Settlement method, terminal, medium and program product based on intelligent fusion terminal

Abstract

A settlement method, terminal, medium and program product based on an intelligent fusion terminal, in which the maximum radio frequency signal strength value of a new commodity in a settlement area is determined as a reference signal strength; it is determined whether the weight of commodities in the settled area has increased; if not, the maximum radio frequency signal strength value of the new commodity in the settled area is determined as the settled signal strength; it is determined whether the ratio of the settled signal strength to the reference signal strength is less than a preset threshold; if it is less than, a continuous distribution surface of the radio frequency signal strength of the new commodity is constructed; the peak position of the continuous distribution surface is calculated, and it is determined whether the peak position of the surface is within a preset range of the settled area; if it is, the new commodity is confirmed as a standard placed commodity; if not, the new commodity is confirmed as a non-standard placed commodity; a preset alarm mode is turned on; if the weight of the commodity increases, the new commodity is confirmed as a standard placed commodity. This application improves the efficiency of intelligent settlement.

Description

Technical Field

[0001] The present application belongs to the field of identification and settlement, and in particular relates to a settlement method, terminal, medium and program product based on an intelligent fusion terminal. Background Art

[0002] With the rapid development of the smart retail industry, traditional manual checkout systems are no longer able to meet the efficiency and service quality demands of the modern business environment. Amidst the growing variety of goods and increasing customer traffic, traditional checkout methods suffer from low settlement efficiency, high labor costs, and high error rates, impacting both merchants' operational efficiency and customers' shopping experience.

[0003] To address these issues, the industry is currently adopting an intelligent identification and settlement system that integrates multiple sensors. This system simultaneously deploys RFID readers, visual cameras, and weight sensors, using multi-dimensional data fusion to identify and verify products. This system accurately completes automatic identification and settlement, significantly improving settlement accuracy and efficiency.

[0004] However, when a product is too light, it may not be recorded in the settled area after settlement. When it is scanned by the RFID reader, the terminal does not detect the weight change in the settled area, resulting in an erroneous judgment that the product has not been operated in accordance with the rules, causing settlement to be interrupted. The administrator needs to authorize to continue settlement, which reduces the efficiency of smart settlement. Summary of the Invention

[0005] The present application provides a settlement method, terminal, medium and program product based on an intelligent fusion terminal, which are used to improve the efficiency of intelligent settlement.

[0006] In a first aspect, the present application provides a settlement method based on an intelligent fusion terminal. After determining that the barcode information of a new product has been scanned, the maximum radio frequency signal strength value of the new product in the settlement area is calculated, and the maximum radio frequency signal strength value is determined as the reference signal strength. The terminal includes a settlement area and a settled area. Several radio frequency antennas are set in the settlement area and the settled area to obtain the radio frequency signal strength of the product.

[0007] Determine whether the weight of the goods in the settled area has increased;

[0008] If the weight of the product has not increased, the maximum RF signal strength value of the new product in the settled area is calculated to obtain the settled signal strength;

[0009] If it is determined that the ratio of the settled signal strength to the reference signal strength is less than a preset threshold, all RF signal data in the settled area are spatially interpolated to construct a continuous distribution surface of the RF signal strength of the new product;

[0010] Calculate the peak position of the continuous distribution surface and determine whether the peak position is within a preset range of the settled area;

[0011] If it is within the preset range, the new product is confirmed as a standard placement product;

[0012] If it is not within the preset range, the new product will be identified as a non-standard placement product;

[0013] Starting a preset alarm mode, and after the preset alarm mode ends, executing a step of calculating the maximum radio frequency signal strength value of the new product in the settled area to obtain the settled signal strength;

[0014] If the weight of the product increases, the new product will be confirmed as a standard placement product.

[0015] By adopting the above technical solution, the baseline signal strength of the new product in the settlement area is calculated and compared with the signal strength in the settled area. Combined with the weight change information of the product, the accuracy of judging whether the product is placed in a standardized manner can be improved. When the ratio is less than the preset threshold, a continuous distribution surface is constructed through spatial interpolation operations, and the compliance of the product placement position is judged based on the peak position of the surface, which can effectively identify the spatial location of the product. The dual verification mechanism of signal strength and weight reduces the risk of misjudgment caused by relying solely on a single sensor. When non-standard placement is detected, the system will activate the preset alarm mode and re-verify after the alarm ends, realizing timely reminders and re-inspections of abnormal situations, improving the accuracy and reliability of the settlement process, and thus improving the efficiency of intelligent settlement.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, the step of calculating the maximum radio frequency signal strength value of the new product in the settlement area specifically includes:

[0017] Obtaining a sequence of radio frequency signal strengths collected by each radio frequency antenna within a settlement area within a preset time window;

[0018] Perform wavelet transform on the RF signal strength sequence to obtain signal characteristic coefficients at different scales;

[0019] Calculate the entropy value of the signal based on the signal characteristic coefficient and remove abnormal data with entropy value greater than the preset threshold;

[0020] Constructing an analytical representation based on the signal strength values ​​excluding the abnormal data;

[0021] Calculate the instantaneous envelope of the analytical representation and obtain the local maximum point;

[0022] Perform linear regression on the local maximum point to obtain the change trend of signal intensity;

[0023] The maximum signal strength in the changing trend is taken as the benchmark signal strength.

[0024] By adopting the above technical solution, wavelet transforms are used to perform multi-scale analysis of RF signal strength sequences. Signal entropy is used to determine and eliminate abnormal data, thereby reducing the impact of environmental interference and measurement noise. By analysing the signal and extracting the instantaneous envelope, the dynamic characteristics of signal strength can be accurately captured. Linear regression of local maximum points is performed to determine the signal strength trend, reducing the impact of instantaneous fluctuations on the determination of baseline signal strength and improving the stability and reliability of baseline signal strength calculation. Data analysis within a preset time window ensures real-time performance while reflecting the overall variation pattern of the signal, making the determination of baseline signal strength more accurate and representative.

[0025] In conjunction with some embodiments of the first aspect, in some embodiments, the step of performing spatial interpolation on all RF signal data in the settled area to construct a continuous distribution surface of the RF signal strength of the new product specifically includes:

[0026] Divide the settled area into a first number of grid cells, record the radio frequency signal strength value of the center point of each grid cell, and obtain a discrete spatial sampling data set;

[0027] Calculating a signal strength change rate of each grid cell based on the spatial sampling data set, and adaptively subdividing the grid cells according to the magnitude of the signal strength change rate to obtain a second number of subdivided grid cells, where the second number is greater than the first number;

[0028] Constructing an interpolation basis function system in the form of a tensor product, where the interpolation basis function system satisfies the interpolation conditions at the nodes of the grid unit;

[0029] Solve the interpolation coefficients that satisfy global smoothness based on the interpolation basis function system;

[0030] Construct a global system of equations containing boundary continuity constraints for the subdivided mesh cells;

[0031] A continuous distribution surface of the radio frequency signal strength of new products is constructed based on the global equations and interpolation coefficients.

