Cloud welfare platform management system, method and equipment based on Internet of Things

Abstract

The invention discloses a cloud welfare platform management system, method and device based on the Internet of Things, and particularly relates to the technical field of the Internet of Things. Comprising a user information determination module, a platform first acquisition module, a platform second acquisition module, a platform preprocessing module, a platform first analysis module, a platform second analysis module, a platform comprehensive judgment module and a platform decision management module. By monitoring the user behavior and the platform performance of the cloud welfare platform, fine and flexible adjustment is carried out, platform operation and user experience are guaranteed to the maximum extent, early warning is carried out on abnormal conditions, perception is carried out based on user interest preference and platform performance stability, flexible adjustment of user preference is carried out, and user experience is improved. On one hand, individual requirements of different users for welfare items can be met, on the other hand, the platform performance is optimized in time to avoid the influence of a load peak on user experience, and the user experience is improved under the condition that normal operation of the platform is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of the Internet of Things. More specifically, the present invention relates to a cloud welfare platform management system, method and device based on the Internet of Things. Background Art

[0002] With the continuous development and popularization of the Internet of Things technology, more and more industries have begun to apply it to actual business. In the field of welfare management, traditional welfare management methods often have many deficiencies, such as low management efficiency, resource waste, information lag and other problems. Therefore, it is necessary to conduct more real-time and accurate analysis of welfare management to improve the management ability of the cloud welfare platform.

[0003] Traditional cloud welfare platform management systems tend to centralize welfare management, with enterprises making unified decisions and formulating welfare policies. The advantage of this method is that it can ensure the fairness and consistency of welfare distribution and reduce management costs at the same time. However, in actual use, there are still some disadvantages. First of all, traditional welfare management relies mostly on manual operations, which not only consumes a large amount of manpower and time, but also easily causes problems such as data entry errors and file loss, resulting in untimely and inaccurate welfare distribution.

[0004] Secondly, traditional cloud welfare platform management systems, such as welfare application approval, still require a large amount of manual intervention and manual operations, lacking intelligent decision-making models and algorithm support. This not only increases the workload of management personnel, but also cannot conduct personalized management of users, affecting the efficiency of welfare management; finally, traditional cloud welfare platform management systems usually do not have real-time monitoring functions, cannot detect abnormal situations in time, have a lag in handling abnormal events and lack a warning mechanism, resulting in a decline in the user experience. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a cloud welfare platform management system, method and device based on the Internet of Things, through the following solutions to solve the problems raised in the above background art.

[0006] In view of this, the present invention aims to propose a cloud welfare platform management system, method and device based on the Internet of Things, which monitors the user behavior and platform performance of the cloud welfare platform, thereby making refined and flexible adjustments, maximizing the balance between platform operation and user experience, and warning of abnormal situations, effectively solving the problems mentioned in the background art.

[0007] To achieve the above object, the present invention provides the following technical solutions: A cloud welfare platform management system based on the Internet of Things specifically includes:

[0008] User information determination module: using a user terminal to determine user information and welfare information;

[0009] Platform first collection module: According to user information and welfare information, use the user behavior perception terminal to monitor user behavior, where the user behavior monitoring objects are platform usage records and welfare receipt records;

[0010] Platform second collection module: Used to perceive the platform performance using the platform performance perception terminal during the platform usage process, where the platform performance perception object is the platform performance;

[0011] Platform preprocessing module: Integrate based on the platform first collection module to obtain user behavior data, and at the same time integrate based on the platform second collection module to obtain platform performance stability data;

[0012] Platform first analysis module: Identify the preference characteristics of welfare items based on user behavior data, and then retrieve the user's historical transaction records to analyze the user's tendency preference characteristics from them;

[0013] Platform second analysis module: Analyze the platform load level based on the platform performance stability data, and at the same time obtain the platform performance during the load peak period, and analyze the platform performance stability by combining the platform load level and the platform performance status during the load peak period;

[0014] Platform comprehensive judgment module: Make a judgment based on user interest preferences and platform performance stability, and thus make decision adjustments;

[0015] Platform decision management module: Interact the decision adjustments and send them to the management personnel through the data transmission terminal according to the preset summary method.

