A data processing method and device for self-service equipment, equipment and storage medium

Abstract

The present disclosure provides a data processing method, device, equipment, and storage medium for a self-service device, which belongs to the field of data processing technology. The method is applied to a first processor in a self-service device, including: evaluating the self-service device data based on a first model to obtain a first data processing time; in response to the first data processing time being less than a first threshold, processing the self-service device data based on the first processor; in response to the first data processing time being greater than or equal to the first threshold, transmitting the self-service device data to a second processor. The data processing method, device, equipment, and storage medium for a self-service device provided by the present disclosure can improve the performance of the self-service device.

Description

Technical Field

[0001] The present disclosure belongs to the field of data processing technology, and more specifically, to a data processing method and device, equipment, and storage medium for a self-service device. Background Art

[0002] With the widespread use of self-service devices, how to process device data efficiently and intelligently has become a focus of the industry. Traditional data processing methods often use a single processor to process data, which cannot be intelligently allocated according to the complexity and duration of the processing task, resulting in low processing efficiency, excessive resource consumption, and even affecting the normal operation of self-service devices.

[0003] In actual applications, self-service devices need to process a wide variety of data, and the processing time varies. Some simple data processing tasks can be completed in a short time, while some complex or large-scale data processing tasks require a longer time. If a single processor is used to process all tasks, it will not only waste processor resources, but also affect the performance of the self-service device due to untimely processing. Summary of the invention

[0004] The purpose of the present disclosure is to provide a data processing method and device, equipment, and storage medium for a self-service device to improve the performance of the self-service device.

[0005] A first aspect of an embodiment of the present disclosure provides a data processing method for a self-service device, which is applied to a first processor in the self-service device, and includes:

[0006] Evaluate the self-service device data based on the first model to obtain a first data processing time;

[0007] In response to the first data processing time being less than a first threshold, processing the self-service device data based on the first processor;

[0008] In response to the first data processing time being greater than or equal to a first threshold, the self-service device data is transmitted to a second processor.

[0009] According to a second aspect of the embodiments of the present disclosure, a data processing device of a self-service device is provided, which is applied to a first processor in the self-service device, and includes:

[0010] A data evaluation module, used to evaluate the self-service device data based on the first model to obtain a first data processing time;

[0011] a data processing module, configured to process the self-service device data based on the first processor in response to the first data processing time being less than a first threshold;

[0012] The data transmission module is used to transmit the self-service device data to a second processor in response to the first data processing time being greater than or equal to a first threshold.

[0013] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned data processing method of a self-service device when executing the computer program.

[0014] In a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the data processing method of the above-mentioned self-service device are implemented.

[0015] The beneficial effects of a data processing method, device, equipment, and storage medium for a self-service device provided by the embodiments of the present disclosure are: the embodiments of the present disclosure evaluate the data through the first model to obtain the first data processing time, so that the system can predict the duration of the processing task in advance and provide an accurate basis for subsequent decision-making. When the first data processing time is less than the first threshold, the first processor processes the data, which can avoid unnecessary data transmission, reduce delays and resource consumption, improve processing efficiency, quickly respond to the business needs of the self-service device, and enhance user experience. When the processing time is greater than or equal to the first threshold, the data is transmitted to the second processor, making full use of the advantages of different processors, allowing the second processor to process complex or time-consuming tasks, and realizing reasonable allocation of resources. It ensures that the self-service device can operate efficiently in various situations, can meet the requirements of fast processing, and can cope with complex tasks, thereby improving the performance of the self-service device. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 A flowchart of a data processing method for a self-service device provided by an embodiment of the present disclosure;

[0018] Figure 2 A structural block diagram of a data processing device of a self-service device provided in one embodiment of the present disclosure;

[0019] Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0020] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present disclosure. However, it should be clear to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present disclosure with unnecessary details.

[0021] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0022] Please refer to Figure 1 , Figure 1 A flowchart of a data processing method of a self-service device provided in an embodiment of the present disclosure is applied to a first processor in the self-service device, including:

[0023] In this embodiment, the self-service device may be a self-service vending machine, which is a device that can provide specific services or complete specific tasks for users with little or no human intervention. The self-service vending machine can be used to sell various commodities, such as beverages, snacks, daily necessities, etc. Users can pay by coin insertion, card swiping, code scanning, etc., and then select the desired commodities, satisfying people's shopping needs anytime and anywhere.

