Passive sensor data transmission optimization method

Abstract

The invention belongs to the technical field of data transmission optimization, and particularly relates to a passive sensor data transmission optimization method. The passive sensor data transmission optimization method provided by the invention is high in data transmission efficiency and good in stability and energy conservation. The method comprises the following steps: step 1, monitoring environmental parameters of a passive sensor, wherein the environmental parameters are used as context information; 2, performing dynamic redundancy threshold calculation according to the context information; step 3, compressing and transmitting the data calculated in the step 2; 4, after the data is compressed and transmitted, feedback information of transmission quality and feedback information of energy utilization efficiency are obtained, the feedback information is returned to the step 2, and closed-loop control is formed.

Description

Technical Field

[0001] The present invention belongs to the technical field of data transmission optimization, and particularly relates to a method for optimizing the data transmission of passive sensors. Background Art

[0002] With the continuous development of the Internet of Things technology, passive sensor networks are increasingly widely used in fields such as environmental monitoring, health monitoring, and industrial control. However, traditional passive sensor data transmission technologies face many challenges in aspects such as low-power transmission, redundant data processing, and stable transmission under low signal-to-noise ratio. First of all, most existing compression algorithms adopt static strategies and cannot be dynamically adjusted according to changes in the environment and sensor states, resulting in low data transmission efficiency. Secondly, passive sensors rely on environmental energy such as microwaves as the power source, and how to optimize energy consumption to extend the service life of the device has become an important issue. In addition, there is a large amount of redundant information in sensor data, and existing technologies have not effectively identified and filtered redundant data, causing waste of bandwidth and computing resources.

[0003] The existing related implementation solutions mainly focus on the topology optimization and energy efficiency improvement of wireless sensor networks.

[0004] Existing Document 1, "Optimization Control Method and Device for the Topology of Wireless Sensor Networks" (inventors: Zheng Yin, etc., patent number: CN117412313A), realizes the topology optimization and energy efficiency improvement of wireless sensor networks through the subgradient algorithm and the block coordinate descent algorithm. This method first selects the initial cluster head nodes and constructs an optimization objective function for transmission power, and uses the Lagrangian dual problem to solve the maximum transmission power of the nodes; by iteratively updating the Lagrange multiplier and power parameters, the optimal transmission power and node-related factors are determined when the multiplier converges; finally, the cluster head structure is optimized in combination with the Euclidean distance, enabling each node in the network to achieve efficient data transmission with optimal power and association configuration, and significantly reducing the overall energy consumption of the network.

[0005] Existing Document 2, "A Method for Optimizing Data Transmission in Wireless Sensor Networks" (inventor: Li Jianpo, authorization announcement number: CN108966354B), improves the network energy efficiency and transmission efficiency through a two-stage strategy. In the intra-cluster data transmission stage, the transmission priority is dynamically allocated according to the remaining energy and data buffer status of the nodes, and the time slot scheduling is optimized to reduce data backlog; in the inter-cluster transmission stage, a comprehensive evaluation model is constructed in combination with parameters such as the energy consumption and transmission distance of the relay nodes, and the optimal path is intelligently selected to complete multi-hop forwarding. Through node state perception and dynamic path planning, this method significantly reduces the overall energy consumption of the network, reduces data transmission delay, and enhances the network stability and reliability in complex environments, and is applicable to Internet of Things scenarios with limited energy.

[0006] In the application of existing technologies in passive sensor networks, although certain achievements have been made in topology optimization and energy efficiency improvement, there are still many deficiencies, especially in dynamic redundant data processing, stable transmission in low signal-to-noise ratio environments, and intelligent compression algorithms. Summary of the Invention

[0007] In view of the above problems, the present invention provides an optimized method for passive sensor data transmission with high data transmission efficiency, good stability and energy conservation.

[0008] To achieve the above object, the present invention adopts the following technical solutions. The present invention includes the following steps:

[0009] Step 1: Monitor the environmental parameters of the passive sensor, and use the environmental parameters as context information;

[0010] Step 2: Calculate the dynamic redundancy threshold according to the context information;

[0011] Step 3: Compress and transmit the data calculated in Step 2;

[0012] Step 4: After the data is compressed and transmitted, obtain the feedback information of the transmission quality and the feedback information of the energy utilization efficiency, and return the feedback information to Step 2 to form a closed-loop control.

[0013] As a preferred solution, the environmental parameters in Step 1 of the present invention include signal-to-noise ratio (SSNR), multipath fading coefficient, environmental temperature, and environmental humidity; the signal-to-noise ratio is the power ratio between the received signal and the environmental noise when the passive sensor sends data to the base station or the receiving end; the multipath fading coefficient is the degree of fading of the received signal due to the multipath propagation characteristics in the channel where the passive sensor is located.

[0014] The multipath fading coefficient can be obtained by the receiving circuit of the passive sensor.

[0015] As another preferred solution, the passive sensor of the present invention converts the 2.4 GHz microwave energy transmitted by the base station into direct current through a microwave rectification circuit (converting radio frequency energy into direct current using a microwave rectification circuit is a conventional technology, and the present invention has made improvements for data transmission and redundancy optimization of passive sensors). The relationship between the conversion efficiency η and the SSNR is:

[0016]

[0017] Where, is the received power; P tx is the transmitted power; k is a constant coefficient, comprehensively reflecting the conversion efficiency of the rectification circuit, antenna coupling loss, and device characteristics.

