Guide robot adaptive communication system and method based on multi-network cooperation

Abstract

The invention discloses a navigation robot self-adaptive communication system and method based on multi-network collaboration, and relates to the technical field of network communication, the system comprises a network management module which is responsible for safely storing encrypted AP information and network topology data and providing a function of receiving AP configuration update through NFC; the signal detection module is responsible for detecting network parameters and generating a network quality evaluation report; the switching control module is responsible for predicting the signal intensity in future set time, realizing microsecond-level decision response by combining a dynamic threshold algorithm and a manufacturer protocol adapter, and providing a decision basis for optimization of network connection; and the dual-channel communication module is integrated with a WiFi chip and a cellular network modem, realizes operator link redundancy through dual SIM (Subscriber Identity Module) card slots, dynamically optimizes WiFi and cellular signal reception by utilizing a built-in intelligent antenna array, and executes network connection optimization and switching operation according to a decision of the switching control module. The navigation robot can automatically adapt to different network environments.

Description

Technical Field

[0001] The present invention relates to the technical field of network communication, and in particular to a tour guide robot adaptive communication system and method based on multi-network collaboration. Background Art

[0002] As an intelligent service device, tour guide robots are widely used in various public places to provide people with convenient and efficient tour guide services. At present, there are several problems that need to be solved in the communication scheme used by tour guide robots, as follows:

[0003] 1. Poor WiFi signal stability: The tour guide robot relies heavily on a single WiFi network for communication. In actual operation, when the tour guide robot needs to work across floors, signal interruptions often occur due to differences in signal strength, interference factors, and WiFi coverage on different floors. This will not only cause the tour guide robot to temporarily lose communication with the control center, making it unable to receive new instructions in a timely manner, but may also cause the ongoing tour guide task to be forced to be interrupted, greatly affecting the user experience and the continuity of the robot's work.

[0004] 2. Obvious delay in network scene switching: In scenarios without WiFi networks, the tour guide robot needs to switch to the 4G network to maintain communication. However, existing communication solutions have significant service delays when performing this network switch. From detecting the loss of WiFi signals and trying to switch to the 4G network, to successfully connecting to the 4G network and restoring stable communication, this process often takes a long time. During this period, the tour guide robot may be unable to communicate, unable to obtain the latest map data, scenic spot introductions and other key information in real time, and unable to provide users with accurate and timely tour guide services.

[0005] 3. AP switching is not intelligent enough: When the tour guide robot moves between different access points (APs), the multi-AP switching mechanism has obvious defects. At present, when the tour guide robot switches APs, it is necessary to manually configure the password of the new AP. This not only increases the workload of the operator, but in actual application scenarios, the tour guide robot may need to switch between different APs frequently, and manual operation is difficult to meet real-time requirements. At the same time, the existing communication solutions lack intelligent decision-making capabilities and cannot automatically select the optimal AP for connection based on real-time conditions such as signal strength and network load. As a result, the tour guide robot may experience problems such as reduced communication quality and slow data transmission during the communication process due to connecting to APs with weak signals or excessive loads. Summary of the invention

[0006] In view of the problems existing in the communication solutions adopted by current guiding robots, such as poor WiFi signal stability, obvious network scenario switching delay, and insufficient intelligence in AP switching, the present invention provides an adaptive communication system and method for guiding robots based on multi-network collaboration.

[0007] In a first aspect, the present invention provides an adaptive communication system for guiding robots based on multi-network collaboration. The technical solutions adopted to solve the above technical problems are as follows:

[0008] An adaptive communication system for guiding robots based on multi-network collaboration, comprising:

[0009] A network management module, responsible for securely storing encrypted AP information and network topology data, and providing the function of receiving AP configuration updates through NFC, so as to provide initial security configuration information for the network connection of the guiding robot;

[0010] A signal detection module, responsible for simultaneously detecting four network parameters of RSSI, signal-to-noise ratio, channel load rate, and bit error rate of the WiFi network and the cellular network, and generating a network quality assessment report once a period, so as to provide real-time data support for the decision-making of subsequent modules;

[0011] A switching control module, responsible for predicting the signal strengths of the WiFi network and the cellular network within a set future time by using the Monte Carlo tree search algorithm, and combining the dynamic threshold algorithm and the manufacturer protocol adapter to achieve a decision response at the microsecond level, so as to provide a decision basis for the optimization of network connection;

[0012] A dual-channel communication module, integrated with a WiFi chip and a cellular network modem, realizes operator link redundancy through a dual SIM card slot, and dynamically optimizes the reception of WiFi and cellular signals by using the built-in intelligent antenna array, and executes the optimization and switching operations of network connection according to the decision of the switching control module;

[0013] A data buffer module, used for temporarily storing data during network switching.

