Office equipment lease management method and system based on cloud platform

Abstract

The invention discloses an office equipment lease management method and system based on a cloud platform, and relates to the technical field of lease management, and the method comprises the following steps: carrying out the dynamic scanning of the frequency band occupation condition of equipment tags according to the real-time signal intensity distribution of each equipment tag after office equipment is connected to a cloud recovery recognition network, and obtaining a frequency band occupation condition of each equipment tag; an independent identification channel is distributed to each equipment label through a self-adaptive frequency spectrum segmentation method; and based on the established independent identification channels, phase offset compensation processing is carried out on edge signals of adjacent identification channels, so that each equipment label signal forms a peak shifting transmission sequence on a time axis. According to the invention, independent identification and stable transmission of multi-device signals are realized through adaptive frequency spectrum segmentation and phase peak shifting transmission, and signal interference and mismatching are avoided; and through dynamic regulation and control ring linkage frequency band distribution, phase compensation, time sequence fingerprints and hierarchical scheduling, adaptive balance and efficient and safe operation of a cloud identification process are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of lease management, and particularly relates to an office equipment lease management method and system based on a cloud platform. BACKGROUND

[0002] In the office equipment lease management process based on the cloud platform, the automatic recovery of the identification tag has signal conflict hidden dangers. Specifically, when the equipment accesses the cloud recovery identification network, the identification signal of the tag will have frequency band overlap or phase interference between multiple devices, resulting in a mismatch when the cloud system parses the uniqueness of the tag. This mismatch will cause the equipment still in the normal lease period to be misjudged as expired equipment, so that it is automatically included in the recovery queue by the system. Such misjudgment not only may cause on-site disputes of the equipment being taken back in advance, but also may cause multiple inconsistencies in information such as lease account period, deposit settlement, and inventory registration. More seriously, the misrecycling behavior may cause the key office terminal of an enterprise to be forced offline without completing data migration, thereby causing business interruption and loss of customer information.

[0003] The above information disclosed in the background section is only intended to enhance the understanding of the background of the present disclosure, and thus it can include information that does not constitute the prior art known to those of ordinary skill in the art. SUMMARY

[0004] The purpose of the present application is to provide an office equipment lease management method and system based on a cloud platform to solve the problems in the background.

[0005] In order to achieve the above-mentioned purpose, the present application provides the following technical scheme: an office equipment lease management method based on a cloud platform, comprising the following steps: Step one, after the office equipment accesses the cloud recovery identification network, the frequency band occupation of the equipment tag is dynamically scanned according to the real-time signal strength distribution of each equipment tag, and each equipment tag is allocated an independent identification channel through an adaptive frequency spectrum segmentation method, so that the signals of each equipment tag in the initial identification stage remain separated; Step two, based on the established independent identification channel, the edge signal of the adjacent identification channel is compensated for phase shift, so that the signals of each equipment tag form a staggered transmission sequence on the time axis, thereby stabilizing the transmission interval between the signals while maintaining the integrity of the overall frequency spectrum; Step three, relying on the formed staggered transmission sequence, the corresponding tag identity time sequence fingerprint is generated in combination with the time delay characteristics of each equipment tag signal, and a unique identification track is constructed for each equipment according to the tag identity time sequence fingerprint, so as to ensure the uniqueness and accurate correspondence of each equipment tag in the cloud parsing process; Step four, based on the generated unique identification track, the cloud issued device recycling instruction to implement hierarchical scheduling control, so that each identification track responds in turn under the control of signal priority, to eliminate the signal interference risk brought by concurrent identification trigger, and ensure the accurate execution of the cloud to the device recycling instruction; Step five, according to the execution result of hierarchical scheduling, a dynamic control loop is constructed to link the frequency band allocation mechanism, phase offset compensation strategy, label identity time sequence fingerprint and hierarchical scheduling process in real time, so that the cloud recycling identification signal keeps adaptive balance and continues stable operation in dynamic environment.

[0006] Preferably, the step of allocating an independent identification channel to each device label by the adaptive spectrum segmentation method comprises: When the office equipment accesses the cloud recycling identification network for the first time, the cloud side starts the signal monitoring process, collects the signal energy intensity, electromagnetic wave peak position and signal fluctuation trend of each office equipment label in different time slices, and constructs a frequency spectrum intensity map reflecting the signal distribution of each label; After completing the spectrum intensity analysis, the cloud side divides the signal overlap area and signal sparse area in the spectrum according to the signal intensity distribution characteristics of each device label, and sets a special frequency interval for each device label, and sets an isolation band between different labels to maintain the signal spacing; The cloud side dynamically adjusts the channel boundary according to the signal intensity change curve in each identification channel, so that the channel width and center frequency move with the change of label signal energy, and the minimum frequency difference between different identification channels is maintained; After the boundary position adjustment of all identification channels is completed, the cloud side binds the unique identification information of each office equipment label with its corresponding identification channel frequency range, establishes a signal identification mapping table and periodically updates the identification channel state, to ensure that the device label signal remains separated in the identification period.

[0007] Preferably, in the process of dynamically adjusting the boundary of the identification channel, when the cloud side detects that the signal intensity in the identification channel is enhanced in a certain frequency band, the boundary of the identification channel is expanded to avoid signal overflow to the adjacent channel, and when the signal energy is weakened, the identification channel boundary is contracted and the spare frequency interval is allocated to the adjacent active channel, to ensure that the spectrum resources are in an optimal state and maintain the signal independent transmission of each identification channel.

[0008] Preferably, the step of performing phase offset compensation processing on the edge signal of the adjacent identification channel comprises: On the basis of the established independent identification channel, the cloud end side continuously monitors the edge signal of each identification channel, collects the starting time point of the signal in each identification channel, the time delay of the signal peak value, the signal energy attenuation curve and the signal phase change trend, to form a time domain mapping diagram of adjacent identification channels; After obtaining the time delay distribution and phase difference characteristics of adjacent identification channels, the cloud end side performs phase offset compensation according to the time domain mapping diagram, so that the signals of adjacent identification channels form staggered transmission relationship on the time axis, the starting time of adjacent signals is delayed or advanced to maintain time sequence balance, and it is ensured that the compensated signals are still within the frequency range of their independent identification channels; After completing the phase offset compensation, the cloud end side unifies and coordinates the time structure of the identification network, periodically scans and slightly adjusts the signal propagation path and delay parameter of all identification channels, to prevent time sequence drift of staggered peak sequence, so as to maintain the stable staggered transmission relationship of signals in each identification channel.

