Air conditioner energy consumption data backhaul and analysis method based on low earth orbit satellite link

Abstract

A method for air conditioning energy consumption data backhaul and analysis based on a low-Earth orbit (LEO) satellite link is disclosed. An energy consumption data acquisition module is deployed at the air conditioning unit. This module connects to the main control board of the air conditioning unit via an RS485 interface to collect real-time operating parameter data. The collected operating parameter data is then transmitted to a data preprocessing unit via a CAN bus. The data preprocessing unit performs format conversion and data compression on the operating parameter data to generate standardized energy consumption data packets. This invention achieves stable acquisition and real-time aggregation of multi-source operating parameters in strong interference environments through the coordination of the RS485 interface and the CAN bus at the terminal side. The preprocessed standardized data packets are seamlessly transmitted to the phased array antenna unit using a high-speed SPI interface, solving the problems of tracking lag and communication interruption of traditional mechanically scanned antennas in scenarios with rapid LEO satellite transits.

Description

Technical Field

[0001] This invention belongs to the field of air conditioning energy consumption analysis technology, specifically a method for air conditioning energy consumption data backhaul and analysis based on low-orbit satellite links. Background Technology

[0002] With the increasing global demand for energy management, real-time energy efficiency monitoring of air conditioning systems distributed across different regions has become an industry trend. Satellite links overcome geographical limitations, transmitting operational parameters collected from terminals to the cloud for in-depth analysis, thereby optimizing energy allocation. However, existing technical solutions face the following technical challenges when dealing with data transmission and analysis in wide-area and complex environments: First, existing equipment often uses a general serial interface to directly connect to the main control board, lacking dedicated anti-interference bus protocol support. This makes key analog signals such as compressor current and refrigerant pressure susceptible to noise pollution during transmission, resulting in data jumps or loss.

[0003] Secondly, most existing satellite communication terminals use mechanical parabolic antennas for signal tracking. In scenarios where low-orbit satellites move at high speeds, the mechanical rotational inertia is large and the response speed is slow, making it difficult to quickly lock onto the satellite beam. This easily leads to communication link interruptions, resulting in a low success rate for transmitting massive amounts of energy-consuming data back, and failing to meet the requirements of continuous monitoring.

[0004] Furthermore, faced with massive amounts of multi-source, heterogeneous energy consumption data, a single processor struggles to process complex energy efficiency calculations, load forecasting, and fault diagnosis algorithms in parallel, resulting in data processing lag and an inability to achieve millisecond-level response times. In addition, existing presentation layers largely rely on static images or non-interactive reports, lacking web-based dynamic visualization tools. This prevents managers from intuitively obtaining real-time energy efficiency status and potential fault warnings for global equipment, leading to weak decision support capabilities. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, the present invention provides a method for air conditioning energy consumption data backhaul and analysis based on low-orbit satellite links, so as to at least partially solve the above-mentioned technical problems.

[0006] The technical solution adopted in this invention is as follows: This invention proposes a method for transmitting and analyzing air conditioning energy consumption data based on low-Earth orbit satellite links, comprising the following steps: S1: Deploy an energy consumption data acquisition module at the air conditioning equipment end. The energy consumption data acquisition module is connected to the main control board of the air conditioning equipment through an RS485 interface to collect the operating parameter data of the air conditioning equipment in real time. The operating parameter data includes compressor operating current, fan speed, indoor and outdoor temperature difference, and refrigerant pressure parameters. S2: The collected operating parameter data is transmitted to the data preprocessing unit via the CAN bus. The data preprocessing unit performs format conversion and data compression on the operating parameter data to generate a standardized energy consumption data packet. S3: Transmit standardized energy consumption data packets to the low-Earth orbit satellite communication module via the SPI interface. The low-Earth orbit satellite communication module includes an antenna unit, a radio frequency transceiver unit, and a baseband processing unit. The antenna unit adopts phased array antenna technology. S4: The low-Earth orbit satellite communication module modulates standardized energy-consuming data packets into radio frequency signals and transmits them to the low-Earth orbit satellite network through the antenna unit; S5: The low-orbit satellite network forwards the received radio frequency signals to the ground receiving station, which demodulates the received radio frequency signals and restores them to standardized energy consumption data packets; S6: The ground receiving station transmits standardized energy consumption data packets to the cloud data center via a fiber optic network. The cloud data center includes a data storage server, a data analysis server, and a data display server. S7: The data analysis server unpacks the received standardized energy consumption data packets, extracts the operating parameter data, and analyzes the operating parameter data based on the preset energy consumption analysis algorithm to generate energy consumption analysis results. S8: Store the energy consumption analysis results to the data storage server, and provide a visual display of the energy consumption analysis results to the user terminal through the data display server.

[0007] In one embodiment of the present invention, the energy consumption data acquisition module includes a current sensor, a temperature sensor, a pressure sensor, and a speed sensor. The temperature sensor uses a PT100 platinum resistance temperature sensor to detect indoor and outdoor temperatures, the pressure sensor uses a piezoresistive pressure sensor to detect refrigerant pressure, and the speed sensor uses a photoelectric encoder to detect fan speed. Each sensor is connected to the corresponding input channel of the data acquisition chip via an analog signal line.

[0008] In one embodiment of the present invention, the data preprocessing unit includes an ARM Cortex-M4 microcontroller, an SDRAM memory, and a Flash memory. The ARM Cortex-M4 microcontroller is connected to the SDRAM memory and the Flash memory respectively via a data bus. The ARM Cortex-M4 microcontroller has a built-in data compression algorithm module. The data compression algorithm module uses the LZ77 compression algorithm to compress the running parameter data, and the compression ratio is set to between 4:1 and 8:1.