[0014] Optionally, the network management module specifically adopts an encryption algorithm, uses the encryption key generated and stored by the hardware security chip to encrypt the AP information and the network topology data, and then stores the encrypted data in a non-volatile storage medium to prevent the data from being illegally stolen or tampered with during the storage process;

[0015] When an external configuration device with NFC function approaches the network management module, the NFC communication interface of the network management module is activated. The external configuration device sends the encrypted AP configuration update data to the network management module. The network management module first performs an integrity check on the data. After the check passes, it decrypts the data using the encryption key in the hardware security chip, and replaces the original configuration data with the decrypted new AP configuration information to complete the update of the AP configuration.

[0016] Optionally, the signal detection module specifically detects four network parameters of RSSI, signal-to-noise ratio, channel load rate, and bit error rate of the WiFi network and the cellular network through the following operations, and generates a network quality assessment report once a period:

[0017] Use the RSSI sensor to sense the intensity of the received signal in real time, convert the received analog signal into a digital signal, and convert the original signal value into a standard RSSI value through the built-in calibration algorithm;

[0018] Based on the preset signal processing circuit and algorithm, filter the received mixed signal, separate the signal component and the noise component, calculate the power of the signal and the noise respectively, and then calculate the signal-to-noise ratio through the power ratio. The calculated signal-to-noise ratio value is used to measure the quality of the signal in the noise environment;

[0019] Statistically count the amount of data transmitted on the channel within a set time period, and at the same time obtain the total transmission capacity of the channel. Divide the amount of transmitted data by the total transmission capacity of the channel to get the channel load rate, so as to evaluate the busyness of the channel;

[0020] Use the bit error rate detection unit to check the received data frame, and count the ratio of the number of error codes to the total number of data codes to get the bit error rate;

[0021] Every time a set time interval passes, use the microprocessor built in the signal detection module to summarize and organize the four network parameters of RSSI, signal-to-noise ratio, channel load rate, and bit error rate of the detected WiFi network and cellular network. Subsequently, comprehensively evaluate the network quality according to the preset evaluation criteria, and generate a network quality assessment report.

[0022] Optionally, taking the four network parameters of RSSI, signal-to-noise ratio, channel load rate, and bit error rate provided by the signal detection module, as well as the historical signal strength data as inputs, the switching control module uses the Monte Carlo tree search algorithm to predict the signal strengths of the WiFi network and the cellular network within a set future time period respectively;

[0023] Meanwhile, the switching control module calculates the real-time threshold using the dynamic threshold algorithm. The FPGA accelerator compares the real-time signal strength values of the WiFi network and the cellular network detected by the signal detection module with the calculated real-time threshold, makes a decision within microseconds based on a preset decision strategy, and the decision result is converted by the manufacturer protocol adapter to enable communication with network devices of different manufacturers.

[0024] Preferably, the expression of the involved dynamic threshold algorithm is as follows:

[0025] Y = X + (v × α) + β,

[0026] β = Σ (success rate of the last 10 handovers) / 10 × 0.2,

[0027] In the formula, Y represents the real-time threshold; X represents the signal strength value set manually; v refers to the moving speed of the tour guide robot, α refers to the compensation coefficient, which is adjusted according to the building material, and different building materials have different signal attenuation degrees; Σ (success rate of the last 10 handovers) represents the sum of the success rates of the last 10 handovers, and β reflects the influence of the success of network handovers in the past period on the real-time threshold.

[0028] Optionally, the WiFi chip integrated in the dual-channel communication module is responsible for scanning the surrounding WiFi hotspots and attempting to connect to the optimal WiFi network according to the AP information stored in the network management module;

[0029] The cellular network modem integrated in the dual-channel communication module establishes a communication connection with the operator's base station through the inserted SIM card to achieve access to the cellular network; the dual-channel communication module detects the operator network status corresponding to the two SIM cards in real time, and automatically switches the data transmission to the operator network corresponding to the other SIM card when the signal of the operator network connected by one SIM card is lower than the preset threshold, ensuring the continuity and stability of network communication.

