Material management method and system based on Beidou positioning system

Abstract

The invention discloses a material management method and system based on a Beidou positioning system, and relates to a positioning and signal processing technology of a warehouse management system. Firstly, storage environment information is obtained, a signal propagation model is established, and a signal multi-path effect and attenuation are calculated. And then satellite signals are processed, adaptive filtering and interference processing are carried out, power consumption is adjusted according to the equipment state, and hardware reliability is evaluated. Meanwhile, AES is adopted to encrypt data and transmit the data according to a protocol, a data interaction interface is established, equipment abnormity is detected through a random forest model, and finally equipment distribution is optimized by applying a K-means clustering algorithm, so that the positioning precision, the equipment reliability and the overall performance of the system can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of material positioning management in the power industry, and particularly relates to a material management method and system based on the Beidou positioning system. Background Art

[0002] The Beidou online positioning technology utilizes the positioning function of the Beidou satellite navigation system to achieve real-time positioning and tracking of objects through computer technology. In modern warehouse management, the Beidou positioning technology has been widely used. Through Beidou positioning, the position information of materials can be obtained in real time for remote tracking and monitoring. Whether inside the warehouse or during transportation, as long as the materials are connected to the Beidou system, managers can view the real-time position of the materials through the specified software or platform. The real-time tracking and monitoring function helps to promptly discover abnormal situations of materials, such as loss, damage, or misdelivery, etc., so as to take corresponding handling measures to ensure the safety and integrity of the materials.

[0003] In complex warehouse management scenarios, positioning devices face many technical difficulties. The main problems are as follows: There are usually a large number of metal devices and shelves in the warehouse environment, and these objects will cause serious multipath effects and signal attenuation to satellite signals, resulting in a decrease in positioning accuracy.

[0004] In addition, there may be electromagnetic interference inside the warehouse, which further affects the positioning performance. Secondly, warehouse management poses strict requirements on the power consumption of positioning devices. The devices need to work continuously for a long time, but limited by the battery capacity, how to extend the battery life while meeting the positioning performance is an urgent problem to be solved. Moreover, positioning devices in the warehouse scenario also need to cope with harsh environmental conditions, such as high temperature, humidity, vibration, etc., which poses challenges to the hardware reliability of the devices. At the same time, the warehouse management system usually involves a large amount of sensitive data, and the positioning devices must adopt strict data encryption and secure transmission mechanisms to ensure the confidentiality and integrity of the data. Finally, in practical applications, the positioning devices also need to be seamlessly integrated with the warehouse management system to achieve real-time data interaction and synchronization, which puts forward higher requirements for the communication protocols and interface standards of the devices. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a material management method and system based on the Beidou positioning system. The present invention overcomes the multipath effects and signal attenuation caused by metal devices and shelves in the warehouse environment through signal processing and modeling, and improves the positioning accuracy.

[0006] To solve the above technical problems, the technical solutions adopted by the present invention are as follows: A material management method and system based on the Beidou positioning system, the steps are as follows: S1 Obtain the distribution information of metal devices and shelves in the storage environment, combine with the preset positions of electromagnetic interference sources, establish a signal propagation model, and calculate the signal multipath effect and attenuation degree; S1.1 Obtain the position distribution information of metal devices and shelves in the storage environment, and use it as the basic data for establishing the signal propagation model; S1.2 According to the preset electromagnetic interference source position information, set corresponding interference nodes in the signal propagation model; S1.3 Through the ray tracing algorithm, simulate the signal propagation path in the storage environment, and calculate the multipath effects such as reflection and diffraction that the signal suffers during propagation; S1.4 Adopt empirical models and machine learning algorithms to calculate the attenuation degree of the signal on different paths according to factors such as signal propagation distance and obstacle material; S1.5 Combine the calculation results of the multipath effect and attenuation degree to obtain the signal strength distribution at different positions in the storage environment; S1.6 Compare the signal strength distribution with the preset threshold, judge which areas may be affected by electromagnetic interference, and mark the potential blind areas; S1.7 According to the blind area distribution, optimize the layout of wireless devices, such as adjusting the antenna direction and adding relay nodes, etc., to improve the overall signal coverage quality of the storage environment.

[0007] S2 According to the signal propagation model, use an adaptive filtering algorithm to process the received satellite signal. If the signal strength is lower than the preset threshold, start the signal enhancement module to compensate for signal attenuation; S2.1 According to the satellite signal propagation model, establish a signal strength prediction model to obtain the predicted signal strength value; S2.2 Obtain the received satellite signal, and use an adaptive filtering algorithm to filter the satellite signal to obtain the filtered satellite signal; S2.3 Judge whether the signal strength of the filtered satellite signal is lower than the preset threshold. If it is lower than the preset threshold, start the signal enhancement module, otherwise do not start the signal enhancement module; S2.4 When starting the signal enhancement module, calculate the signal attenuation amount according to the predicted signal strength value and the actually received signal strength value; S2.5 According to the calculated signal attenuation amount, adaptively adjust the compensation coefficient of the signal enhancement module to obtain the adjusted compensation coefficient; S2.6 Use the adjusted compensation coefficient to perform signal compensation on the filtered satellite signal to obtain the compensated satellite signal; S2.7 Output the compensated satellite signal as the finally processed satellite signal for subsequent signal demodulation, data parsing, and other processing.

[0008] S3 For the electromagnetic interference problem, adopt the frequency-domain filtering method to separate the interference components from the received signal, obtain a pure positioning signal, and improve the positioning accuracy. Frequency-domain filtering and positioning calculation formula: ; Where: : Represents the frequency-domain signal after performing Fourier transform on the time-domain signal . The Fourier transform decomposes the time-domain signal into a combination of sine and cosine waves of different frequencies, enabling the analysis of the frequency components of the signal in the frequency domain.

[0009] : Is the positioning signal containing electromagnetic interference received in the warehousing environment. It is a function of time , indicating the signal strength or amplitude received at different times. In the warehousing environment of the present invention, can be expressed as , where: : Is the amplitude function of the signal, which changes with time and is affected by various factors in the warehousing environment, such as the reflection and absorption of the signal by metal equipment and shelves. Its unit depends on the specific signal strength representation, for example, it can be volts (V) or millivolts (mV).

[0010] : Is the center frequency of the positioning signal, which is a fixed frequency determined by the positioning system used (such as the Beidou positioning system), and the unit is Hertz (Hz).

[0011] : Is the phase function of the signal, which changes with time due to the multipath effect during signal propagation, and the unit is radians (rad).

[0012] : Is the interference signal generated by the electromagnetic interference source in the warehousing environment and is also a function of time. Its characteristics depend on the type and intensity of the interference source and may be periodic or random. The unit is the same as .

[0013] : Imaginary unit, satisfying , and is used to represent the imaginary part in the complex frequency-domain signal. It is an essential element for performing Fourier transform and frequency-domain analysis.

[0014] : is the frequency variable in the Fourier transform, and its range is usually determined according to the signal frequency range of the positioning system. For example, for a satellite positioning system, it may be in the frequency band of [1.5 GHz, 1.6 GHz], with the unit of Hertz (Hz).

[0015] : represents the time variable, covering the entire time period from the start of receiving the signal to the end of receiving the signal, with the unit of seconds (s).

[0016] S3.1 Obtain the received signal containing electromagnetic interference, preprocess the received signal to eliminate noise and distortion in the signal, and obtain the preprocessed signal; S3.2 Convert the preprocessed signal to the frequency domain, using the Fourier transform algorithm to obtain the frequency domain representation of the signal; Specifically, when converting the preprocessed signal to the frequency domain, first preprocess the received signal in S3.1 to eliminate noise and distortion, and obtain a relatively clean , and then convert it to a frequency domain signal through this formula , so as to better analyze the frequency composition of the signal in the frequency domain and find out the frequency characteristics of the interference signal: ; Where: : is the pure signal after frequency domain filtering and converted back to the time domain, that is, the time domain signal after removing interference, and its unit is the same as the same.

[0017] : represents the inverse Fourier transform, which is an operation to convert the frequency domain signal back to the time domain signal. It is the inverse operation of the Fourier transform, enabling us to re-obtain the time domain signal from the frequency domain signal.