[0009] In one embodiment of the present invention, the antenna unit of the low-orbit satellite communication module includes 16 antenna elements arranged in a 4×4 matrix. Each antenna element is connected to a radio frequency transceiver unit via a phase shifter. The radio frequency transceiver unit includes a power amplifier, a low-noise amplifier, a mixer, and a local oscillator. The output terminal of the power amplifier is connected to the input terminal of the antenna element via a microstrip line, and the input terminal of the low-noise amplifier is connected to the output terminal of the antenna element via a microstrip line.

[0010] In one embodiment of the present invention, the baseband processing unit includes an FPGA chip and a DSP chip. The FPGA chip is connected to the DSP chip via a PCIe bus. The FPGA chip has a built-in channel coding module and a modulation and demodulation module. The channel coding module adopts the LDPC coding algorithm with a coding rate between 1 / 2 and 3 / 4. The modulation and demodulation module supports QPSK, 8PSK and 16QAM modulation modes and dynamically selects the modulation mode according to the channel quality.

[0011] In one embodiment of the present invention, the ground receiving station includes a parabolic antenna, a low-noise downconverter, a demodulator, and a network interface unit. The focal length of the parabolic antenna is between 1.2 meters and 2.4 meters. The low-noise downconverter is connected to the feed of the parabolic antenna via a waveguide. The demodulator is connected to the output of the low-noise downconverter via a coaxial cable. The network interface unit is connected to the data output of the demodulator via a gigabit Ethernet interface.

[0012] In one embodiment of the present invention, the data storage server of the cloud data center adopts a distributed storage architecture, including a master storage node and multiple slave storage nodes. The master storage node is connected to the slave storage nodes through a 10 Gigabit Ethernet switch. Each storage node is configured with an SSD solid-state drive and an HDD mechanical hard drive. The SSD solid-state drive is used to store hot data, and the HDD mechanical hard drive is used to store cold data. Data is stored redundantly between the master storage node and the slave storage nodes using RAID5.

[0013] In one embodiment of the present invention, the data analysis server includes a CPU processing unit, a GPU acceleration unit, and an FPGA acceleration unit. The CPU processing unit uses an Intel Xeon processor, the GPU acceleration unit uses an NVIDIA Tesla V100 graphics card, and the FPGA acceleration unit uses a Xilinx UltraScale+ FPGA chip. The CPU processing unit, GPU acceleration unit, and FPGA acceleration unit are interconnected via a PCIe 3.0 bus, and the energy consumption analysis algorithm allocates tasks among the CPU processing unit, GPU acceleration unit, and FPGA acceleration unit.

[0014] In one embodiment of the present invention, the energy consumption analysis algorithm includes an energy efficiency ratio calculation module, a load prediction module, and a fault diagnosis module. The energy efficiency ratio calculation module calculates the energy efficiency ratio of the air conditioning equipment based on the compressor operating current and cooling capacity. The load prediction module uses a time series analysis method to predict the air conditioning load for the next 24 hours. The fault diagnosis module identifies potential faults of the air conditioning equipment based on abnormal patterns in the operating parameter data. The modules exchange data through shared memory.

[0015] In one embodiment of the present invention, the data display server includes a web server, an application server, and a database server. The web server uses Nginx software, the application server uses Tomcat software, and the database server uses a MySQL database. The web server communicates with the user terminal via HTTP / HTTPS protocol, and the application server connects to the database server via JDBC interface. The energy consumption analysis results are transmitted between the web server, the application server, and the user terminal in JSON format. The user terminal uses HTML5Canvas technology to realize the dynamic visualization display of energy consumption data.

[0016] The beneficial effects of the technical solution of this invention are as follows: This invention achieves stable acquisition and real-time aggregation of multi-source operating parameters in strong interference environments through the coordination of RS485 interface and CAN bus on the terminal side. It seamlessly transmits pre-processed standardized data packets to the phased array antenna unit using the SPI high-speed interface, solving the problems of tracking lag and communication interruption of traditional mechanically scanned antennas in scenarios with rapid low-Earth orbit satellite passage. The phased array antenna automatically locks onto the satellite trajectory using electronic beamforming technology, and, in conjunction with the receiving gain of a large-aperture parabolic antenna on the ground, constructs a highly reliable bidirectional link, effectively overcoming the constraints of high free-space path loss and short transmission windows.

[0017] This invention ensures lossless transmission and hierarchical archiving of massive energy consumption data in wide area networks through the linkage of fiber optic backbone network and cloud-based distributed storage architecture. The parallel collaboration mechanism of CPU, GPU and FPGA in heterogeneous computing platform breaks through the computing power bottleneck of single processor in complex energy efficiency model calculation and fault mode identification, realizing millisecond-level response from data acquisition to intelligent analysis. Through dynamic interaction between Web server and HTML5 Canvas technology, abstract algorithm results are transformed into intuitive visualization charts, enabling managers to instantly grasp the energy efficiency status and potential faults of globally distributed air conditioning equipment.

[0018] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0019] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating the steps of the air conditioning energy consumption data backhaul and analysis method based on low-Earth orbit satellite links proposed in this embodiment of the invention. Figure 2 This is a functional diagram of the first method of the air conditioning energy consumption data backhaul and analysis method based on low-orbit satellite link proposed in an embodiment of the present invention. Figure 3 This is a functional diagram of the second method of the air conditioning energy consumption data backhaul and analysis method based on low-orbit satellite link proposed in an embodiment of the present invention; Figure 4 This is a functional diagram of the third method of the air conditioning energy consumption data backhaul and analysis method based on low-orbit satellite link proposed in an embodiment of the present invention. Detailed Implementation

[0020] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0021] The following describes a method for transmitting and analyzing air conditioning energy consumption data based on a low-Earth orbit satellite link, according to an embodiment of the present invention, with reference to the accompanying drawings.

