Resource scheduling method, device and equipment based on intelligent gateway wireless CCO module

Abstract

The embodiment of the invention provides a resource scheduling method, device and equipment based on an intelligent gateway wireless CCO module, and the method comprises the steps: determining predicted equipment data based on historical terminal transmission data of terminal equipment; determining basic spectrum scheduling information according to the predicted equipment data and the data acquisition type; determining a to-be-processed scheduling attribute based on the basic spectrum scheduling information and the predicted equipment data; when the to-be-processed scheduling attribute does not meet the preset scheduling attribute, processing the basic spectrum scheduling information based on a spectrum resource optimization function to obtain an updated to-be-processed scheduling attribute; and taking the basic spectrum scheduling information obtained when the updated to-be-processed scheduling attribute meets the preset scheduling attribute as target spectrum scheduling information. According to the technical scheme provided by the embodiment of the invention, the target spectrum scheduling information is determined based on the predicted equipment data and the spectrum resource optimization function, the iterative optimization of the basic spectrum scheduling information is realized, and the scheduling efficiency is improved.

Description

Technical Field

[0001] The disclosed embodiments relate to the technical field of electric power Internet of Things, and in particular to a resource scheduling method, device and equipment based on a wireless CCO module of an intelligent gateway. Background Art

[0002] With the development of science and technology, the power Internet of Things can connect various types of power terminal equipment, and convert the data of the terminal equipment obtained through the intelligent gateway, and interconnect the network, so that the data of the terminal equipment can be uploaded to the cloud server. As a centralized control and optimization module of the intelligent gateway, the wireless CCO module of the intelligent gateway can schedule and allocate network resources to ensure the transmission quality of data of different traffic types.

[0003] The current method for scheduling and allocating network resources is mainly: the wireless CCO module of the intelligent gateway receives the data reporting request from the terminal device, determines the spectrum resources or communication channels expected by each terminal device according to the data reporting request, polls the communication channel for availability in the order in which the data reporting request is received, and if the channel is occupied, delays a period of time and detects again until the channel is idle, and then allocates the spectrum resources to the corresponding terminal device. However, this method only schedules resources in the order in which the data reporting request is received, which cannot ensure the real-time nature of data transmission and has low scheduling efficiency. Summary of the invention

[0004] The embodiments of the present disclosure provide a resource scheduling method and device based on an intelligent gateway wireless CCO module to determine target spectrum scheduling information based on predicted device data and spectrum resource optimization functions, thereby achieving iterative optimization of basic spectrum scheduling information and improving scheduling efficiency.

[0005] In a first aspect, an 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 includes:

[0006] For at least one edge collaborative area, based on historical terminal transmission data of at least one terminal device in the edge collaborative area within a first preset time period, determining predicted device data of the at least one terminal device at at least one predicted moment within the predicted time period, wherein the first preset time period is a preset time period before a 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;

[0007] Determine basic spectrum scheduling information of a set of devices belonging to the same data collection type according to the predicted device data of the at least one terminal device and a preset data collection type, wherein the set of devices includes at least one terminal device;

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

[0009] Under the condition that the scheduling attributes to be processed do not meet the preset scheduling attributes, the basic spectrum scheduling information is processed based on the spectrum resource optimization function to obtain updated scheduling attributes to be processed, and the basic spectrum scheduling information obtained when the updated scheduling attributes to be processed meet the preset scheduling attributes is used as the target spectrum scheduling information.

[0010] In a second aspect, an embodiment of the present invention further provides a resource scheduling device based on a wireless CCO module of an intelligent gateway, characterized in that it includes:

[0011] A predicted device data determination module, configured to determine, for at least one edge collaborative area, predicted device data of at least one terminal device at at least one predicted moment within a predicted time period based on historical terminal transmission data of at least one terminal device within the edge collaborative area within a first preset time period, wherein the first preset time period is a preset time period before a 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;

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

[0013] A scheduling attribute determination module to be processed, used to determine the scheduling attribute to be processed corresponding to the basic spectrum scheduling information based on the basic spectrum scheduling information corresponding to the data collection type and the prediction device data;

[0014] The target spectrum scheduling information determination module is used to process the basic spectrum scheduling information based on the spectrum resource optimization function to obtain updated scheduling attributes to be processed when the scheduling attributes to be processed do not meet the preset scheduling attributes, and use the basic spectrum scheduling information obtained when the updated scheduling attributes to be processed meet the preset scheduling attributes as the target spectrum scheduling information.

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

[0016] one or more processors;

[0017] a storage device for storing 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 described in any one of the embodiments of the present invention.

[0019] In a fourth aspect, an embodiment of the present invention further provides a storage medium comprising computer executable instructions, which, when executed by a computer processor, are used to execute a resource scheduling method based on a wireless CCO module of an intelligent gateway as described in any one of the embodiments of the present invention.