[0030] Further optionally, the dual-channel communication module is built-in with an intelligent antenna array composed of multiple antenna units; the intelligent antenna array works in cooperation with the signal detection module with the help of a preset signal detection circuit, collects network parameters in real time, and continuously adjusts its own parameters automatically according to the dynamic changes of the collected network parameters through an intelligent algorithm; meanwhile, the intelligent antenna array receives the decision instructions sent by the switching control module in real time to achieve continuous optimization of the reception of WiFi network signals and cellular network signals.

[0031] In a second aspect, the present invention provides a method for an adaptive communication of a tour guide robot based on multi-network collaboration. The technical solution adopted to solve the above technical problems is as follows:

[0032] An adaptive communication method for a navigation robot based on multi-network collaboration, based on the navigation robot adaptive communication system as in the first aspect, the method includes the following steps:

[0033] S1. Pre-build a network topology database containing the SSIDs, encryption passwords, location coordinates, and manufacturer protocol types of multiple APs and load it;

[0034] S2. The dual-channel communication module simultaneously maintains the physical layer links of the WiFi connection and the cellular network;

[0035] S3. The signal detection module simultaneously detects four network parameters of the RSSI, signal-to-noise ratio, channel load rate, and bit error rate of the WiFi network and the cellular network, and generates a network quality assessment report once a period;

[0036] S4. The handover control module takes the four network parameters of the RSSI, signal-to-noise ratio, channel load rate, and bit error rate provided by the signal detection module, as well as the historical signal strength data as inputs, and uses the Monte Carlo tree search algorithm to predict the signal strengths of the WiFi network and the cellular network within a set future time period respectively;

[0037] S5. Combining with the real-time moving speed of the navigation robot, the handover control module uses the dynamic threshold algorithm to calculate the real-time threshold;

[0038] S6. The FPGA accelerator compares the real-time signal strength values of the WiFi network and the cellular network detected by the signal detection module with the calculated real-time threshold, and the FPGA accelerator makes a decision on whether to switch the network within microseconds based on the preset decision strategy;

[0039] S7. The dual-channel communication module dynamically optimizes the reception of the WiFi and cellular signals by using the built-in intelligent antenna array, and executes the optimization and switching operations of the network connection according to the decision of the handover control module.

[0040] Optionally, the involved navigation robot is default set to preferentially use the WiFi network;

[0041] When the current WiFi signal strength detected by the signal detection module is higher than the preset connection threshold, maintain the current WiFi connection;

[0042] When the current WiFi signal strength detected by the signal detection module is higher than the calculated real-time threshold and lower than the preset connection threshold, the navigation robot starts AP scanning through the WiFi chip of the dual-channel communication module. When a better signal strength is found, match the target AP based on the network topology database and call the corresponding manufacturer authentication plug-in to complete the handover. When no better signal strength is found, maintain the current WiFi connection and give an alarm;

[0043] When the current WiFi signal strength detected by the signal detection module is lower than the calculated real-time threshold, indicating that there is no available WiFi at present, the guiding robot activates the cellular network through the cellular network modem of the dual-channel communication module to achieve the switching from the WiFi network to the cellular network. When switching to the cellular network, the guiding robot plays the pre-stored explanation content using the local cache.

[0044] Subsequently, when the signal detection module detects a WiFi signal with a signal strength higher than the calculated real-time threshold, the switching control module makes a decision to switch the network again, and the dual-channel communication module realizes the switching from the cellular network to the WiFi network based on this decision. Otherwise, the cellular connection is maintained.

[0045] A guiding robot adaptive communication system and method based on multi-network collaboration according to the present invention has the following beneficial effects compared with the prior art:

[0046] 1. The present invention can realize the automatic switching between the WiFi network and the cellular network. Even in areas where the WiFi signal is completely absent, the guiding robot can seamlessly switch to the cellular network and continue to complete the guiding and explanation tasks, greatly improving the reliability and stability of the network connection, enhancing the user experience of the guiding and explanation, and solving problems such as instantaneous network interruption during cross-floor movement, high latency and jitter of audio and video transmission in the absence of a WiFi scenario, and connection failure caused by complex authentication processes during multi-AP switching.

[0047] 2. The guiding robot of the present invention can adapt to more scenarios, such as temporary exhibition areas, construction sites, outdoor scenic spots, etc. These areas may not have stable WiFi coverage, but the guiding robot can still rely on the cellular network to complete the guiding and explanation tasks, thus expanding the application scope of the robot.

