resource, data volume to be transmitted, and total terminal configuration quantity of the terminal device received at each time point within the first preset time period. It should be noted that the terminal configuration refers to the available frequency band for the terminal device to transmit data to the upper system. The demand resource refers to the frequency band used by the user end corresponding to the terminal device when the terminal device transmits data to the upper system. The demand resource is usually determined by the engineers who design the terminal device. The data volume to be transmitted refers to the data volume transmitted by each terminal device corresponding to the edge-end collaborative region at each time point within the first preset time period. The total terminal configuration quantity refers to the number of terminal devices corresponding to the edge-end collaborative region. The prediction time period refers to a future time period, usually represented by a time range. For example, the prediction time period can be several hours or several days. The predicted device data refers to the predicted data of the terminal device corresponding to the historical terminal transmission data based on the historical terminal transmission data of the terminal device. At a predicted time within the prediction time period, the predicted bandwidth resource refers to the sum of the available frequency bands for each terminal device to transmit data to the upper system; the predicted transmission data volume refers to the total data volume transmitted by each terminal device; and the device quantity of the terminal device refers to the number of all terminal devices.

[0037] Specifically, for all edge-end collaborative regions, the historical terminal transmission data stored in the cloud server corresponding to the edge-end collaborative region can be obtained through the cloud server. The historical terminal transmission data includes the terminal transmission data of the terminal device received at each time point within the first preset time period by all terminal devices in the edge-end collaborative region. The terminal transmission data can include terminal configuration, demand resource, data volume to be transmitted, and total terminal configuration quantity. Based on the predicted device data of the terminal device at at least one predicted time within the prediction time period, the predicted device data corresponding to the terminal device at each predicted time within a future prediction time period can be determined.

[0038] In the embodiment, data reporting parameters are configured for at least one terminal device in the edge-terminal collaborative region, wherein the data reporting parameters include a data reporting period; historical terminal transmission data corresponding to each reporting period within a first preset time length of at least one terminal device in the edge-terminal collaborative region is obtained; the historical terminal transmission data corresponding to each terminal device is input into a pre-trained data prediction model to obtain predicted device data of the at least one terminal device at at least one predicted time within a preset time length, wherein the at least one predicted time is determined based on the reporting period of the terminal device.

[0039] The data reporting parameters refer to reporting rules when the terminal device reports data to the upper system. The cloud server can configure the reporting parameters based on the data reporting parameters of the terminal device. The data reporting period refers to when and at what time interval the terminal device sends data to the upper system. It should be noted that if the reporting period is too short, the network bandwidth and the backend processing pressure will be greatly increased; if the reporting period is too long, the real-time performance and the ability to discover abnormalities in time will be significantly reduced. For example, the first preset time length is 10 minutes, the initial time for obtaining the historical terminal transmission data within the first preset time length is 10:00, and the data reporting period is 2 minutes. The obtained historical terminal transmission data is the historical terminal transmission data of the terminal device determined at 10:00, 10:02, 10:04, 10:06, 10:08, and 10:10.

[0040] It should be noted that in the embodiment, the historical terminal transmission data corresponding to each terminal device is obtained; the historical terminal transmission data corresponding to each terminal device is input into the data prediction model to output predicted device data of the at least one terminal device at at least one predicted time within a preset time length.

[0041] It should also be noted that the data prediction model refers to a pre-trained data prediction model. In order to improve the accuracy of model training, the historical terminal transmission data corresponding to different terminal devices and different reporting periods can be obtained, and the obtained historical terminal transmission data corresponding to different terminal devices and different reporting periods is used as training data. The sum of all training data constitutes a training sample set. That is, the training sample set includes multiple training data. The training data is only relative and is not specifically limited.

[0042] The model parameters in the data prediction model are initial parameters or a model with default parameters. The predicted device data of the at least one terminal device at the at least one prediction moment within the preset time length is a result output after the current to-be-trained data is input into the to-be-trained data prediction model. It should be noted that the model parameters in the to-be-trained data prediction model do not meet the expected requirements, and therefore, there is a certain difference between the actual predicted device data and the theoretical predicted device data output based on the model parameters at this time. Therefore, based on the actual predicted device data and the theoretical predicted device data corresponding to each group of to-be-trained data, the corresponding error loss value can be determined.

[0043] In this embodiment, the to-be-trained fault type determination model can be a resnet network model. It should be noted that only the predicted device data corresponding to the to-be-trained data is obtained, and the specific model type is not limited. It should be noted that the training parameters can be set to default values before training the to-be-trained fault type determination model. When training the to-be-trained data prediction model, the training parameters in the model can be corrected based on the output result of the to-be-trained data prediction model, that is, the data prediction model can be obtained by correcting the loss function in the to-be-trained data prediction model. Each group of to-be-trained data has a loss value corresponding thereto, which is determined based on the actual predicted device data of each to-be-trained data.

[0044] Specifically, after the to-be-trained data is input into the to-be-trained data prediction model, the to-be-trained data prediction model can obtain the actual predicted device data corresponding to the to-be-trained data. According to the actual predicted device data, the loss value corresponding to the to-be-trained data can be determined, and the model parameters in the to-be-trained data prediction model can be corrected by using the back propagation method.

[0045] Specifically, the training error of the loss function, that is, the loss parameter, can be used as a condition for detecting whether the current loss function reaches convergence, such as whether the training error is less than a preset error or whether the error change trend is stable, or whether the current iteration number is equal to a preset number. If the convergence condition is detected, such as the training error of the loss function is less than the preset error or the error change trend is stable, it indicates that the training of the to-be-trained data prediction model is completed, and at this time, the iteration training can be stopped. If it is detected that the current does not reach the convergence condition, the to-be-trained data can be further obtained to train the to-be-trained data prediction model until the training error of the loss function is within a preset range. When the training error of the loss function reaches convergence, the to-be-trained data prediction model can be used as the data prediction model.