[0022] like Figures 1 to 4 As shown, this embodiment of the invention provides a method for air conditioning energy consumption data backhaul and analysis based on a low-Earth orbit satellite link, including the following steps: S1: Deploy an energy consumption data acquisition module at the air conditioning equipment end. The energy consumption data acquisition module is connected to the main control board of the air conditioning equipment through an RS485 interface to collect the operating parameter data of the air conditioning equipment in real time. The operating parameter data includes compressor operating current, fan speed, indoor and outdoor temperature difference, and refrigerant pressure parameters. S2: The collected operating parameter data is transmitted to the data preprocessing unit via the CAN bus. The data preprocessing unit performs format conversion and data compression on the operating parameter data to generate a standardized energy consumption data packet. S3: Transmits standardized energy consumption data packets to the low-Earth orbit satellite communication module via the SPI interface. The low-Earth orbit satellite communication module includes an antenna unit, a radio frequency transceiver unit, and a baseband processing unit. The antenna unit adopts phased array antenna technology. S4: The low-Earth orbit satellite communication module modulates standardized energy-consuming data packets into radio frequency signals and transmits them to the low-Earth orbit satellite network through the antenna unit; S5: The low-orbit satellite network forwards the received radio frequency signals to the ground receiving station, which demodulates the received radio frequency signals and restores them to standardized energy consumption data packets; S6: The ground receiving station transmits standardized energy consumption data packets to the cloud data center via fiber optic network. The cloud data center includes data storage servers, data analysis servers, and data display servers. S7: The data analysis server unpacks the received standardized energy consumption data packets, extracts the operating parameter data, and analyzes the operating parameter data based on the preset energy consumption analysis algorithm to generate energy consumption analysis results. S8: Store the energy consumption analysis results to the data storage server, and provide a visual display of the energy consumption analysis results to the user terminal through the data display server.

[0023] In specific applications of this invention, at the air conditioning unit end, the energy consumption data acquisition module is embedded inside the air conditioning main control board or established as an independent unit via an RS485 differential serial bus. The RS485 interface uses twisted-pair cable to transmit differential voltage signals. When the compressor is running, the induced current signal generated by the current transformer is converted into a standard voltage by the conditioning circuit. The voltage signal is connected to the input of the RS485 transceiver. The common-mode rejection principle is used to effectively filter out electromagnetic interference generated by motor start-up and shutdown, ensuring the accurate acquisition of compressor operating current data. Simultaneously, the fan speed sensor adopts a photoelectric encoder disk structure, installed at the fan motor shaft end. When the encoder disk rotates, it cuts the light path to generate a pulse sequence. After shaping, the sequence is sent to the acquisition module counter to calculate the speed value in real time. The indoor and outdoor temperature difference is determined by two sets of high-precision thermal resistance sensors placed at the air inlet and outlet respectively. The sensor resistance change is converted into a voltage signal input to the acquisition module. The microprocessor inside the module synchronously samples the two voltages and calculates the difference. The refrigerant pressure parameter is detected by a piezoresistive pressure transmitter. The diaphragm is deformed by pressure, causing an unbalanced output of the Wheatstone bridge. The analog signal is converted into a digital signal after analog-to-digital conversion. The above four data channels are read in a polling manner in the acquisition module at fixed time slices and sent to the data preprocessing unit in master-slave mode via RS485 bus to form the raw operating parameter stream.

[0024] Furthermore, the acquired raw operating parameter stream enters the data preprocessing unit. This unit connects to the acquisition module via a CAN bus. The CAN bus uses a twisted-pair shielded cable structure and supports a multi-master contention mechanism. When multiple sensor data requests occur concurrently, automatic arbitration is performed based on identifier priority to ensure control parameters are transmitted first. Upon receiving the raw data, the microcontroller in the data preprocessing unit first performs a format conversion operation, unifying the quantization units of different sensors to the standard International System of Units (SI). For example, current milliampere values ​​are converted to amperes, temperature Celsius values ​​are rounded to one decimal place, and pressure Pascal values ​​are converted to megapascals. The data is then timestamped. Subsequently, the data compression module starts working, using the LZ77 lossless compression algorithm to scan the data stream, identify recurring byte sequences and replace them with short pointers, generating standardized energy consumption data packets.

[0025] Furthermore, the standardized energy consumption data packets are transmitted to the low-Earth orbit satellite communication module via the SPI high-speed serial interface. The SPI interface adopts a four-wire architecture, including a clock line, a master output / slave input line, a master input / slave output line, and a chip select line. The data preprocessing unit acts as the master, providing a synchronous clock signal, and the low-Earth orbit satellite communication module acts as the slave, responding to the chip select signal to achieve full-duplex data exchange. After the data packets enter the module, they first reach the baseband processing unit. The baseband processing unit has a built-in FPGA logic chip, which performs channel coding on the data packets. LDPC (low-density parity check) coding is used to increase redundant bits and improve error correction capability in weak signal environments. The encoded data is sent to the modem, where QPSK (quadratic phase shift keying) modulation is used to map the binary data into carrier phase changes to generate baseband radio frequency signals.

[0026] Furthermore, the generated baseband RF signal is transmitted to the RF transceiver unit, which includes an up-converter, a power amplifier, and a filtering network. The up-converter mixes the baseband signal with the high-frequency signal generated by the local oscillator, shifting it to the L-band or S-band frequency range suitable for low-Earth orbit satellite communication. The power amplifier boosts the signal power to the transmit level, and the filtering network filters out spurious harmonics to ensure spectral purity. The processed RF signal is then fed to the antenna unit, which employs active phased array antenna technology. It consists of a 4×4 matrix array composed of sixteen microstrip patch antenna elements. Each antenna element integrates a phase shifter and a low-noise amplifier. The baseband processing unit calculates the azimuth and elevation angles of the visible low-Earth orbit satellites at the current moment based on ephemeris data and sends phase control commands to each phase shifter to adjust the radiation phase of each antenna element, ensuring that the array's synthesized beam is precisely pointed towards the satellite, thus achieving dynamic beamforming.

