Wireless communication management system based on artificial intelligence

Abstract

The invention discloses a wireless communication management system based on an artificial intelligence technology. The wireless communication management system comprises an intelligent monitoring module, an intelligent regulation and control module, an intelligent anti-interference module and an intelligent evaluation module, the intelligent monitoring module is used for monitoring the communication equipment and the communication base station in real time; the intelligent regulation and control module is used for calling historical communication information, establishing a first machine learning model according to the historical communication information, predicting next-period equipment communication information and next-period base station communication information and acquiring a communication strategy; the intelligent anti-interference module is used for establishing a second machine learning model to predict interference information according to historical communication information, and formulating an anti-interference strategy according to the interference information; the intelligent evaluation module is used for acquiring communication information in unit time in real time, performing grade evaluation on the communication information and positioning a fault node; by predicting the next-period equipment communication information and the next-period base station communication information, a corresponding communication strategy and an anti-interference strategy are formulated, and detailed scheme support is provided for maintaining the next-period communication stability.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a wireless communication management system based on artificial intelligence. Background Art

[0002] In recent years, with the in-depth development of Internet technology, the development of wireless communication technology in various fields has been promoted. At the same time, the requirements for wireless communication technology have become increasingly stringent. As an important part of science and technology and informatization, wireless communication technology has become a key driving force for today's social and economic development. In recent years, wireless communication technology has made significant progress, and its application has become more and more extensive around the world, and the number of users has continued to grow. However, the development of wireless communication technology still faces a series of technical challenges and market problems.

[0003] At present, the Chinese invention patent with application number 200510130389.2 provides a wireless communication management system to ensure the security function of communication security. When the wireless communication device can perform wireless communication with an external device, it determines whether the parameter value representing the security function of the wireless communication device has a default value, and if the security judgment unit determines that the value of the parameter is the default value, the wireless communication with the external device is restricted. Although the communication security is guaranteed, it does not make a prediction of the future communication situation, nor does it formulate communication strategies and anti-interference strategies based on the future communication situation, which is easy to cause communication congestion and excessive communication interference, and does not conduct a level assessment of the communication situation, which is not conducive to the subsequent formulation of communication strategies and anti-interference strategies. Summary of the invention

[0004] The technical problem solved by the present invention is that the prior art does not predict future communication conditions, nor does it formulate communication strategies and anti-interference strategies based on future communication conditions, which easily leads to communication congestion and excessive communication interference, and does not perform a level assessment of the communication conditions, which is not conducive to the subsequent formulation of communication strategies and anti-interference strategies.

[0005] In order to solve the above technical problems, the present invention provides a wireless communication management system based on artificial intelligence, including an intelligent monitoring module, an intelligent control module, an intelligent anti-interference module and an intelligent evaluation module;

[0006] The intelligent monitoring module is used to monitor the communication equipment and the communication base station in real time, obtain the equipment communication information and the base station communication information, and obtain the communication information according to the equipment communication information and the base station communication information;

[0007] The intelligent control module is used to retrieve historical communication information, establish a first machine learning model based on the historical communication information, and use the first machine learning model to predict the next period of device communication information and the next period of base station communication information, and obtain a communication strategy based on the next period of device communication information and the next period of base station communication information, wherein the historical communication information includes historical device communication information and historical base station communication information;

[0008] The intelligent anti-interference module is used to establish a second machine learning model based on historical communication information, input the communication strategy into the second machine learning model to predict interference information, and formulate an anti-interference strategy based on the interference information;

[0009] The intelligent evaluation module is used to obtain communication information within a unit time in real time and evaluate the communication information, obtain the communication level within a unit time, and locate the faulty node according to the communication level. The communication level is used as the implementation effect evaluation level of the communication strategy and anti-interference strategy within a unit time.

[0010] As a preferred solution of the wireless communication management system based on artificial intelligence described in the present invention, wherein: the intelligent monitoring module includes a device monitoring unit and a base station monitoring unit;

[0011] The device monitoring unit is used to monitor the device at the wireless communication receiving end in real time and obtain the device communication information, wherein the device communication information includes the wireless frequency band, wireless protocol, wireless rate and wireless location;

[0012] The base station monitoring unit is used to monitor the wireless communication base stations in real time and obtain the base station communication information of each wireless communication base station, wherein the base station communication information includes signal coverage, signal power and frequency allocation.