[0046] Specifically, based on the cloud server configuration function, the data reporting parameters of the terminal device are configured through the cloud server. And the data reporting parameters include when and at what time interval the terminal device sends data to the upper system. Based on the data reporting parameters of the terminal device, the historical terminal transmission data corresponding to each reporting period of at least one terminal device in the edge-end collaborative region within the first preset time length is obtained. Based on the pre-trained data prediction model, the historical terminal transmission data corresponding to each terminal device is input, and the predicted device data of at least one terminal device can be output, and the prediction time corresponding to the predicted device data is determined based on the reporting period of the terminal device.

[0047] In S120, based on the predicted device data of the at least one terminal device and the pre-set data collection type, the basic frequency spectrum scheduling information of the device set belonging to the same data collection type is determined, wherein the device set includes at least one terminal device.

[0048] It should be noted that in the power Internet of Things, there are often multiple devices performing the same or similar data collection tasks. For example, multiple vibration sensors simultaneously collect mechanical vibration signals for fault detection; a group of temperature sensors collect environmental temperature to achieve environmental monitoring. Terminal devices that collect the same or similar data collection tasks are referred to as terminal devices of the same data collection type. All terminal devices belonging to the same data collection type are referred to as a device set belonging to the same data collection type. Among them, the devices belonging to the same data collection type often need to follow similar collection periods, frequency bandwidths, reporting periods, etc. Therefore, the basic frequency spectrum scheduling information of the device set belonging to the same data collection type is determined.

[0049] Among them, in wireless communication, different devices are usually allocated different frequency bands or time slots to avoid interference or conflict when uploading data, and the data collected by the terminal device can also be scheduled at a specific sampling frequency or bandwidth. The basic frequency spectrum scheduling information refers to the unified configuration or planning of the sampling frequency, bandwidth occupation, reporting time window and priority of the terminal device of the same data collection type. By determining the basic frequency spectrum scheduling, it can be ensured that the terminal devices of the same data collection type do not interfere with each other in the data collection and transmission link.

[0050] Specifically, according to the data collection task collected by the terminal device, the data collection type of the terminal device is determined. Based on the data collection type of the terminal device, the device set belonging to the same data collection type is determined. For the device set belonging to the same data collection type, the predicted device data of the terminal device is added to determine the basic frequency spectrum scheduling information of the device.

[0051] In the embodiment, when at least two available spectrum ranges are included in the prediction device data of the terminal device, the basic spectrum scheduling information of the device set belonging to the same data collection type is determined based on the at least two available spectrum ranges; and for at least two terminal devices using the same available spectrum range, the basic spectrum scheduling information of the device set belonging to the same data collection is updated according to the device priority of the at least two terminal devices.

[0052] The spectrum range refers to the frequency band interval covered in the prediction bandwidth resource. The device priority refers to the relative importance level allocated to each terminal device, which is usually used to distinguish the order or weight in the data transmission and resource allocation. It should be noted that the calling time slot of the frequency band is allocated to different terminal devices with the same spectrum demand according to the priority order. It should be noted that the priority of the terminal device can be configured by the edge gateway or the cloud server. For example, if a terminal device is related to a high-risk scene such as production safety, device fault alarm, vital sign monitoring, etc., the priority thereof should be significantly higher than that of an ordinary monitoring or background statistical device.

[0053] Specifically, the prediction device data of some terminal devices can include at least two available spectrum ranges, indicating that there are multiple optional spectrum ranges available for scheduling in the network environment of the terminal device. In the device set of the same data collection type, when the prediction device data of a terminal device includes at least two available spectrum ranges, the spectrum used by the terminal device can be set to be less than that used by other terminal devices, so as to avoid mutual interference between devices using the same frequency band when multiple devices simultaneously request uplink bandwidth. According to the above method, the basic spectrum scheduling information of the device set belonging to the same data collection type can be determined. Then, for at least two terminal devices using the same available spectrum range, the data transmission of the high-priority device is ensured first, and then the data transmission of the low-priority device is performed. The basic spectrum scheduling information of the device set belonging to the same data collection is updated based on the device priority of the terminal device

[0054] For example, terminal device A and terminal device B are devices of the same data acquisition type. When terminal device A can use both frequency band 1 and frequency band 2 for data transmission, while terminal device B can only use frequency band 1 for data transmission, the spectrum for data transmission by terminal device A is set to frequency band 1. When device C transmits high-temperature data and device D transmits temperature data, and both devices C and D transmit data based on frequency band 3, device C has a higher priority than device D. Data transmission by the higher-priority device C is prioritized, followed by data transmission by the lower-priority device D. In this case, after allocating different time slots for the same spectrum resources to various terminal devices, the basic spectrum scheduling information for the set of devices acquiring the same data is updated.

[0055] S130. Based on the basic spectrum scheduling information and prediction equipment data corresponding to the data acquisition type, determine the scheduling attribute to be processed corresponding to the basic spectrum scheduling information.

[0056] The scheduling attributes to be processed refer to the scheduling effect under the basic spectrum scheduling strategy. The scheduling effect specifically refers to the reliability, traffic intrusion, resource utilization, and priority guarantee of critical services under the basic spectrum scheduling strategy.

[0057] Specifically, for the same data acquisition type, after obtaining the updated basic spectrum scheduling information and the prediction device data of at least one terminal device, the scheduling attribute to be processed corresponding to the basic spectrum scheduling information of the terminal devices of the same data acquisition type is determined based on the formula.

