Intelligent management method of portable box for subway riding

Abstract

The invention provides a portable box intelligent management method for subway riding, and relates to the technical field of data management, and the method comprises the steps: calling portable box management logs of K stations of a target subway line in a preset historical window, carrying out the index extraction of the logs according to the indexes of the usage amount, the storage amount and the in-cabinet ownership, and carrying out the index extraction of the logs; traversing each index set to carry out index centralized trend analysis, determining mobility factors of K stations, further obtaining deployment coefficients of the K stations, carrying out fluctuation identification to obtain steady-state coefficients of the K stations, identifying the deployment and steady-state coefficients by using a monitoring step length identifier to determine monitoring bandwidth sets of the K stations, and carrying out monitoring on the monitoring bandwidth sets of the K stations; a portable box monitoring transmission branch is constructed for monitoring management, the problems of congestion in the security check process, inconvenience for passengers and increased management cost caused by the fact that articles of the passengers cannot be managed efficiently and conveniently are solved, and the effects of improving the operation efficiency and safety of the whole subway network and ensuring the riding and management experience are achieved.

Description

Technical Field

[0001] The invention relates to the technical field of data management, and in particular to an intelligent management method for a portable box used for subway riding. Background Art

[0002] With the rapid development of urban public transportation, subways, as an important part of urban transportation, have an increasing passenger flow. In order to ensure public safety, subway security inspection has become a necessary procedure for passengers entering the station. Existing subway security inspection methods mainly include X-ray scanning and metal detection of packages and items carried by passengers. However, these methods can usually only statically identify potential dangerous goods, but cannot effectively manage passengers' non-dangerous personal items, such as some small tools or equipment that must be carried but cannot be brought into the carriage. Although these items do not pose a direct safety threat, they often cause cumbersome operations and passenger complaints during the security inspection process, which not only reduces the efficiency of entering the station, but also increases the travel burden of passengers. In addition, the security inspection standards and enforcement strength of different subway stations may be different, resulting in inconsistent passenger experience. Although some subway stations have tried to solve the problem of storing passengers' items by providing temporary storage cabinets, the safety of these solutions cannot be fully guaranteed, and the flexibility of fixed-point storage is poor. It also requires additional human and material support, which increases operating costs.

[0003] In summary, the existing subway management often has technical problems such as being unable to efficiently and conveniently manage passengers' non-dangerous personal belongings, leading to congestion during security checks, inconvenience for passengers, and increased management costs. Summary of the invention

[0004] The present application provides an intelligent management method for a portable case for subway riding, which is used to solve the technical problem that the existing subway management cannot efficiently and conveniently manage passengers' non-dangerous personal belongings, resulting in congestion during security checks, inconvenience for passengers and increased management costs.

[0005] The present application provides a method for intelligently managing a portable box for subway riding, the method comprising: Retrieve the portable box management logs of K stations of the target subway line within a preset historical window to obtain K historical portable box management logs, where K is an integer greater than or equal to 1; extract indicators from the K historical portable box management logs according to the usage indicator, the deposit indicator and the cabinet holding indicator to obtain K usage indicator sets, K deposit indicator sets and K cabinet holding indicator sets; traverse the K usage indicator sets and the K deposit indicator sets to perform indicator concentration trend analysis to determine the K station liquidity factors, and respectively divide the K station liquidity factors by the sum of the K station liquidity factors to obtain the K station deployment coefficients; traverse the K cabinet holding indicator sets to perform fluctuation identification The K station steady-state coefficients are obtained by distinguishing the K station deployment coefficients and the K station steady-state coefficients using a monitoring step identifier to determine a set of K station monitoring bandwidths, wherein the set of K station monitoring bandwidths includes K first station monitoring bandwidths and K second station monitoring bandwidths, and each first station monitoring bandwidth is smaller than the corresponding second station monitoring bandwidth; based on the K first station monitoring bandwidths and the K second station monitoring bandwidths, K station portable box monitoring transmission branches of the target subway line are constructed; the K station portable box monitoring transmission branches are used to monitor and extract the portable box management data of the K stations, and the extracted data is sent to a background management end to manage the portable boxes of the K stations.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: The present application provides an intelligent management method for a portable box for subway riding. By retrieving the portable box management logs of K stations of a target subway line within a preset historical window, K historical portable box management logs are obtained, wherein K is an integer greater than or equal to 1; the K historical portable box management logs are subjected to index extraction according to the usage index, the deposit index and the cabinet holding index, respectively, to obtain K usage index sets, K deposit index sets and K cabinet holding index sets; the K usage index sets and the K deposit index sets are traversed to perform an index concentration trend analysis to determine K station liquidity factors, and the K station liquidity factors are respectively divided by the sum of the K station liquidity factors to obtain K station allocation coefficients; the K cabinet holding index sets are traversed to perform fluctuation identification to obtain K station steady-state coefficients; the K station allocation coefficients and the K station steady-state coefficients are identified by using a monitoring step length identifier to determine the K station monitoring bandwidth set The invention relates to a method for controlling the portable box of a subway line of a target metro line, wherein the K site monitoring bandwidth set includes K first site monitoring bandwidths and K second site monitoring bandwidths, and each first site monitoring bandwidth is smaller than the corresponding second site monitoring bandwidth; based on the K first site monitoring bandwidths and the K second site monitoring bandwidths, K site portable box monitoring transmission branches of the target metro line are constructed; the portable box management data of the K sites are monitored and extracted by using the K site portable box monitoring transmission branches, and the extracted data is sent to the background management end to manage the portable boxes of the K sites, so as to solve the technical problem that the non-dangerous personal belongings of passengers cannot be managed efficiently and conveniently in the existing metro management, resulting in congestion, inconvenience for passengers and increased management costs during security inspection; by using portable boxes and performing intelligent management, effective and convenient management of passengers' belongings is achieved, and the resource allocation of each site is dynamically adjusted, thereby improving the operation efficiency and safety of the entire metro network and ensuring the technical effect of passenger use and management experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 A flow chart of a method for intelligently managing a portable case for subway riding is provided for this application.