[0016] An Internet of Things-based cloud welfare platform management method includes the following steps:

[0017] S1: User information determination: Use the user terminal to determine user information and welfare information;

[0018] S2: Data collection: According to user information and welfare information, use the user behavior perception terminal to monitor user behavior, where the user behavior monitoring objects are platform usage records and welfare receipt records, and use the platform performance perception terminal to perceive the platform performance, where the platform performance perception object is the platform performance;

[0019] S3: Platform preprocessing: Integrate based on the platform first collection module to obtain user behavior data, and at the same time integrate based on the platform second collection module to obtain platform performance stability data;

[0020] S4: Platform analysis: Identify the preference characteristics of welfare items based on user behavior data, and then retrieve the user's historical transaction records to analyze the user's tendency preference characteristics from them;

[0021] S5: Secondary analysis of the platform: Analyze the platform load based on the platform performance stability data, and obtain the platform performance during the peak load period. Analyze the platform performance stability by combining the platform load and the platform performance status during the peak load period.

[0022] S6: Comprehensive judgment of the platform: Make a judgment based on the user interest preferences and the platform performance stability, and then make a decision adjustment.

[0023] S7: Platform decision management: Interact the decision adjustment and send it to the management personnel through the data transmission terminal according to the preset summary method.

[0024] A cloud welfare platform management device based on the Internet of Things, including: a user terminal, an Internet of Things terminal, a user behavior perception terminal, a platform performance perception terminal, and a data transmission terminal;

[0025] User terminal: Assign a unique ID to the user identity.

[0026] Internet of Things terminal: Sense the data of welfare items and the related environment.

[0027] User behavior perception terminal: Perceive the user behavior when the user uses the cloud welfare platform.

[0028] Platform performance perception terminal: Perceive the system performance of the cloud welfare platform monitoring interface.

[0029] Data transmission terminal: Transmit the data collected by the Internet of Things perception terminal, the user behavior perception terminal, and the platform performance perception terminal.

[0030] Combined with all the above technical solutions, the positive effects of the present invention are as follows:

[0031] 1. The present invention uses the user behavior perception focus and the platform performance perception terminal to monitor the cloud welfare platform in real time, integrates the monitored data to obtain user behavior data and platform performance stability data, and makes a perception based on the user interest preferences and the platform performance stability, thereby making a flexible adjustment of the user preferences. On the one hand, it can meet the personalized needs of different users for welfare items, and on the other hand, it can optimize the platform performance in time to avoid the impact of the peak load period on the user experience, which is beneficial to improving the user experience under the condition of ensuring the normal operation of the platform.

[0032] 2. The present invention realizes the personalized adjustment of the user using the cloud welfare platform by increasing the upload of the traded items in the historical transaction records, and then extracting the evaluation stars from the detailed pages of the traded items to obtain the historical preference characteristics and the tendency preference characteristics of the users. On the one hand, it provides a broader and more representative personalized experience for the users, and on the other hand, it helps the management personnel to make personalized adjustments to the welfare items.

[0033] 3. The present invention makes a comprehensive judgment and early warning based on user interest preferences and platform performance stability, can monitor the operation status of the cloud welfare platform and the usage of welfare items in real time, and by setting early warning thresholds and conditions, when the system detects abnormal situations or the usage of welfare items reaches the preset conditions, it will trigger an alarm in a timely manner to notify the management personnel for handling, which helps the enterprise to quickly respond to problems, avoid errors in the welfare management process, and thus improve the management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the overall structure of the present invention.

[0035] Figure 2 It is a schematic diagram of the method flow structure of the present invention.

[0036] Figure 3 It is a schematic diagram of the device structure of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0038] As shown in the appended Figure 1 A cloud welfare platform management system based on the Internet of Things includes a user terminal, an Internet of Things terminal, a user behavior perception terminal, a platform performance perception terminal, and a data transmission terminal.

[0039] In a more specific application of the present invention, the user terminal is used to assign a unique ID to the user identity, including a mobile APP or a web page. After the user views the welfare items that can be claimed in real time through the user terminal and completes the welfare verification by scanning a QR code or an RFID tag, the data is synchronized to the cloud platform in real time.

