Intelligent data acquisition method and system for multifunctional self-service terminal equipment

Abstract

The invention discloses an intelligent data acquisition method and system for multifunctional self-service terminal equipment, and the method comprises the steps: obtaining user interaction data of hotel terminal equipment, carrying out the operation feature analysis, user behavior recognition and feature classification storage, and forming user operation behavior feature data; setting a plurality of time periods to analyze user behaviors, serializing operation behavior characteristic data to form a first behavior sequence, comparing the first behavior sequence with a plurality of behavior modes set based on big data to perform gray correlation analysis, and evaluating user interaction effectiveness. Effective interaction data and invalid interaction data are respectively imported into two queues for storage, and are transmitted to the cloud platform for storage and analysis in the next period, and terminal function display schemes for different users are generated, so that the hotel user feature analysis effect is effectively improved, the user data acquisition and analysis efficiency of the terminal is improved, and the user experience is improved.

Description

Technical Field

[0001] The present invention relates to the field of terminal data analysis, and more specifically, to a data intelligent acquisition method and system for multifunctional self-service terminal devices. Background Art

[0002] With the rapid development of the hotel industry, improving the customer service experience has become the key to hotel competition. Traditional hotel services often rely on manual operations and simple electronic devices, making it difficult to comprehensively capture and deeply analyze the real needs and behavior habits of customers. In order to provide more personalized and intelligent services, hotels have begun to introduce multifunctional terminal devices, which integrate multiple functional modules, such as guest room control, entertainment services, information query, etc., aiming to meet the diverse needs of customers through rich interaction means.

[0003] However, the introduction of multifunctional terminal devices has also brought challenges in data processing. Hotels need to effectively collect, analyze, and utilize the user interaction data generated by these terminal devices to explore the potential needs of customers, optimize service processes, and improve customer satisfaction. However, existing data processing methods often have problems such as data redundancy and low processing efficiency, making it difficult to accurately reflect the real behavior characteristics of customers, unable to screen out effective interaction data in real time, and the traditional terminal technology lacks an efficient acquisition and analysis process for interaction data, lacks redundant analysis of interaction data and judgment of effective data, resulting in difficulty in accurately identifying user behavior characteristics, difficulty in achieving optimized analysis for users, resulting in low practicality of hotel self-service terminals, and thus affecting the accuracy and personalization of hotel services. Summary of the Invention

[0004] The present invention overcomes the defects of the prior art and provides a data intelligent acquisition method and system for multifunctional self-service terminal devices.

[0005] The first aspect of the present invention provides a data intelligent acquisition method for multifunctional self-service terminal devices, including: Based on a preset hotel area, set multifunctional terminal devices, and within an analysis period, obtain user interaction data of the multifunctional terminal devices; Conduct interaction analysis on each user, and according to different functional modules, conduct operation feature analysis, user behavior recognition, and feature classification storage on the user interaction data to form operation behavior feature data of the user; Set multiple time periods within an analysis cycle for user behavior analysis, serialize the operation behavior characteristic data to form a first behavior sequence, set comparison behavior sequences for multiple behavior patterns through user interaction big data, based on the grey relational analysis method, conduct a correlation analysis between the first behavior sequence and the comparison behavior sequences, use the calculated correlation degree as a data validity index, and evaluate the user interaction effectiveness of the first behavior sequence. Based on the evaluation results, screen out valid interaction data and invalid interaction data from the user interaction data in real time; Set two queue storage structures. Within each time period, import the valid interaction data into the first queue and the invalid interaction data into the second queue for storage. In the next analysis cycle, transfer the interaction data to the cloud platform for storage and user feature analysis based on the two queues, and generate a terminal function display plan for individual users.

[0006] In this solution, based on a preset hotel area, set multifunctional terminal devices. Within an analysis cycle, obtain the user interaction data of the multifunctional terminal devices, specifically: Within the preset hotel area, set multiple multifunctional terminal devices, and connect the multifunctional terminal devices to the cloud platform through the Internet; Within an analysis cycle, based on the user-device interaction process, obtain the user interaction data of the multifunctional terminal devices. The user interaction data includes operation instructions, interface access records, gesture operations, click operations, function operations, terminal feedback, and user identification information data.

[0007] In this solution, the multifunctional terminal devices include a data collection module, a function setting module, a storage module, a network connection module, and a data encryption module.

[0008] In this solution, conduct an interaction analysis for each user, and based on different functional modules, conduct an operation characteristic analysis, user behavior recognition, and feature classification storage of the user interaction data to form the operation behavior characteristic data of the user, specifically: Take one user as the analysis object, classify the interaction situations of different functional modules according to the corresponding user interaction data, and conduct recognition based on the operation instructions and operation behaviors. Conduct feature analysis from three dimensions: the operation form, operation content, and operation time of the terminal interaction process, and generate the operation behavior characteristic data.