[0058] Optionally, the scheduling attributes to be processed for the basic spectrum scheduling information are determined based on the amount of data transmitted corresponding to the data acquisition type, the bandwidth resources of at least one terminal device corresponding to each data acquisition type, the first effect produced by the terminal device corresponding to the data acquisition type when transmitting data under the corresponding spectrum resources, and the preset second effect.

[0059] The formula for calculating the scheduling attributes to be processed is:

[0060]

[0061] in, The scheduling attributes to be processed in the basic spectrum scheduling information. For the first in this region The amount of data transmitted corresponding to terminal devices, Under the basic spectrum scheduling strategy, the first Bandwidth resources allocated to terminal devices For the first The first effect of data transmission by terminal devices under this spectrum resource. This is the preset second effect for the edge collaboration region, and its value varies slightly. The evaluation criteria include the first The transmission duration, quality, and energy consumption of data to be transmitted by terminal devices under this spectrum resource. and The evaluation criteria are consistent with those of the other criteria. This refers to the effect of data transmission or processing requests in the edge-coordinated region being less affected by the spectrum allocated to them.

[0062] Specifically, the process involves obtaining the transmitted data volume for each data acquisition type, the bandwidth resources of at least one terminal device for each data acquisition type, and the first effect produced by the terminal device transmitting data under the corresponding spectrum resources for each data acquisition type. For the same data acquisition type, the values ​​of the transmitted data volume, the bandwidth resources of at least one terminal device, and the first effect are multiplied. Then, the results obtained from different data acquisition types are summed. Finally, a preset second effect is added to the summed result to obtain the scheduling attributes to be processed for the basic spectrum scheduling information.

[0063] S140. When the scheduling attribute to be processed does not meet the preset scheduling attribute, the basic spectrum scheduling information is processed based on the spectrum resource optimization function to obtain the updated scheduling attribute to be processed, and the basic spectrum scheduling information obtained when the updated scheduling attribute to be processed meets the preset scheduling attribute is used as the target spectrum scheduling information.

[0064] Among them, the spectrum resource optimization function refers to the core tool for achieving efficient spectrum utilization in wireless communication systems.

[0065] Optionally, the spectrum resource optimization function is:

[0066] ,

[0067] in, For desired spectrum scheduling attributes; For the first Bandwidth resources allocated to terminal devices The preset learning rate parameter, The control scale for the preset learning rate;

[0068] ,

[0069] in, This represents the number of terminal device types within the edge collaboration area.

[0070] It should be noted that in the above spectrum resource optimization function The purpose of this is to use a smaller step size for iterative optimization when the expected scheduling attributes and the scheduling attributes to be processed are relatively close, i.e., the correlation of the effects is high, so as to ensure the accuracy of the iteration results; and to use a larger step size for iterative optimization when the expected scheduling attributes and the scheduling attributes to be processed are relatively close, i.e., the correlation of the effects is low, so as to accelerate the iteration efficiency.

[0071] It should also be noted that target spectrum scheduling information refers to the desired scheduling scheme for the specific allocation of available spectrum resources in a multi-band or multi-channel environment. For example, target spectrum scheduling information may include which frequency band, which channel, which time slot, how much bandwidth to allocate, and the transmission power level, etc.

[0072] Specifically, if the scheduling attributes calculated based on the formula for the scheduling attributes to be processed meet the preset scheduling attributes, the basic spectrum scheduling information is used as the target spectrum scheduling information. If the scheduling attributes calculated based on the formula for the scheduling attributes to be processed do not meet the preset scheduling attributes, then the first... The sensor terminal reallocates spectrum resources, updates the basic spectrum scheduling information based on the spectrum resource allocation results, and recalculates the scheduling attributes to be processed until the scheduling attributes to be processed reach the preset scheduling attributes or the number of iterations reaches the preset iteration threshold. The updated scheduling attributes to be processed are then obtained, and the basic spectrum scheduling information obtained when the updated scheduling attributes to be processed satisfy the preset scheduling attributes is used as the target spectrum scheduling information.

[0073] In this embodiment, the desired spectrum scheduling attributes are obtained; the effect correlation coefficient between the desired spectrum scheduling attributes and the scheduling attributes to be processed is determined; and the basic spectrum scheduling information corresponding to the effect correlation coefficient being greater than a preset correlation coefficient threshold is used as the target spectrum scheduling information.

[0074] The formula for calculating the correlation coefficient is as follows:

[0075]

[0076] in, The correlation coefficient refers to the effect, which quantifies the degree of matching between the "scheduling attribute to be processed" and the "expected spectrum scheduling attribute". The closer the scheduling attribute to be processed and the expected spectrum scheduling attribute are, the better the scheduling execution effect matches expectations, and the higher the correlation coefficient is; conversely, when the deviation is large, the correlation coefficient will decrease. The expected spectrum scheduling attribute is calculated according to a formula of the to-be-processed scheduling attribute. The historical to-be-processed scheduling attributes under each basic spectrum scheduling information are calculated. The average value of each historical to-be-processed scheduling attribute and the user expected scheduling attribute is calculated, and the average value is taken as the expected spectrum scheduling attribute of the region. The user expected scheduling attribute refers to the attribute value set in the theoretical design or target of the scheduling strategy. The experience value is used to avoid the problem that the expected spectrum scheduling attribute is too different from the to-be-processed scheduling attribute, and the effect correlation degree cannot be calculated.