[0009] Preferably, when periodically scanning the signal propagation path and delay parameter of all identification channels, the cloud end side dynamically adjusts the phase offset amplitude of adjacent identification channels according to the time domain mapping diagram of each identification channel, and when detecting that the signal propagation delay exceeds the set threshold, the time interval of adjacent identification channels is automatically recalculated, so that the staggered transmission relationship maintains stable time difference distribution in continuous periods.

[0010] Preferably, the step of constructing a unique identification track for each device according to the tag identity time sequence fingerprint comprises: After forming the staggered transmission sequence, the cloud end side captures the time characteristics of the office equipment tag signal in units of each identification channel, extracts multi-dimensional time parameters such as signal emission time, propagation delay, energy attenuation rate, peak duration and time difference distribution, to obtain the original time characteristic sequence of each device tag; After obtaining the time characteristic sequence, the cloud end side continuously compares the signal arrival time and delay change of the same device tag in different transmission periods, and combines the transmission order of the staggered transmission sequence to form a time delay mapping sequence reflecting the signal propagation law; After forming the time delay mapping sequence, the cloud end side constructs a tag identity time sequence fingerprint according to the sequence, signal emission interval, duration and energy attenuation trend, and maintains the continuity and uniqueness of the time sequence fingerprint through multi-period update; After generating the tag identity time sequence fingerprint, the cloud end side establishes an identification track for each office equipment according to the fingerprint, and associates the identification track with the device rental status, use record and recycling status information, to realize the unique identification and dynamic tracking of the device in the cloud analysis process.

[0011] Preferably, in the process of establishing an identification track for each office equipment according to the label identity time sequence fingerprint, the cloud side compares the office equipment in different geographical locations or use scenarios according to the time delay information and signal propagation characteristics of the identification track, and when the same label identity time sequence fingerprint characteristics are detected, the corresponding identification relationship in the cloud is automatically restored to ensure the continuous identification and unique tracking of the equipment in multiple scene environments.

[0012] Preferably, the step of implementing hierarchical scheduling control on the device recovery instruction issued by the cloud includes: Before preparing to issue the device recovery instruction in the cloud, the cloud side groups and prioritizes the devices to be executed according to the unique identification track of each office equipment, divides the priority levels according to the time distribution information, label identity time sequence fingerprint characteristics, signal strength stability and communication delay parameters in the identification track, and makes the response interval between adjacent levels meet the minimum time sequence difference; After completing the priority level division, the cloud side dynamically schedules the issuance order of the recovery instruction according to the priority level, issues the recovery instruction layer by layer according to the time sequence of the identification track, and sets different response delay parameters for different levels to keep the instruction transmission and the equipment identification time window synchronized; After the devices in each level respond to the recovery instruction in turn, the cloud side executes closed-loop control according to the signal feedback time sequence of the identification track, dynamically adjusts the instruction issuance time of the subsequent level according to the confirmation time of the device response and the update delay of the recovery state, to maintain the continuity and time balance of the hierarchical scheduling process.

[0013] Preferably, the step of constructing a dynamic regulation loop includes: After the hierarchical scheduling process is completed, the cloud side extracts signal distribution information and execution time sequence data according to the recovery instruction response state of each level device, records the signal response time, signal strength change range, frequency band occupation proportion and phase delay change of the identification track when executing the instruction, and compares the current signal propagation time with the time difference of adjacent identification channels to extract the phase overlap factor; After completing the data collection, the cloud side adjusts the frequency band allocation mechanism in real time according to the feedback information, and corrects the phase delay trend of the edge signal by combining the phase offset compensation strategy to form a synergistic regulation of frequency band allocation and phase compensation; After the frequency band allocation and phase compensation adjustment is completed, the cloud side maps and corrects the adjustment results with the label identity time sequence fingerprint to make the label identity time sequence fingerprint consistent with the unique identification track; After the update is completed, the cloud side re-determines the time interval and level response order of the recovery instruction according to the new identification track and signal priority, and feeds back the signal characteristics of the execution result to the dynamic regulation loop to realize the adaptive balance and stable operation of the cloud recovery identification signal.

[0014] The cloud platform-based office equipment leasing management system comprises an adaptive spectrum allocation module, a phase peak-shifting regulation module, a label time sequence fingerprint generation module, a hierarchical scheduling control module and a dynamic linkage regulation module. The adaptive spectrum allocation module, after the office equipment accesses the cloud recycling identification network, dynamically scans the frequency band occupation of the equipment label according to the real-time signal strength distribution of each equipment label, and allocates an independent identification channel to each equipment label through an adaptive spectrum segmentation method. The phase peak-shifting regulation module, based on the established independent identification channel, performs phase offset compensation processing on the edge signals of adjacent identification channels, so that the signals of each equipment label form a peak-shifting transmission sequence on the time axis. The label time sequence fingerprint generation module generates the corresponding label identity time sequence fingerprint in combination with the time delay characteristics of each equipment label signal based on the formed peak-shifting transmission sequence, and constructs a unique identification track for each equipment according to the label identity time sequence fingerprint. The hierarchical scheduling control module, based on the generated unique identification track, implements hierarchical scheduling control on the equipment recycling instructions issued by the cloud, so that each identification track responds in turn under the control of signal priority. The dynamic linkage regulation module constructs a dynamic regulation loop according to the execution result of hierarchical scheduling, and performs real-time linkage control on the frequency band allocation mechanism, the phase offset compensation strategy, the label identity time sequence fingerprint and the hierarchical scheduling process.

[0015] In the above technical solution, the technical effects and advantages provided by the present application are as follows: By using adaptive spectrum segmentation and phase peak-shifting transmission in the equipment access stage, the present application forms an independent and continuous signal channel structure for multiple office equipment in the cloud identification process, thereby eliminating the problems of frequency band overlap and phase interference from the source. This method can realize the orderly distribution and stable transmission of multiple equipment signals in a signal-intensive recycling identification network environment, so that the cloud can maintain the integrity and independence of the signal when identifying the label identity, thereby effectively avoiding the analysis mismatch between equipment labels, ensuring the accuracy of equipment state identification and the consistency of cloud identification results.

[0016] The present application forms a closed-loop linkage of frequency band allocation, phase compensation, time sequence fingerprint and hierarchical scheduling process by constructing a dynamic regulation loop, so that the cloud can automatically adjust the identification strategy according to the equipment running state in different leasing stages. This dynamic regulation method makes the signal transmission maintain adaptive balance under the conditions of multiple equipment concurrency and environmental fluctuations, realizing the continuous and stable operation of the cloud identification process. By continuously updating the identification track of each equipment, the equipment recycling instruction can be executed in sequence, thereby realizing the efficient operation and safe regulation of the cloud office equipment management. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed in the embodiments will be briefly introduced as follows. Obviously, the accompanying drawings in the following description only represent some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained based on these drawings.

