Integrated charging and data dump intelligent device management system

Abstract

The application discloses an intelligent device management system integrated with charging and data dumping, and relates to the technical field of device management, comprising: an identity authentication module, which is used for double authenticating the user identity through biological characteristics and permission tokens, extracting three-dimensional features of tasks from a production system, calculating the adaptation degree of tasks and devices through a feature matching algorithm to generate a candidate device pool; a working condition sensing module, which is used for generating a working condition fingerprint, comparing historical fingerprints through a dynamic time warping algorithm to determine an abnormality level; a charging and storage module, which is used for constructing an energy data balance model according to priority scores and task features, dynamically adjusting charging power and dumping bandwidth, and scheduling devices through a predictive migration mechanism; and a resource management module, which is used for constructing a device full profile, dynamically adjusting device matching rules and user permissions based on an association rule algorithm, and generating a predictive maintenance work order, thereby significantly improving device use safety, matching accuracy and maintenance efficiency.

Description

Technical Field

[0001] This invention relates to the field of equipment management technology, specifically to an intelligent equipment management system that integrates charging and data transfer. Background Technology

[0002] In current device management systems, common solutions mainly rely on a single authentication mechanism and static resource allocation strategy. For example, some systems use passwords or IC cards for user authentication and manage devices through fixed charging and data transfer processes. These systems typically prioritize devices based on their battery level or the urgency of the task to achieve basic charging and data transfer functions.

[0003] However, existing technologies have significant limitations: First, single authentication methods are not secure enough and are vulnerable to forgery or theft, and cannot dynamically assess user operational capabilities, leading to a high risk of device misoperation. Second, static resource allocation strategies cannot adapt to dynamic changes in task requirements, easily resulting in resource waste or task delays. For example, when task urgency changes abruptly or equipment malfunctions, the system struggles to adjust its allocation strategy in a timely manner, impacting overall efficiency. Furthermore, existing systems lack sufficient monitoring and early warning capabilities for device health status. Most systems can only reflect device anomalies through threshold alarms, lacking early identification and predictive maintenance mechanisms for sub-healthy equipment states. This means that equipment failures are often addressed only after they occur, increasing maintenance costs and potentially affecting the execution of critical tasks. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] To address the shortcomings of existing technologies, this invention provides an intelligent device management system that integrates charging and data transfer. By employing dual authentication with biometrics and permission tokens, dynamic resource allocation strategies, and multi-dimensional monitoring of device health status, it solves the problems of insufficient identity authentication security, low resource allocation efficiency, and weak device anomaly early warning capabilities in existing technologies.

[0006] (II) Technical Solution

[0007] To achieve the above objectives, the present invention provides the following technical solution: an intelligent device management system integrating charging and data transfer, comprising:

[0008] The identity authentication module is used to authenticate user identity through both biometrics and permission tokens, construct a device proficiency matrix based on user historical operation data, extract three-dimensional features of tasks from the production system, and calculate the suitability between tasks and devices through feature matching algorithms to generate a candidate device pool.

[0009] The operating condition sensing module is used to collect the current and voltage curves of the equipment in three stages: static, initial charging and initial dumping, to generate an operating condition fingerprint. The abnormal level is determined by comparing the historical fingerprint with the dynamic time warping algorithm. Based on the operating condition factor, historical factor and task factor, the demand priority score is calculated by weighted summation method, and the charging and transfer strategy is executed in stages.

[0010] The charging, transmission and storage module is used to build an energy data balance model based on priority scores and task characteristics, dynamically adjust charging power and dump bandwidth, divide resources into fast charging zone, high-speed dump zone and equalization zone, and schedule equipment through a predictive migration mechanism.

[0011] The resource management module is used to collect the status, operating conditions and related data of the equipment when it is returned to build a complete equipment file. Based on the association rule algorithm, it dynamically adjusts the equipment matching rules and user permissions, and generates predictive maintenance work orders.

[0012] Furthermore, a dual authentication mechanism using biometrics, including face and fingerprint, is adopted. Face acquisition uses an infrared camera, and fingerprint acquisition uses liveness detection technology. A device proficiency matrix is ​​constructed based on the user's historical operation records of a predetermined number of times, and the proficiency is divided into three levels: high, medium, and low, according to the fault trigger rate. Operation instructions for the target device are automatically pushed to users with low proficiency, and auxiliary operation and maintenance permissions are associated with them.