[0020] The technical solution of the embodiment of the present disclosure is as follows: for at least one edge coordination area, based on the historical terminal transmission data of at least one terminal device in the edge coordination area within a first preset time period, the predicted device data of at least one terminal device at at least one predicted moment within the predicted time period is determined, wherein the first preset time period is the preset time period before the current moment, and the predicted device data includes predicted bandwidth resources, predicted transmission data volume and the number of devices of at least one terminal device; then, according to the predicted device data of at least one terminal device and the preset 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 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 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 meet the preset scheduling attribute, the basic spectrum scheduling information is processed based on the spectrum resource optimization function to obtain the updated to-be-processed scheduling attribute, and the basic spectrum scheduling information obtained when the updated to-be-processed scheduling attribute meets the preset scheduling attribute is used as the target spectrum scheduling information, so as to solve the problem of low scheduling efficiency in the prior art that when the wireless CCO module of the intelligent gateway performs resource scheduling on the network resources, the resource scheduling is performed according to the order in which the data reporting request is received. The embodiment of the present invention adopts a method based on predicted device data and spectrum resource optimization function to determine the target spectrum scheduling information, realizes iterative optimization of basic spectrum scheduling information, improves scheduling efficiency, and achieves the effect of improving spectrum resource utilization and ensuring real-time data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale.

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

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

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

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

[0026] Figure 5 A schematic diagram of the structure of a resource scheduling device based on a wireless CCO module of an intelligent gateway provided by an embodiment of the present invention;

[0027] Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0029] Embodiment 1

[0030] Before introducing the technical solution provided by the embodiment of the present disclosure, an exemplary description of the application scenario can be given. The technical solution provided by the embodiment of the present disclosure can be applied in any scenario where target spectrum scheduling information is determined based on predicted device data and spectrum resource optimization function. Optionally, the target spectrum scheduling information can be spectrum scheduling information obtained based on the wireless CCO module of the intelligent gateway.

[0031] Figure 1 It is a flow chart of a resource scheduling method based on an intelligent gateway wireless CCO module provided by an embodiment of the present disclosure. The embodiment of the present disclosure is applicable to situations where target spectrum scheduling information of a terminal device is determined. The method can be executed by a resource scheduling device based on an intelligent 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 the intelligent gateway wireless CCO module provided by the technical solution.

[0032] like Figure 1 As shown, the method includes:

[0033] S110. For at least one edge collaborative area, based on the historical terminal transmission data of at least one terminal device in the edge collaborative area within a first preset time period, determine the predicted device data of at least one terminal device at at least one predicted moment within the predicted time period, wherein the first preset time period is the preset time period before the current moment, and the predicted device data includes predicted bandwidth resources, predicted transmission data volume, and the device quantity of at least one terminal device.

[0034] Among them, Figure 2 As shown in the figure, the collection of all terminal devices that transmit data to an edge gateway and the edge gateway is called an edge coordination area. The edge coordination area is a field divided based on geographical location. A field can include multiple terminal devices. The edge gateway connects all terminal devices in the field to the network and uses the CCO module in the edge gateway to schedule resources so that the sensor terminal can upload data to the cloud server through each node. The edge coordination area allows data to be fully calculated or processed at the "edge side" close to the terminal device, thereby reducing dependence on remote cloud servers. Edge gateway refers to a key node or device deployed at the edge of the network that connects local devices (such as terminal devices) with 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, and can perform data processing, analysis, filtering, and security management operations locally, realizing local management and real-time response to massive IoT devices. The CCO module can be deployed in the edge gateway, which is usually responsible for the control and orchestration functions of the cloud server. The CCO module can manage and coordinate multiple edge gateways and their devices to ensure 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 for further analysis, storage and decision-making, and can send instructions to the edge gateway to achieve management of terminal devices. For example, the CCO module in the edge gateway can allocate network resources to terminal devices in the power system so that sensor terminals can transmit data safely and reliably. It is suitable for various scenarios in the power industry, such as power generation, transmission, substation, distribution, power consumption, dispatching, new energy, water storage, etc.

[0035] It should be noted that terminal devices refer to devices in the network that directly interact with users or the environment. These devices are usually at the end of the network. There are many types of terminal devices, including sensors (such as temperature sensors, humidity sensors), actuators (such as motors, lighting controls), wearable devices (such as smart watches, health monitors), smartphones, tablets, computers, smart home devices (such as smart speakers, smart door locks), etc. In addition, many terminal devices have data collection capabilities, can sense environmental changes, and transmit data to edge gateways or the cloud for further processing and analysis.