[0032] By adopting this technical solution, spatial resolution is dynamically adjusted based on the rate of change of signal strength, providing finer sampling points in areas of drastic signal fluctuations and improving interpolation accuracy. By employing a tensor product interpolation basis function system and introducing boundary continuity constraints, the smoothness and continuity of the interpolation results across the entire region are enhanced. This interpolation method reduces the information loss that can occur with traditional equally spaced sampling and improves the accuracy of the description of key areas through adaptive subdivision. The constructed continuous distribution surface truly reflects the spatial distribution characteristics of RF signal strength, making subsequent judgments based on the peak position of the surface more accurate.

[0033] In conjunction with some embodiments of the first aspect, in some embodiments, before determining that the ratio of the settled signal strength to the reference signal strength is less than a preset threshold, the method further includes:

[0034] Obtain the three-dimensional spatial coordinate information of the settlement area and the settled area, and establish a spatial coordinate system that includes the location of the radio frequency antenna, the boundary of the product placement area, and the distribution of metal objects;

[0035] The arrival time difference and arrival angle of the received signal of each RF antenna are collected based on the spatial coordinate system, and the multipath propagation path map is constructed based on the real-time signal strength.

[0036] Based on the multipath propagation path diagram, the geometric loss, dielectric loss and reflection loss of each path are calculated to obtain the path transmission characteristic parameters;

[0037] Establish a signal propagation compensation matrix based on the path transmission characteristic parameters;

[0038] Perform amplitude and phase compensation on the radio frequency signal based on the signal propagation compensation matrix to obtain a corrected signal strength distribution;

[0039] Calculate the false detection rate and missed detection rate based on the corrected signal strength distribution;

[0040] Update the preset threshold value based on the false detection rate, missed detection rate and preset detection reliability requirements.

[0041] By adopting the above technical solution, a three-dimensional spatial coordinate system is established, which includes the RF antenna position, the boundaries of the product placement area, and the distribution of metal objects. A multipath propagation path map is constructed by combining arrival time difference and arrival angle information, achieving accurate modeling of the RF signal propagation environment. The loss parameters of each propagation path are calculated and a signal propagation compensation matrix is ​​established to compensate for the amplitude and phase of the RF signal, reducing signal distortion caused by multipath propagation. The false detection rate and missed detection rate are calculated based on the corrected signal strength distribution, and the preset threshold is dynamically updated, giving the system adaptive adjustment capabilities, reducing the impact of complex electromagnetic environments on signal propagation, and improving the accuracy of signal strength measurements. By updating the preset threshold in real time, the system can automatically optimize the judgment criteria based on the actual detection results, improving the reliability of judgments on the standardization of product placement.

[0042] In conjunction with some embodiments of the first aspect, in some embodiments, the step of collecting the arrival time difference and arrival angle of the signal received by each radio frequency antenna based on the spatial coordinate system specifically includes:

[0043] A phase-synchronized trigger signal is used to control the sampling timing of each RF antenna to obtain a sampling signal;

[0044] Extracting the in-phase component and the quadrature component of the sampling signal, and calculating the instantaneous phase and envelope of the sampling signal based on the in-phase component and the quadrature component;

[0045] Based on the instantaneous phase, the generalized cross-correlation algorithm is used to calculate the arrival time difference between any two RF antennas;

[0046] An overdetermined set of equations is constructed according to the arrival time difference, and the arrival angle of the sampling signal is determined according to the overdetermined set of equations.

[0047] By adopting the above technical solution and using phase-synchronized trigger signals to control the sampling timing of the RF antennas, it is possible to ensure that all antennas begin sampling at the same time, reducing the errors caused by sampling timing deviations. Extracting the in-phase and quadrature components from the sampled signals and calculating the instantaneous phase and envelope information accurately reflects the phase variation characteristics of the signal during propagation. Using a generalized cross-correlation algorithm to calculate the arrival time difference between antennas can suppress the effects of noise and multipath interference on the time difference estimation. The overdetermined system of equations constructed based on the arrival time difference contains redundant information, and the arrival angle obtained by solving the overdetermined system of equations has higher accuracy and reliability.

[0048] In conjunction with some embodiments of the first aspect, in some embodiments, before calculating the geometric loss, dielectric loss, and reflection loss of each path based on the multipath propagation path graph, the method further includes:

[0049] Calculate the propagation speed and attenuation coefficient of electromagnetic waves in different media based on real-time environmental status data;

[0050] Simulate the propagation characteristics of electromagnetic waves and obtain the main propagation paths;

[0051] Establish an electromagnetic field simulation model based on the main propagation path;

[0052] The electromagnetic field simulation model is used to predict the signal propagation loss of different paths and obtain a path loss estimation database.

[0053] By adopting the above technical solution, electromagnetic wave propagation parameters are calculated based on real-time environmental status data, enabling the propagation model to dynamically adapt to environmental changes. By simulating the propagation characteristics of electromagnetic waves to determine the main propagation paths, computational complexity can be reduced, focusing computing resources on the paths that most significantly impact signal propagation. The established electromagnetic field simulation model takes into account various influencing factors in the actual environment, making the prediction of signal propagation loss more realistic. Pre-establishing a path loss estimation database allows for rapid query of loss information for different propagation paths, reducing the time required for real-time calculations and improving the accuracy of signal propagation characteristic predictions. This provides a reliable theoretical basis for signal strength compensation, thereby enhancing the accuracy of product location determination.

[0054] In conjunction with some embodiments of the first aspect, in some embodiments, using an electromagnetic field simulation model to predict signal propagation losses of different paths specifically includes:

[0055] The electromagnetic field distribution is obtained based on the electromagnetic field simulation model;

[0056] The electromagnetic field distribution is simplified to the superposition of several main propagation modes, and a superposition representation is obtained;

[0057] Based on the superposition representation, the electromagnetic wave propagation equation including frequency dispersion and spatial attenuation characteristics is established;

[0058] Calculate the geometric attenuation, dielectric absorption and scattering loss of each main propagation path to obtain the loss characteristic vector;

[0059] Constructing a path characteristic parameter matrix based on the loss characteristic vector, wherein the characteristic dimensions of the path characteristic parameter matrix include path length, incident angle, number of reflections and loss component;

[0060] A recursive neural network is used to train the path loss prediction model, and the path characteristic parameter matrix is ​​used as input to predict the signal propagation loss of different paths.

