CN117955810B - Communication monitoring method, device, equipment and storage medium based on artificial intelligence - Google Patents
Communication monitoring method, device, equipment and storage medium based on artificial intelligence Download PDFInfo
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Abstract
The application relates to a communication monitoring method, a device, equipment and a storage medium based on artificial intelligence, which are applied to the technical field of communication monitoring, and the method comprises the following steps: acquiring real-time communication network data of a communication network; inputting the real-time communication network data into a preset communication monitoring model for fault analysis to obtain an analysis result; and determining a monitoring processing strategy based on the analysis result, and transmitting the monitoring processing strategy. The application has the effects of monitoring the communication link and ensuring the communication smoothness.
Description
Technical Field
The present application relates to the field of communication monitoring technologies, and in particular, to a communication monitoring method, device, equipment and storage medium based on artificial intelligence.
Background
In mountain areas frequently suffering from geological disasters, real-time monitoring needs to be performed on the geological disasters so as to prepare for taking precautions against the occurrence of the geological disasters, but the geological disasters such as debris flows generally occur in the mountain areas and are affected by the environments of the mountain areas, and even if the geology of the mountain areas is monitored, monitoring data needs to be transmitted to the outside, so that monitoring staff can know the geology of the mountain areas in real time.
In the related art, a manner of fusing a plurality of sensors is generally adopted to monitor geological disasters, monitor data is collected and then transmitted, but in the transmission process, communication equipment is usually disturbed by mountain environments to cause communication interruption or faults, and further the monitor data is partially lost or completely lost in the transmission process to influence the monitoring of the geological disasters, so that a communication monitoring method based on artificial intelligence is needed to monitor a communication link to ensure communication smoothness.
Disclosure of Invention
In order to monitor a communication link and ensure communication smoothness, the application provides a communication monitoring method, device, equipment and storage medium based on artificial intelligence.
In a first aspect, the present application provides a communication monitoring method based on artificial intelligence, which adopts the following technical scheme:
A communication monitoring method based on artificial intelligence, comprising:
acquiring real-time communication network data of a communication network;
inputting the real-time communication network data into a preset communication monitoring model for fault analysis to obtain an analysis result;
and determining a monitoring processing strategy based on the analysis result, and transmitting the monitoring processing strategy.
By adopting the technical scheme, the monitoring of the communication network is realized by acquiring the communication network data in real time, then the real-time data is input into a preset communication monitoring model, the communication monitoring model is utilized to carry out fault analysis on the real-time communication network data, fault information in the communication network is further determined, different monitoring treatment strategies are formulated for different fault information according to analysis results, the monitoring of the communication network can be realized, the accuracy of the monitoring of the communication network is improved, the stability and the reliability of the communication network are improved, and on the other hand, the real-time monitoring of a communication link can be realized by analyzing the real-time communication network data through communication management equipment, so that the smoothness of the communication link is ensured, and the monitoring of geological disasters is further enhanced.
Optionally, before the inputting the real-time communication network data into a preset communication monitoring model to perform fault analysis, the method further includes:
Drawing a communication connection model based on the communication network data, wherein the communication connection model comprises nodes and connecting wires, the nodes refer to communication terminals, and the connecting wires refer to communication links;
Acquiring all historical communication network data of each node and each connecting wire within a preset time period of the communication network, wherein the historical communication network data comprise data flow, signal quality and equipment state;
Performing color coding conversion on the data traffic, the signal quality and the equipment state to obtain data color codes corresponding to the nodes or the connecting lines, wherein the data color codes comprise a first color code corresponding to the data traffic, a second color code corresponding to the signal quality and a third color code corresponding to the equipment state;
dividing all the historical communication network data and the corresponding data color codes into a training set, a verification set and a test set according to a preset proportion;
And training the communication connection model based on the training set, the verification set and the test set to obtain a preset communication monitoring model.
Optionally, the performing color code conversion on the data traffic, the signal quality and the device state to obtain data color codes corresponding to the nodes or the connecting lines includes:
acquiring the transmission speed and the data volume of each node and each connecting line based on the data traffic;
Quantizing the transmission speed and the data volume of each node and each connecting line based on preset data flow to obtain a first color code of the node or the connecting line;
Acquiring the signal intensity and the signal-to-noise ratio of each node and each connecting wire;
determining an intensity difference value according to preset signal intensity and the signal intensity of each node and each connecting line;
Quantizing the signal quality according to the signal-to-noise ratio and the intensity difference value to obtain a second color code of the node or the connecting line;
Acquiring the online time of each node and the connection time of each connecting wire;
In a preset period, performing index quantization on the online time and the connection time to obtain a third color code of the node or the connection line;
and combining the first color code, the second color code and the third color code to obtain data color codes corresponding to the nodes or the connecting lines.
Optionally, inputting the real-time communication network data into a preset communication monitoring model for fault analysis, and obtaining an analysis result includes:
The preset communication monitoring model performs color conversion according to the real-time communication network data to obtain a real-time color code of the node or the connecting line;
performing color display of each node and each connecting line based on the real-time color codes;
If the communication network has a fault, acquiring a color conversion point, wherein the color conversion point is the position where the real-time communication network data change;
Determining a connection line or node based on the color conversion point;
if the color conversion point is a connecting line, determining two connected nodes through the connecting line;
and determining a fault position based on the connecting line and the two nodes, and taking the fault position as an analysis result.