[0036] The first preset time period may be one day or one month before the current time. Historical terminal transmission data refers to 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 may include the terminal configuration, required resources, amount of data to be transmitted, and total number of terminal configurations of the terminal device received at each time point within the first preset duration. 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 required resources refer to the frequency band that the user end corresponding to the terminal device wants the terminal device to use when transmitting data to the upper system. The required resources are usually determined by the terminal design engineer of the terminal device. The amount of data to be transmitted refers to the amount of data transmitted by each terminal device corresponding to the edge coordination area at each time point within the first preset duration. The total number of terminal configurations refers to the number of terminal devices corresponding to the edge coordination area. The predicted duration refers to a certain time period in the future, usually expressed as a time range. For example, the predicted duration can be a few hours or a few days. The predicted device data refers to the predicted data corresponding to the determined terminal device and the historical terminal transmission data based on the historical terminal transmission data of the terminal device. At a certain predicted moment within the predicted duration, the predicted bandwidth resources refer to the sum of the available frequency bands predicted for each terminal device to transmit data to the upper system; the predicted transmission data volume refers to the total data volume of data transmitted by each terminal device; and the number of terminal devices refers to the number of all terminal devices.

[0037] Specifically, for all edge collaboration areas, the historical terminal transmission data stored in the cloud server can be obtained through the corresponding cloud server. The historical terminal transmission data includes the terminal transmission data of the terminal device received by all terminal devices in the edge collaboration area at various time points within the first preset time length. The terminal transmission data may include terminal configuration, required resources, amount of data to be transmitted, and the total number of terminal configurations. Based on the predicted device data of the terminal device at at least one predicted moment within the predicted time length, the predicted device data corresponding to the terminal device at each predicted moment within a certain future predicted time length can be determined.

[0038] In this embodiment, data reporting parameters are configured for at least one terminal device in the edge collaborative area, wherein the data reporting parameters include a data reporting period; based on historical terminal transmission data corresponding to each reporting period of at least one terminal device in the edge collaborative area within a first preset time length, 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 at least one terminal device at at least one predicted moment within the preset time length; wherein at least one preset moment is determined based on the reporting period of the terminal device.

[0039] Among them, the data reporting parameters refer to the reporting rules when the terminal device reports data to the upper system. The data reporting parameters of the terminal device can be configured based on the cloud server. 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 back-end processing pressure will increase significantly; if the reporting period is too long, the real-time performance and the ability to detect anomalies in a timely manner will decrease significantly. For example, the first preset time length is 10 minutes, and the initial time for obtaining historical terminal transmission data within the first preset time length is 10:00, and the data reporting period is 2 minutes. The historical terminal transmission data obtained 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 this 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, and the predicted device data of at least one terminal device at at least one predicted moment within a preset time period is output.

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

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

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

[0044] Specifically, after the data to be trained is input into the prediction model for the data to be trained, the prediction model for the data to be trained can obtain the actual prediction device data corresponding to the data to be trained. According to the actual prediction device data, the loss value corresponding to the data to be trained can be determined, and the model parameters in the prediction model for the data to be trained 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 loss function has reached convergence, such as whether the training error is less than the preset error or whether the error change trend tends to be stable, or whether the current number of iterations is equal to the preset number. If the detection reaches the convergence condition, such as the training error of the loss function is less than the preset error or the error change tends to be stable, it indicates that the training of the prediction model of the data to be trained is completed, and the iterative training can be stopped at this time. If it is detected that the convergence condition is not met at present, the data to be trained can be further obtained to train the prediction model of the data to be trained until the training error of the loss function is within the preset range. When the training error of the loss function reaches convergence, the prediction model of the data to be trained can be used as the data prediction model.

[0046] Specifically, based on the configuration and delivery function of the cloud server, 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 collaboration area 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 predicted time corresponding to the predicted device data is determined based on the reporting period of the terminal device.

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

[0048] It should be noted that in the electric power Internet of Things, there are often multiple devices that jointly perform 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 ambient temperature to achieve environmental monitoring. Terminal devices that collect the same or similar data collection tasks are called terminal devices of the same data collection type. All terminal devices belonging to the same data collection type are called a set of devices belonging to the same data collection type. Among them, devices belonging to the same data collection type often need to follow similar collection cycle, frequency bandwidth, reporting cycle and other rules, so the basic spectrum scheduling information of the set of devices belonging to the same data collection type is determined.

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

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

[0051] In this embodiment, when the predicted device data of the terminal device includes at least two available spectrum ranges, basic spectrum scheduling information of a set of devices belonging to the same data acquisition type is determined based on the at least two available spectrum ranges; for at least two terminal devices in the same available spectrum range, the basic spectrum scheduling information of the set of devices belonging to the same data acquisition type is updated according to the device priorities of the at least two terminal devices.

[0052] Among them, the spectrum range refers to the frequency band interval covered in the predicted bandwidth resources. Device priority refers to the level of relative importance assigned to each terminal device, which is usually used to distinguish the order or weight in links such as data transmission and resource allocation. It should be noted that the call time slots of the frequency band are allocated to different terminal devices with the same spectrum requirements in order of priority. It should be noted that the priority of the terminal device can be configured at the edge gateway or cloud server. For example, if a terminal device is related to high-risk scenarios such as production safety, equipment failure alarm, and vital signs monitoring, its priority should be significantly higher than that of ordinary monitoring or background statistical devices.