[0027] Furthermore, the low-Earth orbit satellite network receives radio frequency signals from the ground. The onboard transponder amplifies, converts, and retransmits the signals, forwarding the uplink signals to ground receiving stations within the coverage area. The ground receiving stations deploy large-aperture parabolic antennas to collect downlink radio frequency signals through feedhorns. The signals are converted into intermediate frequency signals by low-noise downconverters. Demodulators coherently demodulate the intermediate frequency signals to restore the LDPC encoded bitstream. Then, decoders correct transmission errors and restore the data to standardized energy-efficient data packets. The ground receiving stations upload the data packets to the cloud data center through fiber optic networks. The fiber optic links provide a high-bandwidth, low-latency data transmission channel.

[0028] Furthermore, after receiving the data packets, the data storage server stores them in a distributed database according to device ID and time series, creating an index for rapid retrieval. The data analysis server calls a preset energy consumption analysis algorithm to first unpack the data packets and extract parameters such as compressor operating current, fan speed, temperature difference, and pressure. Based on the first law of thermodynamics and combined with the compressor input power and cooling capacity estimation formula, the algorithm calculates the real-time energy efficiency ratio (EER). Simultaneously, the algorithm incorporates a time series prediction model, using historical load data to predict short-term energy consumption trends and combining pressure and temperature anomaly thresholds for fault diagnosis, determining whether there is refrigerant leakage or decreased fan efficiency.

[0029] Furthermore, the data visualization server pushes the analysis report to the user terminal via a web service interface. The user terminal accesses the visualization interface through a browser. The interface uses Canvas drawing technology to dynamically render energy consumption curves, thermal distribution maps, and fault warning lists. The entire system starts with parameter acquisition from the air conditioning end, transmits data through cascaded transmission via CAN bus and SPI interface, achieves wide-area coverage using phased array antennas and low-orbit satellite networks, and finally completes in-depth analysis and visualization in the cloud.

[0030] In one specific implementation, the energy consumption data acquisition module includes a current sensor, a temperature sensor, a pressure sensor, and a speed sensor. The temperature sensor uses a PT100 platinum resistance temperature sensor to detect indoor and outdoor temperatures, the pressure sensor uses a piezoresistive pressure sensor to detect refrigerant pressure, and the speed sensor uses a photoelectric encoder to detect fan speed. Each sensor is connected to the corresponding input channel of the data acquisition chip via an analog signal line.

[0031] The data preprocessing unit includes an ARM Cortex-M4 microcontroller, an SDRAM memory, and a Flash memory. The ARM Cortex-M4 microcontroller is connected to the SDRAM memory and the Flash memory respectively via a data bus. The ARM Cortex-M4 microcontroller has a built-in data compression algorithm module, which uses the LZ77 compression algorithm to compress the running parameter data, with a compression ratio set between 4:1 and 8:1.

[0032] In specific applications, the front end of the system of this invention consists of a current sensor, a PT100 platinum resistance temperature sensor, a piezoresistive pressure sensor, and a photoelectric encoder, forming a sensing layer. Each sensor operates independently, converting physical quantities into electrical signals that are directly connected to the analog input channel of the data acquisition chip. The current sensor outputs a voltage signal proportional to the current when the compressor generates a magnetic field. The PT100 platinum resistance temperature sensor, based on the characteristic that metal resistance changes with temperature, exhibits a linear resistance shift when detecting changes in indoor and outdoor ambient temperature; this resistance change is converted into a differential voltage signal via a bridge circuit. The piezoresistive pressure sensor integrates a Wheatstone bridge; the refrigerant pressure acts on the silicon diaphragm, causing lattice deformation and thus changing the resistivity to generate a microvolt-level voltage output. The photoelectric encoder is mounted on the fan shaft; the rotating code disk cuts the optical path to generate a pulse sequence, with the pulse frequency directly corresponding to the fan speed.

[0033] Furthermore, the generated raw digital sequence is sent to the data preprocessing unit via a parallel or serial interface. This unit uses an ARM Cortex-M4 microcontroller for control logic, and, in conjunction with SDRAM and Flash memory, constructs a high-speed data throughput environment. The Cortex-M4 core has a built-in floating-point unit capable of executing complex filtering algorithms in real time to remove high-frequency noise interference during sensor acquisition, ensuring data quality. SDRAM serves as a high-speed cache, temporarily storing the continuously acquired raw data stream to prevent data loss. The Flash memory is used to solidify system firmware and store historical baseline parameters. The microcontroller calls the built-in data compression algorithm module, employing the LZ77 lossless compression strategy to process the operating parameter data. The LZ77 algorithm scans the data stream using a sliding window mechanism, identifying recurring byte sequence patterns and replacing long strings of repetitive data with offsets and length pointers pointing to previous data, thereby reducing data redundancy. For the periodic characteristics present in the air conditioning operating parameters (such as compressor start-stop cycles and steady-state fluctuations in fan speed), the compression ratio is dynamically adjusted to between 4:1 and 8:1, significantly reducing the amount of data to be transmitted while ensuring data integrity, adapting to the bandwidth limitations of low-Earth orbit satellite links.

