Wireless authentication and privacy infrastructure (WAPI) wireless internet of things system and method in transformer substation

Abstract

The invention discloses a WAPI wireless internet of things system and method in a transformer substation, and relates to the field of WAPI in the transformer substation, and the method comprises the steps: setting the distance between an electromagnetic interference monitoring sensor and equipment, and collecting the electromagnetic interference data of equipment communication in the transformer substation; based on a CNN neural network, performing dynamic sensing and adjustment on a WAPI frequency band and a channel; a communication-first and verification-second processing mechanism is set, so that the reconnection efficiency after disconnection of the equipment and the server is improved; setting a standby communication link, and dynamically switching the standby communication link according to the reconnection times of the equipment and the server; and constructing a WAPI wireless internet of things stability evaluation model, and carrying out comprehensive evaluation on the WAPI wireless internet of things in the transformer substation. The method has the advantages that by dynamically sensing the electromagnetic environment change of the transformer substation, the dynamic adjustment capability of frequency bands, channels, reconnection and switching of the WAPI network is enhanced, the stability of the WAPI network is improved, and the stability and safety of data transmission of equipment are guaranteed.

Description

TECHNICAL FIELD

[0001] The application relates to the field of WAPI in a transformer substation, and particularly relates to a WAPI wireless Internet of Things network system and method in a transformer substation. BACKGROUND

[0002] The transformer substation is a core hub of a power system, and is filled with strong electromagnetic equipment such as transformers and reactors. When the transformer substation is running, wide-band and high-intensity electromagnetic interference (such as harmonic radiation in a 30 MHz-3 GHz frequency band) is generated. As a main communication carrier of the transformer substation equipment and an ASU server, the WAPI wireless Internet of Things network is extremely susceptible to electromagnetic interference. On one hand, the strong magnetic environment can cause a significant decrease in the signal-to-noise ratio (SINR) of the WAPI signal, and can cause problems such as data packet loss and aggravated transmission delay. On the other hand, the complex electromagnetic coupling effect in the transformer substation can cause frequent fluctuations in the channel quality of the WAPI frequency band, and can greatly reduce the connection stability of the equipment and the server.

[0003] Meanwhile, the two-way authentication mechanism adopted by the WAPI protocol can guarantee communication security, but obvious defects are exposed in the strong magnetic interference scene. The traditional two-way authentication process needs to complete multiple handshake interactions. When the equipment is disconnected due to interference, the frequent authentication retries can further aggravate the network load. In particular, when multiple frequency bands of strong magnetic interference are superimposed, the authentication data packet can fail to pass the check due to interference, and can cause a repeated authentication cycle of the equipment and the ASU server. In an extreme case, the authentication queue of the ASU server can be congested, and even a protective collapse of the system can be triggered, causing a long-time interruption of the communication link. Therefore, it is particularly important to improve the stability of the WAPI wireless Internet of Things network in the transformer substation.

[0004] In the application of the existing WAPI network in the transformer substation, a fixed frequency band or channel configuration mode is generally adopted, and the dynamic perception ability of electromagnetic interference is lacked. The deployment positions of the traditional electromagnetic interference monitoring sensors are mainly based on experience, and are not differentiated based on the importance of the equipment communication data. As a result, the interference data collection of the core equipment is not comprehensive, and accurate basis cannot be provided for the optimization of the WAPI network. In addition, the two-way authentication mechanism does not fully consider the communication continuity requirement in the strong magnetic interference scene, and is susceptible to the repeated authentication cycle of the equipment and the ASU server, which can cause the authentication queue of the ASU server to be congested, and even trigger a protective collapse of the system, causing a long-time interruption of the communication link. SUMMARY

[0005] In order to solve the above technical problems, a WAPI wireless Internet of Things network system and method in a substation is provided. This technical solution solves the problems raised in the above background technology, such as the lack of dynamic perception capability of electromagnetic interference, and the fact that the deployment locations of traditional electromagnetic interference monitoring sensors are mostly based on experience, without differentiated layout based on the importance of device communication data, resulting in incomplete interference data collection of core devices and inability to provide accurate basis for WAPI network optimization. Secondly, the two-way authentication mechanism does not fully consider the communication continuity requirements in strong magnetic interference scenarios, and is susceptible to repeated authentication cycles between the device and the ASU server, resulting in congestion of the authentication queue of the ASU server, and even triggering a system protection collapse, causing a long-term interruption of the communication link.

[0006] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0007] A WAPI wireless Internet of Things networking method in a substation, comprising:

[0008] According to the importance of substation equipment communication data, the distance between the electromagnetic interference monitoring sensor and the equipment is set to collect electromagnetic interference data of equipment communication within the substation;

[0009] Based on electromagnetic interference data from device communications and a CNN neural network, the WAPI frequency band and channel are dynamically sensed and adjusted to reduce the disconnection rate between devices and servers.

[0010] According to the two-way authentication process of WAPI wireless Internet of Things, a communication-first-then-verification processing mechanism is set up to improve the reconnection efficiency after the device and server are disconnected;

[0011] Set up a backup communication link and dynamically switch the backup communication link based on the number of reconnections between the device and the server to improve the communication capability between the device and the server;

[0012] Based on the WAPI wireless Internet of Things network and electromagnetic interference historical data, a WAPI wireless Internet of Things network stability assessment model is constructed to conduct a comprehensive assessment of the WAPI wireless Internet of Things network in the substation.

[0013] Preferably, the setting of the distance between the electromagnetic interference monitoring sensor and the equipment according to the importance of the communication data of the substation equipment for collecting the electromagnetic interference data of the communication of the equipment in the substation specifically includes:

[0014] Based on the analytic hierarchy process, the importance of substation equipment communication data is quantitatively evaluated, with the importance of substation equipment communication data as the target layer and grid operation safety, equipment reliability, business and economic benefits, and operation and maintenance decision support as the criterion layer, to obtain the communication data importance coefficient.

[0015] Based on the substation equipment operating parameter data and the importance coefficient of communication data, an optimal model for the distance between electromagnetic interference monitoring sensors and equipment is constructed;

[0016] Obtain the optimal placement and quantity of electromagnetic interference monitoring sensors based on the optimal model of the distance between electromagnetic interference monitoring sensors and equipment;

[0017] Through the electromagnetic interference monitoring sensor group, the electromagnetic interference data of the equipment communication in the substation is collected synchronously in real time.

