Cross-platform office equipment resource dynamic scheduling optimization method and system

Abstract

This invention discloses a method and system for dynamic scheduling and optimization of cross-platform office equipment resources, belonging to the field of scheduling optimization technology. By introducing a dynamic recoding mechanism based on historical node load information, this invention achieves more efficient data synchronization between cross-platform office equipment. Through real-time extraction of node overload frequency and dynamic adjustment of fragmented data paths, this invention effectively avoids hotspot node bottlenecks during synchronization, ensuring the continuity and timeliness of data transmission. Simultaneously, by comparing the predicted synchronization delay with the actual synchronization status, this invention can achieve adaptive correction of node performance, thereby gradually optimizing the data scheduling strategy between devices and improving the overall resource utilization efficiency of the system.

Description

Technical Field

[0001] This invention relates to the field of scheduling optimization technology, and in particular to a method and system for dynamic scheduling optimization of cross-platform office equipment resources. Background Technology

[0002] With the widespread adoption of remote work and multi-device collaboration, enterprises and individual users are placing higher demands on the real-time synchronization and dynamic scheduling of office resources in cross-platform and cross-network environments. Traditional office systems, often based on static device configurations and fixed synchronization mechanisms, struggle to meet the demands of today's complex office scenarios. Especially in situations involving frequent interactions and high concurrency between multiple platforms (such as mobile terminals, cloud platforms, and intranet servers), significant imbalances in device resource scheduling occur, leading to overload on some nodes while others remain idle, severely hindering overall office efficiency. Therefore, dynamically optimizing resource scheduling across cross-platform office devices and implementing a highly reliable state synchronization mechanism are critical technical challenges that current remote office management systems urgently need to address.

[0003] CN119172178B discloses a remote monitoring and management method for mobile office equipment based on the Internet of Things (IoT). It constructs a data prediction model by analyzing deviations in the operational behaviors of different users and abnormal characteristics of authentication frequency, thereby optimizing the identification and isolation of user behavior and improving the security and privacy protection capabilities of the equipment management system. This solution focuses on user behavior identification and risk control, addressing the identity verification problem in a shared device environment. However, its core technical approach remains focused on user-side management and differentiated responses, without addressing the optimization of collaborative scheduling and synchronization mechanisms among office equipment resources. Furthermore, this solution lacks strategies for handling key issues such as uneven resource distribution and synchronization delay control in multi-node data flow paths, and cannot effectively cope with system performance fluctuations caused by resource bottlenecks and synchronization inconsistencies. Therefore, in scenarios involving high-frequency cross-platform office status updates and resource synchronization, it still suffers from technical shortcomings such as insufficient processing capacity, inflexible scheduling, and uncontrollable latency.

[0004] CN116668047A discloses a mobile office management method, device, system, electronic device, and storage medium. It connects clients and internal servers via a cloud server to achieve access scheduling and secure communication of office service resources, focusing on ensuring data access security and connection efficiency. However, this method primarily revolves around access permission management and connection path design for service resources, neglecting the issues of task status data synchronization and fragmented scheduling mechanisms between office devices. In real-world office scenarios, there is a need for real-time synchronization of multi-source heterogeneous status data between different devices. Without support for data fragmentation encoding strategies, node overload frequency modeling, and dynamic scheduling mechanisms based on latency prediction, it will be difficult to cope with the uneven load and synchronization mismatch problems caused by frequent task data interactions. Therefore, while this solution improves access efficiency, it still lacks dynamic optimization capabilities at the resource scheduling level and cannot achieve real-time intelligent adjustment of status data between nodes. Summary of the Invention

[0005] In view of the problems existing in the above-mentioned background art, the present invention is proposed.