[0040] The Internet of Things terminal is used to sense data of welfare items and the related environment, specifically including environmental sensors, RFID tags, and intelligent terminal devices. Among them, the environmental sensors are placed in the welfare warehouse to monitor the temperature and humidity of the welfare warehouse in real time, ensuring that welfare items are stored at appropriate temperatures and humidities. Exemplarily, the environmental sensors periodically collect warehouse data (default interval of 5 minutes), and real-time alarms are issued for abnormal data (such as temperature > 30°C); RFID tags are attached to the packaging of welfare supplies, used to store the name, specifications, production date, shelf life, and supplier of the supplies. The tag information is automatically read through an RFID reader, the welfare supply information is entered into the system, the inventory data is updated, and the outbound information of the items is confirmed during outbound; the intelligent terminal device is used for user identity verification and item collection.

[0041] The user behavior perception terminal is used to perceive user behavior when users use the cloud welfare platform. Specifically, by embedding the SDK into the cloud welfare platform, user behavior is captured in real time. User behavior includes user clicks, swipes, and long presses on web pages, and user voice input is collected through the microphone. And real-time monitoring of user behavior data is carried out, including the user's page stay time, the number of visits to welfare items in one visit, the total number of visits to welfare items, and the number of times of revisiting welfare items.

[0042] It should be understood that the key monitoring of user behavior data is mainly because user behavior directly affects the user's interest in content. Specifically, when the user's page stay time is generally short, it means that the page contains content that the user is not interested in. The more times a welfare item is visited in one visit, the stronger the user stickiness. The higher the number of times of revisiting welfare items, the higher the user's satisfaction and loyalty. By monitoring the user's page stay time, the number of visits to welfare items in one visit, the total number of visits to welfare items, and the number of times of revisiting welfare items, the user activity and usage habits are discovered, so as to discover and improve the page.

[0043] The platform performance perception terminal is used to perceive the system performance of the monitoring interface of the cloud welfare platform. Specifically, performance testing is carried out through LoadRunner, and platform performance data is collected, including platform response time, loading speed, throughput, and concurrent user count.

[0044] It needs to be understood that the platform response time, loading speed, throughput and number of concurrent users have a great impact on the stability of platform performance. The platform response time is the time from the user initiating a request to the platform responding, and the loading speed is the speed at which the platform page is loaded onto the user terminal. The faster the loading speed, the shorter the platform response time and the higher the platform operating performance, thereby improving user experience satisfaction. The throughput is the amount of data successfully processed and transmitted by the system per unit time. High throughput means that the platform can handle more requests per unit time, thereby improving the platform's processing capacity. The number of concurrent users is the number of users whose requests are processed by the platform at the same time. An increase in the number of concurrent users will lead to an increase in the platform load, thereby reducing platform performance.

[0045] The data transmission terminal is used to transmit data collected by the Internet of Things sensing terminal, the user behavior sensing terminal and the platform performance sensing terminal. The data transmission methods include wired transmission and wireless transmission. For example, wired transmission can be carried out through Ethernet to transmit data between sensors and data collection nodes in the welfare warehouse for real-time transmission of the collected data. Wireless transmission can be connected to the user terminal through Wifi for indoor short-distance data transmission.

[0046] The user information determination module determines user information and benefit information using the user terminal.

[0047] In this embodiment, it should be specifically explained that the user information is the user's unique ID. The user's company information and position are provided through registration or login on the mobile APP or web page. The user terminal is used to identify the user information and assign the user a unique ID. The welfare information includes the welfare items that the user can receive and the welfare items that have been received. The user can view the welfare information in real time through the user terminal and complete the welfare verification by scanning the QR code or RFID tag. Once the scan is successful, the platform automatically verifies the validity of the welfare item and marks it as received. The data is synchronized to the cloud platform in real time.

[0048] The first acquisition module of the platform uses the user behavior perception terminal to monitor user behavior based on user information and welfare information, where the user behavior monitoring objects are platform usage records and welfare collection records.

