Intelligent warehouse cargo wireless positioning identification algorithm applied to intelligent logistics

Abstract

The invention discloses an intelligent warehouse goods wireless positioning identification algorithm applied to intelligent logistics, and the algorithm comprises a wireless positioning identification algorithm, the wireless positioning identification algorithm comprises a wireless communication module, a wireless positioning module and a product identification module, the wireless communication module comprises a Bluetooth unit, a WIFI unit, a UWB unit and a safety protection unit, and the product identification module comprises a product identification module. The wireless positioning module comprises a Bluetooth positioning unit, an RFID (Radio Frequency Identification) positioning unit, an ultrasonic positioning unit and a Zigbee positioning unit, and the product identification module comprises a pattern identification unit, an RFID identification unit and a computer vision identification unit. The positioning identification information is transmitted to the background by selecting a proper wireless communication mode through the wireless communication module, such as Bluetooth, WIFI or UWB for encryption protection, the phenomenon of data leakage or attack is avoided, products in logistics are positioned and identified in multiple modes of Bluetooth, RFID or Zigbee, the positioning modes are flexible and diverse, and the practicability is high.

Description

Technical Field

[0001] The present invention relates to the field of positioning and identification technology, and specifically to a wireless positioning and identification algorithm for intelligent warehoused goods applied to smart logistics. Background Art

[0002] IoT technology plays a key role in IoT-enabled smart logistics systems. Sensors, tags, and other devices enable real-time tracking and monitoring of goods, enabling the real-time collection and transmission of logistics information. For example, RFID technology enables real-time tracking and location of goods, significantly improving logistics efficiency and visual management.

[0003] Cargo coding and identification technology in intelligent warehouse management systems is the key to improving warehouse efficiency, reducing labor costs, and improving management accuracy. Cargo identification technology is another key technology in intelligent warehousing. It uses a variety of technical means to achieve rapid and accurate identification of goods. Currently, the main cargo identification technologies include the following:

[0004] Barcode / QR code recognition: This method uses a scanner to read barcodes or QR codes to identify goods. Although this method is relatively mature, it has certain limitations, such as slow scanning speed and high requirements for light and material surface.

[0005] RFID Identification: RFID identification technology uses radio frequency signals to automatically identify goods. Compared with barcode / QR code recognition, RFID identification has higher efficiency and accuracy, and supports simultaneous recognition of multiple tags, making it suitable for large-scale warehousing environments.

[0006] Computer vision recognition: With the continuous development of computer vision technology, its application in smart warehousing is becoming more and more extensive. Through cameras and image processing algorithms, computer vision technology can recognize the appearance, shape and other characteristics of goods, further improving the accuracy and efficiency of recognition.

[0007] However, traditional wireless positioning and identification of warehoused goods has the following disadvantages:

[0008] Traditional wireless positioning and identification of warehouse goods involves a large amount of data, including sensitive information such as customer information and inventory data. If the system's data security measures are not in place, it may lead to data leakage or attacks, causing unpredictable losses to the company. Summary of the Invention

[0009] The purpose of the present invention is to provide an intelligent warehouse cargo wireless positioning and identification algorithm applied to smart logistics, so as to solve the problem proposed in the above background technology that the traditional warehouse cargo wireless positioning and identification involves a large amount of data, including customer information, inventory data and other sensitive information. If the system's data security measures are not in place, it may lead to data leakage or attack, causing unpredictable losses to the enterprise.

[0010] To achieve the above-mentioned objectives, the present invention provides the following technical solutions: a wireless positioning and identification algorithm for intelligent warehouse goods applied to smart logistics, including a wireless positioning and identification algorithm, the wireless positioning and identification algorithm including a wireless communication module, a wireless positioning module and a product identification module, the wireless communication module including a Bluetooth unit, a WIFI unit, a UWB unit and a security protection unit, the wireless positioning module including a Bluetooth positioning unit, an RFID positioning unit, an ultrasonic positioning unit and a Zigbee positioning unit, the product identification module including a pattern recognition unit, an RFID identification unit and a computer vision identification unit, the Bluetooth unit realizes information transmission, data collection and remote monitoring functions between devices through Bluetooth in the logistics system, and the WIFI unit realizes information transmission, data collection and remote monitoring functions between devices through WIFI in the logistics system.

