Near field ad hoc network communication and cooperative control method for intelligent safety helmet and operation equipment based on open source gap

Abstract

The invention discloses a near field ad hoc network communication and cooperative control method for an intelligent safety helmet and operation equipment based on an open source gap. The method comprises the following steps: realizing automatic discovery of equipment through a distributed soft bus; generating a device fingerprint based on hardware features in the trusted execution environment, completing bidirectional authentication and security key negotiation, and establishing a secure ad hoc network; dynamic spectrum management and self-adaptive power control are carried out through cooperative sensing, and an intelligent multi-path routing algorithm is operated to maintain an optimal data transmission path; the intelligent safety helmets serve as control nodes, the states of all the devices are synchronized in real time through distributed data management, cooperative control instructions are generated according to task blueprints and real-time working conditions, and cooperative operation and emergency linkage between the devices are achieved through path distribution. According to the invention, the problems of weak safety, poor anti-interference performance, coordination delay and the like of a traditional scheme in a complex mobile environment are solved, and high-safety, high-reliability and self-adaptive near-field coordination control is realized.

Description

A Near-Field Ad hoc Network Communication and Cooperative Control Method for Smart Safety Helmets and Work Equipment Based on Open Source HarmonyOS Technical Field

[0001] This invention relates to the field of industrial Internet of Things and intelligent collaborative control technology, and in particular to a near-field self-organizing network communication and collaborative control method for smart safety helmets and work equipment based on the open-source HarmonyOS. Background Technology

[0002] In complex work environments such as industrial manufacturing, construction, and emergency rescue, to achieve intelligent and efficient operations, it is typically necessary to network smart safety helmets with various work equipment to form a collaborative work system. These work environments are characterized by frequent equipment movement, varied physical obstacles, and complex electromagnetic interference, placing extremely high demands on the stability, real-time performance, and security of the communication network between equipment.

[0003] Existing technologies suffer from the following main technical shortcomings: traditional Wi-Fi networks rely on fixed access point architectures, which are prone to handover delays and connection interruptions in mobile scenarios; self-organizing network technologies such as Bluetooth Mesh lack dynamic spectrum management capabilities, resulting in drastic fluctuations in communication quality under complex electromagnetic environments; existing security authentication mechanisms are insufficient to prevent device impersonation risks and cannot establish a reliable foundation for collaborative control; and the communication network is disconnected from the upper-layer control system, making it impossible to achieve intelligent collaborative decision-making based on real-time network status. Although the open-source HarmonyOS operating system provides basic capabilities such as distributed soft bus and data management, how to deeply integrate these capabilities with the dynamic wireless environment to build a near-field self-organizing network solution with environmental adaptability, highly reliable communication, and highly secure access remains a pressing technical challenge. In particular, existing technologies have not yet provided systematic solutions for frequent changes in network topology caused by device movement, communication quality assurance under complex electromagnetic interference, and secure and reliable mechanisms for multi-device collaboration. Summary of the Invention

[0004] In view of this, embodiments of the present invention aim to provide a near-field self-organizing network communication and cooperative control method for smart safety helmets and work equipment based on the open-source HarmonyOS, so as to solve or alleviate the technical problems existing in the prior art.

[0005] The technical solution of the embodiment of the present invention is realized as follows: A near-field self-organizing network communication and collaborative control method for an intelligent safety helmet and operation equipment based on OpenHarmony. The method is executed by an intelligent safety helmet equipped with the OpenHarmony operating system and at least one operation equipment, and includes the following steps: S1. Network discovery and secure access phase: After the intelligent safety helmet and the operation equipment are powered on, they are automatically discovered based on the distributed soft bus of OpenHarmony; each device collects its own hardware characteristic data, generates a unique device fingerprint in a trusted execution environment, and completes two-way authentication and secure key negotiation based on the device fingerprint, establishes a secure communication link, and forms a near-field self-organizing network; S2. Dynamic spectrum and routing maintenance phase: Each device in the near-field self-organizing network continuously senses the wireless channel quality of the surrounding environment through the distributed soft bus, and performs dynamic spectrum management based on the sensing results, adaptively selects a communication channel and transmission power; at the same time, each device runs an intelligent multi-path routing algorithm, and dynamically calculates and maintains at least one optimal data transmission path based on the real-time network topology and link quality; S3. Collaborative control and status synchronization phase: The intelligent safety helmet serves as a collaborative control node, based on the established near-field self-organizing network, through the distributed data management ability of OpenHarmony, obtains and synchronizes the working status data of each operation equipment in real time; according to the preset operation task or real-time working condition, generates a collaborative control instruction, and distributes it to the target operation equipment through the optimal data transmission path maintained in S2, to achieve collaborative operation and linkage response among the operation equipment.

[0006] Due to the adoption of the above technical solution in the embodiment of the present invention, it has the following advantages: By integrating the distributed soft bus and trusted execution environment of OpenHarmony, a two-way authentication and dynamic key negotiation mechanism based on multi-dimensional hardware characteristic fingerprints is constructed, which solves the problems of high risk of device impersonation and weak secure access mechanism in traditional self-organizing networks; Through multi-device collaborative sensing and dynamic spectrum management, a comprehensive quality evaluation based on signal-to-noise ratio, channel occupancy rate and signal stability and adaptive channel switching are realized, effectively overcoming the severe fluctuations in communication quality and link interruption caused by fixed channels or simple switching strategies in complex electromagnetic environments; Through an intelligent multi-path routing algorithm and local repair mechanism that integrate link quality, hop count and node load, the reliability of data transmission and network robustness in operation scenarios with frequent movement and changing topologies are significantly improved, avoiding collaborative interruption caused by the failure of a single path; Finally, relying on the distributed data management ability and event-driven status synchronization mechanism of OpenHarmony, the deep coupling of control instruction generation and real-time network status and equipment working conditions is realized, solving the problems of the separation of communication and control layers and delayed collaborative response in traditional systems, thus achieving high-security, high-reliability and adaptive near-field self-organizing network collaborative control as a whole. Brief Description of the Drawings

[0007] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0008] Figure 1 is the overall flowchart of the present invention; Figure 2 is the overall system architecture of the present invention; Figure 3 is the flowchart of dynamic spectrum and routing maintenance of the present invention; Figure 4 is the flowchart of collaborative control and status synchronization of the present invention; Figure 5 is the timing diagram of secure access and device authentication of the present invention; Figure 6 is the flowchart of collaborative control and exception warning of the present invention. Detailed implementation manners

[0009] In the following text, only some exemplary embodiments are briefly described. As those skilled in the art can recognize, the described embodiments can be modified in various different ways without departing from the spirit or scope of the present invention. Therefore, the accompanying drawings and the description are considered to be exemplary in nature rather than restrictive.

[0010] The following will detail the embodiments of the present invention with reference to the accompanying drawings.

[0011] The present invention proposes a near-field self-organizing network communication and collaborative control method for intelligent safety helmets and operating equipment based on OpenHarmony. This method is executed by an intelligent safety helmet equipped with the OpenHarmony operating system and at least one operating equipment, and includes the following steps: S1. Network discovery and secure access stage: After the intelligent safety helmet and the operating equipment are powered on, they are automatically discovered based on the distributed soft bus of OpenHarmony; each device collects its own hardware feature data, generates a unique device fingerprint in the trusted execution environment, and completes two-way authentication and security key negotiation based on this device fingerprint to establish a secure communication link and form a near-field self-organizing network; S2. Dynamic spectrum and routing maintenance stage: Each device in the near-field self-organizing network continuously senses the quality of the wireless channel in the surrounding environment through the distributed soft bus, and performs dynamic spectrum management based on the sensing results, adaptively selecting a communication channel and transmission power; at the same time, each device runs an intelligent multi-path routing algorithm, and dynamically calculates and maintains at least one optimal data transmission path based on the real-time network topology and link quality; S3. Collaborative control and status synchronization stage: The intelligent safety helmet serves as the collaborative control node. Based on the established near-field self-organizing network, through the distributed data management ability of OpenHarmony, it can obtain and synchronize the working status data of each operating equipment in real time; according to the preset operation tasks or real-time working conditions, generate collaborative control instructions, and distribute them to the target operating equipment through the optimal data transmission path maintained in S2 to achieve collaborative operation and linkage response among the operating equipment.