[0018] Preferably, the optimal model of the distance between the electromagnetic interference monitoring sensor and the equipment specifically includes:

[0019] According to the distribution of substation equipment, a plane rectangular coordinate system is established with the control center as the coordinate origin, and the plane coordinates of each substation equipment are obtained;

[0020] Filter and normalize substation equipment operating parameter data to eliminate the impact of data peaks and data dimensions;

[0021] Based on the substation equipment operating parameter data and the importance coefficient of communication data, the optimal distance between the electromagnetic interference monitoring sensor and the equipment is obtained;

[0022] With the device coordinates as the center of the circle and the optimal distance between the electromagnetic interference monitoring sensor and the device as the radius, obtain the common area of ​​the circle formed by multiple devices;

[0023] Based on the common area of ​​multiple devices, combined with the importance coefficient of substation equipment operating parameter data and communication data, an objective function for the optimal location of electromagnetic interference monitoring sensors is constructed;

[0024] According to the objective function of the optimal position of the electromagnetic interference monitoring sensor, the optimal position coordinates of the electromagnetic interference monitoring sensor are obtained based on the gradient descent algorithm;

[0025] The optimal value expression of the distance between the electromagnetic interference monitoring sensor and the equipment is:

[0026]

[0027] Where, d i is the optimal distance between the i-th device and the electromagnetic interference monitoring sensor, k is the environmental impact coefficient, P i is the communication power of the i-th device, I i is the importance coefficient of the communication data of the i-th device, S is the sensitivity of the electromagnetic interference monitoring sensor, and C is the critical threshold of electromagnetic interference;

[0028] The objective function expression for the optimal position of the electromagnetic interference monitoring sensor is:

[0029]

[0030] In the formula, F A is the optimal objective function value of the electromagnetic interference monitoring sensor position, n is the number of devices containing a common area, d(A, B i ) is the distance from the optimal point coordinates to the boundary of the i-th device common area circle.

[0031] Preferably, the electromagnetic interference data of device communication based on the CNN neural network dynamically senses and adjusts the WAPI frequency band and channel, and reduces the disconnection rate of the device and the server, specifically comprising:

[0032] The electromagnetic interference data of device communication and the state data of device communication are normalized to eliminate the influence of data dimension;

[0033] The electromagnetic interference data of device communication and the state data of device communication are taken as input, and the channel adjustment confidence and the frequency band adjustment confidence are taken as output;

[0034] Based on the CNN neural network, the channel adjustment confidence and the frequency band adjustment confidence are obtained;

[0035] A two-level response mode is set to dynamically sense and adjust the WAPI frequency band and channel;

[0036] According to the channel adjustment confidence and the frequency band adjustment confidence, the output instruction is directly mapped to the channel switching instruction of the WAPI protocol stack in combination with the two-level response mode, so as to reduce the disconnection rate of the device and the server;

[0037] The two-level response mode specifically comprises:

[0038] First-level response: according to the obtained channel adjustment confidence, it is judged whether the channel adjustment confidence is greater than the channel preset value, if yes, the CNN neural network is used to evaluate all available channels in the current frequency band in parallel, the channel with the highest SINR and the confidence less than the channel preset value is selected as the available channel, and the channel switching is performed, if no, it indicates that there is no need to replace the channel; if the channel needs to be replaced, and all available channels in the current frequency band are traversed, no available channel is found, the two-level response is adopted to dynamically adjust the WAPI frequency band;

[0039] Secondary response: Triggered when there is no available channel or frequency band adjustment confidence is greater than the frequency band preset value, based on CNN neural network parallel evaluation of all available frequency bands, screening out the frequency band with the minimum comprehensive evaluation threshold and confidence less than the frequency band preset value, and according to the first response mode, screening out the available channel and performing channel switching, if there is no available channel in the frequency band, setting back mechanism, according to the order from small to large, sequentially checking other frequency bands, and continuing to screen the available channel, in the process of checking, if the available channel is found, the channel switching is performed, if all the confidence is less than the frequency band preset value, still no available channel, then an abnormal reminder is sent, and the initial frequency band and channel are returned, and the first response judgment is performed again;

[0040] Preferably, the two-way authentication process according to the WAPI wireless Internet of Things, a communication verification processing mechanism is set up to improve the reconnection efficiency of the device and the server after disconnection, and specifically comprises:

[0041] According to the two-way authentication process of the WAPI wireless Internet of Things, when the first authentication is completed, the ASU generates N preliminary session keys;

[0042] At the same time when the ASU generates N preliminary session keys, an additional short-term authorization token is issued, wherein the authorization token includes: device identity certificate, key derivation parameter and token validity period;

[0043] According to the short-term authorization token and the preliminary session key, when the device is disconnected, a new session key is quickly calculated for communication reconnection, and a certificate verification request is submitted to the ASU asynchronously in the background;

[0044] Determine whether the asynchronous verification is passed, if yes, keep the device normal communication, if no, immediately terminate the communication connection and recycle the PSK sequence and the short-term authorization token.

[0045] Preferably, the setting of the standby communication link dynamically switches the standby communication link according to the reconnection number of the device and the server to improve the communication ability of the device and the server, and specifically comprises:

[0046] Taking the WAPI wireless Internet network as the main link, multiple standby communication links are set up, wherein the main link collects the link state in real time through the system server, and the multiple standby communication links collect the multiple link states through the background server;

[0047] According to the historical data of the reconnection of the device and the system server, based on the expert scoring method, the standby communication link trigger threshold is set, when the reconnection of the device and the system server exceeds the threshold, the standby communication link is switched;

[0048] According to the multiple link state data, a standby communication link switching evaluation model is constructed, and the standby communication link with the highest comprehensive score is selected as the switching target;

[0049] According to the link switching instructions between the device and the server, the device disconnects the main link communication, starts the backup communication link connection, and completes the link establishment through the handshake protocol;

[0050] According to the historical data of the electromagnetic interference monitoring sensor and based on the machine learning algorithm, the electromagnetic interference stability threshold is obtained, and it is detected whether the real-time collected electromagnetic interference is less than the stability threshold. If so, it means that the electromagnetic interference has stabilized and the main link communication connection is restored. If not, the backup communication link connection is started.