[0008] Figure 2 A flow chart of determining the station mobility factor in the intelligent management method of a portable case for subway riding is provided for this application. DETAILED DESCRIPTION

[0009] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0010] In order to effectively reduce security risks, small tools are frequently seized during subway security checks. Although these items are not necessarily high-risk, they need to be restricted according to regulations, resulting in a large number of complaints and disputes from passengers, affecting the smoothness of security checks and the management burden, while reducing the passenger experience. The existing security inspection policies have failed to effectively solve the problem of passengers being unable to take the subway due to carrying daily tools, and a more convenient solution is needed. Therefore, this application proposes the concept of setting up shared convenience boxes and conducting intelligent management, allowing passengers to temporarily store small tool items in special convenience boxes, which are managed and transported through the subway system and retrieved when needed. The convenience boxes are equipped with smart locks and real-time positioning systems, and are effectively managed through big data and mobile applications to ensure safety and convenience. This solution improves the operating efficiency and passenger satisfaction of the subway, and solves the technical problems in the existing subway management that cannot efficiently and conveniently manage passengers' non-dangerous personal belongings, leading to congestion during security checks, inconvenience for passengers and increased management costs. By using portable boxes and conducting intelligent management, it achieves effective and convenient management of passengers' belongings, and dynamically adjusts the resource allocation of each station, thereby improving the operating efficiency and safety of the entire subway network and ensuring the technical effects of passenger use and management experience.

[0011] Examples, such as Figure 1 As shown, the present application provides a method for intelligent management of a portable box for subway riding, the method comprising: Retrieve the portable box management logs of K stations of the target subway line within a preset historical window to obtain K historical portable box management logs, where K is an integer greater than or equal to 1.

[0012] Specifically, the portable box management logs of K stations of the target subway line within the preset historical window are retrieved. The preset historical window refers to a specific time range set, such as the past week, month or longer, which is intended to analyze and evaluate the use of portable boxes in a specific period. K stations refer to stations on the subway line, which can be selected K representative or critical stations, where K is an integer greater than or equal to 1, which means that at least one station is included and can be expanded to include more stations as needed. This selection can be based on the passenger flow of each station, the frequency of use of portable boxes, or other relevant parameters. By retrieving the management logs within the preset historical window of each station, detailed data on the usage, storage and retrieval of portable boxes at each station can be obtained. These management logs include but are not limited to the storage and retrieval time of the portable box, the number of the portable box, the user's information, etc., thereby providing an empirical basis for subsequent data analysis and resource allocation. In specific operations, this step can be performed by the management system of the subway station, and the management system can be configured to automatically extract the above data from the database, or allow the operator to manually select specific query conditions to perform data extraction. This flexible data retrieval method allows managers to flexibly adjust the scope of data collection and analysis according to actual operational needs. In summary, by retrieving and obtaining the portable box management logs of K stations, the use of subway portable boxes can be accurately captured and analyzed, providing data support for further intelligent management and optimization decisions, which not only helps to improve the operational efficiency of the subway system, but also optimizes the passenger experience.

[0013] Indicators are extracted from the K historical portable box management logs according to the usage indicators, deposit indicators and cabinet holding indicators respectively, to obtain K usage indicator sets, K deposit indicator sets and K cabinet holding indicator sets.

