A photovoltaic power station multi-node self-organizing wireless coverage method

Abstract

This invention relates to the field of wireless communication technology and discloses a multi-node self-organizing wireless coverage method for photovoltaic power plants. The method includes: collecting and tracking the physical tilt angle and link quality of the support structure; mapping the tilt angle to a mechanical sector index; constructing a mechanical spectrum fingerprint database and marking poorly performing channels as forbidden states; calculating negatively correlated energy potential weights based on real-time power generation current; fusing and constructing an uplink data transmission path pointing to the aggregation gateway; predicting the mechanical phase trend based on the tilt angle; and, when the trend falls into a communication blind zone and the real-time power generation current meets the maintenance threshold, activating a data retention mode and writing service data into a priority circular buffer. During transmission, forbidden channels are eliminated, and the operating frequency is selected for transmission. This invention effectively solves the problems of dynamic physical shading and energy limitation caused by support structure movement in photovoltaic power plants through coordinated scheduling of mechanical phase mapping and energy channels, achieving adaptive and reliable communication in complex environments.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, specifically to a method for multi-node self-organizing wireless coverage in photovoltaic power plants. Background Technology

[0002] As photovoltaic power plants develop towards large-scale and intelligent deployments, distributed monitoring systems based on wireless self-organizing networks have become the mainstream solution to replace traditional wired deployments. In typical photovoltaic power plant application scenarios, a large number of wireless communication nodes are dispersedly installed near photovoltaic modules, combiner boxes, or inverters, using ZigBee, WiSUN, or proprietary SubGHz protocols to construct a mesh topology network. These nodes are responsible for periodically collecting data on generator voltage, current, and equipment operating status, and aggregating the data to a central gateway through multi-hop relays. Existing communication protocol stacks typically employ static routing metrics based on received signal strength or link quality indicators, rely on a common carrier sense multiple access (CSMA) mechanism for channel contention, and use preset pseudo-random frequency hopping sequences to cope with general electromagnetic interference in the environment to achieve basic data backhaul functionality.

[0003] However, in modern photovoltaic (PV) power plants employing automatic tracking supports, existing technologies exhibit significant limitations. Because the tracking supports must continuously move mechanically to follow the sun's trajectory around the clock, the substantial displacement of their metal structures leads to periodic physical obstruction and severe non-line-of-sight fading in the wireless signal transmission path. Traditional wireless protocols lack the ability to perceive the strong coupling between this mechanical movement and channel quality, often continuing to attempt data transmission even when the supports have rotated to a communication dead zone, resulting in numerous invalid retransmissions and packet loss at the physical layer. Furthermore, existing network protocols fail to effectively integrate the unique energy harvesting characteristics of PV nodes, and cannot dynamically adjust routing overhead based on real-time fluctuations in generated current. This easily leads to critical nodes continuing to bear heavy relay tasks when energy is scarce, potentially causing node energy depletion and network topology breaks, making it difficult to ensure communication reliability and continuity under the dual constraints of mechanical dynamic interference and energy limitations. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a multi-node self-organizing wireless coverage method for photovoltaic power plants, which solves the problems of unstable communication links and data loss caused by dynamic physical shading due to the periodic movement of tracking supports and the limited energy of nodes in existing photovoltaic power plant wireless communication networks.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a multi-node self-organizing wireless coverage method for photovoltaic power stations, comprising the following steps: First, a mechanical spectrum fingerprint database is constructed and maintained. The physical tilt angle and link quality parameters of the tracking bracket are collected via a microcontroller unit. Based on a preset sector granularity, the filtered physical tilt angle is linearly quantized and mapped to generate a unique discrete mechanical sector index. Simultaneously, a two-dimensional taboo bitmap structure is constructed in the storage unit. The row index of this bitmap corresponds to the mechanical sector index, and the column index corresponds to the channel index supported by the physical layer. The packet error rate or link quality indicator of the link is continuously monitored. When the packet error rate of the target channel under the mechanical sector is detected to be consistently higher than the interference judgment threshold, the corresponding position is marked as taboo in the two-dimensional taboo bitmap, thereby associating the physical tilt angle mapping relationship with the channel quality status and forming the mechanical spectrum fingerprint database.

[0006] Secondly, network routes are updated based on the mechanical spectrum fingerprint database and energy potential weights. During route construction, the mechanical spectrum fingerprint database is used to correct link detection results, and path costs are calculated in conjunction with node energy status. Specifically, battery voltage and real-time generation current are collected and normalized preprocessed, and synthesized into a comprehensive energy potential index based on a weighted fusion strategy. A nonlinear cost mapping relationship is constructed using a negative exponential function to convert the comprehensive energy potential index into energy potential weights, making the energy potential weights negatively correlated with real-time generation current, and increasing exponentially when the energy is below the warning threshold. At the same time, the number of forbidden state channels and the number of warning channels under the current mechanical sector are counted, and the mechanical spectrum availability penalty factor reflecting the spectrum scarcity is quantitatively calculated. The expected number of basic link transmissions, the mechanical spectrum availability penalty factor, and the energy potential weights of neighboring nodes are nonlinearly fused to synthesize a composite routing metric, based on which an uplink data transmission path to the aggregation gateway is constructed.

[0007] Next, data retention and buffer management based on mechanical phase prediction is implemented. Based on the historical angular trajectory of the support structure, the current angular velocity and angular acceleration are calculated using weighted least squares, and the transmission delay is estimated using the protocol stack. The predicted angle is then extrapolated using second-order kinematic equations and mapped to a predicted mechanical sector index. Based on this predicted index, the mechanical spectrum fingerprint database is retrieved, the number of taboo and warning channels in the channel state vector is statistically analyzed, and a weighted normalized sector congestion density index is calculated. The priority of the service data to be transmitted is analyzed to generate an urgency factor, and a dynamic congestion tolerance threshold is calculated. When the sector congestion density index exceeds the dynamic congestion tolerance threshold (i.e., falling into a communication dead zone) and the real-time power generation current meets the maintenance threshold, a data retention mode is initiated, data delivery to the radio frequency unit is suspended, and the service data is written to a priority circular buffer. When the buffer is saturated, the retention utility score of the stored data packets is calculated. This score is positively correlated with the service priority and negatively correlated with the retention time. Based on this, a dynamic replacement strategy based on the utility score is executed.

[0008] Finally, frequency selection and transmission based on a fingerprint database are performed when transmission is permitted. Hardware identity features of the nodes are extracted to generate a numerical seed. A normalized time phase offset is calculated using a linear congruent mapping and loaded into a timer to achieve desynchronization at the transmission time. After the delay, taboo channels corresponding to the current sector are removed based on the mechanical spectrum fingerprint database, constructing a set of real-time available channels. A pseudo-random sequence modulo operation remapping is performed, projecting the original frequency hopping index onto the set of available channels to obtain the actual transmission frequency. If the selected frequency is a warning channel, the retention probability is calculated based on the historical bit error rate to determine whether to use it. Finally, service data is transmitted along the uplink data transmission path on the selected operating frequency.

[0009] Furthermore, it includes energy-adaptive aging maintenance for the mechanical spectrum fingerprint database. The aging period of the fingerprint data is dynamically calculated based on the real-time generation current, ensuring a negative correlation between the aging period and the real-time generation current. This means the aging period is shortened when energy is abundant to improve environmental adaptability. An inertial checking strategy is employed: when a node moves to a new mechanical sector, the time difference between the sector's most recent update time and the current time is calculated. If the difference exceeds the aging period, the corresponding status bit is reset, enabling dynamic updates to the fingerprint database.

[0010] This invention provides a method for multi-node self-organizing wireless coverage in photovoltaic power plants. It has the following beneficial effects: 1. This invention maps the physical tilt angle of the photovoltaic support to the quality characteristics of the wireless channel by constructing a mechanical spectrum fingerprint database and a second-order kinematic prediction model. It predefines the communication blind zone using the sector blocking density index. When the mechanical phase is about to fall into the high interference area, it replaces the traditional passive retransmission mechanism with an active avoidance strategy, which reduces the invalid radio frequency transmission caused by metal structure obstruction and ensures the connection stability of the communication link in a dynamic physical environment.

[0011] 2. This invention establishes a network routing mechanism based on nonlinear cost mapping by collecting real-time power generation current and battery voltage and synthesizing a comprehensive energy potential index. When the node energy is lower than the warning threshold, the path overhead increases exponentially, forming a high-impedance routing zone. This forces data traffic to automatically tilt towards nodes with sufficient energy or high charging rates, effectively preventing network topology breaks caused by the depletion of power of individual key nodes and extending the overall lifespan of the passive photovoltaic communication network.

[0012] 3. This invention solves the data retention problem during communication blind spots by performing cross-layer collaborative data retention determination and priority caching management. Under the premise that the node has sufficient maintenance current, a retention mode is initiated. A dynamic replacement strategy based on retention utility scores is used to prioritize the removal of low-priority or outdated data when the buffer is saturated, while retaining high-value alarm information, thus ensuring reliable transmission of critical business data after leaving the blind spot. Attached Figure Description

[0013] Figure 1 This is a diagram of the adaptive networking system architecture for photovoltaic power plants according to the present invention; Figure 2 This is a schematic diagram of the process of the multi-node self-organizing wireless coverage method for photovoltaic power stations according to the present invention; Figure 3 This is a bar chart illustrating the performance comparison between the experimental group and the control group under dynamic interference conditions at a photovoltaic power station in a specific application embodiment of the present invention.

[0014] Among them, 10 is the aggregation gateway; 20 is the wireless communication node; 30 is the remote management server; 40 is the photovoltaic string; 50 is the tracking bracket mechanical structure; 201 is the microcontroller unit; 202 is the radio frequency transceiver unit; 203 is the energy sensing interface; 204 is the attitude acquisition interface; 205 is the storage unit; and 206 is the power management unit. Detailed Implementation

[0015] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] See attached document Figure 1 This invention provides an adaptive networking method for photovoltaic (PV) power plants based on mechanical phase mapping and energy channel coordinated scheduling, which operates within a PV power plant adaptive networking system. This system includes at least one aggregation gateway 10, multiple distributed wireless communication nodes 20, and a remote management server 30.