[0013] Furthermore, the three-dimensional features of the task include timeliness features, functional features, and environmental features: timeliness features include urgency and expected duration, functional features include data requirements and battery life requirements, and environmental features include usage scenarios and network conditions. The task and device compatibility is calculated through feature matching algorithms, and devices with compatibility below the preset compatibility threshold, under maintenance, or incomplete charging / transfer are excluded to generate a candidate device pool.

[0014] Furthermore, real-time monitoring of resource load characteristics is performed to calculate remaining processing capacity and resource release time; resource reservation strategies are dynamically adjusted: high-urgency tasks are given priority to occupy high-priority resources and the reservation time is extended, while ordinary tasks are scheduled to use resources that are about to be released; reserved resources are marked with task and device association identifiers, and are automatically released if they are not claimed within the time limit.

[0015] Furthermore, the acquisition device generates operating condition fingerprints from the current and voltage curves in three stages: static, initial charging, and initial dumping. The real-time fingerprint is compared with the fingerprints in the historical fingerprint database using a dynamic time warping algorithm, and the anomaly level is determined based on the fingerprint similarity range.

[0016] Furthermore, a multi-factor model for demand determination is constructed, which inputs operating condition factors, historical factors, and task factors. Among them, the operating condition factors include real-time power and fingerprint similarity, the historical factors include the number of charging and transmission anomalies in the last 3 times and the battery SOH, and the task factors include task urgency and data volume requirements; the weighted summation method is used to calculate the priority score. When the score ≥ M1, the priority is level one, and urgent charging and transmission are required. When M2 ≤ score < M1, the priority is level two, and normal charging and transmission are performed. When the score < M2, the priority is level three, and only basic maintenance is carried out.

[0017] Furthermore, if the fingerprint similarity < 70% and SOH < 75%, it is determined that the equipment is in a sub-healthy state, and the charging and transmission power is restricted; if the fingerprint similarity ≥ 90% and SOH ≥ 85%, it is determined that the equipment is healthy, and full-power charging and transmission can be carried out; the abnormal probability is calculated based on multi-factor parameters. When the abnormal probability ≥ the preset probability threshold, it is a high risk, and a 5-second observation period is started.

[0018] During the observation period, operating condition data is continuously collected: if the current fluctuation ≤ 5% and the voltage is stable, it is determined as an instantaneous interference, and it is executed normally; if the current suddenly drops > 20%, it is determined as a real anomaly, automatically switched to the safe mode, the charging and transmission is paused, and a fault prompt is pushed to the operation and maintenance terminal.

[0019] Furthermore, the inputs of the energy data balance model include the remaining task time, the current power, the data volume to be transmitted, the upper limit of the charging power, and the upper limit of the dump bandwidth; the charging power and the dump bandwidth are output, satisfying: charging power × remaining time + current power ≥ endurance demand, and dump bandwidth × remaining time ≥ data volume to be transmitted; for level-one priority devices, the upper limit of the charging power and the upper limit of the dump bandwidth are enabled, and for level-three priority devices, the charging power is reduced to 50% of the upper limit and the dump function is turned off.

[0020] Furthermore, the power and the data dump progress are periodically monitored, and the allocation ratio of the charging power and the dump bandwidth is dynamically adjusted; when the network bandwidth drops by more than the set ratio, the local cache breakpoint continuation mechanism is started; the resource partition includes a high-power charging area, a high-speed dump area, and a balanced area; when the number of devices in any partition exceeds the capacity, predictive migration is adopted: calculate the remaining processing time of the device, temporarily store the waiting device in the transition queue, and automatically migrate it after the partition is released; if it is a special task device, the target partition is temporarily expanded, and the partition configuration is restored after the task is completed.

[0021] Furthermore, the three-dimensional data of the device when it is returned is collected to construct a complete device file: status data, operating condition data, and associated data; the association rule algorithm is used to mine the user device association rule and the device task association rule; the device models that are not suitable for long-term tasks are removed, and low-proficiency users are restricted from领用 high-failure-rate devices; when the increase rate of the charging duration and the decrease rate of the battery health exceed the linkage threshold, a predictive maintenance work order is generated.

