Intelligent equipment management system integrating charging and data dump

Abstract

The invention discloses an intelligent equipment management system integrating charging and data dump, and relates to the technical field of equipment management, and the system comprises an identity authentication module which is used for carrying out the dual authentication of a user identity through a biological feature and an authority token, extracting the three-dimensional feature of a task from a production system, and storing the extracted three-dimensional feature; calculating the adaptation degree of the task and the equipment through a feature matching algorithm to generate a candidate equipment pool; the working condition sensing module is used for generating working condition fingerprints, and comparing historical fingerprints through a dynamic time warping algorithm to judge an abnormal level; the charging transmission and storage module is used for constructing an energy data balance model according to the priority score and the task characteristics, dynamically adjusting the charging power and the dump bandwidth, and scheduling equipment through a predictive migration mechanism; and the resource management module is used for constructing a full file of the equipment, dynamically adjusting an equipment matching rule and a user permission based on an association rule algorithm, and generating a predictive maintenance work order, so that the use safety, the matching accuracy and the maintenance efficiency of the equipment are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of device management, in particular to an intelligent device management system integrating charging and data dumping. BACKGROUND

[0002] In the current device management system, the common solution mainly relies on a single identity authentication mechanism and a static resource allocation strategy. For example, some systems use passwords or IC cards for user identity verification and manage devices through fixed charging and data dumping processes. These systems usually perform simple priority sorting according to the power of the device or the urgency of the task to achieve basic charging and transmission functions.

[0003] However, the existing technology has obvious limitations. First, the single identity authentication method is not secure enough and is vulnerable to forgery or theft, and it cannot dynamically assess the user's operational ability, resulting in a high risk of device misuse. Second, the static resource allocation strategy cannot adapt to the dynamic changes in task requirements, which can easily lead to resource waste or task delays. For example, when the task urgency changes suddenly or the device is abnormal, the system has difficulty in adjusting the charging and transmission strategy in a timely manner, affecting overall efficiency. In addition, the existing system lacks the ability to monitor and warn the health status of the device. Most systems can only reflect device abnormalities through threshold alarms, lacking early identification and predictive maintenance mechanisms for sub-healthy devices. This results in device failures often being addressed only after they occur, increasing operational costs and potentially affecting the execution of critical tasks. SUMMARY

[0004] (I) Technical problems solved To address the shortcomings of the prior art, the present application provides an intelligent device management system integrating charging and data dumping. Through dual authentication of biometric features and permission tokens, dynamic resource allocation strategies, and multi-dimensional monitoring of device health status, the present application solves the problems of insufficient security of identity authentication, low efficiency of resource allocation, and weak device abnormality warning capability in the prior art.

[0005] (II) Technical solutions To achieve the above purpose, the present application is implemented by the following technical solutions: an intelligent device management system integrating charging and data dumping, comprising: An identity authentication module for dual authenticating user identity through biometric features and permission tokens, and constructing a device proficiency matrix based on user historical operation data, extracting three-dimensional features of tasks from the production system, calculating the adaptability of tasks and devices through a feature matching algorithm to generate a candidate device pool; The working condition perception module is used for collecting current-voltage curves of the equipment in three stages of static, initial charging and initial dumping to generate a working condition fingerprint, determining an abnormal level by comparing historical fingerprints through a dynamic time warping algorithm, calculating a demand priority score based on working condition factors, historical factors and task factors by using a weighted summation method, and executing a charging and transmission strategy in stages; The charging and transmission and storage module is used for constructing an energy data balance model according to the priority score and task characteristics, dynamically adjusting charging power and dumping bandwidth, dividing resources into fast charging areas, high-speed dumping areas and balanced areas, and scheduling the equipment through a predictive migration mechanism. The resource management module is used for collecting the state, working condition and associated data of the equipment when it is returned to construct a complete file of the equipment, dynamically adjusting equipment matching rules and user permissions based on an associated rule algorithm, and generating a predictive maintenance work order.

[0006] Further, a dual authentication mechanism using biological features including face and fingerprint is adopted, an infrared camera is used for face collection, and a live detection technology is used for fingerprint collection; a device proficiency matrix is constructed based on historical operation records of a user for a predetermined number of times, and is divided into three levels of high, medium and low proficiency according to a fault triggering rate; operation instructions of a target device are automatically pushed to a user with low proficiency, and auxiliary operation and maintenance permissions are associated.