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

[0054] Exemplarily, terminal device A and terminal device B are devices of the same data acquisition type. When terminal device A can use frequency band 1 and frequency band 2 for data transmission, and terminal device B can only use frequency band 1 for data transmission, the spectrum for data transmission of terminal device A is set to frequency band 1. When the data transmitted by device C is high temperature data, and the data transmitted by device D is temperature data, and both device C and device D perform data transmission based on frequency band 3. At this time, the priority of device C is higher than the priority of device D, and the device C with a higher priority is satisfied first for data transmission, and then the device D with a lower priority is satisfied for data transmission. At this time, after allocating different call time slots of the same spectrum resources to each type of terminal device, the update of the basic spectrum scheduling information of the set of devices for the same data collection is completed.

[0055] S130: Determine the scheduling attribute to be processed corresponding to the basic spectrum scheduling information based on the basic spectrum scheduling information corresponding to the data collection type and the predicted device data.

[0056] The pending scheduling attributes refer to the scheduling effect under the basic spectrum scheduling strategy. The scheduling effect specifically refers to the scheduling reliability, intervention traffic, resource utilization, key business priority guarantee and other effects under the basic spectrum scheduling strategy.

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

[0058] Optionally, the scheduling attributes to be processed of the basic spectrum scheduling information are determined based on the amount of transmission data corresponding to the data collection type, the spectrum information 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 when transmitting data under the corresponding spectrum resources, and the preset second effect.

[0059] The calculation formula for the pending scheduling attribute is:

[0060]

[0061] Among them, f(X) is the scheduling attribute to be processed for the basic spectrum scheduling information, m i is the amount of data transmitted by the i-th terminal device in the area, x i is the spectrum information allocated to the i-th type of terminal device under the basic spectrum scheduling strategy, l i is the first effect generated when the data to be transmitted by the i-th type of terminal device is transmitted under the spectrum resource. L is the preset second effect of the edge coordination area, and its value changes slightly. i The evaluation criteria include one or more of the transmission duration, quality, and energy consumption of the data to be transmitted by the i-th terminal device when the data is transmitted under the spectrum resources. i L is consistent with the evaluation criteria of , where L is the effect of the type of data whose transmission or processing request in the edge cooperation area is less affected by the spectrum allocated to it.

[0062] Specifically, the amount of transmission data corresponding to each data collection type, the spectrum information of at least one terminal device corresponding to each data collection type, and the first effect generated by the terminal device corresponding to each data collection type when transmitting data under the corresponding spectrum resources are obtained. For the same data collection type, the value of the amount of transmission data, the value of the spectrum information of at least one terminal device, and the value of the first effect are multiplied. Then, the results obtained from different data collection types are added. Finally, the summed result is added to the preset second effect to obtain the scheduling attributes to be processed of the basic spectrum scheduling information.

[0063] S140. Under the condition that the scheduling attributes to be processed do not meet the preset scheduling attributes, the basic spectrum scheduling information is processed based on the spectrum resource optimization function to obtain updated scheduling attributes to be processed, and the basic spectrum scheduling information obtained when the updated scheduling attributes to be processed meet the preset scheduling attributes 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] Among them, x i is the bandwidth resource reallocated for the i-th data collection type, F(X) is the expected scheduling attribute, η i is the preset learning rate parameter, The control scale for the preset learning rate;

[0068]

[0069] Among them, m i is the number of terminal devices corresponding to the i-th data collection type.

[0070] It should be noted that in the above spectrum resource optimization function The role of is to use a smaller step size for iterative optimization when the expected scheduling attribute is slightly different from the scheduling attribute to be processed, that is, when the effect correlation is high, to ensure the accuracy of the iterative result; when the expected scheduling attribute is significantly different from the scheduling attribute to be processed, that is, when the effect correlation is low, to use a larger step size for iterative optimization to accelerate the iteration efficiency.

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

[0072] Specifically, when the scheduling attribute to be processed calculated based on the calculation formula of the scheduling attribute to be processed meets the preset scheduling attribute, the basic spectrum scheduling information is used as the target spectrum scheduling information. If the scheduling attribute to be processed calculated based on the calculation formula of the scheduling attribute to be processed does not meet the preset scheduling attribute, the spectrum resources are reallocated for the i-th type of sensor terminal, the basic spectrum scheduling information is updated according to the spectrum resource allocation result, and the scheduling attribute to be processed is recalculated until the scheduling attribute to be processed reaches the preset scheduling attribute or the number of iterations reaches the preset iteration number threshold, then the updated scheduling attribute to be processed can be obtained, 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.