[0018] The environmental parameter vector is:

[0019] X = [SSNR, T env , H env , vibration freq_peak , vibration band_energy |

[0020] T env is the environmental temperature parameter, H env is the environmental humidity parameter, vibration freq_peak is the vibration peak frequency, vibration band_energy is the vibration bandwidth energy; the environmental parameter vector serves as context information.

[0021] vibration freq_peak is the main vibration frequency detected by the passive vibration sensor for the target to be measured, and can be monitored through the MEMS accelerometer sensing element in the sensor.

[0022] vibration band_energy is the energy accumulation of the vibration signal of the target component by the passive vibration sensor within a certain frequency band range, and can be obtained by the same set of MEMS sensing elements as the vibration peak frequency through digital signal processing algorithms to accumulate the energy of sub - frequency bands.

[0023] As another preferred solution, the calibration and correction process of k in the present invention:

[0024] Hardware actual measurement calibration: In a complete transmit - receive system, measure multiple groups of input / output power and SSNR data, and through the formula

[0025]

[0026] Use the least - squares or curve fitting method to obtain the optimal k;

[0027] Environment and device correction: Repeat calibration under different temperature and humidity, device aging degree, and antenna matching conditions to obtain the corresponding correction coefficients or correction functions, and dynamically calibrate k.

[0028] Since different hardware designs and environmental factors will lead to different efficiencies, it is necessary to determine through hardware measurement and environmental correction.

[0029] As another preferred solution, step 2 in the present invention includes the relationship part between environmental parameters and redundancy thresholds, the hierarchical coding and redundancy calculation part, and the dynamic adjustment of redundancy thresholds part; environmental parameters determine the setting of redundancy thresholds, and redundancy calculation affects the redundancy threshold adjustment strategy; by calculating the data redundancy, adjust the redundancy threshold according to the deviation between the redundancy and the current redundancy threshold; the updated redundancy threshold guides the data compression and transmission strategy.

[0030] Environmental parameters (such as signal-to-noise ratio, temperature and humidity, etc.) are used to set the redundancy threshold T0. These parameters affect the initial threshold setting. By calculating the data redundancy, the system adjusts the redundancy threshold according to the deviation between the redundancy and the current threshold to achieve intelligent adjustment. The updated redundancy threshold guides the data compression and transmission strategy to ensure the transmission efficiency and stability under different environments. This closed-loop iterates continuously. Environmental changes affect the redundancy threshold, and the redundancy calculation feedback adjusts the threshold, ultimately optimizing the data transmission efficiency.

[0031] As another preferred solution, for the part of the relationship between the environmental parameters and the redundancy threshold in the present invention:

[0032] Set the initial redundancy threshold as T0, and the relationship between the dynamic redundancy threshold T and the environmental parameter vector X is:

[0033] T = T0 × f(X)

[0034]

[0035] where f(X) is a mapping function, indicating how the environmental parameter vector X affects the redundancy threshold T; α SSNR 、α T and α H respectively control the influence weights of SSNR, temperature, and humidity on the redundancy threshold, and SSNR ref 、T ref and H ref are reference values.

[0036] As another preferred solution, for the SSNR in the present invention ref : Select the signal-to-noise ratio value measured in an environment without signal interference or with low background noise. For example, the signal-to-noise ratio measured in an ideal experimental environment can be selected, or the standard value determined when designing the sensor.

[0037] T ref : It is the temperature value measured under standard experimental environmental conditions and is the ideal temperature value set during sensor testing. For example, 25 °C (room temperature) or other preset values.

[0038] H ref : Select the standard humidity value measured under laboratory conditions. For example, a relative humidity of 50%, or the standard humidity in the test environment.

[0039] As another preferred solution, for the hierarchical coding and redundancy calculation part in the present invention:

[0040] The detection data of the passive sensor is D = [d1, d2, …, d n , which is divided into multiple levels according to the redundancy degree, and each level has different thresholds for data redundancy; the redundancy R of the kth levelk is:

[0041]

[0042] where n k represents the number of data points at the k-th level, d i is the original data point collected, is the prediction or expected value at the current level, and the redundancy R k represents the data redundancy degree at this level.

[0043] D = [d1, d2, …, d n , representing a series of data detected by the passive sensor during operation, such as temperature, humidity, and vibration amplitude.

[0044] The division into multiple levels according to the redundancy degree means: dividing the data into several layers according to the size of the prediction residuals of the data, providing more redundancy for the data levels with larger errors, and higher compression for the levels with smaller errors.

[0045] As another preferred solution, the dynamic adjustment of the redundancy threshold part of the present invention:

[0046] The adjustment rule of the redundancy threshold T k is represented by the following formula:

[0047]

[0048] where T k base is the basic redundancy threshold at this level; ɑ k is the adjustment coefficient at this level, controlling the sensitivity of the redundancy threshold to environmental changes; T ref and H ref are set standard values; ɑ T and ɑ H are adjustment coefficients, controlling the influence degree of temperature and humidity changes on the threshold.