[0009] In this solution, multiple time periods are set within an analysis cycle for user behavior analysis, and the operation behavior characteristic data is serialized to form a first behavior sequence. Through user interaction big data, comparison behavior sequences of multiple behavior patterns are set. Based on the grey relational analysis method, the first behavior sequence is analyzed for correlation with the comparison behavior sequences, and the calculated correlation degree is used as the data validity index. Then, the user interaction validity of the first behavior sequence is evaluated, and based on the evaluation results, valid interaction data and invalid interaction data are screened out from the user interaction data in real time. Specifically: Based on a preset time interval, multiple time periods are set within an analysis cycle for user behavior analysis. Based on each time period, the operation behavior characteristic data is serialized to form multiple segments of sequences, and each segment of sequence corresponds to an operation behavior; The multiple segments of sequences are merged to form a first behavior sequence; Preset user behavior characteristics are screened in the user interaction big data to obtain multiple preset user behavior characteristics. The characteristic data of the multiple preset user behavior characteristics is serialized, and comparison behavior sequences of multiple behavior patterns are set; Based on the grey relational analysis method, the comparison behavior sequences are used as the reference sequences, and the first behavior sequence is used as the comparison sequence. Based on each sequence segment, the maximum, minimum differences and absolute differences are calculated, and further the correlation degree between the reference sequence and the comparison sequence is analyzed; The data validity is evaluated by the correlation degree. Based on a preset correlation degree range, the validity of the first behavior sequence is evaluated. Through the validity evaluation process and results, the user interaction data is classified to screen out valid interaction data and invalid interaction data.

[0010] In this solution, two queue storage structures are set. Within each time period, the valid interaction data is imported into the first queue, and the invalid interaction data is imported into the second queue for storage. In the next analysis cycle, based on the two queues, the interaction data is transmitted to the cloud platform for storage and user feature analysis, and a terminal function display scheme for the unit user is generated. Specifically: Two queue storage structures are set. Within each time period, the user interaction data is analyzed and classified in real time. The valid interaction data is imported into the first queue, and the invalid interaction data is imported into the second queue for storage; In the next analysis cycle, based on the two queues, the interaction data is transmitted to the cloud platform for storage and user feature analysis, and a terminal function display scheme for the unit user is generated; The valid interaction data is imported into the first queue, and the invalid interaction data is imported into the second queue for storage. In the next analysis cycle, multiple data segments are output from the two queue data based on the storage order, and each data segment is encrypted and transmitted to the cloud platform for storage and user feature analysis.

[0011] In this solution, the terminal function display solution is specifically as follows: During an analysis period, collect valid interaction data and invalid interaction data through the cloud platform; Based on the valid interaction data, conduct user behavior pattern analysis and interaction characteristic evaluation, and generate the function module priority for a specific user through interaction analysis; Through the invalid interaction data, analyze the interaction frequency of the user-related invalid operation instructions, and based on the interaction frequency, conduct optimization analysis on the interface operation to generate an interface optimization plan; Based on the function module priority and the interface optimization plan, transmit to the multi-functional terminal device and conduct real-time interaction optimization.

[0012] The second aspect of the present invention also provides a data intelligent acquisition system for a multi-functional self-service terminal device. The system includes: a memory and a processor. The memory includes a data intelligent acquisition program for the multi-functional self-service terminal device. When the data intelligent acquisition program for the multi-functional self-service terminal device is executed by the processor, the following steps are implemented: Based on a preset hotel area, set the multi-functional terminal device, and during an analysis period, obtain the user interaction data of the multi-functional terminal device; Conduct interaction analysis on each user, and according to different function modules, conduct operation feature analysis, user behavior recognition and feature classification storage on the user interaction data to form the operation behavior feature data of the user; Set multiple time periods during an analysis period for user behavior analysis, and serialize the operation behavior feature data to form a first behavior sequence. Through the user interaction big data, set the comparison behavior sequences of multiple behavior patterns. Based on the grey relational method, conduct correlation analysis between the first behavior sequence and the comparison behavior sequences, use the calculated correlation degree as the data validity index, and conduct user interaction validity evaluation on the first behavior sequence. Through the evaluation results, screen out valid interaction data and invalid interaction data from the user interaction data in real time; Set two queue storage structures. During each time period, import the valid interaction data into the first queue and import the invalid interaction data into the second queue for storage. In the next analysis period, based on the two queues, transmit the interaction data to the cloud platform for storage and user feature analysis, and generate a terminal function display solution for a unit user.

[0013] The third aspect of the present invention also provides a computer-readable storage medium. The computer-readable storage medium includes a data intelligent acquisition program for the multi-functional self-service terminal device. When the data intelligent acquisition program for the multi-functional self-service terminal device is executed by the processor, the steps of the data intelligent acquisition method for the multi-functional self-service terminal device as described in any one of the above are implemented.