[0006] Therefore, the problem that this invention aims to solve is how to fundamentally address issues such as low synchronization scheduling efficiency, large state synchronization delay, and unpredictable overload nodes among multi-platform office devices.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0008] In a first aspect, the present invention provides a method for dynamic scheduling and optimization of cross-platform office equipment resources, comprising: collecting target status data A that needs to be synchronized among multiple office equipment, and dividing it into fragmented data sets F according to a preset fragmentation rule; extracting the node overload frequency parameter W recorded in the historical status data, and dynamically recoding the fragmented data set F to generate a recoded fragmented data set F′; calculating the parameter L of the expected synchronization delay of the recoded fragmented data set F′ on the current data flow path, and predicting the synchronization status result B based on the parameter L; synchronizing the recoded fragmented data set F′ among cross-platform equipment, and recording the synchronization status data C in real time; comparing the synchronization status data C with the synchronization status result B, and if the delay deviation exceeds a preset threshold, updating the node overload frequency parameter W and adjusting the subsequent fragmented recoding rule.

[0009] As a preferred embodiment of the cross-platform office equipment resource dynamic scheduling optimization method of the present invention, the generation of the fragmented data set F includes: constructing a status record list E = {e1|t1,e2|t2,...,e...} containing all office equipment. n |t n}, where e1~e n For the device nodes to be synchronized, t1~t nThe timestamp of the most recent synchronization record for the corresponding node, where n is the device node; ordered by device node e1 to e n State buffer space from time marker t1 to t n For the newly added target state, extract the data set A that needs to be synchronized, and attach the original node identifier and collection time to each data item; perform primary aggregation on data set A according to the preset content type and node priority dimension, and perform fragmentation processing according to the source node and data generation time sequence to generate fragmented data set F; attach the source node identifier and the time difference Δt between the fragment and the previous time mark to each fragment in fragmented data set F.

[0010] As a preferred embodiment of the cross-platform office equipment resource dynamic scheduling optimization method of the present invention, the generation of the recoded fragment data set F′ includes: retrieving the data forwarding saturation records of each node within the last T periods, extracting the corresponding overload frequency value according to the node number to form a node overload frequency parameter set; calculating the forwarding pressure weight coefficient η of each target node according to the node overload frequency parameter set to form a node weight vector; associating the target synchronization path of each fragment in the fragment data set F with the node weight vector, prioritizing the allocation of high-frequency fragments to high-weight nodes, and generating the recoded fragment data set F′.

[0011] As a preferred embodiment of the cross-platform office equipment resource dynamic scheduling optimization method of the present invention, wherein the forwarding pressure weight coefficient η is inversely proportional to the overload frequency.

[0012] As a preferred embodiment of the cross-platform office equipment resource dynamic scheduling optimization method of the present invention, the association includes: parsing the target synchronization path of each segment in the segmented data set F, extracting all relay node numbers involved in the path, and constructing a path node set corresponding to each segment; calculating the path weighted score of each path based on the node weight vector; and reordering the segmented data set F according to the frequency attribute and path weighted score corresponding to each segment.

[0013] As a preferred embodiment of the cross-platform office equipment resource dynamic scheduling optimization method of the present invention, the parameter L is calculated as follows: For each fragment in the recoded fragment data set F′, the relay node set of the synchronization path and the corresponding node pressure weight vector are called, and the average unit data forwarding time of each node in the T0 period is combined to construct the basic forwarding time sequence of each path; combined with the time difference Δt attached to the fragment, the basic forwarding time sequence is weighted and accumulated according to the node order to obtain the expected synchronization delay parameter L of the corresponding fragment; the prediction of the synchronization status result B includes: based on the parameter L value corresponding to each fragment, combined with the current network bandwidth status and queuing buffer parameters of the target device, the expected time for fragment synchronization is comprehensively evaluated, and combined with the waiting backlog represented by Δt, the synchronization status of each fragment is judged to form the synchronization status result B.

[0014] As a preferred embodiment of the cross-platform office equipment resource dynamic scheduling optimization method of the present invention, the following steps are included: updating the node overload frequency parameter W and adjusting the subsequent sharding recoding rules: if the delay deviation exceeds a preset threshold, retrieve the relay node number in the corresponding path and record it as a set of performance abnormal nodes; count the frequency of abnormal occurrence of each node in the set of performance abnormal nodes and update the node overload frequency parameter W to complete a historical performance status correction; based on the updated W, recalculate the forwarding pressure weight coefficient η and correct the node weight vector; based on the corrected node weight vector, adjust the recoding rules of the subsequent sharding data set so that high-frequency shards avoid unstable nodes.