[0077] Specifically, the expected spectrum scheduling attribute can be determined based on the to-be-processed scheduling attribute and the user expected scheduling attribute. After the expected spectrum scheduling attribute is obtained, the effect correlation coefficient is determined based on the expected spectrum scheduling attribute, the to-be-processed scheduling attribute, and the experience value. When the effect correlation coefficient is greater than a preset correlation coefficient threshold, the corresponding basic spectrum scheduling information is taken as the target spectrum scheduling information.

[0078] In the embodiment, after the target spectrum scheduling information is obtained, the method further includes: determining the transmission order of the to-be-processed data corresponding to the terminal device according to the priority of at least one terminal device corresponding to each spectrum range in the target spectrum scheduling information; or determining the transmission order of the to-be-processed data corresponding to the terminal device according to the data transmission amount information of at least one terminal device corresponding to each spectrum range in the target spectrum scheduling information.

[0079] Each spectrum range can be regarded as a schedulable resource, and the system can allocate resources to different terminals in the resource pool. The to-be-processed data refers to the data sent by the terminal device in uplink. The transmission order refers to the transmission order of the to-be-processed data in uplink. The data transmission amount information refers to the sum of the byte number or bit number of the data sent by the terminal device in uplink.

[0080] Specifically, after obtaining the target spectrum scheduling information, the optimal solution of the spectrum resource allocation of various terminal devices can be obtained, but the data transmission requirements between various terminal devices and different terminal devices of the same type of terminal device can not be the same, and therefore, according to the predicted transmission requirements of the to-be-transmitted data to be received by the current edge collaborative region within a preset time length, a plurality of groups of possible transmission orders of the to-be-processed data corresponding to the determined terminal devices are generated. The scheduling strategy for generating a plurality of groups of possible transmission orders of the to-be-processed data corresponding to the determined terminal devices can include a single scheduling strategy or a multi-scheduling scheduling fusion strategy. The single scheduling strategy can include a terminal device priority strategy and a terminal device data transmission amount information strategy, etc. The multi-scheduling scheduling fusion strategy can be a combination of the single scheduling strategy. According to the terminal device to-be-transmitted data amount prediction result, the transmission time of each group of possible multi-scheduling scheduling fusion strategies is calculated, the multi-scheduling scheduling fusion strategy with the minimum transmission time is taken as the final multi-scheduling scheduling fusion strategy, and the transmission order of the to-be-processed data corresponding to the terminal device is determined according to the final multi-scheduling scheduling fusion strategy.

[0081] For example, as Figure 3As shown, first, the optimal frequency band of the device set of the same data collection type is obtained. The optimal frequency band of the terminal device corresponding to category 1 is M1, the optimal frequency band of the terminal device corresponding to category 2 is M2, and the optimal frequency band of the terminal device corresponding to category 3 is M3. The priority of the first type of terminal device is A > B > C. The priority of the second type of terminal device is D > E > F. The priority of the third type of terminal device is H > L. The transmission order of the data to be processed corresponding to the terminal device can be determined according to the priority order of at least one terminal device corresponding to each frequency spectrum range of M1, M2, and M3 in the target spectrum scheduling information. The transmission order of the data to be processed corresponding to the terminal device can also be determined according to the data transmission amount information of at least one terminal device corresponding to each frequency spectrum range of M1, M2, and M3 in the target spectrum scheduling information, so that the data transmission amount information meets the data transmission amount information of the current time slot. A multi-scheduling scheduling fusion strategy can also be applied, that is, the priority is first considered and then the data transmission amount information is considered. At this time, the scheduling order is: in the current time slot, M1 is allocated to A, M2 is allocated to D, and M3 is allocated to H. Then it is calculated whether the load of A+D+H meets the data transmission amount information limit of the current time slot. If the data transmission amount information is greater than the limit and the data transmission amount of A is greater than the data transmission amount of B, the strategy is adjusted to: M1 is allocated to B, M2 is allocated to D, and M3 is allocated to H. At this time, the resource allocation for the three devices B, D, and H is completed. The remaining A, C, E, F, and L devices are allocated resources in other time slots in the same way. At this time, the obtained strategy is a set of multi-scheduling scheduling fusion strategies. Further, according to the data amount prediction result of the terminal device, the transmission time of each possible multi-scheduling scheduling fusion strategy can be calculated. The multi-scheduling scheduling fusion strategy with the minimum transmission time is taken as the final multi-scheduling scheduling fusion strategy, and the transmission order of the data to be processed corresponding to the terminal device is determined according to the final multi-scheduling scheduling fusion strategy. After determining the transmission order of the data to be processed, a time slot channel allocation table can be generated according to the time slot allocation of the final transmission order of the data to be processed corresponding to the terminal device. For example Figure 4The time slot channel allocation table is shown in the following table, where 1 indicates that the time slot has been allocated to the user, and 0 indicates that it has not been allocated. User 1 is allocated on time slots 1, 3, 5 and 7. User 2 is allocated on time slots 2, 4, 6 and 8. User 3 is allocated on time slots 1, 2, 3 and 4. User 4 is allocated on time slots 5, 6, 7 and 8. After generating the time slot channel allocation table, the time slot channel allocation table is embedded into the downlink beacon frame. The structure of the downlink beacon frame is as follows: frame header: containing synchronization information, identifier, etc. of the beacon frame. The time slot channel allocation field carries the time slot allocation information, which is the structure of the time slot and channel or spectrum resource allocation table. The additional information field contains other control information, such as scheduling information, power control parameters, etc. In the time slot allocation part of each terminal device, the time slot allocation information of the terminal device is stored. The field length can be dynamically adjusted according to the network size to adapt to different numbers of terminal devices and time slots.

[0082] Optionally, when the terminal device corresponding to the to-be-processed data is received, the to-be-processed data is transmitted according to the transmission order.