[0061] By employing this technical solution, the field distribution obtained through electromagnetic field simulation is simplified to a superposition of primary propagation modes, preserving important propagation characteristics while reducing computational complexity. The established electromagnetic wave transmission equation takes into account frequency dispersion and spatial attenuation characteristics, accurately describing the propagation of electromagnetic waves in complex environments. The calculated loss eigenvector comprehensively reflects the characteristics of the propagation path, and the constructed path characteristic parameter matrix includes the key factors affecting signal propagation. A recursive neural network is used to train the path loss prediction model, leveraging the correlation between characteristic parameters to improve the accuracy of loss prediction.

[0062] In the second aspect, an embodiment of the present application provides a settlement terminal based on an intelligent fusion terminal, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and one or more processors call the computer instructions to enable the terminal to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0063] In a third aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions. When the instructions are executed on a terminal, the terminal executes the method described in the first aspect and any possible implementation of the first aspect.

[0064] In a fourth aspect, an embodiment of the present application provides a computer program product, characterized in that when the computer program product is run on a terminal, the terminal executes the method described in any possible implementation manner in the first aspect.

[0065] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0066] 1. The present application provides a settlement method based on an intelligent fusion terminal, which calculates the baseline signal strength of new goods in the settlement area, and compares and analyzes it with the signal strength in the settled area. Combined with the weight change information of the goods, it can improve the accuracy of judging whether the goods are placed in a standardized manner. When the ratio is less than the preset threshold, a continuous distribution surface is constructed through spatial interpolation operations, and the compliance of the placement of the goods is judged based on the peak position of the surface, which can effectively identify the spatial position of the goods. The dual verification mechanism of signal strength and weight reduces the risk of misjudgment caused by relying solely on a single sensor. When non-standard placement is detected, the system will start the preset alarm mode and re-verify after the alarm ends, realizing timely reminders and re-inspections of abnormal situations, improving the accuracy and reliability of the settlement process, and thus improving the efficiency of intelligent settlement.

[0067] 2. The present application provides a settlement method based on an intelligent fusion terminal, establishes a three-dimensional spatial coordinate system including the position of the RF antenna, the boundary of the commodity placement area, and the distribution of metal objects, and constructs a multipath propagation path map in combination with the arrival time difference and arrival angle information, thereby realizing accurate modeling of the RF signal propagation environment. The loss parameters of each propagation path are calculated and a signal propagation compensation matrix is ​​established to perform amplitude and phase compensation on the RF signal, thereby reducing the signal distortion caused by multipath propagation. The false detection rate and missed detection rate are calculated based on the corrected signal strength distribution, and the preset threshold is dynamically updated, so that the system has adaptive adjustment capabilities, reduces the impact of complex electromagnetic environments on signal propagation, and improves the accuracy of signal strength measurement. By updating the preset threshold in real time, the system can automatically optimize the judgment criteria according to the actual detection effect, thereby improving the reliability of the judgment of the standardization of commodity placement.

[0068] 3. The present application provides a settlement method based on an intelligent fusion terminal. By adopting the above-mentioned technical solution, the electromagnetic wave propagation parameters are calculated according to the real-time environmental status data, so that the propagation model can dynamically adapt to environmental changes. By simulating the propagation characteristics of electromagnetic waves to obtain the main propagation path, the computational complexity can be reduced and the computing resources can be concentrated on the path that has the most significant impact on signal propagation. The established electromagnetic field simulation model takes into account various influencing factors in the actual environment, so that the prediction of signal propagation loss is more in line with the actual situation. By pre-establishing a path loss estimation database, the loss information of different propagation paths can be quickly queried, which reduces the time consumption of real-time calculations, improves the accuracy of signal propagation characteristic predictions, and provides a reliable theoretical basis for signal strength compensation, thereby improving the accuracy of product location judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 This is a flow chart of a settlement method based on an intelligent fusion terminal in an embodiment of the present application.

[0070] Figure 2 It is a flow chart of a signal preprocessing method in an embodiment of the present application.

[0071] Figure 3 This is a schematic diagram of the physical device structure of a settlement terminal based on an intelligent fusion terminal provided in an embodiment of the present application. DETAILED DESCRIPTION

[0072] The terms used in the following examples of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of this application, the singular expressions "a," "an," "said," "above," "the," and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in this application refers to any or all possible combinations comprising one or more of the listed items.

[0073] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0074] The following uses an embodiment and combines Figure 1 , a settlement method based on an intelligent fusion terminal in an embodiment of the present application is described:

[0075] See also Figure 1 , which is a flow chart of a settlement method based on an intelligent fusion terminal in an embodiment of the present application.

[0076] S101. Calculate the maximum radio frequency signal strength value of the new product in the settlement area, and determine the maximum radio frequency signal strength value as the reference signal strength;

[0077] After determining that the barcode information of a new product has been scanned, the intelligent fusion terminal calculates the maximum RF signal strength value of the new product in the settlement area and determines the maximum RF signal strength value as the reference signal strength. The terminal includes a settlement area and a settled area, and a number of RF antennas are set up in the settlement area and the settled area to obtain the RF signal strength of the product. Specifically: the RF signal strength sequence collected by each RF antenna in the settlement area within a preset time window is obtained; the RF signal strength sequence is subjected to wavelet transform to obtain signal characteristic coefficients at different scales; the entropy value of the signal is calculated based on the signal characteristic coefficient, and abnormal data with entropy values ​​greater than a preset threshold is removed; an analytical representation is constructed based on the signal strength values ​​excluding the abnormal data; the instantaneous envelope of the analytical representation is calculated to obtain local maximum points; linear regression is performed on the local maximum points to obtain the trend of signal strength changes; the maximum value of the signal strength in the trend of changes is used as the reference signal strength.

[0078] In this step, the intelligent fusion terminal first determines the barcode information of the new product it has scanned, then calculates the maximum RF signal strength value of the new product within the settlement area and determines this maximum value as the reference signal strength. The intelligent fusion terminal includes a settlement area and a settled area, with multiple RF antennas installed in these two areas to obtain the RF signal strength of the product.