[0017] The multiple wireless communication nodes 20 are connected in the form of a wireless ad hoc network, and aggregate data to the aggregation gateway 10 through multi-hop transmission. The aggregation gateway 10 is connected to the remote management server 30 through a fiber optic ring network or a 4G / 5G backhaul link. The topology of the wireless ad hoc network is built based on the low-power lossy network IPv6 routing protocol RPL, and the wireless communication nodes 20 are configured to act as root nodes, router nodes, or leaf nodes according to their network location and connection status.

[0018] The wireless communication node 20 is physically deployed on the power generation unit side of the photovoltaic power station. Specifically, each wireless communication node 20 is integrated into the photovoltaic combiner box or the photovoltaic tracking bracket controller, or installed as an independent module on the photovoltaic module bracket. The wireless communication node 20 is physically coupled with the photovoltaic string 40 and the tracking bracket mechanical structure 50.

[0019] The wireless communication node 20 includes a microcontroller unit 201, a radio frequency transceiver unit 202, an energy sensing interface 203, an attitude acquisition interface 204, a storage unit 205, and a power management unit 206.

[0020] The microcontroller unit 201 is electrically connected to the radio frequency transceiver unit 202, the energy sensing interface 203, the attitude acquisition interface 204, and the storage unit 205, respectively, and is used to perform data processing and logic control. The microcontroller unit 201 is an industrial-grade microcontroller with a floating-point arithmetic unit and is configured to run the network protocol stack and the scheduling algorithm described in this embodiment.

[0021] The radio frequency transceiver unit 202 supports wireless communication in the SubGHz band and is configured to perform physical layer modulation and demodulation, channel monitoring, and spectrum hopping operations. The radio frequency transceiver unit 202 supports channel switching based on frequency hopping spread spectrum technology and has the ability to perform transmit masking on a specified frequency list.

[0022] The energy sensing interface 203 is configured to collect real-time power generation current data of the photovoltaic string 40 associated with it. In one embodiment, the energy sensing interface 203 is directly connected to a Hall current sensor or shunt in the photovoltaic combiner box via an analog-to-digital converter circuit; in another embodiment, the energy sensing interface 203 reads the current register value of the maximum power point tracking controller running in parallel with it via a UART or I2C bus. The real-time power generation current data is transmitted to the microcontroller unit 201 to characterize the current energy adequacy of the node.

[0023] The attitude acquisition interface 204 is configured to acquire real-time mechanical tilt angle data of the tracking bracket mechanical structure 50. In one embodiment, the attitude acquisition interface 204 is an RS485 communication interface connected to the photovoltaic tracking bracket controller, and obtains the current angle value by reading the motor control register; in another embodiment, the attitude acquisition interface 204 is connected to an onboard MEMS accelerometer sensor, and calculates the current physical tilt angle using the gravity component.

[0024] The storage unit 205 includes non-volatile memory and volatile random access memory. The non-volatile memory is used to store firmware programs and historical statistical data; the volatile random access memory is used to construct a circular data buffer and a runtime routing table. The storage unit 205 is configured to store the mechanical spectrum fingerprint database and the queue of service data to be transmitted, as described in subsequent steps.

[0025] The power management unit 206 is connected to the DC bus of the photovoltaic string 40 or an independent power supply battery, and is responsible for providing a stable operating voltage to the components within the wireless communication node 20. The microcontroller unit 201 reads the data from the energy sensing interface 203 to determine whether the energy conditions required to support the storage unit 205 in maintaining data for an extended period of time are currently available.

[0026] The photovoltaic power station adaptive networking system utilizes the aforementioned hardware architecture to construct a communication environment that couples across the physical layer, media access control layer, and network layer. The microcontroller unit 201 dynamically adjusts the operating frequency and transmission timing of the radio frequency transceiver unit 202 by comprehensively analyzing current data from the energy sensing interface 203 and angle data from the attitude acquisition interface 204, thereby achieving adaptive coverage of the physical interference environment of the photovoltaic power station.

[0027] See attached document Figure 2 This invention provides an adaptive networking method for photovoltaic power plants based on mechanical phase mapping and energy channel coordinated scheduling, executed by the aforementioned wireless communication node 20, and mainly includes the following steps: Step S100: Construct and maintain a mechanical spectrum fingerprint database. The microcontroller unit 201 periodically acquires the real-time physical tilt angle of the tracking bracket's mechanical structure 50 via the attitude acquisition interface 204, and monitors the link quality parameters of the current working channel via the radio frequency transceiver unit 202. The microcontroller unit 201 maps continuous physical tilt angles to discrete mechanical sector indices and marks physical channels with link quality below a preset threshold as taboo states, generating a mechanical spectrum fingerprint database stored in the storage unit 205. The microcontroller unit 201 further resets the taboo state records in the mechanical spectrum fingerprint database periodically based on a preset aging period to ensure adaptability to environmental changes.

[0028] Step S200: Update the network routing based on energy potential gradient. The microcontroller unit 201 reads the real-time power generation current of the photovoltaic string 40 through the energy sensing interface 203. The microcontroller unit 201 calculates the energy potential weight based on the real-time power generation current, and the energy potential weight is negatively correlated with the real-time power generation current. The microcontroller unit 201 substitutes the energy potential weight into the routing objective function, calculates the comprehensive path cost based on the physical link detection results, and selects the next-hop parent node based on the principle of minimizing the comprehensive path cost, thereby constructing an uplink data transmission path pointing to the aggregation gateway 10.

[0029] Step S300: Perform cross-layer collaborative data retention determination and cache management. When service data to be transmitted is generated, the microcontroller unit 201 predicts the mechanical phase trend within a preset time period based on the current physical tilt angle and support motion parameters. The microcontroller unit 201 searches the mechanical spectrum fingerprint database to determine whether the predicted mechanical phase trend falls into a communication blind zone. If the prediction result indicates a communication blind zone and the real-time power generation current is higher than the maintenance current threshold, the microcontroller unit 201 initiates a data retention mode, writes the service data into the priority circular buffer in the storage unit 205, and suspends data delivery to the radio frequency transceiver unit 202; otherwise, the service data is added to the transmission queue. During the data retention period, the microcontroller unit 201 executes a buffer overflow protection strategy according to the data packet priority.

[0030] Step S400: Perform hash phase release and dynamic spectrum freeze. When the service data is allowed to be transmitted, the microcontroller unit 201 calculates the deterministic delay parameter based on the unique identifier of the wireless communication node 20 and performs a delay wait to achieve desynchronization of the transmission time. After the delay ends, the microcontroller unit 201 reads the channel state corresponding to the current mechanical sector in the mechanical spectrum fingerprint database again, removes physical channels marked as taboo in the frequency hopping list, and generates a set of available frequencies. The microcontroller unit 201 controls the radio frequency transceiver unit 202 to select the operating frequency from the set of available frequencies and modulates and transmits the service data to the air channel.

[0031] See attached document Figure 2 In the step of constructing and maintaining the mechanical spectrum fingerprint database, this embodiment employs a processing mechanism that converts continuously changing analog physical quantities into discrete indexes that can be processed by a digital system. By reducing the dimensionality of the high-dimensional physical state space, environmental feature memorization under limited storage resources is achieved. This processing flow specifically includes the following steps: Step S101: Acquire and calibrate mechanical phase data.

[0032] The microcontroller unit 201 periodically reads the real-time physical tilt angle of the tracking bracket mechanical structure 50 at a preset sampling frequency through the attitude acquisition interface 204. In this embodiment, the real-time physical tilt angle Defined as the angle between the normal to the photovoltaic module's plane and the normal to the horizontal plane on the east-west vertical tangent, its value is limited to a closed interval []. , [Inside.] Among them, This represents the reverse limit angle of the photovoltaic support (e.g., 60°). This represents the positive limit angle (e.g., +60°). Given the non-ideal factors in industrial environments such as wind loads, vibrations, and mechanical transmission clearances, the microcontroller unit 201 needs to perform signal modulation on the read starting angle data before performing subsequent calculations. Specifically, a low-pass filter algorithm is used to filter out transient jitter components of the high-frequency mechanical natural frequency of the photovoltaic support, in order to obtain a steady-state angle value reflecting the macroscopic attitude of the support. For implementations that acquire data via an accelerometer, the microcontroller unit 201 also needs to introduce a zero-point drift compensation coefficient to correct static deviations caused by sensor installation errors, ensuring that the zero point of the angle data strictly corresponds to the horizontal position of the photovoltaic support.

[0033] Step S102: Perform mechanical sector index calculation.

[0034] In order to convert continuous angle variables into numerical indexes that can be used for table lookup, the microcontroller unit 201 uses a preset sector granularity. Perform a linear quantization mapping operation to compute a unique discrete integer index, i.e., a mechanical sector index. The sector granularity As a key discretization parameter, its value is not arbitrarily set, but determined by the wavelength of the core operating frequency band and the Fresnel zone radius. It is typically set to the minimum angular span that maximizes the difference in path loss between adjacent sectors. (The mechanical sector index is mentioned.) The calculation of is based on the following mathematical expression: ; In the formula, The time after filtering Real-time physical tilt angle; This represents the minimum angular boundary value for the mechanical travel of the support. The angular resolution step size for sector division must satisfy the following conditions: To avoid division errors; This represents the floor function operator; This is an interval clamping function used to ensure that when the input angle exceeds the theoretical range due to sensor noise, the calculation result still converges within the effective index space. Through the above mapping operation, the mechanical angles of the entire stroke are divided into... A series of consecutive mechanical sectors, total number of sectors By the rounding up formula Confirmed. This step converts the physical environment state into a digital logical address, providing a direct index key for subsequent rapid fingerprint retrieval.

