Metadata-based book distribution communication scheduling method

Abstract

This invention relates to a metadata-based book distribution communication scheduling method, belonging to the field of electronic digital data processing technology. The method includes: collecting status metadata of all book terminal devices within a target area; calling a power supply reliability assessment model to quantify real-time operating parameters and historical operating sequence parameters, generating a distribution feasibility weight matrix through matrix fusion processing; generating an adaptive power supply guarantee strategy table; generating a device load prediction curve for future periods based on real-time access popularity parameters; outputting a distribution scheduling sequence; and using a predictive compensation engine to predict trends based on the deviation between feedback data and preset thresholds, generating a final distribution execution instruction set including channel switching instructions, power supply adjustment instructions, and task reordering instructions. The beneficial effects of this invention are improved real-time performance and reliability of resource distribution, making it suitable for efficient management scenarios of large-scale heterogeneous terminal devices in smart libraries.

Description

Technical Field

[0001] This invention relates to the field of electronic digital data processing technology, and more specifically, to a book distribution communication scheduling method based on metadata. Background Technology

[0002] With the deepening of the construction of smart online libraries, the scale and real-time requirements for the distribution of e-book resources continue to rise. The single-channel push mode based on a central server has become the mainstream architecture for resource distribution. This architecture pushes book resources from the cloud server to terminal devices located throughout the library, including self-service borrowing terminals, mobile inventory robots, and tablet reading carts, through centralized management, thus achieving basic automation of resource deployment.

[0003] However, this architecture, which relies solely on fixed communication links and steady-state power supply assumptions, reveals serious flaws in areas with unstable power supply or complex communication environments. When terminal devices enter signal blind spots due to mobile movement, or when the area experiences temporary power fluctuations, the central server cannot detect the attenuation of power supply redundancy and the degradation of multi-channel communication quality in real time. This results in a persistently high rate of interruption in book resource distribution and makes it difficult to dynamically adapt and adjust based on the urgency priority of book resources and the actual load capacity of terminal devices. To address these issues, the industry has proposed several improvement solutions, such as using fixed threshold screening based on collected basic device status information to eliminate unhealthy nodes, employing homogeneous communication link redundancy backup to provide alternative paths, or using a linear evaluation model based on fixed weights for task allocation decisions. However, these technologies all employ static thresholds and rigid decision-making mechanisms, which cannot adapt to dynamic environmental characteristics such as the opening and closing rhythms of libraries and the peak and valley changes in reader traffic; the homogeneous redundancy design completely fails in the face of common-mode faults such as Wi-Fi channel congestion and regional electromagnetic interference; the fixed weight model ignores the uncertainty of equipment status and the time-varying characteristics of resource access popularity, resulting in delayed scheduling decisions and insufficient reliability, making it difficult to meet the core requirements of smart digital libraries for high reliability, low latency, and refined resource distribution. Summary of the Invention

[0004] The purpose of this invention is to provide a metadata-based book distribution communication scheduling method to improve the aforementioned problems. To achieve this purpose, the technical solution adopted by this invention is as follows: Firstly, this application provides a metadata-based book distribution communication scheduling method, including: Collect status metadata of all book terminal devices within the target area and extract attribute metadata of the book resources to be distributed; among which, status metadata includes real-time operating parameters of the power supply module, link quality parameters of each communication channel, available storage parameters of the device, geographical location parameters of the device, and historical running sequence parameters; attribute metadata includes data capacity parameters of the book resources, resource encoding format parameters, real-time access popularity parameters, and distribution priority parameters. The status metadata and attribute metadata are input into the power supply-communication-resource coupling evaluation engine. The power supply reliability evaluation model is called to quantify the real-time operating parameters and historical operating sequence parameters, and output the dynamic power supply health. The communication quality evaluation model is called to perform exponential calculation on the link quality parameters and geographical location parameters, and output the multi-channel stability spectrum. The dynamic power supply health, multi-channel stability spectrum, available storage parameters of the device and data capacity parameters are matched and calculated in multiple dimensions. The distribution priority parameter is introduced as a weighting coefficient, and the distribution feasibility weight matrix is ​​generated through matrix fusion processing. A candidate device list is formed by screening terminal devices with a weighted distribution feasibility value higher than a set threshold. For each device in the candidate device list, each communication channel is sorted according to its link quality parameters. The channel with the highest value is selected as the primary channel and the channel with the second highest value is selected as the backup channel, generating a primary and backup communication channel binding relationship table. Based on real-time operating parameters, the power supply contingency plan library is matched and the operating strategy is dynamically adjusted to generate an adaptive power supply guarantee strategy table. Using the primary and backup communication channel binding relationship table and the adaptive power supply guarantee strategy table as dynamic constraints, the load prediction model is invoked to perform time-series decomposition of historical runtime sequence parameters, and combined with real-time access heat parameters to generate equipment load prediction curves for future periods; the equipment load prediction curves are input into the elastic scheduling optimizer, and under dynamic constraints, the distribution tasks are optimized by time slicing and resource allocation, and the distribution scheduling sequence is output. The distribution and scheduling sequence is sent to the local execution agent of each book terminal device. The local execution agent monitors the changes in power supply and communication status in real time and feeds back the status change data to the predictive compensation engine. The predictive compensation engine makes trend predictions based on the deviation between the feedback data and the preset threshold, and generates the final distribution and execution instruction set, which includes channel switching instructions, power supply adjustment instructions and task reordering instructions.

[0005] Preferably, the process involves inputting state metadata and attribute metadata into the power supply-communication-resource coupling evaluation engine, calling the power supply reliability evaluation model to quantify real-time operating parameters and historical operating sequence parameters, and outputting dynamic power supply health; calling the communication quality evaluation model to perform exponential calculations on link quality parameters and geographical location parameters, and outputting a multi-channel stability spectrum; and performing multi-dimensional matching calculations on the dynamic power supply health, multi-channel stability spectrum, available device storage parameters, and data capacity parameters, introducing a distribution priority parameter as a weighting coefficient, and generating a distribution feasibility weight matrix through matrix fusion processing, including: The power supply reliability assessment model is invoked to perform time-series analysis on the power supply health substructure in the equipment status snapshot structure, extract the time-series window data of the main and backup power supply voltage ratio, calculate the voltage stability index, extract the power supply temperature deviation to calculate the temperature risk index, extract the main and backup current balance to calculate the current balance index, and comprehensively generate dynamic power supply health. The communication quality assessment model is invoked to perform spatial analysis on the communication quality substructure in the equipment status snapshot structure, and for each communication channel, the channel quality comprehensive index is calculated. A device-resource matching evaluation tensor is constructed. The dynamic power supply health is mapped to the first dimension of the tensor, the mean of the multi-channel stability spectrum is mapped to the second dimension of the tensor, and the storage matching degree is calculated by comparing the device's available storage percentage with the number of resource blocks and mapped to the third dimension of the tensor. The resource priority weight coefficient is introduced as a weighting adjustment factor, and a distribution feasibility weight matrix is ​​generated through nonlinear fusion transformation.

[0006] Preferably, terminal devices with a weighted distribution feasibility value higher than a set threshold in the screening and distribution feasibility weight matrix form a candidate device list; for each device in the candidate device list, each communication channel is sorted according to its link quality parameters, the channel with the highest value is selected as the primary channel, and the channel with the second highest value is selected as the backup channel, generating a primary and backup communication channel binding relationship table; based on real-time operating parameters, a power supply contingency plan library is matched and the operating strategy is dynamically adjusted to generate an adaptive power supply guarantee strategy table, including: The distribution feasibility weight matrix is ​​segmented by thresholding. A feasibility threshold is set, and all elements in the matrix are traversed. Device-resource pairs that satisfy the distribution feasibility weight value being greater than the feasibility threshold are extracted to construct a candidate distribution pair set. Each element contains a device identifier, a resource identifier, and a corresponding weight value. For each device in the candidate distribution pair set, retrieve its communication quality substructure, obtain the comprehensive channel quality index of all communication channels of the device, sort the comprehensive channel quality index values ​​in descending order, select the channel identifier ranked first as the primary channel, select the channel identifier ranked second as the backup channel, construct the channel identifier pair, use the link quality parameters of the primary channel as the primary link parameter set, use the link quality parameters of the backup channel as the backup link parameter set, and generate a primary and backup communication channel binding relationship table; For each device in the candidate distribution pair set, its power supply health substructure is retrieved, the deviation between the main and backup power supply voltage ratio and the power supply temperature is extracted, the power supply plan matching engine is invoked, and the pre-stored power supply plan library is retrieved using the deviation between the main and backup power supply voltage ratio and the power supply temperature as query conditions. The matching power supply mode record is located, and the power consumption limit, estimated battery life, and power supply switching delay value in the record are extracted. An adaptive power supply guarantee strategy table is constructed, and the fields in the table include device identifier, power supply mode, power consumption limit, battery life, and switching delay.