[0018] Figure 1 The method flowchart of the office equipment rental management method based on the cloud platform.

[0019] Figure 2 The module schematic diagram of the office equipment rental management system based on the cloud platform. DETAILED DESCRIPTION

[0020] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations may, however, be implemented in many different forms and should not be construed as limited to the examples set forth herein; rather, these example implementations are provided so that this disclosure will be thorough and complete, and will fully convey the scope of example implementations to those skilled in the art.

[0021] The present application provides a cloud platform-based office equipment rental management method as shown in Figure 1 The cloud platform-based office equipment rental management method includes the following steps: Step one, after the office equipment accesses the cloud recycling identification network, the frequency band occupation of the equipment label is dynamically scanned according to the real-time signal strength distribution of each equipment label, and each equipment label is allocated an independent identification channel through an adaptive frequency spectrum segmentation method, so that the signals of each equipment label in the initial identification stage remain separated. The adaptive frequency spectrum segmentation method is used to allocate an independent identification channel to each equipment label, and the specific steps are as follows: When the office equipment accesses the cloud recycling identification network for the first time, the cloud end immediately starts a signal monitoring process to collect the signal emission of all active office equipment labels in real time. Specifically, the cloud end continuously records the signal energy intensity, electromagnetic wave peak position and signal fluctuation trend of each equipment label in different time slices, and normalizes these data in a complete frequency range to construct a frequency spectrum intensity map that can accurately reflect the signal distribution of each label. In this process, the cloud end identifies the area with high signal intensity and long duration as the main signal active area according to the collected signal power curve; at the same time, it detects the area with low signal energy or high noise level to identify the frequency band that may have interference. By continuously superimposing these real-time data and forming a signal energy space distribution matrix, the cloud end can accurately depict the occupation state and mutual relationship of all equipment label signals in the frequency domain in the current network.

[0022] After completing the spectrum intensity analysis, the cloud end side divides the signal overlapping area and the signal sparse area in the spectrum according to the signal intensity distribution characteristics of each device tag in different frequency ranges. In this process, the cloud end side marks the frequency band with high signal energy as a potential interference area, and marks the frequency band with relatively stable signal energy and small change as an available area. Subsequently, the cloud end side allocates a dedicated frequency interval for each device tag according to the number of device tags, their respective signal intensities, signal bandwidth requirements, and distance relationships between them. In the process of interval allocation, the cloud end side sets an isolation band between different tags to maintain a fixed frequency distance between the signals of each device tag to prevent signal sidelobe expansion from causing mutual crosstalk. For device tags with strong signal energy and wide occupied frequency band, the cloud end side allocates a wider frequency range to accommodate the fluctuation range of the signal energy peak; and for device tags with weak signal energy and narrow power distribution, a narrower frequency interval is allocated to improve spectrum utilization. Through this refined frequency allocation method, multiple independent signal identification channels can be established at the physical layer, so that the signal of each office equipment tag runs in an independent frequency band, thereby effectively reducing the possibility of signal overlap and interference.

[0023] After forming the preliminary frequency allocation, the cloud end side continues to monitor each identification channel to which the device tag belongs. Since the office equipment may change position, fluctuate in power, or reflect signals during use, the cloud end side continuously collects the real-time signal intensity change curve in each identification channel and calculates the time delay difference of the signal peak in each channel. According to these change trends, the cloud end side automatically adjusts the channel boundaries so that the width and center frequency of the identification channel can dynamically move with the energy center of the tag signal. For example, when it is detected that the signal intensity in a certain identification channel gradually increases in a certain frequency band, the cloud end side appropriately expands the boundaries of the channel to avoid signal overflow to adjacent channels; and when the signal energy of a certain channel decreases, the cloud end side shrinks its boundaries and allocates the spare frequency resources to the adjacent active channel. At the same time, the cloud end side maintains the interval between different identification channels to be no less than a preset minimum frequency difference according to the previous spectrum occupation information, so as to ensure that each device tag signal can maintain stable independent channel transmission even in the case of high-density device access. This dynamic adjustment process can keep the spectrum resources in an optimal state and prevent identification conflicts caused by signal overlap.

[0024] After the boundary position adjustment of all identification channels is completed, the cloud side uniformly confirms the distribution state of the current identification channel. The cloud side binds the unique identification information of each office equipment tag with the frequency range of the identification channel corresponding thereto, and establishes a one-to-one correspondence relationship in the cloud database to form a complete signal identification mapping table. When a new office equipment accesses the cloud recycling identification network, the cloud side compares the mapping table with the latest spectrum occupation state to determine whether there is a usable frequency interval. If the existing channel can accommodate the new equipment signal, the cloud side automatically allocates an identification channel to the new equipment in the corresponding interval. If the spectrum resource is insufficient, the cloud side re-plans the channel range according to the signal distribution density of the current identification channel, shrinks or reallocates part of the channel, so that the new office equipment tag signal can obtain an independent channel. In the case of a large number of office equipment or frequent access, the cloud side synchronously updates the state of all identification channels at fixed time intervals to ensure that the spectrum occupation map and the identification channel mapping table are consistent, thereby continuously maintaining the separation state of each equipment tag signal in the entire identification period. Through the real-time updating and maintenance mechanism, the signal of each office equipment tag in the cloud recycling identification network can be accurately identified in a stable and interference-free condition.

[0025] Through the above implementation steps, the hierarchical scanning, dynamic division, continuous adjustment and mapping confirmation of the office equipment tag signal in the cloud recycling identification network access stage can be realized, so that each equipment tag signal is always in an independent transmission channel in the entire identification process, thereby ensuring the accuracy of cloud recycling identification and the stability of the identification process.