[0018] : is the frequency response function of the frequency domain filter, determined according to the preset interference frequency range. For an ideal band-stop filter, it can be expressed as: , when or (outside the expected signal frequency range).

[0019] , when (within the interference frequency range).

[0020] Here is the frequency variable, used to represent the frequency of the frequency domain filter, with the unit of Hertz (Hz), and are the lower and upper limits of the known interference frequency range, also in Hertz (Hz).

[0021] S3.3 In the frequency domain, according to the preset interference frequency range, set the filtering parameters of the frequency-domain filter to determine the interference frequency components to be filtered out; S3.4 Apply the frequency-domain filter to the frequency-domain signal to filter out the interference frequency components and obtain the filtered frequency-domain signal; Specifically, in the frequency domain, after setting according to the preset interference frequency range then multiply with to filter out the interference frequency components and obtain the filtered frequency-domain signal.

[0022] S3.5 Convert the filtered frequency-domain signal back to the time domain using the inverse Fourier transform algorithm to obtain the pure time-domain signal after removing interference; Specifically, use to perform the inverse Fourier transform on the filtered frequency-domain signal to obtain the after removing interference for subsequent feature extraction and positioning calculation.

[0023] S3.6 Extract features from the pure time-domain signal to obtain the key feature parameters of the signal, such as signal strength and arrival time, etc., for subsequent positioning calculation; S3.7 Input the extracted signal feature parameters into the positioning algorithm model, combine with the location information of the positioning base station, and calculate the accurate position coordinates of the target through methods such as triangulation to achieve high-precision positioning.

[0024] S4 Dynamically adjust the positioning frequency and the power consumption of the signal processing module according to the battery capacity and the working state of the positioning device. If the device is in a low-battery state, reduce the positioning frequency to extend the battery life; S4.1 Obtain the current battery power information and working state information of the positioning device, compare the power information with the preset power threshold to determine whether the device is in a low-battery state; S4.2 If the device is in a low-battery state, select the corresponding frequency adjustment scheme in the preset positioning frequency adjustment strategy according to the percentage of the battery power to obtain the positioning frequency value to be adjusted; S4.3 Dynamically adjust the working frequency of the positioning module according to the obtained positioning frequency value to reduce the number of positioning operations and thus reduce the power consumption of the positioning module; S4.4 For the signal processing module, reduce the computational amount and complexity of signal processing by reducing the signal sampling rate and the dimension of signal feature extraction, etc., to reduce the power consumption of the signal processing module; S4.5 After adjusting the positioning frequency and signal processing method, continuously monitor the change of the device's battery power, and dynamically optimize the frequency adjustment strategy and signal processing strategy according to the power change trend; In S4.6, the Kalman filtering algorithm is adopted to filter the positioning data obtained after reducing the frequency, reduce the fluctuations and noise of the positioning data, and improve the positioning accuracy; In S4.7, through machine learning algorithms such as decision trees or support vector machines, according to the historical power data and the effect of the positioning frequency adjustment strategy, the frequency adjustment strategy is adaptively optimized to find a balance between extending the battery life and ensuring the positioning accuracy.

[0025] In S5, a hardware redundancy design and environmentally adaptable materials are adopted. For high-temperature, humid, and vibration conditions, a hardware reliability evaluation model is established in advance to judge the operating state of the device in different environments; In S5.1, environmental parameter data such as high temperature, humidity, and vibration in which the device is located are obtained and used as input features; In S5.2, according to the material selection attributes, the material characteristic parameters of the hardware device and components are obtained as input features; In S5.3, the redundancy parameter of the hardware redundancy design scheme is used as an input feature; In S5.4, the above environmental parameters, material characteristic parameters, and redundancy parameters are input into the pre-established hardware reliability evaluation model; In S5.5, the support vector machine algorithm is used to classify the input features to judge the reliability level of the device in the current environment; In S5.6, if the reliability level is lower than the preset threshold, it is judged that the operating state of the device in the current environment is a high-risk state; In S5.7, according to the high-risk operating state of the device, the hardware redundancy and the parameters of the environmentally adaptable materials are automatically adjusted to improve the device reliability until the reliability level meets the requirements.

[0026] In S6, the data generated by the positioning device is obtained, and the data is encrypted using the AES encryption algorithm. Combined with the preset secure transmission protocol, the encrypted data is transmitted to the warehouse management system; Data Encryption and Transmission Formula: ; Where: : represents the encrypted data, which is the result after being processed by the AES encryption algorithm. It is usually represented in binary form and can be regarded as a byte sequence.

[0027] : is an Advanced Encryption Standard (AES) algorithm, which is a symmetric encryption algorithm used to encrypt data to ensure the confidentiality of the data.

[0028] : is the encryption key, which is a binary sequence of a fixed length. Depending on the different modes of the AES encryption algorithm, it can be 128 bits, 192 bits, or 256 bits. For example, in the 128-bit AES mode, , where are binary digits (0 or 1).

[0029] : is the initialization vector, which is a randomly generated byte sequence used to increase the randomness of encryption. In different encryption operations, even with the same plaintext and key, using different will produce different ciphertexts. Its length is usually determined according to the block size of the AES algorithm. For example, in some modes of AES, it can be 16 bytes, that is , are bytes.

[0030] : is the cleaned valid data. In the present invention, it is the data obtained after the original data collected by the positioning device is preprocessed (removing noise data) in S6.1. It can be structured data, such as a data set containing location information (longitude, latitude, altitude), timestamp, and device status. For example, `{ "latitude": 34.56, "longitude":123.45, "timestamp": 1642000000, "device_status": "active"}`. The data type can be JSON or other data structures according to the actual storage format.

[0031] S6.1 Obtain the original data collected by the positioning device, preprocess the original data, remove the noise data, and obtain the cleaned valid data; S6.2 According to the preset AES encryption algorithm, set the encryption key and the initialization vector, and perform encryption processing on the cleaned valid data to obtain the encrypted data; In this sub-step, use the AES algorithm to perform an encryption operation on , and generate the encrypted data and as parameters. .

[0032] S6.3 According to the preset secure transmission protocol, set parameters such as the target address, port number, and timeout time for data transmission, and establish a network connection with the warehouse management system; S6.4 Package the encrypted data according to the format requirements of the secure transmission protocol to generate a data packet that conforms to the protocol specification; S6.5 Through the established network connection, transmit the encapsulated data packets to the specified interface of the warehouse management system and wait for the response of the receiving system; S6.6 If the confirmation response from the warehouse management system is received within the preset timeout period, it is determined that the data transmission is successful; otherwise, retransmission processing is performed; S6.7 According to the business requirements of the warehouse management system, decrypt the received encrypted data, and store the decrypted data in the system database for subsequent business applications.

[0033] S7 According to the communication protocol of the warehouse management system, pre-establish a data interaction interface, and obtain the status information of the positioning device in real time. If the data interaction is abnormal, start the backup communication channel; S7.1 According to the pre-established communication protocol of the warehouse management system, use the Socket communication method to establish a TCP connection to implement the data interaction interface between the system and the positioning device; S7.2 Through the data interaction interface, obtain the status information reported by the positioning device in the form of a JSON format data packet in real time, and parse the JSON data packet to extract the key fields; S7.3 If the status information data packet of the positioning device is not received three times in a row, or the data packet parsing fails, it is determined that the data interaction is abnormal; S7.4 According to the preset backup communication parameters, switch to the backup communication channel, re-establish the Socket connection, and resume data interaction; S7.5 Adopt a heartbeat mechanism to periodically send a heartbeat request data packet to the positioning device. If the heartbeat response is not received, it is judged that the communication is abnormal, and the backup channel is enabled; S7.6 Through statistical analysis of the obtained status information of the positioning device, obtain key parameters such as the device working status and power, and display them in real time in the form of a curve graph; S7.7 Write the status information of the positioning device into the MongoDB database, and build a historical database of the status information based on the time series for big data analysis and fault diagnosis.

[0034] S8 Use the random forest model in the machine learning algorithm to classify the operation data of the positioning device, and judge whether there are hardware failures or signal abnormalities in the device. If an abnormality is detected, trigger the warning mechanism; Device failure and signal abnormality detection formula: ; Where: : is the judgment result of whether there are hardware failures or signal abnormalities in the device. It can be a classification label. For example, indicates that the device is normal, Indicates the existence of a hardware fault, Indicates the existence of a signal anomaly, Indicates other abnormal situations, and its value depends on the classification result.