[0014] The present invention discloses a data intelligent acquisition method and system for a multi-functional self-service terminal device. By obtaining user interaction data of hotel terminal devices, performing operation feature analysis, user behavior recognition, and feature classification storage, user operation behavior feature data is formed. Multiple time periods are set to analyze user behavior, and the operation behavior feature data is serialized to form a first behavior sequence, and a grey relational analysis is performed on the behavior sequence by comparing it with multiple behavior patterns set based on big data to evaluate the effectiveness of user interaction. The effective interaction data and the invalid interaction data are respectively imported into two queues for storage and transmitted to the cloud platform for storage and analysis in the next cycle to generate a terminal function display scheme for different users, effectively improving the hotel user feature analysis effect, improving the efficiency of terminal acquisition and analysis of user data, and enhancing the user experience. Brief Description of the Drawings

[0015] Figure 1 Shows a flowchart of a data intelligent acquisition method for a multi-functional self-service terminal device according to the present invention; Figure 2 Shows a block diagram of a data intelligent acquisition system for a multi-functional self-service terminal device according to the present invention. Detailed Embodiments

[0016] In order to more clearly understand the above objects, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0017] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0018] Figure 1 Shows a flowchart of a data intelligent acquisition method for a multi-functional self-service terminal device according to the present invention.

[0019] As Figure 1 shown, in the first aspect of the present invention, a data intelligent acquisition method for a multi-functional self-service terminal device is provided, including: S102, based on a preset hotel area, set a multi-functional terminal device, and within an analysis period, obtain user interaction data of the multi-functional terminal device; S104, perform interaction analysis on each user, and according to different function modules, perform operation feature analysis, user behavior recognition, and feature classification storage on the user interaction data to form the operation behavior feature data of the user; S106. Set multiple time periods within an analysis cycle for user behavior analysis, serialize the operation behavior characteristic data to form a first behavior sequence. Set comparison behavior sequences for multiple behavior patterns through user interaction big data. Based on the grey relational analysis method, conduct a correlation analysis between the first behavior sequence and the comparison behavior sequences. Take the calculated correlation degree as the data validity index, and conduct a user interaction effectiveness evaluation on the first behavior sequence. Real-time screen out valid interaction data and invalid interaction data from the user interaction data according to the evaluation results; S108. Set two queue storage structures. Within each time period, import the valid interaction data into the first queue and import the invalid interaction data into the second queue for storage. In the next analysis cycle, transfer the interaction data to the cloud platform for storage and user feature analysis based on the two queues, and generate a terminal function display plan for individual users.

[0020] According to an embodiment of the present invention, based on a preset hotel area, set multifunctional terminal devices. Within an analysis cycle, obtain the user interaction data of the multifunctional terminal devices, specifically: Set multiple multifunctional terminal devices within the preset hotel area. The multifunctional terminal devices are connected to the cloud platform through the Internet; Within an analysis cycle, based on the interaction process between the user and the device, obtain the user interaction data of the multifunctional terminal devices. The user interaction data includes operation instructions, interface access records, gesture operations, click operations, function operations, terminal feedback, and user identification information data.

[0021] It should be noted that the user interaction data includes various operation interaction data generated when the user uses various functions of the terminal, and behavior characteristic analysis is performed through this interaction data.

[0022] According to an embodiment of the present invention, the multifunctional terminal device includes a data acquisition module, a function setting module, a storage module, a network connection module, and a data encryption module.

[0023] According to an embodiment of the present invention, conduct an interaction analysis on each user, and perform operation characteristic analysis, user behavior recognition, and feature classification storage on the user interaction data according to different function modules to form the operation behavior characteristic data of the user, specifically: Take one user as the analysis object, classify the interaction situations of different function modules according to the corresponding user interaction data, and perform recognition based on the operation instructions and operation behaviors. Conduct feature analysis from three dimensions of the operation form, operation content, and operation time of the terminal interaction process, and generate operation behavior characteristic data.

[0024] It should be noted that the operation behavior characteristic data includes the characteristic data used by the user in different function modules. The operation behavior characteristic data is the interaction characteristic data based on the terminal platform. Through this data, the user's operation and interaction characteristics can be effectively described and recorded from the perspective of data.

[0025] According to an embodiment of the present invention, multiple time periods are set within an analysis period for user behavior analysis, and the operation behavior characteristic data is serialized to form a first behavior sequence. Through the user interaction big data, comparison behavior sequences of multiple behavior patterns are set. Based on the grey relational analysis method, the first behavior sequence is correlated with the comparison behavior sequences, and the calculated correlation degree is used as the data validity index, and the user interaction validity of the first behavior sequence is evaluated. Through the evaluation results, effective interaction data and invalid interaction data are screened out from the user interaction data in real time. Specifically: Based on a preset time interval, multiple time periods are set within an analysis period for user behavior analysis. Based on each time period, the operation behavior characteristic data is serialized to form multiple segments of sequences, and each segment of sequence corresponds to an operation behavior. The multiple segments of sequences are merged to form a first behavior sequence. Preset user behavior characteristics are screened in the user interaction big data to obtain multiple preset user behavior characteristics. The characteristic data of the multiple preset user behavior characteristics is serialized, and comparison behavior sequences of multiple behavior patterns are set. Based on the grey relational analysis method, the comparison behavior sequence is used as the reference sequence, and the first behavior sequence is used as the comparison sequence. Based on each sequence segment, the maximum, minimum difference and absolute difference are calculated, and the correlation degree between the reference sequence and the comparison sequence is further analyzed. The data validity is evaluated by the correlation degree. Based on a preset correlation degree range, the validity of the first behavior sequence is evaluated. Through the validity evaluation process and results, the user interaction data is classified, and effective interaction data and invalid interaction data are screened out.