Optionally, inputting the real-time communication network data into a preset communication monitoring model for fault analysis, and obtaining an analysis result further includes:
Acquiring a communication log of each node, wherein the communication log comprises the online time of the node, a connectable node and the communication quality of the connectable node;
determining a first monitoring level of the node based on the communication log;
Acquiring fault information of the node, wherein the fault information comprises the node corresponding to the fault position and the fault frequency of the node;
Correcting the first monitoring level based on the fault information to obtain a second monitoring level;
And taking the second monitoring level as an analysis result.
Optionally, after the inputting the real-time communication network data into a preset communication monitoring model to perform fault analysis, the method further includes:
Determining a fault color code based on the color conversion point when the color conversion point occurs on the connection line;
Determining change data of the real-time communication network data based on the fault color code;
and determining fault types according to the change data, wherein the fault types comprise abnormal data flow, signal quality degradation and equipment faults.
Optionally, the determining a monitoring processing policy based on the analysis result includes:
judging whether the communication terminal fails or not based on the analysis result;
if the communication terminal fails, determining the failure times of the failed hardware equipment;
If the failure times of the failed hardware equipment are greater than or equal to preset failure times, a first monitoring and processing strategy is adopted, wherein the first monitoring and processing strategy comprises that a worker performs inspection according to a first monitoring frequency;
if the communication terminal does not have a fault, acquiring load information of the communication network;
A second monitoring process policy is determined based on the load information, the second monitoring process policy including reallocating data traffic and signal quality.
In a second aspect, the present application provides a communication monitoring device based on artificial intelligence, which adopts the following technical scheme:
a communication monitoring device based on artificial intelligence, comprising:
the acquisition module is used for acquiring real-time communication network data of the communication network;
the fault analysis module is used for inputting the real-time communication network data into a preset communication monitoring model to perform fault analysis to obtain an analysis result;
And the determining and displaying module is used for determining a monitoring processing strategy based on the analysis result and sending and displaying the monitoring processing strategy.
By adopting the technical scheme, the monitoring of the communication network is realized by acquiring the communication network data in real time, then the real-time data is input into a preset communication monitoring model, the communication monitoring model is utilized to carry out fault analysis on the real-time communication network data, fault information in the communication network is further determined, different monitoring treatment strategies are formulated for different fault information according to analysis results, the monitoring of the communication network can be realized, the accuracy of the monitoring of the communication network is improved, the stability and the reliability of the communication network are improved, and on the other hand, the real-time monitoring of a communication link can be realized by analyzing the real-time communication network data through communication management equipment, so that the smoothness of the communication link is ensured, and the monitoring of geological disasters is further enhanced.
In a third aspect, the present application provides a communication management apparatus, which adopts the following technical scheme:
A communication management device comprising a processor coupled with a memory;
the memory has stored thereon a computer program capable of being loaded by a processor and executing the artificial intelligence based communication monitoring method according to any of the first aspects.
In a fourth aspect, the present application provides a computer readable storage medium, which adopts the following technical scheme:
A computer readable storage medium storing a computer program capable of being loaded by a processor and executing the artificial intelligence based communication monitoring method according to any one of the first aspects.
Drawings
FIG. 1 is a block diagram of an artificial intelligence based communication monitoring system in accordance with an embodiment of the present application.
Fig. 2 is a schematic flow chart of a communication monitoring method based on artificial intelligence according to an embodiment of the present application.
Fig. 3 is a block diagram of a communication monitoring device based on artificial intelligence according to an embodiment of the present application.
Fig. 4 is a block diagram of a communication management apparatus according to an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the accompanying drawings.
Referring to fig. 1, in order to efficiently implement detection of a geological disaster in a communication network for monitoring the geological disaster, monitor the communication network, and guarantee a communication link, a plurality of monitoring devices are used to monitor the geological disaster, wherein, when monitoring is performed, a communication terminal and a plurality of sensors are arranged in the monitoring devices, the monitoring devices are connected with a communication management device through wireless communication, the communication management device is wirelessly connected with a cloud center, i.e. a camera is wirelessly connected with the communication management device, and the communication management device is wirelessly connected with the cloud center.
The system comprises a plurality of sensors, a cloud center and a wireless connection system, wherein the plurality of sensors comprise a video acquisition camera and a rainfall monitoring sensor, the video acquisition camera can shoot geology in a shooting area, acquire monitoring data, the rainfall monitoring sensor can acquire the rainfall, the plurality of sensors transmit the acquired monitoring data into a communication terminal, the plurality of communication terminals form a network and then transmit the network together into the communication management equipment, and then the communication management equipment transmits the monitoring data into the cloud center, wherein the wireless connection mode comprises 5G communication, beidou communication and satellite communication; so as to ensure the smoothness of the communication link and realize the real-time monitoring of geological disasters.
In this embodiment, the communication management device may receive the monitoring data transmitted by the monitoring device, and transmit the monitoring data to the cloud center through at least any one of 5G communication, beidou communication, or satellite communication.