[0011] As a preferred technical solution of the present invention, an encryption algorithm is provided in the security protection unit. The encryption algorithm includes symmetric encryption and asymmetric encryption. The symmetric encryption uses the same key for encryption and decryption. The calculation formula of the symmetric encryption is:

[0012] C=E(P,e), P=D(C,d),

[0013] Where C is the ciphertext and P is the plaintext. If e=d, then the function P and the function D are symmetric functions, that is, symmetric algorithms;

[0014] The asymmetric encryption uses a pair of keys, one for encryption and the other for decryption. The asymmetric encryption calculation formula is:

[0015] C=E(Pe), P=D(C,d),

[0016] Where C is the ciphertext and P is the plaintext. If e!=d, then the function P and the function D are asymmetric functions, that is, an asymmetric algorithm.

[0017] As a preferred technical solution of the present invention, the Bluetooth positioning unit assumes that there are N base stations or antennas. For a received signal, the received signal power at the i-th base station is set to P i , the phase of the received signal is θ i , then:

[0018]

[0019] Where G is the gain between the transmitting antenna and the receiving antenna, d i is the distance from the receiving point to the i-th base station or antenna, a is the signal attenuation factor; or,

[0020]

[0021] where x i ,y i is the coordinate of the i-th base station or antenna, x and y are the coordinates of the receiving point, and assuming that there are M signals received, then M equations are obtained:

[0022]

[0023] Where φ is the signal phase difference between the receiving point and the i-th base station or antenna. Substituting θi into the above formula, we get a linear equation system about :

[0024]

[0025] Where [A] is an MxN matrix, and [B] are Nx1 and Mx1 vectors respectively.

[0026] As a preferred technical solution of the present invention, the RFID antenna length calculation formula used by the RFID positioning unit is:

[0027]

[0028] Where f represents the operating frequency of the antenna, c represents the speed of light, L represents the length of the antenna, and W represents the width of the antenna. The RSSI positioning algorithm calculates the location of the target tag by measuring the received signal strength. The calculation formula is as follows:

[0029] d = 10 ^(RSSI-A)(10*n) ,

[0030] d is the distance, RSSI is the received signal strength; A is the signal strength at the reference distance; and n is the signal propagation factor, which is usually between 2 and 4.

[0031] As a preferred technical solution of the present invention, the calculation formula of the ultrasonic positioning unit is:

[0032] Horizontal distance: x = vxt;

[0033] Vertical distance:

[0034] Depth: z=vxt,

[0035] Among them, x, y, and z are the coordinates of the discharge source in three-dimensional space.

[0036] As a preferred technical solution of the present invention, the conversion relationship between the Zigbee signal strength RSSI in the Zigbee positioning unit and the distance is expressed by the following formula:

[0037] [RSSI(d)=-(10n\lg d+A)],

[0038] Where A is the signal strength at 1 meter, and n is the signal attenuation factor. Usually, A and n need to be determined through multiple tests to ensure measurement accuracy. The specific formula for calculating distance is:

[0039]

[0040] Among them, A is usually between 45 and 49, and n is between 3.25 and 4.5.

[0041] As a preferred technical solution of the present invention, the pattern recognition unit calculates the following specific steps:

[0042] S1. Select neighborhood: For each pixel P(x,y) in the image, obtain the grayscale values ​​of the 8 adjacent pixels in its neighborhood. Usually, an 8-neighborhood is used, but it can also be adjusted according to specific needs.