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

[0074] The calculation formula of the effect correlation coefficient is:

[0075]

[0076] Among them, ρ refers to the effect correlation coefficient, which is used to quantify the degree of match between the "pending scheduling attributes" and the "expected spectrum scheduling attributes". The closer the pending scheduling attributes and the expected spectrum scheduling attributes are, the more the scheduling execution effect is in line with expectations, and the higher the effect correlation coefficient; on the contrary, when the deviation is large, the effect correlation coefficient will decrease. F(X) is the expected spectrum scheduling attribute. According to the calculation formula of the pending scheduling attributes, calculate the historical pending scheduling attributes under each basic spectrum scheduling information. Calculate the average of each historical pending scheduling attribute and the user's expected scheduling attribute, and use the average as the expected spectrum scheduling attribute for the area. Among them, the user's expected scheduling attribute refers to the attribute value set in the theoretical design or target of the scheduling strategy. α is an empirical value, which is used to avoid the problem that the expected spectrum scheduling attribute and the pending scheduling attribute are too different and the effect correlation cannot be calculated.

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

[0078] In this embodiment, after obtaining the target spectrum scheduling information, the method also includes: determining the transmission order of the data to be processed corresponding to the terminal device based on 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 data to be processed corresponding to the terminal device based on the data transmission volume 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 these resource pools. The data to be processed refers to the data sent by the terminal device in the uplink. The transmission order refers to the order in which the data to be processed is transmitted when it is uploaded. The data transmission volume information refers to the total number of bytes or bits of the data sent by the terminal device in the uplink.

[0080] Specifically, after obtaining the target spectrum scheduling information, the optimal solution for spectrum resource allocation of various types of terminal devices can be obtained, but the data transmission requirements between various types of terminal devices and between different terminal devices of the same type of terminal devices may not be the same. Therefore, it is necessary to generate multiple groups of possible transmission orders of the data to be processed corresponding to the terminal device according to the transmission requirements of the data to be transmitted that the current edge coordination area is about to receive within the preset time length. The scheduling strategy for generating multiple groups of possible transmission orders of the data to be processed corresponding to the terminal device may include a single scheduling strategy or a multi-scheduling scheduling fusion strategy. A single scheduling strategy may include a priority strategy for the terminal device and a data transmission volume information strategy for the terminal device. The multi-scheduling scheduling fusion strategy may be a combination of a single scheduling strategy. The transmission time of each group of possible multi-scheduling scheduling fusion strategies can be calculated based on the prediction result of the amount of data to be transmitted by the terminal device, and the multi-scheduling scheduling fusion strategy with the smallest transmission time is used as the final multi-scheduling scheduling fusion strategy. 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.

[0081] For example, Figure 3As shown, first, the optimal frequency band of the device set of the same data acquisition 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 category of terminal devices is A>B>C. The priority of the second category of terminal devices is D>E>F. The priority of the third category of terminal devices 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 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 volume information of at least one terminal device corresponding to each spectrum range of M1, M2 and M3 in the target spectrum scheduling information to ensure that the data transmission volume information conforms to the data transmission volume information of the current time slot. A multi-scheduling scheduling fusion strategy can also be applied, that is, first according to the priority and then according to the data transmission volume information. At this time, the scheduling order is: in the current time slot, first assign M1 to A, assign M2 to D, and assign M3 to H, and then calculate whether the load of A+D+H meets the data transmission volume information limit of the current time slot. If the data transmission volume information is greater than the limit, and the data transmission volume of A is greater than the data transmission volume of B, the strategy is adjusted to: assign M1 to B, assign M2 to D, and assign M3 to H. At this time, the resource allocation of the three devices B, D, and H is completed. In the same way, the remaining devices A, C, E, F, and L are allocated resources in other time slots. At this time, the strategy obtained is a group of multi-scheduling scheduling fusion strategies. Then, according to the prediction result of the amount of data to be transmitted by the terminal device, the transmission time of each group of possible multi-scheduling scheduling fusion strategies can be calculated, and the multi-scheduling scheduling fusion strategy with the smallest transmission time is used as the final multi-scheduling scheduling fusion strategy. According to the final multi-scheduling scheduling fusion strategy, the transmission order of the data to be processed corresponding to the terminal device is determined. 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 transmission order of the data to be processed corresponding to the final terminal device. Figure 4As shown, 1 indicates that the time slot has been allocated to the user, and 0 indicates that it is not 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 the time slot channel allocation table is generated, the time slot channel allocation table is embedded in the downlink beacon frame. The structure of the downlink beacon frame is as follows: Frame header: Contains synchronization information, identifier, etc. of the beacon frame. The time slot channel allocation field carries the time slot allocation information, which is a time slot and channel or spectrum resource allocation table structure. 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 scale to adapt to different numbers of terminal devices and time slots.

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

[0083] Specifically, in a wireless or wired network system, when a base station (or access point) receives data to be processed by a terminal device, it puts the data 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 and transmits the data to be processed.