[0026] It should be noted that each segment of sequence corresponds to an operation behavior and also corresponds to a segment of operation characteristic data, specifically the operation behavior analyzed in a time period. When performing serialized correlation analysis, correlation calculations are performed based on each segment of sequence. The first behavior sequence and the comparison behavior sequences are both overall sequences, and the overall sequence includes multiple sequence segments. By dividing into multiple sequence segments, the user operation characteristics and behavior patterns can be compared in a refined manner. In the comparison behavior sequences of the multiple behavior patterns, the sequence users compare the user real-time interaction data, and characteristic behaviors that are relevant and similar to the characteristic sequences of the preset patterns are mined. Through this process, the user's effective interaction data can be effectively mined and the invalid interaction data can be judged.

[0027] Effective interaction data refers to data that has value in subsequent processes, plays a role in analyzing user behavior characteristics to a certain extent, and is used for interactive correlation analysis between users. By screening out this data, it is possible to effectively improve the efficiency of subsequent user behavior characteristic analysis, reduce redundant data analysis, improve the accuracy of behavior characteristic recognition, further enhance the user experience of the terminal, and improve the efficiency of data collection and analysis.

[0028] Within an analysis cycle, a user includes at least one first behavior sequence. During the process of real-time terminal data collection and cloud platform data transmission, it includes multiple processes of correlation analysis and data collection and screening of the first behavior sequence. There are various types of comparison behavior sequences, and multiple comparison sequences can be used for evaluation when conducting correlation analysis and effectiveness assessment.

[0029] Through the effectiveness data analysis in the embodiments of the present invention, it is possible to screen out redundant operations, ineffective operations, etc. in the user interaction process, mine effective interaction data, and further provide behavior characteristic analysis based on terminal interaction.

[0030] According to the embodiments of the present invention, two queue storage structures are set. Within each time period, effective interaction data is imported into the first queue, and ineffective interaction data is imported into the second queue for storage. In the next analysis cycle, based on the two queues, the interaction data is transmitted to the cloud platform for storage and user characteristic analysis, and a terminal function display scheme for individual users is generated. Specifically: Set two queue storage structures. Within each time period, real-time analysis and classification of user interaction data are performed. Effective interaction data is imported into the first queue, and ineffective interaction data is imported into the second queue for storage; In the next analysis cycle, based on the two queues, the interaction data is transmitted to the cloud platform for storage and user characteristic analysis, and a terminal function display scheme for individual users is generated; Import effective interaction data into the first queue and ineffective interaction data into the second queue for storage. In the next analysis cycle, multiple data segments are output based on the storage order of the two queues, and each data segment is encrypted and transmitted to the cloud platform for storage and user characteristic analysis.

[0031] It should be noted that queue storage can effectively define the data storage order, and there is user privacy data in the interaction data. When outputting data in the form of a queue, it is possible to perform encrypted transmission in combination with a transmission encryption algorithm, effectively improving data security and transmission efficiency.

[0032] According to the embodiments of the present invention, the terminal function display scheme is specifically as follows: Within an analysis cycle, the cloud platform collects effective interaction data and ineffective interaction data; Based on the effective interaction data, conduct user behavior pattern analysis and interaction feature evaluation, and generate the function module priorities based on specific users through interaction analysis; Through the invalid interaction data, analyze the interaction frequency of the user-related invalid operation instructions, and based on the interaction frequency, conduct optimization analysis on the interface operations to generate an interface optimization plan; Based on the function module priorities and the interface optimization plan, transmit them to the multi-functional terminal device and conduct real-time interaction optimization.

[0033] It should be noted that the optimization analysis of the interface operations is to adjust the interface operation sequence, optimize button navigation, define shortcut functions and other optimization plans to reduce the user's invalid operation behaviors and patterns. The real-time interaction optimization can effectively improve the user's terminal usage experience. Especially for users with a long hotel stay time, efficient interaction plans can be generated for the collected interaction characteristics to improve the subsequent terminal usage experience. Through the embodiments of the present invention, the optimization plans for different users can also be set dynamically and efficiently. The applicability of the terminal device is strong and it has wide application value in hotel terminals.