[0007] Further, the three-dimensional characteristics of a task include timeliness, function and environment: the timeliness includes urgency and estimated duration, the function includes data demand and endurance demand, and the environment includes use scenario and network condition; a task and device adaptation degree is calculated through a feature matching algorithm, and a candidate device pool is generated by excluding devices with an adaptation degree lower than a preset adaptation degree threshold, in maintenance or with incomplete charging and transmission.

[0008] Further, resource load characteristics are monitored in real time, remaining processing capacity and resource release time are calculated, resource reservation strategies are dynamically adjusted: high-urgency tasks preferentially occupy high-priority resources and extend the reservation duration, and ordinary tasks schedule resources to be released; reserved resources are marked with task and device association identifiers, and are automatically released if not used within a timeout period.

[0009] Further, current-voltage curves of the equipment in three stages of static, initial charging and initial dumping are collected to generate a working condition fingerprint; a fingerprint similarity between real-time fingerprints and historical fingerprints in a fingerprint library is compared through a dynamic time warping algorithm, and an abnormal level is determined according to a fingerprint similarity range.

[0010] Further, a multi-factor model for demand judgment is constructed, and working condition factors, historical factors and task factors are input, wherein 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 battery SOH, and the task factors include task urgency and data volume demand; a weighted summation method is used to calculate the priority score, when the score is greater than or equal to M1, the priority is level one, and urgent charging and transmission is needed, when M2 is less than the score and the score is less than M1, the priority is level two, and normal charging and transmission is needed, and when the score is less than M2, the priority is level three, and only basic maintenance is needed.

[0011] Further, if the fingerprint similarity is less than 70% and the SOH is less than 75%, it is judged that the equipment 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 judged that the equipment is healthy, and full-power charging and transmission is allowed, and based on the multi-factor parameters, an abnormal probability is calculated, and when the abnormal probability is greater than or equal to a preset probability threshold, it is high risk, and a 5-second observation period is started. During the observation period, working condition data is continuously collected, if the current fluctuation is less than or equal to 5% and the voltage is stable, it is judged that it is a transient disturbance, and normal execution is performed, if the current drops by more than 20%, it is judged that it is a real abnormality, and the charging and transmission is automatically switched to a safe mode, and the charging and transmission is suspended, and a fault prompt is pushed to the operation and maintenance terminal.

[0012] Further, the input of the energy data balance model includes the remaining time of the task, the current power, the amount of data to be transmitted, the upper limit of the charging power and the upper limit of the dump bandwidth, and the output is the charging power and the dump bandwidth, which satisfy: charging power x remaining time + current power ≥ endurance demand, and dump bandwidth x remaining time ≥ data to be transmitted; the charging power upper limit and the dump bandwidth upper limit are enabled for the equipment of level one priority, and the charging power of the equipment of level three priority is reduced to 50% of the upper limit and the dump function is turned off.

[0013] Further, the power and the dump bandwidth are dynamically adjusted according to the periodic monitoring of the power and the data dump progress, when the network bandwidth decreases by more than a set proportion, a local cache breakpoint resume transmission mechanism is started, and 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, a predictive migration is used: the remaining processing time of the device is calculated, the waiting device is temporarily stored in a transition queue, and after the partition is released, the device is automatically migrated, and if it is a special task device, the target partition is temporarily expanded, and after the task is completed, the partition configuration is restored.

[0014] Further, three-dimensional data collected when the device is returned is used to construct a complete file of the device: state data, working condition data and associated data; an association rule algorithm is used to mine user device association rules and device task association rules; devices of inappropriate long-time tasks are removed, and low-skilled users are restricted from using high-failure-rate devices; when the increase amplitude of charging time and the decrease amplitude of battery health exceed a linkage threshold, a predictive maintenance work order is generated.

[0015] (Three) beneficial effects The application provides a smart device management system integrating charging and data dumping, which has the following beneficial effects: (1) Through dual authentication of biological characteristics and permission tokens, the safety of user identity is ensured, unauthorized access is prevented, a device proficiency matrix is constructed based on user historical operation data, user operation ability is dynamically evaluated, operation instructions are automatically pushed to low-proficiency users and associated auxiliary permissions are provided to reduce the risk of operation errors, a three-dimensional feature of the task and a device adaptation degree matching algorithm are combined to generate an optimal candidate device pool, task execution efficiency and device utilization are improved, and system security, operation standardization and resource allocation accuracy are enhanced.