[0024] The first processor is the core component responsible for the main data processing and control tasks in the self-service equipment (such as self-service vending machines). The first processor can process and calculate various data of the self-service vending machine. For example, it receives the product selection instructions entered by the user through the touch screen or buttons, calculates the product price and change amount, processes the verification and confirmation of payment information, monitors the inventory status inside the vending machine and issues a replenishment reminder when the inventory is insufficient. At the same time, the first processor can also communicate with other external devices, such as data interaction with payment terminals, network servers, etc., to ensure the normal operation of the vending machine and the smooth development of the business.

[0025] S101: Evaluate self-service device data based on a first model to obtain a first data processing time.

[0026] In this embodiment, the first model is a prediction model for evaluating the data processing time of the self-service device. The first model can be obtained by analyzing a large amount of historical data and training with a machine learning algorithm.

[0027] For example, a decision tree can be used as a machine learning model. The decision tree can make branch judgments based on different data features and predict processing time.

[0028] Collect the historical records of the self-service device's past processing of various data, which may include data features (such as data volume, data type, complexity, etc.), actual processing time, and the system status at the time (such as processor load, memory usage, network status, etc.). Perform preprocessing, normalization, and feature extraction on the collected data to obtain target data, which are divided into training sets, validation sets, and test sets. The training set is used to train the selected model, and the validation set is used to evaluate the trained model. Based on the evaluation results, the model parameters are adjusted, different model types are selected, or feature engineering optimization is performed to improve the performance of the model. Finally, the trained first model is deployed to the first processor of the self-service device so that it can evaluate the data processing time in actual operation.

[0029] The first data processing time is obtained by evaluating the first model, and is the time required for the first processor in the self-service device to process the self-service device data. The first data processing time is a predicted value.

[0030] In this embodiment, transaction data, inventory data, etc. are evaluated based on the first model to obtain the first data processing time.

[0031] The first model can consider multiple features of the data. If it is transaction data, you can analyze features such as the transaction amount, the number of commodity types involved, and whether the transaction time point is at a peak period; for inventory data, you can focus on features such as inventory quantity, commodity turnover rate, and inventory ratio of different commodities. At the same time, the first model can also be combined with the current system status of the self-service device, including the load of the first processor, such as the number of tasks currently being processed and the processor utilization rate; memory usage, whether it is close to saturation; and network conditions, such as network latency, bandwidth utilization, etc. Based on these data features and system status information, the first model can calculate through its internal algorithms and rules to predict and output an evaluation result, namely the first data processing time.

[0032] S102: In response to the first data processing time being less than a first threshold, process the self-service device data based on the first processor.

[0033] In this embodiment, the first threshold is a preset time value, which can be comprehensively considered based on user experience requirements and system resource conditions. For example, if the self-service device has a high requirement for response speed and hopes to process data in a short time to meet the user's immediate needs, the first threshold can be set to a relatively small value. If the requirement for response speed is not so strict, the first threshold can be set to a slightly larger value.

[0034] In this embodiment, the first threshold represents the upper limit of the acceptable processing time of the first processor. When the assessed first data processing time is less than this upper limit, it means that the first processor is capable of processing the data within a reasonable time range without transferring the data to other processors, thereby avoiding delays and resource consumption caused by data transmission.

[0035] In response to the self-service device data being transaction data, the first processor may perform operations such as payment verification, inventory update, and transaction record storage; in response to the self-service device data being fault report data, the first processor may perform operations such as fault diagnosis and maintenance request generation;

[0036] When the first processor completes data processing, the processing result can be fed back to other related systems or modules of the self-service device. For example, if it is transaction data processing, the user can be notified of the successful transaction result and the display interface and inventory information of the self-service device can be updated; if it is fault report data processing, a maintenance request can be sent to the relevant maintenance personnel and the fault status information can be updated.

[0037] S103: In response to the first data processing time being greater than or equal to the first threshold, transmitting the self-service device data to the second processor.

[0038] In this embodiment, the second processor is another processing unit in the self-service device other than the first processor, such as a management platform. The second processor has a stronger computing power, a larger memory capacity or a higher processing speed, and can handle more complex or time-consuming tasks. For example, when the first processor estimates that it takes a long time to process certain data, the data is transferred to the second processor to speed up the processing by utilizing its higher processing power.

[0039] In this embodiment, in response to the first data processing time being greater than or equal to the first threshold, the self-service device data is compressed and transmitted to the second processor.