[0026] Step two, based on the established independent identification channel, the edge signal of the adjacent identification channel is compensated for phase shift, so that each equipment tag signal forms a staggered transmission sequence on the time axis, thereby maintaining the integrity of the overall spectrum while stabilizing the transmission interval between signals; Based on the independent identification channel, the edge signal of the adjacent identification channel is compensated for phase shift, and the specific steps are as follows: On the basis of the established independent identification channel, the cloud end side continuously monitors the edge signal of each identification channel to obtain the signal propagation characteristics between adjacent identification channels. Specifically, the cloud end side respectively collects the starting time point of the signal in each identification channel, the time delay of the signal peak value, the signal energy attenuation curve, and the signal phase change trend. Through the analysis of these characteristic data, the cloud end side can accurately judge the signal overlapping area and time intersection interval between adjacent identification channels. For example, when the signal energy peak values of two adjacent identification channels occur close in time axis, it means that the signals of the two channels may interfere with each other in the edge frequency range. At this time, the cloud end side records the position and duration of these overlapping intervals to provide a basis for subsequent phase offset compensation. At the same time, in the monitoring process, the cloud end side dynamically captures the reflection, refraction or device end signal delay on the signal propagation path to ensure that the phase compensation operation can be adjusted based on the real propagation environment. Through such a continuous monitoring process, the phase difference relationship between adjacent identification channels in the time dimension can be clearly depicted, and a time domain mapping diagram reflecting the signal propagation law is formed.

[0027] After obtaining the time delay distribution and phase difference characteristics of the edge signals of each identification channel, the cloud end side performs phase offset compensation operation according to these information, so that the signals of adjacent identification channels form an orderly staggered transmission relationship in time axis. In this process, the cloud end side adjusts the signal start time of each subsequent identification channel one by one with the signal propagation time of the previous identification channel as the reference. When detecting that the signal peak time interval of adjacent identification channels is too short, the cloud end side delays the start time of the signal of the subsequent identification channel to expand its time interval with the previous channel; on the contrary, when the signal interval of two identification channels is too long, the cloud end side appropriately advances the start time of the signal of the subsequent identification channel to maintain the time sequence balance of the whole transmission process. In this way, the signals of adjacent identification channels can form a continuous and non-overlapping transmission sequence in time axis. At the same time, the cloud end side monitors the signal energy distribution after performing phase offset compensation to ensure that the compensated signal is still within its original independent identification channel frequency band range and does not exceed the previous frequency spectrum boundary. In the process of phase adjustment, according to the real-time signal fluctuation of the previous identification channel, the phase offset amplitude of the subsequent identification channel is dynamically optimized, so that the signal staggered transmission state between different device tags forms a stable rhythm relationship in time, thereby effectively avoiding the concentrated burst of signals of multiple identification channels in a short time, causing the cloud analysis end to produce load surge or signal analysis confusion.

[0028] After completing the phase offset compensation and forming the staggered transmission sequence, the cloud end side unifies the time structure of the entire identification network to ensure that the signals between different identification channels can maintain a fixed staggered transmission order within a periodic time framework. At this stage, the cloud end side periodically scans the signal propagation path and delay parameters of all identification channels, and continuously updates the transmission start time and phase difference distribution of each identification channel according to the previously formed time domain map. Through this continuous regulation, each identification channel can automatically follow the staggered relationship formed in the previous period in the new time period, ensuring that the time interval between signals is always stable. At the same time, the cloud end side considers the possible signal fluctuations of different office equipment during use, such as changes in device end transmission power, spatial position movement, or network load increase, and makes minor adjustments to the phase offset values of some identification channels to prevent the staggered sequence from drifting in time. When the signal propagation delay of some identification channels exceeds the set threshold, the cloud end side immediately recalculates the time interval between adjacent identification channels to make the transmission rhythm return to the set stable state. Finally, all identification channels form an orderly and continuously circulating transmission sequence on the time axis, and the signals of different office equipment tags are staggered in time and maintain a constant interval, so that each signal can be analyzed and recorded in an independent time window when analyzed by the cloud.

[0029] Through the above steps, time staggered transmission at the signal level can be achieved based on the established independent identification channels, so that the signals between adjacent identification channels maintain a stable phase relationship and time interval during propagation. In this way, even in a high-density environment where multiple office equipment are transmitting data simultaneously, the cloud recovery identification network can ensure that each office equipment tag signal exists independently in the time dimension and does not overlap and conflict, thereby ensuring the accuracy of cloud signal analysis and the consistency of identification results.

[0030] Step three, relying on the formed staggered transmission sequence, combining the time delay characteristics of each device tag signal to generate the corresponding tag identity time sequence fingerprint, and constructing a unique identification track for each device according to the tag identity time sequence fingerprint to ensure the uniqueness and accurate correspondence of each device tag in the cloud analysis process; According to the tag identity time sequence fingerprint, a unique identification track is constructed for each device, and the specific steps are as follows: After forming the staggered transmission sequence, the cloud end side captures the complete time characteristics of the office equipment tag signals in each identification channel. Specifically, the cloud end side extracts the signal emission time, signal propagation delay, energy decay rate, peak duration, and time difference distribution of the office equipment tag in multiple transmission cycles from each identification channel. Due to the fixed starting order and stable propagation interval of signals from different device tags in the staggered transmission sequence, the cloud end side describes the unique propagation characteristics of each office equipment tag on the time axis through these time parameters. At the same time, the cloud end side combines the phase offset compensation results of the previous stage to analyze the interval length and overlap risk of the signals staggered on the time axis, to ensure that the time characteristics generated subsequently can be extracted in independent time windows, thereby avoiding the mutual influence of multi-device signals in the feature formation process. Through this process, the cloud end side can extract the original time characteristic sequence reflecting the propagation law of each office equipment tag in the time domain.

[0031] After obtaining the time characteristic sequence of each office equipment tag, the cloud end side integrates and summarizes the signal delay law according to the time characteristic information. Specifically, the cloud end side continuously compares the signal arrival time and delay variation of the same device tag in different transmission cycles, analyzes the periodic offset, signal jitter amplitude, and propagation stability on the time axis, and extracts the representative time delay pattern. In this process, the cloud end side combines the transmission order in the staggered transmission sequence formed in the previous stage to ensure that the delay characteristics of each device tag are referenced to the time boundaries of adjacent identification channels, so that the delay variation curve of each device tag maintains a fixed interval structure with other tag signals. In this way, the cloud end side can obtain the independent delay distribution characteristics of each device tag on the time axis, thereby combining the time delay characteristics with the staggered transmission characteristics to form a time delay mapping sequence that can reflect the individualized signal propagation law, which lays a unique time reference for generating the tag identity time sequence fingerprint.