[0035] : It is a Random Forest model, which is an ensemble learning algorithm based on decision trees. It is used to classify the operation data of the device and make a comprehensive judgment based on the results of multiple decision trees to improve the accuracy and robustness of classification.

[0036] : It is a set of key feature parameters extracted after preprocessing the operation data of the positioning device, and can be expressed as , where: : It is the temperature of the device, measured by a temperature sensor, reflecting the working temperature environment of the device, with the unit of degree Celsius (°C).

[0037] : It is the voltage of the device, measured from the power supply module of the device, reflecting the power supply status of the device, with the unit of volt (V).

[0038] : It is the current of the device, measured by a current sensor, reflecting the working current of the device, with the unit of ampere (A).

[0039] : It is the received positioning signal strength, reflecting the signal reception quality, with the unit of decibel-milliwatt (dBm).

[0040] : It is the number of error codes of the device, obtained from the error log or status register of the device, reflecting the error conditions generated during the operation of the device, and is an integer.

[0041] S8.1 Obtain the operation data of the positioning device, preprocess the data, and extract key feature parameters; S8.2 According to the preprocessed data, use the random forest model to classify the operation status of the device to obtain the judgment result on whether the device has a hardware fault or a signal anomaly In this sub-step, the feature parameters extracted in S8.1 are input into the random forest model to obtain the classification result of the operation status of the device to determine whether the device has a hardware fault or a signal anomaly.

[0042] S8.3 If the judgment result indicates that the device has a hardware fault or a signal anomaly, trigger the warning mechanism to generate a warning message; S8.4 By analyzing the historical operation data of the equipment, a feature library of equipment faults and abnormal signals is established to optimize the classification performance of the random forest model; S8.5 The random forest model is evaluated using the cross-validation method, and the model parameters are adjusted according to the evaluation results to improve the classification accuracy of the model; S8.6 The optimized random forest model is deployed into the positioning device monitoring system to classify and judge the equipment operation data in real time, and to detect equipment faults and abnormal signals in a timely manner; S8.7 According to the warning information, combined with the feature library of equipment faults and abnormal signals, the causes of equipment faults are analyzed to provide a basis for subsequent maintenance and optimization.

[0043] S9 Based on the real-time interaction data, the K-means clustering algorithm is used to optimize the distribution of equipment in the storage environment. If the equipment distribution density is too high, the equipment position is adjusted to reduce signal interference; Equipment layout optimization formula:

[0044] Where: : is the clustering result obtained by clustering the real-time interaction data of the equipment through the Kmeans clustering algorithm, usually represented as a set of clustering clusters, and each cluster contains the data of a group of similar equipment. is the clustering result obtained by clustering the real-time interaction data of the equipment through the Kmeans clustering algorithm, usually represented as a set of clustering clusters, and each cluster contains the data of a group of similar equipment.

[0045] : is the Kmeans clustering algorithm, which divides the data into clusters, so that the data points within the clusters have high similarity, while the data points between the clusters have high differences, and the clustering is achieved by minimizing the distance from the data points to the cluster centers.

[0046] : is the real-time interaction data of the equipment in the storage environment, including information such as equipment location and communication signal strength, and can be represented in matrix form: , Where is the th equipment's position coordinates, and the unit can be meters (m) according to the actual storage layout, is the signal strength received by this equipment, and the unit is decibel milliwatt (dBm).

[0047] S9.1 Obtain the real-time interaction data of the equipment in the storage environment, including information such as equipment location and communication signal strength; S9.2 According to the obtained real-time interaction data, use the K-means clustering algorithm to cluster the equipment, and divide the equipment with similar characteristics into the same category; In this sub-step, based on the real-time interaction data of the devices obtained in S9.1 , the Kmeans algorithm is used to divide the devices into different clusters for subsequent evaluation of the rationality of the device layout and optimization: ; Where: is the optimal layout plan of the device layout, which is the final device layout result optimized by the simulated annealing algorithm, including the position information of the devices, and usually stores the position coordinates of the devices in the form of a matrix or data structure.

[0048] is the simulated annealing algorithm, which is a heuristic optimization algorithm. By simulating the physical annealing process, it accepts worse solutions with a certain probability to avoid falling into local optimal solutions, so as to find the global or approximate global optimal layout plan.

[0049] is a set of multiple possible device layout plans generated by the genetic algorithm, which can be expressed as , where: represents the th layout plan position coordinates of the devices, with the unit of meter (m). Each layout plan is different and is generated by the genetic algorithm in S9.5 according to certain genetic operations (such as crossover, mutation).

[0050] For each clustering result in S9.3, analyze the distribution density of the devices within this category, and calculate the average distance and signal interference intensity between the devices; In S9.4, if the distribution density of the devices within a certain cluster is too high, the average distance is less than the preset threshold, and the signal interference intensity is greater than the preset threshold, it is determined that the device layout in this area needs to be optimized; In S9.5, for the area that needs to be optimized, multiple possible device layout plans are generated by the genetic algorithm, and each plan contains the new position coordinates of the devices; In S9.6, the simulated annealing algorithm is used to optimize the generated layout plans. By iteratively adjusting the device positions, the average distance between the devices is maximized and the signal interference is minimized to obtain the optimal layout plan; In this sub-step, the simulated annealing algorithm is used to optimize multiple layout plans in .

[0051] S9.7 According to the optimal layout plan, adjust the actual positions of the equipment in the storage environment, update the equipment location information, and complete the optimization of the equipment layout.

[0052] A material management system based on the Beidou positioning system adopts the described material management method based on the Beidou positioning system. The system includes: A signal propagation modeling module for obtaining the distribution information of metal equipment and shelves in the storage environment, combining the preset positions of electromagnetic interference sources, establishing a signal propagation model, and calculating the signal multipath effect and attenuation degree; a signal processing and enhancement module for processing the received satellite signals according to the signal propagation model using an adaptive filtering algorithm, and if the signal strength is lower than the preset threshold, starting the signal enhancement module to compensate for signal attenuation; an electromagnetic interference suppression module for separating the interference components from the received signals using a frequency domain filtering method to obtain pure positioning signals and improve the positioning accuracy in response to electromagnetic interference problems; a power consumption management module for dynamically adjusting the positioning frequency and the power consumption of the signal processing module according to the battery capacity and the working state of the positioning device, and if the device is in a low power state, reducing the positioning frequency to extend the battery life; a hardware reliability evaluation module for using hardware redundancy design and environmentally adaptable materials to pre-establish a hardware reliability evaluation model for high temperature, humidity, and vibration conditions to judge the operating state of the device in different environments; a data encryption and transmission module for obtaining the data generated by the positioning device, encrypting the data using the AES encryption algorithm, and transmitting the encrypted data to the warehouse management system in combination with the preset secure transmission protocol; a data interaction and communication module for pre-establishing a data interaction interface according to the communication protocol of the warehouse management system to obtain the status information of the positioning device in real time, and if the data interaction is abnormal, starting the backup communication channel; a fault detection and warning module for classifying the operating data of the positioning device using the random forest model in machine learning algorithms to judge whether there are hardware faults or signal abnormalities in the device, and if an abnormality is detected, triggering a warning mechanism; an equipment distribution optimization module for optimizing the distribution of equipment in the storage environment using the K-means clustering algorithm according to the real-time interaction data, and if the equipment distribution density is too high, adjusting the equipment positions to reduce signal interference.

[0053] The present invention can achieve the following beneficial effects: 1. Through signal processing and modeling, the present invention overcomes the multipath effect and signal attenuation caused by metal equipment and shelves in the storage environment, and improves the positioning accuracy. 2. The present invention adjusts the power consumption according to the device state and extends the battery life while ensuring the positioning performance. 3. The present invention uses hardware reliability evaluation and adaptability design to ensure the stable operation of the device in harsh environments such as high temperature, humidity, and vibration. 4. The present invention adopts strict data encryption and secure transmission mechanisms to ensure the confidentiality and integrity of sensitive data. Fifthly, it optimizes communication protocols and interfaces to achieve seamless integration and real-time data interaction and synchronization between the device and the warehouse management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The present invention will be further described below with reference to the drawings and embodiments: Figure 1 is the flowchart of the method of the present invention; Figure 2 is the data conduction flowchart of the present invention; Figure 3 is the system structure diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] The preferred solution is as Figures 1 to 3 shown. A material management method based on the Beidou positioning system has the following steps: S1. Obtain the distribution information of metal devices and shelves in the warehouse environment, combine it with the preset positions of electromagnetic interference sources, establish a signal propagation model, and calculate the signal multipath effect and attenuation degree.