[0034] Furthermore, the compressed, standardized data packets are encapsulated and sent to the low-Earth orbit (LEO) satellite communication module. The compressed data stream output by the data acquisition chip is directly mapped to a clock synchronization signal on the SPI bus. The Cortex-M4 microcontroller, acting as the master, initiates read and write operations, while the LEO satellite communication module, acting as the slave, receives commands. Data is transmitted at high speed in full-duplex mode on the SPI bus, ensuring that compression efficiency does not become a system bottleneck. After completing data packaging, the microcontroller immediately triggers the uplink establishment program of the satellite communication module. The baseband processor within the module performs channel coding and modulation on the data, and the phased array antenna dynamically adjusts the beam direction based on ephemeris data, transmitting the compressed energy-consuming data packets in radio frequency format to the LEO satellite in orbit. The ground receiving station demodulates the signal and transmits it back to the cloud data center via a fiber optic network. The cloud server uses a preset algorithm to reconstruct the data and calculates energy efficiency indicators based on real-time operating conditions. The architecture, which deeply integrates high-precision analog sensing, embedded high-efficiency compression, and satellite broadband communication, solves the problem of difficult data transmission in areas without terrestrial network coverage in traditional air conditioning monitoring systems. At the same time, through the adaptive compression strategy of the LZ77 algorithm, it effectively balances transmission delay and storage costs, enabling continuous, real-time, and economical monitoring and analysis of the energy consumption status of air conditioning equipment globally.

[0035] In one specific implementation, the antenna unit of the low-orbit satellite communication module includes 16 antenna elements arranged in a 4×4 matrix. Each antenna element is connected to a radio frequency transceiver unit via a phase shifter. The radio frequency transceiver unit includes a power amplifier, a low-noise amplifier, a mixer, and a local oscillator. The output of the power amplifier is connected to the input of the antenna element via a microstrip line, and the input of the low-noise amplifier is connected to the output of the antenna element via a microstrip line.

[0036] The baseband processing unit includes an FPGA chip and a DSP chip. The FPGA chip is connected to the DSP chip via a PCIe bus. The FPGA chip has a built-in channel coding module and a modulation and demodulation module. The channel coding module uses the LDPC coding algorithm with a coding rate between 1 / 2 and 3 / 4. The modulation and demodulation module supports QPSK, 8PSK and 16QAM modulation modes and dynamically selects the modulation mode according to the channel quality.

[0037] In a specific application of this invention, the antenna unit consists of a 4×4 planar matrix structure composed of 16 microstrip patch antenna elements. Each element is connected in series with an independent controllable phase shifter, and the control ports of all phase shifters converge to the feedback loop of the RF transceiver unit. When air conditioning energy consumption data needs to be transmitted uplink, the baseband processing unit calculates the real-time azimuth and elevation angles of the visible low-orbit satellites based on ephemeris data, generates a corresponding phase compensation sequence, and drives each phase shifter to adjust the radiation phase after the sequence is processed by the FPGA chip. This causes the radiated waves of the 16 elements to be superimposed in phase in a specific direction in space, forming a high-gain directional beam pointing towards the satellite. The output of the power amplifier is directly connected to the input port of each antenna element via a microstrip line with a characteristic impedance of 50 ohms. The microstrip line is made of a low-loss dielectric substrate to minimize signal attenuation during transmission and ensure that the transmitted power is effectively converted into radiated energy. During downlink reception, the weak radio frequency signal from the satellite is captured by the antenna array elements and transmitted to the input of the low-noise amplifier via the microstrip line. The low-noise amplifier amplifies the signal initially and suppresses front-end thermal noise. Then, it is sent to the mixer and down-converted to the high-frequency carrier generated by the local oscillator to shift the radio frequency signal to the intermediate frequency band for subsequent digital processing.

[0038] Furthermore, the baseband processing unit adopts a dual-core architecture with FPGA and DSP chips working together. Data exchange and command synchronization between the two are achieved through a high-speed PCIe bus. The channel coding module integrated inside the FPGA chip performs LDPC (low-density parity check) encoding on the uplink data, with the coding rate dynamically configured between 1 / 2 and 3 / 4. By adding redundant parity bits, the error correction capability of the data in harsh channel environments is improved. The modulation and demodulation module has built-in multiple modulation mapping tables for QPSK, 8PSK, and 16QAM, and automatically switches the modulation order based on the real-time monitored channel signal-to-noise ratio: during periods when the signal strength fluctuates greatly due to satellite entry or exit, the system prioritizes the QPSK mode with strong anti-interference capability; when the satellite is in the center of the transit area and the link quality is good, it automatically switches to the 16QAM mode with higher spectral efficiency, thereby maximizing bandwidth utilization while ensuring transmission reliability. The DSP chip is responsible for executing complex digital filtering algorithms, Doppler frequency shift compensation calculations, and adaptive equalization processing. It extracts effective information from the raw sampled data stream received from the FPGA, corrects the frequency shift caused by the high-speed relative motion of the satellite, and transmits the processed data back to the upper-layer application interface through the PCIe bus.

[0039] Throughout the communication process, the FPGA chip sends phase update commands to the phase shifter in real time. Combined with the dynamic modulation selection of the DSP chip, the communication link can adapt to the short window period of low-Earth orbit satellites passing overhead rapidly. The RF signal output by the power amplifier is fed to the phased array antenna via a microstrip line. Beamforming technology is used to concentrate energy onto the satellite orbit, overcoming free-space path loss. The downlink signal is picked up by a low-noise amplifier, down-converted by a mixer, and then enters the DSP chip for demodulation and noise reduction. Finally, a standardized energy consumption data packet is restored. The deep integration of the 4×4 phased array antenna, multi-level RF gain control, and LDPC encoding and adaptive modulation and demodulation technology enables continuous and efficient backhaul of air conditioning energy consumption data under complex electromagnetic environments and high-speed motion conditions.

[0040] In one specific implementation, the ground receiving station includes a parabolic antenna, a low-noise downconverter, a demodulator, and a network interface unit. The focal length of the parabolic antenna is between 1.2 meters and 2.4 meters. The low-noise downconverter is connected to the feed of the parabolic antenna via a waveguide. The demodulator is connected to the output of the low-noise downconverter via a coaxial cable. The network interface unit is connected to the data output of the demodulator via a gigabit Ethernet interface.