[0032] After extracting and organizing the time delay mapping sequence of each device tag, the cloud side relies on these time delay characteristics to construct the label identity time sequence fingerprint of each office equipment tag. Specifically, the cloud side combines the time delay mapping sequence formed in the previous stage with multi-dimensional time parameters such as signal transmission interval, signal duration, energy decay trend, etc., so that each office equipment tag has a set of continuous and non-reproducible time sequence characteristics on the time axis. This set of time sequence characteristics constitutes the label identity time sequence fingerprint of the device tag. The formation process of the label identity time sequence fingerprint maintains a corresponding relationship with the staggered transmission sequence, i.e., within a complete transmission cycle, the cloud side can determine the unique identity of each device tag signal by identifying its time sequence distribution. When the device tag signal repeatedly appears in multiple cycles, the cloud side superimposes new time parameters on the original label identity time sequence fingerprint to form an identity feature with dynamic updating capability, so that each label identity can be continuously tracked in the time dimension. The label identity time sequence fingerprint constructed based on time delay characteristics not only ensures the identity uniqueness between different device tags, but also ensures that each signal maintains a fixed analysis order when recognized by the cloud through matching with the staggered transmission sequence in the previous stage.

[0033] After generating the label identity time sequence fingerprint, the cloud side establishes a complete recognition track for each office equipment based on the label identity time sequence fingerprint. This recognition track records the entire time distribution of the device tag signal from the first access to the cloud recycling recognition network to the signal analysis in multiple cycles. The cloud side draws the propagation track of the device signal on the time axis according to the time delay information in the label identity time sequence fingerprint, and associates it with the lease status, usage record, and cloud recycling status information of the device, thereby forming a time-synchronized updated recognition track in the cloud. When the same device is reactivated in different geographical locations or different use scenarios, the cloud side can quickly determine the unique identity of the device and restore its corresponding relationship in the cloud by comparing its new label identity time sequence fingerprint with the stored recognition track. At the same time, when multiple office equipment are simultaneously active, the cloud side distinguishes their time paths through their respective recognition tracks, so that any two devices will not have identity crossover or analysis confusion during the recognition process. By continuously recording and updating the recognition track of each device, the cloud side can dynamically track all office equipment tags, keeping them uniquely identifiable throughout the lease period.

[0034] Through the execution of the above steps, the label identity time sequence fingerprint with independent time identification can be generated on the basis of the time structure of the staggered transmission sequence combined with the delay characteristics of the office equipment label, and a unique identification track is established for each equipment, so that the equipment label always maintains a clear identity correspondence relationship in the cloud analysis process, thereby effectively avoiding label identification confusion and misjudgment problems, and realizing high-precision identity recognition and long-term stable tracking in the cloud office equipment rental management process.

[0035] Step four, based on the generated unique identification track, the device recycling instruction issued by the cloud is implemented hierarchical scheduling control, so that each identification track responds in turn under the control of signal priority, to eliminate the signal interference risk brought by concurrent identification trigger, and ensure the accurate execution of the device recycling instruction by the cloud; Based on the generated unique identification track, the device recycling instruction issued by the cloud is implemented hierarchical scheduling control, and the specific steps are as follows: Before the cloud prepares to issue the device recycling instruction, the cloud side groups and prioritizes all devices to be executed according to the unique identification track of each office equipment generated in the previous stage. Specifically, the cloud side extracts the time distribution information, label identity time sequence fingerprint characteristics, signal strength stability and communication delay parameters with the cloud in the unique identification track of each office equipment. According to these information, the cloud side comprehensively evaluates the communication activity and signal transmission reliability of each device in the current network environment, and divides it into several priority levels. The division of priority levels follows the time sequence distribution of the identification track, that is, the devices with fast signal response speed and high identification track update frequency are classified into the priority layer; while the devices with long signal response interval or track update time lag are classified into the secondary layer. In this process, the cloud side combines the time delay characteristics of the identification track to ensure that the response interval between adjacent levels meets the minimum time difference, thereby forming a clear hierarchical response structure in the time dimension. In this way, the cloud side can determine the unique response level for each device to be recycled, so that the subsequent recycling instruction can be issued in combination of time sequence and priority order.

[0036] After the priority level division is completed, the cloud side dynamically schedules the sequence of the device recycling instruction according to the determined priority level, so that the instruction transmission process is consistent with the time rhythm of each identification track. Specifically, the cloud side sequentially issues the recycling instruction according to the time sequence of the identification track, and maintains a fixed time interval during the issuance of the instruction at each level. The issuance of the instruction at each level is based on the starting time of the identification track of the device in the level, so as to ensure that the signal transmission time is matched with the identification time window of the device, thereby enabling the device to respond immediately when receiving the instruction without overlapping with other levels. At the same time, the cloud side sets different response delay parameters for different levels according to the signal strength change recorded in each identification track, so that the devices with longer signal propagation path or greater signal attenuation can obtain a longer response time window, and the devices with stable signal path are allocated a shorter response window, so as to realize the balanced distribution of the overall response process. Through this hierarchical scheduling and time synchronization control, it can be ensured that all devices respond in turn according to the sequence of their identification tracks during the entire process of the cloud issuing the recycling instruction, so that each recycling instruction has an independent analysis opportunity on the time axis, thereby effectively preventing signal overlap caused by multiple devices responding at the same time.

[0037] After each level device receives and responds to the recycling instruction in turn, the cloud side performs closed-loop control of the recycling scheduling process according to the feedback of all identification tracks, so as to maintain the continuous balance of hierarchical scheduling. Specifically, the cloud side continuously monitors the signal feedback timing of each identification track after the recycling instruction is executed, including the confirmation time of the device response, the duration of the signal return, and the update delay of the recycling state. According to these real-time feedback information, the cloud side dynamically adjusts the issuance time of the instruction at the subsequent level, so that the entire recycling instruction link remains continuous in time without overlapping. For example, when it is detected that the signal response time of a device at a certain level is prolonged or the feedback delay, the cloud side automatically delays the issuance time of the recycling instruction at the next level to ensure that the signal at the current level is completely analyzed before triggering the instruction at the next level. Conversely, when the device at a certain level completes the recycling confirmation in advance, the cloud side can appropriately advance the recycling instruction issuance at the next level, thereby improving the overall efficiency of the recycling process. At the same time, the cloud side continuously refers to the unique identification track and label identity timing fingerprint generated in the previous stage during the entire scheduling process, so as to ensure that the recycling process always matches the time identity of the device. Through this continuous hierarchical scheduling and closed-loop control, the recycling instruction issuance can be adaptively adjusted, so that the devices at each level respond in turn under the control of the signal priority and form a continuous and non-conflicting execution sequence in time.