[0056] Obtain the position distribution information of metal devices and shelves in the warehouse environment and use it as the basic data for establishing the signal propagation model. According to the preset position information of electromagnetic interference sources, set corresponding interference nodes in the signal propagation model. Through the ray tracing algorithm, simulate the propagation path of the signal in the warehouse environment and calculate the multipath effects such as reflection and diffraction that the signal suffers during propagation. Adopt empirical models and machine learning algorithms to calculate the attenuation degree of the signal on different paths according to factors such as signal propagation distance and obstacle material. Combine the calculation results of the multipath effect and attenuation degree to obtain the signal strength distribution at different positions in the warehouse environment. Compare the signal strength distribution with the preset threshold to judge which areas may be affected by electromagnetic interference and mark the potential blind areas. According to the blind area distribution, optimize the layout of wireless devices, such as adjusting the antenna direction, adding relay nodes, etc., to improve the overall signal coverage quality of the warehouse environment.

[0057] For example, first, use a 3D laser scanner to comprehensively scan the warehousing environment to obtain point cloud data, and convert it into a three-dimensional model through point cloud processing software. Mark the specific position coordinates of metal equipment and shelves in the model. For example, the shelves in Area A are located at (5m, 7m, 8m). Then, according to actual measurements, set 2 interference source nodes in the model, located at (2m, 8m, 5m) and (1m, 3m, 1m) respectively. Use the ray tracing algorithm to emit a ray every 1° starting from the antenna of the wireless device to simulate the signal propagation path. When the ray encounters an obstacle, calculate the signal loss caused by reflection and diffraction according to the Fresnel formula and the diffraction integral formula until the ray reaches the receiving end. At the same time, adopt the Okumura-Hata empirical model to estimate the free space propagation loss, and use the support vector machine algorithm to correct the signal attenuation according to the influence of the obstacle material to obtain the corrected path loss value. Superimpose the received power of all rays to obtain the signal strength distribution map of each position in the warehousing environment. Compare the signal strength with the coverage threshold of -85dBm, and mark the area below the threshold as a blind area. For example, the signal strength at (2m, 9m, 6m) in Area C is only -92dBm. Finally, according to the blind area distribution, optimize the layout of the wireless device, move the antenna from the original (8m, 1m, 4m) to (3m, 4m, 7m), and add a relay node at (5m, 6m, 8m). After simulation verification, the overall signal coverage quality of the optimized warehousing environment reaches 97%, meeting the actual application requirements.

[0058] S2. According to the signal propagation model, use an adaptive filtering algorithm to process the received satellite signal. If the signal strength is lower than the preset threshold, start the signal enhancement module to compensate for the signal attenuation.

[0059] According to the satellite signal propagation model, establish a signal strength prediction model to obtain the predicted signal strength value. Obtain the received satellite signal, and use an adaptive filtering algorithm to filter the satellite signal to obtain the filtered satellite signal. Determine whether the signal strength of the filtered satellite signal is lower than the preset threshold. If it is lower than the preset threshold, start the signal enhancement module; otherwise, do not start the signal enhancement module. When starting the signal enhancement module, calculate the signal attenuation amount according to the predicted signal strength value and the actually received signal strength value. According to the calculated signal attenuation amount, adaptively adjust the compensation coefficient of the signal enhancement module to obtain the adjusted compensation coefficient. Use the adjusted compensation coefficient to perform signal compensation on the filtered satellite signal to obtain the compensated satellite signal. Output the compensated satellite signal as the finally processed satellite signal for subsequent signal demodulation and data parsing and other processes.

[0060] For example, according to the satellite signal propagation model, a signal strength prediction model is established. By comprehensively analyzing factors such as satellite orbit parameters, atmospheric refractive index, and ionospheric delay, the predicted signal strength value is obtained. After acquiring the received satellite signal, an adaptive filtering algorithm is used to filter the satellite signal. The adaptive filtering algorithm automatically adjusts the filter parameters according to the statistical characteristics of the signal, effectively removing the Gaussian white noise in the signal and obtaining a filtered satellite signal with a signal-to-noise ratio increased by 6 dB. It is judged whether the strength of the filtered satellite signal is lower than the preset threshold of -130 dBm. If it is lower than this threshold, the signal enhancement module is activated. According to the predicted signal strength value and the actually received signal strength value, the signal attenuation amount is calculated to be 8 dB. According to the signal attenuation amount, the compensation coefficient of the signal enhancement module is adaptively adjusted. Through the least mean square error algorithm, the adjusted compensation coefficient is obtained as 8. The filtered satellite signal is compensated using the adjusted compensation coefficient. The compensation process adopts a method combining nonlinear amplification and spectral equalization. While increasing the signal strength, it ensures that the spectral characteristics of the signal are not damaged. Finally, a compensated satellite signal with a signal-to-noise ratio increased by 12 dB is obtained. The compensated satellite signal is output as the finally processed high-quality satellite signal for subsequent BPSK demodulation, error control coding, etc., ensuring the reliability and stability of satellite communication.

[0061] S3. For the electromagnetic interference problem, a frequency-domain filtering method is adopted to separate the interference components from the received signal, obtain a pure positioning signal, and improve the positioning accuracy.

[0062] The received signal containing electromagnetic interference is acquired, and the received signal is preprocessed to eliminate the noise and distortion in the signal, obtaining the preprocessed signal. The preprocessed signal is transformed into the frequency domain using the Fourier transform algorithm to obtain the frequency-domain representation of the signal. In the frequency domain, according to the preset interference frequency range, the filtering parameters of the frequency-domain filter are set to determine the interference frequency components to be filtered out. The frequency-domain filter is applied to the frequency-domain signal to filter out the interference frequency components, obtaining the filtered frequency-domain signal. The filtered frequency-domain signal is transformed back into the time domain using the inverse Fourier transform algorithm to obtain the pure time-domain signal after removing the interference. Feature extraction is performed on the pure time-domain signal to obtain the key feature parameters of the signal, such as signal strength, arrival time, etc., for subsequent positioning calculations. The extracted signal feature parameters are input into the positioning algorithm model. Combining with the position information of the positioning base station, the accurate position coordinates of the target are calculated through methods such as triangulation to achieve high-precision positioning.

[0063] For example, first, preprocess the received signal containing electromagnetic interference. Use an adaptive filtering algorithm, such as the LMS algorithm, to adaptively adjust the filter coefficients according to the statistical characteristics of the signal, effectively eliminating Gaussian white noise and impulse interference, and increasing the signal-to-noise ratio by more than 10 dB. Then, use the fast Fourier transform (FFT) algorithm to transform the preprocessed signal into the frequency domain. By analyzing the spectral characteristics of the signal, it is determined that the interference frequencies are mainly concentrated between 30 MHz and 50 MHz. According to the interference frequency range, design a Butterworth band-stop filter with a stopband of 30 MHz to 50 MHz and a stopband attenuation greater than 40 dB, and apply it to the frequency-domain signal to effectively filter out the interference frequency components. After filtering, use the inverse fast Fourier transform (IFFT) algorithm to transform the frequency-domain signal back to the time domain to obtain a pure time-domain signal. Extract the features of this signal. Through matched filtering and threshold decision-making, extract the arrival time and intensity features of the signal, where the measurement accuracy of the arrival time is better than 10 ns, and the signal-to-noise ratio of the intensity measurement is greater than 20 dB. Finally, input the extracted signal features into the Chan algorithm for positioning calculation. Combine more than 3 positioning base stations and solve the nonlinear equations to obtain the three-dimensional coordinates of the target, with a positioning accuracy better than 1 m. By comprehensively applying the above algorithms and processes, high-precision positioning in a complex electromagnetic interference environment can be achieved.