[0016] (2) By collecting the current-voltage curve of the device in the static, initial charging and initial dumping stages to generate a working condition fingerprint, historical data is compared based on a dynamic time warping algorithm to accurately determine the device abnormality level, a priority score is calculated based on a multi-factor model, and a charging and dumping strategy is dynamically executed: healthy devices run at full power, sub-healthy devices are protected at limited power, and real-time monitoring of current and voltage fluctuations is performed to identify transient disturbances or real faults, automatically switching to a safe mode, significantly improving device safety, charging and dumping efficiency and abnormal response speed.

[0017] (3) By constructing an energy data balance model, the charging power and dumping bandwidth allocation is dynamically optimized to ensure that the task endurance requirement and data dumping efficiency are met simultaneously, based on a priority grading strategy, first-level task devices run at full power, and third-level task devices run at reduced power to extend the life, real-time monitoring of power and dumping progress is performed, the resource allocation ratio is dynamically adjusted, and a breakpoint resume mechanism is enabled during network fluctuations, by dividing fast charging areas, high-speed dumping areas and balanced areas, combined with a predictive migration algorithm, resources are efficiently scheduled to avoid congestion, significantly improving charging and dumping efficiency, resource utilization and task completion reliability.

[0018] (4) By collecting the state, working condition and associated data of the device when it is returned, a complete device file is constructed, the device matching rules and user permissions are dynamically optimized based on an association rule algorithm, by analyzing the correlation between user operation habits and device performance, low-proficiency users are restricted from using high-failure-rate devices, and devices that are not suitable for the task are removed, when charging duration abnormalities or battery health decline are detected, a predictive maintenance work order is automatically generated to warn of device aging risks in advance, significantly improving device use safety, matching accuracy and maintenance efficiency, extending the device life cycle and reducing operation and maintenance costs. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 is a process schematic diagram of the smart device management system integrating charging and data dumping of the application; Figure 2 is a process schematic diagram of the device management system user pickup of the application. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0021] Please refer to Figure 1 and Figure 2 The present application provides an intelligent device management system integrating charging and data dumping, comprising an identity authentication module, a working condition sensing module, a charging and storage module, and a resource management module, wherein: The identity authentication module is configured to authenticate the user identity through biological features and permission tokens, and to construct a device proficiency matrix according to historical operation data of the user, extract three-dimensional features of tasks from a production system, calculate the adaptability of tasks and devices through a feature matching algorithm to generate a candidate device pool; Specifically, biological features and permission tokens are used for dual authentication, and after authentication, the user device operation archives are retrieved. The biological features include face and fingerprint. Infrared camera is used for face collection, which supports against light and weak light environment. Capacitive type living body detection is used for fingerprint, which prevents forgery. Based on the historical operation data of the user within a predetermined number of times, such as the last 10 device use records, a user device proficiency matrix is constructed. The operation time of the user on different types of devices is counted, such as the average time of user A operating a camera flashlight is 2 minutes, and the time of operating a recording pen is 1 minute. The fault triggering rate is counted, such as the disconnection rate of user B using a certain type of recorder is 5%. The proficiency is divided into three levels of high, medium and low. When the fault triggering rate is ≤2%, the proficiency is high. When 2% < fault triggering rate ≤5%, the proficiency is medium. When the fault triggering rate > 5%, the proficiency is low. If the user's proficiency for the target device is low, the operation guide of the device is automatically pushed, such as the interface needs to be kept stable during dumping, and the auxiliary operation and maintenance permission is associated, such as automatically notifying the administrator for remote guidance when the operation is abnormal. Three-dimensional features of tasks are obtained from the production system, including time limit feature, function feature and environment feature. The time limit feature includes urgency and estimated time length. The urgency is, for example, special level for fault repair, and ordinary for daily inspection. The estimated time length T ≤ 2h is short time, 2h < T ≤ 6h is medium time, and T > 6h is long time. The function feature includes data demand and endurance demand. The data demand is, for example, storing ≥50GB for high-definition video, and transmitting log data in encrypted form. The endurance demand is, for example, the device needs to be full charged for endurance ≥8h for long time task. The environment feature includes use scene and network condition. The use scene is, for example, the device needs to be explosion-proof for explosion-proof level ≥T3. The network condition is, for example, the device needs to support offline caching for field work. Build a capability profile for each device: integrate hardware parameters, historical performance, and task type adaptation, such as battery capacity, interface protocol, explosion-proof level, historical performance such as the last 5 times of charging efficiency, fault frequency, and task type adaptation such as camera flashlight A for long video capture, calculate the adaptation degree of the task and the device through a feature matching algorithm, the feature matching algorithm uses cosine similarity algorithm or Euclidean distance algorithm, full score 100 points, ≥80 points for adaptation, exclude devices with adaptation degree < preset adaptation threshold (such as 80%), in repair and charging transmission incomplete state, get the candidate device pool; Real-time monitoring of charging pile, data interface, local cache and other resource load time characteristics: calculate the current resource remaining processing capacity, such as data interface current occupancy rate 60%, remaining bandwidth ≥500Mbps, and future 1 hour release time, such as device B will complete charging transmission in 20 minutes; Dynamic adjustment of reservation strategy combined with task urgency, for special task, reserve resource for 30 minutes, preferentially occupy idle and high priority resources, such as high-speed data interface; for ordinary task, reserve resource for 10 minutes, if high priority resource is occupied, dispatch the resource to be released, such as interface released in 20 minutes, and remind user 15 minutes in advance that resource will be available; mark the associated identifier of the reserved resource, such as task ID, user ID and device ID, to prevent other users from occupying, and automatically release and push resource release notification if not used within the time limit, and update the candidate device pool.