[0049] In this embodiment, it is necessary to specifically explain that the user behavior monitoring process is as follows:

[0050] Use user behavior sensing terminals to monitor user behavior in real time, including user clicks, slides, and long presses on web pages, and collect user voice input through microphones;

[0051] User log records are obtained based on user behavior, including platform usage records and welfare collection records, and platform usage data is obtained based on the platform usage records. Meanwhile, welfare collection data is obtained based on the welfare collection records. The platform usage data specifically includes the user's item residence time, the number of times welfare items are visited once, the total number of times welfare items are visited, and the number of return visits to welfare items.

[0052] It needs to be explained that the longer the item stays, the greater the user's interest or demand for the item. The number of visits to a welfare item is the number of times the user has visited the welfare item only once. The more times a welfare item is visited, the less interested the user is in the welfare item. The number of return visits to a welfare item is the number of times the user visits the welfare item again after visiting it for the first time, reflecting the user's continued interest in or demand for a welfare item.

[0053] The second acquisition module of the platform is used to perceive the platform performance using the platform performance perception terminal during the use of the platform, where the platform performance perception object is the platform performance.

[0054] In this embodiment, it is necessary to specifically explain that the platform performance perception process is as follows:

[0055] Use platform performance perception terminals to simulate user behavior and perform stress testing on the platform;

[0056] Starting from a lower concurrent access state, set a time interval, divide the timestamp into different time periods, recorded as 1, 2, ..., i, ...n, and gradually increase the load, recording the platform response time, loading speed, throughput and number of concurrent users in different time periods.

[0057] It needs to be explained that the platform response time is the time it takes for the system to start executing and returning results after receiving a request, which reflects the system's response speed and processing power. A shorter platform response time can provide a better user experience, reduce user waiting time, and increase user satisfaction. The loading speed is the time required from the user initiating a request to the page or content being fully loaded and interactive. The faster the loading speed, the less waiting time the user has, which improves user satisfaction and retention. The throughput is the number of requests that the system can handle in a given time period. The number of concurrent users is the number of users accessing or using the system at the same time. A higher throughput means that the system can maintain stable performance under high concurrency.

[0058] It should be further explained that the user behavior simulation builds a user behavior model by analyzing the operating habits of real users, such as page browsing, click frequency, and operation intervals, and uses automated testing tools to simulate various user operations on the platform based on the model, so that the stress test is closer to the actual usage scenario. Starting from a lower concurrent access state, an initial number of concurrent users is set. At the same time, the time interval is determined, and the timestamp is divided into different time periods, which are recorded as 1, 2, ..., i, ...n respectively. At the end of each time period, the load is gradually increased according to a certain strategy. For example, the initial concurrent users are 10 users, and each 10 minutes is a time interval. The number of concurrent users is increased by 10% each time, and the number increases to 11 users in the second time period and 12 users in the third time period. And so on. In each time period, the platform performance perception terminal is used to record the platform response time, loading speed, and throughput of the platform.

[0059] The platform preprocessing module integrates the user behavior data based on the platform's first acquisition module, and integrates the platform's performance stability data based on the platform's second acquisition module.

[0060] In this embodiment, it is necessary to specifically explain that the user behavior data integration process is as follows: the average item residence time of the platform usage data is calculated to obtain the average item residence time, which is specifically expressed as: Where T represents the average item residence time, t represents the item residence time of the user, and k represents the total number of welfare items; the number of welfare item visits N1 and the total number of welfare item visits N t After comparison, we take the percentage and get the bounce rate J, which is specifically expressed as: J = N1 / N t ×100%; N number of times of return visit welfare items h and the total number of visits to welfare items N t After comparison, the percentage is taken to get the return visit rate, which is specifically expressed as: C = N h / N t ×100%;

[0061] Integrate average item dwell time, bounce rate, and return visit rate into user behavior data;

[0062] It needs to be explained that user preference is greatly affected by the time users stay on items, and is also affected by bounce rate and return rate. When users stay on items for a longer time, it means that the user is more interested in the item. In this case, the user's return rate for the item is higher and the bounce rate is lower. On the contrary, when users stay on items for a shorter time, it means that the user is not interested in the item, so the return rate is lower and the bounce rate is higher.