[0040] Data compression can reduce the size of data, thereby reducing the time and bandwidth resources required during the transmission process. For self-service devices, by compressing data, data transmission can be completed faster under the same transmission conditions, improving overall processing efficiency. For example, assuming that uncompressed data takes 10 seconds to transmit, after compression, the transmission time may be shortened to 5 seconds or even shorter. The compressed data can be transmitted to the second processor. The transmission process can be carried out in various ways, such as an internal bus, a network connection or other communication channels.

[0041] For example, it is assumed that there is a self-service vending machine as a self-service device, which mainly sells beverages, snacks, etc. The vending machine is equipped with a first processor and a management platform that can be connected via a network as a second processor.

[0042] When a user purchases a product on his own initiative, new transaction data or inventory data will be generated. The first model combines data features (such as transaction amount, number of product types, whether it is peak period, etc. in transaction data, inventory quantity, product turnover rate, inventory ratio, etc. in inventory data) and system status (processor load, memory usage, network status, etc.) to calculate through internal algorithms and rules to predict the first data processing time.

[0043] Considering that the vending machine needs to respond quickly to meet user needs and the system resources are limited, the first threshold is set to 5 seconds. If the processing time is less than this value, the data is processed by the first processor. When the self-service device data is transaction data, the first processor performs payment verification. If the payment is successful, the inventory information is updated, the transaction record is stored in the local database, and the result of the successful transaction is displayed on the screen to notify the user. When the first data processing time is greater than or equal to 5 seconds, the first processor compresses the data. For example, for larger transaction data records or complex fault report data, a compression algorithm is used to reduce the data volume. The compressed data is then transmitted to the management platform via a network connection. After receiving the data, the management platform analyzes the complex transaction data.

[0044] It can be concluded from the above that this embodiment evaluates the data through the first model to obtain the first data processing time, so that the system can predict the duration of the processing task in advance and provide an accurate basis for subsequent decision-making. When the first data processing time is less than the first threshold, the first processor processes the data, which can avoid unnecessary data transmission, reduce delays and resource consumption, improve processing efficiency, quickly respond to the business needs of the self-service device, and enhance user experience. When the processing time is greater than or equal to the first threshold, the data is transmitted to the second processor, making full use of the advantages of different processors, allowing the second processor to process complex or time-consuming tasks, and realizing reasonable allocation of resources. It ensures that the self-service device can operate efficiently in various situations, can meet the requirements of fast processing, and can cope with complex tasks, thereby improving the performance of the self-service device.

[0045] In an embodiment of the present disclosure, the method further includes: updating the first model based on the first time.

[0046] In this embodiment, the first time may be a preset time interval, such as once a month, once a week, etc.

[0047] In this embodiment, real-time data of the self-service device is obtained in real time, which may include: data characteristics (such as data volume, data type, complexity, etc.), actual processing time, system status at that time (such as processor load, memory usage, network status, etc.), and the final processing result.

[0048] The real-time data and historical data are integrated to form a new data set, and the first model is trained based on the new data set.

[0049] In this embodiment, the first model is retrained using a new data set, including updated historical data and newly collected data. According to the type of the first model, a suitable training algorithm and parameter settings are selected. For example, if it is a machine learning model, it can be trained using optimization algorithms such as stochastic gradient descent, genetic algorithm, etc. After retraining, the updated model is evaluated. The evaluation indicators of the model, such as mean square error, mean absolute error, coefficient of determination, etc., can be calculated using a validation set or cross-validation method. According to the evaluation results, the parameters of the model are adjusted, feature engineering is optimized, or a different model structure is selected to improve the performance and accuracy of the model. When the updated model passes the evaluation and meets the expected performance requirements, it is deployed to the first processor of the self-service device to replace the old model.

[0050] For example, there is a self-service vending machine that mainly sells beverages. In daily use, the vending machine records the relevant information of each transaction data in real time, including data characteristics, actual processing time, system status and processing results. The first time is set to be once a month. When the time node of a month is reached, the newly collected real-time data of this month is integrated with the historical data to form a new data set. The first model is retrained using the new data set. For example, a genetic algorithm is used for optimization training. After the training is completed, the validation set is used for cross-validation to calculate evaluation indicators such as mean square error, mean absolute error and determination coefficient. If the evaluation results show that the model performance has been significantly improved, or the expected performance requirements have been met, the updated model is deployed to the first processor to replace the old model.