[0035] Step S103: Construct the taboo bitmap data structure.

[0036] Based on the index space calculated in step S102, contiguous storage blocks are allocated in storage unit 205 to maintain the mechanical spectrum fingerprint database. In this embodiment, considering the limited random access memory resources of the microcontroller unit 201, the fingerprint database uses a bit-compressed two-dimensional taboo bitmap. The structure is organized. The two-dimensional taboo bitmap... Defined as a dimension The logical matrix, where the row index corresponds to the mechanical sector index. The column index corresponds to the channel index supported by the physical layer of the wireless communication node. ,and , This represents the total number of available frequency points.

[0037] Step S104: Update the channel status flag bit.

[0038] The two-dimensional taboo map Each storage unit in Composed of fixed-width binary bits, it indicates the historical communication quality status of the physical channel under a mechanical sector. As an optimized implementation, each element occupies 2 bits to support a three-state logic representation: binary value 00 defines an unknown state, indicating that the channel has not accumulated sufficient statistical data in the current sector; binary value 01 defines an available state, indicating that the channel has historically good communication quality in this sector; binary value 11 defines a forbidden state, indicating that the channel has definite coherent interference or deep fading in this sector. The microcontroller unit 201 continuously monitors the link quality parameters fed back by the RF transceiver unit 202, including the link quality indicator LQ1 or packet error rate PER. When the system detects in the sector... Down channel The packet error rate consistently exceeds the preset interference detection threshold. When PER > 30% (e.g., when PER > 30%), the microcontroller unit 201 performs a write operation. Setting it to 11 allows the node to actively shield the frequency at the physical layer when it subsequently reaches the same mechanical posture by recording historical communication fault characteristics, thus achieving an adaptive obstacle avoidance effect of learning from mistakes.

[0039] For the data filtering algorithm and sensor calibration method described in step S101, those skilled in the art can implement them using moving average filtering, Kalman filtering, or zero-point compensation algorithms based on lookup tables. The specific calculation process is well-known in the field and will not be elaborated here. Through the above series of processing steps, the photovoltaic power station adaptive networking system effectively transforms the complex dynamic multipath physical environment into a digital lookup table that the microcontroller unit 201 can quickly read, thereby establishing a definite environmental context for subsequent cross-layer resource scheduling.

[0040] See attached document Figure 2 To address the technical challenge of storing full channel state information in resource-constrained embedded environments (such as SRAM capacity less than 64KB) for photovoltaic power plant communication nodes, this embodiment employs a two-dimensional spatial spectrum mapping table based on bit compression technology, namely a taboo bitmap. The core design of this data structure lies in reducing the dimensionality of multi-dimensional features of environmental perception and mapping them to a contiguous physical memory space, thereby preserving... While achieving efficient time-complexity retrieval capabilities, it also reduces memory usage. The specific construction and maintenance process includes the following steps: Step S105: Define the logical dimensions of the bitmap matrix.

[0041] The microcontroller unit 201 allocates a dedicated contiguous address space in volatile memory to construct a logically two-dimensional bitmap matrix. The row dimension of this matrix is ​​determined by the total number of mechanical sectors. The column dimension is determined by the total number of frequency hopping points supported by the radio frequency physical layer. Decision. Under this configuration, any coordinates in the matrix... The only correspondence is when the mechanical support is in the first position When the sector is in attitude, the first The communication quality status of the frequency channel is measured. This data organization method utilizes the spatial correlation of multipath interference in the photovoltaic array, that is, the interference mode exhibits a strong correlation periodicity as the support angle changes, thus providing a structured data foundation for subsequent predictive scheduling.

[0042] Step S106: Configure the polymorphic compression coding scheme.

[0043] To express a rich set of channel quality levels within a limited storage space, this embodiment abandons the traditional byte-level Boolean recording method and adopts a multi-bit compression coding mechanism. Specifically, each state element in the matrix... Fixed bit width assigned As a preferred embodiment, The value is 2, meaning that four discrete physical channel states are represented by two binary bits: Code 00 (Unknown state): Indicates that the channel has not been detected in this state, and the system can initiate a probe transmission to it; Encoding 01 (Available State): Indicates that the historical packet error rate of this channel is lower than the safety threshold at the current angle, and it belongs to the priority scheduling set; Code 10 (Observation State): Indicates that the channel is on the edge of instability, that is, the packet error rate is between the security threshold and the blocking threshold, and is only used as an alternative when there are no other available channels; Code 11 (Taboo State): Indicates that the channel has deterministic coherent interference or deep fading at the current angle, which the RF scheduler must forcibly avoid. This coding mechanism establishes a hysteresis comparison model, avoiding frequent channel state jitter caused by a single transmission failure.

[0044] Step S107: Perform physical address linear mapping.

[0045] Since the physical memory addressing of a microcontroller is typically in bytes (8 bits) or words (32 bits) as the smallest unit, the microcontroller unit 201 needs to perform linear mapping operations to convert logical coordinates... Convert to physical memory address. For a given mechanical sector index... and channel index The corresponding state element's x-byte index address in physical memory Follow the following mapping model: ; ; In the formula, This represents the absolute byte address of the target state element in the microcontroller's physical memory. The microcontroller uses this address instruction to directly locate the specific memory unit (byte) containing the target data. This parameter indicates the starting bit offset of the target status element within the byte. It instructs the microcontroller to perform a logical right shift operation after reading the byte to align the target status bit to the low-order bits of the register. Taboo bitmap The starting base address pointer of the pre-allocated memory block in the microcontroller's volatile memory (SRAM); The mechanical sector index corresponding to the current photovoltaic support posture is represented by the row coordinate of the matrix, and its value is determined by the discretization mapping result in step S102. This indicates the total number of frequency hopping channels supported by the physical layer protocol stack of the radio frequency receiver unit. This value determines the column width of the bitmap matrix and is a fixed configuration constant of the system. : Represents the target physical channel index to be queried, written, or updated, i.e., the column coordinate of the matrix, with a value range of [0, 1, 2, 3]. ]; This represents the binary bit width occupied by a single channel state element. In the preferred configuration of this embodiment, To support the expression of four states; : Indicates the number of bits contained in one byte in a standard computer storage architecture, used to convert bit-level indexes into byte-level indexes; This indicates the floor operator, used to discard the remainder during the calculation process and obtain the complete byte offset; : indicates modulo 8 operation, used to calculate the remainder after dividing by 8, thereby determining the precise location of data within a byte.

[0046] Using this calculation result and bitmasking technology, the microcontroller unit 201 can complete the atomic operations of reading, modifying and writing status bits within one machine cycle.

[0047] Step S108: Build a row-level fast index cache.

[0048] To meet the microsecond-level time slot switching requirements in frequency hopping communication, the system further introduces a row-level pointer buffering mechanism. Whenever the attitude acquisition interface 204 detects a mechanical sector index... When a change occurs, the microcontroller unit 201 immediately triggers a preprocessing interrupt, calculates the starting physical address of the data row corresponding to the new sector in the bitmap, and loads it into the source address register of the direct memory access (DMA) controller. The physical significance is that a direct hardware association of the mechanical attitude memory address is established, so that in the subsequent high-frequency physical packet transmission process, the RF driver does not need to repeat the multiplication and division operations in step S107, but only needs to quickly retrieve the status mask of the current row through DMA, thereby reducing the computational overhead and scheduling delay of the system.

[0049] See attached document Figure 2 Considering the non-stationary nature of the electromagnetic environment of photovoltaic power plants, factors such as seasonal vegetation growth, changes in weather humidity, or temporary additions or removals of surrounding buildings can cause drift in multipath distribution characteristics. A long-term fixed fingerprint database prevents the system from adapting to new environmental conditions. Therefore, this embodiment configures a dynamic aging mechanism based on dual energy environment sensing, aiming to balance the reference value of historical data with adaptability to environmental changes. Specifically, it includes the following execution steps: Step S109: Calculate the energy-adaptive aging cycle.

[0050] The microcontroller unit 201 is configured not to use a fixed time constant to control the validity period of fingerprint data, but instead to dynamically adjust the aging rate based on the energy periodicity unique to the photovoltaic system and the real-time photovoltaic energy status collected by the energy sensing interface 203. The physical principle behind this strategy is that when the generating current of the photovoltaic string 40 is high, it indicates that it is currently daytime with sufficient sunlight, and the wireless communication node 20 has ample energy budget to perform frequent active channel probing and fingerprint updates. In this case, the memory period should be shortened to improve sensitivity to environmental changes. Conversely, at night or on cloudy or rainy days, the system is limited by energy shortages, and a conservative strategy should be adopted to extend the trust period of historical data to reduce probing overhead. Based on this logic, the microcontroller unit 201 calculates the dynamic aging period in real time according to the following nonlinear decay model. : ; In the formula, This represents the maximum valid time window for fingerprint data under the current environmental conditions; For a moment The collected real-time power generation current value; The normalized reference current is taken as the nominal short-circuit current of the photovoltaic module under standard test conditions (STC), and is used to normalize the input quantity to the [0,1] interval; The preset baseline aging time constant for the system represents the maximum memory cycle under zero energy input (such as at night). Its value is usually set to the minimum statistical period of environmental characteristic changes (e.g., 7 days). λ is the energy sensitivity coefficient, typically ranging from [1, 10], used to adjust the response amplitude of the aging cycle to energy changes; λ is the nonlinear adjustment index, typically ranging from [1, 2], used to control the steepness of the adjustment curve. Through this calculation model, the system establishes a positive correlation between energy abundance and environmental exploration rate, realizing adaptive optimal detection under resource-constrained conditions.

[0051] Step S110: Perform a lazy row-level reset.

