A method and apparatus for occlusion detection, a payment device, a storage medium and a program product

Abstract

This application provides an occlusion detection method and apparatus, a payment device, a storage medium, and a program product. Using the technical solution of this application, when the distance sensing data collected by the ranging sensor of the payment device and / or the image sensing data collected by the scanning module meet preset occlusion conditions, the target payment information for payment via the near-field communication module and the scanning module is obtained. This target payment information is then used to determine whether the payment device is occluded. This allows for real-time monitoring of payment devices supporting different payment methods, enabling timely and proactive detection of occlusion during the payment process. Furthermore, the use of multi-source data supports occlusion detection for different payment devices, improving the reliability and intelligence of the payment device in complex scenarios.

Description

Technical Field

[0001] This application relates to the field of electronic payment technology, and in particular to an obstruction detection method and apparatus, payment device, storage medium and program product. Background Technology

[0002] In the process of digitalizing physical retail, payment can be made in various ways. However, payment failures can occur, and device obstruction is one of the reasons for these failures. Current technologies heavily rely on manual offline store visits or user feedback to detect obstructions, making it difficult to proactively and in real-time identify whether payment devices are blocked. This results in delayed problem detection, a lack of flexibility, and a poor payment experience.

[0003] Therefore, how to detect whether the payment device is obstructed in a timely and proactive manner during the payment process has become an urgent problem to be solved. Summary of the Invention

[0004] This application provides an obstruction detection method and apparatus, a payment device, a storage medium, and a program product. The method can detect whether the payment device is obstructed in a timely and proactive manner during the payment process.

[0005] In a first aspect, an occlusion detection method is provided, which is applied to a payment device. The payment device includes a near-field communication module, a scanning module, and a ranging sensor. The method includes: acquiring distance sensing data collected by the ranging sensor and image sensing data collected by the scanning module; when the distance sensing data and / or image sensing data meet preset occlusion conditions, acquiring target payment information for payment made through the near-field communication module and the scanning module; and determining whether the payment device is occluded based on the target payment information.

[0006] In the above technical solution, when the distance sensing data collected by the ranging sensor of the payment device and / or the image sensing data collected by the scanning module meet the preset occlusion conditions, the target payment information for payment via the near-field communication module and the scanning module is obtained. This target payment information is then used to determine whether the payment device is occluded. This solution enables real-time monitoring of payment devices supporting different payment methods. It can proactively and timely detect whether the payment device is occluded during the payment process. Furthermore, the use of multi-source data supports occlusion detection for different payment devices, improving the reliability and intelligence of the payment device in complex scenarios.

[0007] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: when the distance sensing data is less than a preset distance, determining whether the distance sensing data meets the preset occlusion condition based on the duration during which the distance sensing data is less than the preset distance.

[0008] In the above technical solution, when the distance sensing data is less than the preset distance, the duration of the distance sensing data being less than the preset distance is further obtained, and the duration is used to determine whether the distance sensing data meets the preset occlusion conditions. The duration effectively avoids false triggering caused by factors such as normal payment or environmental fluctuations, and improves the occlusion judgment capability of the payment device.

[0009] Combining the first aspect and the above implementation method, based on the duration for which the distance sensing data is less than a preset distance, it is determined whether the distance sensing data meets the preset occlusion condition, including: if the duration for which the distance sensing data is less than the preset distance is greater than or equal to a first preset duration, it is determined that the distance sensing data meets the preset occlusion condition; if the duration for which the distance sensing data is less than the preset distance is less than the first preset duration, it is determined that the distance sensing data does not meet the preset occlusion condition; wherein, if the distance sensing data is greater than or equal to the preset distance, the distance sensing data also does not meet the preset occlusion condition.

[0010] In the above technical solution, the distance sensing data is determined to meet the preset occlusion condition only when the distance sensing data is less than the preset distance and the duration of the distance sensing data being less than the preset distance is greater than or equal to the first preset duration. This ensures the accuracy of detection, reduces invalid judgments caused by interference, and improves the stability of the payment device.

[0011] In conjunction with the first aspect and the above implementation method, the method further includes: when the image perception data includes multiple target noise points, determining whether the image perception data meets the preset occlusion conditions based on the multiple target noise points.

[0012] In the above technical solution, when the image perception data includes multiple target noise points, the determination of whether the image perception data meets the preset occlusion conditions by using multiple target noise points can effectively distinguish between real occlusion and the inherent noise of the scanning module, thereby improving the occlusion judgment capability of the payment device.

[0013] Combining the first aspect and the above implementation method, when multiple target noise points are clustered in the same area, it is determined that the image perception data meets the preset occlusion condition; when multiple target noise points are distributed in different areas, it is determined that the image perception data does not meet the preset occlusion condition; wherein, when the image perception data does not include target noise points, the image perception data also does not meet the preset occlusion condition.

[0014] In the above technical solution, the image perception data is determined to meet the preset occlusion conditions only when multiple target noise points are clustered in the same area. This utilizes the physical characteristic that noise points formed by real occluders in image perception data often exhibit clustered distribution, thereby achieving preliminary occlusion recognition of image perception data.

[0015] In conjunction with the first aspect and the above implementation method, the method also includes: determining the texture features corresponding to the image perception data; and determining whether the image perception data meets the preset occlusion conditions based on the texture features.

[0016] In the above technical solution, the determination of whether the image perception data meets the preset occlusion conditions is made by identifying the texture features corresponding to the determined image perception data. Texture features are introduced as the basis for determining the occlusion of image perception data, which can accurately identify the occlusion state of image data without the need for additional reference objects.

[0017] Combining the first aspect and the above implementation method, determining whether image perception data meets the preset occlusion conditions based on multiple target noise points includes: determining that the image perception data meets the preset occlusion conditions when the texture features remain unchanged at different times; and determining that the image perception data does not meet the preset occlusion conditions when the texture features change at different times.

[0018] In the above technical solution, under the condition that the texture features remain unchanged at different times, the image perception data is determined to meet the preset occlusion conditions. By utilizing the property that the texture features of the image are stable due to the occlusion, the preliminary occlusion recognition of the image perception data is realized.

[0019] In conjunction with the first aspect and the above implementation method, the method further includes: obtaining the target transaction time period, which is determined based on historical transaction information, historical passenger flow data, and historical status data of payment devices; and, during the target transaction time period, performing the steps of obtaining distance sensing data collected by the near-field communication module and image sensing data collected by the scanning module.

[0020] In the above technical solution, distance perception data collected by the near-field communication module and image perception data collected by the scanning module are only acquired during the target transaction time period, avoiding the data consumption caused by continuous data collection and occlusion detection. At the same time, using historical data to determine the target transaction time period can improve the targeting of occlusion detection for different stores.

[0021] Combining the first aspect and the above implementation method, determining whether a payment device is obstructed based on target payment information includes: acquiring target payment information within a first time period when the distance perception data meets the preset obstruction conditions and the image perception data does not meet the preset obstruction conditions; determining that the near-field communication module is obstructed when the target payment information within the first time period indicates that payment was completed through the scanning module but not through the near-field communication module; wherein, the first time period is negatively correlated with historical passenger flow data.