[0022] (III) Beneficial effects

[0023] This invention provides an intelligent device management system that integrates charging and data transfer, and has the following beneficial effects:

[0024] (1) By using biometrics and permission tokens for dual authentication, user identity security is ensured and unauthorized access is prevented. Based on the user's historical operation data, a device proficiency matrix is ​​constructed to dynamically evaluate the user's operation ability. Operation guidance is automatically pushed to users with low proficiency and associated with auxiliary permissions to reduce the risk of operation errors. Combining the task's three-dimensional features with the device adaptability matching algorithm, the optimal candidate device pool is generated to improve task execution efficiency and device utilization, and enhance system security, operational standardization and resource allocation accuracy.

[0025] (2) By collecting the current and voltage curves of the device in the three stages of static, initial charging and initial dumping, the operating condition fingerprint is generated. Combined with the dynamic time warping algorithm to compare historical data, the abnormal level of the device is accurately determined. The priority score is calculated based on the multi-factor model and the charging and transfer strategy is dynamically graded: healthy devices run at full power and sub-healthy devices are protected by power limit. At the same time, by monitoring the current and voltage fluctuations in real time, instantaneous interference or real faults are identified and the safety mode is automatically switched, which significantly improves the safety of the device, charging and transfer efficiency and abnormal response speed.

[0026] (3) By constructing an energy data balance model, the charging power and dump bandwidth allocation are dynamically optimized to ensure that the task endurance requirements and data dump efficiency are met simultaneously. Based on the priority grading strategy, the first-level task equipment operates at full power, and the third-level task equipment reduces power to extend its lifespan. The power and dump progress are monitored in real time, the resource allocation ratio is dynamically adjusted, and the breakpoint resume mechanism is enabled when the network fluctuates. By dividing the fast charging zone, high-speed dump zone and equalization zone, combined with the predictive migration algorithm, resources are efficiently scheduled to avoid congestion and significantly improve charging and transmission efficiency, resource utilization and task completion reliability.

[0027] (4) By collecting the status, working conditions and related data of the equipment when it is returned, a complete equipment file is constructed. Based on the association rule algorithm, the equipment matching rules and user permissions are dynamically optimized. By analyzing the correlation between user operating habits and equipment performance, the use of high failure rate equipment by low-skilled users is restricted, and equipment models that are not compatible with the task are removed. When abnormal charging time or battery health is detected, predictive maintenance work orders are automatically generated to warn of equipment aging risks in advance. This significantly improves the safety of equipment use, matching accuracy and maintenance efficiency, extends the equipment life cycle and reduces operation and maintenance costs. Attached Figure Description

[0028] Figure 1 This is a schematic diagram of the intelligent device management system integrating charging and data transfer according to the present invention.

[0029] Figure 2This is a schematic diagram of the user acquisition process for the equipment management system of the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] Please see Figure 1 and Figure 2 This invention provides an integrated intelligent device management system for charging and data transfer, comprising: an identity authentication module, a working condition sensing module, a charging, data transfer and storage module, and a resource management module, wherein:

[0032] The identity authentication module is used to authenticate user identity through both biometrics and permission tokens, construct a device proficiency matrix based on user historical operation data, extract three-dimensional features of tasks from the production system, and calculate the suitability between tasks and devices through feature matching algorithms to generate a candidate device pool.

[0033] Specifically, it adopts dual authentication of biometrics and permission tokens. After successful authentication, the user's device operation records are retrieved. Biometrics include face and fingerprint. Face capture uses an infrared camera, which supports backlight and low light environments. Fingerprints use capacitive liveness detection to prevent forgery.

[0034] Based on historical operation data of user reservations, such as the last 10 device usage records, a user device proficiency matrix is ​​constructed: the operation time of users for different types of devices is statistically analyzed, such as user A taking an average of 2 minutes to operate a camera flashlight and 1 minute to operate a voice recorder, as well as the fault trigger rate, such as user B having a 5% disconnection rate when using a certain model of recorder;

[0035] The proficiency level is divided into three levels: high, medium, and low. When the fault trigger rate is ≤2%, the proficiency level is high; when 2% < fault trigger rate ≤5%, the proficiency level is medium; and when the fault trigger rate >5%, the proficiency level is low. If the user's proficiency level with the target device is low, the operation guide for that device will be automatically pushed. For example, the interface needs to be kept stable during the transfer and auxiliary operation and maintenance permissions should be associated. If the operation is abnormal, the administrator will be automatically notified for remote guidance.