[0052] To avoid sudden surges in computing resource consumption caused by performing a full scan of the entire fingerprint database in each control cycle, the microcontroller unit 201 employs an attitude check strategy to perform an aging operation. Specifically, when the attitude acquisition interface 204 detects a new mechanical area of ​​motion in the photovoltaic support... At that time, the microcontroller unit 201 triggers a local check interrupt and reads the most recent historical timestamp corresponding to that sector. The microcontroller unit 201 calculates the current system time. and The time difference, and the result calculated in step S109. Compare them. If the time difference... If this happens, the microcontroller unit 201 determines that the historical fingerprint data for that sector is invalid and no longer has any reference value. Subsequently, the microcontroller unit 201 generates a reset mask and performs bitwise logic operations to convert the tabu bitmap... Middle All 11 (taboo state) and 10 (warning state) status bits in the row are forcibly reset to 00 (unknown state), and updated synchronously. The current time is used. This operation ensures that the system verifies the freshness of channel data only when the angle is actually needed, thus distributing the computational load evenly throughout the day's operation of the support structure and eliminating peak computational pressure on the system.

[0053] Step S111: Respond to global environmental mutation events.

[0054] In addition to time-based gradual aging, the system is also equipped with forced reset logic to address sudden structural interference (such as the entry of large construction machinery into the site). The microcontroller unit 201 continuously monitors the physical layer statistical parameters reported by the RF transceiver unit 202, including the average noise floor and the overall packet error rate trend. As a preferred decision logic, when the average rise in noise floor within a set time window (e.g., 1 minute) exceeds a preset mutation threshold (e.g., 6 dB), or when the communication success rate is lower than the system maintenance threshold (e.g., 50%) after passing through more than 3 different mechanical sectors consecutively, the microcontroller unit 201 determines that a structural change has occurred in the current electromagnetic environment. At this time, the microcontroller unit 201 triggers a global reset interrupt, resetting the entire taboo bitmap. Reset to zero, and temporarily adjust sector granularity in the next cycle. Doubling the size, at the cost of sacrificing spatial resolution, accelerates the convergence speed of a new round of environmental features.

[0055] See attached document Figure 2 To achieve adaptive load balancing in photovoltaic array networks with limited resources and unstable energy supply, this embodiment introduces the modeling concept of potential energy fields from physics, abstracting the wireless communication node 20 as discrete potential energy points in the energy field. This model aims to transform the node's current instantaneous power supply capacity (power flow) and energy storage state (energy reservoir) into path metrics recognizable by network layer routing protocols (such as RPL), i.e., energy potential weights. This quantization process is periodically triggered by the specific execution module of the microcontroller unit 201 and includes the following processing steps: Step S201: Collect energy state parameters and perform normalization preprocessing.

[0056] The microcontroller unit 201 periodically reads the operating parameters of the power management module through the energy sensing interface 203, specifically including the terminal voltage of the energy storage unit. and the input current of the photovoltaic power collection unit Given the dimensional differences and measurement noise in the raw sensor data, the microcontroller unit 201 is configured to first perform a sliding window mean filter on the raw sampled values ​​to smooth high-frequency jitter. Subsequently, the system performs a linear normalization operation, mapping the voltage and current data to the dimensionless interval [0,1], to obtain the normalized voltage. With normalized current To ensure the numerical stability of the algorithm, this embodiment introduces a non-zero denominator check mechanism in the normalized division operation: if the calculated benchmark difference (such as...) Approaching zero or less than the machine minimum The system will force the output of a default value to avoid division by zero exceptions. At the same time, interval clamping logic is used to ensure that the normalization result is strictly limited to the closed interval [0,1] to prevent data from going out of bounds due to instantaneous overshoot of the sensor.

[0057] Step S202: Synthesize the comprehensive energy potential index.

[0058] This embodiment posits that a node's routing and forwarding capability depends not only on its current remaining power (i.e., stock) but also on its ability to extract energy from the environment (i.e., incremental energy). Therefore, the microcontroller unit 201, based on a weighted fusion strategy, synthesizes the normalized voltage and current data into a comprehensive energy potential index. This index, at the physical level, characterizes the energy sufficiency of a node at the current moment; the higher the value, the greater the node's potential to undertake data forwarding tasks. The calculation formula is as follows: ; In the formula: The comprehensive energy potential index is a dimensionless scalar in the interval [0,1]. Indicates time The normalized battery voltage reflects the energy storage capacity of the node; Indicates time The normalized photovoltaic input current reflects the energy replenishment rate of the node; This represents the energy storage weighting coefficient, with a value range of (0,1). As a preferred embodiment, The value is typically set to [0.6, 0.7]. The physical basis for this setting is that photovoltaic current fluctuates significantly due to cloud cover, while battery voltage changes relatively smoothly; assigning a higher weight to voltage helps to smooth out the fluctuations. This prevents fluctuations in routing topology caused by brief shadowing, thus maintaining network stability.

[0059] Step S203: Mapping nonlinear routing cost weights. To establish the energy potential difference of the flow at lower water levels during routing, the microcontroller unit 201 constructs a nonlinear cost mapping relationship using a negative exponential function and calculates the final energy potential weights. The core technology of this mapping mechanism aims to construct an energy barrier. When the node has abundant energy ( When the node energy is relatively high, its routing cost remains at a baseline level and changes gradually, allowing traffic to pass freely; however, when the node energy is below the warning threshold, its routing cost increases exponentially, creating a high-impedance region in the network topology, forcing routing protocols to automatically bypass the low-energy node. The energy potential weight is mentioned above. The mathematical model is as follows: ; In the formula: The calculated energy potential weight is used as a routing metric in path calculation. This represents the basic link cost, which typically corresponds to a baseline value for expected transmissions per unit (ETX) (e.g., 10 or 128), representing the minimum overhead under ideal link conditions. This represents the energy warning threshold, typically ranging from [0.2, 0.4]. This threshold defines the edge of the potential energy well. When the value is below this threshold, the weighting curve enters the region of sharp nonlinear rise; This represents the penalty sensitivity curve coefficient, typically ranging from [5, 10]. This parameter controls the rate (i.e., the slope) of weight growth. The larger the value, the more aggressive the system's protection mechanism for low-energy nodes, reflecting the priority given to network lifespan. This represents the maximum penalty factor, used to limit the dynamic range of weight growth. It represents the base of the natural logarithm.

[0060] Based on this, to prevent the calculated weight values ​​from exceeding the position defined by the routing protocol (such as a 16-bit integer) under extremely low power conditions, the microcontroller unit 201 performs saturation and truncation processing on the output results, i.e. ,in This represents the maximum cost allowed by the protocol. Through the above model, this embodiment abstracts the complex physicochemical state of the battery and the stochastic photovoltaic power generation process into a single routing metric with nonlinear protection characteristics.

[0061] See attached document Figure 2 To simultaneously consider the physical reliability of wireless communication, the balance of node energy, and the potential impact of mechanical attitude on channel quality during route construction, this embodiment designs a multi-dimensional feature fusion composite metric calculation mechanism. It abandons the traditional RPL protocol's single-dimensional evaluation method that relies solely on the expected transmission count (ETX), instead deeply coupling the taboo bitmap features generated in the aforementioned steps with energy potential weights. This achieves a three-dimensional balance between communication quality, energy consumption, and environmental risk at the route decision level. The microcontroller unit 201 performs the following metric calculation steps for each neighbor node: Step S204: Obtain and smooth the basic link quality parameters. The microcontroller unit 201 continuously monitors beacon frames or data acknowledgment frames (ACK) from neighboring nodes through the link layer protocol stack, and calculates the expected transmission count (ETX) of the current link in real time based on physical layer statistical data. Given that the wireless channel within the photovoltaic power station is affected by factors such as motor start-stop and inverter switching frequency interference, it exhibits fast fading characteristics, and the instantaneous ETX value often fluctuates drastically. Therefore, the microcontroller unit 201 uses the Exponential Weighted Moving Average (EWMA) algorithm to smooth the original ETX data, obtaining the smoothed basic link quality. This step is based on the low-pass filtering principle in signal processing, aiming to extract the long-term statistical trend of link quality, filter out high-frequency measurement noise, and ensure that subsequent routing decisions will not be misjudged due to transient channel fluctuations.

[0062] Step S205: Quantize the pre-tuned availability penalty factor. To incorporate the impact of the current mechanical attitude of the photovoltaic support on communication quality into the routing decision, the microcontroller unit 201 calls the flag bitmap. Specifically, the system obtains the mechanical sector index from interface 204 based on the current state. Retrieve the first bitmap matrix The system generates row data and counts the number of channels in different states within each row. Based on this statistical result, the system constructs the following mathematical model to calculate the mechanical pretuning availability penalty factor. : ; In the formula: The mechanical pretuning availability penalty factor is given by A dimensionless scalar of the interval. The physical meaning of this value lies in quantifying the scarcity of the spectrum from a current mechanical perspective. The larger the value, the fewer available channels there are, and the higher the probability of communication disruption. Indicates the current number In the mechanical sector row, the total number of channels with status code 10 (warning / grey list); Indicates the current number In the mechanical sector row, the total number of channels with status code 11 (taboo / blacklist); This indicates the total number of frequency hopping channels supported by the physical layer of the radio frequency transceiver unit. In this embodiment, The coefficients are non-zero intrinsic constants (e.g., 50 or 64), which mathematically guarantees that the denominator will not be zero, thus avoiding calculation errors. The weighting coefficient representing the warning status typically ranges from [0.2, 0.5]. This coefficient characterizes the degree to which a channel on the edge of instability reduces the potential success rate of communication. The weighting coefficient representing the taboo state is set to 1.0 as a preferred method, indicating that the channel is completely unusable at the current angle and has the highest penalty weight for the occupation of pre-harmonic resources.

[0063] Step S206: Synthesize the composite routing metric.