[0022] In the above technical solution, the target payment information within the first time period is obtained by combining distance perception data that meets the preset occlusion conditions. The target payment information is then used to determine whether the near-field communication module is occluded. This method can accurately determine whether a single near-field communication module is occluded by combining the data collected by the sensor and the data obtained from the transaction, thus improving the accuracy and specificity of occlusion detection. Furthermore, the time period for occlusion detection can be set according to the actual passenger flow, which improves the flexibility of occlusion detection.

[0023] Combining the first aspect and the above implementation method, determining whether the payment device is obstructed based on the target payment information includes: obtaining the target payment information within a second time period when both the distance perception data and the image perception data meet the preset obstruction conditions; and determining that the near-field communication module and the scanning module are obstructed when the target payment information within the second time period indicates that payment was not completed through the near-field communication module and the scanning module; wherein, the second time period is negatively correlated with historical passenger flow data.

[0024] In the above technical solution, the target payment information within the third time period is obtained by combining distance perception data and image perception data that meet the preset occlusion conditions. The target payment information is then used to determine whether the near-field communication module and the scanning module are occluded. This allows for accurate determination of whether the near-field communication module and the scanning module are occluded by combining the data collected by the sensors and the data obtained from the transaction, thus improving the accuracy and specificity of occlusion detection. Furthermore, the time period for occlusion detection can be set according to the actual passenger flow, improving the flexibility of occlusion detection.

[0025] Combining the first aspect and the above implementation method, determining whether the payment device is obstructed based on the target payment information includes: obtaining the target payment information within a third time period when the image perception data meets the preset obstruction conditions and the distance perception data does not meet the preset obstruction conditions; determining that the scanning module is obstructed when the target payment information within the third time period indicates that the payment was completed through the near-field communication module but not through the scanning module; wherein, the third time period is negatively correlated with historical passenger flow data.

[0026] In the above technical solution, the target payment information within the third time period is obtained by combining image perception data that meets the preset occlusion conditions, and the occlusion of the scanning module is further determined by the target payment information. This can accurately determine whether a single scanning module is occluded by combining the data collected by the sensor and the data obtained from the transaction, thereby improving the accuracy and targeting of occlusion detection. Furthermore, the time period for occlusion detection can be set according to the actual passenger flow, thereby improving the flexibility of occlusion detection.

[0027] In conjunction with the first aspect and the above implementation method, the method further includes: when the payment device is obstructed, sending abnormal obstruction information to the cloud, the abnormal obstruction information being used to indicate that the payment device is obstructed; wherein, the cloud is used to generate a target work order based on the abnormal obstruction information and send the target work order to the target terminal, the target terminal being a terminal device associated with the store where the payment device is located.

[0028] In the above technical solution, when the payment device is obstructed, abnormal obstruction information is sent to the cloud so that the cloud generates a target work order and sends the target work order to the target terminal. This allows for timely issuance of work orders after abnormal obstruction is confirmed, reminding relevant personnel to handle the abnormal obstruction situation, and constructing a closed-loop system from abnormality detection to abnormality resolution.

[0029] Secondly, an occlusion detection device is provided for use in a payment device. The payment device includes a near-field communication module, a scanning module, and a ranging sensor. The device includes: The acquisition module is used to acquire distance sensing data collected by the ranging sensor and image sensing data collected by the scanning module. When the distance sensing data and / or image sensing data meet the preset occlusion conditions, the target payment information for payment through the near-field communication module and the scanning module is acquired. The determination module is used to determine whether the payment device is obstructed based on the target payment information.

[0030] In conjunction with the second aspect, in some implementations of the second aspect, a determining module is used to determine whether the distance sensing data meets the preset occlusion condition based on the duration during which the distance sensing data is less than the preset distance when the distance sensing data is less than the preset distance.

[0031] In combination with the second aspect and the above implementation methods, in some possible implementation methods, the determining module is used to determine that the distance sensing data meets the preset occlusion condition when the duration of the distance sensing data being less than the preset distance is greater than or equal to the first preset duration; and to determine that the distance sensing data does not meet the preset occlusion condition when the duration of the distance sensing data being less than the preset distance is less than the first preset duration; wherein, the distance sensing data also does not meet the preset occlusion condition when the distance sensing data is greater than or equal to the preset distance.

[0032] Combining the second aspect and the above implementation methods, in some possible implementation methods, the determining module is used to determine whether the image perception data meets the preset occlusion conditions based on the multiple target noise points when the image perception data includes multiple target noise points.

[0033] Combining the second aspect and the above implementation methods, in some possible implementation methods, the determining module is used to determine that the image perception data meets the preset occlusion conditions when multiple target noise points are clustered in the same area; and to determine that the image perception data does not meet the preset occlusion conditions when multiple target noise points are distributed in different areas; wherein, if the image perception data does not include target noise points, the image perception data also does not meet the preset occlusion conditions.

[0034] Combining the second aspect and the above implementation methods, in some possible implementation methods, a determining module is used to determine the texture features corresponding to the image perception data; and to determine whether the image perception data meets the preset occlusion conditions based on the texture features.

[0035] Combining the second aspect and the above implementation methods, in some possible implementation methods, the determining module is used to determine that the image perception data meets the preset occlusion conditions when the texture features remain unchanged at different times; and to determine that the image perception data does not meet the preset occlusion conditions when the texture features change at different times.

[0036] Combining the second aspect and the above implementation methods, in some possible implementation methods, the acquisition module is used to acquire the target transaction time period, which is determined based on historical transaction information, historical passenger flow data, and historical status data of payment devices; during the target transaction time period, the steps of acquiring distance sensing data collected by the near-field communication module and image sensing data collected by the scanning module are performed.

[0037] Combining the second aspect and the above implementation methods, in some possible implementation methods, a determining module is used to obtain target payment information within a first time period when the distance perception data meets the preset occlusion conditions and the image perception data does not meet the preset occlusion conditions; if the target payment information within the first time period indicates that payment was completed through the scanning module but not through the near-field communication module, it is determined that the near-field communication module is occluded; wherein, the first time period is negatively correlated with historical passenger flow data.

[0038] Combining the second aspect and the above implementation methods, in some possible implementation methods, a determining module is used to obtain target payment information within a second time period when both distance perception data and image perception data meet preset occlusion conditions; if the target payment information within the second time period indicates that payment was not completed through the near-field communication module and the scanning module, it is determined that the near-field communication module and the scanning module are occluded; wherein, the second time period is negatively correlated with historical passenger flow data.

[0039] Combining the second aspect and the above implementation methods, in some possible implementation methods, a determining module is used to obtain target payment information within a third time period when the image perception data meets the preset occlusion conditions and the distance perception data does not meet the preset occlusion conditions; if the target payment information within the third time period indicates that payment was completed through the near-field communication module but not through the scanning module, it is determined that the scanning module is occluded; wherein, the third time period is negatively correlated with historical passenger flow data.

[0040] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the device further includes a sending module, which is used to send abnormal occlusion information to the cloud when the payment device is obstructed. The abnormal occlusion information is used to indicate that the payment device is obstructed. The cloud is used to generate a target work order based on the abnormal occlusion information and send the target work order to the target terminal, which is a terminal device associated with the store where the payment device is located.

[0041] Thirdly, a payment device is provided, including a memory and a processor. The memory is used to store executable program code; the processor is used to call and run the executable program code from the memory, causing the payment device to perform the occlusion detection method in the first aspect or any possible implementation of the first aspect.