[0083] Specifically, in a wireless or wired network system, when the base station (or access point) receives data to be processed by the terminal device, the data is put into a scheduling queue. Subsequently, the system sorts the data in the queue according to the previously set transmission order or scheduling strategy, allocates appropriate network resources, and finally completes the data transmission to transmit the to-be-processed data.

[0084] The technical scheme of the embodiment of the present disclosure is as follows: for at least one edge-terminal cooperative region, based on historical terminal transmission data of at least one terminal device in the edge-terminal cooperative region within a first preset time length, prediction device data of the at least one terminal device at at least one predicted moment within a prediction time length is determined, wherein the first preset time length is a preset time length before the current moment, and the prediction device data includes predicted bandwidth resources, predicted transmission data volume, and device quantity of the at least one terminal device; then, based on the prediction device data of the at least one terminal device and a pre-set data collection type, basic spectrum scheduling information of a device set belonging to the same data collection type is determined, wherein the device set includes the at least one terminal device; further, based on the basic spectrum scheduling information corresponding to the data collection type and the prediction device data, a to-be-processed scheduling attribute corresponding to the basic spectrum scheduling information is determined; finally, under the condition that the to-be-processed scheduling attribute does not satisfy a preset scheduling attribute, the basic spectrum scheduling information is processed based on a spectrum resource optimization function to obtain an updated to-be-processed scheduling attribute, and the basic spectrum scheduling information obtained when the updated to-be-processed scheduling attribute satisfies the preset scheduling attribute is taken as target spectrum scheduling information, thereby solving the problem of low scheduling efficiency in the prior art that network resources are scheduled based on an intelligent gateway wireless CCO module, and the scheduling is performed according to the receiving order of data reporting requests. The prediction device data and the spectrum resource optimization function are used to determine the target spectrum scheduling information, the iterative optimization of the basic spectrum scheduling information is realized, the scheduling efficiency is improved, and the effects of improving the spectrum resource utilization rate and ensuring the real-time data transmission are achieved.

[0085] Embodiment two

[0086] Figure 5 FIG. 1 is a structural schematic diagram of a resource scheduling device based on an intelligent gateway wireless CCO module provided by the present disclosure, as shown in the figure, the device includes a prediction device data determination module 210, a basic spectrum scheduling information determination module 220, a to-be-processed scheduling attribute determination module 230, and a target spectrum scheduling information determination module 240.

[0087] The prediction device data determination module 210 is configured to determine, for at least one edge collaborative region, prediction device data of at least one terminal device at at least one predicted time within a prediction time length based on historical terminal transmission data of the at least one terminal device within a first preset time length in the edge collaborative region, wherein the first preset time length is a preset time length before a current time, and the prediction device data includes a predicted bandwidth resource, a predicted transmission data amount, and a device quantity of the at least one terminal device; the basic spectrum scheduling information determination module 220 is configured to determine, according to the prediction device data of the at least one terminal device and a preset data collection type, basic spectrum scheduling information of a device set belonging to the same data collection type, wherein the device set includes at least one terminal device; the to-be-processed scheduling attribute determination module 230 is configured to determine a to-be-processed scheduling attribute corresponding to the basic spectrum scheduling information based on the prediction device data and the basic spectrum scheduling information corresponding to the data collection type; and the target spectrum scheduling information determination module 240 is configured to, when the to-be-processed scheduling attribute does not satisfy a preset scheduling attribute, process the basic spectrum scheduling information based on a spectrum resource optimization function to obtain an updated to-be-processed scheduling attribute, and take the basic spectrum scheduling information obtained when the updated to-be-processed scheduling attribute satisfies the preset scheduling attribute as target spectrum scheduling information.

[0088] The technical scheme of the embodiments of the present disclosure is as follows: for at least one edge-terminal cooperative region, based on historical terminal transmission data of at least one terminal device in the edge-terminal cooperative region within a first preset time length, predicted device data of the at least one terminal device at at least one predicted time within a predicted time length is determined, wherein the first preset time length is a preset time length before a current time, the predicted device data includes predicted bandwidth resources, predicted transmission data volume, and a device quantity of the at least one terminal device; then, based on the predicted device data of the at least one terminal device and a pre-set data collection type, basic spectrum scheduling information of a device set belonging to the same data collection type is determined, wherein the device set includes the at least one terminal device; further, based on the basic spectrum scheduling information corresponding to the data collection type and the predicted device data, a to-be-processed scheduling attribute corresponding to the basic spectrum scheduling information is determined; finally, under the condition that the to-be-processed scheduling attribute does not satisfy a preset scheduling attribute, the basic spectrum scheduling information is processed based on a spectrum resource optimization function to obtain an updated to-be-processed scheduling attribute, and the basic spectrum scheduling information obtained when the updated to-be-processed scheduling attribute satisfies the preset scheduling attribute is taken as target spectrum scheduling information, thereby solving the problem of low scheduling efficiency in the prior art when a wireless CCO module of an intelligent gateway performs resource scheduling on network resources according to the receiving order of data reporting requests. The embodiments of the present disclosure determine the target spectrum scheduling information based on the predicted device data and the spectrum resource optimization function, implement iterative optimization of the basic spectrum scheduling information, improve the scheduling efficiency, and achieve the effects of improving the spectrum resource utilization rate and ensuring the real-time data transmission.

[0089] On the basis of each of the above technical solutions, the data reporting parameter configuration module is further configured to configure data reporting parameters for the at least one terminal device in the edge-terminal cooperative region, wherein the data reporting parameters include a data reporting period.