[0051] Preferably, the construction of a WAPI wireless Internet of Things network stability assessment model based on the WAPI wireless Internet of Things network and electromagnetic interference historical data, and the comprehensive assessment of the WAPI wireless Internet of Things network in the substation specifically include:

[0052] Obtain historical data on the operation and electromagnetic interference of the WAPI wireless IoT network, including: channel and frequency band adjustment confidence and adjustment data, electromagnetic interference data, disconnection and reconnection data, and link switching frequency data;

[0053] Filter and normalize WAPI wireless IoT network operation and electromagnetic interference historical data to eliminate the impact of data peaks and data dimensions;

[0054] The inputs are channel and frequency band adjustment confidence and adjustment data, electromagnetic interference data, disconnection and reconnection data, and link switching frequency data, and the WAPI wireless Internet of Things network evaluation value is used as the output.

[0055] The hidden layer performs feature fusion processing on the input through a multi-layer perceptron and sets a multi-level time window through a long short-term memory network;

[0056] According to the frequency of link switching and the mean square error formula, a loss function for evaluating the stability of the WAPI wireless IoT network is constructed.

[0057] A WAPI wireless Internet of Things network stability assessment model is constructed, and a comprehensive assessment of the stability of the WAPI wireless Internet of Things network in the substation is conducted based on the model.

[0058] Furthermore, this solution proposes a WAPI wireless Internet of Things network method system within a substation, which is used to implement the WAPI wireless Internet of Things network method within a substation as described above, including:

[0059] A sensor positioning module, which is used to set the distance between the electromagnetic interference monitoring sensor and the equipment according to the importance of the communication data of the substation equipment, and is used to collect electromagnetic interference data of the communication equipment in the substation;

[0060] A network optimization module is used to dynamically sense and adjust WAPI frequency bands and channels based on electromagnetic interference data from device communications and a CNN neural network to reduce the disconnection rate between devices and servers. A communication-first, verification-later processing mechanism is set up based on the WAPI wireless Internet of Things' two-way authentication process to improve the reconnection efficiency of devices and servers after disconnection. A backup communication link is set up and dynamically switched based on the number of reconnections between the device and the server to improve the communication capability between the device and the server.

[0061] The stability assessment module is used to build a WAPI wireless Internet of Things network stability assessment model based on the WAPI wireless Internet of Things network and electromagnetic interference historical data, and conduct a comprehensive assessment of the WAPI wireless Internet of Things network in the substation.

[0062] Preferably, the network optimization module includes:

[0063] A frequency band and channel adjustment unit, which is used to dynamically sense and adjust the WAPI frequency band and channel based on the CNN neural network according to the electromagnetic interference data of the device communication, thereby reducing the disconnection rate between the device and the server;

[0064] An optimized reconnection unit is used to set a communication-first-verification-later processing mechanism based on the two-way authentication process of the WAPI wireless Internet of Things to improve the reconnection efficiency after the device and the server are disconnected;

[0065] The link switching unit is used to set a backup communication link and dynamically switch the backup communication link according to the number of reconnections between the device and the server, thereby improving the communication capability between the device and the server.

[0066] Compared with the prior art, the present invention has the following beneficial effects:

[0067] The present invention provides a WAPI wireless Internet of Things network method in a substation. By constructing an optimal model of the distance between electromagnetic interference monitoring sensors and equipment, the location distance between sensors and equipment is scientifically set, and the electromagnetic interference data of equipment in the substation is accurately obtained, breaking through the monitoring blind spot problem caused by traditional random point distribution. In combination with the deep learning ability of the CNN neural network, the channel adjustment confidence and the frequency band adjustment confidence are obtained. Through the secondary response mode, the WAPI channel allocation strategy is dynamically adjusted to enable the device communication frequency band to effectively avoid the influence of electromagnetic interference and reduce the disconnection rate between the device and the server. Secondly, through the communication-first-then-verification processing mechanism, the timely connection of device communication is prioritized, and then the background asynchronous verification is used to ensure communication security, thereby improving the connection efficiency of device communication, reducing the delay time of device communication connection, and further integrating short-term authorization orders. The card and the preliminary session key can improve the connection efficiency of device communication while ensuring the security of device communication. Secondly, with the WAPI wireless Internet of Things network as the main link, multiple backup communication links are set up, and a backup communication link switching evaluation model is constructed. The backup communication link with the highest comprehensive score is selected as the switching target, and according to the electromagnetic interference stability threshold, it is switched back to the main link, thereby dynamically switching the backup communication link to improve the communication capability between the device and the server. Finally, a WAPI wireless Internet of Things network stability evaluation model is constructed to conduct a comprehensive evaluation of the stability of the WAPI wireless Internet of Things network in the substation, providing a quantitative decision-making basis for operation and maintenance personnel, thereby realizing dynamic perception of changes in the electromagnetic environment of the substation, strengthening the dynamic adjustment capability of the WAPI network frequency band, channel, reconnection and switching, improving the stability of the WAPI network, and ensuring the stable and secure data transmission of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 This is a flow chart of a WAPI wireless Internet of Things method within a substation according to the present invention;

[0069] Figure 2 The present invention sets the distance between the electromagnetic interference monitoring sensor and the equipment according to the importance of the communication data of the substation equipment, and is used to collect the electromagnetic interference data of the equipment communication in the substation.

[0070] Figure 3 This is a flow chart of the present invention for dynamically sensing and adjusting WAPI frequency bands and channels based on electromagnetic interference data from device communications and using a CNN neural network to reduce the disconnection rate between devices and servers.

[0071] Figure 4 This is a flowchart of the bidirectional authentication process based on the WAPI wireless Internet of Things of the present invention, which sets a communication-first-then-verification processing mechanism to improve the reconnection efficiency after the device and server are disconnected. DETAILED DESCRIPTION

[0072] The following description is used to disclose the present application to enable a person skilled in the art to implement the present application. The preferred embodiments in the following description are only as examples, and other obvious modifications can be made by those skilled in the art.