[0042] Fourthly, a computer-readable storage medium is provided that stores computer program code, which, when executed on a payment device, causes the payment device to perform the occlusion detection method described in the first aspect or any possible implementation thereof.

[0043] Fifthly, a computer program product is provided, comprising: computer program code, which, when run on a payment device, causes the payment device to execute the occlusion detection method in the first aspect or any possible implementation thereof.

[0044] The method provided in this application, when the distance sensing data collected by the ranging sensor of the payment device and / or the image sensing data collected by the scanning module meet preset occlusion conditions, acquires the target payment information for payment via the near-field communication module and the scanning module, and then uses the target payment information to determine whether the payment device is occluded. This method enables real-time monitoring of payment devices supporting different payment methods, and can proactively and timely detect whether the payment device is occluded during the payment process. Furthermore, by employing multi-source data, it supports occlusion detection for different payment devices, improving the reliability and intelligence of the payment device in complex scenarios. Attached Figure Description

[0045] Figure 1 This is a flowchart of generating a target work order provided in an embodiment of this application; Figure 2 This is an illustrative flowchart of an occlusion detection method provided in an embodiment of this application. Figure 1 ; Figure 3 This is an illustrative flowchart of an occlusion detection method provided in an embodiment of this application. Figure 2 ; Figure 4 This is a schematic diagram of the structure of an occlusion detection device provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of a payment device provided in an embodiment of this application. Detailed Implementation

[0046] The technical solutions in this application will be clearly and thoroughly described below with reference to the accompanying drawings. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. "And / or" in the text is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the description of the embodiments of this application, "multiple" refers to two or more than two.

[0047] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0048] Before introducing the solutions of the embodiments of this application, the technical terms that may be involved in the embodiments of this application will be explained first.

[0049] Time-of-Flight (TOF) ranging sensors are ranging devices based on the time-of-flight principle. They primarily calculate the distance between an object and the sensor by emitting light pulses and receiving the reflected light. TOF ranging sensors use tiny transmitters to emit infrared light or laser light, which bounces off any object and returns to the sensor. The distance between the object and the TOF ranging sensor can be measured by the time difference between the emission of the light and its return to the sensor after reflection.

[0050] Near Field Communication (NFC) is a short-range, high-frequency wireless communication technology that allows for contactless, point-to-point data transfer between electronic devices. NFC technology evolved from Radio Frequency Identification (RFID).

[0051] Tap to pay: This is a payment method based on NFC technology. Users simply need to bring their mobile phone close to an NFC-enabled payment device to quickly initiate the payment process. This method reduces the number of steps required by the user, improves payment efficiency, and saves time.

[0052] QR code payment: This is a convenient electronic payment method where users scan a QR code provided by a merchant with their mobile phone, or show their own QR code for the merchant to scan, thereby completing the transaction.

[0053] Before introducing the solutions of the embodiments of this application, we will first introduce the application scenarios of the embodiments of this application.

[0054] In the process of digitalizing physical retail, payment can be made in various ways. However, payment failures can occur during the payment process. Device obstruction is a core pain point that leads to payment failures, data distortion, and low operational efficiency.

[0055] The relevant technologies have the following significant shortcomings in handling obstruction issues: First, problem discovery is passive: They heavily rely on manual offline store visits or user feedback to discover obstructions, making it impossible to proactively and in real-time detect whether payment devices are obstructed, resulting in a delay in problem detection. Second, problems affect usability: If a tap-to-pay device is obstructed at the checkout counter, users will avoid choosing this payment method. Third, problem localization is difficult: The lack of effective device status monitoring and abnormal behavior identification mechanisms makes it difficult to accurately pinpoint whether the transaction failure is due to device obstruction, relocation, or other reasons. Furthermore, because transaction data, customer flow data, and device status data are scattered across different systems, there is a lack of effective fusion and analysis, making it impossible to form a complete store operation profile and dynamic, personalized operational strategies based on device status and store operation data, thus failing to specifically improve the usage rate of high-value users. Fourth, self-recovery is difficult: The natural recovery rate of obstruction issues is very low, posing a risk of long-term failure. Therefore, the above processes suffer from a lack of flexibility and a poor payment experience.

[0056] Therefore, how to detect whether the payment device is obstructed in a timely and proactive manner during the payment process has become an urgent problem to be solved.

[0057] The following is combined Figure 1 The occlusion detection method provided in the embodiments of this application is described by way of example. Figure 1This is a flowchart of generating a target work order provided in an embodiment of this application.

[0058] For example, taking stores as a unit, after a transaction occurs at a store, the transaction location is obtained through transaction information. This is further combined with the location-based services (LBS) module of the payment device to determine the store's location. Based on the store location, points of interest (POIs) are set for different stores. Simultaneously, historical status data of the payment device is obtained from the cloud, extracting the device's power-on and power-off times. Combined with historical customer flow data and historical transaction information, the store's opening time (corresponding to the target transaction time period) is determined. Furthermore, for the ranging sensor, if obstruction is detected, and if the target payment information further confirms that there was a QR code payment but no touch payment, then the near-field communication module is determined to be obstructed, and a first target work order is issued through the cloud. For the ranging sensor and scanning module, if obstruction is detected by both, and if the target payment information further confirms that there were no QR code payments or touch payments within a certain period before and after, then both the near-field communication module and the scanning module are determined to be obstructed, and a second target work order is issued through the cloud. For scanning modules, if there is obstruction during recognition, and if the target payment information is combined to further determine that there was a tap payment but no QR code payment, then the scanning module is determined to be obstructed, and a third target work order is issued through the cloud.

[0059] The following will combine Figure 2 and Figure 3 This section introduces the underlying implementation process upon which this method depends. Figure 2 This is an illustrative flowchart of an occlusion detection method provided in an embodiment of this application. Figure 1 It should be understood that the occlusion detection method 200 can be applied to payment devices, which include near-field communication modules, scanning modules, and ranging sensors. This application embodiment does not limit the type of payment device. For example, as... Figure 2 As shown, the occlusion detection method 200 includes the following steps.

[0060] 201. Acquire distance sensing data collected by the ranging sensor and image sensing data collected by the scanning module.

[0061] In this context, a ranging sensor refers to a sensor capable of measuring distance. In some embodiments, the ranging sensor may include, but is not limited to, one of the following: an ultrasonic ranging sensor, a Time-of-Flight (TOF) ranging sensor, or an infrared ranging sensor. Distance sensing data is data collected by the ranging sensor and is used to characterize the distance between other objects and the near-field communication module. A scanning module refers to a module capable of scanning one-dimensional or two-dimensional barcodes. Image sensing data is data collected by the scanning module and may include data such as a payment code presented by the user.

[0062] It should be noted that the technical solution of this application is mainly applied to payment devices, which are equipped with scanning modules, NFC modules, ranging sensors, LBS modules, and data processing units. The specific appearance and form of the payment device are not limited. The payment device includes at least one scanning module, and each scanning module includes a camera. The payment device supports both QR code payment and tap-to-pay methods. QR code payment is implemented through the scanning module, and tap-to-pay is implemented through the NFC module. The scanning module and NFC module can be connected via an adapter. The scanning module and NFC module can be integrated into one module or formed as independent modules. The adapter can be a KD4 adapter, a specific type of wireless transmission adapter commonly used to connect POS machines or tap-to-pay devices to achieve data transmission and communication between them. Furthermore, the payment device of this application is widely deployed in various physical retail scenarios, such as: traditional store upgrade equipment: smart POS machines (full name: Point of Sale Information Management System), membership service terminals, and product display tags for upgrading traditional retail stores; other IoT terminals: including but not limited to any smart device that needs to perform user identification, transaction processing, and environmental awareness in a retail environment.