[0007] Preferably, the step of using the primary / backup communication channel binding relationship table and the adaptive power supply guarantee strategy table as dynamic constraints, calling the load prediction model to perform time-series decomposition of historical runtime sequence parameters, and combining real-time access popularity parameters to generate device load prediction curves for future periods, includes: The load record sequence of the device in the past period is extracted from the historical runtime sequence parameters. Each load value represents the number of concurrent tasks of the device at the corresponding time. The time series decomposition module of the load prediction engine is called to decompose the load record sequence into trend component, periodic component and random component. The trend component is calculated by linear extrapolation to calculate the trend prediction value of the future period. The periodic component is extracted by Fourier transform and periodic extension to obtain the periodic prediction value. The basic load prediction curve of the device is generated by combining the results. By combining the standardized popularity value of book resources, the impact factor of resource popularity on equipment load is calculated. The impact factor is superimposed on the basic load prediction curve of the equipment to obtain the comprehensive load prediction curve of the equipment. At the same time, the available bandwidth of the primary channel and the available bandwidth of the backup channel bound to the equipment are calculated. The total channel capacity constraint is defined as the sum of the converted values ​​of the available bandwidth of the primary channel and the available bandwidth of the backup channel. The conversion factor is the backup channel capacity conversion factor preset by the system.

[0008] Preferably, the step of inputting the equipment load prediction curve into the elastic scheduling optimizer, optimizing the time slicing and resource allocation of the dispatched tasks under dynamic constraints, and outputting a dispatching sequence includes: The overall load prediction curve of the equipment and the total channel capacity constraint are input into the elastic scheduling optimizer to construct an optimization model with the goal of minimizing the distribution completion time and power consumption. The power consumption limit and endurance time in the adaptive power supply guarantee strategy table are used as the constraint boundary. After mixed integer programming, the distribution scheduling sequence is output. Each task in the sequence includes the task execution time window, target device identifier, resource identifier, designated channel identifier and power supply mode.

[0009] Preferably, the local execution agent that distributes the scheduling sequence to each book terminal device monitors power supply and communication status changes in real time and feeds back the status change data to the predictive compensation engine, including: The distribution and scheduling sequence is encoded into a device-recognizable instruction frame format and sent to the local execution agent of the corresponding book terminal device through the main channel. The local execution agent receives the instruction frame and parses it into an internal task queue. At the same time, it starts the power supply monitoring thread and the communication monitoring thread. The power supply monitoring thread reads the real-time operating parameters of the power supply module at a fixed period, and the communication monitoring thread reads the link quality parameters of the multi-channel communication module at a fixed period. The local execution agent calculates the deviation between the current primary and backup power supply voltage ratio collected by the power supply monitoring thread and the preset voltage ratio threshold in the distribution and scheduling sequence to obtain the power supply deviation. The deviation between the current primary channel quality index collected by the communication monitoring thread and the preset channel quality threshold is calculated to obtain the communication deviation. The power supply deviation and communication deviation are encapsulated into a state change data packet and transmitted back to the predictive compensation engine through the backup channel.

[0010] Preferably, the predictive compensation engine predicts trends based on the deviation between feedback data and a preset threshold, generating a final distribution and execution instruction set that includes channel switching instructions, power supply adjustment instructions, and task rescheduling instructions, including: The predictive compensation engine receives state change data packets from each device, calls the trend prediction filter to perform Kalman filtering on power supply deviation and communication deviation, and predicts the deviation estimate for the next moment. If the estimated power supply deviation exceeds the power supply alarm threshold, a channel switching instruction is generated to switch the primary channel to the backup channel. If the estimated communication deviation is lower than the communication quality threshold, a power supply adjustment instruction is generated to activate the backup power supply module and adjust the power consumption limit. At the same time, the task priority is recalculated based on the deviation estimate, a task reordering instruction is generated, and the final distribution and execution instruction set is combined to achieve real-time adjustment and compensation of the distribution process.

[0011] Secondly, this application also provides a metadata-based book distribution communication scheduling system, including: The data acquisition module is used to collect the status metadata of all book terminal devices within the target area and extract the attribute metadata of the book resources to be distributed. The status metadata includes the real-time operating parameters of the power supply module, the link quality parameters of each communication channel, the available storage parameters of the device, the geographical location parameters of the device, and the historical running sequence parameters. The attribute metadata includes the data capacity parameters of the book resources, the resource encoding format parameters, the real-time access popularity parameters, and the distribution priority parameters. The calling module is used to input status metadata and attribute metadata into the power supply-communication-resource coupling evaluation engine, call the power supply reliability evaluation model to quantify real-time operating parameters and historical operating sequence parameters, and output dynamic power supply health; call the communication quality evaluation model to perform exponential calculations on link quality parameters and geographical location parameters, and output multi-channel stability spectrum; perform multi-dimensional matching calculations on dynamic power supply health, multi-channel stability spectrum, available storage parameters of equipment and data capacity parameters, introduce distribution priority parameters as weighting coefficients, and generate a distribution feasibility weight matrix through matrix fusion processing; The filtering module is used to filter terminal devices in the distribution feasibility weight matrix that are higher than a set threshold to form a candidate device list; for each device in the candidate device list, the communication channels are sorted according to their link quality parameters, the channel with the highest value is selected as the primary channel, and the channel with the second highest value is selected as the backup channel, generating a primary and backup communication channel binding relationship table; based on real-time operating parameters, the power supply contingency plan library is matched and the operating strategy is dynamically adjusted to generate an adaptive power supply guarantee strategy table; The optimization module is used to use the primary and backup communication channel binding relationship table and the adaptive power supply guarantee strategy table as dynamic constraints. It calls the load prediction model to perform time-series decomposition of historical runtime sequence parameters and combines real-time access popularity parameters to generate equipment load prediction curves for future periods. The equipment load prediction curves are then input into the elastic scheduling optimizer to optimize the time slicing and resource allocation of the distribution tasks under dynamic constraints, and output the distribution scheduling sequence. The scheduling module is used to distribute the scheduling sequence to the local execution agent of each book terminal device. The local execution agent monitors the power supply and communication status changes in real time and feeds back the status change data to the predictive compensation engine. The predictive compensation engine makes trend predictions based on the deviation between the feedback data and the preset threshold, and generates the final distribution execution instruction set, which includes channel switching instructions, power supply adjustment instructions and task reordering instructions.

[0012] Thirdly, this application also provides a metadata-based book distribution communication scheduling device, comprising: Memory, used to store computer programs; A processor is used to implement the steps of the metadata-based book distribution communication scheduling method when executing the computer program.

[0013] Fourthly, this application also provides a readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described metadata-based book distribution communication scheduling method.

[0014] The beneficial effects of this invention are as follows: This invention constructs a mapping system that links the full-dimensional status metadata of devices with the attribute metadata of book resources. It incorporates redundant status parameters of power supply modules, quality parameters of multi-channel communication links, device storage capacity parameters, geographical location coordinate parameters, historical fault timing parameters, and data packet capacity parameters, encoding format parameters, access popularity parameters, and distribution priority parameters of book resources into a unified scheduling decision framework, thereby achieving accurate distribution and proactive compensation in complex environments.

[0015] This invention employs a power supply-communication-resource coupling evaluation engine to dynamically generate a distribution feasibility weight matrix. Combined with environmental complexity awareness, it achieves adaptive threshold drift and hierarchical screening of candidate devices, effectively solving the problem of poor adaptability of traditional static threshold mechanisms during peak and off-peak periods in libraries. Based on technical heterogeneity constraints and historical stability assessment, it constructs a primary and backup communication channel binding relationship table to avoid the failure risk of homogeneous redundancy under common-mode interference. It uses a fuzzy inference mechanism to match an adaptive power supply guarantee strategy table, realizing dynamic penalty-based correction of power consumption limits and battery life, adapting to the individual characteristics of device battery aging and load fluctuations. By predicting the power supply and communication deviation trends through a Kalman filter, it generates a collaborative compensation instruction set containing channel switching instructions, power supply adjustment instructions, and task reordering instructions, realizing a paradigm shift from passive response to active intervention.

[0016] This invention enables precise scheduling and proactive compensation for book distribution tasks in the complex environment of libraries with fluctuating power supply and changing communication, significantly reducing the distribution interruption rate and improving the real-time performance and reliability of resource distribution. It is suitable for efficient management scenarios of large-scale heterogeneous terminal devices in smart libraries.

[0017] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the metadata-based book distribution communication scheduling method described in this embodiment of the invention; Figure 2 This is a schematic diagram of the metadata-based book distribution communication scheduling system structure described in this embodiment of the invention; Figure 3 This is a schematic diagram of the metadata-based book distribution communication scheduling device described in an embodiment of the present invention.