[0064] S4. Dynamically adjust the positioning frequency and the power consumption of the signal processing module according to the battery capacity and the working state of the positioning device. If the device is in a low-battery state, reduce the positioning frequency to extend the battery life.

[0065] Obtain the current battery power information and working state information of the positioning device, compare the power information with a preset power threshold to determine whether the device is in a low-battery state; if the device is in a low-battery state, select the corresponding frequency adjustment plan in the preset positioning frequency adjustment strategy according to the percentage of power, and obtain the positioning frequency value that needs to be adjusted; according to the obtained positioning frequency value, dynamically adjust the working frequency of the positioning module to reduce the number of positioning operations and thus reduce the power consumption of the positioning module; for the signal processing module, reduce the computational complexity and amount of signal processing by reducing the signal sampling rate and the dimension of signal feature extraction, etc., to reduce the power consumption of the signal processing module; after adjusting the positioning frequency and signal processing method, continuously monitor the change of the device's battery power, and dynamically optimize the frequency adjustment strategy and signal processing strategy according to the power change trend; use the Kalman filtering algorithm to filter the positioning data obtained after reducing the frequency to reduce the fluctuation and noise of the positioning data and improve the positioning accuracy; through machine learning algorithms, such as decision trees or support vector machines, adaptively optimize the frequency adjustment strategy according to the historical power data and the effect of the positioning frequency adjustment strategy to find a balance between extending the battery life and ensuring the positioning accuracy.

[0066] For example, the positioning device obtains the current battery power information and working status information in real time through the battery management module, compares the power information with a preset power threshold (such as 20%), and determines whether the device is in a low-power state. When the device is in a low-power state, according to the power percentage (such as 15%), select the corresponding frequency adjustment scheme in the preset positioning frequency adjustment strategy (such as when the power is between 10% and 20%, the positioning frequency is adjusted to 50% of the original), and obtain the positioning frequency value that needs to be adjusted. The system adjusts the working frequency of the positioning module dynamically by controlling the working clock and sleep time of the positioning module according to the obtained positioning frequency value. For example, the positioning frequency that was originally performed every 5 seconds is adjusted to every 10 seconds to reduce the number of positioning operations and the power consumption of the positioning module. For the signal processing module, by dynamically adjusting the sampling rate of the ADC (such as reducing from 100Hz to 50Hz), reducing the dimension of signal feature extraction (such as reducing from 20 dimensions to 10 dimensions), etc., reduce the computational amount and complexity of signal processing to reduce the power consumption of the signal processing module. After adjusting the positioning frequency and signal processing method, the system continuously monitors the battery power change of the device through the battery management module, and dynamically optimizes the frequency adjustment strategy and signal processing strategy according to the power change trend (such as the power drops 5% per hour) using the gradient descent algorithm to adapt to the power change. At the same time, the system uses the Kalman filter algorithm to filter the positioning data obtained after reducing the frequency. By establishing a state equation and an observation equation, using the optimal estimate value of the previous moment and the observation value of the current moment, recursively calculate the optimal estimate value of the current moment, reduce the fluctuation and noise of the positioning data, and improve the positioning accuracy. In addition, the system also uses machine learning algorithms (such as decision trees or support vector machines), according to the historical power data and the effect of the positioning frequency adjustment strategy (such as the power drop rate under different frequency adjustment strategies), select the optimal feature attributes using indicators such as information gain or Gini index, and adaptively optimize the frequency adjustment strategy to find a balance between extending the battery life and ensuring the positioning accuracy.

[0067] S5. Adopt hardware redundancy design and environmentally adaptable materials. For high-temperature, humid, and vibration conditions, establish a hardware reliability evaluation model in advance to judge the operating state of the device in different environments.

[0068] Obtain environmental parameter data such as high temperature, humidity, and vibration of the device, and use it as input features. According to the material selection attributes, obtain the material property parameters of the hardware device and components as input features. Use the redundancy parameter of the hardware redundancy design scheme as an input feature. Input the above environmental parameters, material property parameters, and redundancy parameters into a pre-established hardware reliability evaluation model. Use the support vector machine algorithm to classify the input features and determine the reliability level of the device in the current environment. If the reliability level is lower than the preset threshold, it is determined that the operating state of the device in the current environment is a high-risk state. According to the high-risk operating state of the device, automatically adjust the hardware redundancy and environmental adaptability material parameters to improve the device reliability until the reliability level meets the requirements.

[0069] For example, first collect parameter data such as temperature, humidity, and vibration of the device environment through sensors. For example, the temperature sensor collects the device environment temperature of 35 °C, the humidity sensor collects the relative humidity of 80%, and the vibration sensor collects the device vibration frequency of 20 Hz and the amplitude of 5 mm. Then, according to the material selection information of each component of the device, obtain the corresponding material property parameters from the material database. For example, the circuit board uses FR-4 type epoxy glass cloth laminate, and its glass transition temperature is 135 °C and the thermal expansion coefficient is 14×10-6 / °C. At the same time, extract the redundancy parameters of each redundant unit in the device hardware redundancy design scheme. For example, in the dual-machine hot standby scheme, the redundancy of the primary and standby machines is 1. Input the collected environmental parameters, material property parameters, and redundancy parameters into a pre-trained SVM reliability evaluation model for classification prediction, and obtain the reliability level of the device in the current environment as 7. Compare this reliability level with the preset reliability threshold of 9, and determine that the device is currently in a high-risk operating state. Based on the high-risk operating state of the device, automatically adjust the hardware redundancy, upgrade the original dual-machine hot standby scheme to a triple-machine hot standby scheme, and increase the redundancy from 1 to 2. At the same time, according to the environmental temperature and material properties, select a high-temperature-resistant polyimide film material to replace the original epoxy glass cloth laminate, so that the glass transition temperature of the circuit board is increased from 135 °C to 230 °C, and the thermal expansion coefficient is reduced from 14×10-6 / °C to 12×10-6 / °C. After the above adjustments, re-evaluate the device reliability, and obtain that the reliability level is increased to 95, meeting the preset threshold requirements, and the device returns to a safe operating state.

[0070] S106. Obtain the data generated by the positioning device, encrypt the data using the AES encryption algorithm, and transmit the encrypted data to the warehouse management system in combination with the preset secure transmission protocol.

[0071] The original data collected by the positioning device is obtained, and the original data is pre-processed to remove noise data to obtain the cleaned valid data; according to the preset AES encryption algorithm, the encryption key and initial vector are set, and the cleaned valid data is encrypted to obtain the encrypted data; according to the preset security transmission protocol, the target address, port number, timeout time and other parameters of data transmission are set, and a network connection with the warehouse management system is established; the encrypted data is encapsulated according to the format requirements of the security transmission protocol to generate a data packet that complies with the protocol specification; through the established network connection, the encapsulated data packet is transmitted to the designated interface of the warehouse management system, and the response of the receiving system is waited for; if a confirmation response is received from the warehouse management system within the preset timeout period, the data transmission is determined to be successful, otherwise retransmission is performed; according to the business needs of the warehouse management system, the received encrypted data is decrypted, and the decrypted data is stored in the system database for subsequent business applications.

[0072] For example, the raw data collected by the positioning device is preprocessed by the Gaussian filter algorithm, the filter window size is set to 5×5, the standard deviation is 5, the data is smoothed, the high-frequency noise interference is eliminated, and the effective data after cleaning is obtained. According to the AES-256 encryption algorithm, a 256-bit encryption key and a 16-byte random initial vector are set, and the CBC mode is used to encrypt the effective data after cleaning to ensure the confidentiality of the data transmission process. Based on the TCP / IP protocol, the target IP address of the data transmission is set to 1916100, the port number is 8080, and the timeout is 5 seconds. A reliable network connection with the warehouse management system is established through a three-way handshake. The encrypted data is encapsulated according to the custom data frame format. The data frame includes fields such as frame header, data length, encrypted data, and checksum to ensure the integrity and consistency of data transmission. Through the established network connection, the encapsulated data packet is segmented and transmitted to the data receiving interface of the warehouse management system using streaming transmission, and the timer is started to wait for the response confirmation of the receiving system. If the confirmation response frame returned by the warehouse management system is received within the 5-second timeout period, the data transmission is considered successful. Otherwise, a maximum of 3 retransmissions are performed to ensure reliable data delivery. After receiving the encrypted data, the warehouse management system decrypts it according to the agreed encryption algorithm and key, and stores the decrypted data in the MySQL database using Base64 encoding. It also improves data query and analysis efficiency through indexing mechanisms and query optimization technologies, providing data support for business applications such as intelligent scheduling and inventory management.