[0014] Furthermore, the usage indicator is the number of portable boxes taken out from the site by the user, the deposit indicator is the number of portable boxes deposited in the site by the user, and the cabinet holding indicator is the number of portable boxes stored in the site.

[0015] Optionally, immediately after the data collection phase, detailed indicator extraction will be performed on the collected K historical portable box management logs. This process mainly involves three key measurement indicators, namely usage indicator, deposit indicator and cabinet inventory indicator. The usage indicator is the number of times a user takes out a portable box from a specific station. The key of this indicator is to measure the frequency of use of portable boxes at each station and provide management with dynamic insights into the demand for portable boxes. For example, if the usage indicator of a station is abnormally high, it indicates that the station may need to add more portable box supplies or perform more frequent maintenance inspections. The deposit indicator refers to the number of portable boxes deposited by users at each station. This indicator reflects the active use of portable boxes and provides basic data for predicting the possible storage needs of each station. Based on this data, the storage capacity of the station can be adjusted to ensure that the needs of passengers are met. The cabinet inventory index represents the total number of portable boxes stored in each station at any given time. This index is an important parameter for measuring the utilization rate of the station's portable box capacity. It is particularly critical for managers to formulate portable box maintenance and scheduling strategies. By real-time monitoring of the cabinet inventory, the management system can effectively prevent excessive accumulation or shortage of portable boxes and optimize resource allocation. Through the systematic extraction and analysis of the above three indicators, it is possible to accurately evaluate and respond to the actual usage needs of passengers at each subway station, and then adjust and optimize the allocation and management strategy of portable boxes. This data-driven approach not only improves the efficiency of resource use, but also greatly improves the passenger experience. After implementing these measures, subway operating units can manage portable box resources more flexibly, and also provide empirical support for possible future expansion.

[0016] The K usage indicator sets and the K deposit indicator sets are traversed to perform indicator concentration trend analysis, K site liquidity factors are determined, and the K site liquidity factors are respectively divided by the sum of the K site liquidity factors to obtain K site allocation coefficients.

[0017] The K cabinet inventory index sets are traversed to identify fluctuations and obtain K station steady-state coefficients.

[0018] Further, the central tendency analysis of the usage index and the deposit index is performed. The so-called central tendency analysis mainly refers to determining the aggregation trend or average level of data through statistical methods, such as calculating the mean, median, etc. In this step, the average usage and deposit of the portable box at each station are calculated by traversing the K stations on the subway line. This analysis can determine the liquidity factor of each station, which measures the liquidity or usage frequency of the portable box at each station. Among them, the larger the liquidity factor, the larger the flow of people at the station, and the demand and circulation of the portable box are relatively frequent. Next, the liquidity factor of each station is compared with the sum of the liquidity factors of all stations, and the allocation coefficient of each station is calculated. This allocation coefficient reflects the proportion of each station in the allocation of portable box resources relative to the entire subway line, and guides how to adjust the allocation of portable boxes according to actual needs. For example, stations with high liquidity factors may require more portable box supplies or more frequent portable box updates. After that, the fluctuation identification of the set of indicators of the number of portable boxes in the cabinet is performed. The purpose of fluctuation identification is to determine the stability of the number of portable boxes at each station, which is usually achieved by calculating the variance of the number of portable boxes and its inverse. Specifically, the inverse of the variance is defined as the station steady-state coefficient, and the size of this coefficient reflects the degree of fluctuation in the number of portable boxes at the station. The larger the station steady-state coefficient, the smaller the fluctuation in the number of portable boxes at the station, that is, the more stable the number of portable boxes. Such a station does not require frequent monitoring or adjustment of the number of portable boxes; on the contrary, if the steady-state coefficient is small, it means that the number of portable boxes at the station fluctuates greatly, requiring intensive monitoring and possible deployment of portable boxes. Through the above analysis, the portable box resources of subway stations can be managed and optimized more accurately. In order to ensure the balance between the supply and demand of portable boxes at each station, the distribution of portable boxes can be dynamically adjusted according to the deployment coefficient and the steady-state coefficient to ensure the effective use of portable boxes in the subway system, thereby improving passenger satisfaction and subway operation efficiency.

[0019] The K site deployment coefficients and the K site steady-state coefficients are identified using a monitoring step identifier to determine a set of K site monitoring bandwidths, wherein the set of K site monitoring bandwidths includes K first site monitoring bandwidths and K second site monitoring bandwidths, and each first site monitoring bandwidth is smaller than the corresponding second site monitoring bandwidth.