[0022] Through biometric feature and permission token dual authentication, ensure user identity security, prevent unauthorized access, build device proficiency matrix based on user historical operation data, dynamically evaluate user operation ability, automatically push operation guide and associate auxiliary permission for low proficiency users, reduce operation failure risk, combine task three-dimensional feature and device adaptation degree matching algorithm to generate optimal candidate device pool, improve task execution efficiency and device utilization, enhance system security, operation specification and resource allocation accuracy.

[0023] Working condition perception module, for collecting current and voltage curves of device in static, initial charging and initial dumping 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 transmission strategy in stages; Specifically, after the user inserts the device into the position, the interface triggers the dynamic feature collection process: synchronously collect the current and voltage time curves of the device in static (not charging transmission), initial charging and initial dumping three stages, such as charging initial 10 seconds, current from 0.3A to 1.2A, voltage stable at 4.2V, forming device working condition fingerprint; Read the device SN code, retrieve the device's historical working condition fingerprint library from the database, store the working condition curve of the last 20 accesses, compare the real-time device working condition fingerprint with the historical device working condition fingerprint through dynamic time warping algorithm (DTW), and determine the abnormal level according to the fingerprint similarity range. For example, if the fingerprint similarity is ≥90%, it is normal working condition, if 70%≤ fingerprint similarity <90%, it is slight abnormality, and if fingerprint similarity <70%, it is serious abnormality. For special equipment such as explosion-proof recorders, enable the working condition fingerprint template library, pre-store the fingerprint templates of such equipment under different health states, such as when the battery attenuation is 30%, the charging current peak value decreases to 1A, and further improve the judgment accuracy. A multi-factor model is constructed to determine the demand, 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 the task urgency and data volume demand. The demand priority score is calculated using the weighted summation method: demand priority score = (working condition factor x working condition weight) + (historical factor x historical weight) + (task factor x task weight). When the score ≥M1, the priority is level one, urgent charging and transmission, when M2≤score <M1, the priority is level two, normal charging and transmission, and when the score <M2, the priority is level three, only basic maintenance. The values of working condition factors, historical factors, and task factors are the average values of the parameters contained, and each parameter is normalized to [0, 100]. The corresponding weights of working condition factors, historical factors, and task factors can be configured, with default working condition weight = 0.4, historical weight = 0.3, and task weight = 0.3, with a value range of 0~1. M1 and M2 are configurable thresholds with default values of M1=80 and M2=60, which can be adjusted according to specific scenarios. If the fingerprint similarity <70% and SOH <75%, it is determined that the device is sub-healthy, the charging power is limited, the charging current ≤0.8A, and the dump rate ≤USB2.0. If the fingerprint similarity ≥90% and SOH ≥85%, it is determined that the device is healthy and can be fully powered. The abnormal probability is calculated based on multi-factor parameters: abnormal probability = (1-fingerprint similarity) x 0.5 + (historical abnormal times / 3) x 0.3 + (1-SOH / 100) x 0.2. When the abnormal probability ≥a preset probability threshold (such as 30), it is high risk, and a 5-second observation period is started. During the observation period, continuously collect working condition data: if the current fluctuation ≤5% and the voltage is stable, it is determined as transient interference and executed normally. If the current drops by >20%, such as from 1.2A to 0.9A, it is determined as a real abnormality, automatically switches to safety mode, suspends charging and transmission, and pushes the fault prompt to the operation and maintenance terminal.