[0079] Specifically, the intelligent fusion terminal can calculate the baseline signal strength of new products in the following way: first, obtain the RF signal strength sequence collected by each RF antenna in the settlement area within a preset time window; then, perform wavelet transform on the RF signal strength sequence to obtain signal characteristic coefficients at different scales; then, calculate the entropy value of the signal based on the signal characteristic coefficient, and remove abnormal data with entropy values ​​greater than a preset threshold; then, construct an analytical representation based on the signal strength values ​​excluding the abnormal data; finally, calculate the instantaneous envelope of the analytical representation to obtain local maximum points, and perform linear regression on the local maximum points to obtain the changing trend of the signal strength, and take the maximum value of the signal strength in the changing trend as the baseline signal strength.

[0080] S102: Determine whether the weight of the goods in the settled area has increased;

[0081] In this step, the intelligent fusion terminal needs to determine whether the weight of the goods in the settled area has increased. This step is to determine whether the new goods have been placed in the settled area by the customer.

[0082] To determine the weight of goods, the intelligent fusion terminal can install a weight sensor in the settled area to monitor the weight changes of goods in the settled area in real time. When the weight increase is detected, it can be considered that a new product has been placed in the settled area.

[0083] S103. Calculate the maximum radio frequency signal strength value of the new product in the settled area to obtain the settled signal strength;

[0084] If the weight of the product has not increased, the maximum radio frequency signal strength value of the new product in the settled area is calculated to obtain the settled signal strength.

[0085] In this step, if the intelligent fusion terminal determines that the weight of the goods in the settled area has not increased, it is necessary to calculate the maximum radio frequency signal strength value of the new goods in the settled area as the settled signal strength.

[0086] In order to obtain the settled signal strength, the intelligent fusion terminal can adopt a method similar to that of calculating the reference signal strength, that is, first obtain the RF signal strength sequence of the new product received by each RF antenna in the settled area, and then through a series of processing steps such as wavelet transform, abnormal data elimination, analytical representation construction and instantaneous envelope calculation, finally obtain the maximum RF signal strength value of the new product in the settled area as the settled signal strength.

[0087] When calculating settled signal strength, intelligent converged terminals may encounter changes in the RF signal strength distribution caused by the location of new products. To adapt to this change, intelligent converged terminals can introduce adaptive positioning algorithms. Based on the spatial distribution characteristics of RF signal strength, they estimate the location of new products in real time and dynamically adjust RF antenna operating parameters such as transmit power and receive gain based on the location change, thereby ensuring the accuracy of settled signal strength calculations.

[0088] S104: If it is determined that the ratio of the settled signal strength to the reference signal strength is less than a preset threshold, perform spatial interpolation on all radio frequency signal data in the settled area to construct a continuous distribution surface of the radio frequency signal strength of the new product;

[0089] When the intelligent fusion terminal determines that the ratio of the settled signal strength to the reference signal strength is less than a preset threshold, the intelligent fusion terminal performs spatial interpolation operations on all radio frequency signal data in the settled area to construct a continuous distribution surface of the radio frequency signal strength of the new product. Specifically: the settled area is divided into a first number of grid units, and the center point of each grid unit records the radio frequency signal strength value of the center point to obtain a discrete spatial sampling data set; based on the spatial sampling data set, the signal strength change rate of each grid unit is calculated, and the grid units are adaptively subdivided according to the magnitude of the signal strength change rate to obtain a second number of subdivided grid units, and the second number is greater than the first number; an interpolation basis function system in the form of a tensor product is constructed, and the interpolation basis function system satisfies the interpolation conditions at the nodes of the grid units; the interpolation coefficients that satisfy global smoothness are solved according to the interpolation basis function system; a global equation group containing boundary continuity constraints of the subdivided grid units is constructed; and a continuous distribution surface of the radio frequency signal strength of the new product is constructed based on the global equation group and the interpolation coefficients.

[0090] In this step, the intelligent fusion terminal first determines whether the ratio of the settled signal strength to the reference signal strength is less than a preset threshold. If this ratio is less than the threshold, it indicates that the new product may not have been correctly placed in the settled area. Further analysis of the spatial distribution of RF signal strength within the settled area is required to determine the specific location of the new product.

[0091] To construct a continuous distribution surface for the RF signal strength of new products, the intelligent fusion terminal can use a spatial interpolation method. The specific steps are as follows: First, the settled area is divided into several grid cells, and the RF signal strength value at the center point of each grid cell is recorded to obtain a discrete spatial sampling data set; then, the signal strength change rate of each grid cell is calculated based on the spatial sampling data set, and the grid cells are adaptively subdivided according to the magnitude of the change rate to obtain higher-resolution subdivided grid cells; then, an interpolation basis function system in the form of a tensor product is constructed to ensure that the interpolation basis function satisfies the interpolation conditions at the nodes of the grid cells; then, the interpolation coefficients that satisfy global smoothness are solved based on the interpolation basis function system, and a global equation system containing continuity constraints on the boundaries of the subdivided grid cells is constructed; finally, a continuous distribution surface for the RF signal strength of new products is constructed based on the global equation system and the interpolation coefficients.

[0092] S105: Perform spatial interpolation on all radio frequency signal data in the settled area to construct a continuous distribution surface of radio frequency signal strength of new products;

[0093] As can be seen from step S104, if the intelligent fusion terminal determines that the ratio of the settled signal strength to the reference signal strength is less than a preset threshold, it performs spatial interpolation on the RF signal data in the settled area to construct a continuous distribution surface of the RF signal strength of the new product. This step recapitulates this process.

[0094] To achieve spatial interpolation of RF signal strength, intelligent converged terminals can use various interpolation algorithms, such as linear interpolation, spline interpolation, and Kriging interpolation. Kriging interpolation is a spatial interpolation method based on variogram theory. By modeling the spatial correlation of RF signal strength, it can obtain optimal unbiased estimation results. Therefore, it is widely used in the field of RF signal processing.

[0095] When performing spatial interpolation, intelligent converged terminals must comprehensively consider factors such as interpolation accuracy, computational efficiency, and memory overhead, selecting the appropriate interpolation algorithm and parameter settings. For example, in areas where RF signal strength varies dramatically, intelligent converged terminals can use a high-order interpolation algorithm to improve interpolation accuracy; whereas in areas where signal strength varies more gently, low-order interpolation algorithms can be used to reduce computational complexity. Furthermore, intelligent converged terminals can incorporate adaptive data compression technology, which significantly reduces memory overhead and data transmission volume by reducing the dimensionality and creating a sparse representation of RF signal data while maintaining interpolation accuracy.