[0020] In the diagram: 701, Acquisition Module; 702, Calling Module; 703, Filtering Module; 704, Optimization Module; 705, Scheduling Module; 800, Metadata-Based Book Distribution Communication Scheduling Device; 801, Processor; 802, Memory; 803, Multimedia Component; 804, I / O Interface; 805, Communication Component. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0022] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0023] Example 1: This embodiment provides a book distribution communication scheduling method based on metadata.

[0024] See Figure 1 The figure shows that the method includes steps S100, S200, S300, S400 and S500.

[0025] S100: Collect status metadata of all book terminal devices within the target area and extract attribute metadata of the book resources to be distributed; wherein, the status metadata includes real-time operating parameters of the power supply module, link quality parameters of each communication channel, available storage parameters of the device, geographical location parameters of the device, and historical running sequence parameters; the attribute metadata includes data capacity parameters of the book resources, resource encoding format parameters, real-time access popularity parameters, and distribution priority parameters.

[0026] It is understood that step S100 includes S101, S102, and S103, wherein: S101. Through a lightweight acquisition agent deployed on the book terminal device, the raw operating data stream of the power supply module is collected at a fixed sampling period. The data stream includes the instantaneous value of the main power input voltage, the instantaneous value of the backup battery voltage, the power supply temperature sensor value, the effective value of the main power current, and the effective value of the backup battery current. At the same time, the link layer data packets of the multi-channel communication module are collected, and the signal reception strength, data packet error rate, round-trip delay, and available bandwidth of each channel are extracted. The remaining capacity, read and write rate, and number of bad blocks of the storage module are read, and the longitude, latitude, and altitude of the device are obtained from the positioning module. All raw data are marked with a unified timestamp and encapsulated into a raw device status data packet. S102. Parse and convert the original device status data packet into a standardized data structure to construct a device status snapshot structure. This structure includes a unique device identifier, snapshot generation time, power supply health substructure, communication quality substructure, storage status substructure, and geographic location substructure. The power supply health substructure stores the main and backup power supply voltage ratio, power supply temperature deviation, and main and backup current balance. The communication quality substructure stores the identifier of each channel and its corresponding signal strength value, bit error rate, latency, and bandwidth. The storage status substructure stores the available storage percentage, read and write rate, and storage health level. The geographic location substructure stores three-dimensional coordinates, ultimately forming a set of device status metadata. S103. Extract the core attribute fields of the book resources to be distributed from the database of the book resource management system, and construct a resource attribute description vector. This vector includes the unique identifier of the resource, the binary size of the resource file, the resource encoding format, the cumulative number of access requests to the resource in the past 7 days, and the resource distribution priority code. Calculate the number of blocks by dividing the binary size of the resource file into blocks according to a fixed block size, and convert the distribution priority code into a priority weight coefficient to finally form a set of resource attribute metadata.

[0027] S200: Input the status metadata and attribute metadata into the power supply-communication-resource coupling evaluation engine, call the power supply reliability evaluation model to quantify the real-time operating parameters and historical operating sequence parameters, and output the dynamic power supply health; call the communication quality evaluation model to perform exponential calculation on the link quality parameters and geographical location parameters, and output the multi-channel stability spectrum; perform multi-dimensional matching calculation on the dynamic power supply health, multi-channel stability spectrum, available storage parameters of the device and data capacity parameters, introduce the distribution priority parameter as a weighting coefficient, and generate the distribution feasibility weight matrix through matrix fusion processing.

[0028] It is understood that step S200 includes S201, S202, and S203, wherein: S201. The power supply reliability assessment model is invoked to perform time-series analysis on the power supply health substructure in the equipment status snapshot structure. Time-series window data of the primary and backup power supply voltage ratio is extracted, and the voltage stability index is calculated. The power supply temperature deviation is extracted to calculate the temperature risk index, and the primary and backup current balance is extracted to calculate the current balance index. A dynamic power supply health score is generated comprehensively. The formula for calculating the voltage stability index is as follows: In the formula, It is the voltage stability index. Time-series window data of the primary and backup power supply voltage ratio. The standard deviation function is denoted by , and mean is denoted by . S202. Call the communication quality assessment model to perform spatial analysis on the communication quality substructure in the device status snapshot structure, and calculate the comprehensive channel quality index for each communication channel. S203. Construct a device-resource matching evaluation tensor. Map the dynamic power supply health to the first dimension of the tensor, map the mean of the multi-channel stability spectrum to the second dimension, and compare the available storage percentage of the device with the number of resource blocks to calculate the storage matching degree, mapping this to the third dimension. Introduce a resource priority weight coefficient as a weighting adjustment factor, and generate a distribution feasibility weight matrix through nonlinear fusion transformation. The formula for calculating the matrix elements is as follows: In the formula, Let σ represent the feasibility weight value for allocating the i-th device to the j-th resource, and let σ denote the normalization function. This represents the dynamic power supply health of the i-th device. This represents the mean of the multi-channel stability spectrum of the i-th device. This represents the storage matching degree between the i-th device and the j-th resource. This represents the priority weight coefficient of the j-th resource, where i is the device index and j is the resource index.

[0029] It should be noted that, in its implementation, the communication quality assessment model first performs a deep analysis of the communication quality substructure within the device status snapshot structure. After obtaining the raw link-layer parameters for each communication channel, the system does not directly perform weighted calculations but instead first performs context-aware data preprocessing. For example, for signal reception strength parameters, the system combines the device's geographical location parameters with a library building layout knowledge base for correction—if the device is located in an area with dense metal bookshelves or an underground storage room, even if the signal strength value is high, the system will attenuate and correct the value based on historical experience data. This location-aware parameter correction mechanism effectively solves the measurement distortion caused by signal reflection and shielding issues in the special environment of a library.

[0030] When calculating the comprehensive channel quality index, the weighting coefficients β1 to β4 are not statically configured but dynamically adjusted based on the library's actual operational scenarios. For example, during the off-peak period from after the library closes at night until it opens in the morning, the system automatically reduces the latency weight β3 and increases the bandwidth weight β4, because batch data synchronization tasks are more sensitive to throughput during this time. Conversely, during peak daytime hours, the latency weight β3 is increased to ensure a fast response time for readers' borrowing requests. This scenario-adaptive weighting mechanism is a key innovation that distinguishes this method from traditional fixed-weight evaluation.

[0031] When mapping dynamic power supply health to the first dimension of a tensor, the system does not directly fill in a single value, but instead constructs a multi-dimensional vector containing the health confidence interval. Specifically, in addition to the health value itself, the system also calculates the confidence level of that health value, which is determined based on the fluctuation range of power supply parameters in historical runtime sequence parameters. If a device's power supply parameters have fluctuated drastically recently, its health confidence level is low, and it will be assigned a lower weight in subsequent fusion calculations. This dimensional mapping method, which introduces uncertainty quantification, enables scheduling decisions to automatically avoid high-risk devices, improving overall distribution reliability.

[0032] When mapping the mean of the multi-channel stability spectrum to the second dimension of the tensor, the system innovatively introduces "channel quality balance" as an auxiliary dimension. Channel quality balance is obtained by calculating the standard deviation of the quality index of each channel, characterizing the balance of the device's multi-channel capabilities. For example, although a device may have a high average channel quality, if the quality differences between channels are significant (e.g., one channel is excellent, while the others are extremely poor), its channel quality balance will be low. In real-world library scenarios, such devices may experience concentrated loads during peak hours, leading to overload of the main channel, while backup channels cannot effectively distribute load due to large quality differences. By using balance as supplementary information in the second dimension, the system can prioritize devices with balanced channel capabilities during subsequent fusion, achieving better load sharing.

[0033] S300: Select terminal devices with a weighted value higher than a set threshold in the feasibility distribution matrix to form a candidate device list; for each device in the candidate device list, sort each communication channel according to its link quality parameters, select the channel with the highest value as the primary channel and the channel with the second highest value as the backup channel, and generate a primary and backup communication channel binding relationship table; based on real-time operating parameters, match the power supply contingency plan library and dynamically adjust the operating strategy to generate an adaptive power supply guarantee strategy table.

[0034] It is understood that step S300 includes S301, S302, and S303, wherein: S301. The distribution feasibility weight matrix is ​​subjected to threshold segmentation. A feasibility threshold value is set, and all elements in the matrix are traversed. Device-resource pairs that satisfy the distribution feasibility weight value being greater than the feasibility threshold value are extracted to construct a candidate distribution pair set. Each element contains a device identifier, a resource identifier, and a corresponding weight value. It should be noted that, specifically, the system obtains real-time crowd density data from the library's access control system and crowd detection cameras, mapping it to an environmental complexity level. When the crowd density in the reading area exceeds a preset congestion level, the system determines that it is currently under service pressure and automatically increases the environmental sensitivity coefficient, thereby lowering the feasibility threshold and expanding the selection range of candidate devices. This ensures that a sufficient number of terminals can handle the distribution task even in densely populated reader scenarios. Conversely, during the quiet period after the library closes, the environmental sensitivity coefficient decreases, the threshold is raised, and only the devices with the best performance are allowed to perform batch synchronization tasks, ensuring the reliability of task execution.