[0063] 202. When the distance sensing data and / or image sensing data meet the preset occlusion conditions, acquire the target payment information for payment made through the near-field communication module and the scanning module.

[0064] In this context, if the distance sensing data and / or image sensing data meet preset occlusion conditions, it indicates that the payment device may be occluded. Therefore, it is necessary to further obtain the target payment information for payments made through the near-field communication module and scanning module. The preset occlusion conditions are used to initially determine whether the payment device is occluded. In some embodiments, if the distance sensing data and / or image sensing data meet the preset occlusion conditions, the payment device may or may not be occluded. Meeting the preset occlusion conditions can be limited to only the distance sensing data, only the image sensing data, or both. The target payment information is generated after the user makes a payment through the near-field communication module and scanning module in the payment device. In some embodiments, the target payment information can be obtained from the cloud or the payment device's management backend. The target payment information may include, but is not limited to, payment location, payment time, payment result, and payment amount. The payment location can be obtained through the payment device's LBS module, and the payment result can include one of the following: payment success, payment failure, or payment cancellation. Payment time can be represented by a timestamp.

[0065] 203. Determine whether the payment device is obstructed based on the target payment information.

[0066] The method provided in this application, when the distance sensing data collected by the ranging sensor of the payment device and / or the image sensing data collected by the scanning module meet preset occlusion conditions, acquires the target payment information for payment via the near-field communication module and the scanning module, and then uses the target payment information to determine whether the payment device is occluded. This method enables real-time monitoring of payment devices supporting different payment methods, and can proactively and timely detect whether the payment device is occluded during the payment process. Furthermore, by employing multi-source data, it supports occlusion detection for different payment devices, improving the reliability and intelligence of the payment device in complex scenarios.

[0067] It should be noted that steps 201-203 above are a simplified explanation of the occlusion detection method provided in the embodiments of this application. The occlusion detection method provided in the embodiments of this application will be described in more detail below with some examples. See [link to relevant documentation]. Figure 3 , Figure 3 This is an illustrative flowchart of an occlusion detection method provided in an embodiment of this application. Figure 2 .

[0068] It should be understood that the occlusion detection method 300 can be applied to payment devices, which include near-field communication modules, scanning modules, and ranging sensors. This application embodiment does not limit the type of payment device. For example, such as... Figure 3 As shown, the occlusion detection method 300 includes the following steps.

[0069] 301, acquire distance sensing data collected by the ranging sensor and image sensing data collected by the scanning module.

[0070] In this context, a ranging sensor refers to a sensor capable of measuring distance. In some embodiments, the ranging sensor may include, but is not limited to, one of the following: an ultrasonic ranging sensor, a Time-of-Flight (TOF) ranging sensor, or an infrared ranging sensor. Distance sensing data is data collected by the ranging sensor and is used to characterize the distance between other objects and the near-field communication module. A scanning module refers to a module capable of scanning one-dimensional or two-dimensional barcodes. Image sensing data is data collected by the scanning module and may include data such as a payment code presented by the user.

[0071] In one possible implementation, a target transaction time period is obtained, which is determined based on historical transaction information, historical passenger flow data, and historical status data of the payment device. During the target transaction time period, steps are performed to acquire distance sensing data collected by the near-field communication module and image sensing data collected by the scanning module.

[0072] The target transaction time period refers to the operating hours of the store where the payment device is located. In some embodiments, the target transaction time period can be determined by the cloud or the payment device itself, based on historical transaction information, historical customer flow data, and historical status data of the payment device. Historical transaction information is information generated after users completed transactions through the near-field communication module and scanning module in the payment device within a past time period. In some embodiments, historical transaction information can be obtained from the cloud or the management backend of the payment device. Target payment information may include, but is not limited to: transaction location, transaction time, transaction result, transaction amount, etc. Historical customer flow data is data used to characterize the number of customers and their behavior at different times in the past time period. Historical customer flow data can be collected through various technical means, such as video surveillance, infrared counting, or analysis of historical transaction information. Historical status data is used to describe the status of the payment device within a past time period. Historical status data may include, but is not limited to: power-on time, power-off time, abnormal times, etc.

[0073] In some embodiments, different stores have different transaction times, and it is necessary to detect whether the payment device is obstructed during the target transaction time. Therefore, the steps of acquiring distance perception data collected by the near-field communication module and image perception data collected by the scanning module are only executed during the target transaction time, thereby triggering the execution of the occlusion detection method.

[0074] In some embodiments, the distribution of transaction time and number of transactions of a store on different payment channels is determined based on historical transaction information, the peak period pattern of customer traffic and queuing situation is determined based on historical customer flow data, and the time when the store's payment devices are turned on and off is determined based on historical status data of payment devices. Based on the information obtained above, a store operation profile is constructed, which includes the target transaction time period.

[0075] In some embodiments, the store operation profile can be determined by the payment device or by the cloud. It can continuously acquire historical transaction information, historical customer flow data, and historical status data of the payment device to constantly update the store operation profile. Historical transaction information, historical customer flow data, and historical status data of the payment device can be converted into structured tag information and stored in the cloud, providing a data foundation for operational decisions.

[0076] In this implementation, distance perception data collected by the near-field communication module and image perception data collected by the scanning module are only acquired during the target transaction period. This avoids the data consumption caused by continuous data collection and occlusion detection. At the same time, using historical data to determine the target transaction period can improve the targeting of occlusion detection for different stores.

[0077] 302, when the distance sensing data and / or image sensing data meet the preset occlusion conditions, acquire the target payment information for payment made through the near-field communication module and the scanning module.

[0078] In this context, if the distance sensing data and / or image sensing data meet a preset occlusion condition, it indicates that the payment device may be occluded. Therefore, it is necessary to further acquire the target payment information for payment via the near-field communication module and the scanning module. The preset occlusion condition is used to initially determine whether the payment device is occluded. In some embodiments, if the distance sensing data and / or image sensing data meet the preset occlusion condition, the payment device may or may not be occluded. Meeting the preset occlusion condition can be limited to only the distance sensing data, only the image sensing data, or both.

[0079] Target payment information refers to the information generated after a user makes a payment through the near-field communication module and scanning module in the payment device. In some embodiments, target payment information can be obtained from the cloud or the management backend of the payment device. Target payment information may include, but is not limited to: payment location, payment time, payment result, and payment amount. The payment location can be obtained through the LBS module of the payment device, and the payment result can include one of the following: payment successful, payment failed, or payment cancelled. The payment time can be represented by a timestamp.

[0080] In one possible implementation, if the distance sensing data is less than a preset distance, the system determines whether the distance sensing data meets a preset occlusion condition based on the duration during which the distance sensing data is less than the preset distance.

[0081] The distance sensing data is collected by a ranging sensor and is used to characterize the distance between other objects and the near-field communication module. The preset distance can be any suitable size, such as 1 cm or 1.5 cm. The duration is the length of time during which the distance sensing data is less than the preset distance, and is used to characterize the time it takes for the distance sensing data to go from being less than the preset distance to being greater than or equal to the preset distance.