[0012] This embodiment provides a near-field ad hoc network communication and collaborative control method for smart safety helmets and work equipment based on the open-source HarmonyOS operating system. This method is executed by a smart safety helmet running the open-source HarmonyOS operating system and at least one piece of work equipment.

[0013] In the network discovery and secure access phase S1, after the smart safety helmet and work equipment are powered on, they automatically discover themselves using the distributed soft bus based on the open-source HarmonyOS. Devices can periodically broadcast their presence information, and other devices respond upon receiving this, thus establishing an initial connection. Each device can collect its own hardware characteristic data, such as the device model and manufacturer information, and generate a unique device fingerprint in the Trusted Execution Environment (TEE). This device fingerprint is a hash value used to identify the device. Subsequently, based on this device fingerprint, two-way authentication and secure key negotiation can be completed. For example, device A sends an authentication request containing its device fingerprint to device B. After successful verification, device B replies with its own fingerprint for device A to verify. After successful two-way authentication, both parties, based on the verified device fingerprint, execute a key exchange protocol such as Elliptic Curve Diffie-Hellman (ECDH) in the TEE to negotiate and generate a unique, temporary session key. This establishes a secure communication link, forming a near-field ad hoc network.

[0014] In the dynamic spectrum and route maintenance phase S2, each device in the near-field ad hoc network continuously senses the wireless channel quality of its environment via a distributed soft bus. For example, devices can periodically measure the signal strength or noise level of the current channel. Based on the sensing results, dynamic spectrum management is performed, adaptively selecting communication channels and transmit power. For instance, devices can select channels with lower occupancy rates for communication based on measured channel occupancy; transmit power can be easily adjusted based on received signal strength, such as increasing power when the signal is too weak. Simultaneously, each device runs an intelligent multi-path routing algorithm, dynamically calculating and maintaining at least one optimal data transmission path based on real-time network topology and link quality. For example, devices can maintain a simple routing table, recording the next hop and hop count to reach other devices, and selecting the path with the fewest hops as the optimal path.

[0015] In the collaborative control and state synchronization phase S3, as a collaborative control node, the intelligent safety helmet, based on the established near-field self-organizing network, can, through the distributed data management capabilities of OpenHarmony, obtain and synchronize the working status data of each operating equipment in real-time. For example, the intelligent safety helmet can regularly send requests to each operating equipment to obtain information such as its current working mode, battery level, sensor readings, etc. According to the preset operation tasks or real-time working conditions, collaborative control instructions can be generated. For example, when it is detected that the battery level of a certain operating equipment is lower than the threshold, the intelligent safety helmet can generate a charging instruction. This instruction is then distributed to the target operating equipment through the optimal data transmission path maintained in S2, realizing collaborative operation and linkage response among operating equipment. For example, the instruction can be directly sent to the target operating equipment, or relayed and forwarded through other devices in the network.

[0016] In this embodiment, by introducing the distributed capabilities of OpenHarmony, near-field self-organizing network communication and collaborative control between the intelligent safety helmet and operating equipment are achieved. Therefore, problems such as unstable network topology, unreliable communication links, and weak security access mechanisms caused by frequent movement and complex interference of equipment at the operation site are effectively solved. At the same time, by using the intelligent safety helmet as a collaborative control node, deep integration of communication and control is realized, improving the collaborative operation efficiency and overall operation safety among operating equipment.

[0017] In some of the above embodiments, a method for completing two-way authentication and secure key negotiation based on device fingerprints to establish a secure communication link is proposed. However, in practical applications, how to ensure the uniqueness of device identities, the security of the authentication process, and the reliable negotiation of session keys are the key challenges in ensuring the security of near-field self-organizing network communication.

[0018] In response to this, the present invention further proposes a specific method for completing two-way authentication and secure key negotiation based on device fingerprints. First, in the device fingerprint generation stage, the collected MAC address , radio frequency chip feature code , and device hardware serial number are used as multi-dimensional device feature data and input into the hash function in the trusted execution environment to generate the device fingerprint , that is . This step is used to generate a unique and tamper-proof digital identity identifier for each intelligent safety helmet and operating equipment. By integrating multiple hardware-level feature data, the uniqueness of the fingerprint can be maximally guaranteed and forgery can be prevented. Inputting these data into the hash function in the trusted execution environment for processing ensures the security of the fingerprint generation process and prevents external malicious tampering or theft of the original feature data. The hash function The unidirectional nature of fingerprints makes it extremely difficult to reverse engineer the original feature data, further enhancing security. Among these features, the MAC address... This is the physical address of the device's network interface card (such as a Wi-Fi or Bluetooth module), and it is usually unique. Radio frequency chip signature. This is a unique identifier or a specific combination of parameters embedded within the RF chip, such as chip ID, firmware version hash, etc. This information is written at the chip's factory and is not easily changed. Device hardware serial number. A unique identifier assigned by the device manufacturer during production, typically stored in the device's non-volatile memory. A trusted execution environment (TEX) is an isolated, secure execution environment that runs in parallel with the main operating system, providing hardware-level security, such as ARM's TrustZone technology or Intel's SGX technology. Hash function. You can choose standard cryptographic hash functions such as SHA-256 and SHA-3.

[0019] Subsequently, during the device authentication phase, the device initiating the authentication will include its own device fingerprint. The authentication request is encrypted and broadcast to the network. The device to be authenticated decrypts and verifies the device fingerprint in a trusted execution environment. This step verifies the legitimacy of the authentication process. It establishes initial authentication between devices. The device initiating the authentication displays its unique device fingerprint. Encryption followed by broadcasting prevents fingerprints from being eavesdropped on or tampered with during transmission. Upon receiving the encryption request, the device to be authenticated decrypts it using a pre-set key within a trusted execution environment and verifies the fingerprint's legitimacy, ensuring that only devices with valid fingerprints can participate in subsequent communication. Encryption uses a pre-set public key or symmetric key to encrypt the authentication request, such as a shared root key pre-set at the factory for all devices. Broadcasting can be performed via a distributed soft bus or underlying wireless communication protocols (such as Wi-Fi Direct or Bluetooth). Verifying legitimacy may include checking if the fingerprint format is correct, if it matches a list of known valid device fingerprints, or verifying the fingerprint's authenticity through a challenge-response mechanism.

[0020] Finally, during the key negotiation phase, after successful two-way verification, both parties use their respective device fingerprints. and A unique session key is generated through negotiation using a preset key exchange algorithm. This is used to encrypt all subsequent communication data. This step establishes a temporary, secure session key after the device's identity has been verified. This is used to protect subsequent data transmission. The session key is generated based on the device fingerprints of both parties, ensuring that the key is bound to a specific device, and a new key is generated for each session, improving forward security. The preset key exchange algorithm can employ Diffie-Hellman key exchange protocol (DH) or elliptic curve Diffie-Hellman (ECDH), etc. These algorithms allow communicating parties to negotiate a shared secret key over an insecure channel without directly transmitting the key. Device fingerprints can be used as input parameters to the key exchange algorithm, for example, as a seed for private key generation in the DH algorithm, or to derive temporary key pairs required for key exchange, thereby enabling the generated session key. Closely linked to the identities of both parties, a new session key is generated each time a secure communication link is established. Even if a previous session key is compromised, it prevents disruption to current and future communication security. The negotiated session key... All application layer data will be encrypted using symmetric encryption algorithms such as AES (Advanced Encryption Standard) to ensure the confidentiality of data transmission.

[0021] Through the above technical solution, this invention provides a highly secure and reliable device authentication and key negotiation mechanism. First, by integrating multi-dimensional hardware features and generating device fingerprints in a trusted execution environment, the uniqueness of each device's identity and the tamper-proof nature of the fingerprints are ensured, effectively resisting the risks of device forgery and identity impersonation. Second, encrypted authentication requests and decryption verification in a trusted execution environment guarantee the confidentiality and integrity of the authentication process. Finally, key exchange based on the verified device fingerprints enables the negotiation of a unique session key, providing strong encryption protection for all subsequent communication data and significantly improving the security and reliability of the entire collaborative control system.