[0013] As a preferred solution of the wireless communication management system based on artificial intelligence described in the present invention, wherein: the intelligent control module includes an intelligent prediction unit and an intelligent strategy unit;

[0014] The intelligent prediction unit is used to retrieve historical communication information, establish a first machine learning model based on the historical communication information, use the historical device communication information and the historical base station communication information as input to train the first machine learning model, and use the first machine learning model to predict the next period of device communication information and the next period of base station communication information;

[0015] The intelligent strategy unit is used to obtain a communication strategy according to the next period of device communication information and the next period of base station communication information by using the spider peak algorithm.

[0016] As a preferred solution of the wireless communication management system based on artificial intelligence described in the present invention, wherein: the intelligent anti-interference module includes an anti-interference model unit and an anti-interference strategy unit;

[0017] The anti-interference model unit is used to establish a second machine learning model according to the historical communication information, use the historical communication information as input and use the interference information as output to train the second machine learning model, obtain the next period of communication information according to the next period of device communication information and base station communication information, and input the next period of communication information into the second machine learning model to obtain the next period of interference information;

[0018] The anti-interference strategy unit is used to obtain the anti-interference strategy according to the next period of interference information.

[0019] As a preferred solution of the wireless communication management system based on artificial intelligence of the present invention, wherein: the evaluation of communication information to obtain the communication level within a unit time includes:

[0020] The communication information includes signal strength, spectrum utilization, data delay, packet loss rate and bandwidth utilization;

[0021] Signal strength, spectrum utilization, data delay, packet loss rate and bandwidth utilization are evaluated respectively to obtain signal level, spectrum level, delay level, packet loss level and utilization level. The signal level, spectrum level, delay level, packet loss level and utilization level are calculated according to certain weights to obtain the communication level per unit time.

[0022] As a preferred solution of the wireless communication management system based on artificial intelligence described in the present invention, wherein: the intelligent evaluation module includes an intelligent level unit and an intelligent fault unit;

[0023] The intelligent level unit is used to obtain the communication level within a unit time according to the communication information, and if the communication level is less than or equal to the expected threshold, the communication information is sent to the intelligent fault unit for processing;

[0024] The intelligent fault unit is used to locate the faulty node according to the communication information, and the faulty node includes a base station node and a device node.

[0025] As a preferred solution of the wireless communication management system based on artificial intelligence described in the present invention, wherein: locating the faulty node according to the communication information includes:

[0026] Acquire a communication fault data set, input the communication fault data set into a wavelet neural network for learning, and acquire a wavelet neural network model that can determine whether a communication node fails based on communication information;

[0027] The communication information acquired in real time is input into the wavelet neural network model, and the node status of the communication node is acquired and output, wherein the node status includes "fault exists" and "fault does not exist", and the output number 1 indicates the fault exists, and the output number 0 indicates the fault does not exist.

[0028] As a preferred solution of the wireless communication management system based on artificial intelligence described in the present invention, wherein: using the spider bee algorithm to obtain the communication strategy according to the next period of device communication information and the next period of base station communication information includes:

[0029] Set each device node as a spider individual, and set the corresponding hunger level and location information for each spider individual according to the next period of device communication information;

[0030] Each base station node is set as a food source, and the pheromone concentration, food quantity and location information of the food source are set for each food source according to the next base station communication information;

[0031] Each individual spider automatically searches for the best food source based on its own hunger level and location information as well as the pheromone concentration, food quantity and location information of the food source;

[0032] The location information of individual spiders that have found food sources and the matching information between individual spiders and food sources are stored in a memory pool, and the matching information between individual spiders and food sources is used as a communication strategy, which is used to implement the next phase of wireless communication device node and base station node deployment.