[0082] In some embodiments, if the distance sensing data is less than a preset distance, there may be an object in front of the near-field communication module. In order to rule out the possibility of normal payment by the near-field communication module, it is necessary to further obtain the duration of the distance sensing data being less than the preset distance, and determine whether the distance sensing data meets the preset occlusion condition based on the duration.

[0083] In this implementation, when the distance sensing data is less than a preset distance, the duration for which the distance sensing data is less than the preset distance is further obtained, and the duration is used to determine whether the distance sensing data meets the preset occlusion conditions. The duration effectively avoids false triggering caused by factors such as normal payment or environmental fluctuations, and improves the occlusion judgment capability of the payment device.

[0084] In one possible implementation, if the duration for which the distance sensing data is less than a preset distance is greater than or equal to a first preset duration, it is determined that the distance sensing data meets a preset occlusion condition. If the duration for which the distance sensing data is less than the preset distance is less than the first preset duration, it is determined that the distance sensing data does not meet the preset occlusion condition. Furthermore, if the distance sensing data is greater than or equal to the preset distance, the distance sensing data also does not meet the preset occlusion condition.

[0085] The first preset duration can be any suitable value, such as 30 seconds, 1 minute, etc. In some embodiments, the acquisition duration begins when the distance sensing data is less than a preset distance. If the duration is greater than or equal to the first preset duration, the ranging sensor can generate an occlusion sensing signal, thereby determining that the distance sensing data meets the preset occlusion condition. The occlusion sensing signal is the ranging signal captured by the ranging sensor when it is blocked for a long time.

[0086] In some embodiments, if the conditions are not met (i.e., the duration of the distance sensing data being less than a preset distance is greater than or equal to a first preset duration), then the distance sensing data is determined not to meet the preset occlusion condition. If the distance sensing data is greater than or equal to the preset distance, then the distance sensing data also does not meet the preset occlusion condition.

[0087] In this implementation, the distance sensing data is determined to meet the preset occlusion condition only when the distance sensing data is less than the preset distance and the duration of the distance sensing data being less than the preset distance is greater than or equal to the first preset duration. This ensures the accuracy of detection, reduces invalid judgments caused by interference, and improves the stability of the payment device.

[0088] In one possible implementation, when the image perception data includes multiple target noise points, it is determined whether the image perception data meets a preset occlusion condition based on the multiple target noise points.

[0089] Noise refers to uneven, messy bright or dark spots in an image or video, caused by interference or errors during image acquisition or processing, and typically manifests as fine particles in the image. In some embodiments, when the image perception data includes multiple target noise points, it indicates the presence of interference or errors in the image perception data, and it is necessary to further determine whether the image perception data meets preset occlusion conditions based on the multiple target noise points.

[0090] In some embodiments, the image sensing data is mostly in the form of QR codes, and noise-free tag image data can be stored in the payment device. By performing real-time comparative analysis of the image sensing data and the tag image data, isolated pixels with abrupt changes in color and brightness in the image sensing data are detected and identified. If points with abnormal high-frequency signal fluctuations or color deviations are detected in the smooth areas of the image sensing data, they are determined to be noise. The smooth areas can be obtained by comparing the image sensing data and the tag image data.

[0091] In this implementation, when the image perception data includes multiple target noise points, the image perception data can be determined by the target noise points to determine whether the image perception data meets the preset occlusion conditions. This can effectively distinguish between real occlusion and the inherent noise of the scanning module, thereby improving the occlusion judgment capability of the payment device.

[0092] In one possible implementation, if multiple target noise points are clustered in the same area, the image perception data is determined to meet a preset occlusion condition. If multiple target noise points are distributed in different areas, the image perception data is determined not to meet the preset occlusion condition. Furthermore, if the image perception data does not contain target noise points, the image perception data also does not meet the preset occlusion condition.

[0093] In this context, a region refers to a portion of the image sensing data. In some embodiments, the image sensing data is divided into several regions (e.g., a uniform grid, a flat region based on semantic segmentation, or a textured region). The number of target noise points is counted for each region. If multiple target noise points are clustered in the same region, it indicates that part or all of the scanning module may be occluded, and the image sensing data is determined to meet a preset occlusion condition. Conversely, if multiple target noise points are distributed in different regions, it indicates that the data acquired by the scanning module may have other interference, and the image sensing data is determined not to meet the preset occlusion condition. Furthermore, if the image sensing data does not contain target noise points, it also does not meet the preset occlusion condition.

[0094] In this implementation, the image perception data is determined to meet the preset occlusion conditions only when multiple target noise points are clustered in the same area. This utilizes the physical characteristic that noise points formed by real occluders in image perception data often exhibit clustered distribution, thus achieving preliminary occlusion recognition of image perception data.

[0095] In one possible implementation, the texture features corresponding to the image sensing data are determined. Based on the texture features, it is determined whether the image sensing data meets a preset occlusion condition.

[0096] Texture features are global features that reflect the visual characteristics of homogeneous phenomena in an image, reflecting the slowly changing or periodically altered surface structure of an object. In some embodiments, the gray-level co-occurrence matrix (GLCM) can be used to determine the texture features corresponding to the image perceptual data. GLCM is a commonly used statistical method to describe the spatial distribution of pixel gray values ​​in an image. GLCM generates a matrix by calculating the joint probability of gray values ​​appearing in pairs of pixels in the image, and this matrix can be used to extract texture features.

[0097] In some embodiments, the Local Binary Pattern (LBP) can be used to determine the texture features corresponding to the image perception data. LBP is an operator used to describe the local texture features of an image, possessing rotation invariance and grayscale invariance. LBP generates a binary pattern by comparing the grayscale values ​​of the center pixel with those of its neighboring pixels, which is used to represent the texture features corresponding to the local image perception data.

[0098] In this implementation, the determination of whether the image perception data meets the preset occlusion conditions is made by identifying the texture features corresponding to the determined image perception data. Texture features are introduced as the basis for determining the occlusion of image perception data, which can accurately identify the occlusion state of image data without the need for additional reference objects.

[0099] In one possible implementation, if the texture features remain unchanged at different times, the image perception data is determined to satisfy a preset occlusion condition. If the texture features change at different times, the image perception data is determined not to satisfy the preset occlusion condition.

[0100] Since image sensing data is continuously acquired, it is necessary to determine the texture features corresponding to the image sensing data at different moments in the continuous acquisition. In some embodiments, if the texture features remain unchanged at different moments, the image sensing data acquired by the scanning module is considered stable; that is, there is an obstructing object in front of the scanning module, and therefore, the image sensing data can be determined to meet the preset obstruction condition. If the texture features change at different moments, the image sensing data acquired by the scanning module is considered to be changing; that is, there is no obstructing object in front of the scanning module, and therefore, the image sensing data can be determined to not meet the preset obstruction condition.

[0101] In this implementation, while the texture features remain unchanged at different times, it is determined that the image perception data meets the preset occlusion conditions. By utilizing the property that the texture features of the image are stable due to the occlusion, preliminary occlusion recognition of the image perception data is achieved.

[0102] 303, Determine whether the payment device is obstructed based on the target payment information.

[0103] In one possible implementation, if the distance sensing data meets a preset occlusion condition but the image sensing data does not, target payment information within a first time period is acquired. If the target payment information within the first time period indicates that payment was completed via the scanning module but not via the near-field communication module, it is determined that the near-field communication module is occluded. The first time period is negatively correlated with historical passenger flow data.