[0022] In some embodiments of the present invention, a method is proposed in which each device in a near-field ad hoc network continuously senses the wireless channel quality of its environment and performs dynamic spectrum management based on the sensing results to adaptively select the communication channel. However, in real-world, complex operating environments, wireless channel conditions change rapidly. If channel selection is based solely on a single or simple sensing indicator, it may lead to frequent channel switching, unstable communication links, or even an inability to effectively avoid interference, thereby affecting the efficiency and reliability of collaborative operations.

[0023] To address this, the present invention further proposes a specific method for dynamic spectrum management based on sensing results, comprising the following steps. First, each device shares its sensed channels within the 2.4GHz and 5.8GHz frequency bands via a distributed soft bus. Channel quality assessment parameters. The distributed soft bus, provided by the open-source HarmonyOS operating system, is a cross-device, cross-application data transmission and collaboration capability that enables seamless connection and data flow between devices. Here, each device utilizes the distributed soft bus to share its independently perceived wireless channel quality assessment parameters in real time. These parameters typically include, but are not limited to, signal-to-noise ratio, received signal strength indication, channel occupancy rate, and bit error rate, covering all available channels in the commonly used 2.4GHz and 5.8GHz ISM bands. By sharing multi-dimensional channel information, all devices in the network can obtain a global and more comprehensive view of the channel situation, providing a data foundation for subsequent channel selection.

[0024] Secondly, each device, based on all shared parameters, configures each channel... Calculate the overall quality score and make joint decisions The channel with the highest value is selected as the current optimal communication channel. The overall quality score... The calculation formula is: Overall quality score The calculation of channel quality is the core of dynamic spectrum management. After receiving all shared channel quality assessment parameters, each device will perform an assessment of each channel according to a preset formula. The scoring is performed. This formula comprehensively considers the channel. Average signal-to-noise ratio (Reflecting signal quality), channel occupancy rate (Reflecting the level of interference) and the channel variance of received signal strength indication (Reflects channel stability). Among them, These are positive weighting coefficients used to adjust the importance of different factors in the overall score, and their sum is 1 to ensure score normalization. Through multi-dimensional weighted evaluation, the overall performance of the channel can be measured more accurately, avoiding the bias of a single indicator. Ultimately, the devices in the network will negotiate together or the master control device (such as a smart helmet) will make the selection. The channel with the highest value is selected as the current optimal communication channel to ensure communication quality and stability.

[0025] Furthermore, when the current channel quality score is detected... Below the preset first threshold At this time, the smart safety helmet initiates a channel switching command, guiding all devices in the network to synchronously switch to the optimal communication channel. To maintain communication quality, the system will continuously monitor the quality score of the currently used channel. .when Drop to the preset first threshold The following indicates that the current channel performance has significantly deteriorated, and there may be severe interference or attenuation. At this point, the smart safety helmet, acting as the logical control center of the network, will immediately initiate a channel switching command. This command is broadcast to all operating equipment via a distributed soft bus, guiding them to synchronously switch to the already determined optimal communication channel. This mechanism ensures timely and coordinated channel switching when the channel deteriorates, minimizing communication interruption time and guaranteeing the continuity and reliability of collaborative operations.

[0026] Through the above technical solution, each device can share channel quality assessment parameters across multiple frequency bands in real time via a distributed soft bus, thereby obtaining a global and detailed view of the channel condition. Based on this, a comprehensive quality score is introduced. The calculation formula weighted and fused multiple key indicators such as signal-to-noise ratio, channel occupancy, and channel stability, enabling a more comprehensive and accurate evaluation of the overall performance of each channel. This avoids the one-sidedness of evaluating a single indicator, effectively identifying the truly high-quality, low-interference, and stable optimal communication channel. When the current channel quality drops below a preset threshold, the smart helmet can promptly initiate a channel switching command, guiding all devices to synchronously switch to the optimal channel, avoiding communication interruptions or performance degradation caused by channel deterioration. This mechanism significantly improves the channel selection accuracy, communication link stability, and anti-interference capability of near-field ad hoc networks in complex wireless environments, ensuring the continuity and reliability of collaborative operations between the smart helmet and work equipment.

[0027] To achieve truly reliable and conflict-free synchronous handover, this invention introduces a synchronization protocol based on time slot reservation and reliable broadcasting between initiating the handover command and executing the handover action. Specifically: Command encapsulation and time slot reservation: The handover command generated by the smart safety helmet includes not only the target channel number... It also includes a future unified switchover timeline. ( =Current Time +Fixed protection interval ). Sufficient time should be allocated for receiving and processing instructions for all devices.

[0028] Reliable broadcast and intra-network acknowledgment: The smart helmet broadcasts the instruction periodically on the current channel with high priority. Any device that receives the instruction must reply with an acknowledgment (ACK) to the smart helmet. The ACK can be transmitted back via a direct link or a multi-hop relay through a mesh network.

[0029] Command retransmission and integrity check: The smart helmet identifies unacknowledged devices based on ACK feedback. During this period, these devices are retransmitted via the current channel or backup path to ensure that the handover command is reachable across the entire network.

[0030] Global synchronization switching action: When the system time reaches... At that time, all devices that have successfully received the instruction simultaneously switch their wireless communication modules to... .

[0031] New Channel Handshake and State Recovery: After the handover, the smart helmet broadcasts a synchronization beacon on the new channel. Each device replies to this beacon and quickly rebuilds its routing table and security session, restoring service communication.

[0032] This protocol solves the handover timing problem by reserving future time slots and the reliable delivery of instructions by acknowledgment and retransmission, thereby enabling all devices to switch synchronously within the boot network.

[0033] In some embodiments of the present invention, a technical solution is proposed to adaptively select a communication channel by sensing the quality of the wireless channel in the environment and performing dynamic spectrum management. However, in practical applications, even if the optimal communication channel is selected, if the device's transmit power cannot be adjusted according to the real-time link quality, unnecessary interference and energy waste may occur when the link quality is good, while the stability and reliability of communication cannot be guaranteed when the link quality is poor.

[0034] To address this, the present invention further proposes an adaptive transmission power selection step. This step dynamically adjusts the wireless transmission power of the devices based on the real-time communication link quality between the devices, thereby optimizing communication performance, reducing energy consumption, and minimizing interference.

[0035] Specifically, in step S2.1d, the device It will continuously measure its relationship with neighboring devices. Current link quality between The link quality To measure the condition of the wireless communication channel between two devices, an evaluation is based on various physical layer parameters, such as received signal strength indication (RSI). ), signal-to-noise ratio ( ), bit error rate ( (e.g., packet loss rate). Through real-time monitoring... The device can accurately grasp the current health status of the communication link.

[0036] To achieve dynamic adjustment of transmission power, this invention sets two preset thresholds, namely... and ,in Greater than These two thresholds will affect link quality. The area is divided into three zones: high-quality zone ( This invention > This invention ), moderate quality zone ( This invention ≤ This invention This invention ≤ ) and low-quality areas ( This invention The above thresholds can be set empirically or optimized using machine learning methods based on the actual application scenario, device type, and network performance requirements.

[0037] Based on the current link quality With preset threshold and The comparison results show that the equipment Its transmission power will be dynamically adjusted. When link quality is detected Higher than When this time is reached, it indicates that the current communication link is in excellent condition, and the device... Its current transmission power Subtract power decrease step value To reduce unnecessary transmit power. When link quality In and When the value is between 10 and 20, it indicates that the link quality is moderate and stable, and the device is in a good position. It will maintain its current transmission power. The link quality remains unchanged to maintain stable communication. Below When this occurs, it indicates poor link quality and a potential risk of communication interruption. At this time, the device... Its current transmission power Increase power increment step value However, the increased power will not exceed the device's maximum allowable transmission power. That is, take The minimum power after the increase. This represents the current transmit power of the device. The maximum transmit power supported by the device hardware. and It is the preset power adjustment step size, which determines the precision and response speed of power adjustment.