[0043] S2. Compare grayscale values: Compare the grayscale values ​​of the eight adjacent pixels with the grayscale value of the central pixel P(x, y). If the grayscale value of the adjacent pixel is greater than or equal to the central pixel, it is marked as 1; otherwise, it is marked as 0.

[0044] S3. Generate a binary number: Arrange the above results in order to form a binary number, which is the LBP value of the pixel point;

[0045] S4. Calculate the LBP image: Calculate the corresponding LBP value for each pixel in the image according to the above steps to obtain an LBP image, and perform statistical analysis on the LBP image, such as calculating a histogram or counting the frequency of occurrence of different LBP values.

[0046] As a preferred technical solution of the present invention, the F1 value in the computer vision recognition unit is an evaluation indicator that comprehensively considers the precision rate and the recall rate. It is the harmonic mean of the precision rate and the recall rate, and the calculation formula is:

[0047] F1=2*(Precision*Recall) / (Precision+Recall),

[0048] The higher the F1 value, the better the performance in precision and recall.

[0049] Compared with the existing technology, the beneficial effects of the present invention are: by setting up a wireless communication module and a wireless positioning module, the positioning identification information is encrypted and protected and transmitted to the background through the wireless communication module by selecting a suitable wireless communication method, such as Bluetooth, WIFI or UWB, to avoid data leakage or attack. The products in the logistics are positioned and identified in multiple ways through Bluetooth, RFID or Zigbee positioning, and the positioning methods are flexible and diverse and highly practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a schematic diagram of the architecture of the wireless positioning and identification algorithm of the present invention;

[0051] Figure 2 Schematic diagram of the architecture of the wireless communication module of the present invention;

[0052] Figure 3 Schematic diagram of the architecture of the wireless positioning module of the present invention;

[0053] Figure 4 Schematic diagram of the architecture of the product identification module of the present invention.

[0054] In the figure: 1. Wireless positioning and recognition algorithm; 2. Wireless communication module; 21. Bluetooth unit; 22. WIFI unit; 23. UWB unit; 24. Security protection unit; 3. Wireless positioning module; 31. Bluetooth positioning unit; 32. RFID positioning unit; 33. Ultrasonic positioning unit; 34. Zigbee positioning unit; 4. Product recognition module; 41. Pattern recognition unit; 42. RFID recognition unit; 43. Computer vision recognition unit. DETAILED DESCRIPTION

[0055] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0056] See also Figure 1-4The present invention provides a wireless positioning and identification algorithm for intelligent warehouse goods applied to intelligent logistics, including a wireless positioning and identification algorithm 1, the wireless positioning and identification algorithm 1 includes a wireless communication module 2, a wireless positioning module 3 and a product identification module 4, the wireless communication module 2 includes a Bluetooth unit 21, a WIFI unit 22, a UWB unit 23 and a security protection unit 24, the wireless positioning module 3 includes a Bluetooth positioning unit 31, an RFID positioning unit 32, an ultrasonic positioning unit 33 and a Zigbee positioning unit 34, the product identification module 4 includes a pattern recognition unit 41, an RFID recognition unit 42 and a computer vision recognition unit 43, the Bluetooth unit 21 realizes information transmission, data collection and remote monitoring functions between devices in the logistics system through Bluetooth, the WIFI unit 22 realizes information transmission, data collection and remote monitoring functions between devices in the logistics system through WIFI, the UWB unit 23 uses nanosecond or sub-nanosecond pulses for communication; the Bluetooth positioning unit 31 is specifically Bluetooth AoA positioning technology, Bluetooth A oA positioning technology is a method of high-precision positioning using the angle of arrival of the signal. This technology transmits a direction-finding signal through a single antenna. The receiving device has a built-in antenna array. Based on the phase difference between different receiving antennas when the signal passes, the direction and position of the signal source are calculated, thereby achieving high-precision positioning. The RFID positioning unit 32 uses radio signals to obtain position information through non-contact mode recognition and reading and writing, and makes positioning of the position information with small errors in a short time. The signal propagation range is large and the cost is not high. The ultrasonic positioning unit 33 calculates the distance by transmitting ultrasonic signals and measuring the time difference between the transmitted and returned waves. The Zigbee positioning unit 34 uses the Zigbee sensor network for positioning. The pattern recognition unit 41 reads the barcode or QR code information through the scanning device to realize the identification of the goods. The RFID identification unit 42 uses wireless radio frequency signals to realize automatic identification of the goods. The computer vision identification unit 43 uses a camera and image processing algorithm to realize the identification of the appearance and shape characteristics of the goods.