[0034] According to the embodiments of the present invention, it further includes: In the next analysis period, when transmitting the interaction data to the cloud platform based on two queues, alternately output the sequence data through the two queue data and obtain the transmission data; When the transmission data reaches the predetermined data volume, encrypt the transmission data in the form of AES encryption; When the cloud platform receives the transmission data, decrypt it based on the AES encryption algorithm, and screen out the effective interaction data and the invalid interaction data based on the queue order for user behavior evaluation.

[0035] It should be noted that the queue storage has a certain sequence. Both the input storage and the output transmission have a fixed order, providing a more efficient processing flow for encrypted transmission. Before encryption, alternately output the effective interaction data and the invalid interaction data, and pack them into data packets for encryption, effectively preventing the data from being stolen and recognized. Upload it to the cloud platform for corresponding decryption, and screen out the effective and invalid interaction data based on the queue storage order for further analysis of the user behavior characteristics. Through this embodiment, the transmission efficiency can be effectively improved on the premise of ensuring data security.

[0036] The data alternate output means that the first queue outputs a sequence segment of data, and then the second queue outputs a sequence segment of data, and the alternate output obtains the transmission data, effectively improving the security of the interaction data.

[0037] Figure 2 Shows a block diagram of a data intelligent acquisition system for a multi-functional self-service terminal device according to the present invention.

[0038] In a second aspect of the present invention, there is also provided a data intelligent acquisition system 2 for a multi-functional self-service terminal device. The system includes: a memory 21 and a processor 22. The memory 21 includes a data intelligent acquisition program for the multi-functional self-service terminal device. When the data intelligent acquisition program for the multi-functional self-service terminal device is executed by the processor 22, the following steps are implemented: Based on a preset hotel area, set multi-functional terminal devices, and within an analysis period, obtain user interaction data of the multi-functional terminal devices; Conduct interaction analysis for each user, and according to different functional modules, conduct operation feature analysis, user behavior recognition and feature classification storage on the user interaction data to form operation behavior feature data of the user; Set multiple time periods within an analysis period for user behavior analysis, serialize the operation behavior feature data to form a first behavior sequence, set comparison behavior sequences for multiple behavior patterns through user interaction big data, based on the grey relational method, conduct correlation analysis between the first behavior sequence and the comparison behavior sequences, use the calculated correlation degree as a data validity index, and conduct user interaction validity evaluation on the first behavior sequence, and in real time screen out valid interaction data and invalid interaction data from the user interaction data through the evaluation result; Set two queue storage structures. Within each time period, import the valid interaction data into the first queue and import the invalid interaction data into the second queue for storage. In the next analysis period, based on the two queues, transmit the interaction data to the cloud platform for storage and user feature analysis, and generate a terminal function display scheme for individual users.

[0039] According to an embodiment of the present invention, the step of based on a preset hotel area, set multi-functional terminal devices, and within an analysis period, obtain user interaction data of the multi-functional terminal devices is specifically as follows: Within the preset hotel area, set multiple multi-functional terminal devices, and the multi-functional terminal devices are connected to the cloud platform through the Internet; Within an analysis period, based on the interaction process between the user and the device, obtain user interaction data of the multi-functional terminal devices. The user interaction data includes operation instructions, interface access records, gesture operations, click operations, function operations, terminal feedback, and user identification information data.

[0040] It should be noted that the user interaction data includes various operation interaction data generated when the user uses various functions of the terminal, and behavior feature analysis is conducted through this interaction data.

[0041] According to an embodiment of the present invention, the multi-functional terminal device includes a data acquisition module, a function setting module, a storage module, a network connection module, and a data encryption module.

[0042] According to an embodiment of the present invention, for each user, interaction analysis is performed, and according to different functional modules, operation feature analysis, user behavior recognition, and feature classification storage are performed on user interaction data to form operation behavior feature data of the user. Specifically: Taking one user as the analysis object, according to the corresponding user interaction data, the interaction situations of different functional modules are classified, and recognition is performed based on operation instructions and operation behaviors. Feature analysis is performed from three dimensions of the operation form, operation content, and operation time in the terminal interaction process, and operation behavior feature data is generated.

[0043] It should be noted that the operation behavior feature data includes feature data used by the user in different functional modules. The operation behavior feature data is interaction feature data based on the terminal platform. Through this data, the user's operation and interaction characteristics can be effectively described and recorded from a data perspective.