[0104] In cases where the distance perception data meets preset occlusion conditions but the image perception data does not, it indicates that the near-field communication module may be occluded. Further verification using target payment information is needed to determine whether the near-field communication module is indeed occluded. Target payment information may include, but is not limited to, payment location, payment time, payment result, and payment amount. The payment result may include one of the following: payment successful, payment failed, or payment cancelled. The payment result can be used to determine whether the payment has been completed.

[0105] The first time period can be of any suitable length, such as 1 hour, 30 minutes, 10 seconds, etc. In the following embodiments, depending on the length of the first time period, the target payment information within the first time period can include information about a single transaction or information about multiple transactions.

[0106] In some embodiments, when the historical customer flow data indicates a large number of customers, it means that the store will currently generate many transactions, so the first time period can be appropriately reduced. Conversely, when the historical customer flow data indicates a small number of customers, it means that the store will currently generate few transactions, so the first time period can be appropriately increased. That is, the first time period is negatively correlated with the historical customer flow data.

[0107] In some embodiments, if the target payment information in the first time period indicates that the payment was completed through the scanning module but not through the near-field communication module, it may be that the user first attempted to pay by tapping the screen but the payment failed, and then successfully paid by scanning the code, thus determining that the near-field communication module was blocked.

[0108] In this implementation, target payment information within a first time period is obtained by combining distance sensing data that meets preset occlusion conditions. The target payment information is then used to determine whether the near-field communication module is occluded. This approach can accurately determine whether a single near-field communication module is occluded by combining data collected by sensors and data obtained from transactions, thus improving the accuracy and specificity of occlusion detection. Furthermore, the time period for occlusion detection can be set based on actual passenger flow, increasing the flexibility of occlusion detection.

[0109] In one possible implementation, if the distance sensing data and image sensing data meet preset occlusion conditions, target payment information for a second time period is acquired. If the target payment information for the second time period indicates that payment was not completed through the near-field communication module and the scanning module, it is determined that the near-field communication module and the scanning module are occluded.

[0110] In cases where the distance sensing data and image sensing data meet preset occlusion conditions, it is possible that both the near-field communication module and the scanning module are occluded. Further determination of whether the near-field communication module and the scanning module are occluded requires using target payment information. Target payment information may include, but is not limited to, payment location, payment time, payment result, and payment amount. The payment result may include one of the following: payment successful, payment failed, or payment cancelled. The payment result can be used to determine whether the payment has been completed.

[0111] The second time period can be of any suitable size, such as 1 hour, 2 hours, etc. In some embodiments, the second time period can be centered on a target time when the distance perception data and image perception data meet the preset occlusion conditions, and target payment information within the second time period before and after the target time can be obtained to obtain target payment information within a period of time before and after the target time.

[0112] In some embodiments, when the historical customer flow data indicates a large number of customers, it means that the store will currently generate many transactions, so the second time period can be appropriately reduced. Conversely, when the historical customer flow data indicates a small number of customers, it means that the store will currently generate few transactions, so the second time period can be appropriately increased. That is, the second time period is negatively correlated with the historical customer flow data.

[0113] In one embodiment, the second time period is a relatively long duration, designed to reduce the possibility of misjudgment due to occlusion. If the target payment information during the second time period indicates that payment was not completed through the near-field communication module and the scanning module, it can be concluded that both the tap-to-pay and QR code payment methods were successful, thereby determining that the communication module and the scanning module are occluded.

[0114] In this implementation, target payment information within a second time period is obtained by combining distance perception data and image perception data that meet preset occlusion conditions. Furthermore, the target payment information is used to determine whether the near-field communication module and scanning module are occluded. This allows for accurate determination of whether the near-field communication module and scanning module are occluded by combining data collected by sensors and data obtained from transactions, thus improving the accuracy and specificity of occlusion detection. In addition, the time period for occlusion detection can be set according to actual passenger flow, improving the flexibility of occlusion detection.

[0115] In one possible implementation, if the image sensing data meets a preset occlusion condition but the distance sensing data does not, target payment information for a third time period is acquired. If the target payment information for the third time period indicates that payment was completed via the near-field communication module but not via the scanning module, it is determined that the scanning module is occluded.

[0116] In cases where the image perception data meets preset occlusion conditions, but the distance perception data does not, it indicates that the scanning module may be occluded. Further verification using target payment information is needed to determine whether the scanning module is indeed occluded. Target payment information may include, but is not limited to, payment location, payment time, payment result, and payment amount. The payment result may include one of the following: payment successful, payment failed, or payment cancelled. The payment result can be used to determine whether the payment has been completed.

[0117] The third time period can be of any suitable size, such as 15 minutes, 8 seconds, etc. In the following embodiments, depending on the size of the third time period, the target payment information within the third time period can include information about a single transaction or information about multiple transactions.

[0118] In some embodiments, when the historical customer flow data indicates a large number of customers, it means that the store will currently generate many transactions, so the third time period can be appropriately reduced. Conversely, when the historical customer flow data indicates a small number of customers, it means that the store will currently generate few transactions, so the third time period can be appropriately increased. That is, the third time period is negatively correlated with the historical customer flow data.

[0119] In one embodiment, if the target payment information in the third time period indicates that the payment was completed through the near-field communication module but not through the scanning module, it can represent that the user first attempted to pay by scanning a code but failed, and then successfully paid by tapping the code, thereby determining that the scanning module was blocked.

[0120] In this implementation, target payment information within a third time period is obtained by combining image perception data that meets preset occlusion conditions. Further, the occlusion of the scanning module is determined through the target payment information. This allows for accurate determination of whether a single scanning module is occluded by combining data collected by the sensor and data obtained from transactions, thus improving the accuracy and specificity of occlusion detection. Furthermore, the time period for occlusion detection can be set according to actual passenger flow, increasing the flexibility of occlusion detection.

[0121] 304. In the event that the payment device is obstructed, an abnormal obstruction information is sent to the cloud. The abnormal obstruction information indicates that the payment device is obstructed. The cloud uses the abnormal obstruction information to generate a target work order and send the target work order to the target terminal. The target terminal is a terminal device associated with the store where the payment device is located.

[0122] In cases where a payment device is obstructed, the store's checkout layout can be considered unreasonable, thus requiring the transmission of an anomaly obstruction information to the cloud. This information indicates that the payment device is obstructed. The anomaly obstruction information may include, but is not limited to, at least one of the following: payment device location, store location, the obstructed payment device, and the time of the anomaly. In some embodiments, upon receiving the anomaly obstruction information, the cloud generates a target work order based on the information and sends it to the target terminal. The target work order is used to indicate the checkout layout and / or the handling method for the obstructed payment device. The target terminal is a terminal device associated with the store where the payment device is located, and may include, but is not limited to, at least one of the following: the terminal of the store owner where the payment device is located, the terminal of the area manager of the store where the payment device is located, etc.

[0123] In some embodiments, when the near-field communication module is obstructed but the scanning module is not obstructed, the cloud generates a first target work order based on abnormal obstruction information. When both the near-field communication module and the scanning module are obstructed, the cloud generates a second target work order based on abnormal obstruction information. When the scanning module is obstructed but the near-field communication module is not obstructed, the cloud generates a third target work order based on abnormal obstruction information. The first, second, and third target work orders are different. After the cloud sends the target work order to the target terminal, the payment device still needs to continuously execute the obstruction detection method to continuously monitor whether the abnormal obstruction situation has been resolved.