[0020] Exemplarily, the monitoring step length identifier is then used to analyze the deployment coefficients and steady-state coefficients of the K sites to determine the set of monitoring bandwidths for each site. This step is the core part of optimizing the data monitoring frequency and resource allocation in the subway portable box management system. The monitoring step length identifier is a model built based on machine learning technology, which is used to identify and predict the optimal data monitoring frequency. The so-called monitoring bandwidth refers to the time interval for data monitoring, that is, how often the data is collected. In the present invention, the specific task of the monitoring step length identifier is to determine the appropriate monitoring bandwidth for each site based on the deployment coefficient and steady-state coefficient of the site. The deployment coefficient is calculated based on the site mobility factor, which reflects the relative proportion of the flow of portable boxes at the site in the entire subway network. The steady-state coefficient is calculated based on the volatility of the amount of inventory in the cabinet, indicating the fluctuation stability of the number of portable boxes at the site. These two coefficients jointly affect the monitoring density that a site should have in the portable box management system.

[0021] In a specific implementation, the monitoring step length identifier outputs a corresponding monitoring bandwidth set by inputting the deployment coefficients and steady-state coefficients of K stations. This set includes the first monitoring bandwidth and the second monitoring bandwidth of each station, wherein the first monitoring bandwidth is set to a shorter time interval for collecting data more frequently in the case of high mobility or large fluctuations; while the second monitoring bandwidth is longer and is suitable for a stable environment with low mobility or small fluctuations to reduce unnecessary data processing and storage pressure. For example, if a station has a higher deployment coefficient, indicating that the portable boxes at the station are frequently used and circulated, the monitoring step length identifier may recommend a shorter first monitoring bandwidth. On the contrary, if a station has a higher steady-state coefficient, indicating that the number of its portable boxes is relatively stable, the monitoring step length identifier may recommend a longer second monitoring bandwidth. In this way, the monitoring step length identifier can provide an optimized and dynamically adjusted monitoring strategy for each station, ensuring that the portable box management system can effectively respond to the actual needs of different stations, while avoiding resource waste and improving the overall efficiency and responsiveness of the system. This intelligent monitoring strategy is the key to improving the efficiency of subway portable box management and helps to achieve more refined and adaptive operation management.

[0022] K station portable box monitoring transmission branches of the target subway line are constructed based on the K first station monitoring bandwidths and the K second station monitoring bandwidths.

[0023] The portable box monitoring transmission branches of the K sites are used to monitor and extract the portable box management data of the K sites, and the extracted data is sent to the background management end to manage the portable boxes of the K sites.

[0024] Specifically, the monitoring transmission branch refers to a data transmission channel set up specifically for monitoring portable boxes at subway stations. Each station will have different data transmission frequencies and priorities according to the settings of its first monitoring bandwidth and second monitoring bandwidth. The first monitoring bandwidth, which is usually shorter, is used for frequent data updates at stations with frequent user activities or high mobility of portable boxes; while the second monitoring bandwidth is longer and is suitable for stations with relatively less activities or relatively stable use of portable boxes. By constructing these monitoring transmission branches, dynamic monitoring of the use of portable boxes at each station can be achieved. This monitoring includes not only the number of accesses to the portable box, but also multi-dimensional data such as time, frequency, and user type. These data are regularly sent to the background management end through the monitoring transmission branch, and the background management end uses these data for comprehensive analysis to optimize the configuration of portable box resources for the entire subway network. In actual operation, for example, a highly mobile station, such as a city center or a major transportation hub, may be set with a shorter first monitoring bandwidth to ensure that the use of the portable box can be quickly fed back to the background management system. On the contrary, a more remote or less frequently used station may have a lower data update frequency, so the second monitoring bandwidth will be longer. Once the data is extracted by the monitoring transmission branch and sent to the background management end, the background system will adjust the allocation of portable boxes according to the real-time information received, such as reconfiguring the number of portable boxes, adjusting the maintenance plan, etc. This data-driven management method not only improves the efficiency and accuracy of portable box management, but also enhances the subway system's ability to respond to changes in passenger demand, ultimately achieving the goal of improving passenger satisfaction and optimizing operating costs. Through the above steps, the efficient operation of the subway portable box management system is ensured, and the intelligent management of subway portable box resources is achieved by fine-tuning the monitoring frequency and optimizing the data flow.

[0025] Furthermore, if Figure 2 As shown, the K usage indicator sets and the K deposit indicator sets are traversed to perform indicator concentration trend analysis to determine K site liquidity factors, including: The K usage indicator sets are traversed to perform mean calculation to determine the mean values ​​of the K usage indicators.

[0026] The K usage index means are taken as K starting mass centers, and the K starting mass centers are iterated according to a preset mass center iterative update formula to obtain K stage iterative mass centers.