[0033] As a preferred solution of the wireless communication management system based on artificial intelligence described in the present invention, wherein: the anti-interference strategy unit is used to obtain the anti-interference strategy according to the next period of interference information;

[0034] The interference information includes external interference information and multipath interference information of each channel, the external interference information includes interference information generated by other devices of the same frequency, the external interference information includes external interference intensity, external interference devices and external interference frequency, the multipath interference information includes interference information generated by physical obstacles in the communication path, and the multipath interference information includes multipath interference intensity and multipath interference path;

[0035] The communication frequency that the channel needs to skip is obtained according to the external interference frequency, and the interference strength of each channel is obtained according to the external interference strength and the multipath interference strength, and the signal strength of each channel is adjusted according to the interference strength of each channel.

[0036] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any one of the above-mentioned wireless communication management systems based on artificial intelligence.

[0037] Beneficial effects of the present invention: The present invention uses historical communication information to perform machine learning training to obtain a first machine learning model and a second machine learning model, and uses the first machine learning model to predict the next period of device communication information and the next period of base station communication information, and uses the second machine learning model to predict the next period of interference information, so that people can understand the next period of communication conditions, and formulate communication strategies and anti-interference strategies based on the prediction results; formulate corresponding communication strategies and anti-interference strategies based on the prediction of the next period of communication conditions, and provide detailed solution support for maintaining the stability of the next period of communication;

[0038] By monitoring and level-evaluating the communication situation within a unit of time, the effectiveness of the communication strategy and anti-interference strategy is accurately and effectively evaluated, providing reliable case and data support for the subsequent improvement of the communication strategy and anti-interference strategy, and ensuring the stability of wireless communication.

[0039] By evaluating the level of communication conditions within a unit of time, it is determined whether there is a fault in wireless communication. If a fault occurs, the wavelet neural network is used to locate and output the faulty node, which ensures the stability of wireless communication and eliminates node failures for smooth communication. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A schematic diagram of the basic flow of an artificial intelligence-based wireless communication management system provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0042] Example, see Figure 1 , is an embodiment of the present invention, and provides a wireless communication management system based on artificial intelligence, including an intelligent monitoring module, an intelligent control module, an intelligent anti-interference module and an intelligent evaluation module:

[0043] The intelligent monitoring module is used to monitor the communication equipment and the communication base station in real time, obtain the equipment communication information and the base station communication information, and obtain the communication information according to the equipment communication information and the base station communication information;

[0044] The intelligent control module is used to retrieve historical communication information, establish a first machine learning model based on the historical communication information, and use the first machine learning model to predict the next period of device communication information and the next period of base station communication information, and obtain a communication strategy based on the next period of device communication information and the next period of base station communication information, wherein the historical communication information includes historical device communication information and historical base station communication information;

[0045] The intelligent anti-interference module is used to establish a second machine learning model based on historical communication information, input the communication strategy into the second machine learning model to predict interference information, and formulate an anti-interference strategy based on the interference information;

[0046] The intelligent evaluation module is used to obtain communication information within a unit time in real time and evaluate the communication information, obtain the communication level within a unit time, and locate the faulty node according to the communication level. The communication level is used as the implementation effect evaluation level of the communication strategy and anti-interference strategy within a unit time.

[0047] In this embodiment, by using historical communication information for machine learning training, a first machine learning model and a second machine learning model are obtained, and the first machine learning model is used to predict the next period of device communication information and the next period of base station communication information, and the second machine learning model is used to predict the next period of interference information, so that people can understand the next period of communication conditions, and formulate communication strategies and anti-interference strategies based on the prediction results; according to the prediction of the next period of communication conditions, corresponding communication strategies and anti-interference strategies are formulated, providing detailed solution support for maintaining the stability of the next period of communication;

[0048] By monitoring and level-evaluating the communication situation within a unit of time, the effectiveness of the communication strategy and anti-interference strategy is accurately and effectively evaluated, providing reliable case and data support for the subsequent improvement of the communication strategy and anti-interference strategy, and ensuring the stability of wireless communication.

[0049] By evaluating the level of communication conditions within a unit of time, it is determined whether there is a fault in wireless communication. If a fault occurs, the wavelet neural network is used to locate and output the faulty node, which ensures the stability of wireless communication and eliminates node failures for smooth communication.