[0073] Referring to Figure 1 As shown in the figure, a substation WAPI wireless Internet of Things network method comprises:

[0074] According to the importance of substation equipment communication data, the distance between the electromagnetic interference monitoring sensor and the equipment is set to collect the electromagnetic interference data of the equipment communication in the substation;

[0075] According to the electromagnetic interference data of the equipment communication, based on the CNN neural network, the WAPI frequency band and channel are dynamically perceived and adjusted to reduce the disconnection rate of the equipment and the server;

[0076] According to the two-way authentication process of WAPI wireless Internet of Things, a communication-then-verification processing mechanism is set to improve the reconnection efficiency of the equipment and the server after disconnection;

[0077] A backup communication link is set, and according to the reconnection times of the equipment and the server, the backup communication link is dynamically switched to improve the communication ability of the equipment and the server;

[0078] Based on the WAPI wireless Internet of Things network and the electromagnetic interference historical data, a WAPI wireless Internet of Things network stability evaluation model is constructed to comprehensively evaluate the WAPI wireless Internet of Things network in the substation.

[0079] It can be explained that the strong magnetic environment of the substation easily interferes with the connection of the WAPI wireless Internet of Things network, causing the equipment and the server to easily disconnect, resulting in network communication interruption. WAPI adopts a two-way authentication process, and the authentication process is complex and has high latency, resulting in a time-consuming connection of the substation equipment network communication, especially after being disturbed by the multi-frequency strong magnetic interference of the substation, causing repeated connection between the equipment and the ASU server, causing the authentication queue of the ASU server to be congested, and even triggering a protective collapse of the system. Therefore, the present scheme scientifically sets the position distance between the electromagnetic interference monitoring sensor and the equipment, accurately collects the electromagnetic interference data of the equipment, and strengthens the dynamic adjustment capability of the WAPI network frequency band, channel, reconnection and switching, thereby improving the stability of the WAPI network and ensuring the stable and safe transmission of equipment data.

[0080] Referring to Figure 2 As shown in the figure, the distance between the electromagnetic interference monitoring sensor and the equipment is set according to the importance of the substation equipment communication data, and the electromagnetic interference data of the equipment communication in the substation is collected, which specifically comprises:

[0081] Based on the analytic hierarchy process, the importance of substation equipment communication data is quantitatively evaluated, with the importance of substation equipment communication data as the target layer and grid operation safety, equipment reliability, business and economic benefits, and operation and maintenance decision support as the criterion layer, to obtain the communication data importance coefficient.

[0082] Based on the substation equipment operating parameter data and the importance coefficient of communication data, an optimal model for the distance between electromagnetic interference monitoring sensors and equipment is constructed;

[0083] Obtain the optimal placement and quantity of electromagnetic interference monitoring sensors based on the optimal model of the distance between electromagnetic interference monitoring sensors and equipment;

[0084] Through the electromagnetic interference monitoring sensor group, the electromagnetic interference data of the equipment communication in the substation is collected synchronously in real time.

[0085] It can be explained that the deployment locations of traditional electromagnetic interference monitoring sensors are mostly based on experience, and are not differentiated according to the importance of equipment communication data. As a result, the interference data collection of core equipment is incomplete, and it is impossible to provide an accurate basis for WAPI network optimization. Therefore, this solution analyzes the importance coefficient of equipment communication data and the operating parameter data of substation equipment to obtain the optimal placement position and number of electromagnetic interference monitoring sensors and the distance between the equipment, so as to accurately and synchronously collect the electromagnetic interference data of equipment communications in the substation in real time, and provide data support for subsequent WAPI network stability adjustment and analysis.

[0086] The optimal model of the distance between the electromagnetic interference monitoring sensor and the equipment specifically includes:

[0087] According to the distribution of substation equipment, a plane rectangular coordinate system is established with the control center as the coordinate origin, and the plane coordinates of each substation equipment are obtained;

[0088] Filter and normalize substation equipment operating parameter data to eliminate the impact of data peaks and data dimensions;

[0089] Based on the substation equipment operating parameter data and the importance coefficient of communication data, the optimal distance between the electromagnetic interference monitoring sensor and the equipment is obtained;

[0090] With the device coordinates as the center of the circle and the optimal distance between the electromagnetic interference monitoring sensor and the device as the radius, obtain the common area of ​​the circle formed by multiple devices;

[0091] Based on the common area of ​​multiple devices, combined with the importance coefficient of substation equipment operating parameter data and communication data, an objective function for the optimal location of electromagnetic interference monitoring sensors is constructed;

[0092] According to the objective function of the optimal position of the electromagnetic interference monitoring sensor, the optimal position coordinates of the electromagnetic interference monitoring sensor are obtained based on the gradient descent algorithm;

[0093] The optimal value expression of the distance between the electromagnetic interference monitoring sensor and the equipment is:

[0094]

[0095] Where, d i is the optimal distance between the i-th device and the electromagnetic interference monitoring sensor, k is the environmental impact coefficient, P i is the communication power of the i-th device, I i is the importance coefficient of the communication data of the i-th device, S is the sensitivity of the electromagnetic interference monitoring sensor, and C is the critical threshold of electromagnetic interference;

[0096] The objective function expression for the optimal position of the electromagnetic interference monitoring sensor is:

[0097]

[0098] Where, F A is the objective function value for the optimal location of electromagnetic interference monitoring sensors, n is the number of devices containing public areas, d(A, B i ) is the distance from the optimal point coordinate to the boundary of the common area circle of the i-th device.

[0099] It can be explained that the optimal value of the spacing between electromagnetic interference monitoring sensors and equipment can be obtained through the expression of the optimal value of the spacing between a single device. However, in the actual setting process, it is necessary to combine the number of electromagnetic interference monitoring sensors and the layout of substation equipment for dynamic regulation to avoid waste of resources and unreasonable location settings. Therefore, this scheme constructs the objective function for the optimal location of electromagnetic interference monitoring sensors, highlights the influence of the importance coefficient of equipment communication data and communication power, and comprehensively analyzes the electromagnetic interference influence of the common area of ​​the equipment, so as to obtain the optimal electromagnetic interference monitoring sensor location coordinates. Among them, k is the environmental impact coefficient, which can be fitted by the linear regression equation to the environmental impact factors and solved by the least squares method. C is the critical threshold of electromagnetic interference, which refers to the maximum interference intensity that does not affect the normal communication of the equipment. It can be determined by electromagnetic compatibility standards such as GB / T17626.