[0041] The data storage servers in the cloud data center adopt a distributed storage architecture, including a master storage node and multiple slave storage nodes. The master storage node is connected to the slave storage nodes through a 10 Gigabit Ethernet switch. Each storage node is equipped with an SSD solid-state drive and an HDD mechanical hard drive. The SSD solid-state drive is used to store hot data, and the HDD mechanical hard drive is used to store cold data. Data is stored redundantly between the master storage node and the slave storage nodes using RAID5.

[0042] In a specific application of this invention, the ground receiving station, as the first link in the access chain, captures weak radio frequency signals from low-Earth orbit satellites. The antenna aperture size is set in the range of 1.2 meters to 2.4 meters. By increasing the effective receiving area, the gain is improved, and free space path loss is compensated. The parabolic reflector focuses the incident plane wave onto the feed horn at the focal plane. The feed horn is directly coupled to the low-noise downconverter through a waveguide structure. The waveguide transmission method has extremely low insertion loss and excellent shielding performance, ensuring that the high-frequency signal does not experience additional attenuation during the conversion process from the feed to the downconverter.

[0043] Furthermore, after receiving the focused RF signal, the low-noise downconverter first uses its internal high electron mobility transistor (HEMT) for low-noise amplification. Then, it mixes the high-frequency signal generated by the local oscillator with the signal in a mixer, downconverting the high-frequency carrier signal to an intermediate frequency (IF) or baseband frequency. The processed analog IF signal is transmitted to the demodulator via a coaxial cable made of low-loss material to ensure signal integrity. The demodulator integrates a digital signal processing unit that performs synchronization acquisition, equalization filtering, and constellation diagram decision on the input signal, reconstructing the LDPC-encoded digital bitstream. The demodulated digital data packets are then sent to the network interface unit via a gigabit Ethernet interface. The network interface unit is responsible for protocol encapsulation and flow control, packaging the data into IP packets conforming to the TCP / IP standard and uploading them to the cloud data center via the fiber optic backbone.

[0044] The data storage servers in the cloud data center adopt a distributed architecture, consisting of a master storage node and multiple slave storage nodes forming a logical cluster. The master storage node acts as the metadata management hub, recording the physical location, redundant replica status, and access permissions of all data blocks. It establishes high-speed interconnection channels with each slave storage node through a 10 Gigabit Ethernet switch. The 10 Gigabit bandwidth ensures that massive energy consumption data packets can be written in real time, avoiding data buffer overflow caused by write latency at the receiving end. Each storage node is configured with hybrid storage media, including SSD solid-state drives and HDD hard disk drives. The system implements a tiered storage strategy based on data access frequency: newly received air conditioning operating parameters and recent analysis results are marked as hot data and are preferentially written to the SSD array, utilizing its high IOPS characteristics to meet high-frequency query and analysis needs; historical archived data and low-frequency accessed raw logs are automatically migrated to the HDD array, utilizing its large capacity and low cost advantages for long-term storage.

[0045] Furthermore, during the data transfer between the primary and secondary storage nodes, a RAID5 redundant storage mechanism is employed. Data blocks are segmented and distributed across the disks of different nodes, while parity information is calculated and stored simultaneously. When any single storage node fails or a hard drive is damaged, the system reconstructs the lost data using the remaining parity information and data blocks, restoring business continuity without downtime and ensuring efficient operation across the entire chain from satellite signal acquisition to cloud data delivery. The high gain and anti-interference capabilities of the ground receiving station address the problem of weak signal reception, while the tiered management and redundancy protection mechanisms of the cloud storage address the challenges of massive concurrent data writes and long-term secure storage.

[0046] In one specific implementation, the data analysis server includes a CPU processing unit, a GPU acceleration unit, and an FPGA acceleration unit. The CPU processing unit uses an Intel Xeon processor, the GPU acceleration unit uses an NVIDIA Tesla V100 graphics card, and the FPGA acceleration unit uses a Xilinx UltraScale+ FPGA chip. The CPU processing unit, GPU acceleration unit, and FPGA acceleration unit are interconnected via a PCIe 3.0 bus, and the energy consumption analysis algorithm allocates tasks among the CPU processing unit, GPU acceleration unit, and FPGA acceleration unit.

[0047] The energy consumption analysis algorithm includes an energy efficiency ratio calculation module, a load prediction module, and a fault diagnosis module. The energy efficiency ratio calculation module calculates the energy efficiency ratio of the air conditioning equipment based on the compressor operating current and cooling capacity. The load prediction module uses time series analysis to predict the air conditioning load for the next 24 hours. The fault diagnosis module identifies potential faults in the air conditioning equipment based on abnormal patterns in the operating parameter data. All modules exchange data through shared memory.

[0048] In specific applications of this invention, the energy efficiency ratio calculation module involves real-time mathematical calculations of the compressor operating current and cooling capacity. The module is deployed on an FPGA acceleration unit. The FPGA chip utilizes its parallel pipeline architecture to synchronously acquire and perform arithmetic operations on the input multi-channel sensor data, achieving microsecond-level energy efficiency ratio (EER) output. The hard-wired logic circuits inside the FPGA avoid the overhead of general instruction sets, ensuring a stable throughput even when data flows continuously. The load prediction module uses a time series analysis method, which requires processing historical long-cycle data and running complex regression models to predict the air conditioning load for the next 24 hours. This module is offloaded to the GPU acceleration unit.