[0049] The system dynamically adjusts the redundancy threshold according to environmental conditions (such as signal-to-noise ratio, temperature and humidity, etc.). For high-redundancy layer data (such as important temperature and humidity change data), the redundancy threshold is relatively low (for example, T k _high base = 0.1), base ensuring that these key data can maintain a high transmission quality and avoiding losing important information. For low-redundancy layer data (such as non-critical data), the redundancy threshold will be relatively high (for example, T k _low_ base = 0.5), allowing more compression of the data during transmission to reduce bandwidth occupancy and energy consumption. The α kIt can be set to 0.1 (low sensitivity), and the high redundancy layer can set α T = 0.8, α H = 0.8 (great influence of environmental parameters). For the low redundancy layer, α k can be set to 0.3 (high sensitivity), and the low redundancy layer can set α T = 0.2, α H = 0.2 (little influence of environmental parameters).

[0050] As another preferred solution, when SSNR < 20dB in the present invention, T k base rises by 20% - 30%; when SSNR > 25dB, T k base falls by 10% - 15%.

[0051] As another preferred solution, in the present invention, when Rk > Tk (high redundancy layer): differential coding + Huffman static dictionary compression is adopted, and the compression ratio is 5:1;

[0052] When Rk ≤ Tk (low redundancy layer): directly discard adjacent repeated data points, and only retain data with a change amount exceeding ±0.5%.

[0053] As another preferred solution, in step 3 of the present invention, the data compression and transmission of the passive temperature sensor is as follows:

[0054] The original temperature data sequence is D = [d1, d2,..., d n , and the process of its differential coding is:

[0055] Δd i = d i - d i-1 , i = 2, 3,..., n

[0056] where d i is the i-th data point, d i-1 is the previous data point, and Δd i is the difference between data points; the original data sequence is converted into a difference sequence; so as to reduce the amount of data transmitted;

[0057] Based on differential coding, further compression is performed using a Huffman static dictionary. The frequencies of the data points after differential coding are statistically analyzed, and the data points are converted into binary codes through frequency mapping to complete data compression. The compressed data is transmitted through the communication link between the passive temperature sensor and the receiving end. During this process, important data will maintain a low compression ratio to ensure its accurate transmission, while non-critical data will use a high compression ratio to improve transmission efficiency. For data in the low redundancy layer, during data transmission, the overall energy efficiency of the system is further optimized by adjusting the transmission frequency, bandwidth, and power.

[0058] High redundancy layer (important data): This layer contains data that is crucial to the application, such as the change ranges of temperature and humidity, or other key environmental parameters. These data points will be given a low redundancy threshold to ensure their high-fidelity transmission. Since these data are vital to the system's function, they need to be transmitted at a low compression ratio to ensure data integrity and accuracy. Even under relatively large changes in environmental conditions, the system will ensure the stable transmission of these data, thus guaranteeing the system's performance and reliability.

[0059] Low redundancy layer (non-critical data): This layer contains data of relatively low importance to the application, data that can tolerate large errors or have a small impact on the system. These data points will be given a high redundancy threshold, allowing for a higher compression ratio to save bandwidth and energy. By adopting a stronger compression algorithm, the system can efficiently reduce the amount of unimportant data transmitted, thus optimizing resource utilization and reducing unnecessary data transmission.

[0060] As another preferred solution, in step 3 of the present invention, the data compression and transmission of the passive vibration sensor are as follows:

[0061] Time-domain vibration data x n , and the formula for DCT transformation is:

[0062]

[0063] where X k is the transformed frequency-domain data; the low-frequency coefficients (i.e., the parts with smaller k) usually contain more signal energy and reflect the main characteristics of the data. Therefore, the probability distribution is constructed by statistically analyzing the occurrence frequencies of these frequency-domain coefficients.

[0064] P(X k ) is the probability of the k-th low-frequency coefficient X k , representing the frequency of occurrence of the k-th coefficient among all frequency-domain coefficients;

[0065] P(X k ) distribution satisfies:

[0066]

[0067] Arithmetic coding divides the entire interval [0, 1) into sub - intervals, where each sub - interval represents a coefficient. At initialization, low = 0 and high = 1;

[0068] For each coefficient X k , the corresponding interval is [low k , high k . Update the interval:

[0069]

[0070]

[0071] Continue this operation until all coefficients are encoded; the midpoint of the final output interval is used as the encoding result:

[0072] Encodedoutput = Binaryrepresentationofmidpointof[low, high].

[0073] As another preferred solution, the data hierarchical compression and transmission of the passive vibration sensor described in the present invention:

[0074] If SSNR > 25dB, retain the first eight DCT coefficients:

[0075] X k = [X0, X1, …, X7] (SSNR > 25dB)

[0076] If SSNR < 20dB, retain the first twelve DCT coefficients:

[0077] X k = [X0, X1, …, X 11 (SSNR < 20dB).

[0078] Passive sensors can be classified according to their types and data acquisition characteristics. For different types of sensors, the data compression and transmission processing procedures are different. The following is an explanation of the processing procedures for different types of sensors:

[0079] Passive temperature sensor compression and transmission processing:

[0080] Passive temperature sensors are usually used to monitor relatively slowly changing environmental parameters, such as temperature. The data collected by such sensors changes less and has a lower fluctuation range, so the processing procedure is relatively simple. Differential coding and Huffman static dictionary compression strategies are mainly used.