[0084] The technical solution of the embodiment of the present disclosure is as follows: for at least one edge coordination area, based on the historical terminal transmission data of at least one terminal device in the edge coordination area within a first preset time period, the predicted device data of at least one terminal device at at least one predicted moment within the predicted time period is determined, wherein the first preset time period is the preset time period before the current moment, and the predicted device data includes predicted bandwidth resources, predicted transmission data volume and the number of devices of at least one terminal device; then, according to the predicted device data of at least one terminal device and the preset 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 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 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 meet the preset scheduling attribute, the basic spectrum scheduling information is processed based on the spectrum resource optimization function to obtain the updated to-be-processed scheduling attribute, and the basic spectrum scheduling information obtained when the updated to-be-processed scheduling attribute meets the preset scheduling attribute is used as the target spectrum scheduling information, so as to solve the problem of low scheduling efficiency in the prior art that when the wireless CCO module of the intelligent gateway performs resource scheduling on the network resources, the resource scheduling is performed according to the order in which the data reporting request is received. The embodiment of the present invention adopts a method based on predicted device data and spectrum resource optimization function to determine the target spectrum scheduling information, realizes iterative optimization of basic spectrum scheduling information, improves scheduling efficiency, and achieves the effect of improving spectrum resource utilization and ensuring real-time data transmission.

[0085] Embodiment 2

[0086] Figure 5 It is a structural diagram of a resource scheduling device based on an intelligent gateway wireless CCO module provided in an embodiment of 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] A predicted device data determination module 210 is used to determine, for at least one edge collaborative area, predicted device data of at least one terminal device at at least one predicted moment within a predicted time period based on historical terminal transmission data of at least one terminal device in the edge collaborative area within a first preset time period, wherein the first preset time period is a preset time period before a 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; a basic spectrum scheduling information determination module 220 is used to determine a set of devices belonging to the same data collection type based on the predicted device data of the at least one terminal device and a pre-set data collection type. basic spectrum scheduling information, wherein the device set includes at least one terminal device; a module 230 for determining a scheduling attribute to be processed corresponding to the basic spectrum scheduling information based on the basic spectrum scheduling information corresponding to the data collection type and the predicted device data; a module 240 for determining a target spectrum scheduling information, for processing the basic spectrum scheduling information based on a spectrum resource optimization function when the scheduling attribute to be processed does not satisfy a preset scheduling attribute, to obtain an updated scheduling attribute to be processed, and using the basic spectrum scheduling information obtained when the updated scheduling attribute to be processed satisfies the preset scheduling attribute as the target spectrum scheduling information.

[0088] The technical solution of the embodiment of the present disclosure is as follows: for at least one edge coordination area, based on the historical terminal transmission data of at least one terminal device in the edge coordination area within a first preset time period, the predicted device data of at least one terminal device at at least one predicted moment within the predicted time period is determined, wherein the first preset time period is the preset time period before the current moment, and the predicted device data includes predicted bandwidth resources, predicted transmission data volume and the number of devices of at least one terminal device; then, according to the predicted device data of at least one terminal device and the preset 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 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 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 meet the preset scheduling attribute, the basic spectrum scheduling information is processed based on the spectrum resource optimization function to obtain the updated to-be-processed scheduling attribute, and the basic spectrum scheduling information obtained when the updated to-be-processed scheduling attribute meets the preset scheduling attribute is used as the target spectrum scheduling information, so as to solve the problem of low scheduling efficiency in the prior art that when the wireless CCO module of the intelligent gateway performs resource scheduling on the network resources, the resource scheduling is performed according to the order in which the data reporting request is received. The embodiment of the present invention adopts a method based on predicted device data and spectrum resource optimization function to determine the target spectrum scheduling information, realizes iterative optimization of basic spectrum scheduling information, improves scheduling efficiency, and achieves the effect of improving spectrum resource utilization and ensuring real-time data transmission.

[0089] On the basis of the above technical solutions, it also includes: a data reporting parameter configuration module, which is used to configure data reporting parameters for at least one terminal device in the edge collaborative area, wherein the data reporting parameters include a data reporting period.

[0090] On the basis 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] A historical terminal transmission data determination submodule, configured to determine historical terminal transmission data corresponding to each reporting period within a first preset time period based on at least one terminal device in the edge coordination area;

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

[0093] On the basis of the above technical solutions, the basic spectrum scheduling information determination module 220 further includes: a basic spectrum scheduling information determination submodule and a basic spectrum scheduling information update submodule.

[0094] A basic spectrum scheduling information determination submodule, configured to determine basic spectrum scheduling information of a set of devices belonging to the same data collection type based on at least two available spectrum ranges when at least two available spectrum ranges are included in the predicted device data of the terminal device;

[0095] The basic spectrum scheduling information updating submodule is used to update the basic spectrum scheduling information of the device set belonging to the same data collection according to the device priorities of the at least two terminal devices in the same available spectrum range.