[0064] To achieve multi-objective route optimization, the microcontroller unit 201 smooths the link quality. Mechanical spectrum availability penalty factor and the neighbor node energy potential weights calculated in step S203. Nonlinear fusion is performed to calculate the final composite routing metric used for path selection. The synthesis of this metric follows a mathematical model: ; In the formula: This represents the composite routing metric; the smaller the value, the lower the overall cost of the path and the higher its priority. This represents the expected number of transmissions on the underlying link after smoothing, serving as a benchmark metric for the physical quality of the link. This represents the environmental sensitivity gain coefficient, typically ranging from [1, 3]. This coefficient is used to adjust the weighting of the impact of mechanical interference on link quality assessment. The larger the value, the more the system tends to avoid nodes with poor spectral conditions from the current perspective, even if their historical performance is poor. The performance was acceptable, thus reflecting a prevention-oriented routing strategy; This represents the energy potential weight announced by neighboring nodes, reflecting the load-bearing capacity of the next-hop node.

[0065] In this model, the multiplication term Physically, this reflects the expected reduction in transmission efficiency due to environmental risks; that is, the harsher the environment, the greater the expected number of transmissions will be. The additive term... This reflects the cumulative effect of energy costs. This fusion mechanism ensures that routing is based on the dual optimization of communication reliability and energy sustainability, effectively avoiding data flow to energy-trap nodes that have excellent signal strength but are about to run out of power.

[0066] Step S207: Perform parent node selection based on hysteresis threshold.

[0067] The composite routing metric is calculated by traversing all candidate neighbor nodes. Subsequently, the microcontroller unit 201 does not immediately perform a parent node handover. To prevent frequent route switching between two nodes due to minor fluctuations in the wireless channel (i.e., the ping-pong effect), the system introduces hysteresis comparison logic. Specifically, the system only performs a comparison when the metric value of the optimal candidate node is... metric relative to the current parent node Meet the conditions The route update operation is only triggered at that time. As a hysteresis threshold, it is typically set to 10% to 15% of the current path metric as an empirical configuration. This strategy effectively suppresses micro-jitter in the network topology by introducing switching costs, ensuring the stability of the data transmission path over time.

[0068] See attached document Figure 2 In the actual operating environment of a photovoltaic power station, there is a significant system processing delay between the microcontroller 201 initiating wireless transmission and the radio frequency front-end actually radiating electromagnetic waves. This delay includes protocol stack encapsulation time and random backoff time introduced by the CSMA / CA mechanism. Simultaneously, the photovoltaic support is in continuous mechanical motion under the influence of tracking algorithms or wind loads. To address the fingerprint mismatch problem caused by mechanical displacement between the sensing and transmission times, this embodiment constructs a short-time phase prediction model based on second-order kinematics. This model extrapolates the future mechanical state using historical motion trajectories, thereby pre-locking the upcoming mechanical sector. This process is specifically executed by the microcontroller 201 and includes the following processing steps: Step S301: Maintain the motion trajectory within the sliding time window.

[0069] The microcontroller unit 201 allocates a circular buffer in memory to record the most recent data. Mechanical attitude data at each sampling time. The attitude acquisition interface 204 reads the angle values ​​of the photovoltaic support at a fixed sampling frequency (e.g., 10Hz) and sets them as a binary pair. Stored in the buffer. This step establishes a short-term historical motion database, providing the necessary data support for analyzing the instantaneous velocity and acceleration of the support.

[0070] Step S302: Solve for instantaneous kinematic state parameters.

[0071] To capture the dynamic characteristics of the photovoltaic support structure, the microcontroller unit 201 estimates the current angular velocity based on historical data within a sliding window. With angular acceleration Considering the high-frequency quantization noise inherent in the sensor, the system employs weighted least squares to linearly fit the most recent trajectory segment to extract the velocity component, and then calculates the acceleration component through differential calculation. The specific mathematical forms of the weighted least squares method and differential operations are well-known techniques in the field of numerical analysis and will not be elaborated upon here. The physical purpose of this step is to quantify the motion trend of the support in real time, distinguishing between different operating conditions such as stationary, uniform tracking, and variable-speed wind disturbance, providing fundamental state variables for subsequent nonlinear prediction.

[0072] Step S303: Perform forward phase prediction.

[0073] The microcontroller unit 201 estimates the transmission delay based on the protocol stack. The second-order Taylor expansion is used to extrapolate and predict future mechanical angles. This prediction period... This typically covers the maximum expected duration of MAC layer backoff (e.g., 10ms to 50ms). Prediction perspective The calculation follows the following kinematic equations: ; In the formula: This indicates the predicted angular position of the photovoltaic support at the expected radio frequency transmission time. This represents the latest measured angle value. This represents the current angular velocity calculated in step S302, in degrees per second (° / s). This represents the current angular acceleration calculated in step S302, expressed in degrees per square second (° / s²). This indicates the predicted time span. In a preferred embodiment, the system dynamically adjusts this parameter based on the current channel busyness (i.e., the number of CCA detections): the busier the channel, the longer the expected transmission delay. The corresponding increase is taken to compensate for the phase drift caused by backoff.

[0074] Introducing second order The technical objective is that photovoltaic brackets are equipped with high-torque geared motors, whose starting and stopping processes exhibit inertial characteristics. Using only first-order speed prediction will produce significant hysteresis errors during the motor acceleration phase, while second-order models can effectively track this nonlinear speed change, thereby greatly improving the prediction accuracy in dynamic processes.

[0075] Step S304: Map the predicted mechanical sector index.

[0076] From the perspective of obtaining predictions in the continuous domain Next, the control unit 201 needs to map it back to the discrete fingerprint database index space to determine which row of the tabu bitmap should be queried. Based on the aforementioned spatial discretization parameters, the system calculates the predicted sector index. : ; In the formula: This indicates the predicted target mechanical sector index, which will be used for subsequent jump table lookups. This represents the minimum angular boundary of the mechanical travel of the photovoltaic support structure. In this embodiment, the spatial discretization granularity (sector width) of the fingerprint database is represented. A non-zero constant (e.g., 1 degree or 2 degrees) is preset for the system, which mathematically avoids the occurrence of division by zero error; This indicates the total number of sectors, corresponding to the total number of rows in the fingerprint database; This represents the floor function operator; This represents the boundary constraint function. Its physical purpose is to prevent the calculated index value from exceeding the legal addressing range of the fingerprint database due to overshoot in the prediction algorithm, thus ensuring the security of memory access.

[0077] Through the aforementioned prediction mechanism, this embodiment enables the communication system to anticipate the physical environment state at the moment of data transmission. For example, when the support is rapidly sweeping across a metal beam area that strongly blocks frequencies, even though the current angle is still within the safe zone, the prediction algorithm can determine that the support will block the signal exactly 50ms later when data is transmitted. This allows the frequency hopping controller to be instructed in advance to avoid the relevant frequency band, achieving proactive anti-interference under dynamic changes in the physical layer.

[0078] See attached document Figure 2 In the dynamic communication environment of photovoltaic power plants, there are certain extreme mechanical shading angles (such as photovoltaic modules flipping to their extreme positions, causing the metal backsheet to completely block the line-of-sight path), resulting in most channels being in a forbidden or warning state. If data is forcibly transmitted during such communication blackout periods, even frequency hopping technology cannot avoid the risk of packet loss, and will instead increase network interference and node energy consumption.

[0079] However, relying solely on channel quality for data retention decisions leads to excessive storage pressure on nodes. Therefore, this embodiment introduces a data retention mechanism with energy state gating before physical layer transmission. This mechanism not only quantifies and predicts the sector congestion level and the urgency of data services but also intelligently determines, based on the current real-time photovoltaic power generation current, whether to activate the retention buffer mode or force transmission or discard when energy is limited. This logical judgment process is specifically executed by the microcontroller unit 201 and includes the following processing steps: Step S305: Statistically predict the channel availability distribution of the sectors.

[0080] Based on the predicted mechanical sector index locked in step S304 above. The microcontroller unit 201 accesses the taboo bitmap matrix in memory and extracts the first... The system iterates through this vector, counting the number of channels in taboo states for each channel. and the number of channels in warning / gray status. As a preferred implementation, this statistical process is performed only within a millisecond window before the data packet is prepared for transmission. Its purpose is to extract the overall spectrum health from the micro-level frequency state from the current mechanical perspective, serving as the data basis for subsequent risk assessment.

[0081] Step S306: Calculate the sector congestion density index.

[0082] Based on channel state statistics, the microcontroller unit 201 calculates the sector congestion density index, which reflects the degree of communication obstruction in the currently predicted sector. This index, constructed using a weighted normalization method, aims to quantify the crowding-out effect of current mechanical postures on wireless spectrum resources. Its calculation model is as follows: ; In the formula, This represents the sector congestion density index, with a value ranging from [0,1]. A value close to 1 indicates that the vast majority of channels are unavailable under this mechanical angle, and the probability of successful communication is extremely low. This represents the total number of frequency hopping channels configured in the system. In this embodiment, The non-zero positive integer (such as 50) determined by the RF hardware ensures the stability of the division operation mathematically and avoids the abnormality of the denominator being zero. This represents the reduction factor for the early warning channel, typically set within the range of [0.3, 0.6]. Its physical meaning lies in defining the weight of the sub-healthy channel's contribution to the blocking effect; that is, although the early warning channel is not completely disconnected, its higher bit error rate is still considered a partial blocking resource and needs to be factored in. It is included in the total congestion level.

[0083] Step S307: Analyze the urgency and energy status of business data.

[0084] To prevent unnecessary delays from draining the node's power, the microcontroller unit 201 parses the priority identifier in the header of the data packet to be sent. Map it to a normalized urgency factor On the other hand, the real-time power generation current of the photovoltaic modules is read through current sensors. The physical basis for introducing real-time generated current as the decision input is that data retention is essentially a strategy of trading storage for time; maintaining SRAM data and frequent channel monitoring requires additional static current. If it is currently a cloudy or rainy day or night, If the power consumption is too low, the node's battery power supply capacity is limited, and therefore high-power lingering mode should not be supported.