When monitoring is carried out, the monitoring equipment wirelessly transmits monitored monitoring data to the cloud center, and a worker issues an instruction to the monitoring equipment through the cloud center to realize bidirectional data transmission, wherein the monitoring data comprises state data of a communication terminal, alarm data and video data acquired through a video acquisition camera, and the state data of the communication terminal is whether the communication terminal is online or not and whether the communication terminal is in fault or not; the alarm data comprises data exceeding a set monitoring data threshold, for example, when the rainfall is greater than a preset threshold, the rainfall data is alarm data; the video data acquired by the video acquisition camera includes video data of photographed mountain.
It is worth to say that, through the instruction issued by the cloud center, the video data acquired by the camera, the state data of the equipment and the alarm data can be transmitted in a 5G communication mode; the state data, alarm data and instructions issued by the cloud center of the communication terminal can be transmitted in a satellite communication mode; alarm data and instructions issued through the cloud center can be transmitted through Beidou communication. The cloud center is preferably alicloud.
In this embodiment, when monitoring data needs to be transmitted, because each monitoring device is affected by communication of the communication terminal in the process of transmitting data, faults may occur, and at this time, real-time monitoring on the communication network needs to be implemented through the communication management device, so as to ensure normal transmission of the monitoring data, the state data of the device, the alarm data and the issuing instruction of the cloud center.
The embodiment of the application provides a communication monitoring method based on artificial intelligence, which can be executed by communication management equipment, wherein the communication management equipment can be a server or terminal equipment, and the server can be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server for providing cloud computing service. The terminal device may be, but is not limited to, a smart phone, a tablet computer, a desktop computer, etc.
As shown in fig. 2, a communication monitoring method based on artificial intelligence, the main flow of the method is described as follows (steps S101 to S103):
step S101, acquiring real-time communication network data of a communication network.
In this embodiment, when monitoring a geological disaster, not only the internal line of the communication network needs to be monitored, but also whether the communication terminal is damaged needs to be monitored according to the communication network data, and meanwhile, the real-time monitoring of the communication network can be realized by monitoring the data of the real-time communication network, and whether the communication network fails or not can be found in time, so that the communication link is ensured, and the real-time monitoring of the geological disaster is realized.
The communication management device can acquire real-time communication network data of the communication network through satellites, 5G or Beidou, wherein the real-time communication network data comprise data traffic, signal quality and device states, the data traffic comprises transmission speed and data traffic, the signal quality comprises signal strength and signal to noise ratio, and the device states are online and fault of the communication terminal.
Step S102, inputting real-time communication network data into a preset communication monitoring model for fault analysis to obtain an analysis result;
Specifically, in order to perform fault analysis on real-time communication network data more accurately, building and training of a preset communication monitoring model are required to be achieved, so before the real-time communication network data is input into the preset communication monitoring model to perform fault analysis, the method further includes: drawing a communication connection model based on communication network data, wherein the communication connection model comprises nodes and connecting wires, the nodes refer to communication terminals, and the connecting wires refer to communication links; acquiring all historical communication network data of each node and each connecting wire in a preset time period of a communication network, wherein the historical communication network data comprise data flow, signal quality and equipment state; performing color coding conversion on the data traffic, the signal quality and the equipment state to obtain data color codes corresponding to all nodes or all connecting lines, wherein the data color codes comprise a first color code corresponding to the data traffic, a second color code corresponding to the signal quality and a third color code corresponding to the equipment state; dividing all historical communication network data and corresponding data color codes into a training set, a verification set and a test set according to a preset proportion; and training the communication connection model based on the training set, the verification set and the test set to obtain a preset communication monitoring model.
In this embodiment, in order to implement real-time monitoring of a geological disaster communication network system, a preset communication monitoring model needs to be built in a communication management device, and automatic and intelligent monitoring of communication is implemented through the preset communication monitoring model, so when the preset communication monitoring model is built, a communication connection model is firstly built through communication terminals in a communication network and communication network data, that is, each communication terminal in the monitoring device is set as a node in the communication connection model, then communication links between the communication terminals are represented through connecting wires, and finally a communication connection model is formed.
It should be noted that a communication network uses a communication connection model, but a plurality of communication networks may be connected through a communication management device or a server, so as to form a communication connection model set composed of a plurality of communication connection models, and only one communication connection model is taken as an example here.
After a communication connection model is built, the communication management device acquires historical communication network data of each node in a preset time period, in a communication network, the communication network data of a communication link between two nodes are stored in the communication management device or a hard disk, and the communication management device can read the communication network data stored with the communication link and append the historical communication network data of the node and the historical communication network data of the communication link to the communication connection model.
When the communication management equipment monitors the communication network or manually invokes monitoring data of the communication network, the communication management equipment can convert real-time communication network data on the nodes and the connecting lines into corresponding data color codes, and staff can monitor and search communication more conveniently and rapidly according to the colors presented by the data color codes.
When a preset communication monitoring model is built, the historical communication network data of each node and each connecting wire and the corresponding data color codes are divided according to a preset proportion to form a training set, a verification set and a test set, the communication monitoring model is trained through the training set, the verification set and the test set, and the preset communication monitoring model can be obtained after the training is completed.
It should be noted that, when dividing the preset proportions of the training set, the verification set and the test set, the division may be performed by using a random fixed proportion, for example: the preset ratio of the training set, the verification set and the test set may be 7:2:1, or may be 8:1:1, which is not limited herein.