[0063] The platform performance stability data integration process is as follows: The average platform response time is calculated by averaging the platform response time in different time periods to obtain the average platform response time, which is specifically expressed as: Where Tx represents the average platform response time, Tx i represents the platform response time of the i-th time period, and n represents the divided time period; the loading speed fluctuation value Va is obtained by calculating the variance value of the loading speed Vl, which is specifically expressed as: Where Vl i represents the loading speed in the i-th time period;

[0064] Compare the number of concurrent users in different time periods, extract the maximum and minimum numbers of concurrent users, and record the corresponding throughputs. Calculate the variance values ​​to obtain the throughput fluctuation value.

[0065] The average platform response time, loading speed fluctuation value, maximum and minimum concurrent users, and throughput fluctuation value are integrated into platform performance stability data.

[0066] The first analysis module of the platform identifies the preference characteristics of welfare items based on user behavior data, and then retrieves the user's historical transaction records to analyze the user's preference characteristics.

[0067] In this embodiment, it should be specifically explained that the preference feature identification of welfare items based on user behavior data is implemented as follows:

[0068] The user's bounce rate of the welfare item is judged against a preset bounce rate value. If the bounce rate is less than the preset bounce rate value, the welfare item is judged to be an item of interest to the user.

[0069] The return visit rate and return visit rate threshold of the items that the user is interested in are judged. If the return visit rate is greater than the return visit rate threshold, the welfare item is judged to be the user's preferred item;

[0070] Based on the user's preference items, the user's item residence time and average item residence time are weighted, which is specifically expressed as: NA = (t / T)^k, where NA represents the item preference weight, t represents the user's item residence time, T represents the average item residence time, and k represents the adjustment coefficient, which is used to control the growth rate of the weight. If NA is greater than or equal to 1, it means that the user has a high preference for the item. If NA is less than 1, it means that the user has a low preference for the item. The welfare item corresponding to the maximum item preference weight is extracted as the user's preference feature.

[0071] Furthermore, the user preference characteristics are analyzed as follows:

[0072] When there is a historical transaction record of the user, extract the traded items from the user's historical transaction record, and retrieve the evaluation star rating of the traded items from the details page of the traded items;

[0073] Screen out the user-preferred items from the evaluation star ratings of the traded items, and similarly perform preference feature recognition on the user-preferred items to obtain the preference features of the traded items;

[0074] Compare the preference features of the traded items corresponding to each historical transaction record, and extract the preference feature with the highest frequency of occurrence as the user's tendency preference feature.

[0075] The second analysis module of the platform analyzes the platform load level based on the platform performance stability data, and simultaneously obtains the platform performance during the peak load period. Combining the platform load level and the platform performance status during the peak load period, analyze the platform performance stability.

[0076] In this embodiment, it should be specifically noted that the platform load level is further analyzed through the platform performance stability data integrated by the platform preprocessing module, as follows:

[0077] Subtract the average platform response time from the platform response time in the i-th time period and perform absolute value calculation to obtain the platform response time difference value;

[0078] Compare the throughput fluctuation value with the throughput in the i-th time period to obtain the throughput deviation degree;

[0079] Calculate the difference degree between the maximum number of concurrent users and the minimum number of concurrent users, and combine the platform response time difference value and the throughput deviation degree, using the expression Obtain the platform stability evaluation coefficient, where PW represents the platform stability evaluation coefficient, n represents the divided time periods, B max represents the maximum number of concurrent users, B min represents the minimum number of concurrent users, Tx represents the average platform response time, Tx i represents the platform response time in the i-th time period, and O represents the throughput fluctuation value.

[0080] As can be seen from the expression of the above platform stability evaluation coefficient, the smaller the throughput fluctuation value, the shorter the platform response time, and the smaller the difference degree of concurrent users, the more stable the platform performance. This is because during the operation of the platform, when the throughput fluctuation is small, the platform can more reliably meet the needs of users. Therefore, the platform response time is shorter. A small difference degree of concurrent users means that the number of user requests processed by the system in different time periods is relatively stable and there will be no large fluctuations, which helps to achieve the reasonable allocation of system resources and load balancing. When the difference degree of concurrent users is small, the platform can better handle the changes in user requests and avoid performance degradation caused by drastic fluctuations in the number of user requests.

[0081] The platform comprehensive judgment module makes judgments based on user interest preferences and platform performance stability, and then makes decision adjustments respectively.