[0035] During the traversal of matrix elements, the system not only performs simple threshold comparisons but also hierarchically labels candidate devices. Device-resource pairs with weight values ​​in the first interval are marked as "regular candidates," suitable for distributing general book resources with low real-time requirements. Those with weight values ​​in the second interval are marked as "preferred candidates," suitable for distributing popular books or time-sensitive resources, and the system will allocate better communication channels to them. Those with weight values ​​in the highest interval are marked as "special-grade candidates," specifically for distributing emergency notification resources (such as system upgrade packages and security patches). This hierarchical mechanism provides fine-grained scheduling basis for subsequent steps, enabling the system to automatically match devices with different protection levels according to the importance of the resource. For example, for high-priority resources such as new book release notifications, the system will force distribution only to "special-grade candidate" devices and trigger dual backup channel binding and the highest level power supply guarantee strategy to ensure that readers can obtain new book information as soon as possible.

[0036] This method maintains a sliding time window for each device-resource pair. Multiple weight values ​​collected continuously within the window constitute a sequence, and the system calculates the mean of this sequence as the final decision criterion. Only when the mean of the time window continuously exceeds a threshold and reaches the minimum hold time is the device officially added to the candidate set. This temporal stability verification mechanism effectively filters out weight jitter caused by instantaneous disturbances such as location movement and signal obstruction, significantly improving the stability of the candidate device list and the reliability of task allocation.

[0037] S302. For each device in the candidate distribution pair set, retrieve its communication quality substructure, obtain the comprehensive channel quality index of all communication channels of the device, sort the comprehensive channel quality index values ​​in descending order, select the channel identifier ranked first as the primary channel, select the channel identifier ranked second as the backup channel, construct the channel identifier pair, use the link quality parameters of the primary channel as the primary link parameter set, use the link quality parameters of the backup channel as the backup link parameter set, and generate a primary and backup communication channel binding relationship table. It's important to note that in practice, the system first groups all available communication channels of candidate devices by technology type. Typical smart library terminal devices usually support Wi-Fi, 5G cellular, and LoRa communication technologies simultaneously. Within each technology group, the system sorts channels by their overall quality index. The primary channel is selected from the group with the highest quality, while backup channels are strictly limited to the group with a different technology type than the primary channel. For example, for a mobile inventory robot deployed in a library's underground parking garage, if its 5G cellular channel has the highest quality, then the primary channel is locked to 5G, and the backup channel is selected from Wi-Fi and LoRa based on quality, never choosing the other 5G channel. This heterogeneous redundancy design ensures that backup channels provide truly effective backup capabilities when specific technology standards encounter regional interference, greatly improving the resilience of distribution tasks in complex electromagnetic environments.

[0038] Due to hardware aging, the communication modules of some older equipment in the library may experience drastic fluctuations in channel quality indices, manifesting as frequent disconnections and recoveries. Even if such channels currently rank highly in quality, they are unsuitable as backup channels. This method maintains a long-term quality tracking sequence for each channel, calculating its historical mean and variance. The selection of backup channels not only needs to meet technical heterogeneity requirements but also needs to pass a historical stability threshold check—its variance must be lower than the system's preset stability threshold. If a channel currently has high quality but exhibits significant historical fluctuations, the system will proactively reject it as a backup channel, instead selecting a channel with slightly lower current quality but long-term stability. This channel selection mechanism based on historical behavior patterns effectively avoids the risk of "short-term high quality, long-term unreliability" channels, making it particularly suitable for older equipment in the library that has been in service for more than three years.

[0039] The system not only records the instantaneous bandwidth parameters of the channels but also analyzes the capacity variation patterns of each channel at different time periods by combining historical operational data from the library. For example, the library's Wi-Fi channel experiences a sharp drop in capacity during the morning break due to concentrated student internet access, while the 5G cellular channel has ample capacity during the same period as users divert to Wi-Fi. The system encodes this pattern as a "time-period capacity correction coefficient" in the link parameter set and records the time-varying capacity characteristic vector of each channel in the binding relationship table.

[0040] S303. For each device in the candidate distribution pair set, retrieve its power supply health substructure, extract the deviation between the main and backup power supply voltage ratio and the power supply temperature, call the power supply plan matching engine, use the deviation between the main and backup power supply voltage ratio and the power supply temperature as query conditions to retrieve the pre-stored power supply plan library, locate the matching power supply mode record, extract the power consumption limit value, the estimated battery life value and the power supply switching delay value from the record, and construct an adaptive power supply guarantee strategy table. The fields in the table include device identifier, power supply mode, power consumption limit, battery life and switching delay.

[0041] It should be noted that, in specific implementation, the system retrieves the main / backup power supply voltage ratio and power supply temperature deviation from the power supply health substructure for each candidate device. The power supply plan matching engine does not perform precise value queries, but rather employs fuzzy membership inference. The main / backup power supply voltage ratio constitutes a fuzzy input variable, divided into three fuzzy sets: "low," "medium," and "high." Each set uses a Gaussian membership function to characterize its membership curve. The power supply temperature deviation constitutes another fuzzy input variable, divided into three fuzzy sets: "normal," "warning," and "dangerous." Using a pre-defined fuzzy rule table, the engine outputs the fuzzy inference results for the power supply mode. For example, for a window-side reading terminal deployed in direct sunlight, its power supply temperature deviation may remain in the "warning" range for an extended period. Fuzzy inference will automatically match the "backup power-dominated" mode, proactively reducing reliance on the main power supply and extending overall battery life. This flexible matching mechanism based on fuzzy logic effectively avoids the rigidity of traditional binary judgment, making the power supply strategy more closely aligned with the gradual changes in the actual operating state of the device.

[0042] The power consumption limit is not extracted by directly reading a fixed value from the pre-set plan library, but rather by dynamically penalizing and correcting it based on the device's current load status. The system reads real-time operating parameters from the device status snapshot and calculates the device's current power consumption. If the current power consumption exceeds the basic power consumption limit in the pre-set plan library, the actual power consumption limit will be further compressed, with the compression rate directly proportional to the degree of overload. This penalty mechanism effectively prevents overloaded devices from continuing to accept high-power tasks, thus preventing a "power consumption heat accumulation" effect. In actual library scenarios, some multi-functional borrowing terminals need to handle resource distribution tasks while also running foreground services such as screen savers and voice interactions, easily leading to a continuous high-load state. The dynamic penalty mechanism forces these devices to only accept low-power cached tasks or temporarily remove them from the candidate list, preventing device downtime due to power overload and ensuring the continuity of reader services.

[0043] The calculation of battery life estimation incorporates a historical degradation model of battery health. The system not only makes a simple estimate based on the current backup battery voltage but also extracts the battery charge-discharge cycle count and capacity decay curve from the device's historical operating sequence parameters. Mobile devices in the library experience rapid battery aging due to frequent daily charging trips. The system automatically adjusts the battery life estimate by tracking the battery cycle history of each device. When it detects that a device's battery capacity has decayed to below 80% of its initial value, the battery life estimate is shortened accordingly. Based on this, the scheduling system removes the device from the list of long-running tasks and assigns it a shorter, faster task.

[0044] S400 uses the primary / backup communication channel binding relationship table and the adaptive power supply guarantee strategy table as dynamic constraints. It calls the load prediction model to perform time-series decomposition of historical runtime sequence parameters and generates equipment load prediction curves for future periods by combining real-time access heat parameters. The equipment load prediction curves are then input into the elastic scheduling optimizer, which performs time slicing and resource allocation optimization on the distribution tasks under dynamic constraints and outputs the distribution scheduling sequence.

[0045] It is understood that in this step, S400 includes S401, S402, and S403, wherein: S401. Extract the load record sequence of the device in the past time period from the historical runtime sequence parameters, where each load value represents the number of concurrent tasks of the device at the corresponding time. Call the time series decomposition module of the load prediction engine to decompose the load record sequence into trend component, periodic component and random component. Use linear extrapolation to calculate the trend prediction value of the future time period for the trend component. Use Fourier transform to extract the main period for the periodic component and periodic extension to obtain the periodic prediction value. Generate the basic load prediction curve of the device by combining the results. The calculation formula is as follows: In the formula, For the equipment base load prediction curve, This is a trend forecast value. The value is the periodic forecast, where t is a future time. It should be noted that this method innovatively employs a hierarchical temporal decomposition strategy. When decomposing historical runtime parameters, the system first performs coarse-grained periodic extraction, using long-window Fourier transform to identify the principal period at the semester level, filtering out trend abrupt changes caused by long holidays such as winter and summer breaks. Subsequently, medium-grained periodic extraction is performed on the residual sequence to identify weekly cycle patterns and distinguish between weekday and weekend load baseline differences. Finally, fine-grained periodic extraction is performed on the quadratic residual to capture the daily opening and closing rhythms. This multi-period hierarchical stripping method can accurately reconstruct the true cyclical structure of the library's load, avoiding prediction distortion caused by the overlapping of different cyclical components.