[0027] Iteration is continued based on the K stage iteration centroids until a preset iteration stop condition is met, K updated iteration centroids are obtained, and K usage indicators corresponding to the K updated iteration centroids are used as K concentrated usage indicators.

[0028] An indicator concentration trend analysis is performed on the K deposit amount indicator sets to determine K concentrated deposit amount indicators.

[0029] The K centralized usage indicators and the K centralized deposit indicators are weightedly calculated to obtain the K site liquidity factors.

[0030] Furthermore, the method further comprises: ; in, is the stage iteration centroid, is a set of usage indicators, is the i-th usage indicator in the usage indicator set, is the starting centroid, is a Gaussian function.

[0031] In a specific embodiment, the usage indicator set of K sites is traversed and the usage indicator mean of each site is calculated. The purpose of this step is to determine the average usage level of each site to provide a baseline for subsequent data analysis. The usage indicator mean refers to the average number of times the portable box of each site is used within a certain period of time. It is an intuitive indicator to measure the frequency of site usage. Next, these usage indicator means are used as the starting point, that is, as the starting centroid, and the preset centroid iterative update formula is used to iteratively update these starting centroids. The centroid iterative update here is a commonly used technique in cluster analysis, which is used to optimize and refine the usage data representation of each site. By gradually adjusting these centroids, subtle changes in the usage patterns of each site can be more accurately captured. Specifically, the centroid iterative update formula is ,in, is the stage iteration centroid, is a set of usage indicators, is the i-th usage indicator in the usage indicator set, is the starting centroid, is a Gaussian function. This iterative process will continue until the preset iteration stop condition is met, such as the change in the centroid is less than a set threshold, which indicates that the centroid has stabilized and the change caused by further iteration is negligible. At this time, the final centroid obtained is defined as the updated iterative centroid, which represents the centralized usage index of each station and provides a more accurate description of the station usage pattern. At the same time, a similar central tendency analysis is performed on the set of deposit indicators of K stations to determine the centralized deposit index of each station, which is a measure of the centralized tendency of the portable box deposit behavior of each station, providing data support for understanding the station's portable box replenishment and demand. Finally, these centralized usage indicators and centralized deposit indicators are weighted to obtain the liquidity factor of each station. In this step, the weighted calculation takes into account the importance and liquidity needs of different stations to ensure that the liquidity factor can truly reflect the activity of the station in the subway network and the complexity of portable box management. Through the above detailed data processing and analysis steps, a scientific and systematic data support system can be provided for the management of subway portable boxes, making the allocation and scheduling of portable boxes more accurate and efficient, thereby improving the operational efficiency and passenger satisfaction of the entire subway system. This method not only improves the ability of data-driven decision-making, but also optimizes resource utilization and service quality.

[0032] Furthermore, the method further comprises: The distances from the K usage indicator mean value sets to the K stage iteration centroids are traversed and calculated to obtain the K stage centroid deviation distance sets.

[0033] Iteration is continued based on the K phase iteration centroids to obtain K update phase iteration centroids, and a set of K update phase centroid deviation distances is obtained.

[0034] The sum of the K update stage distances of the K update stage centroid deviation distance set and the sum of the K stage distances of the K stage centroid deviation distance set are calculated respectively. When the sum of the K update stage distances is greater than or equal to the sum of the K stage distances, the preset iteration stop condition is met, and the K update stage iteration centroids are used as the K update iteration centroids.

[0035] Specifically, the distances from the K usage index mean sets to the K stage iteration centroids are calculated traversally, with the purpose of evaluating the degree of deviation between the current centroid position and the actual data point (i.e., the mean of the usage index). The so-called distance refers to the Euclidean distance, which is a method of measuring the straight-line distance between two points. Calculating these distances can understand whether the change in the centroid position after each iteration is close enough to the actual usage pattern of the user. Then, the iteration is continued based on the obtained K stage iteration centroids, which means that the centroid position of each site is updated again based on the iteration result of the previous stage to try to reduce the deviation between the actual usage data point and the centroid. This iterative process is carried out by a preset algorithm, such as the least squares method or the gradient descent method, which aims to optimize the usage efficiency and responsiveness of the entire system. Thereafter, an updated stage centroid deviation distance set is obtained, which records the distance between the mean of the usage index of each site and the updated centroid after the new round of iteration. Tracking these distances is crucial to evaluating the efficiency of the iteration process because it directly reflects whether the adjustment of the iteration algorithm is moving in the direction of reducing the overall deviation. Furthermore, the sum of the K update stage distances of the K update stage centroid deviation distance set and the sum of the K stage distances of the K stage centroid deviation distance set are calculated respectively. This step involves comparing two sets of data: one set is the total distance before a new round of iterations, and the other set is the total distance after the iteration. Through this comparison, it can be determined whether the iteration still needs to continue. Finally, when the sum of the K update stage distances is greater than or equal to the sum of the K stage distances, it indicates that further iterations have failed to significantly improve the accuracy of the centroid. At this time, the preset iteration stop condition is met, which means that the current K update iteration centroids are sufficiently accurate relative to the actual usage data and can be used as the final model parameters for subsequent portable box management decisions. Through the above steps, it is ensured that the portable box usage pattern of subway stations is accurately captured through a data-driven method, thereby optimizing the configuration and management of portable boxes and improving service quality and user satisfaction.