[0050] The intelligent monitoring module includes a device monitoring unit and a base station monitoring unit;

[0051] The device monitoring unit is used to monitor the device at the wireless communication receiving end in real time and obtain the device communication information, wherein the device communication information includes the wireless frequency band, wireless protocol, wireless rate and wireless location;

[0052] The base station monitoring unit is used to monitor the wireless communication base stations in real time and obtain the base station communication information of each wireless communication base station, wherein the base station communication information includes signal coverage, signal power and frequency allocation.

[0053] The wireless frequency band represents the frequency range of continuous electromagnetic waves;

[0054] The wireless protocol represents the rules and treaties that the communication device and the communication base station need to comply with to complete the communication or service;

[0055] The wireless rate refers to the speed of transmitting data in wireless communication;

[0056] The wireless position indicates the coordinate position of the communication device;

[0057] The signal power indicates the strength of the communication signal;

[0058] The frequency allocation indicates the allocation of communication frequency bands.

[0059] In this embodiment, by real-time monitoring of communication equipment and communication base stations, device communication information and base station communication information are obtained, providing detailed data basis for real-time understanding of communication information, formulating communication strategies and anti-interference strategies, and evaluating the level of communication conditions within a unit time.

[0060] The intelligent control module includes an intelligent prediction unit and an intelligent strategy unit;

[0061] The intelligent prediction unit is used to retrieve historical communication information, establish a first machine learning model based on the historical communication information, use the historical device communication information and the historical base station communication information as input to train the first machine learning model, and use the first machine learning model to predict the next period of device communication information and the next period of base station communication information;

[0062] The intelligent strategy unit is used to obtain a communication strategy according to the next period of device communication information and the next period of base station communication information by using the spider peak algorithm.

[0063] The first machine learning model is used to predict the next period of device communication information and the next period of base station communication information. The process of establishing the first machine learning model includes:

[0064] Data preparation: Prepare the historical equipment communication information and historical base station communication information data sets that need to be trained, clean and preprocess the data, including missing value processing, outlier processing, and duplicate value processing, and divide the cleaned and processed data sets into training data sets and test data sets;

[0065] Model selection: Select appropriate machine learning algorithms and models. Commonly used algorithms include linear regression, logistic regression, decision tree, random forest, support vector machine, and neural network.

[0066] Model training: Use the training data set to train the selected machine learning model, and use optimization algorithms such as gradient descent to iteratively update the machine learning model parameters to minimize the loss function;

[0067] Model evaluation: During the machine learning model training process, the machine learning model is evaluated to determine the performance of the machine learning model. Evaluation indicators usually include precision, recall rate, and F1 value.

[0068] Model parameter adjustment: According to the results of machine learning model evaluation, the machine learning model parameters are adjusted to further improve the performance of the machine learning model;

[0069] Model preservation and deployment: After the machine learning model training is completed, the trained machine learning model is saved to predict the next device communication information and the next base station communication information.

[0070] The Spider Bee algorithm is based on replicating the hunting, nesting, and mating behaviors of female wasps in nature and is applicable to a variety of optimization problems with different strategy requirements.

[0071] In this embodiment, a first machine learning model is established based on historical communication information, and the first machine learning model is used to predict the next period of device communication information and the next period of base station communication information. A communication strategy is obtained based on the next period of device communication information and the next period of base station communication information, thereby providing effective strategic support for adjusting wireless communications and ensuring smooth and stable communications.

[0072] The intelligent anti-disturbance module includes an anti-disturbance model unit and an anti-disturbance strategy unit;

[0073] The anti-interference model unit is used to establish a second machine learning model according to the historical communication information, use the historical communication information as input and use the interference information as output to train the second machine learning model, obtain the next period of communication information according to the next period of device communication information and base station communication information, and input the next period of communication information into the second machine learning model to obtain the next period of interference information;

[0074] The anti-interference strategy unit is used to obtain the optimal anti-interference strategy according to the next period of interference information.