[0048] 3. The guiding robot of the present invention can automatically adapt to different network environments, reducing the need for complex network configurations, lowering the difficulty of management and maintenance, and making the guiding robot easier to deploy and maintain in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Att Figure 1 is a block diagram of module connections in Embodiment 1 of the present invention;

[0050] Att Figure 2 is a schematic flow chart of the method in Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] To make the technical solutions, technical problems to be solved, and technical effects of the present invention clearer and more understandable, the following describes the technical solutions of the present invention clearly and completely in conjunction with specific embodiments.

[0052] Embodiment 1:

[0053] Reference appendix Figure 1 , this embodiment proposes a navigation robot adaptive communication system based on multi-network collaboration, which includes:

[0054] The network management module is responsible for securely storing encrypted AP information and network topology data, and providing the function of receiving AP configuration updates through NFC, providing initial security configuration information for the network connection of the navigation robot;

[0055] The signal detection module is responsible for simultaneously detecting four network parameters of RSSI, signal-to-noise ratio, channel load rate, and bit error rate of the WiFi network and the cellular network, and generating a network quality assessment report once a period, providing real-time data support for the decision-making of subsequent modules;

[0056] The handover control module is responsible for predicting the signal strength of the WiFi network and the cellular network within a set future time using the Monte Carlo tree search algorithm, and combining the dynamic threshold algorithm and the manufacturer protocol adapter to achieve a decision response at the microsecond level, providing a decision basis for the optimization of the network connection;

[0057] The dual-channel communication module integrates a WiFi chip and a cellular network modem, realizes operator link redundancy through a dual SIM card slot, and dynamically optimizes the reception of WiFi and cellular signals using the built-in intelligent antenna array, and executes the optimization and handover operations of the network connection according to the decision of the handover control module;

[0058] The data buffer module is used for temporarily storing data during network handover.

[0059] In this embodiment, the network management module specifically uses an encryption algorithm, uses the encryption key generated and stored by the hardware security chip to encrypt the AP information and the network topology data, and then stores the encrypted data in a non-volatile storage medium to prevent the data from being illegally stolen or tampered with during storage. When an external configuration device with NFC function approaches the network management module, the NFC communication interface of the network management module is activated, and the external configuration device sends the encrypted AP configuration update data to the network management module. The network management module first performs an integrity check on the data. After the check passes, it uses the encryption key in the hardware security chip to decrypt the data, and replaces the original configuration data with the decrypted new AP configuration information to complete the update of the AP configuration.

[0060] In this embodiment, the signal detection module specifically detects the four network parameters of RSSI, signal-to-noise ratio, channel load rate, and bit error rate of the WiFi network and the cellular network through the following operations, and generates a network quality assessment report once a period:

[0061] The RSSI sensor is used to sense the strength of the received signal in real time, convert the received analog signal into a digital signal, and through the built-in calibration algorithm, convert the original signal value into a standard RSSI value;

[0062] Based on the preset signal processing circuit and algorithm, the received mixed signal is filtered to separate the signal component and the noise component, the power of the signal and the noise is calculated respectively, and then the signal-to-noise ratio is calculated through the power ratio. The calculated signal-to-noise ratio value is used to measure the quality of the signal in the noise environment;

[0063] Within a set time period, the amount of data transmitted on the channel is counted, and at the same time, the total transmission capacity of the channel is obtained. The amount of transmitted data is divided by the total transmission capacity of the channel to obtain the channel load rate, so as to evaluate the busyness of the channel;

[0064] The error rate detection unit is used to check the received data frame and count the ratio of the number of error codes to the total number of data codes to obtain the error rate;

[0065] Every set time interval, the microprocessor built in the signal detection module is used to summarize and sort out the four network parameters of RSSI, signal-to-noise ratio, channel load rate and error rate of the detected WiFi network and cellular network. Subsequently, the network quality is comprehensively evaluated according to the preset evaluation criteria, and a network quality evaluation report is generated.

