Computer-implemented method for determining a movement speed

Abstract

The invention relates to a computer-implemented method for determining a movement speed, comprising the steps of: a. providing a speed value for each first radio tag (TAG) of a plurality of first radio tags (TAG) (S1), b. providing a first radio signal and a first signal-strength profile for each first radio tag (TAG) of the plurality of first radio tags (S2), c. training a machine-learning model on the basis of the first speed values and the first signal-strength profiles in order to determine a corresponding speed value based on at least one signal-strength profile (S3), d. acquiring at least one second initial speed value by applying the trained machine learning model to at least one second signal-strength profile of at least one second radio tag (TAG) (S4), e. acquiring at least one second unwrapped speed value by unwrapping corresponding phase information of the at least one second radio tag (TAG), taking into account the at least one second initial speed value (S5), and f. providing the at least one second unwrapped speed value as the movement speed (S6). The invention also relates to a technical system and to a corresponding computer program product.

Description

[0001]202411002 1 Description Computer-implemented method for determining movement speed Regardless of the grammatical gender of a particular term, persons of male, female, or other gender identities are included. 1. Technical field The invention relates to a computer-implemented method for determining movement speed. Furthermore, the invention is directed to a corresponding technical system and a computer program product. 2. Prior art Radio frequency identification (RFID) tags, e.g., so-called RFID transponders or RFID tags, are very often used to monitor or record goods, merchandise, other objects, or services. Such RFID tags can be read by corresponding readers using high-frequency radio waves.In many cases, the RFID tag is powered by radio waves generated by the reader and therefore requires no separate power supply. RFID tags allow for the easy identification of tagged objects and the transmission of information between the tag and the reader. Using an RFID gate, objects equipped with an RFID tag can be read and / or identified within a typical range of a few meters. A typical RFID gate might, for example, include four antennas arranged in pairs on two separate side posts. However, different configurations or a larger number of antennas are also common. Objects equipped with an RFID tag can generally be detected and read with high reliability by an RFID gate when they come sufficiently close to it.However, pinpointing the location of an RFID tag more precisely is considerably more difficult because, in many cases, only the received signal strengths are available as a basis for measurement. These often allow only a very rough estimate of the RFID tag's distance from the receiving antennas. The received signal strengths are frequently referred to as RSSI values ​​(RSSI: Received Signal Strength Indicator). Furthermore, the recorded signal strengths are often subject to significant noise and are strongly influenced by objects or building elements in the vicinity of the RFID gateway. Finally, the recorded signal strengths also depend on the orientation of the antennas or the RFID tags, as well as on the specific object being tagged. Therefore, there is a need to improve the location resolution. Another need is to improve both location and speed determination.Typically, according to current technology, only the signal strength is considered, and consequently, no other measured parameters are taken into account. Phase information is not yet sufficiently considered when estimating distance. Phase information (or simply phase) essentially measures the path difference between the transmitted RFID antenna pulse ("input wave") and the signal response ("reflected wave") sent back by the RFID tag when the reader and the RFID tag are at a certain distance from each other. The reason for this is that, when evaluating this information, phase information does not allow for a clear conclusion about the distance to moving objects. Moving objects are conventionally subject to phase modulation of electromagnetic oscillation signals. Known approaches for using phase information require assumptions about the expected movement pattern and the speed of the RFID tag being measured.For the special case of a known constant velocity and direction of motion, analytical formulas for "unwrapping" the measured phase values ​​as a function of time are known in order to factor out the phase modulation. However, a disadvantage of these methods is that they also only allow for a rough estimate of the distance. This estimate is subject to high uncertainties or a lack of clarity. Often, without further assumptions about the movement of the RFID tag, it is impossible to distinguish whether the object is at rest or moving between two measurement points by a multiple of the wavelength. Another disadvantage is that the measurement of the phase information is also noisy. The present invention therefore aims to provide a computer-implemented method for determining a motion velocity that is more efficient and reliable. 202411002 3 3.Summary of the Invention: The above-mentioned problem is solved according to the invention by a computer-implemented method for determining a movement speed, comprising the steps: a. Providing a respective speed value for a respective first radio frequency identification (RFID) tag of a plurality of first RFID tags; b. Providing a respective first radio signal and a respective first signal strength profile for the respective first RFID tag of the plurality of first RFID tags; c. Training a machine learning model based on the first speed values ​​and the first signal strength profiles in order to determine a corresponding speed value based on at least one signal strength profile; d. Determining at least one second initial speed value by applying the trained machine learning model to at least one second signal strength profile of at least one second RFID tag; e.Determining at least one second unfolded velocity value by unfolding associated phase information of the at least one second radio frequency identification (RFID) tag, taking into account the at least one second initial velocity value, and providing the at least one second unfolded velocity value as the velocity of movement. Accordingly, the invention is directed to a computer-implemented method for determining a velocity of movement. In other words, it determines the velocity at which an object labeled with an RFID tag is moving. Exemplary objects are the items mentioned