[0038] Through the implementation of the above continuous steps, hierarchical scheduling control of the cloud device recycling instruction based on unique identification track can be realized, so that the recycling instruction responses of different office equipment form an orderly sequence in the time dimension, thereby effectively eliminating the signal interference risk brought by concurrent identification triggering. Through hierarchical management and dynamic regulation of the timing characteristics of the identification track, not only the accurate execution of the recycling instruction in the cloud analysis process is ensured, but also the timing separation of multiple devices in the recycling phase is realized, so that the entire cloud recycling identification network can maintain stable operation under high concurrency.

[0039] Step five, according to the execution result of hierarchical scheduling, a dynamic regulation ring is constructed to link the frequency band allocation mechanism, phase offset compensation strategy, label identity timing fingerprint and hierarchical scheduling process in real time, so that the cloud recycling identification signal can maintain adaptive balance and continuous stable operation in a dynamic environment; According to the execution result of hierarchical scheduling, a dynamic regulation ring is constructed, and the specific steps are as follows: After the hierarchical scheduling process is completed, the cloud end side extracts the signal distribution information and execution timing data of this phase according to the recycling instruction response state of each level device, to initialize the feedback basis of the dynamic regulation ring. Specifically, the cloud end side records the signal response time, signal strength change range, frequency band occupation proportion and phase delay change of each identification track when executing the hierarchical scheduling instruction, and compares and analyzes these real-time data with the frequency band allocation mechanism formed in the previous stage. When it is found that the signal strength of some identification channels appears abnormal fluctuation or the frequency band boundary is inconsistent with the initial state, the cloud end side generates corresponding adjustment signals at the input end of the regulation ring to guide the frequency band allocation optimization of the next stage. At the same time, the cloud end side compares the signal propagation time in the current execution period with the time difference of adjacent identification channels, and extracts the potential factors that may cause phase overlap or signal misplacement. This process constitutes the perception basis of the dynamic regulation ring, so that the cloud can master the dynamic changes of the entire identification network in real time.

[0040] It should be noted that: The cloud end side compares the signal propagation time in the current execution period with the time difference of adjacent identification channels, and extracts the potential factors that may cause phase overlap or signal misplacement, and the specific process is as follows: During the execution of the dynamic regulation process, the cloud end side will continuously record the signal propagation time in each identification channel, and compare it with the signal response time of adjacent identification channels cycle by cycle. When it is detected that the signal arrival time interval of two adjacent identification channels is less than the preset safe time difference threshold (for example, 2.5 milliseconds), the cloud end side judges that there is a risk of phase overlap in this interval; if the signal propagation delay difference exceeds the set upper limit (for example, 8 milliseconds), there may be a signal misplacement phenomenon.

[0041] With specific examples: In a certain execution cycle, the signal propagation time of channel A is identified as 13.2 milliseconds, and the signal propagation time of channel B is identified as 15.4 milliseconds. The time difference between the two is 2.2 milliseconds, which is lower than the set safety threshold of 2.5 milliseconds. At this time, the cloud side will delay the signal transmission timing of channel B by 0.4 milliseconds to ensure that the time interval is restored to more than 2.6 milliseconds, thereby avoiding phase superposition of the two signals in the analysis stage. Through this continuous comparison and time adjustment method, the cloud side can dynamically identify and correct potential phase overlap or signal misplacement problems, ensuring that the signal propagation of each identification channel remains stable, separated, and analyzable.

[0042] After completing the collection and analysis of dynamic data, the cloud side adjusts the frequency band allocation mechanism in real time based on these feedback information, so that it can adapt to the signal offset generated in the hierarchical scheduling execution process. Specifically, the cloud side recalculates the frequency band occupancy ratio of each identification channel, and according to the current recycling instruction execution result and signal strength distribution, the boundaries of some identification channels are slightly moved to alleviate the congestion of local signals. At the same time, the cloud side combines the phase offset compensation strategy of the previous stage to comprehensively evaluate the phase delay trend of the edge signal. If it is found that the signals in a certain frequency interval appear phase aggregation phenomenon after hierarchical scheduling, the cloud side adjusts the phase start time of each signal in this interval appropriately, so that it is redistributed in the time structure of staggered transmission sequence. Through this process, the cloud side can realize synchronous regulation in the frequency and time dimensions, so that the frequency band allocation and phase compensation form a synergistic effect, thereby maintaining the balanced transmission state of multi-channel signals in space and time.

[0043] After the adaptive adjustment of frequency band allocation and phase offset compensation is completed, the cloud end side dynamically matches the adjustment results with the label identity time sequence fingerprint to ensure the continuity of the identification and the accuracy of the analysis results. Specifically, the cloud end side re-maps and corrects the label identity time sequence fingerprint of each device according to the newly adjusted frequency band and phase parameters, recombines the time delay data in the original label identity time sequence fingerprint with the new phase delay data, and makes each label identity time sequence fingerprint remain consistent with the unique identification track of the device. On this basis, the cloud end side analyzes the time offset trend of each identification track to determine whether there is identification time sequence drift caused by frequency band adjustment or phase change. When detecting that the time distribution of some identification tracks is abnormally extended or compressed, the cloud end side re-adjusts the corresponding label identity time sequence fingerprint according to the feedback of adjacent tracks to make it consistent with the actual signal response time. In this way, the cloud end side can dynamically adjust the frequency band and the phase while ensuring the integrity of the device identity in the time sequence dimension, thereby preventing the identity mismatch problem caused by identification delay accumulation. At this time, the label identity time sequence fingerprint is not only the unique identification of identity recognition, but also the stable anchor point of the dynamic regulation loop, playing a core role in connecting the relationship between frequency spectrum control, phase compensation and time scheduling.

[0044] After the adjustment of frequency band allocation, phase compensation and label identity time sequence fingerprint is completed, the cloud end side performs adaptive linkage control on the entire hierarchical scheduling process based on the updated results to form a complete dynamic regulation closed loop. Specifically, the cloud end side re-determines the time interval and hierarchical response order of the recycling instruction according to the new label identification track and signal priority. When the cloud end side detects that some devices cause response delay to increase due to spectrum adjustment, the cloud end side automatically delays the recycling instruction issuing time of the device in the hierarchical level to prevent signal overlap; and when the response period of some devices is shortened due to phase adjustment, the cloud end side appropriately advances the triggering time of the recycling instruction, thereby ensuring that the response rhythm between levels is always coordinated. At the same time, the cloud end side re-enters the signal characteristics of the execution result of each recycling instruction into the feedback end of the regulation loop after each recycling instruction execution is completed, so that the state of frequency band allocation mechanism, phase compensation strategy, label time sequence fingerprint and hierarchical scheduling is continuously updated. Through this cyclic adaptive linkage control, the cloud recycling identification signal can automatically perceive, automatically adjust and automatically balance in the dynamic running process, forming a stable and continuous closed loop regulation structure, thereby maintaining efficient and stable identification and recycling operation in a complex device access environment.