[0066] In this embodiment, taking the four network parameters of RSSI, signal-to-noise ratio, channel load rate and error rate provided by the signal detection module, as well as the historical signal strength data as the input, the switching control module uses the Monte Carlo tree search algorithm to predict the signal strengths of the WiFi network and the cellular network within a future set time period respectively;

[0067] At the same time, the switching control module uses the dynamic threshold algorithm to calculate the real-time threshold. The expression of the dynamic threshold algorithm is as follows:

[0068] Y = X+(v×α)+β,

[0069] β = Σ (success rate of the last 10 handovers) / 10×0.2,

[0070] In the formula, Y represents the real-time threshold; X represents the artificially set signal strength value; v refers to the moving speed of the guiding robot, α refers to the compensation coefficient, which is adjusted according to the building material, and different building materials have different attenuation degrees for signals; Σ (success rate of the last 10 handovers) represents the sum of the success rates of the last 10 handovers, and β reflects the influence of the success of network handovers in the past period of time on the real-time threshold;

[0071] The FPGA accelerator compares the real-time signal strength values of the WiFi network and the cellular network detected by the signal detection module with the calculated real-time threshold, makes a decision within microseconds based on a preset decision strategy, and the decision result is converted by the manufacturer protocol adapter so as to communicate with network devices of different manufacturers.

[0072] In this embodiment, the integrated WiFi chip of the involved dual-channel communication module is responsible for scanning the surrounding WiFi hotspots and attempting to connect to the optimal WiFi network according to the AP information stored in the network management module. The integrated cellular network modem of the dual-channel communication module establishes a communication connection with the operator's base station through the inserted SIM card to achieve access to the cellular network; the dual-channel communication module detects the network status of the operators corresponding to the two SIM cards in real time, and automatically switches the data transmission to the operator network corresponding to the other SIM card when the signal of the operator network connected by one SIM card is lower than the preset threshold, ensuring the continuity and stability of network communication.

[0073] The involved dual-channel communication module is built-in with an intelligent antenna array composed of multiple antenna units; the intelligent antenna array works in cooperation with the signal detection module with the help of a preset signal detection circuit, collects network parameters in real time, and continuously adjusts its own parameters according to the dynamic changes of the collected network parameters through an intelligent algorithm; at the same time, the intelligent antenna array receives the decision instructions sent by the handover control module in real time to achieve continuous optimization of the reception of WiFi network signals and cellular network signals.

[0074] Embodiment 2:

[0075] Reference appendix Figure 2 , based on the navigation robot adaptive communication system described in Embodiment 1, with the navigation robot default set to preferentially use the WiFi network, this embodiment further proposes a navigation robot adaptive communication method based on multi-network collaboration, including the following steps:

[0076] S1. Pre-build and load a network topology database containing the SSIDs, encryption passwords, location coordinates, and manufacturer protocol types of multiple APs.

[0077] S2. The dual-channel communication module simultaneously maintains the physical layer links of the WiFi connection and the cellular network.

[0078] S3. The signal detection module simultaneously detects four network parameters of the RSSI, signal-to-noise ratio, channel load rate, and bit error rate of the WiFi network and the cellular network, and generates a network quality assessment report once a period.

[0079] S4. The switching control module takes the four network parameters of RSSI, signal-to-noise ratio, channel load rate, and bit error rate provided by the signal detection module, as well as the historical signal strength data as inputs, and uses the Monte Carlo tree search algorithm to predict the signal strengths of the WiFi network and the cellular network within a preset future time period (such as 3 seconds).

[0080] S5. Combining the real-time moving speed of the navigation robot, the switching control module calculates the real-time threshold using the dynamic threshold algorithm.

[0081] The expression of the dynamic threshold algorithm is as follows:

[0082] Y = X + (v × α) + β,

[0083] β = Σ (success rate of the last 10 handovers) / 10 × 0.2,

[0084] In the formula, Y represents the real-time threshold; X represents the artificially set signal strength value; v refers to the moving speed of the navigation robot, α refers to the compensation coefficient, which is adjusted according to the building material, and different building materials have different attenuation degrees for signals; Σ (success rate of the last 10 handovers) represents the sum of the success rates of the last 10 handovers, and β reflects the influence of the success of network handovers in the past period on the real-time threshold.