[0046] Specifically, the linear extrapolation of the trend component does not use a globally uniform slope, but rather a piecewise adaptive extrapolation mechanism. The system identifies acceleration and deceleration points in device load growth based on historical runtime parameters. For example, after a library launches a new large-scale e-book database, the resource download load undergoes a three-stage trend evolution: "slow growth – rapid surge – stabilization." Traditional linear extrapolation would severely underestimate the load peak during the rapid surge phase. This method automatically divides the time axis into multiple intervals by detecting curvature changes in the trend component, fitting an independent linear model within each interval. When the curvature exceeds a set threshold, the system determines that it has entered a rapidly changing interval, automatically reducing the extrapolation step size and increasing the prediction update frequency, thereby ensuring that trend prediction can quickly respond to changes in library operations.

[0047] Traditional methods typically discard random components as noise, but this method recognizes that random components in a library setting actually contain important signals of sudden events, such as sudden network attacks or abnormal downloads caused by system vulnerabilities. The system performs anomaly detection on random components; when the amplitude of a random component exceeds three times the historical standard deviation, it is identified as a sudden event impact. At this point, the system triggers a sudden event recording mechanism, marking that period as an anomaly and reducing the weight of historical data from that period in subsequent predictions to prevent the mislearning of abnormal patterns. Simultaneously, the system feeds back the sudden event information to the operations and maintenance system, triggering a security audit process. This intelligent identification and processing mechanism for random components not only improves the robustness of predictions but also enhances the system's security protection capabilities.

[0048] S402. Combining the standardized popularity value of book resources, calculate the impact factor of resource popularity on equipment load, superimpose the impact factor onto the basic load prediction curve of the equipment to obtain the comprehensive load prediction curve of the equipment. At the same time, calculate the available bandwidth of the primary channel and the available bandwidth of the backup channel bound to the equipment, define the total channel capacity constraint as the sum of the converted values ​​of the available bandwidth of the primary channel and the available bandwidth of the backup channel, and the conversion factor is the backup channel capacity conversion factor preset by the system.

[0049] It's important to note that the calculation of the popularity impact factor not only relies on the current standardized popularity value of the book resources but also incorporates the prediction of popularity decay trends. In a library setting, resource access popularity typically exhibits an evolutionary pattern of "pulse-like surge - exponential decay - long-tail plateau," for example, new book recommendation announcements initially experience extremely high popularity, followed by rapid decay. This method analyzes the access time series of resources over the past seven days, fits a popularity decay curve, and predicts the popularity decay rate for future periods. The calculation of the impact factor incorporates a decay adjustment coefficient. When the predicted popularity is in a rapid decay phase, the system automatically reduces the intensity of the impact factor to avoid excessive impact on load prediction after the popularity has passed its peak. This proactive popularity impact adjustment mechanism allows the load prediction curve to anticipate the lifecycle of resource popularity and rationally allocate equipment load windows.

[0050] Furthermore, the definition of total channel capacity constraint breaks through the traditional simplified model of "linear addition of primary and backup capacities," innovatively proposing a differentiated capacity conversion strategy of "hot standby - cold standby." In the communication environment of a library, backup channels are not always in a full-capacity standby state. To reduce equipment power consumption, backup channels are usually in low-power monitoring mode, and their available bandwidth is only a portion of the theoretical value. This method dynamically adjusts the capacity conversion factor based on the technology type and current state of the backup channel. For example, for a 5G backup channel in DRX (Discontinuous Reception) power-saving mode, its capacity conversion factor is set to 0.3, indicating that only 30% of the theoretical bandwidth can be used for burst load sharing; while for a Wi-Fi backup channel in active scanning mode, the conversion factor can be increased to 0.7.

[0051] S403. Input the overall load prediction curve of the equipment and the total channel capacity constraint into the elastic scheduling optimizer to construct an optimization model with the goal of minimizing the distribution completion time and power consumption. Use the power consumption limit and endurance time in the adaptive power supply guarantee strategy table as the constraint boundary. After mixed integer programming, output the distribution scheduling sequence. Each task in the sequence includes the task execution time window, target device identifier, resource identifier, designated channel identifier and power supply mode.

[0052] It should be noted that when the scheduling sequence includes urgent security update tasks, the system will increase the weight coefficient of the distribution completion time in real time, allowing for the sacrifice of some power consumption indicators to ensure task timeliness; when all tasks are regular resource synchronization tasks, the power consumption weight coefficient will be increased to pursue optimal energy consumption. This adaptive adjustment of target weights based on task attributes enables the scheduler optimizer to always output the scheduling scheme that best fits the actual needs under different business scenarios.

[0053] The softening of constraints is a key innovation in this method for addressing library uncertainties. Under strict constraints, even slight deviations in equipment load prediction curves or channel capacity constraints can lead the optimization model to determine that there is no feasible solution, resulting in task distribution blockage. This method introduces slack variables to the power consumption limit and battery life constraints, allowing for brief and limited exceedances of the constraint boundaries in extreme cases, but requiring compensation within subsequent time windows. For example, during peak resource download periods in exam week, some devices may briefly exceed their power consumption limits due to sudden requests for popular resources. The system will not immediately reject the task allocation but will record the extent and duration of the exceedance and prioritize allocating low-power tasks to these devices for "energy compensation" in subsequent scheduling. This soft constraint mechanism significantly improves the solution space coverage of the scheduling optimizer, ensuring the continuity of task distribution during peak library business periods.

[0054] The scheduling sequence output by this method adopts a flexible structure of "time window + trigger condition". Each task not only includes an execution time window, but also carries channel switching trigger conditions and power supply mode switching trigger conditions. For example, the time window of a task is set to the period after the library closes, and a channel switching condition is bound to it: "Automatically switch to the backup channel when the error rate of the primary channel exceeds one-thousandth". This condition-triggered flexible scheduling sequence enables the local execution agent to autonomously respond to local state changes without calling back to the central scheduling system, significantly reducing response latency. Combined with the predictive compensation mechanism in subsequent steps, a two-level scheduling architecture of "central optimization + edge autonomy" is formed, which not only ensures global optimality, but also achieves rapid local response, perfectly adapting to the complex and ever-changing operating environment of the library.

[0055] S500 distributes the scheduling sequence to the local execution agent of each book terminal device. The local execution agent monitors the changes in power supply and communication status in real time and feeds back the status change data to the predictive compensation engine. The predictive compensation engine predicts the trend based on the deviation between the feedback data and the preset threshold, and generates the final distribution execution instruction set, which includes channel switching instructions, power supply adjustment instructions and task reordering instructions.

[0056] It is understood that in this step, S500 includes S501, S502, and S503, wherein: S501. The distribution scheduling sequence is encoded into a device-recognizable instruction frame format and sent to the local execution agent of the corresponding book terminal device through the primary channel. The local execution agent receives the instruction frame and parses it into an internal task queue. At the same time, it starts the power supply monitoring thread and the communication monitoring thread. The power supply monitoring thread reads the real-time operating parameters of the power supply module at a fixed period, and the communication monitoring thread reads the link quality parameters of the multi-channel communication module at a fixed period. The local execution agent calculates the deviation between the current primary and backup power supply voltage ratio collected by the power supply monitoring thread and the preset voltage ratio threshold in the distribution scheduling sequence to obtain the power supply deviation. S502. The deviation between the current primary channel quality index collected by the communication monitoring thread and the preset channel quality threshold is calculated to obtain the communication deviation. The power supply deviation and communication deviation are encapsulated into a state change data packet and transmitted back to the predictive compensation engine through the backup channel.

[0057] In practice, the power supply monitoring thread and communication monitoring thread within the local execution agent adopt an asynchronous parallel architecture. The two threads run with independent sampling periods and do not block each other. The power supply monitoring thread directly reads real-time register data from the power management chip through the device driver interface to obtain core parameters such as the primary / backup power supply voltage ratio and current balance. The communication monitoring thread, on the other hand, captures the underlying statistical data of the communication module in real time through socket programming and calculates the channel quality index. The data collected by both threads first enters a local buffer for time alignment to ensure that power supply deviation and communication deviation are calculated based on snapshots at the same time, avoiding misjudgments caused by sampling time differences.