[0038] Through the above technical solution, the present invention can be based on the device Its neighboring equipment Real-time link quality between Dynamically adjust the transmission power When link quality Better than the preset threshold When this happens, the system automatically reduces its transmission power, effectively reducing interference to surrounding equipment and significantly lowering equipment power consumption. When link quality... Below the preset threshold At this time, the system will appropriately increase the transmission power to enhance signal strength and ensure a reliable communication connection even under poor channel conditions. Regarding link quality... When the transmit power is between two thresholds, it remains stable, avoiding unnecessary frequent adjustments. This adaptive power control strategy, combined with dynamic spectrum management, enables the entire near-field ad hoc network to achieve more efficient spectrum utilization and lower energy consumption while ensuring communication stability and reliability, thereby improving the overall performance and robustness of the network.

[0039] In some embodiments of the present invention, although a method for dynamically calculating and maintaining at least one optimal data transmission path using an intelligent multi-path routing algorithm is proposed in near-field ad hoc networks, in actual industrial operating environments, wireless channel conditions are complex and variable. Equipment mobility, interference, and sudden traffic can all cause a single optimal path to deteriorate rapidly or even fail. If data transmission relies solely on a single path, a problem with that path could lead to interruptions in the transmission of critical control commands or delays in state synchronization, severely impacting the reliability, real-time performance, and security of collaborative operations.

[0040] To address this, the present invention further proposes a specific method for running an intelligent multi-path routing algorithm during the dynamic spectrum and route maintenance phase to dynamically calculate and maintain at least one optimal data transmission path, including the following steps: First, each device periodically exchanges information with neighboring devices, including link quality scores. , number of jumps and node load status Routing information. To ensure the real-time nature and accuracy of routing decisions, devices in a near-field ad hoc network periodically exchange routing information with their neighbors. This routing information includes link quality scores. , number of jumps and node load status Among them, link quality score Used to quantify the performance and reliability of direct communication links between devices, such as by comprehensively evaluating metrics like signal-to-noise ratio, bit error rate, or received signal strength. Hop count This indicates the number of intermediate devices a data packet must pass through from the source device to the destination device. Node load status. This reflects the current resource usage of the device, such as CPU utilization, memory usage, or the length of the pending data queue, used to assess the device's ability to process data packets and potential congestion risks. By periodically exchanging this multi-dimensional information, each device can promptly perceive changes in network topology, fluctuations in link quality, and the distribution of node load, providing comprehensive data support for subsequent path selection.

[0041] Secondly, using a path cost evaluation algorithm, a comprehensive calculation is performed from the source node. To the target node a certain path The comprehensive value of the path The algorithm takes hop count, path bottleneck link quality, and total path load as input parameters, and its calculation formula is as follows: After obtaining the latest routing information, each device will use a path cost evaluation algorithm to comprehensively calculate the cost from the source node. To the target node any potential path The comprehensive value of the path This algorithm comprehensively evaluates the quality of a path, not just its length. Specifically, it uses hop count, bottleneck link quality, and total path load as key input parameters. In the calculation formula, Item represents path The sum of the reciprocals of the quality of all links, where For any pair of adjacent devices on the path and The link quality score between the links. This item reflects the overall transmission cost of the path; the worse the link quality, the larger its reciprocal, and the greater its contribution to the total cost. The item directly introduces the path. Number of jumps This is used to measure the length of the path; fewer hops indicate lower transmission latency. The item represents the path. The load value of the node with the highest load among all nodes is the bottleneck node load of the path. This item can effectively reflect the congestion risk of the path and avoid choosing a path that passes through a high-load node. , , The corresponding normalized weighting coefficients allow for flexible adjustments to the importance of the three influencing factors—link quality, hop count, and node load—based on the actual application scenario. For example, they can be appropriately increased in scenarios with high real-time requirements. The weighting, and can be increased in scenarios with high reliability requirements. The weighting of data transmission paths. Through comprehensive evaluation across multiple dimensions, the truly optimal data transmission path can be identified more accurately.

[0042] Finally, a multi-path routing table is built and dynamically updated to aggregate the cost of each path. The path with the lowest cost is designated as the primary path, and all other paths with a cost below a preset threshold are designated as backup paths. To further improve the robustness and reliability of the network, each device calculates the combined cost of all possible paths. Then, a multi-path routing table will be built and dynamically updated. In this routing table, the total cost of each path is... The path with the shortest length will be designated as the primary path, used for carrying regular data transmission. Meanwhile, to address potential failures or performance degradation on the primary path, all other paths will be considered together at a lower cost. Paths falling below a preset threshold will be identified and stored as backup paths. This preset threshold can be configured based on the dynamics of the network environment and the reliability requirements of the application, ensuring that the backup paths have sufficient quality to take over the function when the primary path fails. In this way, the device not only has an optimal path but also multiple available high-quality alternative paths, thereby significantly enhancing the continuity of data transmission and the fault tolerance of the network.

[0043] Through the above technical solution, this invention effectively solves the problem that in dynamic industrial operating environments, a single optimal path may rapidly deteriorate or fail, leading to interruptions in the transmission of collaborative control commands and delays in state synchronization. Specifically, by periodically exchanging multi-dimensional routing information, including link quality scores, hop counts, and node load status, each device can comprehensively perceive the real-time network status. Based on this, by utilizing a comprehensive path cost evaluation algorithm that incorporates key factors such as the reciprocal of link quality, hop count, and bottleneck node load, the true merits of paths can be calculated more accurately, avoiding the limitations of routing selection based solely on a single indicator (such as hop count), thus selecting a truly reliable and efficient data transmission path. Furthermore, by constructing and dynamically updating a multi-path routing table, not only is the path with the lowest overall cost designated as the primary path, but other paths with costs below a preset threshold are also designated as backup paths. This multi-path strategy significantly improves the robustness and fault tolerance of near-field ad hoc networks. When the performance of the primary path degrades or fails, the system can quickly switch to the backup path to ensure the continuous and real-time transmission of critical control commands and status data. This greatly enhances the reliability and safety of the collaborative operation between the smart safety helmet and the work equipment, ensuring the smooth progress of industrial operations.

[0044] In some of the embodiments of the present invention described above, a near-field ad hoc network communication and cooperative control method for smart safety helmets and work equipment based on open-source HarmonyOS is proposed. This method can dynamically calculate and maintain at least one optimal data transmission path. However, in actual working environments, the quality of wireless channels may change rapidly, causing the established optimal path or backup path to suddenly fail, thereby triggering data transmission interruption and affecting the real-time performance and reliability of cooperative control.

[0045] To address this, the present invention further proposes a link repair step. This link repair step aims to enhance the robustness of the near-field ad hoc network, ensuring that data transmission can be quickly restored in the event of a link failure, thereby guaranteeing the continuity of collaborative control between the smart safety helmet and the work equipment.

[0046] Specifically, when a data packet fails to be transmitted on the primary path, the sending device will immediately select a path from the set of backup paths, taking into account the overall cost. The smallest alternative path is retransmitted. After sending a data packet, the sending device continuously listens for acknowledgment (ACK) messages from the target device. If no ACK is received within a preset time, or if a negative acknowledgment (NACK) is received, the primary path transmission is considered to have failed. At this point, the sending device queries its internally stored routing table, which contains multiple pre-calculated alternative paths and their corresponding comprehensive path costs. The transmitting device will quickly identify the total cost of paths in the current set of alternative paths. The shortest path is selected, and previously failed data packets are immediately retransmitted along that path. This process is implemented at the link layer or network layer to minimize retransmission latency.

[0047] If all available paths fail, the smart helmet will trigger a local route rediscovery process, quickly reconstructing a communication path to the target device based on the current network topology. When the sending device has attempted retransmission through all alternative paths and still fails, it indicates that all known paths in the current network are unavailable. At this point, the smart helmet, acting as the network's logical control center, will initiate a local route rediscovery mechanism. This mechanism does not perform a network-wide route reconstruction but instead employs a restricted flood search. The reconstruction process uses a restricted flood search, with a search radius... Dynamically adjusted based on network size. Search radius. The calculation formula is .in, The maximum allowed search radius is used to limit the flooding range and prevent network congestion; The estimated number of nodes in the current network is obtained using the logarithmic function. The search range can be adaptively adjusted according to the network size, ensuring a smaller search range when there are fewer nodes and an appropriate expansion of the search range when there are more nodes to improve the success rate; This is a constant offset used to fine-tune the calculated radius; This indicates rounding up to the nearest integer, ensuring the search radius is an integer. The smart helmet sends route discovery requests with this limited radius to its neighboring nodes. Neighboring nodes continue forwarding within the radius limit until the target device is found or all paths have been explored. Once a new available path is discovered, the smart helmet updates its routing table and notifies relevant devices to use the new path for communication.