[0051] From the above, it can be concluded that this embodiment periodically (such as monthly or weekly) updates and retrains the first model based on real-time and historical data, and strictly evaluates its performance. This embodiment ensures that the model can continuously adapt to changes in the data processing needs of self-service devices, effectively improves the model's prediction accuracy and processing efficiency, thereby further optimizing resource allocation and enhancing the response speed and user experience of self-service devices.

[0052] In one embodiment of the present disclosure, updating the first model based on the first time includes:

[0053] In response to a first time, obtaining a performance indicator of a first processor;

[0054] The first model is updated based on the performance indicator of the first processor.

[0055] In this embodiment, the performance indicator of the first processor may include:

[0056] Processor load: reflects the amount of tasks currently being processed by the first processor and the proportion of computing resources it occupies. If the processor load is high, it may affect the data processing speed;

[0057] Processing speed: This can be evaluated by measuring the amount of data processed by the first processor per unit time. Changes in processing speed may indicate that the performance of the processor has been affected by changes in the system environment.

[0058] Memory usage: The first processor includes the size of the used memory and the ratio of the remaining memory. If the memory is insufficient, the data processing efficiency may decrease.

[0059] Temperature: The first processor generates heat during operation. If the temperature is too high, it may affect its performance or even cause failure.

[0060] In this embodiment, the performance index of the first processor can be obtained in real time through a built-in monitoring tool or sensor. For example, the interface of the operating system can read information such as the load and memory usage of the processor. The temperature sensor can measure the temperature of the processor.

[0061] The first model is retrained using the new performance indicator data together with the historical data. The performance indicator can be added to the model as a new feature variable, or the structure and parameters of the model can be adjusted to adapt to the new performance changes.

[0062] For example, if a linear relationship is found between processor load and data processing time, a coefficient related to processor load can be added to the model to improve the prediction accuracy of the model.

[0063] After retraining, evaluate the updated model and use the validation set or cross-validation method to calculate the model's evaluation indicators, such as mean square error, mean absolute error, determination coefficient, etc. Based on the evaluation results, further adjust the model parameters or optimize feature engineering to ensure that the model performance is improved.

[0064] From the above, it can be concluded that this embodiment monitors and responds to changes in the performance indicators of the first processor in real time, taking it as an important basis for model updating. This embodiment can dynamically adjust and optimize the first model, effectively improving the adaptability and accuracy of the model when processing complex tasks, while ensuring that the self-service device can still maintain efficient and stable data processing capabilities when facing different performance conditions, thereby enhancing the user experience and overall system performance.

[0065] In an embodiment of the present disclosure, it also includes: determining the first time of the first model based on the spatiotemporal regularity of the self-service device.

[0066] In this embodiment, the operation of the self-service device will be affected by time and space factors. In terms of time, different time periods have different data processing requirements and modes. For example, during the daytime on weekdays, the small transaction data of the self-service vending machine is generally more, while at night or on weekends, there will be more large transactions or special demand data. In terms of space, the geographical location of the self-service device, the surrounding environment, and the characteristics of the population served will affect its data processing. For example, if the self-service vending machine is located in an office building area, there may be a higher data processing volume during the daytime on weekdays because office workers have a greater demand for beverages, snacks, etc.; if it is located near a shopping mall, the data processing volume may increase significantly on weekends and holidays because the consumption demand of the shopping crowd is relatively strong.

[0067] In this embodiment, the time and space rules of the self-service equipment are analyzed to determine the first time.

[0068] Time pattern analysis includes: analyzing the historical data of self-service equipment.

[0069] Collect data processing records of self-service devices in different time periods, which can include data volume, processing time, data type, etc. For example, for a self-service vending machine, you can record the number of commodity transactions and the amount of commodity sales at different time periods every day. Perform statistical analysis on the collected historical data to find out the temporal patterns. You can use data analysis tools such as time series analysis and cluster analysis. For example, through time series analysis, it is found that the data processing volume of self-service devices has increased significantly on certain days of the week or at certain time periods every day.

[0070] Spatial pattern analysis includes: analysis of the geographical location, surrounding environment and service population of the self-service equipment.

[0071] Consider the impact of the location of the self-service device on data processing. Population density, economic development level, consumption habits and other factors in different regions will lead to different data processing modes of self-service devices. For example, self-service devices located in a busy commercial area may have a higher transaction frequency and more complex data types.