[0085] Step S308: Perform a data retention decision under multidimensional constraints.

[0086] To strike a balance between avoiding packet loss, ensuring timeliness, and maximizing energy availability, the microcontroller unit 201 incorporates a Boolean decision logic with energy gating. The system only sets the data stagnation flag when both severe communication congestion and sufficient energy are simultaneously met. This decision logic follows the mathematical relationship described below: ; Among them, dynamic blocking tolerance threshold Defined as: ; In the formula: This indicates the Boolean decision result for data retention. If TRUE, the system initiates retention mode, suspending data delivery to the radio frequency unit; if FALSE, retention is not performed, and the system attempts to transmit directly or discard the data. This represents the sustaining current threshold. This threshold is calculated based on the minimum static power consumption of the microcontroller unit 201 and the storage unit 205 in data hold mode, ensuring that the system is only allowed to execute the strategy of waiting for a better channel when the photovoltaic energy input is greater than the sustaining consumption, thus preventing node power loss due to holding onto data. This represents the basic blocking tolerance threshold, typically ranging from [0.6, 0.8], defining the system's baseline tolerance to harsh environments. This represents the urgency compensation gain, typically ranging from [0.5, 1.0]. Its physical function is to enhance the penetration capability of high-priority data; that is, for urgent data, the system dynamically increases the blocking tolerance threshold. This makes it harder for the system to trigger delays (making it more likely to force the transmission), thus ensuring the timeliness of critical business operations.

[0087] Through the above-mentioned decision logic, this embodiment realizes a cross-layer collaborative flow control mechanism: when the support sweeps through a high interference area and has sufficient energy, non-urgent data is automatically stored temporarily; however, once energy is scarce or emergency fault data is encountered, the system will block the stagnation logic, prioritize the execution of basic communication functions or quickly release resources, and avoid the complete paralysis of nodes due to the pursuit of perfect channels.

[0088] See attached document Figure 2After the aforementioned data retention mode is activated, as the photovoltaic support continues to reside in the communication dead zone, the business data to be sent will continuously accumulate in the limited SRAM storage space of the microcontroller unit 201. If the traditional first-in-first-out (FIFO) strategy is simply used, high-value emergency alarm data will be rejected from being written due to a full buffer, or blocked by low-value historical data. To address this, this embodiment constructs a priority circular buffer with an adaptive aging mechanism in the storage unit 205. In the saturated state where the storage space is exhausted, it intelligently identifies and removes the least cost-effective old data, thereby dynamically freeing up space to accommodate new high-value data. This management strategy is specifically executed by the microcontroller unit 201 and includes the following processing steps: Step S309: Monitor the buffer capacity and write pointer status. The microcontroller unit 201 maintains the write pointer pointing to the circular buffer storage space. With read pointer Address wraparound is managed using modulo operations. In each new data packet... Before requesting a write, the system calculates the logical distance between two pointers to assess the current remaining space. Greater than the data packet length The system directly performs the write operation and updates the pointer; if If the buffer is insufficient, it indicates that the buffer is saturated. In this case, the system does not directly discard the new data, but triggers an eviction decision based on value assessment. This step, as a boundary check mechanism for memory management, ensures that, with limited hardware resources, data write operations will not cause memory address out-of-bounds errors or unexpected overwrite errors.

[0089] Step S310: Quantify the retention effectiveness of data packets in the buffer.

[0090] To determine which stored data packet should be sacrificed, the microcontroller unit 201 iterates through the header information of all currently stored data packets in the buffer and calculates the value of each stored data packet. Retention utility score This calculation is based on the principle of multidimensional weighting, aiming to unify discrete business priorities with a continuous time dimension into a scalar evaluation system. The calculation formula is as follows: ; In the formula: Indicates the first The retention utility score of each existing data packet indicates that the higher the score, the greater the current value of the data packet and the higher the priority for it to be retained by the system. Indicates the first The service priority level of each data packet, with values ​​ranging from a set of positive integers. ( (Represents the highest priority). This parameter directly maps to the business importance of the data content; for example, the priority level of inverter hardware fault alarms is set higher than that of regular voltage telemetry data. Indicates the current system clock time; Indicates the first The timestamp of the first data packet entering the buffer; This represents the priority weighting coefficient, which is usually set to [10, 100] and is used to amplify the dominant role of the business level. This represents the time aging decay coefficient, typically ranging from [0.1, 1.0], with units of s⁻¹. This item reflects the freshness characteristic of information: as the retention time increases, the reference value of historical data gradually decreases (for example, instantaneous power fluctuations from 5 minutes ago have no reference value for buying time control), and its retention utility decreases linearly, thus increasing the probability of being replaced by new data.

[0091] Step S311: Perform conditional expulsion based on utility comparison.

[0092] After calculating the retention utility score of all data packets in the buffer, the microcontroller unit 201 identifies the data packet with the lowest score as a candidate victim, denoted as... Its score is At the same time, the system uses the same model to calculate the new data packets to be written. The initial utility score is (at this time Get the current time (The aging term is 0). Subsequently, the system performs the following comparison and replacement operations: ; In the formula: This indicates the final memory storage operation instruction executed; Indicates the release of old data packets. The memory block occupied, and the new data packet Write to this location; This indicates that new data packets to be written should be discarded, and the current state of the buffer should be maintained. This represents the hysteresis threshold, and its value is a non-negative constant (e.g., 5.0).

[0093] In this embodiment, the physical purpose of setting this threshold is to prevent frequent memory replacement operations: only when the value of the new data is higher than the worst old data (exceeding...) will the new data be replaced. Replacement is only performed when there is sufficient buffer space. This delayed eviction mechanism effectively avoids repeated write-erase contention between two data packets of similar value at the buffer edge, thereby reducing the dynamic power consumption and fragmentation risk of SRAM.

[0094] Through the above strategy, this embodiment implements a dynamic memory survival-of-the-fittest mechanism: under extreme conditions of communication disruption, the buffer will automatically evolve into a container that stores only high-priority and freshest data, ensuring that when the communication link is restored, the first batch of data sent by the node contains the most business-value information.

[0095] See attached document Figure 2 In large-scale mesh networks constructed from photovoltaic arrays, if all nodes use the same factory default settings to periodically broadcast beacons or make routing requests, broadcast storms can easily occur during the initial power-on phase or synchronization period, leading to catastrophic congestion and collisions in the physical layer channels. While conventional random backoff algorithms can alleviate instantaneous collisions, they cannot fundamentally eliminate collision peaks caused by the superposition of periodic behaviors of homogeneous devices.

[0096] To achieve ordered interleaving of node behaviors in the time domain, this embodiment introduces a deterministic desynchronization mechanism based on hardware unique identifiers. This mechanism utilizes the immutable identity information of nodes to generate a fixed time phase offset, ensuring that adjacent nodes are naturally staggered on the time axis, thereby reducing the probability of channel contention. This calculation process is specifically executed by the microcontroller unit 201 and includes the following processing steps: Step S401: Extract hardware identity features and generate a numerical seed.

[0097] The microcontroller unit 201 reads the unique media access control address or the microcontroller's globally unique identifier (UUID) embedded within the radio frequency transceiver unit 202. Since the original hardware identifier is a non-numerical byte sequence, and IDs assigned by different manufacturers exhibit local continuity, the system employs a multinomial rolling hash algorithm to compress and map the identity byte stream to generate a desynchronized value seed that is uniformly distributed in the numerical space, in order to obtain a highly distinguishable discrete value. The specific hash operation process is a well-known technique in the field of computer science, and this embodiment will not elaborate on the specific formula. Its technical purpose is to map discrete and disordered hardware IDs into a uniformly distributed integer space, thereby eliminating the numerical clustering phenomenon caused by the continuous allocation of manufacturer IDs.

[0098] Step S402: Calculate the normalized time phase offset.

[0099] After obtaining the numerical seed, the system needs to map it to a predefined desynchronization time window. Within, to calculate the time phase offset specific to that node. To ensure the fairness and randomness of time resource allocation, the microcontroller unit 201 uses a linear congruential mapping method for calculation, and its mathematical model is defined as follows: ; In the formula, This represents the inherent time delay of a node in a periodic task, measured in milliseconds (ms). This offset remains constant after system initialization and constitutes the node's temporal fingerprint. The desynchronization seed generated in step S401 is a non-negative integer; The parameter represents the mapping space modulus. In this embodiment, this parameter is set to a sufficiently large non-zero prime number. Choosing a large prime number as the denominator not only mathematically avoids the abnormal risk of division by zero, but also minimizes mapping collisions and ensures that the hash value can be uniformly projected into the standardized interval [0,1). This indicates the maximum allowed desynchronization jitter window width. In a preferred implementation, this parameter is typically set to a value within the communication cycle. 20% to 50%. For example, if the heartbeat broadcast cycle is 10 seconds, then A window of 2 seconds is acceptable. The physical basis for setting this window is an engineering trade-off: it is necessary to provide a sufficiently wide time span to accommodate the distributed transmission of a large number of nodes, while avoiding the slowing down of network convergence or the delay jitter exceeding the tolerance of the control loop due to an excessively large offset window.

[0100] Step S403: Load the deterministic periodic task timer.

[0101] In obtaining the exclusive phase offset Subsequently, when initializing periodic communication tasks (such as neighbor discovery and routing table reporting), the microcontroller unit 201 adds this offset to the base periodic timer. Through this process, even if multiple nodes power on at the same time (i.e., possess the same...), the offset remains constant. Due to their different hardware IDs, the calculated This will also be quite different, forcing them to automatically stagger their transmission times on the timeline. Compared to a completely random backoff mechanism, this deterministic desynchronization scheme makes network traffic patterns predictable, facilitating subsequent troubleshooting and time slot planning.