[0044] According to an embodiment of the present invention, multiple time periods are set within an analysis cycle for user behavior analysis, and the operation behavior feature data is serialized to form a first behavior sequence. Through user interaction big data, comparison behavior sequences of multiple behavior patterns are set. Based on the grey relational analysis method, the first behavior sequence is correlated with the comparison behavior sequences, and the calculated correlation degree is used as an index for data validity, and the user interaction validity of the first behavior sequence is evaluated. Through the evaluation results, effective interaction data and invalid interaction data are screened out from the user interaction data in real time. Specifically: Based on a preset time interval, multiple time periods are set within an analysis cycle for user behavior analysis. Based on each time period, the operation behavior feature data is serialized to form multiple segments of sequences, and each segment of sequence corresponds to an operation behavior; The multiple segments of sequences are combined to form a first behavior sequence; Preset user behavior features are screened in the user interaction big data to obtain multiple preset user behavior features. The feature data of the multiple preset user behavior features is serialized, and comparison behavior sequences of multiple behavior patterns are set; Based on the grey relational analysis method, the comparison behavior sequence is used as the reference sequence, and the first behavior sequence is used as the comparison sequence. Based on each sequence segment, the maximum, minimum difference, and absolute difference are calculated, and further the correlation degree between the reference sequence and the comparison sequence is analyzed; The data validity is evaluated by the correlation degree. Based on a preset correlation degree range, the validity of the first behavior sequence is evaluated. Through the validity evaluation process and results, the user interaction data is classified, and effective interaction data and invalid interaction data are screened out.

[0045] It should be noted that each sequence corresponds to an operation behavior and also corresponds to a piece of operation feature data, specifically, the operation behavior obtained by analyzing a time period. When performing serialized association analysis, the association calculation is performed based on each sequence. The first behavior sequence and the comparison behavior sequence are both overall sequences, and the overall sequence includes multiple sequence segments. By dividing into multiple sequence segments, the user operation characteristics and behavior patterns can be refined. In the comparison behavior sequences of the multiple behavior patterns, the sequence users compare the real-time interaction data of the users, and dig out the characteristic behaviors that are correlated and similar to the characteristic sequences of the preset patterns. Through this process, the user's effective interaction data can be effectively mined and the invalid interaction data can be judged.

[0046] Effective interaction data refers to data that will be useful in the future and has a certain role in analyzing user behavior characteristics and the interaction correlation between users. By screening out this data, the efficiency of subsequent user behavior characteristic analysis can be effectively improved, redundant data analysis can be reduced, the accuracy of behavioral characteristic identification can be improved, and the user experience of the terminal can be further improved as well as the efficiency of data collection and analysis.

[0047] In one analysis cycle, a user includes at least one first behavior sequence. The process of collecting data in the real-time terminal and transmitting data to the cloud platform includes multiple correlation analysis and data collection and screening processes of the first behavior sequence. There are multiple comparison behavior sequences. When conducting correlation analysis and effectiveness evaluation, multiple comparison sequences can be used for evaluation.

[0048] Through the effectiveness data analysis in the embodiment of the present invention, it is possible to screen out superfluous operations, redundant operations, invalid operations, etc. in the user interaction process, mine out effective interaction data, and further provide behavioral feature analysis based on terminal interaction.

[0049] According to an embodiment of the present invention, the two queue storage structures are set, and in each time period, valid interaction data is imported into the first queue, and invalid interaction data is imported into the second queue for storage. In the next analysis cycle, the interaction data is transmitted to the cloud platform based on the two queues for storage and user feature analysis, and a terminal function display scheme for unit users is generated, specifically: Two queue storage structures are set up to analyze and classify user interaction data in real time in each time period, import valid interaction data into the first queue, and import invalid interaction data into the second queue for storage; In the next analysis cycle, the interaction data is transmitted to the cloud platform based on two queues for storage and user feature analysis, and a terminal function display plan for unit users is generated; Import the valid interaction data into the first queue and the invalid interaction data into the second queue for storage. In the next analysis cycle, output multiple data segments based on the storage order of the data in the two queues, and encrypt and transmit each data segment to the cloud platform for storage and user feature analysis.

[0050] It should be noted that queue storage can effectively define the data storage order, and there is user privacy data in the interaction data. When outputting data in the form of a queue, it can be encrypted and transmitted in combination with the transmission encryption algorithm, effectively improving data security and transmission efficiency.

[0051] According to the embodiments of the present invention, the terminal function display solution is specifically as follows: In an analysis cycle, collect valid interaction data and invalid interaction data through the cloud platform; Based on the valid interaction data, conduct user behavior pattern analysis and interaction feature evaluation, and generate the function module priority for a specific user through interaction analysis; Through the invalid interaction data, analyze the interaction frequency of the user's related invalid operation instructions, and based on the interaction frequency, conduct optimization analysis on the interface operation to generate an interface optimization plan; Transmit the function module priority and the interface optimization plan to the multi-functional terminal device and conduct real-time interaction optimization.

[0052] It should be noted that the optimization analysis of the interface operation is to adjust the interface operation order, button navigation optimization, quick function definition and other optimization plans to reduce the user's invalid operation behaviors and patterns. The real-time interaction optimization can effectively improve the user's terminal usage experience. Especially for users with a long hotel stay, efficient interaction plans can be generated for the collected interaction features, improving the subsequent terminal usage experience. Through the embodiments of the present invention, the optimization plans for different users can also be set efficiently and dynamically, and the applicability of the terminal device is strong, which has wide application value in hotel terminals.