[0073] S7. According to the communication protocol of the warehouse management system, a data interaction interface is established in advance to obtain the status information of the positioning device in real time. If the data interaction is abnormal, the backup communication channel is started.

[0074] According to the pre-established communication protocol of the warehousing management system, use the Socket communication method to establish a TCP connection to implement the data interaction interface between the system and the positioning device; through the data interaction interface, in the form of JSON format data packets, obtain the status information reported by the positioning device in real time, and parse the JSON data packets to extract key fields; if the status information data packets of the positioning device are not received continuously three times, or the data packet parsing fails, it is determined that the data interaction is abnormal; according to the preset backup communication parameters, switch to the backup communication channel and re-establish the Socket connection to resume data interaction; adopt a heartbeat mechanism to regularly send heartbeat request data packets to the positioning device, if the heartbeat response is not received, it is judged that the communication is abnormal and the backup channel is enabled; through statistical analysis of the obtained status information of the positioning device, key parameters such as the device working status and power are obtained and displayed in real time in the form of a curve graph; write the status information of the positioning device into the MongoDB database, and based on the time series, construct a historical database of the status information for big data analysis and fault diagnosis.

[0075] For example, according to the pre-established communication protocol of the warehousing management system, use the Socket communication method to establish a TCP connection to implement the data interaction interface between the system and the positioning device. Through the data interaction interface, in the form of JSON format data packets, obtain the status information reported by the positioning device in real time every 5 seconds, and use the FastJSON library to parse the JSON data packets to extract key fields such as device ID, timestamp, longitude and latitude, and power. If the status information data packets of the positioning device are not received continuously three times, or the data packet parsing fails, it is determined that the data interaction is abnormal. According to the preset backup communication parameters, switch to the backup 4G communication channel and re-establish the Socket connection to resume data interaction. Adopt a heartbeat mechanism to send heartbeat request data packets to the positioning device every 30 seconds. If the heartbeat response is not received continuously three times, it is judged that the communication is abnormal and the backup channel is enabled. Through statistical analysis of the obtained status information of the positioning device, use the sliding window algorithm to calculate key parameters such as the average working current and power change rate of the device in the recent 10 minutes, and display them in real time in the form of a curve graph. Write the status information of the positioning device into the MongoDB database, and based on the time series, construct a historical database of the status information. Use the time series analysis algorithm to perform big data analysis on the historical working status of the device to achieve device fault warning and diagnosis.

[0076] S8. Use the random forest model in the machine learning algorithm to classify the operation data of the positioning device to judge whether there are hardware failures or signal abnormalities in the device. If an abnormality is detected, trigger the warning mechanism.

[0077] Obtain the operation data of the positioning device, preprocess the data, and extract key feature parameters. According to the preprocessed data, use the random forest model to classify the device operation status, and obtain the judgment result on whether there is a hardware fault or signal anomaly in the device. If the judgment result indicates that there is a hardware fault or signal anomaly in the device, trigger the warning mechanism and generate warning information. By analyzing the historical operation data of the device, establish a feature library of device faults and abnormal signals to optimize the classification performance of the random forest model. Use the cross-validation method to evaluate the random forest model, and adjust the model parameters according to the evaluation results to improve the classification accuracy of the model. Deploy the optimized random forest model to the positioning device monitoring system to classify and judge the device operation data in real time, and timely detect device faults and abnormal signals. According to the warning information, combined with the feature library of device faults and abnormal signals, analyze the causes of device faults to provide a basis for subsequent maintenance and optimization.

[0078] For example, first, obtain the operation data from the positioning device, including parameters such as the working voltage, current, and temperature of the device. Preprocess the obtained data to remove outliers and noise, and extract key feature parameters, such as the average voltage and the current fluctuation range. Then, input the preprocessed data into the random forest model for classification. The random forest model integrates multiple decision trees and synthesizes the classification results of each decision tree to obtain the judgment result on whether there is a hardware fault or signal anomaly in the device. If the judgment result indicates that there is a fault or anomaly in the device, trigger the warning mechanism, generate a warning message containing information such as the fault type and severity, and send it to relevant personnel. At the same time, by analyzing the historical operation data of the device, establish a feature library of device faults and abnormal signals. The feature library contains the distribution of feature parameters of various faults and anomalies, such as the average voltage being lower than 5V and the current fluctuation range being greater than 1A during a fault. Use the data in the feature library to train and optimize the random forest model to improve the classification accuracy of the model. Use the 5-fold cross-validation method to evaluate the optimized model. If the classification accuracy is higher than 95%, deploy the model to the positioning device monitoring system to classify and judge the device operation data in real time. When the monitoring system detects a fault or anomaly in the device, combined with the data in the feature library, analyze the cause of the fault. For example, if the device voltage continuously drops below 2V and the current fluctuation range is greater than 5A, it can be judged as a power supply circuit fault, and the power supply module needs to be checked. Through the analysis of the cause of the fault, it can provide data support for subsequent maintenance and optimization, and improve the reliability and stability of the device operation.

[0079] S9. According to the real-time interaction data, use the K-means clustering algorithm to optimize the distribution of devices in the warehousing environment. If the device distribution density is too high, adjust the device positions to reduce signal interference.

[0080] Obtain the real-time interaction data of devices in the warehousing environment, including information such as device location and communication signal strength. According to the obtained real-time interaction data, use the K-means clustering algorithm to cluster the devices, and divide the devices with similar characteristics into the same category. For each clustering result, analyze the distribution density of the devices within the category, and calculate the average distance and signal interference strength between the devices. If the distribution density of the devices within a certain clustering is too high, the average distance is less than the preset threshold, and the signal interference strength is greater than the preset threshold, then it is determined that the device layout in this area needs to be optimized. For the area that needs to be optimized, use the genetic algorithm to generate multiple possible device layout plans, and each plan contains the new position coordinates of the devices. Use the simulated annealing algorithm to optimize the generated layout plans, and through iterative adjustment of the device positions, maximize the average distance between the devices and minimize the signal interference to obtain the optimal layout plan. According to the optimal layout plan, adjust the actual positions of the devices in the warehousing environment, update the device location information, and complete the optimization of the device layout.

[0081] For example, a large number of wireless sensor devices are deployed in the warehousing environment, and these devices collect environmental data in real time and communicate through a wireless network. The system obtains the interaction data such as the position coordinates and signal strength of the devices every 5 minutes and transmits the data to the cloud server. After receiving the data, the server uses the K-means clustering algorithm to cluster the devices. By calculating the Euclidean distance between the devices as the similarity measure, set the number of clusters K = 5 and iterate 20 times to obtain the clustering result. For each cluster, analyze the distribution density of the devices in it and calculate the average distance between the devices. If the average distance is less than 2 meters and the number of devices in the same area exceeds 10, it is considered that the device distribution in this area is too dense. At the same time, calculate the signal interference strength between the devices. When the average signal-to-interference ratio is lower than 15 dB, it is determined that there is strong signal interference. For the area with dense distribution and serious signal interference, the device layout needs to be optimized and adjusted. The system uses the genetic algorithm to automatically generate 10 possible layout plans, and each plan corresponds to a chromosome encoding the new position coordinates of the devices. Generate new layout plans through crossover and mutation, and iterate 500 times to obtain a set of optimized layouts. Then use the simulated annealing algorithm for further optimization, set the initial temperature T0 = 100, and the temperature drops by 10% each time iteration. Stop the annealing process when T < 1. The optimization goal is to maximize the average distance between the devices and minimize the signal interference strength. After optimization, the optimal plan for the device layout is obtained, the average distance is increased to more than 5 meters, and the average signal-to-interference ratio reaches more than 20 dB. Finally, according to the optimized plan, guide the staff to adjust the actual deployment positions of the devices, update the device location information, and complete the layout optimization. Through a series of processes such as continuous data collection, clustering analysis, algorithm optimization, and plan deployment, the dynamic optimization of the device layout in the warehousing environment is realized, effectively reducing device interference and improving communication quality and system stability.