[0015] Secondly, this invention provides a cross-platform office equipment resource dynamic scheduling and optimization system, comprising: a status acquisition module, used to acquire target status data A that needs to be synchronized among multiple office devices, and divide it into fragmented data sets F according to preset fragmentation rules; a recoding module, used to extract the node overload frequency parameter W recorded in historical status data, and dynamically recode the fragmented data set F to generate a recoded fragmented data set F′; a delay prediction module, used to calculate the expected synchronization delay parameter L of the recoded fragmented data set F′ on the current data flow path, and predict the synchronization status result B based on the parameter L; a synchronization execution module, used to synchronize the recoded fragmented data set F′ among cross-platform devices, and record the synchronization status data C in real time; and a deviation adjustment module, used to compare the synchronization status data C with the synchronization status result B, and if the delay deviation exceeds a preset threshold, update the node overload frequency parameter W and adjust the subsequent fragmented recoding rules.

[0016] Thirdly, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, they implement the steps of the cross-platform office equipment resource dynamic scheduling and optimization method as described in the first aspect of the present invention.

[0017] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of the cross-platform office equipment resource dynamic scheduling optimization method as described in the first aspect of the present invention.

[0018] The beneficial effects of this invention are as follows: By introducing a dynamic recoding mechanism based on historical node load information, this invention achieves more efficient data synchronization between cross-platform office devices. Through real-time extraction of node overload frequency and dynamic adjustment of fragmented data paths, this invention effectively avoids hotspot node bottlenecks during the synchronization process, ensuring the continuity and timeliness of data transmission. Simultaneously, by comparing the predicted synchronization delay with the actual synchronization status, this invention can achieve adaptive correction of node performance, thereby gradually optimizing the data scheduling strategy between devices and improving the overall resource utilization efficiency of the system. In summary, this invention not only significantly reduces the average latency of data synchronization in cross-platform office scenarios but also possesses dynamic load balancing and abnormal node avoidance capabilities, ensuring stability and real-time performance during multi-device collaboration, and is suitable for resource scheduling optimization needs in complex heterogeneous office network environments. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart for a method to dynamically schedule and optimize cross-platform office equipment resources.

[0021] Figure 2 The structure diagram of the cross-platform office equipment resource dynamic scheduling optimization system. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] As mentioned in the background section, with the widespread adoption of remote work and multi-device collaboration, enterprises and individual users have placed higher demands on the real-time synchronization and dynamic scheduling of office resources in cross-platform and cross-network environments. Traditional office systems, often based on static device configurations and fixed synchronization mechanisms, struggle to meet the demands of today's complex office scenarios. Especially in situations involving frequent interactions and high concurrency between multiple platforms (such as mobile terminals, cloud platforms, and intranet servers), significant imbalances in device resource scheduling occur, leading to overload on some nodes while others remain idle, severely hindering overall office efficiency. Therefore, how to dynamically optimize resource scheduling across cross-platform office devices and achieve a highly reliable state synchronization mechanism is a critical technical challenge that current remote office management systems urgently need to address.

[0026] Figure 1 This is a flowchart illustrating a cross-platform office equipment resource dynamic scheduling optimization method according to an embodiment of the present invention. Figure 1 As shown, the cross-platform office equipment resource dynamic scheduling optimization method includes:

[0027] S1: Collect target status data that needs to be synchronized between multiple office devices, and divide it into a data set F according to the preset data sharding rules.

[0028] A preferred method for generating the fragmented data set F includes the following steps:

[0029] (1) Construct a status record list E = {e1|t1,e2|t2,...,e...} containing all office equipment. n |t n}, where e1~e n For the device nodes to be synchronized, t1~t n This is the timestamp of the most recent synchronization record for the corresponding node, where n is the device node.

[0030] It should be noted that this list can be understood as an information table containing the current operating status, unique device number, and last successful synchronization time of multiple office devices (such as printers, scanners, shared terminals, etc.). The process of building this status record list includes: periodically capturing operating status information from the status monitoring module of each device (such as system log manager, status probe, etc.); transmitting the captured information to the aggregation server through the intranet control node; and performing data fusion, indexing, and unified list formatting on the aggregation server (such as using JSON or structured database tables).