[0075] The second machine learning model is used to predict the next period of interference information.

[0076] In this embodiment, a second machine learning model is established based on historical communication information, and the second machine learning model is used to obtain next-period interference information based on next-period communication information, and an anti-interference strategy is obtained based on the next-period interference information, thereby providing effective strategic support for ensuring the smooth operation of wireless communications and resisting communication interference.

[0077] The evaluating of the communication information to obtain the communication level within a unit time includes:

[0078] The communication information includes signal strength, spectrum utilization, data delay, packet loss rate and bandwidth utilization;

[0079] Signal strength, spectrum utilization, data delay, packet loss rate and bandwidth utilization are evaluated respectively to obtain signal level, spectrum level, delay level, packet loss level and utilization level. The signal level, spectrum level, delay level, packet loss level and utilization level are calculated according to certain weights to obtain the communication level per unit time.

[0080] The signal strength indicates the strength of the communication signal between the device and the base station;

[0081] The frequency utilization rate is an indicator to measure the efficiency of wireless communication in utilizing spectrum resources;

[0082] The data delay represents the transmission time of the communication signal from the transmitting end to the receiving end;

[0083] The packet loss rate indicates the proportion of signals lost during transmission;

[0084] The bandwidth utilization rate represents the ratio between the available bandwidth and the actual bandwidth;

[0085] In this embodiment, the certain weight is 3:2:2:2:1.

[0086] In this embodiment, by performing level evaluation on the communication information within a unit time, the effectiveness of the communication strategy and the anti-interference strategy within the unit time can be understood through the level evaluation, and whether the wireless communication fails can be determined through the level evaluation, which is conducive to preventing and detecting communication failures and ensuring smooth communication.

[0087] The intelligent assessment module includes an intelligent grade unit and an intelligent fault unit;

[0088] The intelligent level unit is used to obtain the communication level within a unit time according to the communication information, and if the communication level is less than or equal to the expected threshold, the communication information is sent to the intelligent fault unit for processing;

[0089] The intelligent fault unit is used to locate the faulty node according to the communication information, and the faulty node includes a base station node and a device node.

[0090] In this embodiment, by evaluating the level of communication information within a unit time, the effectiveness of the implementation of the communication strategy and the anti-interference strategy can be understood, and whether the wireless communication fails and the faulty node is located according to the communication level, effectively ensuring the smoothness of the wireless communication.

[0091] The locating the faulty node according to the communication information comprises:

[0092] Acquire a communication fault data set, input the communication fault data set into a wavelet neural network for learning, and acquire a wavelet neural network model that can determine whether a communication node fails based on communication information;

[0093] The communication information acquired in real time is input into the wavelet neural network model, and the node status of the communication node is acquired and output, wherein the node status includes "fault exists" and "fault does not exist", and the output number 1 indicates the fault exists, and the output number 0 indicates the fault does not exist.

[0094] The wavelet neural network is a new type of layered, multi-resolution artificial neural network constructed based on wavelet analysis theory and wavelet transform.

[0095] In this embodiment, a wavelet neural network is used to learn the communication fault set to obtain a wavelet neural network model, and the wavelet neural network model is used to locate the faulty node according to real-time communication information, which is beneficial to subsequent fault processing and troubleshooting of communication faults.

[0096] The communication strategy obtained by using the spider bee algorithm according to the next period of device communication information and base station communication information includes:

[0097] Set each device node as a spider individual, and set the corresponding hunger level and location information for each spider individual according to the next period of device communication information;

[0098] Each base station node is set as a food source, and the pheromone concentration, food quantity and location information of the food source are set for each food source according to the next base station communication information;

[0099] Each individual spider automatically searches for the best food source based on its own hunger level and location information as well as the pheromone concentration, food quantity and location information of the food source;

[0100] The location information of the individual spiders that have found the food source and the matching information between the individual spiders and the food source are stored in a memory pool, and the matching information between each individual spider and the food source is used as a communication strategy, which is used to implement the next phase of wireless communication deployment.