[0048] Through the above technical solution, this invention effectively solves the problem of data transmission interruption due to link failure in dynamically changing near-field ad hoc network environments. When the primary path fails, the system can quickly switch to a pre-calculated backup path for retransmission, significantly reducing packet loss rate and transmission latency, and ensuring timely delivery of collaborative control commands. When all preset paths fail, the smart helmet can trigger an intelligent local route rediscovery mechanism, which quickly reconstructs the communication path through a limited flooding search with dynamically adjusted search radius, avoiding the high overhead and long-term interruption caused by full-network route reconstruction. This greatly enhances the robustness and self-healing capability of the near-field ad hoc network, ensuring the continuity and reliability of collaborative control between the smart helmet and the work equipment, especially in harsh or variable working environments, effectively guaranteeing work safety and efficiency.

[0049] In some of the embodiments of the present invention, a smart safety helmet is proposed to acquire and synchronize the working status data of each piece of equipment in real time. However, in practical applications, if a traditional polling mechanism or a simple broadcast method is used for data synchronization, it may lead to a waste of network bandwidth, insufficient real-time data synchronization, and difficulty in ensuring data consistency. Especially in a dynamic and resource-constrained environment under a near-field ad hoc network, how to achieve efficient and reliable real-time synchronization of status data is the key.

[0050] In response, this invention further proposes that in step S3, the real-time acquisition and synchronization of the working status data of each piece of equipment is specifically achieved through the distributed data management function of the open-source HarmonyOS: the smart safety helmet subscribes to the key status variables of all working equipment in the network; when the key status variable of any piece of equipment changes, the status update notification and data are automatically published to the smart safety helmet through the near-field self-organizing network, so as to achieve real-time and consistent synchronization of status data.

[0051] Specifically, the distributed data management function of the open-source HarmonyOS is a core capability provided by its operating system, enabling seamless data sharing and collaboration across devices. This function shields the complexity of the underlying network by building a unified distributed data layer, allowing applications to access data on remote devices as if they were local data. It supports multiple data models, such as distributed key-value stores or distributed relational databases, and provides data subscription, publishing, synchronization, and data consistency guarantee mechanisms, providing fundamental support for distributed application development.

[0052] The smart safety helmet uses a "subscription" mechanism to express its interest in specific data items to all work equipment in the near-field ad hoc network. Key state variables are parameters crucial to collaborative operations, reflecting the current operating status or task progress of the equipment, such as operating mode, sensor readings, battery level, fault indications, or task completion status. The subscription model eliminates the need for the smart safety helmet to continuously query data; instead, it waits for the subscribed parties to proactively push data.

[0053] When any subscribed critical state variable within the work equipment changes, the work equipment immediately triggers an event and automatically publishes a status update notification and data containing the latest state value through the established near-field ad hoc network. This event-driven publishing mechanism ensures timely data updates, avoids unnecessary network traffic, and utilizes the near-field ad hoc network as the underlying communication channel.

[0054] Real-time synchronization means that updates to status data can be transmitted from the work equipment to the smart helmet with extremely low latency, ensuring that the smart helmet uses the most up-to-date data when making decisions. Consistent synchronization, on the other hand, guarantees that in a distributed system, the status data of the work equipment acquired by the smart helmet remains highly consistent with the actual status of the work equipment itself, avoiding collaborative control errors or efficiency reductions caused by data inconsistencies.

[0055] Through the above technical solution, the smart safety helmet can leverage the distributed data management capabilities of the open-source HarmonyOS to efficiently acquire operational equipment status data using a publish / subscribe model. When key status variables of the operational equipment change, it can automatically publish update notifications and data to the smart safety helmet, avoiding the network resource waste and synchronization delays caused by traditional polling mechanisms. This event-driven synchronization mechanism ensures the real-time nature and consistency of status data, enabling the smart safety helmet, as a collaborative control node, to promptly generate and distribute collaborative control commands based on the latest and most accurate operational equipment status information. This significantly improves the response speed, reliability, and overall efficiency of collaborative operations in a near-field ad hoc network environment.

[0056] In some of the embodiments of the present invention described above, the smart safety helmet can acquire and synchronize the working status data of each piece of equipment in real time. However, in the actual collaborative operation process, how to accurately generate and issue collaborative control commands based on these real-time status data and in combination with preset work tasks, so as to ensure that the work equipment responds in a coordinated manner as expected, is a key challenge to achieve efficient and accurate collaborative operation.

[0057] To address this, the present invention further proposes a specific method for generating collaborative control instructions in step S3. Specifically, the smart safety helmet has a built-in collaborative operation scheduler. This collaborative operation scheduler is a software module or hardware unit integrated inside the smart safety helmet, used to manage, coordinate, and optimize the collaborative operation process of multiple pieces of equipment. It can function as an application running on the open-source HarmonyOS operating system mounted on the smart safety helmet, or as an independent microcontroller module responsible for key functions such as task parsing, status comparison, instruction generation, and distribution, guiding the overall operation. The scheduler parses the expected action sequence and triggering conditions of each piece of equipment based on the received task blueprint. The task blueprint is a predefined data structure or file describing the collaborative operation process and objectives, such as an XML or JSON format configuration file, or a script generated by a graphical programming tool. It contains information such as the overall objectives of the operation, the roles of each piece of equipment, the expected action sequence, the dependencies between actions, triggering conditions, and time constraints. The parsing module inside the scheduler reads the task blueprint and converts it into an executable internal data structure, thereby transforming the high-level task description into an operable basis for generating instructions for individual devices.

[0058] Based on this, the scheduler compares the actual state vector of the work equipment obtained from the distributed data management in real time. The expected state vector required by the task blueprint And calculate the state deviation degree. Among them, the actual state vector This is a collection of the actual working status of each piece of equipment at the current moment, including position, attitude, sensor readings, actuator status, etc. This data is acquired in real time through the distributed data management capabilities of the open-source HarmonyOS. Expected State Vector This represents the expected state of each piece of equipment in the current operational phase, dynamically generated based on the task blueprint and the current operational progress. State Deviation. It quantifies the degree of difference between the actual state and the expected state, and its calculation formula is as follows: In the formula, The norm of a vector, such as the Euclidean norm (L2 norm), is used to calculate the square root of the sum of the squares of the differences between its components. By calculating the state deviation, the system can provide a quantitative indicator to determine whether the actual operation deviates from expectations. When the deviation... Less than the tolerance threshold Furthermore, it automatically generates the corresponding control instruction set when specific triggering logic conditions are met. Tolerance threshold. The preset, allowed deviation range is defined, but the triggering logic conditions, in addition to the deviation degree, must also meet other conditions, such as a specific time window or the occurrence of a certain event. The scheduler continuously monitors... The scheduler checks other logical conditions and, once the conditions are met, selects or dynamically generates a set of control instructions from a predefined instruction library based on the task blueprint and the current state. This ensures that instructions are generated only when necessary and when conditions are ripe, avoiding unnecessary or incorrect instruction issuance.

[0059] By incorporating a collaborative work scheduler into the smart safety helmet and introducing a work task blueprint, this invention can decompose complex collaborative work tasks into executable sequences of expected actions and triggering conditions. The scheduler acquires real-time data on the actual status of the work equipment and precisely compares it with the expected status required by the task blueprint. By calculating the state deviation, the difference between the actual operation and the expectation is quantified. When the deviation is within an acceptable range and meets the preset triggering logic, the system can automatically and accurately generate collaborative control commands. This effectively solves the problem of how to intelligently make decisions and generate control commands based on real-time status data and task requirements in complex work environments, ensuring that the work equipment can respond in conjunction with the preset collaborative logic, significantly improving the automation level and execution accuracy of collaborative operations.