[0031] At the same time, for each device node, this step also records the timestamp of its most recent synchronization record. This timestamp is extracted from the device's historical synchronization log to identify the time point when the device's last data status was successfully synchronized, ensuring that the starting state of the synchronization operation is accurate and controllable.

[0032] (2) According to equipment nodes e1 to e n State buffer space from time marker t1 to t n For newly added target states, extract the data set A that needs to be synchronized, and attach the original node identifier and collection time to each data item.

[0033] For example, based on the last synchronization timestamp of each device recorded in the status list, backtracking from the current moment to t n The process involves extracting target status information newly added within the device node's status cache during this period. This target status information may include newly added items in the device task queue, configuration file change information, usage log data, and user interaction records. Extracting this information requires data extraction on each device node. New data entries in the status cache are scanned based on timestamps. Each extracted data item must have two key fields forcibly appended: the original node identifier (i.e., the source of the data item) and the current collection time, forming a set of data entries with clear traceability and collection timestamps.

[0034] Unlike the crude approach of full synchronization or simple incremental synchronization in traditional methods, this method uses the time mark of each node as the boundary to accurately filter out newly generated or changed data within a time period from the node state buffer, thereby achieving fine-grained and time-series continuous state acquisition.

[0035] Specifically, each collected data item is appended with its original node identifier and collection time, ensuring that the data retains its source and time-series attributes throughout the subsequent sharding and scheduling processes. This effectively improves data traceability and timeliness, avoids data misalignment or out-of-order issues during synchronization, and significantly optimizes the reliability and accuracy of synchronization in multi-device environments.

[0036] (3) Perform primary aggregation on data set A according to the preset content type and node priority dimension, and perform sharding processing according to the source node and data generation time sequence to generate sharded data set F.

[0037] Preferably, in this embodiment of the invention, primary aggregation refers to the preliminary merging and organization of the collected target status data set based on content type (such as printing status, scanning task, fax activity, etc.) and node priority dimensions (such as device model priority, usage frequency priority, management level priority) without changing the original data granularity, so as to facilitate subsequent fragmentation processing and synchronization efficiency improvement.

[0038] In the initial aggregation process, each record in dataset A is first iterated through, and a content type label and node priority score are assigned to it. For example, a print job status record from a primary print device, with a calculated node priority score of 0.85, will be labeled as "Print Category - Priority 0.85". Then, the first round of grouping is performed based on content type; for example, all print-related data is grouped together first, followed by scanning, exception, and so on. This step ensures that similar data is grouped in order, facilitating data consistency during subsequent sharding.

[0039] Next, within each content type group, nodes are sorted according to their priority scores, from highest to lowest. This step is called priority dimension aggregation, and its core purpose is to improve the timeliness of data synchronization for critical devices.

[0040] Finally, it is important to emphasize that the primary aggregation method of this invention is specifically optimized for the temporal consistency of data from multiple sources. During the aggregation process, the collection timestamp of each data item is calculated, and the incrementality of the time series is ensured within the same data group. Even if there is a microsecond-level time difference between the data reported by different devices, alignment is achieved through a built-in time correction mechanism to ensure the timeline consistency of the fragmented data set F during subsequent synchronization.

[0041] This differs from existing technologies that simply aggregate by device but ignore the time sequence, significantly improving the logical coherence of multi-device synchronization.

[0042] (4) Add the source node identifier and the time difference Δt between the previous time stamp and each fragment in the fragmented data set F.

[0043] The source node identifier is used to quickly locate the original device node of the shard during the synchronization process, which facilitates rapid backtracking and accurate error repair in case of anomalies.

[0044] S2: Extract the node overload frequency parameter W recorded in the historical state data, and dynamically recode the fragmented data set F to generate the recoded fragmented data set F′.

[0045] S2.1: Retrieve the data forwarding saturation records of each node within the last T periods, extract the corresponding overload frequency values ​​according to the node number, and form a node overload frequency parameter set.