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

[0097] The submodule for obtaining the scheduling attributes to be processed is used to determine the scheduling attributes to be processed of the basic spectrum scheduling information based on the amount of transmission data corresponding to the data collection type, the spectrum information 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 when transmitting data under the corresponding spectrum resources, and the preset second effect.

[0098] On the basis of the above technical solutions, the target spectrum scheduling information determination module 240 further includes: a desired spectrum scheduling attribute acquisition submodule, an effect correlation coefficient determination submodule and a target spectrum scheduling information acquisition submodule.

[0099] An expected spectrum scheduling attribute acquisition submodule, used to acquire expected spectrum scheduling attributes;

[0100] An effect correlation coefficient determination submodule, used to determine an effect correlation coefficient between the desired spectrum scheduling attribute and the to-be-processed scheduling attribute;

[0101] The target spectrum scheduling information acquisition submodule is used to use the basic spectrum scheduling information corresponding to when the effect correlation coefficient is greater than a preset correlation coefficient threshold as the target spectrum scheduling information.

[0102] On the basis of the above technical solutions, the target spectrum scheduling information determination module 240 further includes: a spectrum resource optimization function submodule.

[0103] Spectrum resource optimization function submodule, used for

[0104]

[0105] Among them, xi is the bandwidth resource reallocated for the i-th data collection type, F(X) is the scheduling attribute to be processed, η i is the preset learning rate parameter, The control scale for the preset learning rate;

[0106]

[0107] Among them, m i is the number of terminal devices corresponding to the i-th data collection type.

[0108] Based on the above technical solutions, a transmission order determination module is also included.

[0109] A transmission order determination module is used to determine the transmission order of the data to be processed 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 to determine the transmission order of the data to be processed corresponding to the terminal device according to the data transmission volume information of at least one terminal device corresponding to each spectrum range in the target spectrum scheduling information.

[0110] On the basis of the above technical solutions, a data transmission module to be processed is also included.

[0111] The module for transmitting data to be processed is used to transmit the data to be processed according to the transmission sequence when receiving the data to be processed corresponding to the terminal device.

[0112] The resource scheduling device based on the intelligent gateway wireless CCO module provided in the embodiment of the present disclosure can execute the resource scheduling method based on the intelligent gateway wireless CCO module provided in any embodiment of the present disclosure, and has the corresponding functional modules and beneficial effects of the execution method.

[0113] It is worth noting that the various units and modules included in the above-mentioned device are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the embodiments of the present disclosure.

[0114] Embodiment 3

[0115] Figure 6 Schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure. Figure 6 , which shows an electronic device (eg, Figure 6The terminal device in the embodiment of the present disclosure may include but is not limited to mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0116] like Figure 6 As shown, the electronic device 500 may include a processing device (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 a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An edit / output (I / O) interface 505 is also connected to the bus 504.

[0117] Typically, the following devices may be connected to the I / O interface 505: an input device 506 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 507 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 508 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 509. The communication device 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 6 The electronic device 500 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0118] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through 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, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

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

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

[0121] Embodiment 4

[0122] The embodiment of the present disclosure provides a computer storage medium on which a computer program is stored. When the program is executed by a processor, the resource scheduling method based on the intelligent gateway wireless CCO module provided in the above embodiment is implemented.

[0123] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, 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, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., 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 any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0125] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0126] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device:

[0127] For at least one edge collaborative area, based on historical terminal transmission data of at least one terminal device in the edge collaborative area within a first preset time period, determining predicted device data of the at least one terminal device at at least one predicted moment within the predicted time period, wherein the first preset time period is a preset time period before a 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;

[0128] Determine basic spectrum scheduling information of a set of devices belonging to the same data collection type according to the predicted device data of the at least one terminal device and a preset data collection type, wherein the set of devices includes at least one terminal device;

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

[0130] Under the condition that the scheduling attributes to be processed do not meet the preset scheduling attributes, the basic spectrum scheduling information is processed based on the spectrum resource optimization function to obtain updated scheduling attributes to be processed, and the basic spectrum scheduling information obtained when the updated scheduling attributes to be processed meet the preset scheduling attributes is used as the target spectrum scheduling information.

[0131] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may 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 may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0132] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0133] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, constitute a limitation on the unit itself.

[0134] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0135] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, 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 foregoing.