[0081] Differential coding: By recording the differences between adjacent data points instead of directly transmitting the original data values, the amount of data transmitted is reduced.

[0082] Huffman coding: By assigning shorter codes to symbols with higher frequencies, the data volume is further reduced and the data transmission efficiency is optimized.

[0083] Compressed transmission processing of passive humidity sensors:

[0084] The data collected by passive humidity sensors usually changes relatively slowly, similar to temperature sensors. The differential coding and static compression strategy can also be adopted in the process of humidity data processing. The specific processing steps are as follows:

[0085] Differential coding: Similar to temperature sensors, the change of humidity data is small, and differential coding is used to reduce the amount of data.

[0086] Huffman coding: Further compress the data after differential coding, and optimize the data transmission through the Huffman static dictionary.

[0087] Compressed transmission processing of passive vibration sensors:

[0088] Passive vibration sensors are usually used to monitor relatively fast-changing environmental parameters, such as vibration. Since vibration data changes frequently and contains a lot of high-frequency information, the processing process is relatively complex. Discrete cosine transform (DCT) and arithmetic coding techniques are mainly used.

[0089] DCT (Discrete Cosine Transform): Convert the time-domain data into frequency-domain data, remove the high-frequency part with low energy, so as to reduce the amount of data.

[0090] Arithmetic coding: Further compress the frequency-domain data. By estimating the probability distribution of the occurrence of each coefficient, it is converted into a compact binary code to reduce the transmission volume.

[0091] As another preferred solution, in step 4 of the present invention, the way to obtain the feedback information of the transmission quality is: obtain the feedback information of the transmission quality through the detection module on the base station side or the passive sensor side.

[0092] As another preferred solution, the feedback information of the transmission quality described in the present invention includes bit error rate BER, packet loss rate PLR and link quality indicator LQI.

[0093] Secondly, in step 4 of the present invention, the way to obtain the feedback information of the energy utilization efficiency is: obtain the feedback information of the energy utilization efficiency through the energy detection module.

[0094] In addition, the energy detection module described in the present invention is used to monitor the output power of the microwave rectifier circuit.

[0095] Advantages of the present invention

[0096] By introducing context-aware modeling, dynamic redundancy threshold calculation, and hierarchical compression and transmission steps, the present invention can flexibly adjust the data compression strategy according to real-time environmental changes and feedback from the passive sensor status, thereby effectively improving the data transmission efficiency and reducing the waste of bandwidth and computing resources caused by redundant data; ensuring the efficiency, stability, and energy conservation of passive sensor data transmission in complex environments.

[0097] Through an accurate redundant data identification and filtering mechanism, the present invention can maintain the stability and reliability of data transmission in an environment with low signal-to-noise ratio, avoiding the bandwidth waste and network instability caused by improper processing of redundant data in traditional technologies. That is, assuming the sensor data is D = [d1, d2, …, d n , it is divided into multiple levels according to the redundancy degree, and each level has a different threshold for data redundancy. The redundancy degree R k of the k-th level is defined as:[[]]

[0098]

[0099] where n k represents the number of data points at the k-th level, d i is the original data point collected, is the prediction or expected value at the current level. The redundancy degree R k represents the data redundancy degree of this level.

[0100] The adjustment rule of the redundancy threshold T k is expressed by the following formula:[[]]

[0101]

[0102] where T k base is the basic redundancy threshold of this level; α k is the adjustment coefficient of this level, controlling the sensitivity of the redundancy threshold to environmental changes; T ref and H ref are set standard values; α T and α H are adjustment coefficients, controlling the influence degree of temperature and humidity changes on the threshold.

[0103] By hierarchical coding and data compression, the present invention optimizes the data compression process, further improves the data transmission efficiency, and overcomes the problems of insufficient stability and lack of adaptability of compression strategies in the prior art.

[0104] According to the sensor type and environmental changes, the present invention adopts a hierarchical coding technology to process data in layers according to redundancy. Different compression strategies are applied to each data layer according to its importance and redundancy degree, so as to optimize the use of transmission bandwidth while ensuring data integrity and accuracy. That is, passive temperature sensors are usually used to monitor relatively slowly changing environmental parameters, such as temperature. The collected data of such sensors changes little and has a low fluctuation range, so the processing process is relatively simple. Differential coding and Huffman static dictionary compression strategies are mainly adopted. Compression and transmission processing of passive vibration sensors: Passive vibration sensors are usually used to monitor relatively fast-changing environmental parameters, such as vibration. Since vibration data changes frequently and contains a lot of high-frequency information, the processing process is relatively complex. Discrete cosine transform (DCT) and arithmetic coding technologies are mainly adopted. In hierarchical coding, the redundancy threshold is dynamically adjusted according to real-time environmental parameter changes. For each layer, the redundancy threshold T k can be adjusted according to the following formula:

[0105]

[0106] where T k base is the basic redundancy threshold of this layer, and α k is the adjustment coefficient of this layer, which controls the sensitivity of the redundancy threshold to environmental changes. T ref and H ref are standard values set by the system. Usually, these values are measured under ideal conditions without special environmental changes. α T and α H are adjustment coefficients, which control the influence degree of temperature and humidity changes on the threshold.