[0060] In some of the embodiments of the present invention described above, although collaborative control commands can be generated based on the task blueprint and real-time operating conditions to achieve coordinated response between work equipment, sudden safety anomalies may occur in actual working environments. Relying solely on conventional command distribution and execution processes may not be able to respond to these emergencies in a timely and effective manner, thus posing potential safety risks and affecting the safety of personnel and equipment.

[0061] To address this, the present invention further proposes an abnormal alarm linkage step. Specifically, when the smart safety helmet identifies a safety anomaly through its own sensors or from the status data synchronized from any working equipment, it immediately suspends the current collaborative work task. For example, the smart safety helmet can integrate accelerometers, gyroscopes, gas sensors, or heart rate sensors. When the data from these sensors exceeds a preset threshold, the built-in processing unit of the smart safety helmet will determine it as a safety anomaly. Simultaneously, the smart safety helmet continuously analyzes the working status data synchronized from various working equipment through distributed data management, such as temperature, pressure, posture, or fault codes. Once an anomaly is detected, it will also be identified as a safety anomaly event. Upon identifying such an event, the smart safety helmet will immediately stop issuing new collaborative control commands to the working equipment and send a pause command to all working equipment currently performing tasks, putting them into a safe standby state to prevent the abnormal situation from worsening.

[0062] Based on this, the system will generate the highest priority alarm and control commands, and broadcast them to all associated work equipment via the near-field ad hoc network, triggering the emergency response mode of all devices. The alarm and control commands may include "emergency stop," "equipment lock," "evacuate the site," or "audible and visual alarm," etc., to deal with emergencies. The highest priority is assigned by the collaborative work scheduler built into the smart safety helmet when generating the command, and its priority value is... satisfy This formula ensures the priority values ​​for emergency commands. This command always has a higher priority than any regular command, ensuring it is processed first in any network transmission and device execution queue. Upon receiving this highest priority command, all associated equipment will immediately enter a preset emergency response mode, such as the robotic arm stopping and locking, the drone hovering or returning to base, and the mobile robot stopping and issuing an alarm.

[0063] Through the above technical solution, this invention introduces an abnormal alarm linkage mechanism into the collaborative control method of smart safety helmets and work equipment. When the smart safety helmet identifies a safety anomaly through its own sensors or from the status data synchronized with the work equipment, it can react quickly and immediately suspend the currently ongoing collaborative work task, effectively preventing the further escalation of potential dangers. Furthermore, the system can generate alarm and control commands with the highest priority and, utilizing the broadcast capability of near-field ad hoc networks, quickly and reliably distribute these commands to all associated work equipment. This is achieved by assigning priority values ​​to emergency commands. Set to a higher priority than the maximum priority of regular instructions. of This ensures that the command is prioritized in any network transmission and device execution queue, thereby triggering all devices to synchronously enter emergency response mode. This significantly improves the safety response speed and coordination consistency of the entire operating system, effectively protecting the lives of personnel and the property of equipment, and compensating for the shortcomings of relying solely on conventional collaborative control in responding to sudden safety incidents.

[0064] In some embodiments of the present invention, a near-field ad hoc network is proposed to establish between the smart safety helmet and the work equipment to achieve communication and collaborative control. However, in actual working environments, equipment may move frequently, and wireless signals are easily blocked and interfered with, resulting in unstable communication links. A single communication path cannot guarantee the reliability of data transmission and the robustness of the network, thereby affecting the efficiency and safety of collaborative operations.

[0065] In this regard, the present invention further proposes that the network topology of the near-field self-organizing network is a dynamic mesh network, wherein the smart safety helmet serves as the logical control center and data aggregation point, the work equipment serves as a network node, and any node can communicate directly with other nodes within the communication range or communicate through multi-hop relays.

[0066] Specifically, the near-field ad hoc network topology is a dynamic mesh network, meaning that each device node in the network is connected to at least one other node, and typically to multiple nodes, forming multi-path redundancy. This structure allows data to be transmitted from the source node to the destination node via multiple paths, ensuring data can reach the destination even if some links fail. The dynamic mesh network also possesses self-organizing and self-healing capabilities, enabling real-time adjustments and optimizations based on factors such as node movement and changes in link quality. This can be achieved by running distributed routing protocols, such as AODV, on each device. These protocols allow nodes to automatically discover neighbors, establish and maintain routing tables, and adaptively adjust data transmission paths according to network changes. For example, when a node moves or link quality degrades, the routing protocol can quickly discover new available paths, ensuring communication continuity.

[0067] The smart safety helmet plays a dual role in this network, functioning as both a logical control center and a data aggregation point. As the logical control center, it is responsible for generating and distributing collaborative control commands, coordinating the actions of various pieces of equipment. As the data aggregation point, it collects status data from all equipment and may perform preliminary processing or upload it to a higher-level system. The smart safety helmet can have a built-in high-performance processor and sufficient storage space to run a collaborative work scheduler and data management module. As the logical control center, it can periodically send heartbeat packets or query commands to the equipment to confirm its online status and availability. As the data aggregation point, it can receive status data published by the equipment through a subscription mechanism and store it locally or forward it to the cloud.

[0068] The work equipment, acting as network nodes, is the device that performs specific work tasks. Within the mesh network, it functions as a regular node, responsible for receiving control commands from the smart helmet, executing corresponding actions, and reporting its own status to the helmet. The work equipment has a built-in communication module and microcontroller, enabling it to run the mesh network protocol stack and communicate with other nodes. It can drive actuators based on received control commands, collect its own status data through sensors, and then transmit this data to the smart helmet via the network.

[0069] Any node can communicate directly with other nodes within its communication range or via multi-hop relay. Direct communication allows two nodes to establish a connection and exchange data directly within each other's wireless signal coverage. Multi-hop relay communication allows data to be forwarded through one or more intermediate nodes when the source and destination nodes are not within each other's direct communication range, eventually reaching the destination node. Each node transmits data via its wireless communication module. When a node needs to send data to another node, it first checks if the destination node is within its direct communication range. If not, it queries an available multi-hop path using a routing protocol and sends the data packet to the next relay node on that path. Each relay node receives the data packet and forwards it to the next node based on routing information until the data reaches its destination. This mechanism significantly enhances network coverage and robustness.

[0070] By employing the aforementioned technical solution, the network topology of the near-field ad hoc network is configured as a dynamic mesh network, and the roles of the smart safety helmet and operational equipment are clearly defined. This invention effectively solves the problems of poor reliability and limited coverage of traditional single-path communication in complex operational environments. The dynamic mesh network allows for multiple data transmission paths. When a link fails due to obstruction, interference, or equipment movement, data can be automatically relayed through other available paths, greatly enhancing network robustness and communication continuity. The smart safety helmet, acting as the logical control center and data aggregation point, can centrally manage and coordinate various operational equipment. Through the multi-hop capability of the mesh network, control commands are effectively distributed to more distant or obstructed operational equipment, while simultaneously aggregating status data from all equipment, ensuring the real-time performance and accuracy of collaborative control. The operational equipment, as network nodes, can not only execute commands and report status but also act as relay nodes to forward data from other devices, thereby expanding the network coverage. This enables seamless communication and collaborative operation between devices even in large or obstacle-ridden operational areas, improving overall operational efficiency and safety.

[0071] This invention also provides an example of a practical application of the method of this invention: I. Application Scenario and System Composition: This example simulates an emergency repair operation following a partial collapse inside a 3.2-kilometer-long mountain highway tunnel. The following devices equipped with the open-source HarmonyOS system are deployed on-site to construct an autonomous collaborative operation unit: Smart Safety Helmet (Command Node): 1 unit, model HC-01, serving as the logic control center and data aggregation point.

[0072] Smart safety helmets (work node): 2 units, model HC-02, worn by frontline workers.

[0073] Excavation robot: 1 unit, model EX-10, responsible for cleaning and loading excavated soil.

[0074] Two transport robots, model TR-20, are responsible for the transfer of construction waste.

[0075] II. Specific implementation, data calculation basis and decision-making logic of each stage: S1: Network discovery and secure access stage: This stage is used to quickly establish a trusted and secure Mesh self-organizing network in an environment without preset infrastructure.