[0053] The third aspect of the present invention also provides a computer-readable storage medium, which includes a data intelligent acquisition program for a multi-functional self-service terminal device. When the data intelligent acquisition program for the multi-functional self-service terminal device is executed by a processor, the steps of the data intelligent acquisition method for the multi-functional self-service terminal device as described in any one of the above are realized.

[0054] The present invention discloses a data intelligent acquisition method and system for a multi-functional self-service terminal device. By obtaining user interaction data of hotel terminal devices, operation feature analysis, user behavior recognition, and feature classification storage are performed to form user operation behavior feature data. Multiple time periods are set to analyze user behavior, and the operation behavior feature data is serialized to form a first behavior sequence, and gray relational analysis is performed on the behavior sequence by comparing it with multiple behavior patterns set based on big data to evaluate the effectiveness of user interaction. The effective interaction data and the invalid interaction data are respectively imported into two queues for storage and transmitted to the cloud platform for storage and analysis in the next cycle to generate a terminal function display scheme for different users, effectively improving the hotel user feature analysis effect, improving the efficiency of terminal acquisition and analysis of user data, and enhancing the user experience.

[0055] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed may be through some interfaces, and the indirect coupling or communication connection of the devices or units may be electrical, mechanical, or other forms.

[0056] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units; they may be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0057] In addition, in each embodiment of the present invention, the various functional units can all be integrated in one processing unit, or each unit can be separately used as a unit, or two or more units can be integrated in one unit; the above-mentioned integrated units can be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0058] Those of ordinary skill in the art can understand that all or part of the steps for implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above method embodiments; and the foregoing storage medium includes: various media such as a removable storage device, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk, or an optical disc that can store program codes.

[0059] Alternatively, if the above integrated units of the present invention are implemented in the form of software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes, such as removable storage devices, ROM, RAM, magnetic disks, or optical discs.

[0060] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention.

Claims

1. A data intelligent collection method for a multifunctional self-service terminal device, characterized in that: include: Based on the preset hotel area, set up multi-functional terminal devices, and obtain user interaction data of multi-functional terminal devices within one analysis cycle; Conduct interaction analysis for each user, and perform operation feature analysis, user behavior identification, and feature classification storage on user interaction data according to different functional modules to form user operation behavior feature data; In one analysis cycle, multiple time periods are set to analyze user behavior, and the operation behavior feature data is serialized to form a first behavior sequence. Through the user interaction big data, a comparison behavior sequence of multiple behavior patterns is set. Based on the grey correlation method, the first behavior sequence and the comparison behavior sequence are analyzed for correlation. The calculated correlation degree is used as the data validity indicator, and the user interaction validity of the first behavior sequence is evaluated. Through the evaluation results, valid interaction data and invalid interaction data are screened out from the user interaction data in real time; Two queue storage structures are set up. In each time period, valid interaction data is imported into the first queue, and invalid interaction data is imported into the second queue for storage. In the next analysis cycle, the interaction data is transmitted to the cloud platform based on the two queues for storage and user feature analysis, and a terminal function display plan for unit users is generated.

2. The method for intelligent data collection for a multifunctional self-service terminal device according to claim 1, characterized in that: The multifunctional terminal device is set based on the preset hotel area, and the user interaction data of the multifunctional terminal device is obtained within an analysis cycle, specifically: In the preset hotel area, multiple multi-functional terminal devices are set up, and the multi-functional terminal devices are connected to the cloud platform through the Internet; During an analysis cycle, based on the user-device interaction process, the user interaction data of the multi-function terminal device is obtained. The user interaction data includes operation instructions, interface access records, gesture operations, click operations, function operations, terminal feedback, and user identification information data.

3. The data intelligent collection method for a multifunctional self-service terminal device according to claim 1, characterized in that: The multi-energy terminal device includes a data acquisition module, a function setting module, a storage module, a network connection module and a data encryption module.

4. The method for intelligent data collection for a multifunctional self-service terminal device according to claim 1, characterized in that: The interactive analysis of each user is performed, and the user interaction data is subjected to operation feature analysis, user behavior identification and feature classification storage according to different functional modules to form the user's operation behavior feature data, specifically: Taking a user as the analysis object, the interaction situations of different functional modules are classified according to the corresponding user interaction data, and identification is performed based on operation instructions and operation behaviors. Feature analysis is performed in three dimensions: operation form, operation content, and operation time of the terminal interaction process, and operation behavior feature data is generated.