[0036] Obtain the three-dimensional characteristics of tasks from the production system, including timeliness characteristics, functional characteristics, and environmental characteristics. Among them, the timeliness characteristics include urgency and estimated duration. The urgency levels are, for example, special level for fault repair and ordinary level for daily inspection. The estimated duration T≤2h is short, 2h<T≤6h is medium, and T>6h is long. The functional characteristics include data requirements and battery life requirements. The data requirements are, for example, for high-definition video, the storage needs to be ≥50GB, and for log data, encrypted transmission is required. The battery life requirements are, for example, for long-duration tasks, the device needs to have a full charge battery life of ≥8h. The environmental characteristics include usage scenarios and network conditions. The usage scenarios are, for example, for explosion-proof environments, the device's explosion-proof level needs to be ≥T3, and for network conditions, such as field operations, the device needs to support offline caching.

[0037] Build a capability profile for each device: Integrate hardware parameters, historical performance, and adapted task types. The hardware parameters are, for example, battery capacity, interface protocol, and explosion-proof level. The historical performance is, for example, the charging and transmission efficiency in the last 5 times and the number of faults. The adapted task types are, for example, camera flashlight A is adapted to long-duration video acquisition. Calculate the adaptability of the task to the device through a feature matching algorithm. The feature matching algorithm uses the cosine similarity algorithm or the Euclidean distance algorithm, with a full score of 100 points. ≥80 points means adaptation. Exclude devices with an adaptability < preset adaptability threshold (such as 80%) and those in the maintenance and uncompleted charging and transmission states to obtain a candidate device pool.

[0038] Real-time monitor the load timeliness characteristics of resources such as charging piles, data interfaces, and local caches: Calculate the remaining processing capacity of the current resources, such as the current occupancy rate of the data interface is 60% and the remaining bandwidth ≥500Mbps, and the release time within the next 1 hour, such as device B will complete charging and transmission in 20 minutes.

[0039] Dynamically adjust the reservation strategy in combination with the task urgency. For special-level tasks, reserve resources for 30 minutes and preferentially occupy idle and high-priority resources, such as high-speed data interfaces. For ordinary tasks, reserve resources for 10 minutes. If high-priority resources are occupied, schedule resources that will be released soon, such as an interface that will be released in 20 minutes, and remind the user 15 minutes in advance that the resources will be available. Mark associated identifiers for the reserved resources, such as task ID, user ID, and device ID, to prevent other users from occupying them. If not claimed after the timeout, they will be automatically released and a resource release notification will be pushed, and at the same time, update the candidate device pool.

[0040] Ensure user identity security through dual authentication of biometrics and permission tokens to prevent unauthorized access. Build a device proficiency matrix based on the user's historical operation data to dynamically evaluate the user's operation ability. Automatically push operation guides to low-proficiency users and associate auxiliary permissions to reduce the risk of operation errors. Combine the three-dimensional characteristics of tasks with the device adaptability matching algorithm to generate an optimal candidate device pool, improve the task execution efficiency and device utilization rate, and enhance the system security, operation standardization, and resource allocation accuracy.

[0041] The operating condition perception module is used to collect the current-voltage curves of the device in three stages: static, initial charging, and initial dumping, so as to generate an operating condition fingerprint. It determines the abnormal level by comparing the historical fingerprint through the dynamic time warping algorithm, calculates the demand priority score based on the operating condition factor, historical factor, and task factor, and executes the charging and transmission strategy in different levels;

[0042] Specifically, after the user inserts the device into the仓位, the interface triggers the dynamic feature collection process: synchronously collect the current and voltage time curves of the device in three stages: static (not charging and transmitting), initial charging, and initial dumping. For example, within 10 seconds of initial charging, the current rises from 0.3A to 1.2A, and the voltage stabilizes at 4.2V, forming the device operating condition fingerprint;