[0049] Furthermore, the fault diagnosis module identifies potential faults based on abnormal patterns in operating parameter data. The module employs a hybrid execution strategy: some feature extraction is performed by the FPGA, leveraging its low latency to monitor current waveform distortion and transient signals such as pressure spikes in real time; complex pattern matching and decision tree inference are handled by the GPU, utilizing its powerful floating-point computing capabilities to perform cluster analysis on multi-dimensional feature vectors, identifying signs of refrigerant leakage, fan efficiency decline, or compressor wear. Analysis modules exchange data efficiently through a shared memory area, while the CPU processing unit maintains a global status register, coordinating data read / write permissions among modules to ensure that the energy efficiency ratio results, load forecasts, and fault diagnosis conclusions are aligned on the timeline. Once the FPGA completes the instantaneous energy efficiency calculation, it directly writes the results to the shared memory buffer. The GPU then reads the data and updates the prediction model based on historical trends. Simultaneously, the fault diagnosis module calls the latest data to trigger the anomaly detection logic.

[0050] Furthermore, after the raw data stream enters the server, the CPU performs preprocessing and task distribution, the FPGA performs high-frequency real-time computing, and the GPU performs large-scale concurrent analysis and prediction. The three work seamlessly together through the PCIe 3.0 bus and shared memory. The heterogeneous computing architecture makes full use of the performance characteristics of various processors. The CPU is responsible for logic control and task scheduling, the FPGA is responsible for low-latency deterministic computing, and the GPU is responsible for high-throughput parallel computing. Together, they solve the defects of slow response and low throughput of traditional single CPU architecture when processing multi-source heterogeneous energy consumption data. The system can complete the entire process from parameter unpacking to energy efficiency assessment, load extrapolation, and fault early warning within milliseconds after the data arrives. The generated structured analysis results are written to the data storage server in real time and pushed to the user terminal through the data display server.

[0051] In one specific implementation, the data display server includes a web server, an application server, and a database server. The web server uses Nginx software, the application server uses Tomcat software, and the database server uses MySQL database. The web server communicates with the user terminal via HTTP / HTTPS protocol, and the application server connects to the database server via JDBC interface. The energy consumption analysis results are transmitted between the web server, application server, and user terminal in JSON format. The user terminal uses HTML5Canvas technology to realize the dynamic visualization display of energy consumption data.

[0052] In practical applications, this invention uses a MySQL database as the underlying storage engine to store structured data such as energy efficiency ratio calculations, load forecast curves, and fault diagnosis reports from a data analysis server. The database server is configured with a master-slave replication cluster to ensure data consistency and read-write separation capabilities during high-concurrency reads. The application server deploys Tomcat middleware and establishes a long-lived connection to the MySQL database via a JDBC interface. When a user initiates a query request, the application server parses the SQL statement, extracts energy consumption data from the database for a specified time range, specific device number, or region, and performs secondary aggregation and formatting in memory, converting the raw data into lightweight data objects conforming to the JSON standard.

[0053] Furthermore, the web server uses Nginx software as a reverse proxy and static resource distribution node, directly providing HTTP / HTTPS encrypted communication services to user terminals. Nginx leverages its event-driven non-blocking I / O model to efficiently handle a large number of concurrent WebSocket connections and HTTP requests, load balancing user terminal access traffic across multiple backend application server instances. User terminals access the system through a browser, using HTML5 Canvas technology to render dynamic charts. When a user terminal initiates a data refresh command, the request is transmitted to Nginx via the HTTPS protocol. Nginx forwards the request to the application server, which reads the latest analysis results from MySQL, encapsulates them into a JSON data packet, compresses it, and sends it back to the user terminal. The user terminal's JavaScript engine receives the JSON data, parses the numerical sequences and metadata, and calls the Canvas API to draw line charts, heatmaps, or bar charts. The Canvas drawing engine renders the graphics directly on the client's graphics card, avoiding the bandwidth consumption caused by server-side image generation, achieving millisecond-level chart updates and interactive responses.

[0054] Furthermore, the MySQL database stores global energy consumption status, the application server executes complex business logic and data transformation, Nginx ensures secure access and traffic scheduling under high concurrency, user terminals utilize local computing power to complete graphics rendering, and JSON format serves as a unified data exchange standard, shielding the differences between different operating systems and browser kernels, ensuring seamless cross-platform transmission of energy consumption analysis results. HTML5 Canvas technology allows users to intuitively view real-time energy efficiency trends, 24-hour load forecast curves, and fault warning information, supporting zoom, pan, and drill-down interactive operations. This solution, which deeply integrates high-performance web services, Java enterprise application logic, and relational databases, solves the problems of difficult deployment, delayed data updates, and multi-terminal compatibility issues inherent in traditional C / S architectures.

[0055] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0056] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A method for transmitting and analyzing air conditioning energy consumption data based on low-Earth orbit satellite links, characterized in that, Includes the following steps: S1: Deploy an energy consumption data acquisition module at the air conditioning equipment end. The energy consumption data acquisition module is connected to the main control board of the air conditioning equipment through an RS485 interface to collect the operating parameter data of the air conditioning equipment in real time. The operating parameter data includes compressor operating current, fan speed, indoor and outdoor temperature difference, and refrigerant pressure parameters. S2: The collected operating parameter data is transmitted to the data preprocessing unit via the CAN bus. The data preprocessing unit performs format conversion and data compression on the operating parameter data to generate a standardized energy consumption data packet. S3: Transmit standardized energy consumption data packets to the low-Earth orbit satellite communication module via the SPI interface. The low-Earth orbit satellite communication module includes an antenna unit, a radio frequency transceiver unit, and a baseband processing unit. The antenna unit adopts phased array antenna technology. S4: The low-Earth orbit satellite communication module modulates standardized energy-consuming data packets into radio frequency signals and transmits them to the low-Earth orbit satellite network through the antenna unit; S5: The low-Earth orbit satellite network forwards the received radio frequency signals to the ground receiving station, which demodulates the received radio frequency signals and restores them to standardized energy consumption data packets; S6: The ground receiving station transmits standardized energy consumption data packets to the cloud data center via a fiber optic network. The cloud data center includes a data storage server, a data analysis server, and a data display server. S7: The data analysis server unpacks the received standardized energy consumption data packets, extracts the operating parameter data, and analyzes the operating parameter data based on the preset energy consumption analysis algorithm to generate energy consumption analysis results. S8: Store the energy consumption analysis results to the data storage server, and provide a visual display of the energy consumption analysis results to the user terminal through the data display server.