[0085] S6. The FPGA accelerator compares the real-time signal strength values of the WiFi network and the cellular network detected by the signal detection module with the calculated real-time threshold, and the FPGA accelerator makes a decision on whether to switch the network within microseconds based on a preset decision strategy, specifically as follows:

[0086] (a) When the current WiFi signal strength detected by the signal detection module is higher than the preset connection threshold, maintain the current WiFi connection;

[0087] (b) When the current WiFi signal strength detected by the signal detection module is higher than the calculated real-time threshold and lower than the preset connection threshold, the navigation robot starts AP scanning through the WiFi chip of the dual-channel communication module. When a better signal strength is found, match the target AP based on the network topology database and call the corresponding vendor authentication plugin to complete the handover. When no better signal strength is found, maintain the current WiFi connection and give an alarm;

[0088] (c) When the current WiFi signal strength detected by the signal detection module is lower than the calculated real-time threshold, indicating that there is no available WiFi at present, the navigation robot activates the cellular network through the cellular network modem of the dual-channel communication module to achieve the handover from the WiFi network to the cellular network.

[0089] After switching from WiFi network to cellular network, the tour guide robot uses the local cache to play the pre-stored explanation content. The signal detection module continuously detects the signal strength of the WiFi network. When the signal detection module detects a WiFi signal with a signal strength higher than the calculated real-time threshold, the switching control module makes a decision to switch networks again. The dual-channel communication module switches from cellular network to WiFi network based on the decision, otherwise the cellular connection is maintained.

[0090] S7, the dual-channel communication module uses the built-in smart antenna array to dynamically optimize WiFi and cellular signal reception, and performs network connection optimization and switching operations based on the decisions of the switching control module.

[0091] The following scenario is used as an example to explain the above solution in detail.

[0092] Scenario 1: Ordinary building tour (switching between floors of a three-story building)

[0093] Network topology: 3 APs are deployed on each floor (Cisco AP1-3 floors), and Huawei AP4 is deployed in the stairwell.

[0094] Switching process:

[0095] 1.1. When the tour guide robot was moving on the east side of the second floor, it detected that the AP2 signal strength dropped to -68dBm (speed = 0.8m / s).

[0096] 1.2. Calculation of real-time threshold value Y: Set the basic threshold value X to -65dBm, the current moving speed v of the tour guide robot to 0.8m / s, the compensation coefficient α is adjusted to 0.15 according to the building material, and the sum of the success rates of the last 10 switches is calculated to be 0.95, then the real-time threshold value Y = -65 + 0.8 × 0.15 + 0.95 × 0.2 = -63.9dBm.

[0097] 1.3. Trigger AP scanning and find that the signal of AP3 on the 3rd layer is -61dBm. The signal strength is better than the currently connected AP2 and meets the preset switching conditions.

[0098] 1.4. Call the Cisco authentication plug-in to complete 802.11r fast switching (taking 172ms).

[0099] Verification: The voice explanation was smooth (delay ≤ 153ms), and the navigation data packet loss rate dropped from 12% to 0.3%. This shows that the network quality was effectively improved through this network switching, meeting the requirements of the tour guide robot for network stability and data transmission quality, and proving the effectiveness and reliability of the network switching strategy and process.

[0100] Scenario 2: Underground parking lot tour (no WIFI)

[0101] 4G Cellular Network Activation Process:

[0102] 2.1. When entering the parking lot, the WiFi signal strength < the calculated real-time threshold Y. Assume that Y = -80 dBm at this time;

[0103] 2.2. When the WiFi signal does not meet the requirements, quickly switch to the 4G cellular network and establish a dedicated bearer channel for the voice communication of the tour guide robot (the establishment time is about 487 ms) to ensure the quality and stability of the voice communication;

[0104] 2.3. During the network switching process, the tour guide robot preferentially uses the local cache to play the pre-stored explanation content to achieve seamless connection;

[0105] 2.4. Conduct bandwidth optimization: Dynamically degrade the video stream resolution from 1080p to 480p, saving 63% of the traffic. This shows that the tour guide robot has the ability to dynamically adjust the data transmission strategy according to the network conditions, and reasonably utilizes network resources to reduce the operation cost on the premise of ensuring the basic functions.

[0106] In summary, by adopting an adaptive communication system and method for a tour guide robot based on multi-network collaboration of the present invention, automatic switching between the WiFi network and the cellular network can be achieved, and problems such as instantaneous network interruption during cross-floor movement, high latency jitter in audio and video transmission in the absence of a WiFi scenario, and connection failure caused by a complex authentication process during multi-AP switching are solved.

[0107] The above application of specific examples has elaborated in detail the principle and implementation manner of the present invention. These embodiments are only used to help understand the core technical content of the present invention. Based on the above specific embodiments of the present invention, any improvements and modifications made by those skilled in the art of this technology without departing from the principle of the present invention shall fall within the scope of patent protection of the present invention.