[0043] Read the device SN code, retrieve the historical operating condition fingerprint database of the device from the database, store the operating condition curves of the last 20 accesses, and determine the abnormal level by comparing the fingerprint similarity between the real-time device operating condition fingerprint and the historical device operating condition fingerprint through the dynamic time warping algorithm (DTW). For example, when the fingerprint similarity ≥ 90% is normal operating condition, 70% ≤ fingerprint similarity < 90% is slightly abnormal, and fingerprint similarity < 70% is severely abnormal; for special devices, such as explosion-proof recorders, enable the operating condition fingerprint template library, and pre-store the fingerprint templates of this type of device in different health states. For example, when the battery decays by 30%, the peak charging current drops to 1A, further improving the determination accuracy;

[0044] Construct a multi-factor model for demand determination, input the operating condition factor, historical factor, and task factor. Among them, the operating condition factor includes the real-time power and fingerprint similarity, the historical factor includes the number of charging and transmission anomalies in the last 3 times and the battery SOH, and charging and transmission anomalies such as disconnection and verification failure, and the task factor includes the task urgency and data volume requirements;

[0045] Use the weighted summation method to calculate the demand priority score: demand priority score = (operating condition factor × operating condition weight) + (historical factor × historical weight) + (task factor × task weight). When the score ≥ M1, the priority is level one, and urgent charging and transmission are required. When M2 ≤ score < M1, the priority is level two, and normal charging and transmission are required. When the score < M2, the priority is level three, and only basic maintenance is required. The values of the operating condition factor, historical factor, and task factor are taken as the average values of the included parameters, and each parameter is normalized to [0, 100]. The corresponding weights of the operating condition factor, historical factor, and task factor can be configured. The default operating condition weight = 0.4, historical weight = 0.3, task weight = 0.3, and the value range is 0 to 1. M1 and M2 are configurable thresholds, and the default values are M1 = 80 and M2 = 60, which are adjusted according to the specific scenario;

[0046] If the fingerprint similarity is <70% and the State of Harmony (SOH) is <75%, the device is considered sub-healthy, and charging power is limited, with charging current ≤0.8A and dump rate ≤USB2.0. If the fingerprint similarity is ≥90% and the SOH is ≥85%, the device is considered healthy and can be charged and transferred at full power. The anomaly probability is calculated based on multi-factor parameters: Anomaly probability = (1 - fingerprint similarity) × 0.5 + (historical anomaly count / 3) × 0.3 + (1 - SOH / 100) × 0.2. When the anomaly probability is ≥ a preset probability threshold (e.g., 30), it is considered high risk, and a 5-second observation period is initiated.

[0047] During the observation period, operating condition data is continuously collected: if the current fluctuation is ≤5% and the voltage is stable, it is judged as a momentary interference and normal operation is performed; if the current drops by more than 20%, such as from 1.2A to 0.9A, it is judged as a real anomaly, automatically switches to safe mode, suspends charging and transmission, and pushes a fault prompt to the operation and maintenance terminal. The operation and maintenance terminal is a dedicated operating device or tool interface used by IT operation and maintenance personnel for daily management, fault diagnosis, configuration adjustment and monitoring.

[0048] By collecting current and voltage curves of the device in three stages—static, initial charging, and initial dumping—a working condition fingerprint is generated. Combined with a dynamic time warping algorithm and compared with historical data, the device's abnormality level is accurately determined. Based on a multi-factor model, a priority score is calculated, and a dynamic graded charging and transfer strategy is implemented: healthy devices operate at full power, while sub-healthy devices are protected by power limiting. At the same time, by monitoring current and voltage fluctuations in real time, transient interference or real faults are identified, and a safety mode is automatically switched, significantly improving device safety, charging and transfer efficiency, and abnormal response speed.

[0049] The charging, transmission and storage module is used to build an energy data balance model based on priority scores and task characteristics, dynamically adjust charging power and dump bandwidth, divide resources into fast charging zone, high-speed dump zone and equalization zone, and schedule equipment through a predictive migration mechanism.