5. The method for intelligent data collection for a multifunctional self-service terminal device according to claim 4, characterized in that: The method sets multiple time periods for user behavior analysis within an analysis cycle, and serializes the operation behavior feature data to form a first behavior sequence. Through the user interaction big data, a comparison behavior sequence of multiple behavior patterns is set. Based on the grey correlation method, the first behavior sequence and the comparison behavior sequence are analyzed for correlation. The calculated correlation degree is used as a data validity indicator, and the user interaction validity of the first behavior sequence is evaluated. Through the evaluation results, valid interaction data and invalid interaction data are screened out in real time from the user interaction data. Specifically, Based on the preset time interval, multiple time periods are set within an analysis cycle to analyze user behavior. Based on each time period, the operation behavior feature data is serialized to form multiple sequences, each of which corresponds to an operation behavior. Merging the multiple sequences into a first behavior sequence; Screening preset user behavior features in user interaction big data to obtain multiple preset user behavior features, serializing feature data for the multiple preset user behavior features, and setting comparison behavior sequences for multiple behavior patterns; Based on the grey correlation method, the contrast behavior sequence is taken as the reference sequence, and the first behavior sequence is taken as the comparison sequence. The maximum, minimum and absolute differences are calculated based on each sequence segment, and the correlation between the reference sequence and the comparison sequence is further analyzed. The data validity is evaluated by correlation, and the validity of the first behavior sequence is evaluated based on the preset correlation range. Through the validity evaluation process and results, the user interaction data is classified to screen out valid interaction data and invalid interaction data.

6. The data intelligent collection method for a multifunctional self-service terminal device according to claim 5, characterized in that: The two queue storage structures are set up. In each time period, valid interaction data is imported into the first queue, and invalid interaction data is imported into the second queue for storage. In the next analysis cycle, the interaction data is transmitted to the cloud platform based on the two queues for storage and user feature analysis, and a terminal function display scheme for unit users is generated, specifically: Two queue storage structures are set up to analyze and classify user interaction data in real time in each time period, import valid interaction data into the first queue, and import invalid interaction data into the second queue for storage; In the next analysis cycle, the interaction data is transmitted to the cloud platform based on two queues for storage and user feature analysis, and a terminal function display plan for unit users is generated; The valid interaction data is imported into the first queue, and the invalid interaction data is imported into the second queue for storage. In the next analysis cycle, the two queue data are output as multiple data segments based on the storage order, and each data segment is encrypted and transmitted to the cloud platform for storage and user feature analysis.

7. The method for intelligent data collection for a multifunctional self-service terminal device according to claim 6, characterized in that: The terminal function display scheme is specifically as follows: In one analysis cycle, valid interaction data and invalid interaction data are collected through the cloud platform; Analyze user behavior patterns and evaluate interaction characteristics based on effective interaction data, and generate functional module priorities based on specific users through interaction analysis; Analyze the interaction frequency of invalid operation instructions related to users through invalid interaction data, and optimize the interface operation based on the interaction frequency to generate an interface optimization plan; Based on the functional module priority and interface optimization plan, it is transmitted to multi-energy terminal devices and real-time interactive optimization is performed.

8. A data intelligent collection system for multifunctional self-service terminal equipment, characterized in that: The system includes: a memory and a processor. The memory includes a data intelligent collection program for a multifunctional self-service terminal device. When the data intelligent collection program for a multifunctional self-service terminal device is executed by the processor, the following steps are implemented: Based on the preset hotel area, set up multi-functional terminal devices, and obtain user interaction data of multi-functional terminal devices within one analysis cycle; Conduct interaction analysis for each user, and perform operation feature analysis, user behavior identification, and feature classification storage on user interaction data according to different functional modules to form user operation behavior feature data; In one analysis cycle, multiple time periods are set to analyze user behavior, and the operation behavior feature data is serialized to form a first behavior sequence. Through the user interaction big data, a comparison behavior sequence of multiple behavior patterns is set. Based on the grey correlation method, the first behavior sequence and the comparison behavior sequence are analyzed for correlation. The calculated correlation degree is used as the data validity indicator, and the user interaction validity of the first behavior sequence is evaluated. Through the evaluation results, valid interaction data and invalid interaction data are screened out from the user interaction data in real time; Two queue storage structures are set up. In each time period, valid interaction data is imported into the first queue, and invalid interaction data is imported into the second queue for storage. In the next analysis cycle, the interaction data is transmitted to the cloud platform based on the two queues for storage and user feature analysis, and a terminal function display plan for unit users is generated.

9. The data intelligent collection system for multifunctional self-service terminal equipment according to claim 8, characterized in that: The multifunctional terminal device is set based on the preset hotel area, and the user interaction data of the multifunctional terminal device is obtained within an analysis cycle, specifically: In the preset hotel area, multiple multi-functional terminal devices are set up, and the multi-functional terminal devices are connected to the cloud platform through the Internet; During an analysis cycle, based on the user-device interaction process, the user interaction data of the multi-function terminal device is obtained. The user interaction data includes operation instructions, interface access records, gesture operations, click operations, function operations, terminal feedback, and user identification information data.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a data intelligent collection program for a multifunctional self-service terminal device. When the data intelligent collection program for a multifunctional self-service terminal device is executed by a processor, the steps of the data intelligent collection method for a multifunctional self-service terminal device as described in any one of claims 1 to 7 are implemented.

Images