[0072] Consider environmental factors around the self-service equipment, such as other facilities nearby, traffic conditions, etc. For example, a self-service vending machine located near a shopping mall has a higher data processing demand during specific time periods (such as holidays).

[0073] Consider the demographics of the people served by the self-service devices and understand their demographics, including age distribution, occupation type, consumer preferences, etc. These factors can affect the type of data and processing requirements. For example, self-service vending machines for young people will have more interactive data and real-time requirements.

[0074] In this embodiment, the time rule and the space rule are comprehensively analyzed to determine the best time to update the first model.

[0075] For example, if it is found that the vending machines located in a commercial center process the largest amount of data and the longest processing time in the afternoons on holidays and weekends, the first model can be updated on the night before the holiday or on the morning of the weekend to better cope with high-load data processing tasks.

[0076] In this embodiment, the spatiotemporal regularity of the self-service device is updated based on the environmental information of the area where the self-service device is set.

[0077] As the usage and environment of the self-service equipment change, the spatiotemporal laws will also change. Therefore, it is necessary to re-analyze the spatiotemporal laws regularly and dynamically adjust the update time of the first model. For example, if the economic development of a region leads to changes in population mobility, which affects the data processing mode of the self-service equipment, then the first time needs to be adjusted accordingly.

[0078] From the above, it can be concluded that, by determining the first time based on the spatiotemporal law, this embodiment can update the first model when it is most needed, thereby better adapting to the data processing needs of the self-service device, which helps to improve the accuracy and effectiveness of the model and ensure that the self-service device can operate efficiently and stably.

[0079] In an embodiment of the present disclosure, it also includes: adjusting the first threshold based on the priority of the self-service device data.

[0080] In this embodiment, the priority of the self-service device data is determined based on the data type and time sensitivity of the self-service device data, and the first threshold is adjusted based on the priority of the self-service device data.

[0081] Different types of data may have different priorities. For example, for a vending machine, payment information in transaction data may have a higher priority because it is directly related to the security of funds and the completion of the transaction. Inventory query data may have a relatively lower priority.

[0082] Some data may be very time-sensitive and need to be processed as soon as possible. For example, if a self-service device fails, it needs to be processed in a timely manner to avoid affecting user use and causing greater losses. This type of data has a higher priority. Some statistical analysis data may not be so time-sensitive and have a relatively lower priority.

[0083] In this embodiment, if the priority of the self-service device data is high, the first threshold is adjusted lower based on the first adjustment step; if the priority of the self-service device data is low, the first threshold is adjusted higher based on the first adjustment step.

[0084] In this embodiment, first, this embodiment will have a default first threshold value, which is set after comprehensively considering the performance of the self-service device, user experience, and system resource status. When the self-service device generates data, this embodiment can evaluate the priority of the data based on the above-mentioned basis for determining the priority. For example, when payment information data is detected, this embodiment can identify it as high-priority data; and when it is just ordinary commodity browsing data, it can be identified as low-priority data.

[0085] The first adjustment step is a fixed value used to adjust the first threshold, and the size of the first adjustment step determines the amplitude of each adjustment of the first threshold.

[0086] If the data is determined to be of high priority, this embodiment may lower the first threshold based on the first adjustment step size.

[0087] Exemplarily, assuming that the initial first threshold is T and the first adjustment step is ΔT1, if the current data priority is high, the new first threshold will become T-ΔT1.

[0088] The purpose of this is to allow the first processor to process high-priority data more quickly, or to transfer the data to the second processor in a shorter time, so as to ensure that the high-priority data can be processed in a timely manner.

[0089] If the data is determined to be of low priority, this embodiment may increase the first threshold based on the first adjustment step.

[0090] Exemplarily, assuming that the initial first threshold is T, the new first threshold becomes T+ΔT1.

[0091] This is because for low-priority data, the first processor can be given a relatively long processing time to avoid unnecessary data transmission to the second processor, thereby saving system resources and improving overall efficiency.

[0092] From the above, it can be concluded that this embodiment optimizes the data processing flow of the self-service device by dynamically adjusting the first threshold, flexibly adjusts the processing priority according to the priority of the data, ensures that high-priority data is processed quickly, and reduces the occupation of system resources by low-priority data, thereby improving the system response speed, resource utilization and overall operating efficiency.