Claims

1. A tour guide robot adaptive communication system based on multi-network collaboration, characterized in that: It includes: The network management module is responsible for securely storing encrypted AP information and network topology data, and provides the function of receiving AP configuration updates via NFC, providing initial security configuration information for the network connection of the tour guide robot; The signal detection module is responsible for simultaneously detecting the four network parameters of the WiFi network and the cellular network, namely RSSI, signal-to-noise ratio, channel load rate and bit error rate, and regularly generates a network quality assessment report to provide real-time data support for the decision-making of subsequent modules; The switching control module is responsible for using the Monte Carlo tree search algorithm to predict the signal strength of the WiFi network and the cellular network within a set time in the future. It combines the dynamic threshold algorithm and the manufacturer's protocol adapter to achieve microsecond-level decision response and provide a decision basis for the optimization of network connections. Dual-channel communication module, integrating WiFi chip and cellular network modem, realizes carrier link redundancy through dual SIM card slots, and uses built-in smart antenna array to dynamically optimize WiFi and cellular signal reception, and performs network connection optimization and switching operations according to the decision of the switching control module; The data buffer module is used to temporarily store data during network switching.

2. The adaptive communication system for tour guide robots based on multi-network collaboration according to claim 1, characterized in that: The network management module specifically adopts an encryption algorithm and uses an encryption key generated and stored by a hardware security chip to encrypt AP information and network topology data, and then stores the encrypted data in a non-volatile storage medium to prevent the data from being illegally stolen or tampered with during storage; When an external configuration device with NFC function is close to the network management module, the NFC communication interface of the network management module is activated, and the external configuration device sends the encrypted AP configuration update data to the network management module. The network management module first performs an integrity check on the data. After the check passes, the encryption key in the hardware security chip is used to decrypt the data, and the decrypted new AP configuration information replaces the original configuration data to complete the AP configuration update.

3. The adaptive communication system for tour guide robots based on multi-network collaboration according to claim 1, characterized in that: The signal detection module specifically detects four network parameters of the WiFi network and the cellular network, namely RSSI, signal-to-noise ratio, channel load rate and bit error rate, through the following operations, and generates a network quality assessment report regularly: The RSSI sensor senses the strength of the received signal in real time and converts the received analog signal into a digital signal. The built-in calibration algorithm converts the original signal value into a standard RSSI value. Based on the preset signal processing circuit and algorithm, the received mixed signal is filtered to separate the signal component and the noise component, and the power of the signal and the noise are calculated respectively. Then, the signal-to-noise ratio is calculated through the power ratio. The calculated signal-to-noise ratio value is used to measure the quality of the signal in a noisy environment. The amount of data transmitted on the channel within a set time period is counted, and the total transmission capacity of the channel is obtained at the same time. The amount of data transmitted is divided by the total transmission capacity of the channel to obtain the channel load rate, which is used to evaluate the busyness of the channel; The received data frame is checked by using the bit error rate detection unit, and the ratio of the number of error codes to the total number of data codes is counted to obtain the bit error rate; At set intervals, the microprocessor built into the signal detection module will summarize and organize the four network parameters of the detected WiFi network and cellular network, including RSSI, signal-to-noise ratio, channel load rate and bit error rate. Then, the network quality will be comprehensively evaluated according to the pre-set evaluation criteria to generate a network quality evaluation report.

4. The adaptive communication system for tour guide robots based on multi-network collaboration according to claim 1, characterized in that: Taking the four network parameters of RSSI, signal-to-noise ratio, channel load rate and bit error rate provided by the signal detection module and the historical signal strength data as input, the switching control module uses the Monte Carlo tree search algorithm to predict the signal strength of the WiFi network and the cellular network in the future set time period respectively; At the same time, the switching control module uses a dynamic threshold algorithm to calculate the real-time threshold. The FPGA accelerator compares the real-time signal strength values ​​of the WiFi network and the cellular network detected by the signal detection module with the calculated real-time threshold, and makes a decision within microseconds based on a preset decision strategy. The decision result is converted through a manufacturer's protocol adapter so that it can communicate with network devices from different manufacturers.