[0096] S106, calculating the peak position of the continuous distribution surface, and determining whether the peak position of the surface is within a preset range of the settled area;

[0097] In this step, the intelligent fusion terminal needs to calculate the peak position of the continuous distribution surface of the new product radio frequency signal strength obtained in step S105, and determine whether the peak position is within the preset range of the settled area.

[0098] To calculate the peak location of the surface, the intelligent fusion terminal can use numerical optimization algorithms, such as gradient descent and Newton's method, to find the maximum point on the surface through an iterative search. To improve the efficiency and reliability of peak search, the intelligent fusion terminal can also introduce a heuristic search strategy. Based on the distribution characteristics of the RF signal strength, it can pre-estimate the possible location of the peak and then perform a detailed search within the local area, thereby accelerating convergence and avoiding local extrema.

[0099] S107: Confirm the new product as a non-standard placement product and activate the preset alarm mode;

[0100] If it is not within the preset range, the new product is confirmed as an irregularly placed product, and after the preset alarm mode ends, the step of calculating the maximum radio frequency signal strength value of the new product in the settled area is executed to obtain the settled signal strength, and then returning to step S101, the execution is repeated until the peak position of the surface is within the preset range of the settled area.

[0101] In this step, if the intelligent fusion terminal determines that the peak position of the radio frequency signal strength of the new product is not within the preset range of the settled area, the new product is identified as an irregularly placed product and the preset alarm mode is activated.

[0102] To remind customers to place new items correctly in the checkout area, the intelligent fusion terminal can use a variety of alarm methods, such as voice prompts, flashing lights, and screen displays. Furthermore, to guide customers through the correct placement process, the intelligent fusion terminal can also use visual methods to display the current and target locations of new items on the screen in real time, and use arrows and other indicators to guide customers to move the items to the correct location.

[0103] After the preset alarm mode is activated, the intelligent fusion terminal needs to continuously monitor the location changes of the new product until it is confirmed that the new product has been correctly placed. To prevent customers from delaying the placement operation, the intelligent fusion terminal can set an alarm timeout mechanism. If the new product location change is not detected within the preset time, the manual intervention process will be automatically triggered, requesting staff assistance.

[0104] Furthermore, while in alert mode, the intelligent converged terminal can also record relevant information about irregular placement events, such as the time of occurrence, duration, and customer behavior characteristics, for subsequent user behavior analysis and optimization. By mining and modeling this data, the intelligent converged terminal can learn and predict customers' irregular placement behavior patterns, enabling proactive measures to reduce the frequency of irregular placement events, improve checkout efficiency, and enhance the customer experience.

[0105] S108. Confirm the new product as a standard placement product.

[0106] If the result of step S102 is that the weight of the commodity has increased, the new commodity is confirmed as a standard placement commodity. At the same time, if the peak position of the surface is within the preset range of the settled area, this step is also executed.

[0107] For newly placed items, the intelligent converged terminal automatically updates the list of settled items and their amounts, displaying the settlement information on the screen for customers to review and confirm. Furthermore, to enhance the customer shopping experience, the intelligent converged terminal can also recommend related products or promotions based on the customer's purchase history and preferences, encouraging additional purchases.

[0108] After confirming that the new item is properly placed, the intelligent fusion terminal continues to monitor the status of the settled area until the customer completes checkout and payment for all items. To prevent customers from accidentally taking or missing items during the checkout process, the intelligent fusion terminal uses visual recognition technology to track the quantity and location changes of items in the settled area in real time, comparing them with the settlement list to promptly identify and correct errors. Furthermore, the intelligent fusion terminal uses human-computer interaction to guide customers in correctly operating the settlement device, such as prompting them to place items in designated areas and guiding them through the payment process, thereby improving settlement accuracy and efficiency.

[0109] In the above embodiment, the baseline signal strength of the new product in the settlement area is calculated and compared with the signal strength in the settled area. Combined with the information on the change in product weight, the accuracy of judging whether the product is placed in a standardized manner can be improved. When the ratio is less than the preset threshold, a continuous distribution surface is constructed through spatial interpolation operations, and the compliance of the product placement position is judged based on the peak position of the surface, which can effectively identify the spatial position of the product. The dual verification mechanism of signal strength and weight reduces the risk of misjudgment caused by relying solely on a single sensor. When non-standard placement is detected, the system will start the preset alarm mode and re-verify after the alarm ends, realizing timely reminders and re-inspections of abnormal situations, improving the accuracy and reliability of the settlement process, and thereby improving the efficiency of intelligent settlement.

[0110] In order to further improve the accuracy and reliability of the system judgment, before executing the above settlement method, this application also provides a signal preprocessing solution. This solution compensates and corrects the radio frequency signal by establishing an accurate spatial electromagnetic environment model, providing a more reliable signal basis for the judgment of the normativeness of product placement. On this basis, the system can dynamically adjust the judgment threshold so that the above dual verification mechanism based on signal strength and weight can operate more stably. Figure 2 , a signal preprocessing method in an embodiment of the present application is described:

[0111] See also Figure 2 , which is a flow chart of a signal preprocessing method in an embodiment of the present application.

[0112] S201. Obtain three-dimensional spatial coordinate information of the settlement area and the settled area, and establish a spatial coordinate system including the location of the radio frequency antenna, the boundary of the product placement area, and the distribution of metal objects;

[0113] The intelligent fusion terminal obtains the three-dimensional spatial coordinate information of the settlement area and the settled area, establishing a spatial coordinate system that includes the location of the RF antenna, the boundaries of the product placement area, and the distribution of metal objects. This step obtains the three-dimensional spatial information of the settlement area and the settled area to establish a three-dimensional spatial coordinate system that includes the specific location information of the RF antenna, the boundaries of the product placement area, and the distribution of metal objects. By establishing such a complete three-dimensional spatial coordinate system, the intelligent fusion terminal can accurately grasp the spatial position relationship between various objects, providing a coordinate basis for subsequent signal processing and determining the compliance of product placement.

[0114] In specific implementations, the intelligent fusion terminal can obtain the three-dimensional spatial coordinate information of the settlement area and the settled area through various methods. For example, the intelligent fusion terminal can use a three-dimensional vision sensor to collect three-dimensional point cloud data of the settlement area and the settled area, and then generate a complete three-dimensional spatial model through point cloud stitching and three-dimensional reconstruction technology. In addition, the intelligent fusion terminal can also use multiple two-dimensional vision sensors to collect images of the settlement area and the settled area from different angles, and then use binocular stereo vision technology or structured light technology to extract three-dimensional spatial information. To obtain the position of the RF antenna and the distribution of metal objects, the intelligent fusion terminal can use manual calibration to enter the position information of the RF antenna and metal objects into the system.