[0046] For example, firstly, historical status data from the last T periods (e.g., T could be the last 7 days or the last 1000 synchronization operations) is retrieved, and records of data forwarding saturation for each node are filtered out. A data forwarding saturation record refers to an event where a node reaches or exceeds a preset forwarding capacity limit within a certain period. For example, if node X triggers a forwarding saturation alarm 5 times within T periods, its corresponding overload frequency value is 5. Using the node number as an index, the overload frequency values ​​of all nodes are extracted, ultimately forming a node overload frequency parameter set.

[0047] S2.2: Based on the node overload frequency parameter set, calculate the forwarding pressure weight coefficient η for each target node to form a node weight vector. The forwarding pressure weight coefficient η is inversely proportional to the overload frequency, which can be expressed as:

[0048]

[0049] Among them, W i Let λ be the overload frequency of node i within period T, and λ be a coefficient for adjusting sensitivity (e.g., λi).

[0050] =0.1); Through this formula, when W i When η = 0 (i.e., the node is not overloaded), η = 1, indicating the node's full-load bearing capacity; while when W = 0, η = 1, indicating the node's full-load bearing capacity. i As the node increases, η decreases accordingly, dynamically reflecting the decline in the node's carrying capacity. The set of η values ​​for all nodes constitutes the node weight vector for the current period.

[0051] S2.3: Associate the target synchronization path of each fragment in the fragmented data set F with the node weight vector, prioritize the allocation of high-frequency fragments to high-weight nodes, and generate a recoded fragmented data set F′.

[0052] Furthermore, the association includes the following steps: parsing the target synchronization path of each fragment in the fragmented data set F, extracting all relay node numbers involved in the path, and constructing the path node set corresponding to each fragment; calculating the path weighted score of each path based on the node weight vector; and reordering the fragmented data set F according to the frequency attribute and path weighted score corresponding to each fragment.

[0053] For example, firstly, the data structure of each shard in set F is parsed to extract its target synchronization path, and the numbers of all relay nodes involved in the path are identified. For instance, a shard's synchronization path may pass through nodes e, b, and c in sequence, then the path node set corresponding to this path is {e, b, c}. The system performs this parsing operation sequentially on all shards in set F to construct a complete shard-path node association table.

[0054] Furthermore, based on the aforementioned node weight vector, a path weighted score is calculated for each synchronization path. Specifically, the path weighted score S... k The calculation method is as follows:

[0055]

[0056] Among them, P k This represents the set of path nodes for partitioning. Considering the dilution effect of path length (i.e., node hop count) on the score, this invention, based on the initial multiplication value, counts the number of node hops n in the path. e And based on the node hop count n e Calculate the dilution correction factor to avoid the imbalance where long paths have artificially low scores due to increased hop counts, while short paths have artificially high scores due to fewer hop counts. k The value accurately reflects the overall carrying capacity of the synchronization path. A higher value indicates less path pressure and suitability for frequent fragmentation; a lower value indicates a more fragile path and the need to reduce the load.

[0057] Based on the frequency attribute of each shard (i.e., the expected frequency of synchronous triggering of the shard in the business) and the path weighted score S k The fragmented data set F is reordered to generate a new recoded fragmented data set F′.

[0058] The specific sorting logic is as follows:

[0059] For high-frequency fragments, priority is given to allocating them to the path node set with higher path weighting scores to ensure that high-frequency synchronization tasks receive sufficient resource support.

[0060] Low-frequency fragmentation allows allocation to paths with slightly lower weight values, thereby balancing network load.

[0061] According to this rule, the priority of all fragments is rearranged, and the final recoding result F′ is formed.

[0062] It should be noted that the division of fragment frequency attributes is based on the trigger frequency statistics of historical synchronization tasks of fragments, combined with a sliding window weighted model with a certain period. Specifically, the actual number of times each fragment participates in synchronization within the most recent T periods is statistically analyzed, and the frequency index is determined accordingly. To distinguish between high and low frequencies, this invention adopts the quantile dynamic threshold method to avoid the problem of poor adaptability caused by fixed thresholds. The specific process is as follows: First, the frequency indexes of all fragments are sorted in ascending order; the Q quantile (e.g., Q = 0.7, i.e., the 70th percentile, etc., which is not uniquely limited here) is selected as the frequency division threshold; if the frequency index of a fragment is greater than or equal to the frequency division threshold, it is marked as a high-frequency fragment; otherwise, it is marked as a low-frequency fragment.