The time sequence may be used to divide, for example, when historical communication network data is collected, the time of the communication terminal joining the communication network is taken as the starting time, the first cut-off time of the first preset period added to the starting time is taken as the training period, the second cut-off time of the second preset period added to the first cut-off time is taken as the verification period, and the third preset period added to the second cut-off time is taken as the test period.
For example, the starting time of a communication terminal joining a communication network is 2023, 1 month and 1 day, the first preset period is 1 month, that is, the first cutoff time is 2023, 2 months and 1 day, the first cutoff time is 2023, 1 month and 1 day to 2023, 2 months and 1 day are training periods, all communication network data in the training periods are data of a training set, the second preset period is 10 months, the second cutoff time is 2023, 12 months and 1 day, 2023, 2, 1 month and 1 day to 2023, 12 months and 1 day are test periods, all communication network data in the test periods are data of a test set, the third preset period is 1 month, the third cutoff time is 2024, 1 month and 1 day is 2023, 12, 1 month and 1 day to 2024, 1 month and 1 day are data of a verification set.
In another alternative embodiment, all the historical communications network data may also be divided into K subsets, with K-1 subsets being selected as training sets at a time, and the remaining subset being the validation set, for K training and validation.
The above three embodiments for dividing the training set, the verification set and the test set are not limited herein, and may be one kind or may be a combination of two or more kinds.
Further, performing color code conversion on the data traffic, the signal quality and the device state, respectively, to obtain data color codes corresponding to each node or each connecting line includes: acquiring the transmission speed and the data volume of each node and each connecting line based on the data traffic; quantizing the transmission speed and the data quantity of each node and each connecting wire based on preset data flow to obtain a first color code of the node or the connecting wire; acquiring the signal intensity and the signal-to-noise ratio of each node and each connecting wire; determining an intensity difference value according to preset signal intensity and the signal intensity of each node and each connecting line; quantizing the signal quality according to the signal-to-noise ratio and the intensity difference value to obtain a second color code of the node or the connecting line; acquiring the online time of each node and the connection time of each connecting wire; in a preset period, carrying out index quantification on the online time and the connection time to obtain a third color code of the node or the connection line; and combining the first color code, the second color code and the third color code to obtain the data color code corresponding to each node or each connecting line.
In this embodiment, since the communication network data includes data traffic, signal quality and device status, when converting the data traffic, signal quality and device status into data color codes, the data traffic, signal quality and device status need to be converted into RGB values that can be identified by the communication management device, that is, red values that specify that the data traffic corresponds to RGB modes, green values that specify that the signal quality corresponds to RGB modes, blue values that specify that the device status corresponds to RGB modes, only a combination form is shown herein, and combinations of other colors are also possible, such as red values that each of the data traffic, signal quality and device status corresponds to or red values that each of two of the data traffic, signal quality and device status correspond to;
When two of the data traffic, the signal quality, and the device status correspond to the same RGB color, the remaining RGB colors may be 0, and when the data traffic and the signal quality correspond to red, the device status corresponds to green, the blue value is 0.
The following is an illustration of the data traffic in one node for red, signal quality for green, and device status for blue.
In the present embodiment, since each node and each connection line includes data traffic, signal quality, and device status, each node and each connection line can be displayed by the RGB color mode.
Specifically, the data traffic includes transmission speed and data volume, the signal quality includes signal strength and signal-to-noise ratio, and the device status includes on-line time of the node and connection time of the connection line.
In calculating the first color code, the transmission speed and the data amount of each node are quantized, and then the quantized transmission speed and data amount are linearly mapped with RGB data.
In one possible implementation, since the transmission speed is transmitted at a speed of megabits in the communication network, the transmission speed can reach hundred megabits according to the current transmission speed, when the transmission speed is more than 1 megabit and less than hundred megabits, the transmission speed is directly converted into a part of a red value, and the data volume is basically in G, when the data volume is more than 1G and less than 100G, the data volume is directly converted into a part of a red value, for example, the transmission speed is 24M/S, the data volume is 24G, the red value at this time is 24+24=48, the first color code at this time is 48, and when the decimal number occurs, the carry is performed in a rounding manner, so that an error is allowed.
When calculating the signal quality, each communication terminal is provided with a signal intensity detection device when transmitting, an intensity difference value is determined through the signal intensity and the preset signal intensity, and then a second color code is determined according to the signal-to-noise ratio and the intensity difference value.
If the signal intensity of a node is 80, the preset signal intensity is 30, the intensity difference is 50, the signal-to-noise ratio is 20db, and the green value is 50+20=70, i.e. the second color code is 70.
It should be noted that the preset signal strength is set manually according to the region where the node is located.
When calculating the third color code, quantization is required in a preset period according to the on-line time of the node and the connection time of the connection line, so as to obtain the third color code, and since the communication terminal at the node may be off-line or the communication link may be disconnected irregularly, quantization is required according to the on-line time and the connection time.
I.e., 220 hours of on-line time for a node, then the third color code corresponding to that node is 220.
It should be noted that the preset period is ten days, that is, 240 hours.
When the first color code, the second color code and the third color code are obtained, the first color code, the second color code and the third color code are combined according to an RGB mode to obtain the data color code of the node, and therefore the data color code is displayed in a preset communication monitoring model.