[0076] Device fingerprint generation and two-way authentication: Data acquisition: After each device boots up, its Trusted Execution Environment (TEE) securely reads the following immutable hardware characteristics: MAC address ( For example, the Wi-Fi MAC of the command node HC-01 is AA:BB:CC:DD:EE:FF.

[0077] Radio frequency chip signature ( ): A 128-bit unique identifier read from the read-only memory area of ​​the Wi-Fi / Bluetooth chip, such as 0x1A2B3C4D5E6F7890.

[0078] Device hardware serial number ( ): A globally unique serial number burned into the device during production, such as SN-20240510-001.

[0079] Calculation basis: The national cryptographic SM3 hash algorithm is called within the trusted execution environment (as the hash function). ), for the spliced ​​feature data Perform calculations to generate a 256-bit device fingerprint. ).

[0080] Calculation formula:

[0081] Example: Command node HC-01 calculates its device fingerprint. .

[0082] Authentication and Key Negotiation: HC-01 broadcast includes The authentication request is encrypted using a pre-configured temporary group key. Upon receiving it, the EX-10 excavator robot decrypts it within its trusted execution environment and verifies it. Is it on the pre-authorized command equipment fingerprint list? After successful verification, the EX-10 replies with its own fingerprint. After successful two-way authentication, both parties negotiate a unique session key based on the Elliptic Curve Diffie-Hellman (ECDH, curve: sm2p256v1) protocol, using their respective device fingerprints as derived parameters. It is used to encrypt all subsequent communications.

[0083] S2: Dynamic Spectrum and Routing Maintenance Phase: This phase is used to ensure the reliability and efficiency of communication links in complex electromagnetic environments.

[0084] Dynamic Spectrum Management: Data Measurement: Every 5 seconds, all devices share the following parameters for each channel within the 2.4GHz and 5.8GHz ISM bands they are aware of via a distributed soft bus: Average Signal-to-Noise Ratio (SNR). ): Measurements in the channel via the physical layer The ratio of the received beacon frame signal to the noise power (unit: dB).

[0085] Channel occupancy rate ( ): Channel idle assessment (CCA) statistics are used to determine the channel idleness within a 1-second measurement period. The percentage of time that was judged as "busy" (unit: %).

[0086] Received signal strength variance ( ): The variance of the RSSI values ​​(in dBm) of all beacon frames received within 1 second is used to quantify channel stability.

[0087] Calculation basis and decision-making: Each device operates independently for each channel. Calculate the overall quality score .

[0088] formula:

[0089] Parameter setting basis: In this embodiment, based on the high stability requirements within the tunnel, the weighting coefficients are set as follows: (Emphasis on signal quality) (Emphasizing low interference) (Emphasizing stability), and .

[0090] Calculation Example: Assume the measurement results for 2.4GHz Ch.6 and 5.8GHz Ch.149 are as follows:

[0091] Decision-making logic: due to And the current working channel Below the switching threshold Command node HC-01 initiates a network-wide channel switching command, and all devices simultaneously switch to Ch.149.

[0092] Adaptive transmit power control: data measurement: equipment (e.g., the TR-20-1 transport robot) continuously measures its relationship with neighboring devices. Link quality between (e.g., command node HC-01) In this embodiment, Defined as the average success rate of receiving the last 10 data packets (in %).

[0093] Calculation basis and decision: TR-20-1 is based on a preset link quality threshold. and Dynamically adjust its transmission power .

[0094] Strategy Formula:

[0095] Parameter setting basis: The maximum transmit power limited by the device hardware is set to 20dBm.

[0096] and Set to 2dB to balance response speed and power oscillation.

[0097] Calculation example: The current power of TR-20-1 is The measured value was similar to that of HC-01. lower than According to the strategy, it will increase the power to .

[0098] Intelligent multipath routing maintenance: Data exchange: Each device exchanges route advertisements with its neighbors every second, containing the following information: Link quality score ( ): As defined above, the reception success rate.

[0099] Node load status ( ): The smoothed average of the current CPU utilization of the device (in %).

[0100] Number of jumps ( ): The minimum number of hops to other nodes in the network.

[0101] Calculation basis and decision: Calculation starts from the source node (HC-01) to the target node Path of (TR-20-2) Comprehensive value .

[0102] formula:

[0103] Parameter setting basis: This scenario emphasizes low latency and reliability; weights are set accordingly. (Focusing on link quality) (Focus on number of jumps) (Light load node load), and then normalize it.

[0104] Calculation example: Assume there are two paths: Path P1: HC-01->EX-10->TR-20-2, number of hops Each link 98%, 40% (bottleneck). Node load: 10%, 60%, 20%.

[0105] Path P2: HC-01->HC-02 (worker)->TR-20-1->TR-20-2, number of jumps Each link 95%, 90%, 85%. Node load: 10%, 30%, 25%, 20%.

[0106] Cost calculation:

[0107]

[0108] Decision logic: Although P1 has fewer hops, it has a very poor bottleneck link ( ), leading to its overall cost Much higher Therefore, the system sets P2 as the primary path and sets its cost below the backup path threshold (e.g., ...). Column P1 of ) is the alternative path.

[0109] S3: Collaborative Control and State Synchronization Stage: This stage realizes intelligent collaboration and safety emergency response based on real-time state.

[0110] Status Synchronization and Collaborative Command Generation: Data Synchronization: All operational equipment uses the open-source HarmonyOS distributed data management system to publish its key statuses (such as GPS / odometer fusion location, battery voltage, robotic arm joint angles, and task state machines) as "state variables." The command node HC-01 subscribes to all these variables, and any update is synchronized across the entire network within milliseconds.

[0111] Instruction generation calculation basis: The collaborative operation scheduler within HC-01 maintains a task blueprint (e.g., "transport the excavated soil from coordinate A to coordinate B"). The scheduler calculates the actual state vector in real time. (Obtained from synchronized data) and the blueprint's expected state vector deviation .

[0112] formula: ,in The Euclidean norm is used.

[0113] Triggering logic: When Less than the tolerance threshold (For example, when the position deviation is <0.5 meters and the mission status is "ready"), the next stage command is automatically generated (such as sending the "go to load point" command to TR-20-1).

[0114] Abnormal alarm and emergency response data measurement: The nine-axis IMU sensor of the worker's safety helmet HC-02 detected continuous abnormal vibration (>2g) for 3 seconds; at the same time, the lidar of the excavator robot EX-10 detected new debris 3 meters ahead.

[0115] Calculation Basis and Decision-Making: Anomaly Judgment: The HC-02 local algorithm determined that the vibration pattern matched the characteristics of "falling rocks," while the EX-10 algorithm determined that loose debris had intruded into the safe working area. Both were marked locally as the highest priority safety events.

[0116] Command generation and broadcasting: After the event is marked, the device immediately generates an "Emergency Stop (E-STOP)" command and assigns it priority. .

[0117] formula:

[0118] Parameter setting basis: The highest priority of regular instructions in this system. To ensure absolute priority, an emergency increment is set. .therefore, .

[0119] Network behavior: This command is broadcast via the Mesh network in a flooded manner with the highest priority. Upon receiving it, all nodes immediately interrupt any current tasks and enter the preset emergency mode (the robot locks its drive motors, and the safety helmet emits an audible and visual alarm).

[0120] III. Conclusion: This embodiment verifies that the method can effectively achieve trusted interconnection, intelligent collaboration and safe linkage between personnel and intelligent equipment in extreme industrial scenarios with no network, strong interference and high dynamics, significantly improving the overall efficiency and inherent safety level of complex operations.