[0063] Through the above-mentioned recoding mechanism, the present invention effectively avoids the imbalance phenomenon of high-load nodes being continuously overloaded and low-load nodes being idle in the prior art, dynamically improves the resource scheduling efficiency of the synchronization network, and ensures the balanced and stable operation of data synchronization tasks in a large-scale office equipment environment.

[0064] S3: Calculate the parameter L of the expected synchronization delay of the recoded fragmented data set F′ on the current data stream path, and predict the synchronization status result B based on the parameter L.

[0065] S3.1: For each fragment in the recoded fragment data set F′, call the relay node set of the synchronization path and the corresponding node pressure weight vector, and combine the average unit data forwarding time of each node in the past few cycles to construct the basic forwarding time sequence of each path.

[0066] It should be noted that during multi-shard data synchronization, latency directly determines data consistency and the final synchronization timeliness. However, in existing technologies, shard synchronization latency is often only roughly estimated as the sum of the forwarding times of a single node, failing to fully consider differences in node load and dynamic changes in shard urgency, leading to significant prediction errors. Therefore, the operation of this invention first establishes the recoded shard data set F′ as the basic data object for latency calculation.

[0067] Furthermore, unlike conventional static path time calculation methods such as Hop Count or average link bandwidth, this invention employs a node load weight vector as a dynamic adjustment factor. Each node calculates its forwarding load capacity index based on historical overload frequency and current periodic load, accurately reflecting the fluctuations in its processing capacity over different time periods. The calculated weights are then matched one-to-one with the path node set to establish a path model with dynamic load characteristics for each shard.

[0068] S3.2: Combining the time difference Δt attached to the fragment, the basic forwarding time sequence is weighted and accumulated according to the node order to obtain the expected synchronization delay parameter L of the corresponding fragment.

[0069] In conventional synchronization systems, path delay is often measured by link rate or historical average, ignoring the recent fluctuations in node latency. This invention, however, statistically analyzes the average time taken to forward a unit data packet for each relay node over the past T periods, and uses this statistical value as a parameter characterizing node latency.

[0070] Specifically, for each node in the path, the recent periodic statistics table is queried to extract the average forwarding time per unit byte or unit fragment. Then, the forwarding times of all nodes are concatenated in the path order to form a basic forwarding time sequence. This sequence not only reflects the path length and the number of nodes, but also dynamically reflects the current congestion and time differences of each segment of the path, thereby significantly improving the accuracy of latency estimation.

[0071] After completing the construction of the basic time-consuming sequence, a weighted accumulation operation was further performed on the basic forwarding time-consuming sequence according to the node order, taking into account the time difference Δt attached to the fragment.

[0072] It should be noted that in data synchronization scenarios, some shards may become delayed and backed up due to network fluctuations, and their synchronization latency tolerance and priority differ significantly from those of real-time shards. To address this, this invention normalizes the Δt value and weights it with the time consumption of each node in the basic latency sequence to achieve dynamic compensation or improvement for the path latency of delayed shards. By summing the weighted sequence node by node, the expected synchronization latency parameter L of the shard can be obtained, accurately reflecting the expected synchronization time of the shard from its current state to the target device.

[0073] S3.3: The prediction of the synchronization state result B includes the following steps:

[0074] (1) Based on the parameter L value corresponding to each fragment, combined with the current network bandwidth status and queuing buffer parameters of the target device, comprehensively evaluate the estimated time for fragment synchronization to be completed.