Through the mode, the preset communication monitoring model is trained, after training is completed, the real-time communication network data is input into the preset communication monitoring model, and then the preset communication monitoring model performs fault analysis according to the input real-time communication network data to obtain an analysis result.
Specifically, inputting real-time communication network data into a preset communication monitoring model for fault analysis, and obtaining an analysis result includes: the method comprises the steps that a preset communication monitoring model performs color conversion according to real-time communication network data to obtain real-time color codes of nodes or connecting wires; performing color display of each node and each connecting line based on the real-time color codes; if the communication network has a fault, acquiring a color conversion point, wherein the color conversion point is a position where real-time communication network data change; determining a connection line or node based on the color conversion point; if the color conversion point is a connecting line, determining two connected nodes through the connecting line; and determining a fault position based on the connecting line and the two nodes, and taking the fault position as an analysis result.
In this embodiment, when real-time communication network data is input to a preset communication monitoring model, the real-time communication network data is first converted into a real-time color code, and then color display is performed in a communication management device according to the real-time communication network data; when the communication network is judged to have faults or anomalies according to the real-time communication network data in the communication network, the color in a preset communication network model starts to change, namely, a color conversion point appears at the fault point, at the moment, a connecting line or a node with faults can be determined according to the position of the color conversion point, and when the color conversion point appears on the node, the fault or anomalies of the communication terminal represented by the node are indicated; when the color conversion point appears on the connection line, it is indicated that a fault occurs when two nodes transmit data, and at this time, the fault position needs to be determined by the two nodes and the connection line, and then the fault position is taken as an analysis result.
For example, when a packet loss occurs in the data traffic, that is, when the node a sends a packet of the data traffic to the node B, the red value of the node a is 150, but when the red value of the node B is 100, the green value and the blue value are unchanged, that is, it may be determined that a color conversion point occurs in a connection line between the node a and the node B, and during the transmission, the data traffic is disturbed or intercepted, and it may be determined that a fault occurs during the transmission.
Further, inputting the real-time communication network data into a preset communication monitoring model for fault analysis, and obtaining an analysis result further includes: acquiring a communication log of each node, wherein the communication log comprises the online time of the node, the connectable node and the communication quality of the connectable node; determining a first monitoring level of the node based on the communication log; acquiring fault information of nodes, wherein the fault information comprises the nodes corresponding to the fault positions and the fault frequency of the nodes; correcting the first monitoring level based on the fault information to obtain a second monitoring level; and taking the second monitoring level as an analysis result.
In this embodiment, since the location of some nodes is protected by a firewall with a high security coefficient, or an anti-shielding and anti-interference device is provided, so that the communication quality of the nodes in the communication network transmission is relatively high, the monitoring of the nodes can be relaxed, but when the signal quality is poor, or the communication terminal is in an area with relatively poor communication, frequent monitoring of the nodes is required.
Therefore, in this embodiment, it is necessary to determine a first monitoring level of each node according to the communication log of the node, then correct the first monitoring level according to the fault information of the node to obtain a second monitoring level, and then use the second monitoring level as an analysis result.
Further, after inputting the real-time communication network data into a preset communication monitoring model to perform fault analysis, the method further comprises: determining a fault color code based on the color conversion point when the color conversion point occurs on the connection line; determining change data of the real-time communication network data based on the fault color code; and determining fault types according to the change data, wherein the fault types comprise abnormal data flow, signal quality degradation and equipment faults.
In this embodiment, when the color conversion point is located on the connection line, the fault color code is determined according to the color conversion point, that is, the change data of the communication network data is determined by the RGB data of the color conversion point and the RGB data of the nodes at two ends of the connection line.
Step S103, determining a monitoring processing strategy based on the analysis result, and transmitting the monitoring processing strategy.
Specifically, whether the communication terminal fails or not is judged based on the analysis result; if the communication terminal fails, determining the failure times of the failed hardware equipment; if the number of faults of the hardware equipment with faults is greater than or equal to the preset number of faults, a first monitoring and processing strategy is adopted, wherein the first monitoring and processing strategy comprises that a worker carries out inspection according to a first monitoring frequency; if the communication terminal does not have a fault, load information of the communication network is obtained; a second monitoring process policy is determined based on the load information, the second monitoring process policy including reallocating data traffic and signal quality.
In this embodiment, when the communication network fails, the failure may be caused by the failure or the hardware damage of the hardware device of the communication terminal, or may be caused by the interference or the excessive occupation of network resources during the transmission of the communication network data, which results in uneven load distribution and thus causes communication congestion, thereby affecting the transmission efficiency.
When the hardware of the communication network fails, the communication terminal may be interfered by external factors such as temperature, humidity and vibration in some areas, so that a false fault condition occurs to the communication terminal, wherein the false fault condition is a condition that the communication terminal does not actually fail, but the communication network data is abnormal due to the environmental factors; at this time, the preset fault times are set in the communication management device, and because the external factor is an accidental event, if the number of times of the faults of the communication terminal is greater than or equal to the preset fault times, the fault is truly generated, if the number of times of the faults is smaller than the preset fault times, the fault is represented as a false fault condition, and when the communication terminal truly generates the fault, the hardware device of the node needs to be monitored and processed according to the first monitoring and processing strategy.