[0090] On the basis of each of the above technical solutions, the predicted device data determination module 210 further includes a historical terminal transmission data determination submodule and a predicted device data acquisition submodule.

[0091] The historical terminal transmission data determination submodule is configured to determine historical terminal transmission data of each reporting period of the at least one terminal device within the first preset time length based on the historical terminal transmission data.

[0092] The predicted device data acquisition submodule is configured to input the historical terminal transmission data corresponding to each terminal device into a pre-trained data prediction model to obtain predicted device data of the at least one terminal device at at least one predicted time within a preset time length, wherein the at least one predicted time is determined based on a reporting period of the terminal device.

[0093] On the basis of each of the technical solutions above, the basic spectrum scheduling information determination module 220 further comprises a basic spectrum scheduling information determination sub-module and a basic spectrum scheduling information update sub-module.

[0094] The basic spectrum scheduling information determination sub-module is configured to, when the terminal device's predicted device data comprises at least two available spectrum ranges, determine the basic spectrum scheduling information of a device set belonging to a same data collection type based on the at least two available spectrum ranges.

[0095] The basic spectrum scheduling information update sub-module is configured to, for at least two terminal devices of a same available spectrum range, update the basic spectrum scheduling information of a device set belonging to a same data collection according to the device priorities of the at least two terminal devices.

[0096] On the basis of each of the technical solutions above, the to-be-processed scheduling attribute determination module 230 further comprises a to-be-processed scheduling attribute acquisition sub-module.

[0097] The to-be-processed scheduling attribute acquisition sub-module is configured to determine the to-be-processed scheduling attribute of the basic spectrum scheduling information according to the transmission data volume corresponding to the data collection type, the bandwidth resources of at least one terminal device corresponding to each data collection type, the first effect produced by the terminal device corresponding to the data collection type when transmitting data under the corresponding spectrum resources, and a preset second effect.

[0098] On the basis of each of the technical solutions above, the target spectrum scheduling information determination module 240 further comprises an expected spectrum scheduling attribute acquisition sub-module, an effect correlation coefficient determination sub-module, and a target spectrum scheduling information acquisition sub-module.

[0099] The expected spectrum scheduling attribute acquisition sub-module is configured to acquire an expected spectrum scheduling attribute.

[0100] The effect correlation coefficient determination sub-module is configured to determine an effect correlation coefficient between the expected spectrum scheduling attribute and the to-be-processed scheduling attribute.

[0101] The target spectrum scheduling information acquisition sub-module is configured to take the basic spectrum scheduling information corresponding to the effect correlation coefficient greater than a preset correlation coefficient threshold value as the target spectrum scheduling information.

[0102] On the basis of each of the technical solutions above, the target spectrum scheduling information determination module 240 further comprises a spectrum resource optimization function sub-module.

[0103] The spectrum resource optimization function sub-module is configured to

[0104] ,

[0105] wherein, for a desired spectrum scheduling attribute, for a preset learning rate parameter, for a control scale of the preset learning rate;

[0106] ;

[0107] wherein, is the number of terminal device types in the edge collaborative region.

[0108] On the basis of each of the above technical solutions, further comprising a transmission sequence determination module.

[0109] The transmission sequence determination module is configured to determine a transmission sequence of the to-be-processed data corresponding to the terminal device according to a priority of at least one terminal device corresponding to each spectrum range in the target spectrum scheduling information; or determine the transmission sequence of the to-be-processed data corresponding to the terminal device according to data transmission amount information of at least one terminal device corresponding to each spectrum range in the target spectrum scheduling information.

[0110] On the basis of each of the above technical solutions, further comprising a to-be-processed data transmission module.

[0111] The to-be-processed data transmission module is configured to transmit the to-be-processed data according to the transmission sequence when the to-be-processed data corresponding to the terminal device is received.

[0112] The resource scheduling apparatus based on the intelligent gateway wireless CCO module provided by the embodiments of the present disclosure can execute the resource scheduling method based on the intelligent gateway wireless CCO module provided by any of the embodiments of the present disclosure, and has the corresponding function modules and beneficial effects of executing the method.

[0113] It should be noted that each unit and module included in the above apparatus is only divided according to the function logic, but is not limited to the above division, as long as the corresponding functions can be implemented; in addition, the specific names of each functional unit are only for convenient distinction, and do not limit the protection scope of the embodiments of the present disclosure.

[0114] Embodiment Three

[0115] Figure 6 is a structural schematic diagram of an electronic device provided by an embodiment of the present disclosure. Hereinafter, with reference to Figure 6 which shows an electronic device (for example, a mobile phone) suitable for implementing an embodiment of the present disclosure. Figure 6The diagram below shows the structure of the terminal device or server 500. The terminal device in this embodiment may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and vehicle terminals (e.g., vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0116] like Figure 6 As shown, electronic device 500 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 502 or a program loaded from storage device 508 into random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of electronic device 500. The processing unit 501, ROM 502, and RAM 503 are interconnected via bus 504. An edit / output (I / O) interface 505 is also connected to bus 504.

[0117] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 508 including, for example, magnetic tapes, hard disks, etc.; and communication devices 509. Communication device 509 allows electronic device 500 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 An electronic device 500 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0118] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by the processing device 501, it performs the functions defined in the methods of embodiments of this disclosure.

[0119] Names of messages or information exchanged between multiple devices in the embodiments of the present disclosure are only for illustrative purposes, and are not used to limit the scope of the messages or information.

[0120] The electronic device provided by the embodiments of the present disclosure and the resource scheduling method based on the wireless CCO module of the intelligent gateway provided by the above embodiments belong to the same inventive concept, and the technical details not described in detail in the present embodiment can be referred to the above embodiments, and the present embodiment has the same beneficial effects as the above embodiments.