[0075] It should be noted that calculating only the delay L cannot directly guide synchronization scheduling decisions, while the synchronization state result B, as a specific judgment result of the synchronization state, can provide a clear classification basis for the system. In this invention, the prediction operation of the synchronization state result B value is first based on the parameter L value corresponding to each segment, ensuring that the quantified data of the expected delay of each segment becomes the basis for decision-making; secondly, it is corrected and evaluated in combination with the current network bandwidth status and queuing buffer parameters of the target device. The final expected duration is obtained by dividing the parameter L by the average available bandwidth and multiplying by (1 minus the current buffer occupancy ratio of the device). That is, the parameter L is reduced when the average available bandwidth is large and amplified when the current buffer occupancy ratio of the device is close to 1, thereby dynamically reflecting the bandwidth increase and congestion risk. A very small positive number (such as 0.01) is added in the calculation to prevent division by zero error.

[0076] (2) Based on the waiting backlog represented by Δt, the synchronization status of each segment is judged to form the synchronization status result B.

[0077] Finally, this invention combines the waiting backlog represented by Δt to determine the synchronization status of each slice, forming a synchronization status result B. A comparison operation between the estimated duration and Δt is performed on each slice to determine the probability of successful slice synchronization.

[0078] Specifically, if the expected duration is less than the Δt value, meaning the expected synchronization duration is within an acceptable time limit, then the fragment is determined to be in a normal synchronization state, and is recorded as the normal identifier in the status result B value; if the expected duration is greater than or close to the Δt value, meaning the expected synchronization duration has exceeded or is close to the synchronization time limit, then the fragment is determined to have a synchronization timeout risk, and is recorded as a risk or timeout status identifier in the B value.

[0079] After completing the above-mentioned judgment of each segment, the status identification results of each segment are summarized to form the overall synchronization status prediction result B.

[0080] S4: Synchronize the recoded fragmented data set F′ across cross-platform devices and record the synchronization status data C in real time.

[0081] First, based on the target synchronization path of each fragment, a cross-platform data transmission task is initiated, scheduling each node in the optimized path to forward data sequentially according to its weight. To improve transmission efficiency, an intermediate protocol stack based on heterogeneous interface adaptation is preferred to ensure compatibility with the data interface limitations of different platform devices.

[0082] During the synchronization process, the aggregation server monitors the status and monitors each synchronization event of the recoded shard in real time, recording information including but not limited to: synchronization start time, transmission delay of each relay node in the path, whether failure retry occurs, actual completion time, and final confirmation flag.

[0083] Specifically, when recording the synchronization status data C, this invention incorporates the time difference Δt between the segments and the real-time completion time into the recording system. This design aims to directly quantify whether the segments have completed synchronization within the time limit during subsequent synchronization status determination. The above information is uniformly structured and recorded as the synchronization status data C.

[0084] S5: Compare the synchronization status data C with the synchronization status result B. If the delay deviation exceeds the preset threshold, update the node overload frequency parameter W and adjust the subsequent fragment recoding rules.

[0085] First, the core of the comparison is to compare the estimated synchronization completion time of each shard with the actual synchronization completion time, calculate the latency deviation between the two, i.e., |actual duration - estimated duration|, and compare it with a preset threshold. If the latency deviation exceeds the preset threshold, it indicates that there is an unexpected synchronization anomaly in that shard, triggering the following adjustment mechanism.

[0086] A better approach is to update the node overload frequency parameter W and adjust subsequent fragment recoding rules, including:

[0087] (1) If the delay deviation exceeds the preset threshold, retrieve the number of each relay node in the corresponding path and record it as a set of abnormal performance nodes; count the frequency of abnormal occurrence of each node in the set of abnormal performance nodes, and update the node overload frequency parameter W to complete a historical performance status correction.

[0088] (2) Based on the updated W, recalculate the forwarding pressure weight coefficient η and correct the node weight vector.

[0089] (3) Based on the corrected node weight vector, adjust the recoding rules of subsequent sharded data sets so that high-frequency shards avoid unstable nodes.

[0090] As can be seen, this invention, by comparing the predicted synchronization state result B with the actual synchronization state data C, promptly detects abnormal nodes and performance deviations in network transmission, achieving dynamic correction of the node overload frequency parameter W and adaptive adjustment of the recoding strategy. This mechanism can effectively identify unstable relay nodes, preventing high-frequency data from continuing to pass through performance bottleneck paths, thereby optimizing fragmentation scheduling and network load distribution. By updating the node weight vector and actively avoiding high-risk paths, the stability and success rate of data synchronization are significantly improved, reducing the risk of synchronization failure caused by transmission delays, congestion, or node failures, and enhancing the system's robustness and intelligent scheduling capabilities.