[0036] Furthermore, traversing the K cabinet inventory indicator sets to identify fluctuations and determining K site steady-state factors of the K sites includes: The K cabinet inventory index sets are traversed to perform fluctuation variance calculation, and the inverse of the calculation results are respectively used as the steady-state factors of the K sites.

[0037] The K site deployment coefficients are obtained by respectively dividing the K site steady-state factors by the sum of the K site steady-state factors.

[0038] Optionally, the K sets of in-cabinet inventory indexes are traversed to calculate the fluctuation variance. In this step, the in-cabinet inventory index of each site, that is, the number of portable boxes recorded within a given time, is used to calculate the variance. Variance is a measure of the degree of dispersion of a set of values ​​in statistics, which can indicate the consistency and stability of the changes in the number of portable boxes at each site. The calculation formula for the variance is to sum the squares of the differences between each value and the mean and average them. The larger the result, the greater the fluctuation of the data. Next, the inverse of the calculated variance is defined as the site steady-state factor. In statistics, the inverse of the variance can be regarded as a measure of stability. The smaller the variance, the larger its inverse, indicating that the data is more stable. Therefore, the size of the site steady-state factor directly reflects the stability of the number of portable boxes at each site. Sites with higher steady-state factors indicate that the number of portable boxes fluctuates less and is relatively stable; conversely, the fluctuation is larger and the stability is lower. Next, in order to further apply these steady-state factors to the actual portable box management, it is necessary to compare the steady-state factor of each station with the sum of the steady-state factors of all stations to obtain the station allocation coefficient. This coefficient is calculated by dividing the steady-state factor of a single station by the sum of the steady-state factors of all stations. It is used to indicate the relative stability weight of each station in the entire subway network. This coefficient can be used to dynamically adjust the allocation of portable boxes, giving priority to resource allocation for stations with large fluctuations and low stability, so as to enhance the adaptability and responsiveness of the entire system. Through the above analysis and calculation, not only can the storage stability of the portable boxes at each station be accurately evaluated, but also the resource allocation can be optimized according to the actual stability data, thereby improving the overall efficiency and effectiveness of subway portable box management. This data-based approach ensures the scientificity and practicality of resource allocation, and significantly improves the quality of subway portable box services and passenger satisfaction.

[0039] Furthermore, the K site allocation coefficients and the K site steady-state coefficients are identified by using a monitoring step length identifier to determine a set of K site monitoring bandwidths, including: A plurality of sample site deployment coefficients, a plurality of sample site steady-state coefficients and a plurality of sample site monitoring bandwidth sets are obtained as training data.

[0040] The framework constructed based on the feedforward neural network is supervised and trained using the training data to learn the mapping relationship between the site allocation coefficient and the site steady-state coefficient and the site monitoring bandwidth set until the training converges to obtain the trained monitoring step identifier.