[0050] Specifically, based on demand priority and task three-dimensional characteristics, an energy data balance model is constructed. Input parameters include remaining task time, current device battery level, amount of data to be transferred, charging power limit, and dump bandwidth limit. Output parameters are charging power and dump bandwidth. The energy data balance model is constructed based on linear programming theory, with the core formula as follows: Objective function: Within the remaining task time, device battery level ≥ task endurance requirement and data dump completion rate ≥ 100%. Constraints: Charging power × remaining task time + current device battery level ≥ task endurance requirement; Dump bandwidth × remaining task time ≥ amount of data to be transferred; Charging power ≤ charging power limit; Dump bandwidth ≤ dump bandwidth limit.

[0051] For Level 1 priority devices, the charging power is the upper limit of the charging power and the dump bandwidth is the upper limit of the dump bandwidth. For Level 3 priority devices, only charging is performed, the charging power is half of the upper limit of the charging power, the battery life is extended, and the dump function is turned off.

[0052] Real-time monitoring of energy data balance: Periodically monitors power consumption and data transfer progress, and dynamically adjusts the allocation ratio of charging power and transfer bandwidth. For example, every 5 seconds, it calculates the difference between the current power consumption and the 50% target power consumption, and the difference between the transmitted data and the 50% target data. If the difference between the current power consumption and the 50% target power consumption is greater than 0 and the difference between the transmitted data and the 50% target data is less than 0, it reduces the charging power by 10% and increases the transfer bandwidth by 10% to prioritize data completion. If the difference between the current power consumption and the 50% target power consumption is less than 0 and the difference between the transmitted data and the 50% target data is greater than 0, it increases the charging power by 10% and reduces the transfer bandwidth by 10% to prioritize battery life.

[0053] If a network bandwidth drop exceeds a set percentage, such as 30%, from 500Mbps to 350Mbps, the local cache will be automatically started to resume interrupted transmission: the untransmitted data will be temporarily stored in the local cache, the data block checksum will be recorded, and the transmission will resume from the interrupted point after the network is restored, thus avoiding data retransmission.

[0054] The charging piles, data interfaces and other resources are divided into three dynamic zones: fast charging zone, high-speed storage zone and balanced zone. The fast charging zone has a charging power of ≥1.2A and is suitable for first-priority devices. The high-speed storage zone has a bandwidth of ≥3Gbps and is suitable for devices with data volume >100GB. The balanced zone has a power of 0.8~1.0A and a bandwidth of 1~2Gbps and is suitable for second-priority devices.

[0055] When the number of devices in any partition exceeds the capacity, predictive migration is adopted: calculate the remaining processing time of the current device. For example, if device C still needs 15 minutes to complete charging and transfer, the waiting device will be temporarily stored in the transition queue and automatically migrated after the partition is released. If it is a special task device, the target partition can be temporarily expanded, such as upgrading one interface in the equalization zone to the fast charging zone. After the task is completed, the partition configuration will be restored.

[0056] By constructing an energy data balance model, the charging power and data dumping bandwidth allocation are dynamically optimized to ensure that the task's endurance requirements and data dumping efficiency are met simultaneously. Based on a priority-based hierarchical strategy, first-level task devices operate at full power, while third-level task devices reduce power to extend their lifespan. The power consumption and dumping progress are monitored in real time, and the resource allocation ratio is dynamically adjusted. A breakpoint resume mechanism is enabled when the network fluctuates. By dividing the network into fast charging zones, high-speed dumping zones, and balanced zones, and combining predictive migration algorithms, resources are efficiently scheduled to avoid congestion, significantly improving charging and data transfer efficiency, resource utilization, and task completion reliability.

[0057] The resource management module is used to collect the status, operating conditions and related data of the equipment when it is returned to build a complete equipment file. Based on the association rule algorithm, it dynamically adjusts the equipment matching rules and user permissions, and generates predictive maintenance work orders.

[0058] Specifically, the system collects three-dimensional data when the device is returned, including status data, operating condition data, and related data. The status data includes the final battery level, total charging and transfer time, transfer completion rate, and battery SOH change. The operating condition data includes the current fluctuation curve and the number of abnormal triggers during the charging and transfer process, such as disconnection and overheating. The related data includes the user's device proficiency level, task type, and resource partition usage records. A complete file for each use of the device is constructed, and the task ID and user ID are associated and stored in a distributed database.