[0082] In this embodiment, it should be specifically noted that the decision adjustment includes user interest preference adjustment and platform performance adjustment. The user interest preference adjustment specifically includes matching the user's preference characteristics with the user's tendency preference characteristics to analyze the user's interest preferences. The analysis of the user's interest preferences is as follows:

[0083] Match the user's preference characteristics with the user's tendency preference characteristics, perform intersection calculation, and use the successfully matched preference characteristics as the user's interest preferences. Exemplarily, assume that the set corresponding to the user's preference characteristics is {A, B, C, D}, and the set corresponding to the user's tendency preference characteristics is {D, E, F} at this time. Then the result of the intersection calculation is D, and then D is used as the user's interest preference. If the result of the intersection calculation is an empty set, then compare the item preference weights corresponding to the user's preference characteristics and the user's tendency preference characteristics respectively, and select the highest item preference weight as the user's interest preference.

[0084] The specific operation of the platform performance adjustment is as follows:

[0085] Compare the platform stability evaluation coefficient with the set stability evaluation preset value. If there is a situation where the platform stability evaluation coefficient is less than the set stability evaluation preset value, it is determined that platform performance adjustment is required, and an alarm is issued and the resource allocation of the platform is dynamically adjusted. If there is a situation where the platform stability evaluation coefficient is greater than or equal to the set stability evaluation preset value, it is determined that platform performance adjustment is not required.

[0086] The platform decision management module interactively displays the decision adjustment and sends it to the management personnel through the data transmission terminal according to the preset summary method.

[0087] In this embodiment, it should be specifically noted that the interactive display process is as follows: The analyzed user interest preferences are sorted in descending order according to the evaluation star rating, and the user interest preference ranked first is extracted from the descending order and displayed on the user page. At the same time, the platform provides personalized content and service recommendations for the user to improve user satisfaction and loyalty; The preset summary methods include any one of report summary and chart summary. When determining the preset summary method, it is necessary to select according to the actual suitability between the platform performance change and the preset summary method.

[0088] A management method for an Internet of Things-based cloud welfare platform, comprising the following steps:

[0089] S1: User information determination: Use the user terminal to determine user information and welfare information;

[0090] S2: Data collection: According to the user information and welfare information, use the user behavior perception terminal to monitor user behavior, where the user behavior monitoring object is the platform usage record and welfare receipt record, and use the platform performance perception terminal to perceive the platform performance, where the platform performance perception object is the platform performance;

[0091] S3: Platform preprocessing: Integrate based on the platform's first collection module to obtain user behavior data, and at the same time integrate based on the platform's second collection module to obtain platform performance stability data;

[0092] S4: Platform analysis: Identify the preference characteristics of welfare items based on the user behavior data, and then retrieve the user's historical transaction records to analyze the user's tendency preference characteristics from them;

[0093] S5: Platform secondary analysis: Analyze the platform load level based on the platform performance stability data, and at the same time obtain the platform performance during the load peak period, and analyze the platform performance stability by combining the platform load level and the platform performance status during the load peak period;

[0094] S6: Platform comprehensive judgment: Make a judgment based on the user interest preference and the platform performance stability, and thus make a decision adjustment;

[0095] S7: Platform decision management: Interact the decision adjustment and send it to the management personnel through the data transmission terminal according to the preset summary method.

[0096] An Internet of Things-based cloud welfare platform management device, comprising: a user terminal, an Internet of Things terminal, a user behavior perception terminal, a platform performance perception terminal, and a data transmission terminal;

[0097] User terminal: Assign a unique ID to the user identity;

[0098] Internet of Things terminal: Perceive data of welfare items and related environments;

[0099] User behavior perception terminal: Perceives user behavior when the user uses the cloud welfare platform;

[0100] Platform performance perception terminal: Perceives the system performance of the monitoring interface of the cloud welfare platform;

[0101] Data transmission terminal: Transmits the data collected by the Internet of Things perception terminal, the user behavior perception terminal, and the platform performance perception terminal.