[0102] See attached document Figure 3In scenarios where photovoltaic modules continuously move along the sun's trajectory, the availability of the physical channel is no longer static but exhibits time-varying characteristics highly coupled with mechanical angles. Traditional frequency-hopping spread spectrum (FHSS) mechanisms rely on statically defined pseudo-random sequences and lack the ability to detect real-time environmental shading. This leads to the RF unit transmitting at high power on invalid frequencies blocked by the metal backplane, resulting in energy waste and data loss. Therefore, this embodiment constructs a dynamic spectrum mask mechanism between the link layer and the physical layer. As a real-time logical filter, it redirects and maps the standard frequency-hopping sequence based on the current mechanical spectrum fingerprint, ensuring that each transmission is strictly limited to a physically accessible spectrum window. This execution process is specifically performed by the microcontroller unit 201 in conjunction with the RF transceiver unit 202, and includes the following processing steps: Step S404: Construct a set of real-time available channels.

[0103] Based on the current mechanical sector index determined in the preceding steps, the microcontroller unit 201 retrieves the corresponding taboo bitmap vector from the mechanical spectrum fingerprint database of the storage unit 205. This bitmap vector identifies the availability status of the entire frequency band channel under the current mechanical attitude. To construct a frequency pool for actual transmission, the system parses this bitmap, removes all channels marked as taboo, and reorganizes the remaining available and warning channels into a dynamic set of available channels. As a preferred implementation, the set is structurally represented as a compact one-dimensional indexed array, with its elements ordered in ascending order of physical frequencies. This step isolates invalid frequency bands in severely attenuated regions from the source through a real-time filtering mechanism.

[0104] Step S405: Perform modulo operation remapping of the pseudo-random sequence.

[0105] To avoid physical obstruction while retaining the anti-interference and multiple access characteristics of frequency hopping technology, the microcontroller unit 201 does not directly use the original index generated by the standard frequency hopping algorithm. Instead, it uses a modular arithmetic mapping method to project the original index onto the set of available channels. In detail, let the original frequency hopping index generated by the standard pseudo-random sequence generator be... The actual transmission frequency index after spectral masking is then... The calculation model is as follows: ; In the formula, This indicates the actual channel logical index that is ultimately transmitted to the radio frequency transceiver unit 202 for synthesizing the carrier frequency; This represents the set of available channels constructed in step S404; This represents an array address fetch operation, used to extract the channel index of a specific location within a set; This represents the original frequency hopping index, which is generated following pseudo-random logic defined by standard communication protocols. Represents the set of currently available channels The total number of elements in the array. Specifically, considering the extreme condition where all channels are unavailable ( This embodiment includes a pre-check logic before performing the modulo operation: if If the value approaches 0, the system will directly trigger the aforementioned data retention mode, suspending physical layer transmission, thereby mathematically avoiding the abnormal risk of the modulo operation denominator being zero.

[0106] Step S406: Apply a probabilistic early warning channel avoidance strategy.

[0107] In the constructed set of available channels In this process, some channels are in a warning state, meaning that although they are not completely disconnected, they face a high risk of multipath fading. To balance warning utilization and transmission reliability, this embodiment introduces a probabilistic avoidance mechanism during the mapping process. When the mapping result... When a warning channel is pointed to, the system does not use it immediately, but instead calculates the retention probability of that channel. Based on this, a decision is made as to whether to reselect. The calculation model for the retention probability is defined as follows: ; In the formula, This represents the probability of retaining and using the early warning channel at the current moment, with a value range of [0,1]. This represents the historical bit error rate of the warning channel within a preset sliding window, with a value range of [0,1]. The physical purpose of introducing this parameter is to dynamically assess its current risk level using the channel's past communication quality; the worse the historical performance, the lower the probability of it being retained. This represents the sensitivity avoidance coefficient, typically set to a range of [5, 20]. This coefficient is used to adjust the system's degree of rejection of interference. The larger the value is set, the steeper the function curve, and the more the system tends to skip the warning channel with a high bit error rate and instead look for cleaner spectrum resources.

[0108] If based on The random decision result is to discard; the microcontroller unit 201 will then process the original index. A linear offset operation is performed, and the modulo operation mapping in step S405 is re-executed until a channel that meets the conditions is selected or the maximum number of retries is reached. Through the above dynamic spectrum masking mechanism, this embodiment achieves deep decoupling between physical layer hopping frequency modulation and mechanical environment state, ensuring that it always operates within the physically reachable effective spectrum before frequency preservation.

[0109] Combination Figure 3To enable those skilled in the art to more intuitively understand the operational logic of this invention in actual industrial settings, the following description is based on a typical scenario of a 50MW photovoltaic power station in Northwest China.

[0110] Figure 3 This is a bar chart illustrating the performance comparison between the experimental group and the control group under dynamic interference conditions at a photovoltaic power station in a specific application embodiment of the present invention. (a) The communication reliability comparison shows that the data packet delivery success rate of the experimental group is as high as 96.2%, while that of the control group is only 78.5%.

[0111] (b) Energy efficiency comparison (average energy consumption) shows that the experimental group consumed only 8.1 mJ per bit of data transmitted, which is 34.7% lower than the control group's 12.4 mJ.

[0112] (c) Network lifetime comparison (network lifespan) indicates that the network lifespan of the experimental group reached 23.8 days, which is 64.1% longer than the 14.5 days of the control group.

[0113] (d) Network stability comparison (routing oscillation) shows that the number of routing oscillations in the experimental group was only 15 times / hour, which was significantly lower than the 128 times / hour in the control group (suppressed by 88.3%).

[0114] Specific application examples: 1. Scenarios and Hardware Configuration In this application embodiment, a subarray area consisting of 40 photovoltaic tracking brackets is selected in a photovoltaic power station. Each tracking bracket is equipped with a set of wireless communication nodes 20.

[0115] Hardware platform: The microcontroller unit 201 uses an STM32L4 series low-power MCU; the RF transceiver unit 202 uses an SX1262 LoRa RF chip, operating in the 470MHz to 510MHz frequency band, and is configured with 50 frequency hopping channels. =50); The attitude acquisition interface 204 connects to the RS485 bus to read the encoder value of the rotary drive motor.

[0116] Environmental characteristics: Between 10:00 and 14:00 every day, the photovoltaic brackets in this area will adjust to a suitable angle according to the sun's trajectory. At this time, the metal backplate will cause severe non-line-of-sight (NLOS) shading to the antenna.

[0117] 2. Demonstration of the running process Assume the current time is 11:30 AM and the weather is cloudy turning sunny.

[0118] Phase 1: Active Avoidance of Mechanical Spectrum Fingerprint (corresponding to steps S405, S101, and S104) Wireless communication node A detects the current mechanical tilt angle for The microcontroller unit 201 retrieved the mechanical spectrum fingerprint database and found that in this sector ( Under this condition, the bitmap state corresponding to channels 12 to 18 in the channel index list is 11 (forbidden state), indicating that these frequency points are in the coherent interference zone of the metal bracket. When node A needs to send a set of inverter voltage data, the original index generated by the standard frequency hopping algorithm... It points precisely to channel 15. At this point, the dynamic spectrum masking mechanism intervenes, using modular arithmetic... The transmission frequency is forcibly remapped to channel 42 (an available channel in the clearance zone), thereby avoiding invalid packet transmission on channel 15 and ensuring successful data transmission in one go.

[0119] Phase Two: Data Retention in Energy Channel Coordination (corresponding to steps S305 and S308) At 11:35, the cloud cover suddenly thickened, and the photovoltaic panel output current plummeted to 2A (normalized). Simultaneously, the tracking bracket performs wind-resistant action, rotating to... Limiting angles.

[0120] The prediction algorithm of node A calculates the sector congestion density index for the next 50ms. The value is as high as 0.85 (meaning 85% of the channels are unavailable). At this point, the system determines that although the environment is severely congested, due to... Too low (energy deficiency), not meeting the retention conditions ( =FALSE). Decision execution: Node A abandons the data retention strategy and sends immediately instead. Despite the extremely high risk of packet loss, the system chooses a best-effort survival strategy to avoid draining the battery and causing node crashes due to maintaining SRAM data and frequent listening in low-power conditions.

[0121] Phase 3: Route Self-Healing Based on Potential Energy Gradient (corresponding to steps S201 and S207) At 12:00, the clouds dispersed and sunlight resumed. Due to battery aging, Node B, although having normal photovoltaic input, experienced a decrease in terminal voltage. Long-term low. According to the formula... The energy potential weight calculated by node B Node A, which is higher than neighbor node Co, calculates the composite routing metric. At that time, it was found that the path cost passing through node B was due to The number of nodes increased too rapidly. Therefore, the RPL routing protocol automatically switched the next-hop parent node of node A from node B to the more powerful node C.

[0122] Effect: Node B is protected and only processes its own necessary data, no longer undertaking forwarding tasks, thereby extending its lifespan and avoiding network topology breakage caused by single-point energy depletion.

[0123] Experimental verification and effect comparison: To verify the beneficial effects of the photovoltaic power station adaptive networking method based on mechanical phase mapping and energy channel coordinated scheduling proposed in this invention, a semi-physical simulation platform with 20 nodes was built in the laboratory, and a comparative test was conducted with the traditional RPL routing + standard FHSS frequency hopping scheme.

[0124] 1. Experimental setup Experimental group: Wireless communication nodes running the complete scheme of this invention.

[0125] Control group: Wireless communication nodes running the standard RPL protocol (based on ETX metric) and standard pseudo-random frequency hopping sequences.

[0126] Test conditions: The photovoltaic support is simulated to reciprocate periodically at a speed of 0.5 degrees / second, and random metal plates are introduced to simulate dynamic multipath fading; at the same time, the energy input fluctuation of the photovoltaic panel under the cycle of sunny days, cloudy days and nights is simulated through a programmable power supply.