[0100] Reference Figure 3 As shown, the WAPI frequency band and channel are dynamically sensed and adjusted based on the CNN neural network according to the electromagnetic interference data of device communication to reduce the disconnection rate between the device and the server. Specifically, the following steps are performed:

[0101] Normalize the electromagnetic interference data and status data of device communication to eliminate the impact of data dimension;

[0102] The electromagnetic interference data of the device communication and the status data of the device communication are used as input quantities, and the channel adjustment confidence and the frequency band adjustment confidence are used as output quantities;

[0103] Based on the CNN neural network, the channel adjustment confidence and frequency band adjustment confidence are obtained;

[0104] Set the secondary response mode to dynamically sense and adjust WAPI frequency bands and channels;

[0105] Based on the channel adjustment confidence level and the frequency band adjustment confidence level, combined with the secondary response mode, the output command is directly mapped to the channel switching command of the WAPI protocol stack, reducing the disconnection rate between the device and the server.

[0106] The secondary response mode is specifically:

[0107] Level 1 response: Based on the obtained channel adjustment confidence, determine whether the channel adjustment confidence is greater than the channel preset value. If so, all other available channels in the current frequency band are evaluated in parallel based on the CNN neural network, and the channel with a confidence less than the channel preset value and the highest SINR is selected as the available channel, and channel switching is performed. If not, it means that there is no need to change the channel. If a channel change is required and no other available channels in the current frequency band are found, a level 2 response is adopted to dynamically adjust the WAPI frequency band.

[0108] Level 2 response: Triggered when there is no available channel or the frequency band adjustment confidence is greater than the frequency band preset value. Based on the CNN neural network, all other available frequency bands are evaluated in parallel, and the frequency band with the smallest comprehensive evaluation threshold and confidence less than the frequency band preset value is screened out. According to the level 1 response mode, available channels are screened out and channel switching is performed. If there is no available channel in the frequency band, a fallback mechanism is set up to check other frequency bands in order from small to large according to the comprehensive evaluation threshold, and continue to screen available channels. During the screening process, if an available channel is found, channel switching is performed. If there is still no available channel after traversing all frequency bands with confidence less than the frequency band preset value, an abnormal reminder is issued, the system returns to the original frequency band and channel, and the level 1 response judgment is re-performed.

[0109] It can be explained that electromagnetic waves in different frequency bands have different sensitivities to electromagnetic interference. High-intensity electromagnetic interference may generate strong induced currents and noise in specific frequency bands, resulting in a decrease in signal quality. By dynamically adjusting channels and frequency bands, when it is found that the currently used channel or frequency band is severely interfered with, it is promptly switched to other channels or frequency bands with better quality, thereby ensuring the clear and accurate transmission of communication signals. Therefore, this solution uses the deep learning capabilities of the CNN neural network to obtain the channel adjustment confidence and the frequency band adjustment confidence, and proposes a two-level response mode. Combined with the channel adjustment confidence and the frequency band adjustment confidence, the WAPI channel and frequency band allocation strategy is dynamically adjusted to enable device communication to effectively avoid the impact of electromagnetic interference and reduce the disconnection rate between the device and the server. Among them, the channel adjustment confidence and the frequency band adjustment confidence refer to the degree of confidence that the current channel and frequency band need to be switched. When the confidence is greater than the preset value, it indicates that the current channel and frequency band need to be switched;

[0110] The comprehensive evaluation threshold expression is:

[0111] λ=β1x1+β2x2+β3x3+β4x4+β0

[0112] Where λ is the comprehensive evaluation threshold, β0 is the equilibrium constant, β1, β2, β3, and β4 are the weights of the average interference intensity of the frequency band, handover complexity, device compatibility, and the number of frequency band and channel loads, respectively; x1, x2, x3, and x4 are the quantized values ​​of the average interference intensity of the frequency band, handover complexity, device compatibility, and the number of frequency band and channel loads, respectively;

[0113] Among them, the values ​​of β0, β1, β2, β3 and β4 can be obtained based on historical data using the least squares method or machine learning algorithm.

[0114] Reference Figure 4 As shown, according to the two-way authentication process of the WAPI wireless Internet of Things, a communication-first-verification processing mechanism is set to improve the reconnection efficiency of the device and the server after disconnection, specifically including:

[0115] According to the two-way authentication process of WAPI wireless IoT, when completing the first authentication, the ASU generates N preliminary session keys;

[0116] When the ASU generates N preliminary session keys, it also issues a short-term authorization token. The authorization token contains: device identity certificate, key derivation parameters, and token validity period.

[0117] Based on the short-term authorization token and the preliminary session key, when the device is disconnected, a new session key is quickly calculated to reconnect the communication. At the same time, the background asynchronously submits a certificate verification request to ASU.

[0118] Determine whether the asynchronous verification is passed. If so, maintain normal communication between the devices. If not, immediately terminate the communication connection and recycle the PSK sequence and short-term authorization token.

[0119] It can be explained that the two-way authentication process of the WAPI wireless Internet of Things requires multiple handshake interactions. When the device is disconnected due to interference, frequent authentication retries will further increase the network load, especially when strong magnetic interference in multiple bands is superimposed. The device is more likely to be disconnected and the frequency of authentication retries will be higher, resulting in a sharp increase in network load. For devices that have successfully connected for the first time, the authentication process can be appropriately simplified to improve the efficiency of device reconnection. Therefore, this solution combines short-term authorization tokens and preliminary session keys. When the device is disconnected, a new session key is quickly calculated to reconnect the communication. At the same time, the background asynchronously submits a certificate verification request to the ASU. The efficiency of device reconnection is effectively improved through the communication-first-then-verification processing mechanism. The complexity and dynamics of the new session key are increased by combining the short-term authorization token and the preliminary session key, reducing the security risk of a single preliminary session key or a short-term authorization token. Among them, the method of calculating the new session key by combining the short-term authorization token and the preliminary session key follows the following principles:

[0120] The importance coefficient of device communication data is divided into three intervals: low importance data, medium importance data and high importance data;

[0121] Low-importance data: When the importance coefficient of device communication data is within the low-importance data range, a short-term authorization token-dominated mode is adopted. After quickly verifying the identity through the token, a simple session key, such as HMAC-SHA256 (token + timestamp), is directly generated to reduce key negotiation overhead.

[0122] Medium-importance data: When the importance coefficient of device communication data is within the medium-importance data range, the pre-session key-dominated mode is adopted. A new session key is generated through the Diffie-Hellman key exchange using the generated pre-session key and the device identity ID.