[0093] In one embodiment of the present disclosure, in response to the first data processing time being less than a first threshold, processing the self-service device data based on the first processor includes:

[0094] Preprocessing the self-service device data to obtain first data;

[0095] Classify the first data based on the type of the first data to obtain a plurality of first sub-data;

[0096] The first sub-data is processed based on a target algorithm, where the target algorithm is an algorithm in an algorithm library corresponding to the first sub-data.

[0097] There may be some noise and outliers in the self-service device data. In order to make the data more accurate and reliable, the self-service device data is preprocessed to improve the data quality and unify the data format.

[0098] In this embodiment, data cleaning, data standardization and data format conversion are performed on the self-service device data to obtain first data.

[0099] First, check the errors, missing values ​​and outliers in the data. For example, in the transaction data of the vending machine, there may be negative amounts or unreasonable transaction times. These data need to be identified and corrected or deleted. Secondly, standardize the value range of the data so that data with different characteristics are comparable. For example, normalize the data such as commodity prices and quantities so that their value ranges are within a specific range. Thirdly, convert data in different formats into a unified format, such as converting text data into digital codes. Finally, obtain the first data.

[0100] In this embodiment, the classification standard of the self-service device data is determined according to the type of the self-service device data.

[0101] For example, data can be classified according to its source (such as transaction data, inventory data, fault report data, etc.), structure (such as structured data, semi-structured data, unstructured data, etc.), or business meaning (such as payment-related data, product information data, user behavior data, etc.).

[0102] Based on the determined classification standard, the preprocessed first data is classified to obtain a plurality of first sub-data.

[0103] For example, the data of self-service vending machines can be divided into transaction sub-data, inventory sub-data, fault sub-data, etc.

[0104] In this embodiment, an algorithm library including multiple target algorithms is constructed, and based on the types of multiple first sub-data, a corresponding target algorithm is selected from the algorithm library, and the corresponding first sub-data is processed based on the selected target algorithm.

[0105] A pre-built algorithm library containing a variety of algorithms that can cover the processing needs of different types of data.

[0106] For example, for transaction data, encryption algorithms can be used to ensure the security of payment information, and data analysis algorithms can be used to mine user purchasing behavior patterns; for inventory data, inventory management algorithms can be used to optimize and predict inventory; for fault report data, fault diagnosis algorithms can be used to determine the cause of the fault and solution.

[0107] According to the type of the first sub-data, a corresponding target algorithm is selected from an algorithm library.

[0108] Exemplarily, when processing transaction sub-data, an encryption algorithm and a data analysis algorithm are selected; when processing inventory sub-data, an inventory management algorithm is selected; when processing fault sub-data, a fault diagnosis algorithm is selected.

[0109] The corresponding first sub-data are processed using the selected target algorithm.

[0110] Exemplarily, an encryption algorithm is used to encrypt the payment information in the transaction sub-data, and a data analysis algorithm is used to perform user behavior analysis on the transaction sub-data; an inventory management algorithm is used to update and predict inventory on the inventory sub-data; a fault diagnosis algorithm is used to perform fault analysis and diagnosis on the fault sub-data, and generate corresponding maintenance suggestions.

[0111] It can be concluded from the above that this embodiment effectively improves the data quality and ensures the accuracy and reliability of the data by preprocessing and classifying the self-service device data and performing targeted processing based on the target algorithm in the algorithm library. At the same time, the most appropriate algorithm is used to process different types of data, thereby improving the data processing efficiency and the operating efficiency of the self-service equipment.

[0112] In one embodiment of the present disclosure, in response to the first data processing time being greater than or equal to the first threshold, transmitting the self-service device data to the second processor includes:

[0113] In response to the self-service device data being of the first data type, transmitting the self-service device data to the second processor based on the first transmission parameter;

[0114] In response to the self-service device data being of a second data type, transmitting the self-service device data to the second processor based on a second transmission parameter;

[0115] The real-time requirement of the first data type is higher than the real-time requirement of the second data type.

[0116] In this embodiment, the first data type and the second data type can be divided according to the characteristics of the self-service device data and business requirements.

[0117] For example, in a vending machine, transaction data can be classified as the first data type because transaction data has a high real-time requirement and needs to be processed in time to ensure smooth transactions. Inventory statistics data can be classified as the second data type because inventory statistics data has a relatively low real-time requirement and can be processed within a certain period of time.