[0124] In this implementation, when the payment device is obstructed, abnormal obstruction information is sent to the cloud so that the cloud can generate a target work order and send the target work order to the target terminal. This allows for timely issuance of work orders after abnormal obstruction is confirmed, reminding relevant personnel to handle the abnormal obstruction situation, and constructing a closed-loop system from abnormality detection to abnormality resolution.

[0125] In summary, the method provided in this application, when the distance sensing data collected by the ranging sensor of the payment device and / or the image sensing data collected by the scanning module meet preset occlusion conditions, acquires the target payment information for payment via the near-field communication module and the scanning module, and then uses the target payment information to determine whether the payment device is occluded. This method enables real-time monitoring of payment devices supporting different payment methods, and can proactively and timely detect whether the payment device is occluded during the payment process. Furthermore, by employing multi-source data, it supports occlusion detection for different payment devices, improving the reliability and intelligence of the payment device in complex scenarios.

[0126] It should be understood that the above examples are provided to help those skilled in the art understand the embodiments of this application, and are not intended to limit the embodiments of this application to the specific values ​​or scenarios illustrated. Those skilled in the art can obviously make various equivalent modifications or changes based on the above examples, and such modifications or changes also fall within the scope of the embodiments of this application.

[0127] The above text combined Figures 1 to 3 The occlusion detection method provided in the embodiments of this application is described in detail below; the following will be combined with Figure 4 and Figure 5 The apparatus embodiments of this application are described in detail below. It should be understood that the apparatus in the embodiments of this application can perform the various methods described in the foregoing embodiments of this application, that is, the specific working processes of the various products described below can be referred to the corresponding processes in the foregoing method embodiments.

[0128] Figure 4 This is a schematic diagram of the structure of an occlusion detection device provided in an embodiment of this application. Figure 4 As shown, the occlusion detection device 400 includes: an acquisition module 401 and a determination module 402. Wherein: The acquisition module 401 is used to acquire distance sensing data collected by the ranging sensor and image sensing data collected by the scanning module. When the distance sensing data and / or image sensing data meet the preset occlusion conditions, the target payment information for payment through the near-field communication module and the scanning module is acquired. The determination module 402 is used to determine whether the payment device is obstructed based on the target payment information.

[0129] In one possible implementation, the determining module 402 is used to determine whether the distance sensing data meets the preset occlusion condition based on the duration during which the distance sensing data is less than the preset distance when the distance sensing data is less than the preset distance.

[0130] In one possible implementation, the determining module 402 is configured to determine that the distance sensing data meets the preset occlusion condition when the duration of the distance sensing data being less than the preset distance is greater than or equal to the first preset duration; and to determine that the distance sensing data does not meet the preset occlusion condition when the duration of the distance sensing data being less than the preset distance is less than the first preset duration; wherein, the distance sensing data also does not meet the preset occlusion condition when the distance sensing data is greater than or equal to the preset distance.

[0131] In one possible implementation, the determining module 402 is used to determine whether the image sensing data meets a preset occlusion condition based on the multiple target noise points when the image sensing data includes multiple target noise points.

[0132] In one possible implementation, the determining module 402 is used to determine that the image perception data meets the preset occlusion condition when multiple target noise points are clustered in the same area; and to determine that the image perception data does not meet the preset occlusion condition when multiple target noise points are distributed in different areas; wherein, if the image perception data does not include target noise points, the image perception data also does not meet the preset occlusion condition.

[0133] In one possible implementation, the determining module 402 is used to determine the texture features corresponding to the image perception data; and to determine whether the image perception data meets the preset occlusion conditions based on the texture features.

[0134] In one possible implementation, the determining module 402 is used to determine that the image perception data meets the preset occlusion conditions when the texture features remain unchanged at different times; and to determine that the image perception data does not meet the preset occlusion conditions when the texture features change at different times.

[0135] In one possible implementation, the acquisition module 401 is used to acquire a target transaction time period, which is determined based on historical transaction information, historical passenger flow data, and historical status data of payment devices; during the target transaction time period, the steps of acquiring distance sensing data collected by the near-field communication module and image sensing data collected by the scanning module are performed.

[0136] In one possible implementation, the determining module 402 is used to obtain target payment information within a first time period when the distance perception data meets the preset occlusion conditions and the image perception data does not meet the preset occlusion conditions; if the target payment information within the first time period indicates that payment was completed through the scanning module but not through the near-field communication module, it is determined that the near-field communication module is occluded; wherein, the first time period is negatively correlated with historical passenger flow data.

[0137] In one possible implementation, the determining module 402 is used to obtain target payment information within a second time period when both distance sensing data and image sensing data meet preset occlusion conditions; if the target payment information within the second time period indicates that payment was not completed through the near-field communication module and the scanning module, it is determined that the near-field communication module and the scanning module are occluded; wherein, the second time period is negatively correlated with historical passenger flow data.

[0138] In one possible implementation, the determining module 402 is used to obtain target payment information within a third time period when the image perception data meets the preset occlusion conditions and the distance perception data does not meet the preset occlusion conditions; if the target payment information within the third time period indicates that payment was completed through the near-field communication module but not through the scanning module, it is determined that the scanning module is occluded; wherein, the third time period is negatively correlated with historical passenger flow data.

[0139] In one possible implementation, the device further includes a sending module for sending abnormal occlusion information to the cloud when the payment device is obstructed. The abnormal occlusion information indicates that the payment device is obstructed. The cloud is used to generate a target work order based on the abnormal occlusion information and send the target work order to a target terminal, which is a terminal device associated with the store where the payment device is located.

[0140] The division of modules in the above-described occlusion detection device is for illustrative purposes only. In other embodiments, the occlusion detection device can be divided into different modules as needed to complete all or part of the functions of the above-described occlusion detection device.

[0141] The various modules in the occlusion detection device provided in this application embodiment can be implemented in the form of a computer program. This computer program can run on a server or client payment device. The program modules constituted by this computer program can be stored in the memory of the server or client payment device. When the computer program is executed by a processor, it implements all or part of the steps of the method described in this application embodiment.

[0142] It should be noted that the aforementioned occlusion detection device 400 is embodied in the form of a functional unit. The term "module" here can be implemented in software and / or hardware, without specific limitations.

[0143] For example, a "module" can be a software program, a hardware circuit, or a combination of both that implements the above functions. The hardware circuit may include an application-specific integrated circuit (ASIC), electronic circuitry, a processor (e.g., a shared processor, a proprietary processor, or a group processor) and memory for executing one or more software or firmware programs, integrated logic circuitry, and / or other suitable components that support the described functions.

[0144] Therefore, the units of the various examples described in the embodiments of this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0145] Figure 5 This is a schematic diagram of the structure of a payment device provided in an embodiment of this application.

[0146] For example, such as Figure 5 As shown, the payment device 500 includes a memory 501 and a processor 502. The memory 501 stores executable program code 5011, and the processor 502 is used to call and execute the executable program code 5011 to perform an occlusion detection method.