2. The method for air conditioning energy consumption data backhaul and analysis based on low-Earth orbit satellite links according to claim 1, characterized in that, The energy consumption data acquisition module includes a current sensor, a temperature sensor, a pressure sensor, and a speed sensor. The temperature sensor uses a PT100 platinum resistance temperature sensor to detect indoor and outdoor temperatures. The pressure sensor uses a piezoresistive pressure sensor to detect refrigerant pressure. The speed sensor uses a photoelectric encoder to detect fan speed. Each sensor is connected to the corresponding input channel of the data acquisition chip via an analog signal line.

3. The method for air conditioning energy consumption data backhaul and analysis based on low-Earth orbit satellite links according to claim 2, characterized in that, The data preprocessing unit includes an ARM Cortex-M4 microcontroller, an SDRAM memory, and a Flash memory. The ARM Cortex-M4 microcontroller is connected to the SDRAM memory and the Flash memory respectively via a data bus. The ARM Cortex-M4 microcontroller has a built-in data compression algorithm module. The data compression algorithm module uses the LZ77 compression algorithm to compress the running parameter data, with a compression ratio set between 4:1 and 8:

1.

4. The method for air conditioning energy consumption data backhaul and analysis based on low-Earth orbit satellite links according to claim 3, characterized in that, The antenna unit of the low-orbit satellite communication module includes 16 antenna elements arranged in a 4×4 matrix. Each antenna element is connected to the radio frequency transceiver unit via a phase shifter. The radio frequency transceiver unit includes a power amplifier, a low-noise amplifier, a mixer, and a local oscillator. The output of the power amplifier is connected to the input of the antenna element via a microstrip line, and the input of the low-noise amplifier is connected to the output of the antenna element via a microstrip line.

5. The method for air conditioning energy consumption data backhaul and analysis based on low-Earth orbit satellite links according to claim 4, characterized in that, The baseband processing unit includes an FPGA chip and a DSP chip. The FPGA chip is connected to the DSP chip via a PCIe bus. The FPGA chip has a built-in channel coding module and a modulation and demodulation module. The channel coding module adopts the LDPC coding algorithm with a coding rate between 1 / 2 and 3 / 4. The modulation and demodulation module supports QPSK, 8PSK and 16QAM modulation modes and dynamically selects the modulation mode according to the channel quality.

6. The method for air conditioning energy consumption data backhaul and analysis based on low-Earth orbit satellite links according to claim 5, characterized in that, The ground receiving station includes a parabolic antenna, a low-noise downconverter, a demodulator, and a network interface unit. The focal length of the parabolic antenna is between 1.2 meters and 2.4 meters. The low-noise downconverter is connected to the feed of the parabolic antenna via a waveguide. The demodulator is connected to the output of the low-noise downconverter via a coaxial cable. The network interface unit is connected to the data output of the demodulator via a gigabit Ethernet interface.

7. The method for air conditioning energy consumption data backhaul and analysis based on low-Earth orbit satellite links according to claim 6, characterized in that, The data storage server in the cloud data center adopts a distributed storage architecture, including a master storage node and multiple slave storage nodes. The master storage node is connected to the slave storage nodes through a 10 Gigabit Ethernet switch. Each storage node is equipped with an SSD solid-state drive and an HDD mechanical hard drive. The SSD solid-state drive is used to store hot data, and the HDD mechanical hard drive is used to store cold data. Data is stored redundantly between the master storage node and the slave storage nodes using RAID5.

8. The method for air conditioning energy consumption data backhaul and analysis based on low-Earth orbit satellite links according to claim 7, characterized in that, The data analysis server includes a CPU processing unit, a GPU acceleration unit, and an FPGA acceleration unit. The CPU processing unit uses an Intel Xeon processor, the GPU acceleration unit uses an NVIDIA Tesla V100 graphics card, and the FPGA acceleration unit uses a Xilinx UltraScale+ FPGA chip. The CPU processing unit, GPU acceleration unit, and FPGA acceleration unit are interconnected via a PCIe 3.0 bus, and the energy consumption analysis algorithm allocates tasks among the CPU processing unit, GPU acceleration unit, and FPGA acceleration unit.

9. The method for air conditioning energy consumption data backhaul and analysis based on low-Earth orbit satellite links according to claim 8, characterized in that, The energy consumption analysis algorithm includes an energy efficiency ratio calculation module, a load prediction module, and a fault diagnosis module. The energy efficiency ratio calculation module calculates the energy efficiency ratio of the air conditioning equipment based on the compressor operating current and cooling capacity. The load prediction module uses time series analysis to predict the air conditioning load for the next 24 hours. The fault diagnosis module identifies potential faults in the air conditioning equipment based on abnormal patterns in operating parameter data. All modules exchange data through shared memory.

10. The method for air conditioning energy consumption data backhaul and analysis based on low-Earth orbit satellite links according to claim 9, characterized in that, The data display server includes a web server, an application server, and a database server. The web server uses Nginx software, the application server uses Tomcat software, and the database server uses MySQL database. The web server communicates with the user terminal via HTTP / HTTPS protocol, and the application server connects to the database server via JDBC interface. Energy consumption analysis results are transmitted between the web server, application server, and user terminal in JSON format. The user terminal uses HTML5Canvas technology to realize dynamic visualization of energy consumption data.

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