above, such as packages in logistics centers, in warehouses, or items in a production plant or factory (e.g., on conveyor belts).Unless otherwise specified, all steps of the computer-implemented method can be performed by at least one computing unit, which can also be referred to as a data processing device. In particular, the data processing device, which comprises at least one processing circuit configured or adapted for carrying out a computer-implemented method according to the invention, can perform the steps of the computer-implemented method. For this purpose, a computer program can be stored in the data processing device, in particular one processing circuit, which includes instructions that, when executed by the data processing device, in particular the at least one processing circuit, cause the data processing device to execute the computer-implemented method. In the first and second method steps, the input data for the speed prediction are provided.For each of the majority of radio frequency identification (RFID) tags, the first speed value, the first radio signal, and the first signal strength curve are provided. The input data can also be considered training data for the training phase. As is common in machine learning, the data can be divided into training data, validation data, and / or test data for the different phases. The input data can be received via one or more input interfaces. Additionally or alternatively, the output data, such as the movement speed, can also be sent via one or more output interfaces. The interfaces can be configured as serial or parallel interfaces. Advantageously, the interfaces ensure efficient and smooth data transmission between processing units. Data can be exchanged bidirectionally without data congestion.The input data can be in the form of measured values ​​and / or acquired by one or more sensor units. The velocity value can be acquired by a sensor. The sensors can be based, among other things, on position detection using optical measurement systems (e.g., camera images and light barriers) or on radar, lidar, or other radio wave-based localization systems (e.g., real-time localization systems, RTLS). The radio signal can be provided by measuring the received radio wave-based radio tag signals. The signal strength profile and / or the profile of the phase information values ​​can be determined from the received radio signal. In a further process step, the machine learning model is trained in the training phase based on the training data. The model is trained to predict the velocity value.The machine learning model can also be referred to as a machine learning model. In a further step, the trained machine learning model is applied to unknown data for speed prediction. Thus, different initial and subsequent data sets are used for training and the actual application of the model. The application of the trained model results in the second initial speed value. The initial speed value can also be referred to as the modeled speed or current speed value. This serves as input for determining the second unfolded speed value by unfolding the associated phase information of at least one other radio tag. In other words, the phase information is dynamically unfolded, taking the modeled speed into account.In the final process step, the at least one second unfolded velocity value is provided as the movement speed after unfolding. The present invention thus ensures that the movement speed is determined reliably and efficiently. During the unfolding step, the initial velocity value is used, which is not predefined but determined dynamically. In contrast to the prior art, no fixed and potentially erroneous assumptions about the constant speed of the radio frequency identification (RFID) tags are required for using the phase information. Consequently, the prediction of the movement speed and the resulting movement speed are also more accurate and reliable. The prediction is subject to fewer uncertainties and errors. In one embodiment, the first RFID tags are transported multiple times through a predetermined area under varying transport conditions.Accordingly, the RFID tags are moved through the defined area multiple times to generate a high-quality data foundation. In other words, the training data is expanded. A large and diverse training dataset for the initial training of the model has proven particularly advantageous with regard to prediction. In another embodiment, the model is a neural network. Accordingly, different models can be used. The machine learning model can be flexibly selected depending on the underlying technical system, the RFID gate, user preferences, or other conditions. In another embodiment, the velocity value is defined as a velocity vector, where the velocity vector preferably has at least two entries with a magnitude and a direction.Accordingly, the velocity value is represented as a multidimensional vector, similar to a two-dimensional vector. In a further development, the unfolding is performed using numerical or analytical unfolding, or machine learning. Thus, the determination of at least one second initial velocity value, as well as the determination of at least one second unfolded velocity value, can be carried out using machine learning (unfolding). In other words, the two steps are performed using machine learning and can be combined for this purpose. A single model or two different models can be used. Alternatively, only the determination of at least one second initial velocity value is performed using machine learning. A different approach is used for the unfolding.In other words, only one step is performed using machine learning. In a further embodiment, the computer-implemented method also includes: reconstructing at least one position and / or at least one direction of movement of the at least one second radio tag based on the at least one second unfolded velocity value, and / or tracking at least one associated object depending on the at least one position, where the at least one associated object is labeled with the at least one second radio tag. Accordingly, the second unfolded velocity value can be used to locate the one or more second radio tags. Consequently, the associated position can be reliably and efficiently determined based on the predicted velocity value.In addition to or as an alternative to location tracking, object tracking can be performed. In a further embodiment, the computer-implemented method also includes: - outputting the at least one second initial velocity value, the at least one second developed velocity value, and / or other related data to a display unit; - storing the at least one second initial velocity value, the at least one second developed velocity