[0024] 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.

[0025] 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. 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. 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. 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. 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. 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. 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.

[0026] 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.

[0027] 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.

[0028] 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. 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. 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. 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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. An intelligent device management system integrating charging and data transfer, characterized in that: include: 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. 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. 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. 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.

2. The intelligent device management system integrating charging and data transfer according to claim 1, characterized in that: 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 proficiency is divided into three levels: high, medium, and low, according to the fault trigger rate. Automatically push operation instructions for the target device to users with low proficiency, and associate them with auxiliary operation and maintenance permissions.

3. The intelligent device management system integrating charging and data transfer according to claim 2, characterized in that: The three-dimensional features of the task include timeliness, 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.

4. The intelligent device management system integrating charging and data transfer according to claim 1, characterized in that: Real-time monitoring of resource load characteristics, calculation of remaining processing capacity and resource release time; dynamic adjustment of resource reservation strategy: high-urgency tasks prioritize the use of high-priority resources and extend the reservation time, while ordinary tasks schedule resources that are about to be released; marking the task and device association identifiers for reserved resources, and automatically releasing them if they are not claimed within the time limit.

5. The intelligent device management system integrating charging and data transfer according to claim 1, characterized in that: The device generates operating condition fingerprints by collecting 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.

6. The intelligent device management system integrating charging and data transfer according to claim 5, characterized in that: Build a multi-factor model for demand determination, and input 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 data transfer anomalies in the last 3 times and the battery SOH, and the task factors include task urgency and data volume requirements; use the weighted summation method to calculate the priority score. When the score ≥ M1, the priority is level one and urgent charging and data transfer are required; when M2 ≤ score < M1, the priority is level two and normal charging and data transfer are performed; when the score < M2, the priority is level three and only basic maintenance is carried out.

7. The intelligent device management system integrating charging and data dumping according to claim 6, characterized in that: If the fingerprint similarity < 70% and the SOH < 75%, it is determined that the device is in a sub-healthy state and the charging and data transfer power is restricted; if the fingerprint similarity ≥ 90% and the SOH ≥ 85%, it is determined that the device is healthy and full-power charging and data transfer can be performed; calculate the anomaly probability based on multi-factor parameters. When the anomaly probability ≥ the preset probability threshold, it is a high risk and a 5-second observation period is started; Continuously collect operating condition data during the observation period: if the current fluctuation ≤ 5% and the voltage is stable, it is determined as an instantaneous interference and executed normally; if the current suddenly drops > 20%, it is determined as a real anomaly, automatically switch to the safe mode, suspend charging and data transfer, and push a fault prompt to the operation and maintenance terminal.

8. The intelligent device management system integrating charging and data dumping according to claim 1, characterized in that: The input of the energy data balance model includes 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 data transfer bandwidth; the output is the charging power and the data transfer bandwidth, satisfying: charging power × remaining time + current power ≥ the endurance requirement, and data transfer bandwidth × remaining time ≥ the data volume to be transmitted; devices with level one priority enable the upper limit of the charging power and the upper limit of the data transfer bandwidth, and the charging power of devices with level three priority is reduced to 50% of the upper limit, and the data transfer function is turned off.

9. The intelligent device management system integrating charging and data dumping according to claim 8, characterized in that: Periodically monitor the power and data transfer progress, and dynamically adjust the allocation ratio of the charging power and the data transfer bandwidth; when the network bandwidth drops by more than the set ratio, start the local cache breakpoint resumption mechanism; the resource partition includes a high-power charging area, a high-speed data transfer area, and a 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 devices in the transition queue, and automatically migrate them 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.

10. The intelligent device management system integrating charging and data dumping according to claim 1, characterized in that: Collect the three-dimensional data when the device is returned to construct a complete device file: status data, operating condition data, and associated data; use the association rule algorithm to mine the user device association rules and the device task association rules; remove the device models that are not suitable for long-term tasks, and restrict low-proficiency users 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联动 threshold, generate a predictive maintenance work order.

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