[0121] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in the present invention, and these should all be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A near-field self-organizing network communication and cooperative control method for smart safety helmets and work equipment based on open-source HarmonyOS, characterized in that, The described method is executed by a smart safety helmet equipped with the open-source HarmonyOS and at least one operating equipment, and includes the following steps: S1. Network discovery and secure access phase: After the smart safety helmet and the operating equipment are powered on, they are automatically discovered based on the distributed soft bus of the open-source HarmonyOS; each device collects its own hardware feature data, generates a unique device fingerprint in the trusted execution environment, and completes two-way authentication and secure key negotiation based on the device fingerprint to establish a secure communication link and form a near-field self-organizing network; S2. Dynamic spectrum and routing maintenance phase: Each device in the near-field self-organizing network continuously senses the wireless channel quality of the surrounding environment through the distributed soft bus, and performs dynamic spectrum management based on the sensing results, adaptively selecting a communication channel and transmission power; at the same time, each device runs an intelligent multi-path routing algorithm, and dynamically calculates and maintains at least one optimal data transmission path based on the real-time network topology and link quality; S3. Cooperative control and status synchronization phase: The smart safety helmet serves as a cooperative control node, and based on the established near-field self-organizing network, it can obtain and synchronize the working status data of each operating equipment in real time through the distributed data management ability of the open-source HarmonyOS; according to the preset operation tasks or real-time working conditions, it generates cooperative control instructions, and distributes them to the target operating equipment through the optimal data transmission path maintained in S2, so as to achieve cooperative operation and linkage response among the operating equipment.

2. The near-field self-organizing network communication and cooperative control method for smart safety helmets and work equipment based on open-source HarmonyOS as described in claim 1, characterized in that, Step S1, which involves completing two-way authentication and security key negotiation based on the device fingerprint, specifically includes: S1.1, inputting the collected MAC address M, RF chip signature R, and device hardware serial number S as multi-dimensional device feature data into the hash function H in the trusted execution environment to generate the device fingerprint F, i.e., F = H(M, R, S); S1.2, the device initiating authentication broadcasts an authentication request containing its own device fingerprint F to the network after encryption, and the device to be authenticated decrypts and verifies the legitimacy of the device fingerprint F in the trusted execution environment; S1.3, after successful two-way authentication, both parties, based on their respective device fingerprints F... A and F B It employs a preset key exchange algorithm to negotiate and generate a unique session key K, which is used to encrypt all subsequent communication data.

3. The near-field self-organizing network communication and cooperative control method for smart safety helmets and work equipment based on open-source HarmonyOS as described in claim 1, characterized in that, The dynamic spectrum management based on the sensing results in step S2 specifically includes: S2.1a, each device shares the channel quality assessment parameters of each channel i in the 2.4GHz and 5.8GHz frequency bands it senses through a distributed soft bus; S2.1b, each device calculates a comprehensive quality score Q for each channel i based on all the shared parameters. i And jointly decide Q i The channel with the highest value is selected as the current optimal communication channel; the overall quality score Q is... i The formula for calculating Q is: i =α·SNR i -β·Occupancy i -γ·Variance(RSSI i In the formula, SNR i Occupancy represents the average signal-to-noise ratio of channel i. i Represents the occupancy rate of channel i, Variance(RSSI) i S2.1c: When the current channel quality score Q is detected, α, β, γ represent the variance of the received signal strength indication on channel i (used to characterize channel stability), and α, β, γ are the positive weight coefficients of each influencing factor, and α + β + γ = 1; current When the value is below a preset first threshold θ1, the smart safety helmet initiates a channel switching command to guide all devices in the network to switch synchronously to the optimal communication channel.

4. The near-field self-organizing network communication and cooperative control method for smart safety helmets and work equipment based on open-source HarmonyOS as described in claim 3, characterized in that, Step S2 further includes an adaptive transmission power selection step, specifically: S2.1d, device u measures the current link quality LQ between itself and its neighboring device v. uv and compared with two preset thresholds and low ( By comparing the data, its transmission power P is dynamically adjusted. tx_u The mathematical expression for the adjustment strategy is: In the formula, For the current transmission power, P max ΔP is the maximum allowable transmit power of the device. dec and ΔP inc These are the step values ​​for decreasing and increasing power, respectively. This formula ensures that when the link quality is good, interference and power consumption are reduced, when the quality is insufficient, power is increased to ensure stability, and power is kept stable within the threshold range.

5. The near-field self-organizing network communication and cooperative control method for smart safety helmets and work equipment based on open-source HarmonyOS as described in claim 1, characterized in that, In step S2, running the intelligent multi-path routing algorithm to dynamically calculate and maintain at least one optimal data transmission path specifically includes: S2.2a. Each device periodically exchanges routing information including link quality score LQ, hop count h, and node load status L with its neighbor devices; S2.2b. Using the path cost evaluation algorithm, comprehensively calculate the path comprehensive cost value C(p) of a certain path p from the source node s to the target node d; the algorithm takes the hop count, the quality of the path bottleneck link, and the total path load as input parameters, and its calculation formula is: In the formula, Let h(p) represent the sum of the reciprocals of the quality of all links on path p (reflecting the overall transmission cost), and h(p) be the path hop count. S2.2c represents the maximum load of all nodes on path p (reflecting the path congestion risk), and w1, w2, w3 are the corresponding normalized weight coefficients; S2.2c, construct and dynamically update the multi-path routing table, set the path with the smallest comprehensive cost C(p) as the primary path, and set the other paths with costs below the preset threshold as backup paths.

6. The near-field self-organizing network communication and cooperative control method for smart safety helmets and work equipment based on open-source HarmonyOS as described in claim 5, characterized in that, The method further includes a link repair step: S2.2d. When the data packet transmission fails on the main path, the sending device immediately selects the standby path with the smallest cost value C from the standby path set for retransmission; S2.2e. If all available paths fail, the smart safety helmet triggers a local routing rediscovery process, and quickly reconstructs the communication path to the target device based on the current network topology; the reconstruction process uses a restricted flooding search, and its search range radius R is dynamically adjusted according to the network scale: In the formula, R max The maximum allowed search radius is given by N, where N is the estimated number of nodes in the current network, and Δ is a constant offset. This indicates rounding up to the nearest integer.

7. The near-field self-organizing network communication and cooperative control method for smart safety helmets and work equipment based on open-source HarmonyOS as described in claim 1, characterized in that, In step S3, obtaining and synchronizing the working status data of each operating equipment in real time is specifically realized through the distributed data management function of the open-source HarmonyOS: The smart safety helmet subscribes to the key status variables of all operating equipment in the network; when the key status variable of any operating equipment changes, it automatically notifies and publishes the status update and data to the smart safety helmet through the near-field self-organizing network, so as to achieve real-time and consistent synchronization of the status data.

8. The near-field self-organizing network communication and cooperative control method for smart safety helmets and work equipment based on open-source HarmonyOS according to claim 1 or 7, characterized in that, The generation of collaborative control instructions in step S3 specifically includes: S3.1, the smart safety helmet has a built-in collaborative operation scheduler, which parses the expected action sequence and triggering conditions of each piece of equipment based on the received task blueprint; S3.2, the scheduler compares the actual state vector of the equipment obtained from distributed data management in real time. The expected state vector required by the task blueprint And calculate the state deviation degree D: In the formula, |·| represents the norm of the vector; when the deviation degree D is less than the tolerance threshold ε and the specific triggering logic condition is met, the corresponding control instruction set is automatically generated.

9. The near-field self-organizing network communication and cooperative control method for smart safety helmets and work equipment based on open-source HarmonyOS as described in claim 8, characterized in that, The method further includes an abnormal alarm linkage step: S3.3, when the smart safety helmet identifies a safety anomaly through its own sensors or from the status data synchronized from any working equipment, it immediately suspends the current collaborative work task; S3.4, it generates the highest priority alarm and control command and broadcasts it to all associated working equipment through the near-field self-organizing network, triggering the emergency response mode of all equipment; the highest priority is assigned by the scheduler when generating the command, and its priority value P emergency Satisfy: P emergency =P max In the formula +ΔP, P max ΔP is the maximum priority value for regular instructions, and ΔP is the emergency increment, ensuring that the instruction is processed with priority in any queue.

10. The near-field self-organizing network communication and cooperative control method for smart safety helmets and work equipment based on open-source HarmonyOS according to claim 1, characterized in that, The network topology of the near-field self-organizing network is a dynamic mesh network, wherein the smart safety helmet serves as the logical control center and data aggregation point, and the work equipment serves as a network node. Any node can communicate directly with other nodes within the communication range or communicate through multi-hop relays.

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