[0102] Secondly: In the attached drawings of the disclosed embodiments of the present invention, only the structures related to the disclosed embodiments are involved. For other structures, reference can be made to the general design. Without conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0103] Finally: The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A cloud welfare platform management system based on the Internet of Things, characterized in that: It includes user terminals, IoT sensing terminals, user behavior sensing terminals, platform performance sensing terminals and data transmission terminals, including: User information determination module: determines user information and benefit information using a user terminal; The first collection module of the platform: using the user behavior sensing terminal to monitor user behavior based on user information and welfare information, where the user behavior monitoring objects are platform usage records and welfare collection records; The second platform acquisition module is used to sense the platform performance using the platform performance sensing terminal during the use of the platform, where the platform performance sensing object is the platform performance; Platform preprocessing module: integrates user behavior data based on the first acquisition module of the platform, and integrates platform performance stability data based on the second acquisition module of the platform; The first analysis module of the platform: Identify the preference characteristics of welfare items based on user behavior data, and then retrieve the user's historical transaction records to analyze the user's preference characteristics; The second analysis module of the platform: analyzes the platform load level based on the platform performance stability data, obtains the platform performance during the peak load period, and analyzes the platform performance stability by combining the platform load level and the platform performance during the peak load period; Platform comprehensive judgment module: Make judgments based on user interest preferences and platform performance stability, and make decisions accordingly; Platform decision management module: interactively adjust the decision and send it to the management personnel through the data transmission terminal according to the preset summary method.

2. According to claim 1, a cloud welfare platform management system based on the Internet of Things is characterized by: The user information is the user's unique ID. The user's company information and position are provided through registration or login on the mobile APP or web page. The user terminal is used to identify the user information and assign the user a unique ID. The welfare information is the welfare items that the user can receive and the welfare items that have been received. The user can view the welfare information in real time through the user terminal and complete the welfare verification by scanning the QR code or RFID tag. Once the scan is successful, the platform automatically verifies the validity of the welfare item and marks it as received. The data is synchronized to the cloud platform in real time.

3. According to the cloud welfare platform management system based on the Internet of Things according to claim 1, it is characterized by: The user behavior monitoring process is as follows: Use user behavior sensing terminals to monitor user behavior in real time, including user clicks, slides, and long presses on web pages, and collect user voice input through microphones; User log records are obtained based on user behavior, including platform usage records and welfare collection records, and platform usage data is obtained based on the platform usage records. Meanwhile, welfare collection data is obtained based on the welfare collection records. The platform usage data specifically includes the user's item residence time, the number of times welfare items are visited once, the total number of times welfare items are visited, and the number of return visits to welfare items.

4. According to claim 1, a cloud welfare platform management system based on the Internet of Things is characterized by: The platform performance perception process is as follows: Use platform performance perception terminals to simulate user behavior and perform stress testing on the platform; Starting from a lower concurrent access state, set a time interval, divide the timestamp into different time periods, recorded as 1, 2, ..., i, ...n, and gradually increase the load, recording the platform response time, loading speed, throughput and number of concurrent users in different time periods.

5. According to claim 1, a cloud welfare platform management system based on the Internet of Things is characterized by: The user behavior data integration process is as follows: the average item residence time of the platform usage data is calculated to obtain the average item residence time, the bounce rate is obtained by comparing the number of visits to the welfare items in sequence with the total number of visits to the welfare items, and the return visit rate is obtained by comparing the number of return visits to the welfare items with the total number of visits to the welfare items. Integrate average item dwell time, bounce rate, and return visit rate into user behavior data; The platform performance stability data integration process is as follows: the average value is calculated by the platform response time in different time periods to obtain the average platform response time, and the loading speed fluctuation value is obtained by calculating the variance value through the loading speed; Compare the number of concurrent users in different time periods, extract the maximum and minimum numbers of concurrent users, and record the corresponding throughputs. Calculate the variance values ​​to obtain the throughput fluctuation value. The average platform response time, loading speed fluctuation value, maximum and minimum concurrent users, and throughput fluctuation value are integrated into platform performance stability data.