[0121] Embodiment four

[0122] The embodiments of the present disclosure provide a computer storage medium, which stores a computer program, and the program is executed by a processor to implement the resource scheduling method based on the wireless CCO module of the intelligent gateway provided by the above embodiments.

[0123] It should be noted that the computer readable medium of the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples of computer readable storage media can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or component. In the present disclosure, the computer readable signal medium can include a data signal carried in a baseband or as a part of a carrier wave, which carries computer readable program code. Such a propagated data signal can take many forms, including but not limited to an electromagnetic signal, an optical signal or any suitable combination of the above. The computer readable signal medium can also be any computer readable medium other than the computer readable storage medium, which can send, propagate or transmit a program for use by or in conjunction with an instruction execution system, device or component. The program code contained in the computer readable medium can be transmitted by any suitable medium, including but not limited to a wire, a cable, an RF (radio frequency) or the like, or any suitable combination of the above.

[0124] In some embodiments, the server can communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with digital data communications of any form or medium (e.g., a communications network). Examples of communications networks include local area networks ("LAN"), wide area networks ("WAN"), internetworks (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.

[0125] The computer readable medium described above can be included in the electronic device described above; or can exist separately, without being assembled into the electronic device.

[0126] The computer readable medium described above carries one or more programs, when the one or more programs are executed by the electronic device, cause the electronic device to:

[0127] For at least one edge collaborative region, based on historical terminal transmission data of at least one terminal device in the edge collaborative region within a first preset time length, determine predicted device data of the at least one terminal device at at least one predicted time within a predicted time length, wherein the first preset time length is a preset time length before the current time, and the predicted device data includes predicted bandwidth resources, predicted transmission data volume, and device quantity of the at least one terminal device;

[0128] According to the predicted device data of the at least one terminal device and a pre-set data collection type, determine basic spectrum scheduling information of a device set belonging to the same data collection type, wherein the device set includes at least one terminal device;

[0129] Based on the basic spectrum scheduling information corresponding to the data collection type and the predicted device data, determine a to-be-processed scheduling attribute corresponding to the basic spectrum scheduling information;

[0130] Under the condition that the to-be-processed scheduling attribute does not satisfy a preset scheduling attribute, process the basic spectrum scheduling information based on a spectrum resource optimization function to obtain an updated to-be-processed scheduling attribute, and take the basic spectrum scheduling information obtained when the updated to-be-processed scheduling attribute satisfies the preset scheduling attribute as target spectrum scheduling information.

[0131] Computer program code for carrying out operations of the present disclosure can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0132] The computer program instructions can also be loaded onto a computer or other programmable information processing apparatus to cause a series of operations to be performed on the computer or other programmable information processing apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable information processing apparatus implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0133] The units described in the embodiments of the present disclosure can be implemented by hardware, software, or a combination of hardware and software. In some cases, the names of the units do not constitute a limitation on the units themselves.

[0134] The functions described in this specification can be implemented in part or in whole through one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Program-specific Integrated Circuits (ASICs), Program-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

[0135] In the context of this disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium would include a lined- up electrical connection, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0136] The above description is only preferred embodiments of the present disclosure and the explanation of the applied technical principles. It should be understood by those skilled in the art that the disclosure range involved in the present disclosure is not limited to the technical solutions formed by the specific combinations of the above technical features, and also covers other technical solutions formed by any combination of the above technical features or equivalent features without departing from the above disclosed concept. For example, the technical solutions formed by replacing the above features with the technical features disclosed in the present disclosure (but not limited to) having similar functions.

[0137] In addition, although each operation is described in a particular order, this should not be understood as requiring the operations to be performed in the specific order shown or in a sequential order. In certain circumstances, multitasking and parallel processing can be advantageous. Similarly, although several implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Certain features described in the context of separate embodiments can also be combined in a single embodiment. Conversely, various features described in the context of a single embodiment can also be separated and implemented in multiple embodiments.

[0138] Although the subject matter has been described in language specific to structural features and / or methodological acts, it is to be understood that the subject defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

1. A resource scheduling method based on an intelligent gateway wireless CCO module, characterized in that, The method comprises: For at least one edge collaborative region, based on the historical terminal transmission data of at least one terminal device in the edge collaborative region within a first preset time length, determine the predicted device data of the at least one terminal device at at least one predicted time within a predicted time length, wherein the first preset time length is a preset time length before the current time, and the predicted device data comprises predicted bandwidth resources, predicted transmission data volume, and the number of devices of the at least one terminal device; According to the predicted device data of the at least one terminal device and the pre-set data collection type, determine the basic frequency spectrum scheduling information of the device set belonging to the same data collection type, wherein the device set comprises at least one terminal device; Based on the basic frequency spectrum scheduling information corresponding to the data collection type and the predicted device data, determine the to-be-processed scheduling attribute corresponding to the basic frequency spectrum scheduling information; If the to-be-processed scheduling attribute does not meet the preset scheduling attribute, process the basic frequency spectrum scheduling information based on a spectrum resource optimization function to obtain an updated to-be-processed scheduling attribute, and take the basic frequency spectrum scheduling information obtained when the updated to-be-processed scheduling attribute meets the preset scheduling attribute as target frequency spectrum scheduling information; The method further comprises: Configure data reporting parameters for at least one terminal device in the edge collaborative region, wherein the data reporting parameters comprise a data reporting period; The formula for determining the to-be-processed scheduling attribute of the base frequency spectrum scheduling information is: ; in, The scheduling attributes to be processed in the basic spectrum scheduling information. For the first edge within the collaborative region The amount of data transmitted corresponding to terminal devices, For the first Bandwidth resources allocated to terminal devices For the first The first effect produced when data to be transmitted by terminal devices is transmitted under the corresponding spectrum resources; The evaluation criteria should include at least the first The transmission duration, quality, and energy consumption of the data to be transmitted by the terminal device under the corresponding spectrum resources; This is the preset second effect for the edge-coordinated region; Correspondingly, based on the historical terminal transmission data of at least one terminal device in the edge collaborative region within a first preset time length, determine the predicted device data of the at least one terminal device at at least one predicted time within a predicted time length, comprising: ； wherein, is a desired spectral scheduling property; is a preset learning rate parameter; is a control scale of the preset learning rate. ； wherein, is the number of terminal device types in the edge collaborative region.