[0118] When the first data processing time is greater than or equal to the first threshold, it indicates that the first processor cannot process the self-service device data within an acceptable time, and the data needs to be transferred to the second processor for processing. Before transmitting the data, the type of the self-service device data needs to be determined.

[0119] In this embodiment, it can be determined whether the data belongs to the first data type or the second data type through a specific identifier of the data, a source channel, or certain fields in the data structure.

[0120] Exemplarily, if the data comes from a withdrawal transaction module and has a specific transaction identifier, it can be determined as the first data type; if the data comes from a device status monitoring module and has a specific status identifier, it can be determined as the second data type.

[0121] In this embodiment, the transmission parameters refer to the parameters used when transmitting the self-service device data to the second processor, including the transmission mode, transmission rate, data format, etc. Different data types require different transmission parameters to meet their specific real-time requirements and processing requirements. For example, for the first data type with high real-time requirements, it may be necessary to use a high-speed transmission mode, a large transmission bandwidth, and an optimized data format to ensure that the data can be quickly transmitted to the second processor for processing. For the second data type with low real-time requirements, a relatively slow transmission mode, a small transmission bandwidth, and a simpler data format can be used to save transmission resources.

[0122] For example, when the self-service device data is determined to be withdrawal transaction request data (first data type), a high-speed transmission channel can be automatically selected and given priority transmission rights to ensure that the data can quickly reach the second processor for processing. When the data is determined to be device operation status monitoring data (second data type), a normal transmission channel can be selected and a larger data compression ratio can be used for transmission to save transmission resources.

[0123] After the transmission parameters are determined, the self-service device data can be transmitted to the second processor. After receiving the data, the second processor can perform corresponding processing according to the data type.

[0124] From the above, it can be concluded that this embodiment effectively improves data processing efficiency and resource utilization by dividing the self-service device data into a first data type and a second data type according to the real-time requirements, and transmitting the data to the second processor based on different transmission parameters, thereby ensuring that transaction data with higher real-time requirements can be processed quickly, while saving transmission resources for inventory statistical data with lower real-time requirements, thereby improving the overall performance and user experience of the self-service device.

[0125] Corresponding to the data processing method of a self-service device in the above embodiment, Figure 2 This is a structural block diagram of a data processing device of a self-service device provided by an embodiment of the present disclosure. For the sake of convenience, only the parts related to the embodiment of the present disclosure are shown. Figure 2 The data processing device 20 of the self-service device is applied to the first processor in the self-service device, including: a data evaluation module 21, a data processing module 22, and a data transmission module 23.

[0126] The data evaluation module 21 is used to evaluate the self-service device data based on the first model to obtain the first data processing time;

[0127] The data processing module 22 is configured to process the self-service device data based on the first processor in response to the first data processing time being less than the first threshold;

[0128] The data transmission module 23 is configured to transmit the self-service device data to the second processor in response to the first data processing time being greater than or equal to the first threshold.

[0129] In one embodiment of the present disclosure, a data processing device 20 of a self-service device further includes: a first updating module; the first updating module is specifically used to:

[0130] The first model is updated based on the first time.

[0131] In one embodiment of the present disclosure, the model updating module is further configured to:

[0132] In response to a first time, obtaining a performance indicator of a first processor;

[0133] The first model is updated based on the performance indicator of the first processor.

[0134] In one embodiment of the present disclosure, a data processing device 20 of a self-service device further includes: a second updating module; the second updating module is specifically used to:

[0135] The first time of the first model is determined based on the spatiotemporal law of the self-service device.

[0136] In one embodiment of the present disclosure, a data processing device 20 of a self-service device further includes: a threshold adjustment module; the threshold adjustment module is specifically used to:

[0137] The first threshold is adjusted based on the priority of the self-service device data.

[0138] In one embodiment of the present disclosure, the data processing module 22 is further configured to:

[0139] Preprocessing the self-service device data to obtain first data;

[0140] Classify the first data based on the type of the first data to obtain a plurality of first sub-data;

[0141] The first sub-data is processed based on a target algorithm, where the target algorithm is an algorithm in an algorithm library corresponding to the first sub-data.

[0142] In one embodiment of the present disclosure, the data transmission module 23 is further configured to:

[0143] In response to the self-service device data being of the first data type, transmitting the self-service device data to the second processor based on the first transmission parameter;

[0144] In response to the self-service device data being of a second data type, transmitting the self-service device data to the second processor based on a second transmission parameter;

[0145] The real-time requirement of the first data type is higher than the real-time requirement of the second data type.