[0115] S202: collecting the arrival time difference and arrival angle of the signal received by each radio frequency antenna based on the spatial coordinate system, and constructing a multipath propagation path map in combination with the real-time signal strength;

[0116] The arrival time difference and arrival angle of the received signal of each RF antenna are collected based on the spatial coordinate system, and a multipath propagation path diagram is constructed in combination with the real-time signal strength. Specifically: a phase-synchronized trigger signal is used to control the sampling timing of each RF antenna to obtain a sampling signal; the in-phase component and the orthogonal component of the sampling signal are extracted, and the instantaneous phase and envelope of the sampling signal are calculated based on the in-phase component and the orthogonal component; based on the instantaneous phase, a generalized cross-correlation algorithm is used to calculate the arrival time difference between any two RF antennas; an overdetermined set of equations is constructed based on the arrival time difference, and the arrival angle of the sampling signal is determined based on the overdetermined set of equations.

[0117] The intelligent converged terminal collects the arrival time difference and arrival angle of the signal received by each RF antenna based on a spatial coordinate system and, combined with real-time signal strength, constructs a multipath propagation path map. This step utilizes the established three-dimensional spatial coordinate system to collect information such as the arrival time difference and arrival angle of the signal received by each RF antenna. Combined with real-time measured signal strength data, it constructs a graphical model describing the signal's multipath propagation path. The multipath propagation path map reflects the signal's propagation in space, including direct and reflected paths, providing important reference information for subsequent signal compensation and correction.

[0118] In specific implementations, the intelligent fusion terminal can construct a multipath propagation path map in a variety of ways. First, the intelligent fusion terminal can use phase synchronization trigger signals to control the sampling timing of each RF antenna, ensuring that the signal samples collected by each antenna are synchronized in time. Then, the intelligent fusion terminal can extract the in-phase and quadrature components of the sampled signal and calculate characteristic parameters such as the instantaneous phase and envelope of the sampled signal based on these components. Using these characteristic parameters, the intelligent fusion terminal can use a generalized cross-correlation algorithm to calculate the arrival time difference between any two RF antennas, and construct an overdetermined system of equations based on the arrival time difference to solve the arrival angle of the sampled signal. Finally, the intelligent fusion terminal can comprehensively analyze data such as the location information of each RF antenna, the arrival time difference and arrival angle of the signal, and real-time signal strength to construct a complete multipath propagation path map.

[0119] Before executing step S203, the propagation speed and attenuation coefficient of electromagnetic waves in different media are calculated based on real-time environmental status data; the propagation characteristics of electromagnetic waves are simulated to obtain the main propagation paths; an electromagnetic field simulation model is established based on the main propagation paths; and the signal propagation loss of different paths is predicted using the electromagnetic field simulation model to obtain a path loss estimation database. Predicting the signal propagation loss of different paths using the electromagnetic field simulation model specifically includes: simulating the electromagnetic field distribution based on the electromagnetic field simulation model; simplifying the electromagnetic field distribution into a superposition of several main propagation modes to obtain a superposition representation; establishing an electromagnetic wave transmission equation containing frequency dispersion and spatial attenuation characteristics based on the superposition representation; calculating the geometric attenuation, medium absorption, and scattering loss of each main propagation path to obtain a loss eigenvector; constructing a path characteristic parameter matrix based on the loss eigenvector, wherein the characteristic dimensions in the path characteristic parameter matrix include path length, incident angle, number of reflections, and loss component; using a recursive neural network to train the path loss prediction model, and using the path characteristic parameter matrix as input to predict the signal propagation loss of different paths.

[0120] S203, calculating the geometric loss, dielectric loss, and reflection loss of each path based on the multipath propagation path diagram to obtain path transmission characteristic parameters;

[0121] Based on the multipath propagation path map, the intelligent converged terminal calculates the geometric loss, dielectric loss, and reflection loss of each path to obtain path transmission characteristic parameters. This step uses the constructed multipath propagation path map to analyze and calculate the signal loss along each propagation path, obtaining a set of parameters that reflect the path transmission characteristics. These transmission characteristic parameters include geometric diffusion loss, dielectric absorption loss, and reflection and refraction loss experienced by the signal during propagation, and are crucial for subsequent signal compensation and correction.

[0122] In specific implementations, the intelligent converged terminal can obtain the loss parameters for each propagation path through a combination of theoretical calculations and empirical estimations. For geometric loss, the intelligent converged terminal can use a free-space propagation model to calculate the signal's distance attenuation and diffusion loss based on the distance between the RF antenna and the product in the spatial coordinate system and the geometric characteristics of the propagation path. For dielectric loss, the intelligent converged terminal can calculate the propagation velocity and attenuation coefficient of electromagnetic waves in media such as air and shelves based on environmental parameters such as temperature, humidity, and air pressure within the settlement area, and estimate dielectric absorption loss based on the length of the propagation path. For reflection loss, the intelligent converged terminal can use simplified electromagnetic theoretical models (such as the Fresnel reflection model) to calculate reflection loss based on parameters such as the material, roughness, and angle of incidence of reflective surfaces such as shelves, floors, and ceilings. Furthermore, the intelligent converged terminal can use electromagnetic field simulation software to create a three-dimensional model and mesh the settlement area, simulate and calculate signal loss along each propagation path, and generate a path loss estimation database.

[0123] S204: Establish a signal propagation compensation matrix according to the path transmission characteristic parameters, and perform amplitude and phase compensation on the radio frequency signal based on the signal propagation compensation matrix to obtain a corrected signal strength distribution;

[0124] The intelligent converged terminal establishes a signal propagation compensation matrix based on the path transmission characteristic parameters. Based on this matrix, it compensates the RF signal for amplitude and phase, resulting in a corrected signal strength distribution. This step uses the estimated path transmission characteristic parameters to construct a matrix model describing the signal propagation compensation rules. This matrix is ​​then used to correct the amplitude and phase of the received RF signal, ultimately yielding a compensated signal strength spatial distribution. This step aims to mitigate the effects of multipath on signal strength measurements and improve the accuracy and reliability of signal strength distribution.