[0059] The Apriori association rule algorithm was used to uncover hidden patterns, including user device association and device task association. For example, in user device association, the anomaly rate was 8% higher when a less skilled user used a model X recorder than when a more skilled user used it, indicating that a more stable device should be matched for less skilled users. In device task association, the SOH decrease rate of model Y camera flashlight was 0.5% higher per cycle during long-term tasks than during short-term tasks, indicating that model Y is not suitable for long-term tasks.

[0060] Based on the correlation mining results, the device task matching rules are adjusted. For example, the Y-type camera flashlight is removed from the long-term task candidate pool and added to the short-term task pool. For users with low proficiency, their access to high-failure-rate devices is restricted, such as the X-type dashcam. Permissions are unlocked after the user's proficiency level is increased to high level.

[0061] When the increase in charging time and the decrease in battery health exceed the linkage threshold, a predictive maintenance work order is generated. For example, if the charging time increases by 20% compared to the historical average and the state of health (SOH) decreases by more than 1%, it is determined that the battery is degrading faster and the charging interval needs to be shortened. If the number of disconnections during the dump is more than 2 and there are no network anomalies, it is determined that the device interface is aging and the interface needs to be replaced. For devices with a battery SOH of less than 70% and an aging interface, a predictive maintenance work order is generated and pushed to the operation and maintenance terminal, indicating the recommended replacement time, such as replacing the battery before SOH is less than 65%. At the same time, the priority score of the device is reduced, such as to 60% of the original score, to avoid being assigned to critical tasks.

[0062] By collecting data on the status, operating conditions, and related information of the equipment upon return, a complete equipment profile is constructed. Based on association rule algorithms, equipment matching rules and user permissions are dynamically optimized. By analyzing the correlation between user operating habits and equipment performance, the system restricts inexperienced users from using high-failure-rate equipment and removes equipment models that are not compatible with tasks. When abnormal charging time or declining battery health is detected, predictive maintenance work orders are automatically generated to provide early warnings of equipment aging risks. This significantly improves equipment safety, matching accuracy, and maintenance efficiency, extends equipment lifespan, and reduces operation and maintenance costs.

[0063] In the application, the various formulas mentioned are all calculated by removing dimensions and taking their numerical values. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The coefficients in the formulas are set by those skilled in the art according to the actual situation.

[0064] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, and combinations thereof. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0065] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0066] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A smart device management system that integrates charging and data dumping, characterized by: Comprise: Identity authentication module, for double authentication of user identity by biological characteristics and permission token, and constructing device proficiency matrix according to user historical operation data: statistics of user operation time length on different types of devices, and failure trigger rate, the proficiency is divided into high, medium and low three levels, when the failure trigger rate is less than or equal to 2%, the proficiency is high, when 2% < failure trigger rate ≤ 5%, the proficiency is medium, when the failure trigger rate is greater than 5%, the proficiency is low; Extract three-dimensional features of tasks from production system, calculate the adaptation degree of tasks and devices through feature matching algorithm to generate candidate device pool; Working condition perception module, for collecting current voltage curve of device in static, charging initial and dumping initial three stages to generate working condition fingerprint, comparing historical fingerprint through dynamic time warping algorithm to determine abnormal level, calculating demand priority score based on working condition factor, historical factor and task factor, and executing charging strategy by classification; Charging and storage module, for constructing energy data balance model according to priority score and task characteristics, the input of energy data balance model includes task remaining time, current power, data volume to be transmitted, charging power upper limit and dumping bandwidth upper limit; The output of charging power and dumping bandwidth meets: charging power × remaining time + current power ≥ endurance demand, and dumping bandwidth × remaining time ≥ data volume to be transmitted; The first priority device starts charging power upper limit and dumping bandwidth upper limit, the third priority device reduces charging power to 50% of the upper limit, and closes the dumping function; Dynamically adjust charging power and dumping bandwidth, divide resources into fast charging area, high speed dumping area and balance area, and schedule devices through predictive migration mechanism; Resource management module, for collecting the state, working condition and associated data of the device when it is returned to construct the device file, dynamically adjusting the device matching rule and user permission based on the associated rule algorithm, and generating predictive maintenance work order.