[0101] In this embodiment, the spider peak algorithm is used to obtain the communication strategy, which provides detailed and effective strategy support for improving the quality of wireless communication and saves a lot of manpower and material resources for formulating the strategy.

[0102] The anti-interference strategy unit is used to obtain the anti-interference strategy according to the next period of interference information;

[0103] The interference information includes external interference information and multipath interference information of each channel, the external interference information includes interference information generated by other devices of the same frequency, the external interference information includes external interference intensity, external interference devices and external interference frequency, the multipath interference information includes interference information generated by physical obstacles in the communication path, and the multipath interference information includes multipath interference intensity and multipath interference path;

[0104] The communication frequency that the channel needs to skip is obtained according to the external interference frequency, and the interference strength of each channel is obtained according to the external interference strength and the multipath interference strength, and the signal strength of each channel is adjusted according to the interference strength of each channel.

[0105] In this embodiment, an anti-interference strategy is obtained according to the next period of interference information, which provides detailed strategy support for anti-interference of wireless communication and is conducive to improving the communication quality of wireless communication.

[0106] In this embodiment, by using historical communication information for machine learning training, a first machine learning model and a second machine learning model are obtained, and the first machine learning model is used to predict the next period of device communication information and the next period of base station communication information, and the second machine learning model is used to predict the next period of interference information, so that people can understand the next period of communication conditions, and formulate communication strategies and anti-interference strategies based on the prediction results; according to the prediction of the next period of communication conditions, corresponding communication strategies and anti-interference strategies are formulated, providing detailed solution support for maintaining the stability of the next period of communication;

[0107] By monitoring and level-evaluating the communication situation within a unit of time, the effectiveness of the communication strategy and anti-interference strategy is accurately and effectively evaluated, providing reliable case and data support for the subsequent improvement of the communication strategy and anti-interference strategy, and ensuring the stability of wireless communication.

[0108] By evaluating the level of communication conditions within a unit of time, it is determined whether there is a fault in wireless communication. If a fault occurs, the wavelet neural network is used to locate and output the faulty node, which ensures the stability of wireless communication and eliminates node failures for smooth communication.

[0109] It should be understood by those skilled in the art that the embodiments of the present invention can be provided as methods, systems or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program codes. Among them, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk. These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0110] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A wireless communication management system based on artificial intelligence technology, characterized in that: Including intelligent monitoring module, intelligent control module, intelligent anti-disturbance module and intelligent evaluation module; The intelligent monitoring module is used to monitor the communication equipment and the communication base station in real time, obtain the equipment communication information and the base station communication information, and obtain the communication information according to the equipment communication information and the base station communication information; The intelligent control module is used to retrieve historical communication information, establish a first machine learning model based on the historical communication information, and use the first machine learning model to predict the next period of device communication information and the next period of base station communication information, and obtain a communication strategy based on the next period of device communication information and the next period of base station communication information, wherein the historical communication information includes historical device communication information and historical base station communication information; The intelligent anti-interference module is used to establish a second machine learning model based on historical communication information, input the communication strategy into the second machine learning model to predict interference information, and formulate an anti-interference strategy based on the interference information; The intelligent evaluation module is used to obtain communication information within a unit time in real time and evaluate the communication information, obtain the communication level within a unit time, and locate the faulty node according to the communication level. The communication level is used as the implementation effect evaluation level of the communication strategy and anti-interference strategy within a unit time.

2. The wireless communication management system based on artificial intelligence technology as claimed in claim 1, characterized in that: The intelligent monitoring module includes a device monitoring unit and a base station monitoring unit; The device monitoring unit is used to monitor the device at the wireless communication receiving end in real time and obtain the device communication information, wherein the device communication information includes the wireless frequency band, wireless protocol, wireless rate and wireless location; The base station monitoring unit is used to monitor the wireless communication base stations in real time and obtain the base station communication information of each wireless communication base station, wherein the base station communication information includes signal coverage, signal power and frequency allocation.