5. The adaptive communication system for tour guide robots based on multi-network collaboration according to claim 4 is characterized in that: The expression of the dynamic threshold algorithm is as follows: Y=X+(v×α)+β, β = Σ(switching success rate in the last 10 times) / 10×0.2, Where Y represents the real-time threshold; X represents the artificially set signal strength value; v refers to the moving speed of the guide robot; α refers to the compensation coefficient, which is adjusted according to the building material. Different building materials have different degrees of signal attenuation; Σ(switching success rate of the last 10 times) represents the sum of the success rates of the last 10 switches; β reflects the impact of the success of network switching in the past period of time on the real-time threshold.

6. The adaptive communication system for tour guide robots based on multi-network collaboration according to claim 1, characterized in that: The WiFi chip integrated in the dual-channel communication module is responsible for scanning the surrounding WiFi hotspots and trying to connect to the optimal WiFi network according to the AP information stored in the network management module; The cellular network modem integrated in the dual-channel communication module establishes a communication connection with the operator's base station through the inserted SIM card to achieve access to the cellular network; the dual-channel communication module detects the operator network status corresponding to the two SIM cards in real time, and automatically switches data transmission to the operator network corresponding to the other SIM card when the signal of the operator network connected to one SIM card is lower than a preset threshold, thereby ensuring the continuity and stability of network communication.

7. The adaptive communication system for tour guide robots based on multi-network collaboration according to claim 6, characterized in that: The dual-channel communication module has a built-in intelligent antenna array composed of multiple antenna units; the intelligent antenna array works in conjunction with the signal detection module with the help of a preset signal detection circuit to collect network parameters in real time, and automatically adjusts its own parameters based on the dynamic changes of the collected network parameters through an intelligent algorithm; at the same time, the intelligent antenna array receives the decision instructions sent by the switching control module in real time to achieve continuous optimized reception of WiFi network signals and cellular network signals.

8. A tour guide robot adaptive communication method based on multi-network collaboration, characterized in that: Based on the guide robot adaptive communication system as claimed in claim 1, the method comprises the following steps: S1, pre-build and load a network topology database containing SSIDs, encryption passwords, location coordinates and vendor protocol types of multiple APs; S2, dual-channel communication module simultaneously maintains the physical layer link of WiFi connection and cellular network; S3, the signal detection module simultaneously detects the four network parameters of the WiFi network and the cellular network, namely RSSI, signal-to-noise ratio, channel load rate and bit error rate, and generates a network quality assessment report regularly; S4, the switching control module uses the four network parameters of RSSI, signal-to-noise ratio, channel load rate and bit error rate provided by the signal detection module, as well as the historical signal strength data as input, and uses the Monte Carlo tree search algorithm to predict the signal strength of the WiFi network and the cellular network in the future set time period respectively; S5, combining the real-time moving speed of the tour guide robot, the switching control module uses a dynamic threshold algorithm to calculate the real-time threshold; S6, the FPGA accelerator compares the real-time signal strength values ​​of the WiFi network and the cellular network detected by the signal detection module with the calculated real-time threshold, and the FPGA accelerator makes a decision on whether to switch networks within microseconds based on a preset decision strategy; S7, the dual-channel communication module uses the built-in smart antenna array to dynamically optimize WiFi and cellular signal reception, and performs network connection optimization and switching operations based on the decisions of the switching control module.

9. The adaptive communication method of a tour guide robot based on multi-network collaboration according to claim 8, characterized in that: The tour guide robot is set by default to use the WiFi network first; When the current WiFi signal strength detected by the signal detection module is higher than a preset connection threshold, the current WiFi connection is maintained; When the current WiFi signal strength detected by the signal detection module is higher than the calculated real-time threshold and lower than the preset connection threshold, the tour guide robot starts AP scanning through the WiFi chip of the dual-channel communication module. When a better signal strength is found, the robot matches the target AP based on the network topology database and calls the corresponding manufacturer authentication plug-in to complete the switch. If no better signal strength is found, the robot maintains the current WiFi connection and issues an alert. When the current WiFi signal strength detected by the signal detection module is lower than the calculated real-time threshold, indicating that no WiFi is currently available, the tour guide robot activates the cellular network through the cellular network modem of the dual-channel communication module to switch from the WiFi network to the cellular network. When switching to the cellular network, the tour guide robot uses the local cache to play the pre-stored explanation content; Subsequently, when the signal detection module detects a WiFi signal with a signal strength higher than the calculated real-time threshold, the switching control module makes a decision to switch networks again, and the dual-channel communication module implements switching from the cellular network to the WiFi network based on the decision, otherwise the cellular connection is maintained.

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