value, and / or other related data in a storage unit; and / or - transmitting the at least one second initial velocity value, the at least one second developed velocity value, and / or other related data to a processing unit. Accordingly, one or more actions can be initiated after the movement velocity has been provided as an output value of the method according to the invention.The measures can be carried out simultaneously, sequentially, or in stages. First, the movement speed can be displayed to a user on a display unit of a processing unit. Furthermore, the movement speed can be stored, and the speed value itself or in the form of a corresponding message or notification can be transmitted to another unit, such as an end device, a control unit, or another processing unit. The receiving processing unit can also initiate further corresponding measures upon receipt. These measures include, for example, control actions. For instance, a processing unit of a technical system can receive the movement speed and trigger a control action based on it. The processing unit can also check the movement speed, approve it, and / or reject it before initiating an action.This has the advantage that any measures taken after the speed prediction is made can be implemented reliably and promptly. Furthermore, the invention relates to a technical system for carrying out the above method. The invention also relates to a computer program product comprising a computer program that includes means for carrying out the above-described method when the computer program is executed on a program-controlled device. A computer program product, such as a computer program means, can be provided or delivered, for example, as a storage medium, such as a memory card, USB stick, CD-ROM, DVD, or in the form of a downloadable file from a server in a network. This can be done, for example, in a wireless communication network by transmitting a corresponding file containing the computer program product or the computer program means.A suitable program-controlled device is, in particular, a control unit such as an industrial control PC, a programmable logic controller (PLC), or a microprocessor for a smart card or the like. 4. Brief Description of the Drawings In the following detailed description, preferred embodiments of the invention are further described with reference to the following figures. FIG 1 shows a schematic flowchart of the method according to the invention. FIG 2 shows a schematic representation of an RFID gate with four antennas according to the prior art. FIG 3 shows a schematic flowchart of the method according to an embodiment of the invention. 5. Description of Preferred Embodiments Preferred embodiments of the present invention are described below with reference to Figure 1.Figure 1 schematically illustrates a flowchart of the inventive method with process steps S1 to S6. Prediction of the initial velocity value: According to one embodiment of the invention, the machine learning model is trained using labeled training data such that, given a series of RSSI time series as input, it outputs the velocity value of the RFID tags (TAGs). The RSSI time series can be available as separate series for each recording antenna at the RFID gate. The velocity value can be represented as a two-dimensional vector with magnitude and direction (e.g., left / right / up / down). The machine learning model can be implemented as a neural network, such as a convolutional neural network (CNN). By calculating the difference between the position data, velocity vectors can be calculated and provided as ground truth labels during the training of the neural network.In this way, a model can be generated in advance, which is then used to deduce the current speed of the RFID tag from the RSSI values ​​received separately for each RFID tag per antenna. Dynamic unfolding of the phase information taking into account the initial speed value: Using the trained machine learning model, the phase information can then be dynamically unfolded. According to one embodiment of invention 202411002 10, the RSSI values ​​and phase measurements received during operation per antenna (usually both measured quantities lie on the same time grid) are bundled in sliding time windows (typically 1-3 s in length), so that an evaluation of the trained model can be performed based on the RSSI values. It is assumed that the speed remains almost constant over the duration of the time window.For fast passages through the RFID gate, the time window can be reduced. The initial speed value is then used as input to the unwrapping algorithm. During unwrapping, the initial speed value is optimized to achieve a maximally consistent and temporally unwrapping sequence of phase values. The corresponding unwrapping speed value can therefore deviate from the initial value within certain limits. According to one embodiment of the invention, a consistent and converging unwrapping is performed using the initial speed value. This allows the individual and antenna-specific phase measurements within the time window to be converted into local position changes x between two measurement times. From these position changes, a trajectory accurate to the centimeter (a time series of distance values) can be determined.If, for example, relative distances of at least two or three antennas can be reconstructed, any ambiguities in the movement between 2D (plane) and 3D (space) can be consistently resolved. In the next time step, the aggregation window is advanced, and the steps are repeated. In this way, trajectories (chains of position changes) can also be piecemeal and consistently assembled over longer periods. Numerical or analytical unfolding methods can be used for this process. Alternatively, the unfolding can be performed or supplemented by a machine learning model. For example, a neural network can be created and trained that directly learns the relationship between the uncorrected phase data, the predicted tag velocity, and the position changes known from camera tracking. This allows, among other things, the analysis of the trajectory and the predicted movement patterns.The limitation that the speed of the RFID tag is assumed to be approximately constant over the length of the displacement time window is circumvented. This has proven particularly advantageous for fast movement patterns. Figure 2 shows a schematic representation of an RFID gate with four antennas according to the prior art. A moving RFID tag is arranged in the center of the RFID gate. Figure 3 shows a schematic flowchart of the method according to an embodiment of the invention. First, the speed prediction takes place in a first part. This is followed by the deployment in a second part.