[0123] High-importance data: When the importance coefficient of device communication data is within the high-importance data range, a fusion mechanism of a preliminary session key and a short-term authorization token is adopted. The short-term authorization token is used as the basis for identity authentication, and the preliminary session key is used as the encryption seed. A high-strength session key is generated through the key derivation function HKDF.

[0124] The step of setting a backup communication link and dynamically switching the backup communication link according to the number of reconnections between the device and the server to improve the communication capability between the device and the server specifically includes:

[0125] With the WAPI wireless IoT network as the primary link, multiple backup communication links are set up. The primary link collects link status in real time through the system server, while the multiple backup communication links collect multi-link status through the background server.

[0126] Based on the historical data of device and system server reconnections and the expert scoring method, a backup communication link trigger threshold is set. When the device and system server reconnection rate exceeds this threshold, the backup communication link is switched.

[0127] Based on the multi-link status data, a backup communication link switching evaluation model is constructed to select the backup communication link with the highest comprehensive score as the switching target;

[0128] According to the link switching instructions between the device and the server, the device disconnects the main link communication, starts the backup communication link connection, and completes the link establishment through the handshake protocol;

[0129] According to the historical data of the electromagnetic interference monitoring sensor and based on the machine learning algorithm, the electromagnetic interference stability threshold is obtained, and it is detected whether the real-time collected electromagnetic interference is less than the stability threshold. If so, it means that the electromagnetic interference has stabilized and the main link communication connection is restored. If not, the backup communication link connection is started.

[0130] It can be explained that after the CNN neural network-based dynamic perception and adjustment of the WAPI frequency band and channel and multiple unsuccessful reconnections between the device and the server, in order to ensure the normal transmission of device data and guarantee data support for the safety detection of the substation, it is necessary to switch to a backup communication link that can pass normally. Therefore, this solution uses the WAPI wireless Internet of Things network as the main link, sets up multiple backup communication links, and constructs a backup communication link switching evaluation model to select the backup communication link with the highest comprehensive score as the switching target. At the same time, the main link communication connection is restored through the electromagnetic interference stability threshold, thereby ensuring the normal transmission of device data and the safety detection of the substation;

[0131] The standby communication link switching evaluation model expression is:

[0132]

[0133] Where R i is the evaluation value of switching the ith standby communication link, are the weights of the backup communication link packet loss rate, backup communication link transmission delay and backup communication link reconnection times, respectively. i is the current packet loss rate of the ith backup communication link, T i is the current transmission delay of the ith backup communication link, T i-max is the maximum transmission delay of the ith backup communication link within one year, M iis the number of reconnections of the ith backup communication link in the last hour, M i-max is the maximum number of reconnections of the ith backup communication link within one year.

[0134] The WAPI wireless Internet of Things network stability assessment model is constructed based on the WAPI wireless Internet of Things network and electromagnetic interference historical data, and the comprehensive assessment of the WAPI wireless Internet of Things network in the substation specifically includes:

[0135] Obtain historical data on the operation and electromagnetic interference of the WAPI wireless IoT network, including: channel and frequency band adjustment confidence and adjustment data, electromagnetic interference data, disconnection and reconnection data, and link switching frequency data;

[0136] Filter and normalize WAPI wireless IoT network operation and electromagnetic interference historical data to eliminate the impact of data peaks and data dimensions;

[0137] The inputs are channel and frequency band adjustment confidence and adjustment data, electromagnetic interference data, disconnection and reconnection data, and link switching frequency data, and the WAPI wireless Internet of Things network evaluation value is used as the output.

[0138] The hidden layer performs feature fusion processing on the input through a multi-layer perceptron and sets a multi-level time window through a long short-term memory network;

[0139] According to the frequency of link switching and the mean square error formula, a loss function for evaluating the stability of the WAPI wireless IoT network is constructed.

[0140] A WAPI wireless Internet of Things network stability assessment model is constructed, and a comprehensive assessment of the stability of the WAPI wireless Internet of Things network in the substation is conducted based on the model.

[0141] It can be explained that by integrating channel and frequency band adjustment confidence and adjustment data, electromagnetic interference data, disconnection and reconnection data, and link switching frequency data, based on a neural network algorithm and setting multi-level time windows, the stability of the WAPI wireless IoT network in the substation is comprehensively evaluated through short-term fluctuations and long-term dependence. This is used to guide the optimization and maintenance of the WAPI wireless IoT network in the substation, improve the overall stability and reliability of the WAPI wireless IoT network in the substation, and ensure the normal operation of the substation business. On the other hand, the link switching frequency is an important indicator for evaluating the overall stability of the WAPI wireless IoT network in the substation. The size of the switching frequency directly determines the overall stability of the WAPI wireless IoT network. Therefore, when constructing the loss function for the WAPI wireless IoT network stability assessment, it is necessary to increase the importance of the link switching frequency through a weighted approach.

[0142] The loss function expression of the WAPI wireless Internet of Things network stability evaluation is:

[0143]

[0144] Where, L loss is the loss function value of the WAPI wireless Internet of Things network stability assessment, L1 is the loss function value obtained by solving the linear regression formula constructed by channel and frequency band adjustment confidence and adjustment data, electromagnetic interference data, disconnection and reconnection data, L2 is the loss function value of the link switching frequency data, δ is the weight of the link switching frequency data, and L i∈{1,2} In order to make L1 and L2 both conform to the formula, m is the number of sample groups, For L i The j-th input value corresponding to the loss function, For L i The preset value corresponding to the loss function;

[0145] For ease of understanding, it can be interpreted as: when i=1, is the j-th input value corresponding to the L1-th loss function, that is, the j-th input value solved by the linear regression formula constructed by adjusting the confidence and adjustment data of the channel and frequency band, the electromagnetic interference data, and the disconnection and reconnection data. is the preset value corresponding to the L1 loss function. When i=2, is the jth calculation input value corresponding to the L2 loss function, that is, the jth input value of the link switching frequency data, is the preset value corresponding to the L2 loss function.

[0146] Furthermore, based on the same inventive concept as the aforementioned WAPI wireless Internet of Things network method within a substation, this solution proposes a WAPI wireless Internet of Things network method system within a substation, comprising:

[0147] A sensor positioning module, which is used to set the distance between the electromagnetic interference monitoring sensor and the equipment according to the importance of the communication data of the substation equipment, and is used to collect electromagnetic interference data of the communication equipment in the substation;

[0148] A network optimization module is used to dynamically sense and adjust WAPI frequency bands and channels based on electromagnetic interference data from device communications and a CNN neural network to reduce the disconnection rate between devices and servers. A communication-first, verification-later processing mechanism is set up based on the WAPI wireless Internet of Things' two-way authentication process to improve the reconnection efficiency of devices and servers after disconnection. A backup communication link is set up and dynamically switched based on the number of reconnections between the device and the server to improve the communication capability between the device and the server.