[0125] In specific implementations, the intelligent fusion terminal can build a signal propagation compensation matrix based on theoretical foundations of signal processing and the estimated path transmission characteristic parameters. The row and column dimensions of this matrix correspond to the number of RF antennas, and the matrix element values ​​reflect the signal compensation coefficients between different antennas. These compensation coefficients must comprehensively consider factors such as the amplitude attenuation and phase delay of the RF signal along each propagation path and are calculated using the geometric characteristics and loss parameters of the propagation path. After obtaining the compensation matrix, the intelligent fusion terminal multiplies the matrix with the original signal received by each RF antenna to obtain the compensated signal amplitude and phase values. Finally, the intelligent fusion terminal uses the compensated signal amplitude to recalculate the signal strength distribution map at each location in the space, which serves as the basis for subsequent judgment of product placement compliance.

[0126] S205. Calculate the false detection rate and missed detection rate based on the corrected signal strength distribution;

[0127] The intelligent converged terminal calculates the false detection rate and missed detection rate based on the corrected signal strength distribution. This step uses the compensated and corrected signal strength distribution to analyze and calculate the false detection rate and missed detection rate indicators of the system's judgment of product placement compliance. The false detection rate is the probability of misidentifying a product that meets the placement specifications as not meeting them, while the missed detection rate is the probability of misidentifying a product that does not meet the placement specifications as meeting them. These two indicators reflect the accuracy and reliability of product placement compliance judgment and are important criteria for evaluating system performance.

[0128] In specific implementations, the intelligent fusion terminal can use statistical hypothesis testing methods to calculate the false positive rate and missed detection rate. First, the intelligent fusion terminal needs to divide the corrected signal strength distribution map into two areas: those that meet the placement specifications and those that do not, based on a pre-set judgment threshold. Then, the intelligent fusion terminal can select a set of product samples with known placement specifications and, based on the position of these samples in the signal strength distribution map, calculate the difference between the statistical judgment results and the actual placement specifications. By calculating the proportion of samples that meet the specifications that are mistakenly judged as not meeting the specifications, the system's false positive rate can be obtained. Similarly, by calculating the proportion of samples that do not meet the specifications that are missed as meeting the specifications, the system's missed detection rate can be obtained.

[0129] S206: Update the preset threshold according to the false detection rate, missed detection rate and preset detection reliability requirement.

[0130] The intelligent converged terminal updates the preset thresholds based on the false detection rate, missed detection rate, and pre-set detection reliability requirements. This step uses the calculated false detection rate and missed detection rate indicators, combined with the system's pre-set detection reliability requirements, to dynamically adjust the threshold parameters for determining product placement compliance. This adaptive threshold update improves the sensitivity of the judgment threshold while meeting detection reliability requirements, reducing the false detection rate and missed detection rate, and further enhancing overall system performance.

[0131] In specific implementations, intelligent converged terminals can use closed-loop control to automatically adjust their judgment thresholds. First, the intelligent converged terminal needs to preset an initial detection reliability target based on business needs and operational experience. For example, the terminal should keep both the false detection rate and missed detection rate below 5%. The intelligent converged terminal then compares the current false detection rate and missed detection rate with the preset targets and calculates the threshold adjustment based on the magnitude and sign of the differences. For example, if the false detection rate is higher than expected and the missed detection rate is lower than expected, the intelligent converged terminal can appropriately lower the judgment threshold to reduce false detections. Conversely, if the missed detection rate is higher than expected and the false detection rate is lower than expected, the intelligent converged terminal should increase the judgment threshold to reduce missed detections. Through repeated threshold adjustments and performance evaluation, the intelligent converged terminal can continuously approach the optimal judgment threshold, achieving adaptive optimization of detection performance.

[0132] In the above embodiment, a three-dimensional spatial coordinate system is established that includes the position of the RF antenna, the boundary of the product placement area, and the distribution of metal objects. The arrival time difference and arrival angle information are combined to construct a multipath propagation path map, thereby achieving accurate modeling of the RF signal propagation environment. The loss parameters of each propagation path are calculated and a signal propagation compensation matrix is ​​established to perform amplitude and phase compensation on the RF signal, thereby reducing the signal distortion caused by multipath propagation. The false detection rate and missed detection rate are calculated based on the corrected signal strength distribution, and the preset threshold is dynamically updated, so that the system has adaptive adjustment capabilities, reduces the impact of complex electromagnetic environments on signal propagation, and improves the accuracy of signal strength measurement. By updating the preset threshold in real time, the system can automatically optimize the judgment criteria based on the actual detection effect, thereby improving the reliability of the judgment of the standardization of product placement.

[0133] The following describes the terminal in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , which is a schematic diagram of the physical device structure of a settlement terminal based on an intelligent fusion terminal provided in an embodiment of the present application.

[0134] It should be noted that Figure 3 The structure of the terminal shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0135] like Figure 3 As shown, the terminal includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes, such as the methods described in the above embodiments, based on programs stored in a read-only memory (ROM) 302 or programs loaded from a storage unit 308 into a random access memory (RAM) 303. RAM 303 also stores various programs and data required for terminal operation. CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to bus 304.

[0136] The following components are connected to the I / O interface 305: an input section 306 including a camera, infrared sensor, and the like; an output section 307 including a liquid crystal display (LCD) and speakers; a storage section 308 including a hard disk and the like; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. Removable media 311, such as a magnetic disk, optical disk, magneto-optical disk, or semiconductor memory, is installed in the drive 310 as needed, so that computer programs read from the media can be installed in the storage section 308 as needed.

[0137] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for executing the methods illustrated in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via the communication section 309 and / or installed from removable media 311. When executed by the central processing unit (CPU) 301, the computer program performs the various functions defined in the present invention.

[0138] It should be noted that the computer-readable medium described in the embodiments of the present invention may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium may include a data signal transmitted in baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal may take any of a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof.

[0139] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the terminals, methods, and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the boxes may also occur in an order different from that marked in the accompanying drawings. For example, two boxes shown in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, as well as the combination of boxes in the block diagram or flowchart, can be implemented using a dedicated hardware-based terminal that performs the specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0140] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the terminal described in the above embodiments, or may exist independently and not incorporated into the terminal. The storage medium carries one or more computer programs, and when executed by a processor of a terminal, the terminal implements the methods provided in the above embodiments.

[0141] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0142] As used in the above embodiments, the term “when” may be interpreted to mean “if” or “after” or “in response to determining that” or “in response to detecting that”, depending on the context. Similarly, the phrases “upon determining that” or “if (stated condition or event) is detected” may be interpreted to mean “if determining that” or “in response to determining that” or “upon detecting (stated condition or event)” or “in response to detecting (stated condition or event)”, depending on the context.

[0143] In the above embodiments, all or part of the embodiments can be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, hard disk, tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive).