3. The wireless communication management system based on artificial intelligence technology as claimed in claim 1, characterized in that: The intelligent control module includes an intelligent prediction unit and an intelligent strategy unit; The intelligent prediction unit is used to retrieve historical communication information, establish a first machine learning model based on the historical communication information, use the historical device communication information and the historical base station communication information as input to train the first machine learning model, and use the first machine learning model to predict the next period of device communication information and the next period of base station communication information; The intelligent strategy unit is used to obtain a communication strategy according to the next period of device communication information and the next period of base station communication information by using the spider peak algorithm.

4. The wireless communication management system based on artificial intelligence technology as claimed in claim 1, characterized in that: The intelligent anti-disturbance module includes an anti-disturbance model unit and an anti-disturbance strategy unit; The anti-interference model unit is used to establish a second machine learning model according to the historical communication information, use the historical communication information as input and use the interference information as output to train the second machine learning model, obtain the next period of communication information according to the next period of device communication information and the next period of base station communication information, and input the next period of communication information into the second machine learning model to obtain the next period of interference information; The anti-interference strategy unit is used to obtain the anti-interference strategy according to the next period of interference information.

5. The wireless communication management system based on artificial intelligence technology as claimed in claim 1, characterized in that: The evaluating of the communication information to obtain the communication level within a unit time includes: The communication information includes signal strength, spectrum utilization, data delay, packet loss rate and bandwidth utilization; Signal strength, spectrum utilization, data delay, packet loss rate and bandwidth utilization are evaluated respectively to obtain signal level, spectrum level, delay level, packet loss level and utilization level. The signal level, spectrum level, delay level, packet loss level and utilization level are calculated according to certain weights to obtain the communication level per unit time.

6. The wireless communication management system based on artificial intelligence technology as claimed in claim 1, characterized in that: The intelligent assessment module includes an intelligent grade unit and an intelligent fault unit; The intelligent level unit is used to obtain the communication level within a unit time according to the communication information, and if the communication level is less than or equal to the expected threshold, the communication information is sent to the intelligent fault unit for processing; The intelligent fault unit is used to locate the faulty node according to the communication information, and the faulty node includes a base station node and a device node.

7. The wireless communication management system based on artificial intelligence technology as claimed in claim 6, characterized in that: The locating the faulty node according to the communication information comprises: Acquire a communication fault data set, input the communication fault data set into a wavelet neural network for learning, and acquire a wavelet neural network model that can determine whether a communication node fails based on communication information; The communication information acquired in real time is input into the wavelet neural network model to obtain the node status of the communication node and output it. The node status includes "fault exists" and "no fault exists". The output number 1 indicates the presence of a fault, and the output number 0 indicates the absence of a fault.

8. The wireless communication management system based on artificial intelligence technology as claimed in claim 3, characterized in that: The communication strategy obtained by using the spider bee algorithm according to the next period of device communication information and the next period of base station communication information includes: Set each device node as a spider individual, and set the corresponding hunger level and location information for each spider individual according to the next period of device communication information; Each base station node is set as a food source, and the pheromone concentration, food quantity and location information of the food source are set for each food source according to the next base station communication information; Each individual spider automatically searches for the best food source based on its own hunger level and location information as well as the pheromone concentration, food quantity and location information of the food source; The location information of individual spiders that have found food sources and the matching information between individual spiders and food sources are stored in a memory pool, and the matching information between individual spiders and food sources is used as a communication strategy, which is used to implement the next phase of wireless communication device node and base station node deployment.

9. The wireless communication management system based on artificial intelligence technology as claimed in claim 4, characterized in that: The anti-interference strategy unit is used to obtain the anti-interference strategy according to the next period of interference information; The interference information includes external interference information and multipath interference information of each channel, the external interference information includes interference information generated by other devices of the same frequency, the external interference information includes external interference intensity, external interference devices and external interference frequency, the multipath interference information includes interference information generated by physical obstacles in the communication path, and the multipath interference information includes multipath interference intensity and multipath interference path; The communication frequency that the channel needs to skip is obtained according to the external interference frequency, and the interference strength of each channel is obtained according to the external interference strength and the multipath interference strength, and the signal strength of each channel is adjusted according to the interference strength of each channel.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, it implements any one of claims 1-9, an artificial intelligence-based wireless communication management system.

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