[0058] The calculation of communication deviation not only involves a simple comparison of the difference between the current channel quality index and the threshold, but also innovatively introduces channel quality trend assessment. The system uses short-term historical data (typically from the past 5 minutes) maintained by the local execution agent to fit a trend line of the channel quality index. If the current quality index is slightly higher than the threshold, but the trend line shows a rapid decline, the communication deviation will be amplified in the calculation, triggering an early warning mechanism. This trend-enhanced deviation calculation method allows the system to complete channel switching before channel quality substantially deteriorates, significantly reducing the probability of task interruption due to switching delays. During the lunchtime peak hours in the library, Wi-Fi channels quickly become congested due to concentrated student access. The trend-enhanced mechanism can predict channel saturation in advance and complete backup channel switching before readers actually experience any lag.

[0059] The data encapsulation process employs a lightweight binary encoding format, rather than a redundant text protocol. The header field of the status change data packet includes a unique device identifier, packet sequence number, timestamp, and checksum. The payload field compactly arranges key information such as power supply deviation, communication deviation, current power supply mode, and current channel identifier. The sequence number field is used by the predictive compensation engine to detect packet loss. If multiple consecutive sequence numbers are missing, the engine automatically determines that the backup channel may be abnormal and triggers a channel health check process. This built-in self-diagnostic mechanism for transmission reliability eliminates the need for additional heartbeat detection messages, saving bandwidth while ensuring the complete delivery of status information.

[0060] The S503 predictive compensation engine receives status change data packets from various devices, calls a trend prediction filter to perform Kalman filtering on power supply deviation and communication deviation, and predicts the deviation estimate for the next moment. It determines whether the power supply deviation estimate exceeds the power supply alarm threshold. If it does, it generates a channel switching instruction to switch the primary channel to the backup channel. If the communication deviation estimate is lower than the communication quality threshold, it generates a power supply adjustment instruction to activate the backup power supply module and adjust the power consumption limit value. At the same time, it recalculates the task priority based on the deviation estimate, generates a task reordering instruction, and integrates the final distribution and execution instruction set to achieve real-time adjustment and compensation of the distribution process.

[0061] It's important to note that the Kalman filtering process does not employ a fixed-parameter, general model. Instead, it adaptively optimizes the model parameters to account for the changing characteristics of library equipment states. In the Kalman filter for power supply deviation, the process noise covariance is positively correlated with the load fluctuation of the equipment's power supply module. For high-load equipment that simultaneously handles distribution tasks and reader interaction services, the random disturbances in the power supply voltage are greater. The filter automatically increases the process noise parameter, reducing the confidence in single observations and relying more on the smoothing trend of historical states. Conversely, for low-load equipment that only performs background synchronization, the process noise parameter is smaller, allowing the filter to quickly respond to actual power supply state changes. This parameter adaptation mechanism based on equipment load characteristics enables the Kalman filter to maintain an optimal balance between prediction accuracy and response speed across different types of equipment.

[0062] Kalman filtering for communication bias introduces a compensation term for external interference factors. In library environments, communication quality is often affected by periodic interference from external devices. For example, the wireless microphone system in the lecture hall performs a channel hopping every two hours, causing a momentary impact on nearby Wi-Fi channels. This method obtains the schedule of such interference sources by interfacing with the library's reservation system and encodes it as an adjustment factor in the interference covariance matrix. When the filter predicts an impending interference period, it automatically increases the measurement noise covariance, reducing the sensitivity to sudden changes in observations and avoiding misjudgments caused by momentary interference. This external knowledge-injected filtering mechanism significantly improves prediction stability in complex electromagnetic interference environments.

[0063] The threshold determination for the deviation prediction adopts a tiered alarm strategy, rather than a simple out-of-bounds trigger. The system sets two levels of thresholds: a "warning threshold" and an "emergency threshold." When the predicted power supply deviation exceeds the warning threshold, the predictive compensation engine first generates a "channel pre-switching instruction." This instruction is sent to the local execution agent, requiring it to pre-activate the handshake authentication process of the backup channel while maintaining the current primary channel, completing identity authentication and parameter negotiation. When the deviation continues to worsen and exceeds the emergency threshold, a formal "channel switching instruction" is generated. At this point, the backup channel is already in a ready state, and the switching process can be completed in milliseconds, greatly reducing service interruption time.

[0064] The final generated instruction set is not a simple stack of instructions, but a structured sequence of instructions that has undergone instruction conflict resolution and execution timing optimization. The system checks the mutual exclusion relationships between different instructions. For example, if a channel switching instruction and a power supply mode switching instruction are executed simultaneously, it may cause the device to restart. The system automatically inserts timing intervals to ensure that instructions are executed safely in sequence. At the same time, the instruction set also includes the expected execution result and timeout rollback strategy for each instruction. If the local execution agent fails to complete the instruction execution within the specified time, the predictive compensation engine will activate the rollback plan, enabling a third redundant channel or a third backup power supply, forming a multi-layered nested fault defense system. This refined design and security verification mechanism of the instruction set ensures that compensation instructions can be executed correctly and reliably in the highly heterogeneous environment of library equipment, achieving self-healing capabilities under complex fault scenarios.

[0065] Example 2: like Figure 2 As shown, this embodiment provides a metadata-based book distribution communication scheduling system. See [link to documentation]. Figure 2 The system includes: Acquisition module 701: Used to collect status metadata of all book terminal devices in the target area and extract attribute metadata of the book resources to be distributed; wherein, the status metadata includes the real-time operating parameters of the power supply module, the link quality parameters of each communication channel, the available storage parameters of the device, the geographical location parameters of the device, and the historical running sequence parameters; the attribute metadata includes the data capacity parameters of the book resources, the resource encoding format parameters, the real-time access popularity parameters, and the distribution priority parameters. Module 702 is used to input status metadata and attribute metadata into the power supply-communication-resource coupling evaluation engine, call the power supply reliability evaluation model to quantify real-time operating parameters and historical operating sequence parameters, and output dynamic power supply health; call the communication quality evaluation model to perform exponential calculation on link quality parameters and geographical location parameters, and output multi-channel stability spectrum; perform multi-dimensional matching calculation on dynamic power supply health, multi-channel stability spectrum, available storage parameters of equipment and data capacity parameters, introduce distribution priority parameters as weighting coefficients, and generate distribution feasibility weight matrix through matrix fusion processing; Filtering module 703: Used to filter terminal devices in the distribution feasibility weight matrix that are higher than a set threshold to form a candidate device list; for each device in the candidate device list, sort each communication channel according to its link quality parameters, select the channel with the highest value as the primary channel and the channel with the second highest value as the backup channel, and generate a primary and backup communication channel binding relationship table; based on real-time operating parameters, match the power supply contingency plan library and dynamically adjust the operating strategy to generate an adaptive power supply guarantee strategy table; Optimization module 704: It is used to call the load prediction model to perform time-series decomposition of historical runtime sequence parameters with the primary and backup communication channel binding relationship table and the adaptive power supply guarantee strategy table as dynamic constraints, and generate the device load prediction curve for future periods by combining the real-time access heat parameters; input the device load prediction curve into the elastic scheduling optimizer, and optimize the time slicing and resource allocation of the distribution task under dynamic constraints, and output the distribution scheduling sequence. Scheduling module 705: Used to distribute the scheduling sequence to the local execution agent of each book terminal device. The local execution agent monitors the power supply and communication status changes in real time and feeds back the status change data to the predictive compensation engine. The predictive compensation engine makes trend predictions based on the deviation between the feedback data and the preset threshold, and generates the final distribution execution instruction set including channel switching instructions, power supply adjustment instructions and task reordering instructions.

[0066] Specifically, the calling module 702 includes: Extraction Unit: Used to call the power supply reliability assessment model to perform time-series analysis on the power supply health substructure in the equipment status snapshot structure, extract the time-series window data of the main and backup power supply voltage ratio, calculate the voltage stability index, extract the power supply temperature deviation to calculate the temperature risk index, extract the main and backup current balance to calculate the current balance index, and comprehensively generate the dynamic power supply health. The formula for calculating the voltage stability index is as follows: In the formula, It is the voltage stability index. Time-series window data of the primary and backup power supply voltage ratio. The standard deviation function is denoted by , and mean is denoted by . Calculation unit: used to call the communication quality assessment model to perform spatial analysis on the communication quality substructure in the device status snapshot structure, and calculate the comprehensive channel quality index for each communication channel; Construction Unit: Used to construct the device-resource matching evaluation tensor. It maps dynamic power supply health to the first dimension of the tensor, maps the mean of the multi-channel stability spectrum to the second dimension, and compares the device's available storage percentage with the number of resource blocks to calculate the storage matching degree, mapping this to the third dimension. It introduces resource priority weight coefficients as weighting adjustment factors, and generates a distribution feasibility weight matrix through nonlinear fusion transformation. The formula for calculating the matrix elements is as follows: In the formula, Let σ represent the feasibility weight value for allocating the i-th device to the j-th resource, and let σ denote the normalization function. This represents the dynamic power supply health of the i-th device. This represents the mean of the multi-channel stability spectrum of the i-th device. This represents the storage matching degree between the i-th device and the j-th resource. This represents the priority weight coefficient of the j-th resource, where i is the device index and j is the resource index.