[0149] A stability assessment module, which is used to construct a WAPI wireless Internet of Things network stability assessment model based on WAPI wireless Internet of Things network and electromagnetic interference historical data, and conduct a comprehensive assessment of the WAPI wireless Internet of Things network in the substation;

[0150] The network optimization module includes:

[0151] A frequency band and channel adjustment unit, which is used to dynamically sense and adjust the WAPI frequency band and channel based on the CNN neural network according to the electromagnetic interference data of the device communication, thereby reducing the disconnection rate between the device and the server;

[0152] An optimized reconnection unit is used to set a communication-first-verification-later processing mechanism based on the two-way authentication process of the WAPI wireless Internet of Things to improve the reconnection efficiency after the device and the server are disconnected;

[0153] The link switching unit is used to set a backup communication link and dynamically switch the backup communication link according to the number of reconnections between the device and the server, thereby improving the communication capability between the device and the server.

[0154] In summary, the advantages of the present invention are: by dynamically sensing changes in the electromagnetic environment of the substation, the dynamic adjustment capabilities of the WAPI network frequency band, channel, reconnection and switching are enhanced, the stability of the WAPI network is improved, and the stable and secure data transmission of the equipment is ensured.

[0155] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A WAPI wireless Internet of Things network method in a substation, characterized in that: include: According to the importance of substation equipment communication data, the distance between the electromagnetic interference monitoring sensor and the equipment is set to collect electromagnetic interference data of equipment communication within the substation; Based on electromagnetic interference data from device communications and a CNN neural network, the WAPI frequency band and channel are dynamically sensed and adjusted to reduce the disconnection rate between devices and servers. According to the two-way authentication process of WAPI wireless Internet of Things, a communication-first-then-verification processing mechanism is set up to improve the reconnection efficiency after the device and server are disconnected; Set up a backup communication link and dynamically switch the backup communication link based on the number of reconnections between the device and the server to improve the communication capability between the device and the server; Based on the WAPI wireless Internet of Things network and electromagnetic interference historical data, a WAPI wireless Internet of Things network stability assessment model is constructed to conduct a comprehensive assessment of the WAPI wireless Internet of Things network in the substation.

2. A WAPI wireless Internet of Things network method in a substation according to claim 1, characterized in that: The distance between the electromagnetic interference monitoring sensor and the equipment is set according to the importance of the communication data of the substation equipment to collect the electromagnetic interference data of the communication of the equipment in the substation, which specifically includes: Based on the analytic hierarchy process, the importance of substation equipment communication data is quantitatively evaluated, with the importance of substation equipment communication data as the target layer and grid operation safety, equipment reliability, business and economic benefits, and operation and maintenance decision support as the criterion layer, to obtain the communication data importance coefficient. Based on the substation equipment operating parameter data and the importance coefficient of communication data, an optimal model for the distance between electromagnetic interference monitoring sensors and equipment is constructed; Obtain the optimal placement and quantity of electromagnetic interference monitoring sensors based on the optimal model of the distance between electromagnetic interference monitoring sensors and equipment; Through the electromagnetic interference monitoring sensor group, the electromagnetic interference data of the equipment communication in the substation is collected synchronously in real time.

3. A WAPI wireless Internet of Things network method in a substation according to claim 2, characterized in that: The optimal model of the distance between the electromagnetic interference monitoring sensor and the equipment specifically includes: According to the distribution of substation equipment, a plane rectangular coordinate system is established with the control center as the coordinate origin, and the plane coordinates of each substation equipment are obtained; Filter and normalize substation equipment operating parameter data to eliminate the impact of data peaks and data dimensions; Based on the substation equipment operating parameter data and the importance coefficient of communication data, the optimal distance between the electromagnetic interference monitoring sensor and the equipment is obtained; With the device coordinates as the center of the circle and the optimal distance between the electromagnetic interference monitoring sensor and the device as the radius, obtain the common area of ​​the circle formed by multiple devices; Based on the common area of ​​multiple devices, combined with the importance coefficient of substation equipment operating parameter data and communication data, an objective function for the optimal location of electromagnetic interference monitoring sensors is constructed; According to the objective function of the optimal position of the electromagnetic interference monitoring sensor, the optimal position coordinates of the electromagnetic interference monitoring sensor are obtained based on the gradient descent algorithm; The optimal value expression of the distance between the electromagnetic interference monitoring sensor and the equipment is: Where, d i is the optimal distance between the i-th device and the electromagnetic interference monitoring sensor, k is the environmental impact coefficient, P i is the communication power of the i-th device, I i is the importance coefficient of the communication data of the i-th device, S is the sensitivity of the electromagnetic interference monitoring sensor, and C is the critical threshold of electromagnetic interference; The objective function expression for the optimal position of the electromagnetic interference monitoring sensor is: Where, F A is the objective function value for the optimal location of electromagnetic interference monitoring sensors, n is the number of devices containing public areas, d(A, B i ) is the distance from the optimal point coordinate to the boundary of the common area circle of the i-th device.