[0107] In the data compression of vibration sensors, the present invention combines the dynamic adjustment of SSNR to retain the number of DCT coefficients, and determines which frequency domain coefficients to retain according to the signal-to-noise ratio. In this way, a small number of low-frequency components are retained under high SSNR conditions, while more frequency domain coefficients are retained under low SSNR conditions (traditional DCT does not consider the influence of environmental changes on compression efficiency).

[0108] The present invention can effectively identify and filter redundant data, realize the dynamic identification and filtering of redundant data, and avoid wasting bandwidth and computing resources. That is: Hierarchical redundancy quantization (core of step 2):

[0109] Divide the sensor data into multiple layers according to the prediction residual, and calculate the redundancy of each layer:

[0110]

[0111] where is generated by using the moving window mean prediction. When Rk When it is lower than the current dynamic threshold Tk, it is determined that the data of this layer is redundant data.

[0112] Environment adaptive threshold adjustment:

[0113] Dynamically correct the threshold according to the real-time signal-to-noise ratio (SSNR):

[0114]

[0115] When SSNR < 20dB, T k base Rises by 20% - 30%, relaxes the redundancy determination standard to ensure transmission stability; when SSNR > 25dB, T k base Falls by 10% - 15%, implements more stringent redundancy filtering.

[0116] Hierarchical compression execution:

[0117] R k > T k (High redundancy layer): Adopts differential coding + Huffman static dictionary compression, and the compression ratio is 5:1;

[0118] R k ≤T k (Low redundancy layer): Directly discard adjacent duplicate data points, and only retain data with a change amount exceeding ±0.5%. Description of the drawings

[0119] The present invention will be further described below in conjunction with the drawings and specific embodiments. The protection scope of the present invention is not limited only to the description of the following content.

[0120] Figure 1 It is the flowchart of the data transmission optimization system of the present invention. Specific embodiments

[0121] As shown in the figure, in the passive sensor network, data acquisition is the starting point of the entire system. Each passive sensor is responsible for real-time monitoring of specific environmental parameters, including temperature, humidity, and vibration parameters. In terms of power supply, the passive sensor works by receiving microwave energy from the base station and feeds back the collected data to the base station in the form of wireless signals. Since the passive sensor completely relies on external microwave power supply, the stability of the energy supply is highly coupled with the data transmission efficiency, and it is necessary to dynamically optimize to reduce the impact of energy dependence on data integrity.

[0122] To reasonably optimize data collection, the present invention adopts context-aware modeling. In this stage, first, the base station monitors environmental parameters in real time. The key parameters include signal-to-noise ratio (SSNR), multipath fading coefficient, environmental temperature, and humidity. Environmental changes will directly affect the data quality of sensors. The signal-to-noise ratio (SSNR) is particularly crucial. An environment with a low signal-to-noise ratio often leads to errors in data transmission.

[0123] The passive sensor converts the 2.4 GHz microwave energy transmitted by the base station into direct current through a microwave rectification circuit. The relationship between its conversion efficiency η and SSNR is:

[0124]

[0125] where is the received power, P tx is the transmitted power. This formula reveals the inherent coupling between energy supply and data transmission quality, and decoupling needs to be achieved through a dynamic threshold compensation mechanism.

[0126] Context awareness provides real-time feedback on the current environmental state, thereby dynamically adjusting the processing strategy for passive sensor data. Assume that X represents a set of key parameter vectors in the environment:

[0127] X = [SSNR, T env , H env , vibration freq_peak , vibration band_energy

[0128] These environmental parameters, namely the environmental temperature parameter, environmental relative humidity parameter, vibration peak frequency, and vibration bandwidth energy, will be input into the system model as context information to help judge data redundancy, transmission priority, and compression strategy. Based on the passive sensor ID, the system automatically identifies the type of passive sensor and loads the feature models applicable to different passive sensors. For temperature sensors, the loaded model is the temperature fluctuation model; for vibration sensors, the loaded model is the vibration frequency change model.

[0129] In the dynamic redundancy threshold calculation step, Context-aware Dynamic Threshold Layered Coding (CDT-LC) is adopted as the core of the data redundancy perception mechanism. The purpose is to dynamically adjust the data redundancy threshold by real-time feedback on environmental changes and sensor states, thereby achieving efficient data compression and transmission.

[0130] ​First, the system obtains the environmental parameters related to data transmission through the base station. The key parameters include signal-to-noise ratio (SSNR), multipath fading coefficient, environmental temperature, and humidity. These environmental parameters directly affect the validity and redundancy of data.

[0131] Set the initial redundancy threshold as T0 and adjust this threshold according to real-time environmental feedback. Assume the relationship between the dynamic redundancy threshold T and the environmental parameter X is:

[0132] T = T0 × f(X)

[0133] Where f(X) is a mapping function indicating how environmental parameters affect the redundancy threshold. For example, when the SSNR is low, to ensure stable data transmission, the redundancy threshold can be appropriately relaxed; while changes in temperature and humidity affect the fluctuation range of data, thus affecting the setting of the redundancy threshold.