When the communication terminal does not have a fault, it can be determined that the reason for the fault of the communication network data is due to interference or the situation that network resources are excessively occupied, at this time, load information of the communication network needs to be acquired, whether the situation is due to the situation that the network resources are excessively occupied or not is determined, if the situation is due to the situation that the network resources are excessively occupied, data flow is required to be automatically adjusted according to the load information, and redistribution is performed according to the data flow and signal quality, so that balanced distribution of loads is ensured, network congestion and performance degradation are avoided, and transmission efficiency is improved.
Fig. 3 is a block diagram of a communication monitoring device 200 based on artificial intelligence according to an embodiment of the present application.
As shown in fig. 3, the artificial intelligence based communication monitoring apparatus 200 mainly includes:
an acquisition module 201, configured to acquire real-time communication network data of a communication network;
the fault analysis module 202 is configured to input real-time communication network data into a preset communication monitoring model for fault analysis, so as to obtain an analysis result;
the determining and displaying module 203 is configured to determine a monitoring processing policy based on the analysis result, and send and display the monitoring processing policy.
As an optional implementation manner of this embodiment, the fault analysis module 202 is further specifically configured to, before inputting the real-time communication network data into the preset communication monitoring model to perform fault analysis, obtain an analysis result, the method further includes: drawing a communication connection model based on communication network data, wherein the communication connection model comprises nodes and connecting wires, the nodes refer to communication terminals, and the connecting wires refer to communication links; acquiring all historical communication network data of each node and each connecting wire in a preset time period of a communication network, wherein the historical communication network data comprise data flow, signal quality and equipment state; performing color coding conversion on the data traffic, the signal quality and the equipment state to obtain data color codes corresponding to all nodes or all connecting lines, wherein the data color codes comprise a first color code corresponding to the data traffic, a second color code corresponding to the signal quality and a third color code corresponding to the equipment state; dividing all historical communication network data and corresponding data color codes into a training set, a verification set and a test set according to a preset proportion; and training the communication connection model based on the training set, the verification set and the test set to obtain a preset communication monitoring model.
As an optional implementation manner of this embodiment, the fault analysis module 202 is further specifically configured to perform color-coding conversion on the data traffic, the signal quality, and the device status, and the obtaining data color codes corresponding to each node or each connection line includes: acquiring the transmission speed and the data volume of each node and each connecting line based on the data traffic; quantizing the transmission speed and the data quantity of each node and each connecting wire based on preset data flow to obtain a first color code of the node or the connecting wire; acquiring the signal intensity and the signal-to-noise ratio of each node and each connecting wire; determining an intensity difference value according to preset signal intensity and the signal intensity of each node and each connecting line; quantizing the signal quality according to the signal-to-noise ratio and the intensity difference value to obtain a second color code of the node or the connecting line; acquiring the online time of each node and the connection time of each connecting wire; in a preset period, carrying out index quantification on the online time and the connection time to obtain a third color code of the node or the connection line; and combining the first color code, the second color code and the third color code to obtain the data color code corresponding to each node or each connecting line.
As an optional implementation manner of this embodiment, the fault analysis module 202 is further specifically configured to input real-time communication network data into a preset communication monitoring model for fault analysis, where the obtaining an analysis result includes: the method comprises the steps that a preset communication monitoring model performs color conversion according to real-time communication network data to obtain real-time color codes of nodes or connecting wires; performing color display of each node and each connecting line based on the real-time color codes; if the communication network has a fault, acquiring a color conversion point, wherein the color conversion point is a position where real-time communication network data change; determining a connection line or node based on the color conversion point; if the color conversion point is a connecting line, determining two connected nodes through the connecting line; and determining a fault position based on the connecting line and the two nodes, and taking the fault position as an analysis result.
As an optional implementation manner of this embodiment, the fault analysis module 202 is further specifically configured to input real-time communication network data into a preset communication monitoring model for fault analysis, where obtaining an analysis result further includes: acquiring a communication log of each node, wherein the communication log comprises the online time of the node, the connectable node and the communication quality of the connectable node; determining a first monitoring level of the node based on the communication log; acquiring fault information of nodes, wherein the fault information comprises the nodes corresponding to the fault positions and the fault frequency of the nodes; correcting the first monitoring level based on the fault information to obtain a second monitoring level; and taking the second monitoring level as an analysis result.
As an optional implementation manner of this embodiment, the fault analysis module 202 is further specifically configured to perform fault analysis after inputting the real-time communication network data into the preset communication monitoring model, and after obtaining the analysis result, the method further includes: determining a fault color code based on the color conversion point when the color conversion point occurs on the connection line; determining change data of the real-time communication network data based on the fault color code; and determining fault types according to the change data, wherein the fault types comprise abnormal data flow, signal quality degradation and equipment faults.
As an optional implementation manner of this embodiment, the determining and displaying module 203 is further specifically configured to determine, based on the analysis result, a monitoring processing policy, including: judging whether the communication terminal fails or not based on the analysis result; if the communication terminal fails, determining the failure times of the failed hardware equipment; if the number of faults of the hardware equipment with faults is greater than or equal to the preset number of faults, a first monitoring and processing strategy is adopted, wherein the first monitoring and processing strategy comprises that a worker carries out inspection according to a first monitoring frequency; if the communication terminal does not have a fault, load information of the communication network is obtained; a second monitoring process policy is determined based on the load information, the second monitoring process policy including reallocating data traffic and signal quality.