6. The cloud welfare platform management system based on the Internet of Things according to claim 1 is characterized by: The identification of preference characteristics of welfare items based on user behavior data is implemented as follows: The user's bounce rate of the welfare item is judged against a preset bounce rate value. If the bounce rate is less than the preset bounce rate value, the welfare item is judged to be an item of interest to the user. The return visit rate and return visit rate threshold of the items that the user is interested in are judged. If the return visit rate is greater than the return visit rate threshold, the welfare item is judged to be the user's preferred item; Based on the user's preference items, the user's item residence time and the average item residence time are weighted, which is specifically expressed as: NA = (t / T)^k, where NA represents the item preference weight, t represents the user's item residence time, T represents the average item residence time, and k represents the adjustment coefficient, which is used to control the growth rate of the weight, and extract the welfare item corresponding to the maximum item preference weight as the user's preference feature.

7. The cloud welfare platform management system based on the Internet of Things according to claim 1 is characterized in that: The user preference characteristics are analyzed as follows: When the user has a historical transaction record, the transaction item is extracted from the user's historical transaction record, and the rating star of the transaction item is retrieved from the details page of the transaction item; Filter out user-preferred items from the rating stars of the transaction items, and identify the preference features of the user-preferred items in the same way to obtain the preference features of the transaction items; The preference features of the transaction items corresponding to each historical transaction record are compared, and the preference features with the highest frequency of occurrence are extracted as the user's tendency preference features.

8. The cloud welfare platform management system based on the Internet of Things according to claim 1 is characterized by: The platform load level is further analyzed by the platform performance stability data integrated by the platform preprocessing module, as follows: The platform response time difference value is obtained by subtracting the average platform response time from the platform response time in the i-th time period and calculating the absolute value; By comparing the throughput fluctuation value with the throughput in the i-th time period, the degree of throughput deviation is obtained; The difference between the maximum number of concurrent users and the minimum number of concurrent users is calculated, and combined with the platform response time difference and throughput deviation, the expression is used The platform stability evaluation coefficient is obtained, where PW represents the platform stability evaluation coefficient, n represents the divided time period, and B max Indicates the maximum number of concurrent users, B min represents the minimum number of concurrent users, Tx represents the average platform response time, Tx i represents the platform response time in the i-th time period, Oa i represents the throughput in the i-th time period, and O represents the throughput fluctuation value.

9. A cloud welfare platform management method based on the Internet of Things, according to any one of claims 1-8, a cloud welfare platform management system based on the Internet of Things, characterized in that: The following steps are involved: S1: User information determination: Determine user information and benefit information using the user terminal; S2: Data collection: User behavior monitoring is performed using user behavior sensing terminals based on user information and welfare information, where the user behavior monitoring objects are platform usage records and welfare collection records, and platform performance sensing is performed using platform performance sensing terminals, where the platform performance sensing object is platform performance; S3: Platform preprocessing: Integrate the user behavior data based on the first acquisition module of the platform, and integrate the platform performance stability data based on the second acquisition module of the platform; S4: Platform analysis: Identify the preference characteristics of welfare items based on user behavior data, and then retrieve the user's historical transaction records to analyze the user's preference characteristics, thereby matching the preference characteristics of welfare items with the user's preference characteristics to analyze the user's interest preferences; S5: Secondary analysis of the platform: Analyze the platform load level based on the platform performance stability data, obtain the platform performance during the peak load period, and analyze the platform performance stability based on the platform load level and the platform performance status during the peak load period; S6: Comprehensive judgment of the platform: Comprehensive judgment is made based on user interest preferences and platform performance stability, and decision adjustments are made accordingly; S7: Platform decision management: The decision adjustments are interactively sent to the management personnel through the data transmission terminal according to the preset summary method.

10. A cloud welfare platform management device based on the Internet of Things, according to any one of claims 1-8, a cloud welfare platform management system based on the Internet of Things, characterized in that: include: User terminal, IoT terminal, user behavior perception terminal, platform performance perception terminal and data transmission terminal, wherein the user terminal: assigns a unique ID to the user identity; IoT terminal: perceives data of welfare items and related environment; User behavior perception terminal: perceives user behavior when the user uses the cloud welfare platform; Platform performance perception terminal: perceives the system performance of the cloud welfare platform monitoring interface; Data transmission terminal: Transmits data collected by IoT sensing terminals, user behavior sensing terminals, and platform performance sensing terminals.

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