[0041] Furthermore, in order to construct an effective monitoring step length identifier, it is necessary to collect sufficient training data, which includes the deployment coefficients, steady-state coefficients and corresponding monitoring bandwidth sets from multiple sample sites. Here, the deployment coefficient refers to the weight calculated based on the station steady-state factor, which reflects the importance of each site in the subway network and the priority of resource allocation; while the steady-state coefficient describes the stability of the fluctuation of the number of portable boxes at each site, and the two together determine the monitoring needs and frequency of the site. Next, using these training data, supervised training will be performed based on the framework of the feedforward neural network. The feedforward neural network is a deep learning model that processes data through forward connections between layers and does not contain feedback connections. This network structure is particularly suitable for processing complex pattern recognition and prediction tasks, such as predicting the optimal monitoring bandwidth of different sites in the present invention. During the training process, the network gradually learns how to predict the appropriate monitoring bandwidth based on the input site deployment coefficients and steady-state coefficients by repeatedly adjusting the network weights. Supervised training means that each input (site deployment coefficient and steady-state coefficient) corresponds to an expected output (monitoring bandwidth set), and the training goal is to minimize the error between the network prediction value and the actual value. This is usually achieved by defining a loss function (such as mean square error), and then using gradient descent or other optimization algorithms to adjust the network parameters to minimize the loss function value. The training process will continue until the model converges, that is, when the network's prediction error no longer decreases significantly, or when it reaches a predetermined training cycle or error threshold. This stage is a key link to ensure that the model can reliably predict the monitoring bandwidth and is also the process of model optimization. In this way, the monitoring step length identifier learns how to dynamically adjust the monitoring frequency according to the actual operating conditions and fluctuation characteristics of the site, so that data collection is both efficient and meets actual needs. After obtaining the trained monitoring step length identifier, this model will be deployed to the actual subway station portable box monitoring system. In operation, the system will input the current deployment coefficient and steady-state coefficient of each site into the identifier in real time, and the identifier will output the corresponding monitoring bandwidth recommendation. For example, for a given site, if its steady-state coefficient is high (i.e., the inventory fluctuation is small), the monitoring step length identifier may recommend a longer monitoring interval to reduce unnecessary data processing and communication burden; conversely, for sites with large fluctuations, a shorter monitoring interval is recommended to ensure that any important changes can be captured in time. Such an intelligent monitoring system not only optimizes the use of resources and reduces operation and maintenance costs, but also improves the flexibility and responsiveness of subway portable box management. It allows subway operators to understand and respond to passengers' actual needs more accurately, improving service quality and passenger satisfaction. In addition, through continuous data monitoring and analysis, the system can also help operators identify potential operational problems or optimization points, thereby further improving the overall efficiency and safety of the subway system.

[0042] Furthermore, the method further comprises: Extracting K monitoring extraction data sets of the K sites received by the background management terminal within a preset monitoring window.

[0043] Information entropy is calculated for the K monitoring and extraction data sets to determine K information entropies.

[0044] Based on the sizes of the K information entropies, resource balancing is performed on the K processing units in the background management terminal that manage the K sites, and the configured K processing units are used to manage the portable boxes of the K sites.

[0045] Specifically, in order to effectively manage the portable boxes at subway stations and optimize resource allocation, a method based on information entropy calculation is introduced to evaluate and adjust the monitoring and management strategies of each station. First, K monitoring extraction data sets of the K stations within the preset monitoring window are extracted from the background management terminal. The preset monitoring window refers to a specific time range set by the administrator, such as the past 24 hours or a week. This time window is used to capture all activity data related to the use of the portable box, including but not limited to the number of accesses, access time, user interaction, etc. of the portable box. This step is the starting point of the data processing flow and provides the original input for subsequent analysis. Next, information entropy calculation is performed on these monitoring extraction data sets to determine the K information entropies of each station. Information entropy is a statistic that measures the randomness or uncertainty of data, and its calculation formula is based on the probability of different events in the data set. In the present invention, the information entropy of each station reflects the irregularity and complexity of its portable box usage pattern. High information entropy means that the use of the portable box at the station during the inspection period is more frequent and irregular, and vice versa, it means that the usage pattern is more stable. Finally, based on the calculated size of the K information entropies, the K processing units that manage the K sites in the background management end are configured with resource balancing, which means that the system will dynamically allocate management resources according to the size of the information entropy of each site, such as increasing the monitoring intensity of high information entropy sites to ensure that those sites with large demand changes and high uncertainty can be effectively responded to. At the same time, for sites with low information entropy, resource allocation can be appropriately reduced to optimize the overall operational efficiency. The system will manage the portable boxes of each site more intelligently and efficiently using the configured K processing units. In this way, the background system can not only ensure the reasonable allocation of resources, but also improve the service quality and user satisfaction of the entire subway network. This data-driven dynamic management method provides a highly adaptive and responsive management mechanism for the subway portable box system, effectively improving operational efficiency and passenger experience.

[0046] Through the technical solutions of the above-mentioned embodiments, the intelligent management method of portable boxes for subway riding provided by the present application solves the technical problems existing in the existing subway management that the non-dangerous personal belongings of passengers cannot be managed efficiently and conveniently, resulting in congestion during security checks, inconvenience to passengers and increased management costs. By using portable boxes and performing intelligent management, effective and convenient management of passengers' belongings is achieved, and the resource allocation of each station is dynamically adjusted, thereby improving the operational efficiency and safety of the entire subway network and ensuring the technical effects of riding and management experience.