[0057] The security protection unit 24 is provided with an encryption algorithm, which includes symmetric encryption and asymmetric encryption. Symmetric encryption uses the same key for encryption and decryption. The calculation formula for symmetric encryption is:

[0058] C=E(P,e), P=D(C,d),

[0059] Where C is the ciphertext and P is the plaintext. If e=d, then the function P and the function D are symmetric functions, that is, symmetric algorithms;

[0060] Asymmetric encryption uses a pair of keys, one for encryption and the other for decryption. The asymmetric encryption calculation formula is:

[0061] C=E(Pe), P=D(C,d),

[0062] Where C is the ciphertext and P is the plaintext. If e!=d, then the function P and the function D are asymmetric functions, that is, an asymmetric algorithm.

[0063] The Bluetooth positioning unit 31 assumes that there are N base stations or antennas. For a received signal, let the received signal power at the i-th base station be P i , the phase of the received signal is θ i , then:

[0064]

[0065] Where G is the gain between the transmitting antenna and the receiving antenna, d i is the distance from the receiving point to the i-th base station or antenna, a is the signal attenuation factor; or,

[0066]

[0067] where x i ,y i is the coordinate of the i-th base station or antenna, x and y are the coordinates of the receiving point, and assuming that there are M signals received, then M equations are obtained:

[0068]

[0069] Where φ is the signal phase difference between the receiving point and the i-th base station or antenna. Substituting θi into the above formula, we get a linear equation system about :

[0070]

[0071] Where [A] is an MxN matrix, and [B] are Nx1 and Mx1 vectors respectively.

[0072] The calculation formula for the RFID antenna length used by the RFID positioning unit 32 is:

[0073]

[0074] Where f represents the operating frequency of the antenna, c represents the speed of light, L represents the length of the antenna, and W represents the width of the antenna. The RSSI positioning algorithm calculates the location of the target tag by measuring the received signal strength. The calculation formula is as follows:

[0075] d = 10 ^(RSSI-A)(10*n) ,

[0076] d is the distance, RSSI is the received signal strength; A is the signal strength at the reference distance; and n is the signal propagation factor, which is usually between 2 and 4.

[0077] The calculation formula of the ultrasonic positioning unit 33 is:

[0078] Horizontal distance: x = vxt;

[0079] Vertical distance:

[0080] Depth: z=vxt,

[0081] Among them, x, y, and z are the coordinates of the discharge source in three-dimensional space.

[0082] The conversion relationship between the Zigbee signal strength RSSI in the Zigbee positioning unit 34 and the distance is expressed by the following formula:

[0083] [RSSI(d)=-(10n\lg d+A)],

[0084] Where A is the signal strength at 1 meter, and n is the signal attenuation factor. Usually, A and n need to be determined through multiple tests to ensure measurement accuracy. The specific formula for calculating distance is:

[0085]

[0086] Among them, A is usually between 45 and 49, and n is between 3.25 and 4.5.

[0087] The specific calculation steps of the pattern recognition unit 41 are:

[0088] S1. Select neighborhood: For each pixel P(x,y) in the image, obtain the grayscale values ​​of the 8 adjacent pixels in its neighborhood. Usually, an 8-neighborhood is used, but it can also be adjusted according to specific needs.