4. A WAPI wireless Internet of Things network method in a substation according to claim 3, characterized in that: Dynamically sensing and adjusting the WAPI frequency band and channel based on the CNN neural network based on the electromagnetic interference data of device communications to reduce the disconnection rate between the device and the server specifically includes: Normalize the electromagnetic interference data and status data of device communication to eliminate the impact of data dimension; The electromagnetic interference data of the device communication and the status data of the device communication are used as input quantities, and the channel adjustment confidence and the frequency band adjustment confidence are used as output quantities; Based on the CNN neural network, the channel adjustment confidence and frequency band adjustment confidence are obtained; Set the secondary response mode to dynamically sense and adjust WAPI frequency bands and channels; Based on the channel adjustment confidence level and the frequency band adjustment confidence level, combined with the secondary response mode, the output command is directly mapped to the channel switching command of the WAPI protocol stack, reducing the disconnection rate between the device and the server. The secondary response mode is specifically: Level 1 response: Based on the obtained channel adjustment confidence, determine whether the channel adjustment confidence is greater than the channel preset value. If so, all other available channels in the current frequency band are evaluated in parallel based on the CNN neural network, and the channel with a confidence less than the channel preset value and the highest SINR is selected as the available channel, and channel switching is performed. If not, it means that there is no need to change the channel. If a channel change is required and no other available channels in the current frequency band are found, a level 2 response is adopted to dynamically adjust the WAPI frequency band. Level 2 response: Triggered when there is no available channel or the frequency band adjustment confidence is greater than the frequency band preset value. Based on the CNN neural network, all other available frequency bands are evaluated in parallel, and the frequency band with the smallest comprehensive evaluation threshold and confidence less than the frequency band preset value is screened out. According to the level 1 response mode, available channels are screened out and channel switching is performed. If there is no available channel in the frequency band, a fallback mechanism is set up to check other frequency bands in order from small to large according to the comprehensive evaluation threshold, and continue to screen available channels. During the checking process, if an available channel is found, channel switching is performed. If there is still no available channel after traversing all frequency bands with confidence less than the frequency band preset value, an abnormal reminder is issued, returning to the original frequency band and channel, and re-judging the level 1 response.

5. A WAPI wireless Internet of Things method in a substation according to claim 4, characterized in that: The two-way authentication process of the WAPI wireless Internet of Things is used to set up a communication-first-verification processing mechanism to improve the reconnection efficiency of the device and the server after the disconnection, which specifically includes: According to the two-way authentication process of WAPI wireless IoT, when completing the first authentication, the ASU generates N preliminary session keys; When the ASU generates N preliminary session keys, it also issues a short-term authorization token. The authorization token contains: device identity certificate, key derivation parameters, and token validity period. Based on the short-term authorization token and the preliminary session key, when the device is disconnected, a new session key is quickly calculated to reconnect the communication. At the same time, the background asynchronously submits a certificate verification request to ASU. Determine whether the asynchronous verification is passed. If so, maintain normal communication between the devices. If not, immediately terminate the communication connection and recycle the PSK sequence and short-term authorization token.

6. A WAPI wireless Internet of Things network method in a substation according to claim 5, characterized in that: The step of setting a backup communication link and dynamically switching the backup communication link according to the number of reconnections between the device and the server to improve the communication capability between the device and the server specifically includes: With the WAPI wireless IoT network as the primary link, multiple backup communication links are set up. The primary link collects link status in real time through the system server, while the multiple backup communication links collect multi-link status through the background server. Based on the historical data of device and system server reconnections and the expert scoring method, a backup communication link trigger threshold is set. When the device and system server reconnection rate exceeds this threshold, the backup communication link is switched. Based on the multi-link status data, a backup communication link switching evaluation model is constructed to select the backup communication link with the highest comprehensive score as the switching target; According to the link switching instructions between the device and the server, the device disconnects the main link communication, starts the backup communication link connection, and completes the link establishment through the handshake protocol; According to the historical data of the electromagnetic interference monitoring sensor and based on the machine learning algorithm, the electromagnetic interference stability threshold is obtained, and it is detected whether the real-time collected electromagnetic interference is less than the stability threshold. If so, it means that the electromagnetic interference has stabilized and the main link communication connection is restored. If not, the backup communication link connection is started.

7. A WAPI wireless Internet of Things method in a substation according to claim 6, characterized in that: The WAPI wireless Internet of Things network stability assessment model is constructed based on the WAPI wireless Internet of Things network and electromagnetic interference historical data, and the comprehensive assessment of the WAPI wireless Internet of Things network in the substation specifically includes: Obtain historical data on the operation and electromagnetic interference of the WAPI wireless IoT network, including: channel and frequency band adjustment confidence and adjustment data, electromagnetic interference data, disconnection and reconnection data, and link switching frequency data; Filter and normalize WAPI wireless IoT network operation and electromagnetic interference historical data to eliminate the impact of data peaks and data dimensions; The inputs are channel and frequency band adjustment confidence and adjustment data, electromagnetic interference data, disconnection and reconnection data, and link switching frequency data, and the WAPI wireless Internet of Things network evaluation value is used as the output. The hidden layer performs feature fusion processing on the input through a multi-layer perceptron and sets a multi-level time window through a long short-term memory network; According to the frequency of link switching and the mean square error formula, a loss function for evaluating the stability of the WAPI wireless IoT network is constructed. A WAPI wireless Internet of Things network stability assessment model is constructed, and a comprehensive assessment of the stability of the WAPI wireless Internet of Things network in the substation is conducted based on the model.

8. A WAPI wireless Internet of Things network method system in a substation, characterized in that: A method for implementing a WAPI wireless Internet of Things network in a substation according to any one of claims 1 to 7, comprising: A sensor positioning module, which is used to set the distance between the electromagnetic interference monitoring sensor and the equipment according to the importance of the communication data of the substation equipment, and is used to collect electromagnetic interference data of the communication equipment in the substation; A network optimization module is used to dynamically sense and adjust WAPI frequency bands and channels based on electromagnetic interference data from device communications and a CNN neural network to reduce the disconnection rate between devices and servers. A communication-first, verification-later processing mechanism is set up based on the WAPI wireless Internet of Things' two-way authentication process to improve the reconnection efficiency of devices and servers after disconnection. A backup communication link is set up and dynamically switched based on the number of reconnections between the device and the server to improve the communication capability between the device and the server. The stability assessment module is used to build a WAPI wireless Internet of Things network stability assessment model based on the WAPI wireless Internet of Things network and electromagnetic interference historical data, and conduct a comprehensive assessment of the WAPI wireless Internet of Things network in the substation.

9. A WAPI wireless Internet of Things network method system in a substation according to claim 8, characterized in that: The network optimization module includes: A frequency band and channel adjustment unit, which is used to dynamically sense and adjust the WAPI frequency band and channel based on the CNN neural network according to the electromagnetic interference data of the device communication, thereby reducing the disconnection rate between the device and the server; An optimized reconnection unit is used to set a communication-first-verification-later processing mechanism based on the two-way authentication process of the WAPI wireless Internet of Things to improve the reconnection efficiency after the device and the server are disconnected; The link switching unit is used to set a backup communication link and dynamically switch the backup communication link according to the number of reconnections between the device and the server, thereby improving the communication capability between the device and the server.

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