[0045] Through the execution of the above steps, a dynamic regulation loop with self-sensing and self-regulation capabilities can be constructed on the basis of the hierarchical scheduling execution result, so that real-time linkage is formed between the frequency band allocation, phase offset compensation, label identity timing fingerprint and hierarchical scheduling process, and full-process adaptive adjustment of signal identification and recycling scheduling is realized. This step can maintain the balance state and stable operation of the cloud recycling identification signal under the conditions of multiple device concurrent access, dynamic signal change and transmission environment fluctuation, and provides continuous and reliable signal support and dynamic coordination capability for the entire cloud office equipment rental management process.

[0046] The present application adopts adaptive frequency spectrum segmentation and phase peak-shaving transmission in the device access stage, so that multiple office equipment forms an independent and continuous signal channel structure in the cloud identification process, eliminating the frequency band overlap and phase interference problem from the source. This method can realize the orderly distribution and stable transmission of multiple device signals in a signal-intensive recycling identification network environment, so that the cloud can maintain the integrity and independence of the signal when identifying the label identity, thereby effectively avoiding the analysis mismatch between device labels, ensuring the accuracy of device state identification and the consistency of cloud identification results.

[0047] The present application constructs a dynamic regulation loop to form a closed-loop linkage between frequency band allocation, phase compensation, timing fingerprint and hierarchical scheduling process, so that the cloud can automatically adjust the identification strategy according to the device operating state at different rental stages. This dynamic regulation method enables the signal transmission to maintain adaptive balance under the conditions of multiple device concurrency and environmental fluctuation, realizing the continuous and stable operation of the cloud identification process. By continuously updating the identification trajectory of each device, it can ensure that the device recycling instructions are executed in sequence according to the timing, thereby realizing efficient operation and safe regulation of cloud office equipment management.

[0048] The present application provides an office equipment rental management system based on a cloud platform as shown in Figure 2 The office equipment rental management system based on a cloud platform comprises an adaptive frequency spectrum allocation module, a phase peak-shaving regulation module, a label timing fingerprint generation module, a hierarchical scheduling control module and a dynamic linkage regulation module. The adaptive frequency spectrum allocation module dynamically scans the frequency band occupation of the device label according to the real-time signal intensity distribution of each device label after the office equipment accesses the cloud recycling identification network, and allocates an independent identification channel to each device label through an adaptive frequency spectrum segmentation method. The phase peak-shaving regulation module compensates the phase offset of the edge signal of adjacent identification channels based on the established independent identification channel, so that the signals of each device label form a peak-shaving transmission sequence on the time axis. The label timing fingerprint generation module generates the corresponding label identity timing fingerprint in combination with the time delay characteristics of each device label signal based on the formed peak-shaving transmission sequence, and constructs a unique identification trajectory for each device according to the label identity timing fingerprint. The hierarchical scheduling control module implements hierarchical scheduling control on the device recycling instruction issued by the cloud based on the generated unique identification track, so that each identification track responds in turn under the control of signal priority; The dynamic linkage control module constructs a dynamic control loop according to the execution result of the hierarchical scheduling, and performs real-time linkage control on the frequency band allocation mechanism, the phase offset compensation strategy, the label identity time sequence fingerprint and the hierarchical scheduling process.

[0049] The office equipment leasing management method based on the cloud platform is implemented through the office equipment leasing management system based on the cloud platform, and the specific method and process of the office equipment leasing management system based on the cloud platform are described in the above embodiments of the office equipment leasing management method based on the cloud platform, which will not be repeated here.

[0050] The above only describes some exemplary embodiments of the present application by way of illustration, without doubt, for those skilled in the art, the described embodiments can be modified in various ways without departing from the spirit and scope of the present application. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the present application.

Claims

1. A cloud platform-based office equipment rental management method, characterized by, The method comprises the following steps: Step one, after the office equipment accesses the cloud recycling identification network, the frequency band occupation of the equipment label is dynamically scanned according to the real-time signal strength distribution of each equipment label, and each equipment label is allocated an independent identification channel through an adaptive frequency spectrum segmentation method; Step two, based on the established independent identification channel, the edge signal of the adjacent identification channel is phase offset compensated to form a staggered transmission sequence of each equipment label signal on the time axis; Step three, relying on the formed staggered transmission sequence, the corresponding label identity time sequence fingerprint is generated combined with the time delay characteristics of each equipment label signal, and a unique identification track is constructed for each equipment according to the label identity time sequence fingerprint; Step four, based on the generated unique identification track, the device recycling instruction issued by the cloud is implemented hierarchical scheduling control, so that each identification track responds in turn under the control of signal priority; Step five, according to the execution result of hierarchical scheduling, a dynamic control loop is constructed to realize real-time linkage control of frequency band allocation mechanism, phase offset compensation strategy, label identity time sequence fingerprint and hierarchical scheduling process.