[0089] S2. Compare grayscale values: Compare the grayscale values ​​of the eight adjacent pixels with the grayscale value of the central pixel P(x, y). If the grayscale value of the adjacent pixel is greater than or equal to the central pixel, it is marked as 1; otherwise, it is marked as 0.

[0090] S3. Generate a binary number: Arrange the above results in order to form a binary number, which is the LBP value of the pixel point;

[0091] S4. Calculate the LBP image: Calculate the corresponding LBP value for each pixel in the image according to the above steps to obtain an LBP image, and perform statistical analysis on the LBP image, such as calculating a histogram or counting the frequency of occurrence of different LBP values.

[0092] The F1 value in the computer vision recognition unit 43 is an evaluation indicator that comprehensively considers the precision rate and the recall rate. It is the harmonic mean of the precision rate and the recall rate. The calculation formula is:

[0093] F1=2*(Precision*Recall) / (Precision+Recall),

[0094] The higher the F1 value, the better the performance in precision and recall.

[0095] In the present invention, the Bluetooth unit 21 realizes information transmission, data collection and remote monitoring functions between devices in the logistics system through Bluetooth, the WIFI unit 22 realizes information transmission, data collection and remote monitoring functions between devices in the logistics system through WIFI, and the UWB unit 23 uses nanosecond or sub-nanosecond pulses for communication; the Bluetooth positioning unit 31 is specifically Bluetooth AoA positioning technology, which is a method of high-precision positioning using the angle of arrival of the signal. This technology transmits a direction-finding signal through a single antenna, and the receiving end device has a built-in antenna array. According to the phase difference generated between different receiving antennas when the signal passes, the direction and position of the signal source are calculated, thereby achieving high-precision positioning, RFI The D positioning unit 32 uses radio signals to obtain position information through non-contact recognition and reading and writing, and can locate the position information with a small error in a short time. The signal propagation range is large and the cost is low. The ultrasonic positioning unit 33 calculates the distance by emitting ultrasonic signals and measuring the time difference between the emitted and returned waves. The Zigbee positioning unit 34 uses the Zigbee sensor network for positioning. The pattern recognition unit 41 reads the barcode or QR code information through the scanning device to realize the identification of the goods. The RFID identification unit 42 uses wireless radio frequency signals to realize automatic identification of the goods. The computer vision recognition unit 43 uses a camera and image processing algorithm to realize the identification of the appearance and shape characteristics of the goods.

[0096] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A wireless positioning and identification algorithm for intelligent warehouse goods applied to smart logistics, comprising a wireless positioning and identification algorithm (1), characterized in that: The wireless positioning and recognition algorithm (1) comprises a wireless communication module (2), a wireless positioning module (3) and a product recognition module (4); the wireless communication module (2) comprises a Bluetooth unit (21), a WIFI unit (22), a UWB unit (23) and a safety protection unit (24); the wireless positioning module (3) comprises a Bluetooth positioning unit (31), an RFID positioning unit (32), an ultrasonic positioning unit (33) and a Zigbee positioning unit (34); and the product recognition module (4) comprises a pattern recognition unit (41), an RFID recognition unit (42) and a computer vision recognition unit (43).

2. The intelligent warehouse cargo wireless positioning and identification algorithm applied to smart logistics according to claim 1 is characterized by: The security protection unit (24) is provided with an encryption algorithm, which includes symmetric encryption and asymmetric encryption. The symmetric encryption uses the same key for encryption and decryption. The calculation formula of the symmetric encryption is: C=E(P,e), P=D(C,d), Where C is the ciphertext and P is the plaintext. If e=d, then the function P and the function D are symmetric functions, that is, symmetric algorithms; The asymmetric encryption uses a pair of keys, one for encryption and the other for decryption. The asymmetric encryption calculation formula is: C=E(Pe), P=D(C,d), Where C is the ciphertext and P is the plaintext. If e!=d, then the function P and the function D are asymmetric functions, that is, an asymmetric algorithm.