[0082] The present invention provides a material management system based on the Beidou positioning system, which mainly includes: a signal propagation modeling module for obtaining the distribution information of metal devices and shelves in the storage environment, combining the preset positions of electromagnetic interference sources, establishing a signal propagation model, and calculating the signal multipath effect and attenuation degree; a signal processing and enhancement module for processing the received satellite signals using an adaptive filtering algorithm according to the signal propagation model, and if the signal strength is lower than the preset threshold, starting the signal enhancement module to compensate for signal attenuation; an electromagnetic interference suppression module for separating the interference components from the received signals using a frequency-domain filtering method to obtain pure positioning signals and improve the positioning accuracy in response to the electromagnetic interference problem; a power consumption management module for dynamically adjusting the positioning frequency and the power consumption of the signal processing module according to the battery capacity and the working state of the positioning device, and if the device is in a low power state, reducing the positioning frequency to extend the battery life; a hardware reliability evaluation module for using hardware redundancy design and environmentally adaptable materials to pre-establish a hardware reliability evaluation model for high temperature, humidity, and vibration conditions to judge the operating state of the device in different environments; a data encryption and transmission module for obtaining the data generated by the positioning device, encrypting the data using the AES encryption algorithm, and transmitting the encrypted data to the warehouse management system in combination with the preset secure transmission protocol; a data interaction and communication module for pre-establishing a data interaction interface according to the communication protocol of the warehouse management system to obtain the status information of the positioning device in real time, and if the data interaction is abnormal, starting a backup communication channel; a fault detection and warning module for classifying the operating data of the positioning device using the random forest model in machine learning algorithms to judge whether there are hardware faults or signal abnormalities in the device, and if an abnormality is detected, triggering a warning mechanism; and a device distribution optimization module for optimizing the device distribution in the storage environment using the K-means clustering algorithm according to the real-time interaction data, and if the device distribution density is too high, adjusting the device positions to reduce signal interference.

[0083] The above embodiments are only the preferred technical solutions of the present invention and should not be regarded as limitations on the present invention. The protection scope of the present invention should be the technical solutions recorded in the claims, including the equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, the equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. A material management method based on Beidou positioning system, characterized in that The following steps are involved: Obtain the distribution information of metal equipment and shelves in the storage environment, combine it with the preset electromagnetic interference source location, establish a signal propagation model, and calculate the signal multipath effect and attenuation degree; according to the signal propagation model, use the adaptive filtering algorithm to process the received satellite signal. If the signal strength is lower than the preset threshold, start the signal enhancement module to compensate for the signal attenuation; for the electromagnetic interference problem, use the frequency domain filtering method to separate the interference component from the received signal, obtain a pure positioning signal, and improve the positioning accuracy; according to the battery capacity and the working status of the positioning device, dynamically adjust the positioning frequency and the power consumption of the signal processing module. If the device is in a low-power state, reduce the positioning frequency to extend the battery life; Adopting hardware redundancy design and environmentally adaptable materials, a hardware reliability assessment model is pre-established for high temperature, humidity and vibration conditions to determine the operating status of the equipment in different environments; obtaining the data generated by the positioning device, encrypting the data using the AES encryption algorithm, and transmitting the encrypted data to the warehouse management system in combination with the preset secure transmission protocol; According to the communication protocol of the warehouse management system, a data interaction interface is established in advance to obtain the status information of the positioning device in real time. If the data interaction is abnormal, the backup communication channel is activated; The random forest model in the machine learning algorithm is used to classify the operating data of the positioning equipment to determine whether the equipment has hardware failure or signal abnormality. If an abnormality is detected, the early warning mechanism is triggered. Based on real-time interactive data, the K-means clustering algorithm is used to optimize the equipment distribution in the storage environment. If the equipment distribution density is too high, the equipment position is adjusted to reduce signal interference.

2. The material management method based on the Beidou positioning system according to claim 1, characterized in that: The method of obtaining the distribution information of metal equipment and shelves in the storage environment, combining the preset electromagnetic interference source location, establishing a signal propagation model, and calculating the signal multipath effect and attenuation degree includes: Obtain the location distribution information of metal equipment and shelves in the storage environment and use it as the basic data for establishing the signal propagation model; According to the preset electromagnetic interference source location information, the corresponding interference node is set in the signal propagation model; Through ray tracing algorithms, the propagation path of signals in the storage environment is simulated, and the multipath effects including reflection and diffraction of the signals during propagation are calculated; Using empirical models and machine learning algorithms, the attenuation of signals on different paths is calculated based on the signal propagation distance and obstacle material. The signal strength distribution at different locations in the storage environment is obtained by combining the calculation results of multipath effect and attenuation degree; Compare the signal strength distribution with the preset threshold to determine which areas may be affected by electromagnetic interference and mark potential blind spots; According to the distribution of blind spots, optimize the layout of wireless devices to improve the overall signal coverage quality of the warehouse environment.

3. The material management method based on Beidou positioning system according to claim 1, characterized in that: The method uses an adaptive filtering algorithm to process the received satellite signal according to the signal propagation model, and if the signal strength is lower than a preset threshold, starts a signal enhancement module to compensate for signal attenuation, including: According to the propagation model of satellite signals, a signal strength prediction model is established to obtain the predicted signal strength value; Acquire the received satellite signal, and use an adaptive filtering algorithm to filter the satellite signal to obtain a filtered satellite signal; Determine whether the signal strength of the filtered satellite signal is lower than a preset threshold, if it is lower than the preset threshold, start the signal enhancement module, otherwise do not start the signal enhancement module; When the signal enhancement module is started, the signal attenuation is calculated based on the predicted signal strength value and the actual received signal strength value; Adaptively adjusting the compensation coefficient of the signal enhancement module according to the calculated signal attenuation to obtain an adjusted compensation coefficient; Using the adjusted compensation coefficient to perform signal compensation on the filtered satellite signal to obtain a compensated satellite signal; The compensated satellite signal is output as the final processed satellite signal for subsequent signal demodulation and data analysis.

4. The material management method based on the Beidou positioning system according to claim 1 is characterized in that: In order to solve the electromagnetic interference problem, the frequency domain filtering method is used to separate the interference components from the received signal, obtain a pure positioning signal, and improve the positioning accuracy, including: Acquire a received signal containing electromagnetic interference, preprocess the received signal, eliminate noise and distortion in the signal, and obtain a preprocessed signal; Convert the preprocessed signal to the frequency domain and use the Fourier transform algorithm to obtain the frequency domain representation of the signal; In the frequency domain, according to the preset interference frequency range, the filtering parameters of the frequency domain filter are set to determine the interference frequency components that need to be filtered out; Applying a frequency domain filter to the frequency domain signal to filter out interference frequency components and obtain a filtered frequency domain signal; The filtered frequency domain signal is converted back to the time domain, and the inverse Fourier transform algorithm is used to obtain the pure time domain signal after removing interference; Extract features from pure time-domain signals to obtain key characteristic parameters of the signals, including signal strength and arrival time, for subsequent positioning calculations; The extracted signal feature parameters are input into the positioning algorithm model, combined with the location information of the positioning base station, and the precise location coordinates of the target are calculated through the triangulation method.

5. The material management method based on Beidou positioning system according to claim 1 is characterized in that: The method of dynamically adjusting the positioning frequency and the power consumption of the signal processing module according to the battery capacity and the working state of the positioning device, and reducing the positioning frequency to extend the battery life if the device is in a low power state, includes: Obtain the current battery power information and working status information of the positioning device, compare the power information with the preset power threshold, and determine whether the device is in a low power state; If the device is in a low-battery state, the corresponding frequency adjustment scheme is selected from the preset positioning frequency adjustment strategy according to the battery percentage to obtain the positioning frequency value that needs to be adjusted; According to the obtained positioning frequency value, the working frequency of the positioning module is dynamically adjusted to reduce the number of positioning operations to reduce the power consumption of the positioning module; For the signal processing module, the amount of signal processing calculation and complexity can be reduced by reducing the signal sampling rate and the dimension of signal feature extraction, so as to reduce the power consumption of the signal processing module; After adjusting the positioning frequency and signal processing method, continuously monitor the battery power changes of the device, and dynamically optimize the frequency adjustment strategy and signal processing strategy according to the battery power change trend; The Kalman filter algorithm is used to filter the positioning data obtained after the frequency is reduced, so as to reduce the fluctuation and noise of the positioning data and improve the positioning accuracy. Through a machine learning algorithm, the frequency adjustment strategy is adaptively optimized according to historical power data and the effect of the positioning frequency adjustment strategy to find a balance between extending battery life and ensuring positioning accuracy; the machine learning algorithm includes a decision tree or a support vector machine.