2. The method of claim 1, wherein, Based on the historical terminal transmission data of at least one terminal device in the edge collaborative region within a first preset time length, input the historical terminal transmission data corresponding to each reporting period of each terminal device into a pre-trained data prediction model to obtain the predicted device data of the at least one terminal device at at least one predicted time within a preset time length; The at least one preset time is determined based on the reporting period of the terminal device. The method further comprises: According to the predicted device data of the at least one terminal device and the pre-set data collection type, determine the basic frequency spectrum scheduling information of the device set belonging to the same data collection type, comprising: ​ 3. The method of claim 1, wherein, ​ In the prediction device data of the terminal device, at least two available frequency spectrum ranges are included, and based on the at least two available frequency spectrum ranges, the basic frequency spectrum scheduling information of a device set belonging to the same data collection type is determined; For at least two terminal devices of the same available frequency spectrum range, the basic frequency spectrum scheduling information of the device set belonging to the same data collection type is updated according to the device priority of the at least two terminal devices.

4. The method of claim 1, wherein, The method further comprises: obtaining an expected frequency spectrum scheduling attribute; determining the effect correlation coefficient between the expected frequency spectrum scheduling attribute and the to-be-processed scheduling attribute; when the effect correlation coefficient is greater than a preset correlation coefficient threshold, the basic frequency spectrum scheduling information corresponding to the effect correlation coefficient is taken as the target frequency spectrum scheduling information.

5. The method of claim 1, wherein, After obtaining the target frequency spectrum scheduling information, the method further comprises: determining the transmission order of the to-be-processed data corresponding to the terminal device according to the priority of at least one terminal device corresponding to each frequency spectrum range in the target frequency spectrum scheduling information; or determining the transmission order of the to-be-processed data corresponding to the terminal device according to the data transmission amount information of at least one terminal device corresponding to each frequency spectrum range in the target frequency spectrum scheduling information.

6. The method of claim 5, wherein, The method further comprises: when the to-be-processed data corresponding to the terminal device is received, transmitting the to-be-processed data according to the transmission order.

7. A resource scheduling device based on intelligent gateway wireless CCO module, characterized in that, Comprise: a prediction device data determination module, configured to, for at least one edge-end collaboration region, determine prediction device data of at least one terminal device at at least one prediction time within a prediction time length based on historical terminal transmission data of the at least one terminal device within a first preset time length in the edge-end collaboration region, wherein the first preset time length is a preset time length before the current time, and the prediction device data comprises predicted bandwidth resources, predicted transmission data amount, and device quantity of the at least one terminal device; a basic frequency spectrum scheduling information determination module, configured to determine basic frequency spectrum scheduling information of a device set belonging to the same data collection type according to the prediction device data of the at least one terminal device and a preset data collection type, wherein the device set comprises at least one terminal device; a to-be-processed scheduling attribute determination module, configured to determine to-be-processed scheduling attributes corresponding to the basic frequency spectrum scheduling information of the data collection type based on the basic frequency spectrum scheduling information and the prediction device data; a target frequency spectrum scheduling information determination module, configured to, when the to-be-processed scheduling attributes do not satisfy a preset scheduling attribute, process the basic frequency spectrum scheduling information based on a frequency spectrum resource optimization function to obtain updated to-be-processed scheduling attributes, and take the basic frequency spectrum scheduling information obtained when the updated to-be-processed scheduling attributes satisfy the preset scheduling attribute as target frequency spectrum scheduling information; The to-be-processed scheduling attribute determination module further includes a to-be-processed scheduling attribute acquisition submodule, configured to determine the to-be-processed scheduling attribute of the basic spectrum scheduling information according to the transmission data volume corresponding to the data collection type, the bandwidth resource of at least one terminal device corresponding to each data collection type, the first effect generated by the terminal device corresponding to the data collection type in data transmission under the corresponding spectrum resource, and a preset second effect. The formula for determining the to-be-processed scheduling attribute of the basic spectrum scheduling information is as follows: ； in, The scheduling attributes to be processed in the basic spectrum scheduling information. For the first edge within the collaborative region The amount of data transmitted corresponding to terminal devices, For the first Bandwidth resources allocated to terminal devices For the first The first effect produced when data to be transmitted by a terminal device is transmitted under the corresponding spectrum resources; The evaluation criteria should include at least the first The transmission duration, quality, and energy consumption of the data to be transmitted by the terminal device under the corresponding spectrum resources; This is the preset second effect for the edge-coordinated region; The spectrum resource optimization function is as follows: ； wherein, is a desired spectral scheduling attribute; is a preset learning rate parameter; is a control scale of the preset learning rate; ； wherein, is the number of terminal device types within the edge collaborative region.

8. An electronic device, comprising: The electronic device includes: one or more processors; a storage device configured to store one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the resource scheduling method based on the intelligent gateway wireless CCO module according to any one of claims 1-6.

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