In one example, a module in any of the above apparatuses may be one or more integrated circuits configured to implement the above methods, for example: one or more application specific integrated circuits (application specific integratedcircuit, ASIC), or one or more digital signal processors (DIGITAL SIGNAL processor, DSP), or one or more field programmable gate arrays (field programmable GATE ARRAY, FPGA), or a combination of at least two of these integrated circuit forms.
For another example, when a module in an apparatus may be implemented in the form of a scheduler of processing elements, the processing elements may be general-purpose processors, such as a central processing unit (central processing unit, CPU) or other processor that may invoke a program. For another example, the modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the apparatus and modules described above may refer to the corresponding process in the foregoing method embodiment, which is not repeated herein.
Fig. 4 is a block diagram of a communication management apparatus 300 according to an embodiment of the present application.
As shown in fig. 4, the communication management device 300 includes a processor 301 and a memory 302, and may further include an information input/information output (I/O) interface 303, one or more of a communication component 304, and a communication bus 305.
Wherein the processor 301 is configured to control the overall operation of the communication management device 300 to perform all or part of the steps of the artificial intelligence based communication monitoring method described above; the memory 302 is used to store various types of data to support operation at the communication management device 300, which may include, for example, instructions for any application or method operating on the communication management device 300, as well as application-related data. The Memory 302 may be implemented by any type or combination of volatile or non-volatile Memory devices, such as one or more of static random access Memory (Static Random Access Memory, SRAM), electrically erasable programmable Read-Only Memory (ELECTRICALLY ERASABLE PROGRAMMABLE READ-Only Memory, EEPROM), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM), programmable Read-Only Memory (Programmable Read-Only Memory, PROM), read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
The I/O interface 303 provides an interface between the processor 301 and other interface modules, which may be a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 304 is used for wired or wireless communication between the communication management device 300 and other devices. Wireless Communication, such as Wi-Fi, bluetooth, near field Communication (NFC for short), 2G, 3G, or 4G, or a combination of one or more thereof, the corresponding Communication component 304 can include: wi-Fi part, bluetooth part, NFC part.
The communication management device 300 may be implemented by one or more Application Specific Integrated Circuits (ASIC), digital signal Processor (DIGITAL SIGNAL Processor, DSP), digital signal processing device (DIGITAL SIGNAL Processing Device, DSPD), programmable logic device (Programmable Logic Device, PLD), field programmable gate array (Field Programmable GATE ARRAY, FPGA), controller, microcontroller, microprocessor or other electronic components for performing the artificial intelligence based communication monitoring method as set forth in the above embodiments.
Communication bus 305 may include a pathway to transfer information between the aforementioned components. The communication bus 305 may be a PCI (PERIPHERAL COMPONENT INTERCONNECT, peripheral component interconnect standard) bus or an EISA (Extended Industry Standard Architecture ) bus, or the like. The communication bus 305 may be divided into an address bus, a data bus, a control bus, and the like.
The communication management apparatus 300 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), car terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like, and may also be servers and the like.
The application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the steps of the communication monitoring method based on artificial intelligence when being executed by a processor.
The computer readable storage medium may include: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
The above description is only illustrative of the preferred embodiments of the present application and of the principles of the technology employed. It will be appreciated by persons skilled in the art that the scope of the application is not limited to the specific combinations of the features described above, but also covers other embodiments which may be formed by any combination of the features described above or their equivalents without departing from the spirit of the application. Such as the above-mentioned features and the technical features having similar functions (but not limited to) applied for in the present application are replaced with each other.
Claims (8)
1. A communication monitoring method based on artificial intelligence, comprising:
acquiring real-time communication network data of a communication network;
inputting the real-time communication network data into a preset communication monitoring model for fault analysis to obtain an analysis result;
determining a monitoring processing strategy based on the analysis result, and transmitting the monitoring processing strategy;
Before the real-time communication network data is input into a preset communication monitoring model to perform fault analysis, and an analysis result is obtained, the method further comprises the following steps:
Drawing a communication connection model based on the communication network data, wherein the communication connection model comprises nodes and connecting wires, the nodes refer to communication terminals, and the connecting wires refer to communication links;
Acquiring all historical communication network data of each node and each connecting wire within a preset time period of the communication network, wherein the historical communication network data comprise data flow, signal quality and equipment state;
Performing color coding conversion on the data traffic, the signal quality and the equipment state to obtain data color codes corresponding to the nodes or the connecting lines, wherein the data color codes comprise a first color code corresponding to the data traffic, a second color code corresponding to the signal quality and a third color code corresponding to the equipment state;
dividing all the historical communication network data and the corresponding data color codes into a training set, a verification set and a test set according to a preset proportion;
training the communication connection model based on the training set, the verification set and the test set to obtain a preset communication monitoring model;
the step of performing color code conversion on the data traffic, the signal quality and the equipment state to obtain data color codes corresponding to the nodes or the connecting lines comprises the following steps:
acquiring the transmission speed and the data volume of each node and each connecting line based on the data traffic;
Quantizing the transmission speed and the data volume of each node and each connecting line based on preset data flow to obtain a first color code of the node or the connecting line;
Acquiring the signal intensity and the signal-to-noise ratio of each node and each connecting wire;
determining an intensity difference value according to preset signal intensity and the signal intensity of each node and each connecting line;
Quantizing the signal quality according to the signal-to-noise ratio and the intensity difference value to obtain a second color code of the node or the connecting line;
Acquiring the online time of each node and the connection time of each connecting wire;
In a preset period, performing index quantization on the online time and the connection time to obtain a third color code of the node or the connection line;
and combining the first color code, the second color code and the third color code to obtain data color codes corresponding to the nodes or the connecting lines.