[0127] 2. Performance Indicator Comparison ; ; 3. Experimental conclusions and charts show that, under the unique high-dynamic mechanical disturbance and energy-constrained environment of photovoltaic power plants: Anti-interference capability: The mechanical spectrum fingerprinting technology of this invention effectively transforms the blind transmission of the physical layer into deterministic transmission based on environment awareness, eliminating the communication black hole phenomenon caused by mechanical obstruction.

[0128] Energy efficiency: By combining energy potential routing and data retention strategies, the system automatically degrades services to survive during periods of energy scarcity and increases throughput during periods of abundant energy, achieving a perfect fit with the characteristics of photovoltaic power generation.

[0129] System stability: The deterministic desynchronization mechanism and priority buffer management ensure that critical operation and maintenance data (such as fault alarms) can still be transmitted with low latency under large-scale networking and sudden congestion conditions.

[0130] In summary, the solution of this invention is superior to existing general wireless networking technologies in the vertical field of photovoltaic power plants.

Claims

1. A method for multi-node self-organizing wireless coverage of photovoltaic power stations, characterized in that, include: The physical tilt angle and link quality of the tracking bracket are collected by the microcontroller unit (201), the physical tilt angle of the tracking bracket is mapped to the mechanical sector index, and the target channel in the mechanical spectrum fingerprint database is marked as a forbidden channel. Based on the mechanical spectrum fingerprint database, the link quality is corrected, and the negative correlation energy potential weight is calculated by combining the real-time power generation current. The uplink data transmission path pointing to the aggregation gateway (10) is then constructed. Based on the physical tilt angle of the tracking bracket, the mechanical phase trend is determined. When it is determined that the mechanical phase trend falls into the communication blind zone and the real-time power generation current is higher than the maintenance threshold, the data retention mode is activated and the service data is written into the priority ring buffer. When transmission is permitted, the forbidden channels are eliminated based on the mechanical spectrum fingerprint database to generate an available frequency set, the operating frequency is selected, and the service data is transmitted along the uplink data transmission path.

2. The method for multi-node self-organizing wireless coverage of a photovoltaic power station according to claim 1, characterized in that, The process of mapping the physical tilt angle of the tracking bracket to a mechanical sector index and marking the target channel in the mechanical spectrum fingerprint database as a forbidden channel specifically includes the following steps: A linear quantization mapping operation is performed on the physical tilt angle of the tracking bracket after filtering, based on a preset sector granularity, to generate a unique discrete integer index; A two-dimensional taboo bitmap structure is constructed in the storage unit (205), wherein the row index of the two-dimensional taboo bitmap corresponds to the mechanical sector index, and the column index corresponds to the channel index supported by the physical layer of the wireless communication node (20). Continuously monitor the link quality indicator or packet error rate. When the packet error rate of the current working channel under the corresponding mechanical sector index is continuously higher than the interference judgment threshold, the current working channel is determined to be the target channel. The microcontroller (201) modifies the data in the storage unit (205) and sets the storage unit corresponding to the mechanical sector index and the target channel in the two-dimensional taboo bitmap to the taboo state, thereby completing the marking of the target channel on the taboo state channel.

3. The method for multi-node self-organizing wireless coverage of a photovoltaic power station according to claim 2, characterized in that, The method also includes performing energy-adaptive aging maintenance on the mechanical spectrum fingerprint database, specifically including the following steps: The aging period of the state record in the mechanical spectrum fingerprint database is dynamically calculated based on the real-time power generation current. The real-time power generation current is negatively correlated with the aging period, that is, the aging period when energy is abundant is shorter than the aging period when energy is scarce. Using a lazy checking strategy, when the wireless communication node (20) moves to a new mechanical sector index, the time difference between the most recent update timestamp of the state record corresponding to the new mechanical sector index and the current time is calculated; When the time difference exceeds the aging period, a reset mask is generated and bit logic operations are used to force the state bits of the row corresponding to the new mechanical sector index in the two-dimensional taboo bitmap to be reset to an unknown state, thereby completing the energy adaptive aging maintenance for the mechanical spectrum fingerprint library.

4. The method for multi-node self-organizing wireless coverage of a photovoltaic power station according to claim 1, characterized in that, The calculation of the negatively correlated energy potential weights specifically includes: Collect energy state parameters and perform normalization preprocessing on battery voltage and the real-time power generation current; Based on a weighted fusion strategy, the normalized battery voltage and the normalized real-time power generation current are combined to form a comprehensive energy potential index. A nonlinear cost mapping relationship is constructed using a negative exponential function to map the comprehensive energy potential index to the energy potential weight. When the comprehensive energy potential index is lower than the energy warning threshold, it rises exponentially to form a high impedance region in network routing. The comprehensive energy potential index is converted into the energy potential weight that is negatively correlated with the real-time power generation current through the nonlinear cost mapping relationship.

5. A method for multi-node self-organizing wireless coverage of a photovoltaic power station according to claim 1, characterized in that, The construction of the uplink data transmission path pointing to the aggregation gateway (10) specifically includes the following steps: Based on the mechanical spectrum fingerprint database, the number of forbidden state channels and the number of warning channels under the current mechanical sector index are statistically analyzed, and the mechanical spectrum availability penalty factor reflecting the degree of spectrum scarcity is quantitatively calculated as an environmental correction weight. The link quality is obtained and smoothed to obtain the expected number of transmissions for the smoothed base link. The mechanical spectrum availability penalty factor is superimposed on the expected number of transmissions of the basic link, and combined with the energy potential weights announced by neighboring nodes within the communication range for nonlinear fusion to synthesize a composite routing metric. The composite routing metric of all neighboring nodes is calculated and the node with the best composite routing metric is selected as the optimal candidate node. When the composite routing metric of the optimal candidate node is better than the composite routing metric of the current parent node by more than the hysteresis threshold, a parent node switching operation is performed to update the next-hop node, and the uplink data transmission path pointing to the aggregation gateway (10) is constructed based on the updated next-hop node.

6. The method for multi-node self-organizing wireless coverage of a photovoltaic power station according to claim 1, characterized in that, Determining the mechanical phase trend specifically includes the following steps: Maintain the historical angle trajectory within the sliding time window, and use the weighted least squares method to fit the data within the sliding time window to calculate the current angular velocity and angular acceleration; The predicted time span is determined based on the transmission delay estimated by the protocol stack, and the predicted angle is calculated by extrapolation using the second-order kinematic equations containing the angular velocity and the angular acceleration. Based on the spatial discretization granularity of the fingerprint database, the predicted angle is mapped to a predicted mechanical sector index, and the mechanical phase trend is determined by the generated predicted mechanical sector index.

7. A method for self-organizing wireless coverage of a photovoltaic power station with multiple nodes according to claim 1, characterized in that, Determining whether a mechanical phase trend falls into a communication dead zone involves the following steps: Based on the determined mechanical sector index, the mechanical spectrum fingerprint database is retrieved, the number of taboo state channels and the number of warning channels in the corresponding channel state vector are counted, and the weighted normalized sector blocking density index is calculated. The priority of the data packets of the service data to be sent is parsed to generate a normalized urgency factor and a dynamic blocking tolerance threshold is calculated. The higher the urgency factor, the higher the dynamic blocking tolerance threshold. Perform a multi-condition Boolean decision, and determine the state in which the mechanical phase trend falls into the communication blind zone by using the quantitative relationship between the sector blocking density index and the dynamic blocking tolerance threshold.

8. A method for self-organizing wireless coverage of a photovoltaic power station with multiple nodes according to claim 7, characterized in that, Initiating the data retention mode specifically includes the following steps: In response to the mechanical phase trend being defined as falling into the communication blind zone, the real-time power generation current is obtained; The real-time power generation current is compared with the maintenance threshold to confirm the data maintenance capability of the current node; When the real-time power generation current is greater than the maintenance threshold, it is determined that the start-up condition is met, the operation of setting the data retention flag is performed and the data delivery to the radio frequency transceiver unit (202) is suspended, thus completing the start-up of the data retention mode.

9. A method for multi-node self-organizing wireless coverage of a photovoltaic power station according to claim 1, characterized in that, Writing the business data into the priority circular buffer specifically includes the following steps: When the priority ring buffer is saturated and a new data packet is requested to be written, all existing data packets in the priority ring buffer are traversed and the retention utility score of each existing data packet is calculated. The retention utility score is defined to be positively correlated with the service priority level of the stored data packets and negatively correlated with the retention time of the stored data packets in the priority circular buffer. The stored data packet with the lowest retention utility score is identified as a candidate victim. When the initial utility score of the new stored data packet to be written is higher than the retention utility score of the candidate victim and the difference exceeds the eviction delay threshold, the memory space occupied by the candidate victim is released and the new stored data packet is written. The writing of the business data to the priority circular buffer is completed through a dynamic replacement strategy based on the retention utility score.

10. A method for self-organizing wireless coverage of a photovoltaic power station with multiple nodes according to claim 2, characterized in that, Selecting the operating frequency specifically includes the following steps: Extract the hardware identity features of the wireless communication node (20) and generate a numerical seed. Calculate the normalized time phase offset using the linear congruential mapping method. Load the time phase offset into the periodic task timer to achieve desynchronization of the transmission time. In response to the end of the delay of the periodic task timer, the mechanical spectrum fingerprint database is read to construct a real-time available channel set that has eliminated the taboo state channels; A pseudo-random sequence generation algorithm is used to generate an original frequency hopping index. A modulo operation is performed on the length of the real-time available channel set for remapping. The original frequency hopping index is then projected onto the real-time available channel set to obtain the actual transmission frequency index. When the actual transmitting radio point index points to the warning state channel, the retention probability is calculated based on the historical bit error rate of the warning state channel, and a decision is made on whether to re-execute the mapping selection based on the retention probability. The final actual transmitting radio point index is determined through the retention probability filtering mechanism to complete the selection of the operating frequency.

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