[0134] One of the cores of CDT-LC is hierarchical coding, which allows the system to divide data into different levels according to the importance and redundancy of the data. At each level, the system compresses through different redundancy thresholds to preferentially transmit important data.

[0135] Assume the data of the sensor is D = [d1, d2, …, d n , and divide it into multiple levels according to the redundancy degree. Each level has a different threshold for data redundancy. The redundancy degree R k of the k-th level is defined as:

[0136]

[0137] Where n k represents the number of data points in the k-th level, d i is the original data point collected, is the prediction or expected value of the current level. The redundancy degree R k represents the data redundancy degree of this level.

[0138] In hierarchical coding, the redundancy threshold is dynamically adjusted according to real-time environmental parameter changes. For each level, the adjustment rule of the redundancy threshold T k can be expressed by the following formula:

[0139]

[0140] Where T k base is the base redundancy threshold of this level, and α k is the adjustment coefficient of this level, controlling the sensitivity of the redundancy threshold to environmental changes. T ref and H refis the standard value set by the system. Under normal circumstances, these values are measured under ideal conditions without special environmental changes. α T and α H are adjustment coefficients that control the degree of influence of temperature and humidity changes on the threshold value.

[0141] When the SSNR is low, the redundancy threshold will be appropriately increased to ensure effective data compression and transmission even in the case of poor signals. Changes in temperature and humidity also affect the dynamic adjustment of the redundancy threshold, enabling the system to be flexibly optimized under different environmental conditions.

[0142] After completing the calculation of data redundancy, the next task is to compress and transmit the valid data. According to the sensor type, data characteristics, and environmental status, different compression algorithms are selected to optimize the transmission efficiency, save bandwidth, reduce energy consumption, and ensure the integrity and stability of the data during transmission.

[0143] The data collected by the temperature sensor (low-frequency slow-varying) usually changes slowly and has small fluctuations. Therefore, for temperature data, a combination of differential coding and Huffman static dictionary is used for compression. The core of differential coding is to record the differences between adjacent data points instead of directly transmitting the original data values. Since the temperature data changes little, the differences between adjacent data points are usually small, which can significantly reduce the amount of data transmitted.

[0144] Assume the original temperature data sequence is D = [d1, d2, …, d n , and the process of its differential coding is as follows:

[0145] Δd i = d i - d i-1 , i = 2, 3, …, n

[0146] where d i is the i-th data point, d i-1 is the previous data point, and Δd i is the difference between data points. In this way, the original data sequence is transformed into a difference sequence. Through differential coding, the original data sequence is transformed into a difference sequence, thereby reducing the data fluctuations and facilitating compression.

[0147] On the basis of differential coding, the present invention uses the Huffman static dictionary for further compression. Huffman coding is a lossless compression algorithm that achieves the effect of compressing data by assigning shorter codes to symbols with higher frequencies and longer codes to symbols with lower frequencies. Huffman coding can assign shorter binary codes to frequently occurring data according to the signal frequency, thereby reducing redundancy and bandwidth requirements.

[0148] The data collected by the vibration sensor (high-frequency transient) changes rapidly and usually contains a large amount of high-frequency information. Since there is a large amount of high-frequency parts with low energy in the vibration signal, traditional compression methods cannot effectively reduce the data volume. Therefore, the present invention uses the discrete cosine transform (DCT) technology for frequency domain conversion and combines it with an adaptive coefficient selection method to optimize the data compression effect.

[0149] DCT is a commonly used signal compression technology, especially suitable for processing periodically changing data. By converting time-domain data into frequency-domain data, DCT can extract the main components of the signal and discard the high-frequency parts with low energy, thereby reducing the data volume. For the time-domain vibration data x n , the formula for DCT transformation is:

[0150]

[0151] where x n is the original vibration data point, and X k is the transformed frequency-domain data. By performing DCT transformation on the vibration data, the data can be converted from the time domain to the frequency domain to obtain the frequency component X k .

[0152] After obtaining the frequency-domain data X k , the system dynamically adjusts the number of DCT coefficients to be retained according to the current SSNR (signal-to-noise ratio). Generally, the data in the low-frequency part can better represent the main features of the signal, while the high-frequency part contains less energy and makes less contribution to the overall features of the signal.

[0153] Set a threshold to determine how many frequency-domain coefficients to retain according to the current SSNR. If the SSNR is high, the first 8 low-frequency coefficients are retained:

[0154] X k = [X0, X1, …, X7] (SSNR > 25 dB)

[0155] If the SSNR is low, the first 12 coefficients are retained:

[0156] X k = [X0, X1, …, X 11 (SSNR < 20 dB)

[0157] In this way, the system can dynamically adjust the compression ratio while ensuring the data transmission quality, improving the data compression efficiency.

[0158] For the retained low-frequency DCT coefficients, the present invention further compresses them using arithmetic coding. First, estimate the probability distribution of the occurrence of each coefficient. Let P(X k) represents the probability of the k-th low-frequency coefficient X k , and the probability distribution satisfies:

[0159]

[0160] Next, arithmetic coding divides the entire interval [0, 1) into subintervals, where each subinterval represents a symbol (coefficient). At initialization, low = 0 and high = 1.