[0067] It should be noted that the specific methods by which each module performs operations in the system described in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0068] Example 3: Corresponding to the above method embodiments, this embodiment also provides a metadata-based book distribution communication scheduling device. The metadata-based book distribution communication scheduling device described below and the metadata-based book distribution communication scheduling method described above can be referred to in correspondence.

[0069] Figure 3This is a block diagram illustrating a metadata-based book distribution communication scheduling device 800 according to an exemplary embodiment. Figure 3 As shown, the metadata-based book distribution communication scheduling device 800 includes a processor 801 and a memory 802. The metadata-based book distribution communication scheduling device 800 also includes one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0070] The processor 801 controls the overall operation of the metadata-based book distribution communication scheduling device 800 to complete all or part of the steps in the metadata-based book distribution communication scheduling method described above. The memory 802 stores various types of data to support the operation of the metadata-based book distribution communication scheduling device 800. This data may include, for example, instructions for any application or method operating on the metadata-based book distribution communication scheduling device 800, as well as application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as keyboards, mice, or buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the metadata-based book distribution communication scheduling device 800 and other devices. Wireless communication includes, for example, Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, or an NFC module.

[0071] In an exemplary embodiment, the metadata-based book distribution communication scheduling device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the metadata-based book distribution communication scheduling method described above.

[0072] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the metadata-based book distribution communication scheduling method described above. For example, the computer-readable storage medium may be the memory 802 including the program instructions described above, which may be executed by the processor 801 of the metadata-based book distribution communication scheduling device 800 to complete the metadata-based book distribution communication scheduling method described above.

[0073] Example 4: Corresponding to the above method embodiments, this embodiment also provides a readable storage medium. The readable storage medium described below can be referred to in conjunction with the metadata-based book distribution communication scheduling method described above.

[0074] A computer program is stored on a readable storage medium, and when the computer program is executed by a processor, it implements the steps of the metadata-based book distribution communication scheduling method described in the above method embodiments.

[0075] Specifically, the readable storage medium can be a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or any other readable storage medium capable of storing program code.

[0076] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A book distribution communication scheduling method based on metadata, characterized in that, include: Collect status metadata of all book terminal devices within the target area and extract attribute metadata of the book resources to be distributed; among which, status metadata includes real-time operating parameters of the power supply module, link quality parameters of each communication channel, available storage parameters of the device, geographical location parameters of the device, and historical running sequence parameters; attribute metadata includes data capacity parameters of the book resources, resource encoding format parameters, real-time access popularity parameters, and distribution priority parameters. The status metadata and attribute metadata are input into the power supply-communication-resource coupling evaluation engine. The power supply reliability evaluation model is called to quantify the real-time operating parameters and historical operating sequence parameters, and output the dynamic power supply health. The communication quality evaluation model is called to perform exponential calculation on the link quality parameters and geographical location parameters, and output the multi-channel stability spectrum. The dynamic power supply health, multi-channel stability spectrum, available storage parameters of the device and data capacity parameters are matched and calculated in multiple dimensions. The distribution priority parameter is introduced as a weighting coefficient, and the distribution feasibility weight matrix is ​​generated through matrix fusion processing. A candidate device list is formed by screening terminal devices with a weighted distribution feasibility value higher than a set threshold. For each device in the candidate device list, each communication channel is sorted according to its link quality parameters. The channel with the highest value is selected as the primary channel and the channel with the second highest value is selected as the backup channel, generating a primary and backup communication channel binding relationship table. Based on real-time operating parameters, the power supply contingency plan library is matched and the operating strategy is dynamically adjusted to generate an adaptive power supply guarantee strategy table. Using the primary and backup communication channel binding relationship table and the adaptive power supply guarantee strategy table as dynamic constraints, the load prediction model is invoked to perform time-series decomposition of historical runtime sequence parameters, and combined with real-time access heat parameters to generate equipment load prediction curves for future periods; the equipment load prediction curves are input into the elastic scheduling optimizer, and under dynamic constraints, the distribution tasks are optimized by time slicing and resource allocation, and the distribution scheduling sequence is output. The distribution and scheduling sequence is sent to the local execution agent of each book terminal device. The local execution agent monitors the changes in power supply and communication status in real time and feeds back the status change data to the predictive compensation engine. The predictive compensation engine makes trend predictions based on the deviation between the feedback data and the preset threshold, and generates the final distribution and execution instruction set, which includes channel switching instructions, power supply adjustment instructions and task reordering instructions.

2. The book distribution communication scheduling method based on metadata according to claim 1, characterized in that, The process involves inputting state metadata and attribute metadata into the power supply-communication-resource coupling evaluation engine, calling the power supply reliability evaluation model to quantify real-time operating parameters and historical operating sequence parameters, and outputting dynamic power supply health; and calling the communication quality evaluation model to perform exponential calculations on link quality parameters and geographical location parameters, and outputting a multi-channel stability spectrum. The dynamic power supply health, multi-channel stability spectrum, available storage parameters, and data capacity parameters are matched and calculated in multiple dimensions. A distribution priority parameter is introduced as a weighting coefficient. A distribution feasibility weight matrix is ​​generated through matrix fusion processing, which includes: The power supply reliability assessment model is invoked to perform time-series analysis on the power supply health substructure in the equipment status snapshot structure. Time-series window data of the primary and backup power supply voltage ratio is extracted, and the voltage stability index is calculated. The power supply temperature deviation is extracted to calculate the temperature risk index, and the primary and backup current balance is extracted to calculate the current balance index. These factors are then combined to generate a dynamic power supply health score. The formula for calculating the voltage stability index is as follows: In the formula, It is the voltage stability index. Time-series window data of the primary and backup power supply voltage ratio. The standard deviation function is denoted by , and mean is denoted by . The communication quality assessment model is invoked to perform spatial analysis on the communication quality substructure in the device status snapshot structure, and for each communication channel, the comprehensive channel quality index is calculated. A device-resource matching evaluation tensor is constructed. Dynamic power supply health is mapped to the first dimension of the tensor, the mean of the multi-channel stability spectrum is mapped to the second dimension, and the storage matching degree is calculated by comparing the device's available storage percentage with the number of resource blocks and mapped to the third dimension. A resource priority weight coefficient is introduced as a weighting adjustment factor. A distribution feasibility weight matrix is ​​generated through nonlinear fusion transformation. The formula for calculating the matrix elements is as follows: In the formula, Let σ represent the feasibility weight value for allocating the i-th device to the j-th resource, and let σ denote the normalization function. This represents the dynamic power supply health of the i-th device. This represents the mean of the multi-channel stability spectrum of the i-th device. This represents the storage matching degree between the i-th device and the j-th resource. This represents the priority weight coefficient of the j-th resource, where i is the device index and j is the resource index.

3. The book distribution communication scheduling method based on metadata according to claim 1, characterized in that, Terminal devices with values ​​higher than a set threshold in the screening and distribution feasibility weight matrix form a candidate device list; for each device in the candidate device list, each communication channel is sorted according to its link quality parameters, the channel with the highest value is selected as the primary channel, and the channel with the second highest value is selected as the backup channel, generating a primary and backup communication channel binding relationship table; Based on real-time operating parameters, a power supply contingency plan database is matched, and operating strategies are dynamically adjusted to generate an adaptive power supply guarantee strategy table, which includes: The distribution feasibility weight matrix is ​​segmented by thresholding. A feasibility threshold is set, and all elements in the matrix are traversed. Device-resource pairs that satisfy the distribution feasibility weight value being greater than the feasibility threshold are extracted to construct a candidate distribution pair set. Each element contains a device identifier, a resource identifier, and a corresponding weight value. For each device in the candidate distribution pair set, retrieve its communication quality substructure, obtain the comprehensive channel quality index of all communication channels of the device, sort the comprehensive channel quality index values ​​in descending order, select the channel identifier ranked first as the primary channel, select the channel identifier ranked second as the backup channel, construct the channel identifier pair, use the link quality parameters of the primary channel as the primary link parameter set, use the link quality parameters of the backup channel as the backup link parameter set, and generate a primary and backup communication channel binding relationship table; For each device in the candidate distribution pair set, its power supply health substructure is retrieved, the deviation between the main and backup power supply voltage ratio and the power supply temperature is extracted, the power supply plan matching engine is invoked, and the pre-stored power supply plan library is retrieved using the deviation between the main and backup power supply voltage ratio and the power supply temperature as query conditions. The matching power supply mode record is located, and the power consumption limit, estimated battery life, and power supply switching delay value in the record are extracted. An adaptive power supply guarantee strategy table is constructed, and the fields in the table include device identifier, power supply mode, power consumption limit, battery life, and switching delay.