[0136] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

[0137] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0138] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. A resource scheduling method based on a wireless CCO module of an intelligent gateway, characterized in that: include: For at least one edge collaborative area, based on historical terminal transmission data of at least one terminal device in the edge collaborative area within a first preset time period, determining predicted device data of the at least one terminal device at at least one predicted moment within the predicted time period, wherein the first preset time period is a preset time period before a 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; Determine basic spectrum scheduling information of a set of devices belonging to the same data collection type according to the predicted device data of the at least one terminal device and a preset data collection type, wherein the set of devices includes at least one terminal device; Determine, based on the basic spectrum scheduling information corresponding to the data collection type and the predicted device data, a scheduling attribute to be processed corresponding to the basic spectrum scheduling information; Under the condition that the scheduling attributes to be processed do not meet the preset scheduling attributes, the basic spectrum scheduling information is processed based on the spectrum resource optimization function to obtain updated scheduling attributes to be processed, and the basic spectrum scheduling information obtained when the updated scheduling attributes to be processed meet the preset scheduling attributes is used as the target spectrum scheduling information.

2. The method according to claim 1, characterized in that: The method further comprises: Configuring data reporting parameters for at least one terminal device in the edge coordination area, wherein the data reporting parameters include a data reporting period; Accordingly, based on the historical terminal transmission data of at least one terminal device in the edge cooperation area within the first preset time period, the predicted device data of the at least one terminal device at at least one predicted time within the predicted time period is determined, including: Based on historical terminal transmission data corresponding to each reporting period within a first preset time period of at least one terminal device in the edge coordination area; Inputting the historical terminal transmission data corresponding to each terminal device into a pre-trained data prediction model to obtain the predicted device data of at least one terminal device at at least one predicted time within a preset time period; Among them, the at least one preset moment is determined based on the reporting period of the terminal device.

3. The method according to claim 1, characterized in that The determining, according to the predicted device data of the at least one terminal device and the preset data collection type, basic spectrum scheduling information of a set of devices belonging to the same data collection type includes: When the predicted device data of the terminal device includes at least two available spectrum ranges, determining basic spectrum scheduling information of a set of devices belonging to the same data collection type based on the at least two available spectrum ranges; For at least two terminal devices in 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 priorities of the at least two terminal devices.

4. The method according to claim 1, characterized in that The determining, based on the basic spectrum scheduling information corresponding to the data collection type and the predicted device data, a scheduling attribute to be processed corresponding to the basic spectrum scheduling information includes: Determine the scheduling attributes to be processed of the basic spectrum scheduling information based on the amount of transmission data corresponding to the data collection type, the spectrum information 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 when transmitting data under the corresponding spectrum resources, and the preset second effect.

5. The method according to claim 1, characterized in that The method further comprises: Obtaining expected spectrum scheduling attributes; Determining an effect correlation coefficient between the desired spectrum scheduling attribute and the to-be-processed scheduling attribute; The basic spectrum scheduling information corresponding to when the effect correlation coefficient is greater than a preset correlation coefficient threshold is used as the target spectrum scheduling information.

6. The method according to claim 1, characterized in that The spectrum resource optimization function is: Among them, x i is the bandwidth resource reallocated for the i-th data collection type, F(X) is the scheduling attribute to be processed, η i is the preset learning rate parameter, The control scale for the preset learning rate; Among them, m i is the number of terminal devices corresponding to the i-th data collection type.

7. The method according to claim 1, characterized in that After obtaining the target spectrum scheduling information, the method further includes: Determine 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, The transmission order of the to-be-processed data corresponding to the terminal device is determined according to the data transmission volume information of at least one terminal device corresponding to each spectrum range in the target spectrum scheduling information.

8. The method according to claim 7, characterized in that The method further comprises: When the data to be processed corresponding to the terminal device is received, the data to be processed is transmitted according to the transmission order.

9. A resource scheduling device based on an intelligent gateway wireless CCO module, characterized in that: include: A predicted device data determination module, configured to determine, for at least one edge collaborative area, predicted device data of at least one terminal device at at least one predicted moment within a predicted duration based on historical terminal transmission data of at least one terminal device within the edge collaborative area within a first preset duration, wherein the first preset duration is a preset duration before a 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; A basic spectrum scheduling information determination module, configured to determine basic spectrum scheduling information of a set of devices belonging to the same data collection type according to the predicted device data of the at least one terminal device and a preset data collection type, wherein the set of devices includes at least one terminal device; A scheduling attribute determination module to be processed, used to determine the scheduling attribute to be processed corresponding to the basic spectrum scheduling information based on the basic spectrum scheduling information corresponding to the data collection type and the prediction device data; The target spectrum scheduling information determination module is used to process the basic spectrum scheduling information based on the spectrum resource optimization function to obtain updated scheduling attributes to be processed when the scheduling attributes to be processed do not meet the preset scheduling attributes, and use the basic spectrum scheduling information obtained when the updated scheduling attributes to be processed meet the preset scheduling attributes as the target spectrum scheduling information.

10. An electronic device, characterized in that: The electronic device comprises: one or more processors; a storage device for storing one or more programs, When one or more programs are executed by one or more processors, the one or more processors implement the resource scheduling method based on the intelligent gateway wireless CCO module as claimed in any one of claims 1 to 8.

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