2. The method according to claim 1, wherein inputting the real-time communication network data into a preset communication monitoring model for fault analysis, and obtaining an analysis result comprises:
The preset communication monitoring model performs color conversion according to the real-time communication network data to obtain a real-time color code of the node or the connecting line;
performing color display of each node and each connecting line based on the real-time color codes;
If the communication network has a fault, acquiring a color conversion point, wherein the color conversion point is the position where the real-time communication network data change;
Determining a connection line or node based on the color conversion point;
if the color conversion point is a connecting line, determining two connected nodes through the connecting line;
and determining a fault position based on the connecting line and the two nodes, and taking the fault position as an analysis result.
3. The method of claim 2, wherein inputting the real-time communication network data into a preset communication monitoring model for fault analysis, and obtaining the analysis result further comprises:
Acquiring a communication log of each node, wherein the communication log comprises the online time of the node, a connectable node and the communication quality of the connectable node;
determining a first monitoring level of the node based on the communication log;
Acquiring fault information of the node, wherein the fault information comprises the node corresponding to the fault position and the fault frequency of the node;
Correcting the first monitoring level based on the fault information to obtain a second monitoring level;
And taking the second monitoring level as an analysis result.
4. A method according to claim 3, wherein after said inputting the real-time communication network data into a preset communication monitoring model for fault analysis, the method further comprises:
Determining a fault color code based on the color conversion point when the color conversion point occurs on the connection line;
Determining change data of the real-time communication network data based on the fault color code;
and determining fault types according to the change data, wherein the fault types comprise abnormal data flow, signal quality degradation and equipment faults.
5. The method of claim 1, wherein the determining a monitoring processing policy based on the analysis result comprises:
judging whether the communication terminal fails or not based on the analysis result;
if the communication terminal fails, determining the failure times of the failed hardware equipment;
If the failure times of the failed hardware equipment are greater than or equal to preset failure times, a first monitoring and processing strategy is adopted, wherein the first monitoring and processing strategy comprises that a worker performs inspection according to a first monitoring frequency;
if the communication terminal does not have a fault, acquiring load information of the communication network;
A second monitoring process policy is determined based on the load information, the second monitoring process policy including reallocating data traffic and signal quality.
6. A communication monitoring device based on artificial intelligence, comprising:
the acquisition module is used for acquiring real-time communication network data of the communication network;
the fault analysis module is used for inputting the real-time communication network data into a preset communication monitoring model to perform fault analysis to obtain an analysis result;
The determining and displaying module is used for determining a monitoring processing strategy based on the analysis result and sending and displaying the monitoring processing strategy;
The fault analysis module is further specifically configured to perform fault analysis by inputting real-time communication network data into a preset communication monitoring model, and before obtaining an analysis result, the method further includes: drawing a communication connection model based on communication network data, wherein the communication connection model comprises nodes and connecting wires, the nodes refer to communication terminals, and the connecting wires refer to communication links; acquiring all historical communication network data of each node and each connecting wire in a preset time period of a communication network, wherein the historical communication network data comprise data flow, signal quality and equipment state; performing color coding conversion on the data traffic, the signal quality and the equipment state to obtain data color codes corresponding to all nodes or all connecting lines, wherein the data color codes comprise a first color code corresponding to the data traffic, a second color code corresponding to the signal quality and a third color code corresponding to the equipment state; dividing all historical communication network data and corresponding data color codes into a training set, a verification set and a test set according to a preset proportion; training the communication connection model based on the training set, the verification set and the test set to obtain a preset communication monitoring model;
the fault analysis module is further specifically configured to perform color code conversion on the data traffic, the signal quality and the device status, and the obtaining the data color code corresponding to each node or each connecting line includes: acquiring the transmission speed and the data volume of each node and each connecting line based on the data traffic; quantizing the transmission speed and the data quantity of each node and each connecting wire based on preset data flow to obtain a first color code of the node or the connecting wire; acquiring the signal intensity and the signal-to-noise ratio of each node and each connecting wire; determining an intensity difference value according to preset signal intensity and the signal intensity of each node and each connecting line; quantizing the signal quality according to the signal-to-noise ratio and the intensity difference value to obtain a second color code of the node or the connecting line; acquiring the online time of each node and the connection time of each connecting wire; in a preset period, carrying out index quantification on the online time and the connection time to obtain a third color code of the node or the connection line; and combining the first color code, the second color code and the third color code to obtain the data color code corresponding to each node or each connecting line.
7. A communication management device comprising a processor coupled to a memory;
The processor is configured to execute a computer program stored in the memory to cause the communication management device to perform the method of any one of claims 1 to 5.
8. A computer readable storage medium comprising a computer program or instructions which, when run on a computer, cause the computer to perform the method of any one of claims 1 to 5.
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