[0047] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0048] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. An intelligent management method for a portable case for subway riding, characterized in that: The method comprises: Retrieve the portable box management logs of K stations of the target subway line within a preset historical window to obtain K historical portable box management logs, where K is an integer greater than or equal to 1; Extracting indicators from the K historical portable box management logs according to the usage indicator, the deposit indicator, and the cabinet inventory indicator, respectively, to obtain K usage indicator sets, K deposit indicator sets, and K cabinet inventory indicator sets; Traversing the K usage indicator sets and the K deposit indicator sets to perform indicator concentration trend analysis, determine K site liquidity factors, and respectively divide the K site liquidity factors by the sum of the K site liquidity factors to obtain K site allocation coefficients; Traversing the K cabinet inventory index sets to identify fluctuations, and obtaining K station steady-state coefficients; Using a monitoring step identifier to identify the K site deployment coefficients and the K site steady-state coefficients, and determine a set of K site monitoring bandwidths, wherein the set of K site monitoring bandwidths includes K first site monitoring bandwidths and K second site monitoring bandwidths, and each first site monitoring bandwidth is smaller than the corresponding second site monitoring bandwidth; Constructing K station portable box monitoring transmission branches of the target subway line based on the K first station monitoring bandwidths and the K second station monitoring bandwidths; The portable box monitoring transmission branches of the K sites are used to monitor and extract the portable box management data of the K sites, and the extracted data is sent to the background management end to manage the portable boxes of the K sites.

2. The intelligent management method for a portable case for subway riding according to claim 1, characterized in that: Traversing the K usage indicator sets and the K deposit indicator sets to perform indicator concentration trend analysis, and determining K site liquidity factors, including: Traversing the K usage indicator sets to perform mean calculations and determine the mean values ​​of the K usage indicators; Taking the K usage index means as K starting mass centers, iterating the K starting mass centers according to a preset mass center iterative update formula to obtain K stage iterative mass centers; Iteration is continued based on the K stage iteration centroids until a preset iteration stop condition is met, K update iteration centroids are obtained, and K usage indicators corresponding to the K update iteration centroids are used as K concentrated usage indicators; Performing an indicator concentration trend analysis on the K deposit amount indicator sets to determine K concentrated deposit amount indicators; The K centralized usage indicators and the K centralized deposit indicators are weightedly calculated to obtain the K site liquidity factors.

3. The intelligent management method for a portable case for subway riding as claimed in claim 2, characterized in that: include: ； in, is the stage iteration centroid, is a set of usage indicators, is the i-th usage indicator in the usage indicator set, is the starting centroid, is a Gaussian function.

4. The intelligent management method for a portable case for subway riding as claimed in claim 3, characterized in that: include: Traversing and calculating the distances from the K usage indicator mean value sets to the K stage iteration centroids, and obtaining the K stage centroid deviation distance sets; Continue to iterate based on the K stage iterative centroids to obtain K update stage iterative centroids, and obtain a set of deviation distances of the K update stage centroids; The sum of the K update stage distances of the K update stage centroid deviation distance set and the sum of the K stage distances of the K stage centroid deviation distance set are calculated respectively. When the sum of the K update stage distances is greater than or equal to the sum of the K stage distances, the preset iteration stop condition is met, and the K update stage iteration centroids are used as the K update iteration centroids.

5. The intelligent management method for a portable case for subway riding according to claim 1, characterized in that: Traversing the K cabinet inventory index sets to identify fluctuations, and determining K station steady-state factors of the K stations, including: Traversing the K cabinet inventory index sets to perform fluctuation variance calculations, and taking the inverse of the calculation results as the steady-state factors of the K sites respectively; The K site deployment coefficients are obtained by respectively dividing the K site steady-state factors by the sum of the K site steady-state factors.

6. The intelligent management method for a portable case for subway riding according to claim 1, characterized in that: The K site allocation coefficients and the K site steady-state coefficients are identified by using a monitoring step length identifier to determine a set of K site monitoring bandwidths, including: Obtaining multiple sample site deployment coefficients, multiple sample site steady-state coefficients, and multiple sample site monitoring bandwidth sets as training data; The framework constructed based on the feedforward neural network is supervised and trained using the training data to learn the mapping relationship between the site allocation coefficient and the site steady-state coefficient and the site monitoring bandwidth set until the training converges to obtain the trained monitoring step identifier.

7. The intelligent management method for a portable case for subway riding as claimed in claim 1, characterized in that: include: Extracting K monitoring extraction data sets of the K sites received by the background management terminal within a preset monitoring window; Performing information entropy calculation on the K monitoring and extraction data sets to determine K information entropies; Based on the sizes of the K information entropies, resource balancing is performed on the K processing units in the background management terminal that manage the K sites, and the configured K processing units are used to manage the portable boxes of the K sites.

8. The intelligent management method for a portable case for subway riding as claimed in claim 1, characterized in that: The usage index is the number of portable boxes taken out from the site by the user, the deposit index is the number of portable boxes deposited in the site by the user, and the cabinet holding index is the number of portable boxes stored in the site.

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