2. The intelligent device management system integrated with charging and data dumping according to claim 1, characterized in that: A dual authentication mechanism using biological characteristics including face and fingerprint is adopted, an infrared camera is used for face collection, and a live detection technology is adopted for fingerprint collection; A device proficiency matrix is constructed based on the historical operation records of the user for a predetermined number of times, and the proficiency is divided into three levels of high, medium and low according to the failure trigger rate; Operation instructions of target device are automatically pushed to users with low proficiency, and auxiliary operation and maintenance permissions are associated.

3. The intelligent device management system integrated with charging and data dumping according to claim 2, characterized in that: Task three-dimensional features include time limit feature, function feature and environment feature: time limit feature includes urgency and expected duration, function feature includes data demand and endurance demand, and environment feature includes use scene and network condition, the adaptation degree of task and device is calculated through feature matching algorithm, and the candidate device pool is generated by excluding devices with adaptation degree lower than the preset adaptation degree threshold, in maintenance or with incomplete charging and dumping.

4. The intelligent device management system integrated with charging and data dumping according to claim 1, characterized in that: Real-time monitoring of resource load characteristics, calculating the remaining processing capacity and resource release time; dynamically adjusting the resource reservation strategy: high emergency task priority occupies high priority resource and extends the reservation time, ordinary task scheduling will release the resource; mark the reserved resource with task and device association identifier, and automatically release if not used within the time limit.

5. The integrated charging and data dump intelligent device management system according to claim 1, characterized in that: The acquisition device generates working condition fingerprints in three stages of static, initial charging and initial dumping by collecting current and voltage curves; the dynamic time warping algorithm is used to compare the real-time fingerprint with the historical fingerprint library; the abnormality level is determined according to the fingerprint similarity range.

6. The integrated charging and data dump intelligent device management system according to claim 5, characterized in that: A demand determination multi-factor model is constructed, and working condition factors, historical factors and task factors are input; the working condition factors include real-time power and fingerprint similarity, the historical factors include the number of abnormal charging and transmission in the last three times and the battery SOH, and the task factors include task urgency and data volume demand; the weighted summation method is used to calculate the priority score, and when the score is greater than or equal to M1, the priority is first level, and urgent charging and transmission; when M2 is less than or equal to the score and the score is less than M1, the priority is second level, and normal charging and transmission; when the score is less than M2, the priority is third level, and only basic maintenance.

7. The integrated charging and data dump intelligent device management system according to claim 6, characterized in that: If the fingerprint similarity is less than 70% and the SOH is less than 75%, it is determined that the device is sub-healthy, and the charging and transmission power is limited; if the fingerprint similarity is greater than or equal to 90% and the SOH is greater than or equal to 85%, it is determined that the device is healthy, and the full power charging and transmission can be performed; based on the multi-factor parameter calculation, when the abnormal probability is greater than or equal to the preset probability threshold, it is high risk, and a 5-second observation period is started; During the observation period, the working condition data is continuously collected: if the current fluctuation is less than or equal to 5% and the voltage is stable, it is determined that it is a transient disturbance, and it is executed normally; if the current drops by more than 20%, it is determined that it is a real abnormality, and the charging and transmission are automatically switched to the safety mode, and the charging and transmission are suspended, and the fault prompt is pushed to the operation and maintenance terminal.

8. The integrated charging and data dump intelligent device management system according to claim 1, characterized in that: Periodically monitor the power and data dump progress, and dynamically adjust the allocation ratio of charging power and dumping bandwidth; when the network bandwidth decreases by more than a set proportion, start the local cache breakpoint resume transmission mechanism; the resource partition includes high-power charging area, high-speed dumping area and balanced area; when the number of devices in any partition exceeds the capacity, use predictive migration: calculate the remaining processing time of the device, temporarily store the waiting device in the transition queue, and automatically migrate after the partition is released; if it is a special task device, temporarily expand the target partition, and restore the partition configuration after the task is completed.

9. The integrated charging and data dump intelligent device management system according to claim 1, characterized in that: Three-dimensional data construction device full file when the collection device is returned: state data, working condition data and associated data; using association rule algorithm to mine user equipment association rules and equipment task association rules; remove equipment models that do not adapt to long-time tasks, limit low-skilled users to use high-failure-rate equipment; when the increase of charging time and the decrease of battery health exceed the linkage threshold, generate predictive maintenance work orders.

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