[0161] For each symbol X k (the corresponding interval is [low k , high k ), update the interval:

[0162]

[0163]

[0164] Continue this operation until all symbols are encoded. The midpoint of the final output interval is used as the encoding result:

[0165] Encoded output = Binary representation of midpoint of [low, high]

[0166] In this way, arithmetic coding can convert DCT coefficients into a compact binary code, further reducing the amount of data storage and transmission.

[0167] The entire process of data acquisition, processing, compression, and transmission constitutes a highly integrated closed-loop control system. Each link coordinates with each other through a real-time feedback mechanism to ensure the efficiency and stability of data transmission. In this system, the operations and decisions of each step can be adjusted in real time according to environmental changes and data characteristics to cope with dynamic working conditions.

[0168] The core of closed-loop control lies in context-aware modeling. The system can dynamically adjust the processing method of redundant data according to the sensor type and current environmental conditions. Through real-time obtained environmental parameters, such as signal-to-noise ratio (SSNR), the system flexibly adjusts the redundancy standard to avoid unnecessary data transmission, while improving transmission efficiency and reducing energy consumption. As the environment changes, the calculation of the redundancy threshold can respond in a timely manner, relaxing the threshold when the signal is weak to ensure the stability of data transmission. On the contrary, when the signal is good, the amount of redundant data transmission is reduced to further improve efficiency.

[0169] The compression transmission strategy is also a key link in closed-loop control. The system will select an appropriate compression strategy based on real-time data characteristics and environmental conditions to optimize data transmission. When the redundancy is high, a stronger compression method is adopted; in adverse environments, lightweight compression is selected, that is:

[0170] If SSNR > 25dB, retain the first eight DCT coefficients:

[0171] X k = [X0, X1, …, X7] (SSNR > 25dB)

[0172] If SSNR < 20dB, retain the first twelve DCT coefficients:

[0173] X k = [X0, X1, …, X 11 (SSNR < 20dB).

[0174] For data in the low redundancy layer, during data transmission, the overall energy efficiency of the system is further optimized by adjusting the transmission frequency, bandwidth, and power.

[0175] Ensure the stability and accuracy of data transmission. The system dynamically adjusts the transmission frequency to adapt to changes in data volume and signal conditions, thereby avoiding unnecessary bandwidth waste and extending the service life of sensor devices.

[0176] The detection data of the passive sensor is D = [d1, d2, …, d n , which is divided into multiple levels according to the redundancy degree, and each level has a different threshold for data redundancy; the redundancy R k of the k-th level is:

[0177]

[0178] where n k represents the number of data points in the k-th level, d i is the original data point collected, is the prediction or expected value of the current level, and the redundancy R k represents the data redundancy degree of this level.

[0179] Preprocess the data and only transmit the data with practical significance, reducing the transmission burden of invalid data. This filtering mechanism can be automatically optimized according to the changes in real-time data flow and historical trends to ensure the efficiency of data transmission.

[0180] Finally, through the feedback mechanism of the closed-loop control system, the system can respond in real time to changes in signal quality and environmental conditions, flexibly adjust various operations, thereby maximizing data transmission efficiency and ensuring that the system can still operate stably even in complex environments. The present invention effectively guarantees the high efficiency and low power consumption characteristics of the system during long-term operation, and further improves the overall architecture of the passive sensor data transmission optimization system based on context awareness and CDT-LC dynamic redundancy threshold calculation.

[0181] Collect environmental parameters dynamically at the millisecond level, applicable to passive vibration sensors for monitoring mechanical shocks and passive temperature sensors for monitoring instantaneous temperature spikes. Fixed sampling windows such as 1ms, 10ms, 20ms, up to 1000ms can be adopted, and data continuity is ensured through timestamp alignment.

[0182] Collect environmental parameters stably at the second level, applicable to monitoring of temperature and humidity, slow-changing parameters such as normal operation of equipment, etc. Interval periodic sampling can be adopted at intervals of 1s, 2s, 3s, 10s, 100s, etc.

[0183] In the data compression and transmission of the passive temperature sensor, the binary code enters the periodic transmission buffer linkage through the low-frequency channel, reducing the channel occupancy rate to below 15%.

[0184] It can be understood that the above specific description of the present invention is only for explaining the present invention and is not limited to the technical solutions described in the embodiments of the present invention. Those of ordinary skill in the art should understand that the present invention can still be modified or equivalently replaced to achieve the same technical effect; as long as the use requirements are met, they are all within the protection scope of the present invention.

Claims

1. An optimization method for passive sensor data transmission, characterized in that It includes the following steps: Step 1: Monitor the environmental parameters of the passive sensor, and the environmental parameters serve as context information; Step 2: Calculate the dynamic redundancy threshold according to the context information; Step 3: Compress and transmit the data calculated in Step 2; Step 4: After the data is compressed and transmitted, obtain the feedback information of the transmission quality and the feedback information of the energy utilization efficiency, and return the feedback information to Step 2 to form a closed-loop control.