2. The cloud platform-based office equipment rental management method of claim 1, wherein, The step of allocating an independent identification channel for each equipment label through an adaptive frequency spectrum segmentation method comprises: When the office equipment accesses the cloud recycling identification network for the first time, the cloud side starts a signal monitoring process, collects the signal energy intensity, electromagnetic wave peak position and signal fluctuation trend of each office equipment label in different time slices, and constructs a frequency spectrum intensity graph reflecting the signal distribution of each label; After completing the frequency spectrum intensity analysis, the cloud side divides the signal overlap area and signal sparse area in the frequency spectrum according to the signal intensity distribution characteristics of each equipment label, and defines a special frequency interval for each equipment label, and sets an isolation band between different labels to maintain the signal spacing; The cloud side dynamically adjusts the channel boundary according to the signal intensity change curve in each identification channel, so that the channel width and center frequency move with the change of label signal energy, and the minimum frequency difference between different identification channels is maintained; After the boundary position of all identification channels is adjusted, the cloud side binds the unique identification information of each office equipment label with its corresponding identification channel frequency range, establishes a signal identification mapping table and periodically updates the identification channel state. 3.The cloud platform-based office equipment rental management method of claim 2, wherein, During the process of dynamically adjusting the boundary of the identification channel, when the cloud side detects that the signal intensity in the identification channel is enhanced in a certain frequency band, the boundary of the identification channel is expanded; when the signal energy is weakened, the boundary of the identification channel is contracted and the spare frequency interval is allocated to the adjacent active channel. 4.The cloud platform-based office equipment rental management method of claim 2, wherein, The step of performing phase offset compensation on the edge signal of the adjacent identification channel comprises: On the basis of the established independent identification channel, the cloud side continuously monitors the edge signal of each identification channel, collects the starting time point of the signal in each identification channel, the time delay of the signal peak, the signal energy decay curve and the signal phase change trend, and forms a time domain mapping diagram of adjacent identification channels; After obtaining the time delay distribution and phase difference characteristics of adjacent identification channels, the cloud side performs phase offset compensation according to the time domain mapping diagram, so that the signals of adjacent identification channels form staggered transmission relationship on the time axis, the starting time of adjacent signals is delayed or advanced to maintain time sequence balance, and it is ensured that the compensated signals are still within the frequency range of their independent identification channels; After completing the phase offset compensation, the cloud side uniformly coordinates the time structure of the identification network, periodically scans and adjusts the signal propagation path and delay parameters of all identification channels, and prevents time sequence drift of the staggered sequence. 5.The cloud platform-based office equipment rental management method of claim 4, wherein, When the cloud side periodically scans the signal propagation path and delay parameters of all identification channels, the phase offset amplitude of adjacent identification channels is dynamically fine-tuned according to the time domain mapping diagram of each identification channel. When the signal propagation delay exceeds the set threshold, the time interval of adjacent identification channels is automatically recalculated. 6.The cloud platform-based office equipment rental management method of claim 4, wherein, The steps of constructing a unique identification track for each device according to the tag identity time sequence fingerprint include: After forming the staggered transmission sequence, the cloud side captures the time characteristics of the office equipment tag signals in units of each identification channel, extracts multi-dimensional time parameters, and obtains the original time characteristic sequence of each device tag; After obtaining the time characteristic sequence, the cloud side continuously compares the signal arrival time and delay change of the same device tag in different transmission cycles, combines the transmission order of the staggered transmission sequence, and forms a time delay mapping sequence reflecting the signal propagation law; After forming the time delay mapping sequence, the cloud side constructs a tag identity time sequence fingerprint according to the sequence, signal transmission interval, duration and energy attenuation trend, and maintains the continuity and uniqueness of the time sequence fingerprint through multi-cycle update; After generating the tag identity time sequence fingerprint, the cloud side establishes an identification track for each office equipment according to the fingerprint, and associates the identification track with the device rental status, use record and recycling status information, realizing the unique identification and dynamic tracking of the device in the cloud analysis process. 7.The cloud platform-based office equipment rental management method of claim 6, wherein, In the process of establishing an identification track for each office equipment according to the tag identity time sequence fingerprint, the cloud side compares office equipment in different geographical locations or use scenarios according to the time delay information and signal propagation characteristics of the identification track. When the same tag identity time sequence fingerprint feature is detected, the corresponding identification relationship in the cloud is automatically restored. 8.The cloud platform-based office equipment rental management method of claim 6, wherein, The steps of implementing hierarchical scheduling control on the device recycling instructions issued by the cloud include: Before the cloud prepares to issue the device recycling instruction, the cloud side groups and prioritizes the devices to be executed according to the unique identification track of each office equipment, divides the priority level according to the time distribution information in the identification track, the tag identity time sequence fingerprint feature, the signal strength stability and the communication delay parameter, and makes the response interval between adjacent levels meet the minimum time difference; After completing the priority level division, the cloud side dynamically schedules the issuance order of the recycling instruction according to the priority level, issues the recycling instruction layer by layer according to the time sequence of the identification track, and sets different response delay parameters for different levels to keep the instruction transmission and device identification time window synchronized; After the hierarchical devices respond to the recycling instruction in turn, the cloud side executes closed-loop control according to the signal feedback time sequence of the identified track, dynamically adjusts the instruction issuing time of the hierarchy according to the confirmation time of the device response and the update delay of the recycling state. 9.The cloud platform-based office equipment rental management method of claim 8, wherein, The step of constructing the dynamic regulation loop comprises: After the hierarchical scheduling process is completed, the cloud side extracts signal distribution information and execution time sequence data according to the recycling instruction response state of each hierarchical device, records the signal response time, signal intensity change range, frequency band occupation ratio and phase delay change of the identified track when executing the instruction, compares the current signal propagation time with the time difference of the adjacent identification channel, and extracts the phase overlap factor; After the data collection is completed, the cloud side adjusts the frequency band allocation mechanism in real time according to the feedback information, and corrects the phase delay trend of the edge signal in combination with the phase offset compensation strategy, so that the frequency band allocation and phase compensation form a synergistic regulation; After the frequency band allocation and phase compensation adjustment are completed, the cloud side maps and corrects the adjustment results with the tag identity time sequence fingerprint in time, so that the tag identity time sequence fingerprint is consistent with the unique identified track; After the update is completed, the cloud side re-determines the time interval and hierarchical response order of the recycling instruction according to the new identified track and signal priority, and feeds back the signal characteristics of the execution result to the dynamic regulation loop.

10. A cloud platform-based office equipment rental management system for implementing the cloud platform-based office equipment rental management method according to any one of claims 1 to 9, characterized by, It comprises an adaptive frequency spectrum allocation module, a phase peak-shaving regulation module, a tag time sequence fingerprint generation module, a hierarchical scheduling control module and a dynamic linkage regulation module. The adaptive frequency spectrum allocation module dynamically scans the frequency band occupation of the device tag according to the real-time signal intensity distribution of each device tag after the office equipment accesses the cloud recycling identification network, and allocates an independent identification channel for each device tag through an adaptive frequency spectrum segmentation method; The phase peak-shaving regulation module performs phase offset compensation processing on the edge signal of the adjacent identification channel based on the established independent identification channel, so that the device tag signals form a peak-shaving transmission sequence on the time axis; The tag time sequence fingerprint generation module generates the corresponding tag identity time sequence fingerprint in combination with the time delay characteristics of each device tag signal based on the formed peak-shaving transmission sequence, and constructs a unique identified track for each device according to the tag identity time sequence fingerprint; The hierarchical scheduling control module implements hierarchical scheduling control on the device recycling instruction issued by the cloud based on the generated unique identified track, so that each identified track responds in turn under the control of the signal priority; The dynamic linkage regulation module constructs a dynamic regulation loop according to the execution result of the hierarchical scheduling, and performs real-time linkage control on the frequency band allocation mechanism, the phase offset compensation strategy, the tag identity time sequence fingerprint and the hierarchical scheduling process.

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