[0144] Those skilled in the art will appreciate that all or part of the process steps in the above-described method embodiments can be implemented by a computer program instructing the relevant hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can include the process steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A settlement method based on an intelligent fusion terminal, characterized in that: include: After determining that the barcode information of a new product has been scanned, calculating the maximum radio frequency signal strength value of the new product in the settlement area, and determining the maximum radio frequency signal strength value as the reference signal strength, the terminal includes the settlement area and the settled area, and a plurality of radio frequency antennas are provided in the settlement area and the settled area to obtain the radio frequency signal strength of the product; Determining whether the weight of the merchandise in the settled area has increased; If the weight of the product has not increased, calculating the maximum radio frequency signal strength value of the new product in the settled area to obtain the settled signal strength; When it is determined that the ratio of the settled signal strength to the reference signal strength is less than a preset threshold, performing a spatial interpolation operation on all radio frequency signal data in the settled area to construct a continuous distribution surface of the radio frequency signal strength of the new product; Calculating a peak position of the continuous distribution surface, and determining whether the peak position of the surface is within a preset range of the settled area; If the new product is within the preset range, the new product is confirmed as a standard placement product; If the new product is not within the preset range, the new product is identified as a non-standard placement product; Activating a preset alarm mode, and after the preset alarm mode ends, executing the step of calculating the maximum radio frequency signal strength value of the new product in the settled area to obtain the settled signal strength; If the weight of the commodity increases, the new commodity is confirmed as a standard placement commodity.

2. The method according to claim 1, characterized in that The step of calculating the maximum radio frequency signal strength value of the new product in the settlement area specifically includes: Obtaining a sequence of radio frequency signal strengths collected by each radio frequency antenna within a settlement area within a preset time window; Performing wavelet transform on the radio frequency signal strength sequence to obtain signal characteristic coefficients at different scales; Calculating the entropy value of the signal based on the signal characteristic coefficient, and removing abnormal data with an entropy value greater than a preset threshold; constructing an analytical representation based on the signal strength values ​​excluding the abnormal data; Calculating the instantaneous envelope of the analytical representation to obtain a local maximum point; Performing linear regression on the local maximum point to obtain a change trend of the signal intensity; The maximum value of the signal strength in the change trend is used as the reference signal strength.

3. The method according to claim 1, characterized in that The step of performing spatial interpolation operation on all radio frequency signal data in the settled area to construct a continuous distribution surface of the radio frequency signal strength of the new product specifically includes: Dividing the settled area into a first number of grid units, recording the radio frequency signal strength value of the center point of each grid unit, to obtain a discrete spatial sampling data set; Calculating a signal strength change rate of each of the grid cells based on the spatial sampling data set, and adaptively subdividing the grid cells according to the magnitude of the signal strength change rate to obtain a second number of subdivided grid cells, where the second number is greater than the first number; constructing an interpolation basis function system in a tensor product form, wherein the interpolation basis function system satisfies an interpolation condition at the nodes of the grid unit; Solving interpolation coefficients that satisfy global smoothness according to the interpolation basis function system; constructing a global system of equations including boundary continuity constraints for the subdivided grid cells; A continuous distribution surface of the radio frequency signal strength of the new product is constructed based on the global equation group and the interpolation coefficients.

4. The method according to claim 1, wherein Before determining that the ratio of the settled signal strength to the reference signal strength is less than a preset threshold, the method further includes: Obtaining three-dimensional spatial coordinate information of the settlement area and the settled area, and establishing a spatial coordinate system including the location of the radio frequency antenna, the boundary of the product placement area, and the distribution of metal objects; Collecting the arrival time difference and arrival angle of the signal received by each of the radio frequency antennas based on the spatial coordinate system, and constructing a multipath propagation path map in combination with the real-time signal strength; Calculating the geometric loss, dielectric loss, and reflection loss of each path based on the multipath propagation path graph to obtain path transmission characteristic parameters; Establishing a signal propagation compensation matrix according to the path transmission characteristic parameters; performing amplitude and phase compensation on the radio frequency signal based on the signal propagation compensation matrix to obtain a corrected signal strength distribution; Calculating the false detection rate and the missed detection rate according to the corrected signal strength distribution; The preset threshold is updated according to the false detection rate, the missed detection rate and the preset detection reliability requirement.

5. The method according to claim 4, characterized in that The step of collecting the arrival time difference and arrival angle of the signal received by each of the radio frequency antennas based on the spatial coordinate system specifically includes: Using a phase synchronization trigger signal to control the sampling timing of each of the radio frequency antennas to obtain a sampling signal; Extracting an in-phase component and a quadrature component of the sampled signal, and calculating an instantaneous phase and an envelope of the sampled signal based on the in-phase component and the quadrature component; Based on the instantaneous phase, a generalized cross-correlation algorithm is used to calculate the arrival time difference between any two of the radio frequency antennas; An overdetermined set of equations is constructed according to the arrival time difference, and the arrival angle of the sampling signal is determined according to the overdetermined set of equations.

6. The method according to claim 4, characterized in that Before calculating the geometric loss, dielectric loss, and reflection loss of each path based on the multipath propagation path graph, the method further includes: Calculate the propagation speed and attenuation coefficient of electromagnetic waves in different media based on real-time environmental status data; Simulating the propagation characteristics of the electromagnetic wave to obtain a main propagation path; Based on the main propagation path, establishing an electromagnetic field simulation model; The electromagnetic field simulation model is used to predict signal propagation losses of different paths to obtain a path loss prediction database.

7. The method according to claim 6, characterized in that The method of predicting signal propagation losses of different paths by using the electromagnetic field simulation model specifically includes: Simulating the electromagnetic field distribution based on the electromagnetic field simulation model; Simplifying the electromagnetic field distribution into a superposition of several main propagation modes to obtain a superposition representation; Establishing an electromagnetic wave transmission equation including frequency dispersion and spatial attenuation characteristics based on the superposition representation; Calculating the geometric attenuation, dielectric absorption and scattering losses of each of the main propagation paths to obtain a loss characteristic vector; Constructing a path characteristic parameter matrix according to the loss characteristic vector, wherein characteristic dimensions in the path characteristic parameter matrix include path length, incident angle, number of reflections and loss component; A recursive neural network is used to train a path loss prediction model, and the path characteristic parameter matrix is ​​used as input to predict the signal propagation loss of different paths.

8. A settlement terminal based on an intelligent fusion terminal, characterized in that: The terminal includes: One or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the terminal to execute the method according to any one of claims 1 to 7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on a terminal, the terminal is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a terminal, the terminal is enabled to execute the method according to any one of claims 1 to 7.

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