2. The method for optimizing the data transmission of a passive sensor according to claim 1, characterized in that The environmental parameters in Step 1 include the signal-to-noise ratio, the multipath fading coefficient, the environmental temperature, and the environmental humidity; the signal-to-noise ratio is the power ratio between the received signal and the environmental noise when the passive sensor sends to the base station or the receiving end; the multipath fading coefficient is the fading degree of the received signal due to the multipath propagation characteristics in the channel where the passive sensor is located.

3. The method for optimizing passive sensor data transmission according to claim 2, characterized in that The passive sensor converts the 2.4 GHz microwave energy transmitted by the base station into direct current through a microwave rectifier circuit, and the relationship between the conversion efficiency η and the SSNR is: Among them, is the received power; P tx is the transmitted power; k is a constant coefficient, comprehensively reflecting the conversion efficiency of the rectifier circuit, antenna coupling loss and device characteristics; The environmental parameter vector is: X = [SSNR, T env , H env , vibration freq_peak , vibration band_energy ​ T env is the environmental temperature parameter, H env is the environmental humidity parameter, vibration freq_peak is the vibration peak frequency, vibration band_energy is the vibration bandwidth energy; the environmental parameter vector serves as context information.

4. The method for optimizing the data transmission of a passive sensor according to claim 1, wherein Step 2 includes the relationship part between the environmental parameters and the redundancy threshold, the hierarchical coding and redundancy calculation part, and the dynamic adjustment of the redundancy threshold part; the environmental parameters determine the setting of the redundancy threshold, and the redundancy calculation affects the redundancy threshold adjustment strategy; by calculating the data redundancy, adjust the redundancy threshold according to the deviation between the redundancy and the current redundancy threshold; the updated redundancy threshold guides the data compression and transmission strategy.

5. The method for optimizing the data transmission of a passive sensor according to claim 4, wherein The relationship part between the environmental parameters and the redundancy threshold: Set the initial redundancy threshold as T0, and the relationship between the dynamic redundancy threshold T and the environmental parameter vector X is: T = T0 × f(X) Among them, f(X) is a mapping function, indicating how the environmental parameter vector X affects the redundancy threshold T; α SSNR , α T and α H respectively control the influence weights of SSNR, temperature, and humidity on the redundancy threshold. SSNR ref , T ref and H ref are reference values; SSNR ref : The signal-to-noise ratio measured by selecting an environment with no signal interference or low background noise; T ref : is the temperature value measured under standard experimental environmental conditions and is the ideal temperature value set during sensor testing; H ref : Select the standard humidity value measured under laboratory conditions.

6. The method for optimizing the data transmission of a passive sensor according to claim 4, wherein The hierarchical coding and redundancy calculation part: The detection data of the passive sensor is D = [d1, d2, …, d n , which is divided into multiple levels according to the redundancy degree, and each level has a different threshold for data redundancy; the redundancy degree R k of the k-th level is as follows: Among them, n k represents the number of data points at the k-th level, d i is the original data point collected, is the prediction or expected value at the current level, and the redundancy R k represents the data redundancy degree at this level.

7. The method for optimizing the data transmission of a passive sensor according to claim 4, characterized in that The dynamic adjustment of the redundancy threshold part: Redundancy threshold T k The adjustment rule is expressed by the following formula: Among them, T k base is the basic redundancy threshold of this level; ɑ k is the adjustment coefficient of this level, controlling the sensitivity of the redundancy threshold to environmental changes; T ref and H ref are the set standard values; ɑ T and ɑ H are adjustment coefficients, controlling the influence degree of temperature and humidity changes on the threshold.

8. The passive sensor data transmission optimization method according to claim 7, wherein When SSNR < 20 dB, T k base rises by 20% - 30%; When SSNR > 25 dB, T k base decreases by 10% to 15%.

9. The method for optimizing the data transmission of a passive sensor according to claim 1, wherein In Step 3, the data compression and transmission of the passive temperature sensor is as follows: The original temperature data sequence is D = [d1, d2, …, d n , and the process of its differential coding is as follows: Δd i = d i - d i-1 , i = 2, 3, …, n where d i is the i-th data point, d i-1 is the previous data point, and Δd i is the difference between data points; the original data sequence is transformed into a difference sequence; Based on differential coding, further compression is performed using a Huffman static dictionary. Statistically analyze the frequencies of the data points after differential coding, and convert the data points into binary codes through frequency mapping to complete data compression; the compressed data is transmitted through the communication link between the passive temperature sensor and the receiving end.

10. The method for optimizing the data transmission of a passive sensor according to claim 1, characterized in that In Step 3, the data compression and transmission of the passive vibration sensor is as follows: Time-domain vibration data x n , and the formula for DCT transformation is: Among them, X k is the transformed frequency-domain data; P(X k ) is the probability of the k-th low-frequency coefficient X k , representing the frequency at which the k-th coefficient appears among all frequency-domain coefficients; P(X k ) distribution satisfies: Arithmetic coding divides the entire interval [0,1) into sub-intervals, and each sub-interval represents a coefficient. At initialization, low = 0, high = 1; For each coefficient X k , the corresponding interval is [low k , high k , update the interval: Continue this operation until all coefficients are encoded; the midpoint of the final output interval is used as the coding result: Encodedoutput = Binaryrepresentationofmidpointof[low,high].

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