[0146] See also Figure 3 , Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 3 The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303 and one or more memories 304. The processors 301, input devices 302, output devices 303 and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of the modules in the above-mentioned device embodiments, such as Figure 2 The functions of modules 21 to 23 are shown.

[0147] It should be understood that in the embodiment of the present disclosure, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0148] The input device 302 may include a touch panel, a fingerprint collection sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.

[0149] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0150] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of the data processing method of a self-service device provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device described in the embodiments of the present disclosure, which will not be repeated here.

[0151] In another embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by the processor, all or part of the processes in the above-mentioned embodiment method are implemented, and the computer program can also be completed by instructing the relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0152] The computer-readable storage medium may be an internal storage unit of the electronic device of any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the computer-readable storage medium may also include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0153] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0154] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0155] In the several embodiments provided in the present application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or it can be an electrical, mechanical or other form of connection.

[0156] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present disclosure.

[0157] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0158] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these modifications or replacements should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A data processing method for a self-service device, applied to a first processor in the self-service device, characterized in that: include: Evaluate the self-service device data based on the first model to obtain a first data processing time; In response to the first data processing time being less than a first threshold, processing the self-service device data based on the first processor; In response to the first data processing time being greater than or equal to a first threshold, transmitting the self-service device data to a second processor; Also includes: updating the first model based on a first time; Also includes: Determine the first time of the first model based on the spatiotemporal law of the self-service device; Update the spatiotemporal regularity of the self-service device based on the environmental information of the area where the self-service device is set; Also includes: adjusting the first threshold based on the priority of the self-service device data; determining a priority of the self-service device data based on a data type and time sensitivity of the self-service device data, and adjusting a first threshold based on the priority of the self-service device data; If the priority of the self-service device data is high, the first threshold is lowered based on the first adjustment step; if the priority of the self-service device data is low, the first threshold is increased based on the first adjustment step; In response to the first data processing time being greater than or equal to a first threshold, transmitting the self-service device data to a second processor, comprising: In response to the self-service device data being of the first data type, transmitting the self-service device data to the second processor based on the first transmission parameter; In response to the self-service device data being of a second data type, transmitting the self-service device data to a second processor based on a second transmission parameter; The real-time requirement of the first data type is higher than the real-time requirement of the second data type.

2. A data processing method for a self-service device as claimed in claim 1, characterized in that: The updating of the first model based on the first time includes: In response to a first time, obtaining a performance indicator of a first processor; The first model is updated based on the performance indicator of the first processor.

3. The data processing method of a self-service device according to claim 1, characterized in that: In response to the first data processing time being less than a first threshold, processing the self-service device data based on the first processor includes: Preprocessing the self-service device data to obtain first data; Classify the first data based on its type to obtain a plurality of first sub-data; The first sub-data is processed based on a target algorithm, where the target algorithm is an algorithm in an algorithm library corresponding to the first sub-data.

4. A data processing device for a self-service device, applied to a first processor in the self-service device, characterized in that: include: A data evaluation module, used to evaluate the self-service device data based on the first model to obtain a first data processing time; a data processing module, configured to process the self-service device data based on the first processor in response to the first data processing time being less than a first threshold; A data transmission module, configured to transmit the self-service device data to a second processor in response to the first data processing time being greater than or equal to a first threshold; Also includes: updating the first model based on a first time; Also includes: Determine the first time of the first model based on the spatiotemporal law of the self-service device; Update the spatiotemporal regularity of the self-service device based on the environmental information of the area where the self-service device is set; Also includes: adjusting the first threshold based on the priority of the self-service device data; determining a priority of the self-service device data based on a data type and time sensitivity of the self-service device data, and adjusting a first threshold based on the priority of the self-service device data; If the priority of the self-service device data is high, the first threshold is lowered based on the first adjustment step; if the priority of the self-service device data is low, the first threshold is increased based on the first adjustment step; In response to the first data processing time being greater than or equal to a first threshold, transmitting the self-service device data to a second processor, comprising: In response to the self-service device data being of the first data type, transmitting the self-service device data to the second processor based on the first transmission parameter; In response to the self-service device data being of a second data type, transmitting the self-service device data to a second processor based on a second transmission parameter; The real-time requirement of the first data type is higher than the real-time requirement of the second data type.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

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