3. The intelligent warehouse cargo wireless positioning and identification algorithm for smart logistics according to claim 1 is characterized by: The Bluetooth positioning unit (31) assumes that there are N base stations or antennas. For a received signal, the received signal power at the i-th base station is set to P i , the phase of the received signal is θ i , then: Where G is the gain between the transmitting antenna and the receiving antenna, d i is the distance from the receiving point to the i-th base station or antenna, a is the signal attenuation factor; or, where x i ,y i is the coordinate of the i-th base station or antenna, x and y are the coordinates of the receiving point, and assuming that there are M signals received, then M equations are obtained: Where φ is the signal phase difference between the receiving point and the i-th base station or antenna. Substituting θi into the above formula, we get a linear equation system about : Where [A] is an MxN matrix, and [B] are Nx1 and Mx1 vectors respectively.

4. The intelligent warehouse cargo wireless positioning and identification algorithm for smart logistics according to claim 1 is characterized by: The calculation formula for the RFID antenna length used by the RFID positioning unit (32) is: Where f represents the operating frequency of the antenna, c represents the speed of light, L represents the length of the antenna, and W represents the width of the antenna. The RSSI positioning algorithm calculates the location of the target tag by measuring the received signal strength. The calculation formula is as follows: d＝10 ^(RSSI-A)(10*n) ， d is the distance, RSSI is the received signal strength; A is the signal strength at the reference distance; and n is the signal propagation factor, which is usually between 2 and 4.

5. The intelligent warehouse cargo wireless positioning and identification algorithm applied to smart logistics according to claim 1 is characterized by: The calculation formula of the ultrasonic positioning unit (33) is: Horizontal distance: x = vxt; Vertical distance: Depth: z=vxt, Among them, x, y, and z are the coordinates of the discharge source in three-dimensional space.

6. The intelligent warehouse cargo wireless positioning and identification algorithm applied to smart logistics according to claim 1 is characterized by: The conversion relationship between the Zigbee signal strength RSSI in the Zigbee positioning unit (34) and the distance is expressed by the following formula: [RSSI(d)=-(10n\lg d+A)], Where A is the signal strength at 1 meter, and n is the signal attenuation factor. Usually, A and n need to be determined through multiple tests to ensure measurement accuracy. The specific formula for calculating distance is: Among them, A is usually between 45 and 49, and n is between 3.25 and 4.

5.

7. The intelligent warehouse cargo wireless positioning and identification algorithm applied to smart logistics according to claim 1 is characterized by: The pattern recognition unit (41) calculates the following specific steps: S1. Select neighborhood: For each pixel P(x,y) in the image, obtain the grayscale values ​​of the 8 adjacent pixels in its neighborhood. Usually, an 8-neighborhood is used, but it can also be adjusted according to specific needs. S2. Compare grayscale values: Compare the grayscale values ​​of the eight adjacent pixels with the grayscale value of the central pixel P(x, y). If the grayscale value of the adjacent pixel is greater than or equal to the central pixel, it is marked as 1; otherwise, it is marked as 0. S3. Generate a binary number: Arrange the above results in order to form a binary number, which is the LBP value of the pixel point; S4. Calculate the LBP image: Calculate the corresponding LBP value for each pixel in the image according to the above steps to obtain an LBP image, and perform statistical analysis on the LBP image, such as calculating a histogram or counting the frequency of occurrence of different LBP values.

8. The intelligent warehouse cargo wireless positioning and identification algorithm applied to smart logistics according to claim 1 is characterized by: The F1 value in the computer vision recognition unit (43) is an evaluation index that comprehensively considers the precision rate and the recall rate. It is the harmonic mean of the precision rate and the recall rate. The calculation formula is: F1=2*(Precision*Recall) / (Precision+Recall), The higher the F1 value, the better the performance in precision and recall.

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