6. The material management method based on Beidou positioning system according to claim 1 is characterized in that: The hardware redundancy design and environmental adaptability materials are used to establish a hardware reliability evaluation model in advance for high temperature, humidity and vibration conditions to determine the operating status of the equipment in different environments, including: Obtain the environmental parameter data of the equipment, including high temperature, humidity, and vibration, and use it as input features; According to the material selection attributes, the material characteristic parameters of hardware devices and components are obtained as input features; The redundancy parameter of the hardware redundancy design scheme is used as an input feature; Input the above environmental parameters, material characteristic parameters and redundancy parameters into a pre-established hardware reliability assessment model; Use support vector machine algorithm to classify input features and determine the reliability level of the equipment in the current environment; If the reliability level is lower than the preset threshold, the device is judged to be in a high-risk state in the current environment; According to the high-risk operating status of the equipment, the hardware redundancy and environmental adaptability material parameters are automatically adjusted to improve the equipment reliability until the reliability level meets the requirements.

7. The material management method based on Beidou positioning system according to claim 1 is characterized in that: The data generated by the positioning device is obtained, the data is encrypted using the AES encryption algorithm, and the encrypted data is transmitted to the warehouse management system in combination with a preset secure transmission protocol, including: Obtain the original data collected by the positioning device, pre-process the original data, remove the noise data, and obtain the cleaned valid data; According to the preset AES encryption algorithm, the encryption key and initial vector are set, and the cleaned valid data is encrypted to obtain the encrypted data; According to the preset secure transmission protocol, set the target address, port number and timeout period for data transmission, and establish a network connection with the warehouse management system; Encapsulate the encrypted data according to the format requirements of the security transmission protocol to generate a data packet that complies with the protocol specifications; Transmit the encapsulated data packet to the designated interface of the warehouse management system through the established network connection and wait for the response of the receiving system; If a confirmation response is received from the warehouse management system within the preset timeout period, the data transmission is considered successful, otherwise retransmission is performed; According to the business requirements of the warehouse management system, the received encrypted data is decrypted and stored in the system database for subsequent business applications.

8. The material management method based on Beidou positioning system according to claim 1 is characterized in that: According to the communication protocol of the warehouse management system, a data interaction interface is pre-established to obtain the status information of the positioning device in real time. If the data interaction is abnormal, the backup communication channel is started, including: According to the pre-established warehouse management system communication protocol, the Socket communication method is used to establish a TCP connection to realize the data interaction interface between the system and the positioning device; Through the data interaction interface, the status information reported by the positioning device is obtained in real time in the form of JSON format data packets, and the JSON data packets are parsed to extract key fields; If the positioning device status information data packet is not received for three consecutive times, or the data packet parsing fails, it is determined that the data interaction is abnormal; According to the preset backup communication parameters, switch to the backup communication channel, re-establish the Socket connection, and resume data interaction; The heartbeat mechanism is used to periodically send heartbeat request packets to the positioning device. If no heartbeat response is received, it is judged as a communication abnormality and the backup channel is activated; By statistically analyzing the acquired positioning device status information, key parameters including device working status and power are obtained and displayed in real time in the form of a curve chart; The positioning device status information is written into the MongoDB database, and based on the time series, a historical database of the status information is constructed for big data analysis and fault diagnosis.

9. The material management method based on Beidou positioning system according to claim 1, characterized in that: The random forest model in the machine learning algorithm is used to classify the operating data of the positioning device to determine whether the device has hardware failure or signal abnormality. If an abnormality is detected, an early warning mechanism is triggered, including: Obtain the operating data of the positioning equipment, pre-process the data, and extract key characteristic parameters; Based on the preprocessed data, the random forest model is used to classify the equipment operation status to determine whether the equipment has hardware failure or signal abnormality. If the judgment result shows that the device has hardware failure or signal abnormality, the early warning mechanism is triggered and early warning information is generated; By analyzing the historical operation data of the equipment, a feature library of equipment failures and abnormal signals is established to optimize the classification performance of the random forest model; The random forest model was evaluated using the cross-validation method, and the model parameters were adjusted according to the evaluation results to improve the classification accuracy of the model; Deploy the optimized random forest model to the positioning equipment monitoring system to classify and judge the equipment operation data in real time, and promptly detect equipment failures and abnormal signals; Based on the early warning information and the feature library of equipment failure and abnormal signals, the cause of equipment failure is analyzed to provide a basis for subsequent maintenance and optimization.

10. The material management method based on Beidou positioning system according to claim 1, characterized in that: The K-means clustering algorithm is used to optimize the equipment distribution in the storage environment based on the real-time interactive data. If the equipment distribution density is too high, the equipment position is adjusted to reduce signal interference, including: Obtain real-time interactive data of equipment in the warehouse environment, including equipment location and communication signal strength; Based on the acquired real-time interaction data, the K-means clustering algorithm is used to cluster the devices, and the devices with similar characteristics are divided into the same category; For each clustering result, analyze the distribution density of devices in the category and calculate the average distance between devices and signal interference strength; If the device distribution density in a cluster is too high, the average distance is less than the preset threshold, and the signal interference intensity is greater than the preset threshold, it is determined that the device layout in the area needs to be optimized; For the area that needs to be optimized, multiple possible equipment layout plans are generated through genetic algorithms, each of which contains the new location coordinates of the equipment; The generated layout scheme is optimized by using simulated annealing algorithm. By iteratively adjusting the device positions, the average distance between devices is maximized, signal interference is minimized, and the optimal layout scheme is obtained. According to the optimal layout plan, adjust the actual location of the equipment in the storage environment, update the equipment location information, and complete the equipment layout optimization.

11. A material management system based on Beidou positioning system, characterized in that: A material management method based on the Beidou positioning system according to any one of claims 1 to 10 is adopted, and the system comprises: The signal propagation modeling module is used to obtain the distribution information of metal equipment and shelves in the storage environment, and to establish a signal propagation model in combination with the preset electromagnetic interference source position, and calculate the signal multipath effect and attenuation degree; the signal processing and enhancement module is used to process the received satellite signal according to the signal propagation model using an adaptive filtering algorithm. If the signal strength is lower than the preset threshold, the signal enhancement module is started to compensate for the signal attenuation; the electromagnetic interference suppression module is used to use the frequency domain filtering method to separate the interference components from the received signal to obtain a pure positioning signal and improve the positioning accuracy in order to solve the electromagnetic interference problem; the power consumption management module is used to dynamically adjust the positioning frequency and the power consumption of the signal processing module according to the battery capacity and the working status of the positioning device. If the device is in a low-power state, the positioning frequency is reduced to extend the battery life; the hardware reliability evaluation module is used to use hardware redundant design and environmentally adaptable materials to pre-build the hardware reliability evaluation module for high temperature, humidity and vibration conditions. A hardware reliability evaluation model is established to determine the operating status of the equipment in different environments; a data encryption and transmission module is used to obtain the data generated by the positioning device, encrypt the data using the AES encryption algorithm, and transmit the encrypted data to the warehouse management system in combination with the preset security transmission protocol; a data interaction and communication module is used to pre-establish a data interaction interface according to the communication protocol of the warehouse management system, and obtain the status information of the positioning device in real time. If the data interaction is abnormal, the backup communication channel is started; a fault detection and early warning module is used to classify the operating data of the positioning device using the random forest model in the machine learning algorithm to determine whether the device has a hardware fault or a signal abnormality. If an abnormality is detected, the early warning mechanism is triggered; an equipment distribution optimization module is used to optimize the equipment distribution in the storage environment using the K-means clustering algorithm based on real-time interaction data. If the equipment distribution density is too high, the equipment position is adjusted to reduce signal interference.

Images