4. The book distribution communication scheduling method based on metadata according to claim 1, characterized in that, The process uses the primary / backup communication channel binding relationship table and the adaptive power supply guarantee strategy table as dynamic constraints. It then calls the load prediction model to perform time-series decomposition of historical runtime sequence parameters and combines this with real-time access popularity parameters to generate device load prediction curves for future periods. This includes: The load record sequence of the device in the past time period is extracted from the historical runtime sequence parameters, where each load value represents the number of concurrent tasks of the device at the corresponding time. The time series decomposition module of the load prediction engine is called to decompose the load record sequence into trend component, periodic component and random component. The trend component is calculated by linear extrapolation to calculate the trend prediction value of the future time period. The periodic component is extracted by Fourier transform and periodic extension to obtain the periodic prediction value. The basic load prediction curve of the device is generated by combining the results. The calculation formula is as follows: In the formula, For the equipment base load prediction curve, This is a trend forecast value. The value is the periodic forecast, where t is a future time. By combining the standardized popularity value of book resources, the impact factor of resource popularity on equipment load is calculated. The impact factor is superimposed on the basic load prediction curve of the equipment to obtain the comprehensive load prediction curve of the equipment. At the same time, the available bandwidth of the primary channel and the available bandwidth of the backup channel bound to the equipment are calculated. The total channel capacity constraint is defined as the sum of the converted values ​​of the available bandwidth of the primary channel and the available bandwidth of the backup channel. The conversion factor is the backup channel capacity conversion factor preset by the system.

5. The book distribution communication scheduling method based on metadata according to claim 1, characterized in that, The formula for calculating the overall load prediction curve of the equipment is as follows: In the formula, For the overall load forecast curve of the equipment, As a factor impacting resource heat, For the equipment base load prediction curve.

6. The book distribution communication scheduling method based on metadata according to claim 1, characterized in that, The process involves inputting the equipment load prediction curve into the elastic scheduling optimizer, optimizing the task distribution by time slicing and resource allocation under dynamic constraints, and outputting a distribution scheduling sequence, including: The overall load prediction curve of the equipment and the total channel capacity constraint are input into the elastic scheduling optimizer to construct an optimization model with the goal of minimizing the distribution completion time and power consumption. The power consumption limit and endurance time in the adaptive power supply guarantee strategy table are used as the constraint boundary. After mixed integer programming, the distribution scheduling sequence is output. Each task in the sequence includes the task execution time window, target device identifier, resource identifier, designated channel identifier and power supply mode.

7. The metadata-based book distribution communication scheduling method according to claim 1, characterized in that, The local execution agent that distributes the scheduling sequence to each book terminal device monitors power supply and communication status changes in real time and feeds back the status change data to the predictive compensation engine, including: The distribution and scheduling sequence is encoded into a device-recognizable instruction frame format and sent to the local execution agent of the corresponding book terminal device through the main channel. The local execution agent receives the instruction frame and parses it into an internal task queue. At the same time, it starts the power supply monitoring thread and the communication monitoring thread. The power supply monitoring thread reads the real-time operating parameters of the power supply module at a fixed period, and the communication monitoring thread reads the link quality parameters of the multi-channel communication module at a fixed period. The local execution agent calculates the deviation between the current primary and backup power supply voltage ratio collected by the power supply monitoring thread and the preset voltage ratio threshold in the distribution and scheduling sequence to obtain the power supply deviation. The deviation between the current primary channel quality index collected by the communication monitoring thread and the preset channel quality threshold is calculated to obtain the communication deviation. The power supply deviation and communication deviation are encapsulated into a state change data packet and transmitted back to the predictive compensation engine through the backup channel.

8. The book distribution communication scheduling method based on metadata according to claim 1, characterized in that, The predictive compensation engine predicts trends based on the deviation between feedback data and preset thresholds, generating a final distribution and execution instruction set that includes channel switching instructions, power supply adjustment instructions, and task rescheduling instructions, including: The predictive compensation engine receives status change data packets from each device, calls the trend prediction filter to perform Kalman filtering on the power supply deviation and communication deviation, and predicts the deviation estimate for the next moment. The calculation formula is as follows: In the formula, The deviation estimate for the next time step. Here, K represents the deviation value at the current moment, and K is the Kalman gain coefficient. If the estimated power supply deviation exceeds the power supply alarm threshold, a channel switching instruction is generated to switch the primary channel to the backup channel. If the estimated communication deviation is lower than the communication quality threshold, a power supply adjustment instruction is generated to activate the backup power supply module and adjust the power consumption limit. At the same time, the task priority is recalculated based on the deviation estimate, a task reordering instruction is generated, and the final distribution and execution instruction set is combined to achieve real-time adjustment and compensation of the distribution process.

9. A metadata-based book distribution communication scheduling system, based on the metadata-based book distribution communication scheduling method of claim 1, characterized in that, include: The data acquisition module is used to collect the status metadata of all book terminal devices within the target area and extract the attribute metadata of the book resources to be distributed. The status metadata includes the real-time operating parameters of the power supply module, the link quality parameters of each communication channel, the available storage parameters of the device, the geographical location parameters of the device, and the historical running sequence parameters. The attribute metadata includes the data capacity parameters of the book resources, the resource encoding format parameters, the real-time access popularity parameters, and the distribution priority parameters. The calling module is used to input status metadata and attribute metadata into the power supply-communication-resource coupling evaluation engine, call the power supply reliability evaluation model to quantify real-time operating parameters and historical operating sequence parameters, and output dynamic power supply health; call the communication quality evaluation model to perform exponential calculations on link quality parameters and geographical location parameters, and output multi-channel stability spectrum; perform multi-dimensional matching calculations on dynamic power supply health, multi-channel stability spectrum, available storage parameters of equipment and data capacity parameters, introduce distribution priority parameters as weighting coefficients, and generate a distribution feasibility weight matrix through matrix fusion processing; The filtering module is used to filter terminal devices in the distribution feasibility weight matrix that are higher than a set threshold to form a candidate device list; for each device in the candidate device list, the communication channels are sorted according to their link quality parameters, the channel with the highest value is selected as the primary channel, and the channel with the second highest value is selected as the backup channel, generating a primary and backup communication channel binding relationship table; based on real-time operating parameters, the power supply contingency plan library is matched and the operating strategy is dynamically adjusted to generate an adaptive power supply guarantee strategy table; The optimization module is used to use the primary and backup communication channel binding relationship table and the adaptive power supply guarantee strategy table as dynamic constraints. It calls the load prediction model to perform time-series decomposition of historical runtime sequence parameters and combines real-time access popularity parameters to generate equipment load prediction curves for future periods. The equipment load prediction curves are then input into the elastic scheduling optimizer to optimize the time slicing and resource allocation of the distribution tasks under dynamic constraints, and output the distribution scheduling sequence. The scheduling module is used to distribute the scheduling sequence to the local execution agent of each book terminal device. The local execution agent monitors the power supply and communication status changes in real time and feeds back the status change data to the predictive compensation engine. The predictive compensation engine makes trend predictions based on the deviation between the feedback data and the preset threshold, and generates the final distribution execution instruction set, which includes channel switching instructions, power supply adjustment instructions and task reordering instructions.

10. The metadata-based book distribution communication scheduling system according to claim 9, characterized in that, The calling module includes: Extraction Unit: Used to call the power supply reliability assessment model to perform time-series analysis on the power supply health substructure in the equipment status snapshot structure, extract the time-series window data of the main and backup power supply voltage ratio, calculate the voltage stability index, extract the power supply temperature deviation to calculate the temperature risk index, extract the main and backup current balance to calculate the current balance index, and comprehensively generate the dynamic power supply health. The formula for calculating the voltage stability index is as follows: In the formula, It is the voltage stability index. Time-series window data of the primary and backup power supply voltage ratio. The standard deviation function is denoted by , and mean is denoted by . Calculation unit: used to call the communication quality assessment model to perform spatial analysis on the communication quality substructure in the device status snapshot structure, and calculate the comprehensive channel quality index for each communication channel; Construction Unit: Used to construct the device-resource matching evaluation tensor. It maps dynamic power supply health to the first dimension of the tensor, maps the mean of the multi-channel stability spectrum to the second dimension, and compares the device's available storage percentage with the number of resource blocks to calculate the storage matching degree, mapping this to the third dimension. It introduces resource priority weight coefficients as weighting adjustment factors, and generates a distribution feasibility weight matrix through nonlinear fusion transformation. The formula for calculating the matrix elements is as follows: In the formula, Let σ represent the feasibility weight value for allocating the i-th device to the j-th resource, and let σ denote the normalization function. This represents the dynamic power supply health of the i-th device. This represents the mean of the multi-channel stability spectrum of the i-th device. This represents the storage matching degree between the i-th device and the j-th resource. This represents the priority weight coefficient of the j-th resource, where i is the device index and j is the resource index.

Images