[0147] For example, memory 501 can be used to store related programs of the occlusion detection method provided in the embodiments of this application; processor 502 can call the related programs of the occlusion detection method stored in memory 501 to execute the occlusion detection method of the embodiments of this application; for example, acquiring distance sensing data collected by the ranging sensor and image sensing data collected by the scanning module; when the distance sensing data and / or image sensing data meet the preset occlusion conditions, acquiring the target payment information for payment through the near-field communication module and the scanning module; and determining whether the payment device is occluded based on the target payment information.

[0148] This embodiment can divide the device into functional modules based on the above method example. For example, each module can correspond to a separate function, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0149] When the functional modules are divided according to their respective functions, the device may also include a processing module and a communication module. It should be noted that all relevant content regarding the steps involved in the above method embodiments can be referenced from the functional descriptions of the corresponding functional modules, and will not be repeated here.

[0150] It should be understood that the apparatus provided in this embodiment is used to perform the above-described occlusion detection method, and therefore can achieve the same effect as the above-described implementation method.

[0151] When using integrated units, the device may include a processing module and a storage module. The processing module may be a processor or a controller that can implement or execute various exemplary logic blocks, modules, and circuits shown in conjunction with the disclosure of this application. The processor may also be a combination of functions that implement computing capabilities, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc., and the storage module may be a memory.

[0152] In addition, the device provided in the embodiments of this application may specifically be a chip, component or module. The chip may include a connected processor and a memory. The memory is used to store instructions. When the processor calls and executes the instructions, the chip can execute an occlusion detection method provided in the above embodiments.

[0153] This application also provides a computer-readable storage medium storing computer program code. When the computer program code is run on a computer, the computer executes the above-described related method steps to implement the occlusion detection method provided in the above embodiments. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, Digital Video Discs (DVDs), Compact Disc Read-Only Memory (CD-ROMs), microdrives, and magneto-optical disks, read-only memory (ROMs), random access memory (RAMs), erasable programmable read-only memory (EPROMs), electrically erasable programmable read-only memory (EEPROMs), dynamic random access memory (DRAMs), video random access memory (VRAMs), flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of media or device suitable for storing instructions and / or data.

[0154] This application also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned related steps to implement the occlusion detection method provided in the above embodiments.

[0155] The computer-readable storage medium, computer program product, or chip provided in this application are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0156] Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0157] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0158] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An occlusion detection method, characterized in that, Applied to a payment device, the payment device including a near-field communication module, a scanning module, and a ranging sensor, the method includes: Acquire distance sensing data collected by the ranging sensor and image sensing data collected by the scanning module; When the distance sensing data and / or the image sensing data meet the preset occlusion conditions, the target payment information for payment through the near-field communication module and the scanning module is obtained; Based on the target payment information, it is determined whether the payment device is obstructed.

2. The method according to claim 1, characterized in that, The method further includes: If the distance sensing data is less than a preset distance, the distance sensing data is determined to meet the preset occlusion condition based on the duration for which the distance sensing data is less than the preset distance.

3. The method according to claim 2, characterized in that, The step of determining whether the distance sensing data meets the preset occlusion condition based on the duration during which the distance sensing data is less than the preset distance includes: If the duration for which the distance perception data is less than the preset distance is greater than or equal to the first preset duration, it is determined that the distance perception data satisfies the preset occlusion condition. If the duration for which the distance perception data is less than the preset distance is less than the first preset duration, it is determined that the distance perception data does not meet the preset occlusion condition. Where the distance sensing data is greater than or equal to the preset distance, the distance sensing data also does not meet the preset occlusion condition.

4. The method according to claim 1, characterized in that, The method further includes: If the image perception data includes multiple target noise points, determine whether the image perception data meets the preset occlusion condition based on the multiple target noise points.

5. The method according to claim 4, characterized in that, The step of determining whether the image perception data satisfies the preset occlusion condition based on the plurality of target noise points includes: When multiple target noise points are clustered in the same area, it is determined that the image perception data satisfies the preset occlusion condition; When the multiple target noise points are distributed in different areas, it is determined that the image perception data does not meet the preset occlusion condition; Where the target noise is not included in the image perception data, the image perception data also does not meet the preset occlusion condition.

6. The method according to claim 1, characterized in that, The method further includes: Determine the texture features corresponding to the image perception data; Based on the texture features, determine whether the image perception data meets the preset occlusion conditions.

7. The method according to claim 6, characterized in that, The step of determining whether the image perception data satisfies the preset occlusion condition based on the plurality of target noise points includes: If the texture features remain unchanged at different times, it is determined that the image perception data satisfies the preset occlusion condition; If the texture features change at different times, it is determined that the image perception data does not meet the preset occlusion condition.

8. The method according to claim 1, characterized in that, The method further includes: The target transaction time period is obtained, which is determined based on historical transaction information, historical customer flow data, and historical status data of the payment device; During the target transaction time period, the step of acquiring the distance sensing data collected by the near-field communication module and the image sensing data collected by the scanning module is performed.

9. The method according to claim 1, characterized in that, Determining whether the payment device is obstructed based on the target payment information includes: If the distance perception data meets the preset occlusion condition and the image perception data does not meet the preset occlusion condition, the target payment information within the first time period is obtained. If the target payment information during the first time period indicates that the payment was completed through the scanning module but not through the near-field communication module, it is determined that the near-field communication module is blocked. The first time period shows a negative correlation with historical passenger flow data.

10. The method according to claim 1, characterized in that, Determining whether the payment device is obstructed based on the target payment information includes: When both the distance perception data and the image perception data meet the preset occlusion conditions, the target payment information within the second time period is obtained; If the target payment information during the second time period indicates that payment was not completed through the near-field communication module and the scanning module, it is determined that the near-field communication module and the scanning module are blocked. The second time period shows a negative correlation with historical passenger flow data.

11. The method according to claim 1, characterized in that, Determining whether the payment device is obstructed based on the target payment information includes: If the image perception data meets the preset occlusion condition and the distance perception data does not meet the preset occlusion condition, the target payment information within the third time period is obtained. If the target payment information during the third time period indicates that the payment was completed through the near-field communication module but not through the scanning module, it is determined that the scanning module is blocked. The third time period is negatively correlated with historical passenger flow data.

12. The method according to any one of claims 1 to 10, characterized in that, The method further includes: If the payment device is obstructed, an abnormal obstruction message is sent to the cloud, which indicates that the payment device is obstructed. The cloud platform is used to generate a target work order based on the abnormal occlusion information and send the target work order to the target terminal, which is a terminal device associated with the store where the payment device is located.

13. An occlusion detection device, characterized in that, Applied to payment devices, the payment device includes a near-field communication module, a scanning module, and a ranging sensor, the device comprising: The acquisition module is used to acquire distance sensing data collected by the ranging sensor and image sensing data collected by the scanning module. When the distance sensing data and / or the image sensing data meet the preset occlusion conditions, the module acquires the target payment information for payment made through the near-field communication module and the scanning module. The determination module is used to determine whether the payment device is obstructed based on the target payment information.

14. A payment device, characterized in that, include: Memory, used to store executable program code; A processor is configured to call and run the executable program code from the memory, causing the payment device to perform the method as described in any one of claims 1 to 12.

15. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed on the payment device, cause the payment device to perform the method as described in any one of claims 1 to 12.

16. A computer program product, characterized in that, When the computer program product is run on a computer, it causes the computer to perform the method as described in any one of claims 1 to 12.

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