Claims

202411002 12 Claims 1. Computer-implemented method for determining a movement speed, comprising the steps a. providing a respective speed value for a respective first radio frequency identification tag (RFID) of a plurality of first RFIDs (RFIDs) (S1), b. providing a respective first radio signal and a respective first signal strength profile for the respective first RFID of the plurality of first RFIDs (RFIDs) (S2), c. training a machine learning model based on the first speed values ​​and the first signal strength profiles to determine a corresponding speed value based on at least one signal strength profile (S3), d. determining at least one second initial speed value by applying the trained machine learning model to at least one second signal strength profile of at least one second RFID (RFID) (S4), e.

1. Determining at least one second unfolded velocity value by unfolding associated phase information of the at least one second radio frequency identification tag (RFID) taking into account the at least one second initial velocity value (S5), and f. Providing the at least one second unfolded velocity value as the movement velocity (S6).

2. Computer-implemented method according to claim 1, wherein the first RFIDs (RFIDs) are transported multiple times through a predetermined area under varied transport conditions.

3. Computer-implemented method according to claim 1 or claim 2, wherein the model is a neural network.

4. Computer-implemented method according to any one of the preceding claims, wherein the velocity value is defined as a velocity vector, wherein the... 202411002 13. Velocity vector preferably comprising at least two entries with a magnitude and a direction.

5. Computer-implemented method according to any one of the preceding claims, wherein the unfolding is performed using numerical unfolding, analytical unfolding, or machine learning.

6. Computer-implemented method according to any one of the preceding claims, further comprising: - reconstructing at least one position and / or at least one direction of movement of the at least one second radio tag (TAG) based on the at least one second unfolded velocity value, and / or - tracking at least one associated object as a function of the at least one position, wherein the at least one associated object is labeled with the at least one second radio tag (TAG). 7.A computer-implemented method according to one of the preceding claims, further comprising: - outputting the at least one second initial speed value, the at least one second developed speed value and / or other associated data on a display unit, - storing the at least one second initial speed value, the at least one second developed speed value and / or other associated data in a storage unit, and / or - transmitting the at least one second initial speed value, the at least one second developed speed value and / or other associated data to a computing unit. 202411002 14 8. Technical system for carrying out the method according to one of the preceding claims.

9. Computer program product comprising a computer program comprising means for carrying out the method according to one of claims 1 to 7 when the computer program is executed on a program-controlled device.

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