[0091] Furthermore, this embodiment also provides a cross-platform office equipment resource dynamic scheduling and optimization system, including,

[0092] The status acquisition module 100 is used to collect target status data A that needs to be synchronized between multiple office devices, and divide it into fragmented data sets F according to preset fragmentation rules;

[0093] The recoding module 200 is used to extract the node overload frequency parameter W recorded in the historical state data and dynamically recode the fragmented data set F to generate the recoded fragmented data set F′.

[0094] The delay prediction module 300 is used to calculate the parameter L of the expected synchronization delay of the recoded fragment data set F′ on the current data stream path, and predict the synchronization status result B based on the parameter L;

[0095] The synchronization execution module 400 is used to synchronize the recoded fragmented data set F′ across cross-platform devices and record the synchronization status data C in real time.

[0096] The deviation adjustment module 500 is used to compare the synchronization status data C with the synchronization status result B. If the delay deviation exceeds a preset threshold, the node overload frequency parameter W is updated and the subsequent fragment recoding rules are adjusted.

[0097] This embodiment also provides a computer device applicable to the cross-platform office equipment resource dynamic scheduling and optimization method, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to realize the cross-platform office equipment resource dynamic scheduling and optimization method proposed in the above embodiment.

[0098] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0099] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements the method for dynamic scheduling and optimization of cross-platform office equipment resources as proposed in the above embodiment.

[0100] In summary, this invention achieves more efficient data synchronization between cross-platform office devices by introducing a dynamic recoding mechanism based on historical node load information. Through real-time extraction of node overload frequency and dynamic adjustment of fragmented data paths, this invention effectively avoids hotspot node bottlenecks during synchronization, ensuring the continuity and timeliness of data transmission. Simultaneously, by comparing the predicted synchronization delay with the actual synchronization status, this invention can adaptively correct node performance, thereby gradually optimizing data scheduling strategies between devices and improving the overall resource utilization efficiency of the system. In conclusion, this invention not only significantly reduces the average latency of data synchronization in cross-platform office scenarios but also possesses dynamic load balancing and abnormal node avoidance capabilities, ensuring stability and real-time performance during multi-device collaboration, and is suitable for resource scheduling optimization needs in complex heterogeneous office network environments.

[0101] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for dynamic scheduling and optimization of cross-platform office equipment resources, characterized by: include: Collect target status data that needs to be synchronized between multiple office devices, and divide it into a data set F according to a preset data sharding rule; Extract the node overload frequency parameter W recorded in the historical status data, and dynamically recode the fragmented data set F to generate the recoded fragmented data set F′; Calculate the parameter L of the expected synchronization delay of the recoded fragmented data set F′ on the current data stream path, and predict the synchronization state result B based on the parameter L; The recoded fragmented data set F′ is synchronized across cross-platform devices, and the synchronization status data C is recorded in real time; If the delay deviation exceeds a preset threshold, the node overload frequency parameter W is updated and the subsequent fragment recoding rules are adjusted by comparing the synchronization status data C with the synchronization status result B. The generation of the recoded fragment data set F′ includes: retrieving the data forwarding saturation records of each node within the last T periods, extracting the corresponding overload frequency values ​​according to the node number to form a node overload frequency parameter set; and calculating the forwarding pressure weight coefficient of each target node based on the node overload frequency parameter set. A node weight vector is formed; the target synchronization path of each fragment in the fragmented data set F is associated with the node weight vector, and high-frequency fragments are preferentially allocated to high-weight nodes to generate a recoded fragmented data set F′. The association includes: parsing the target synchronization path of each fragment in the fragmented data set F, extracting all relay node numbers involved in the path, and constructing a path node set corresponding to each fragment; calculating the path weighted score of each path based on the node weight vector; and reordering the fragmented data set F according to the frequency attribute and path weighted score corresponding to each fragment.

2. The cross-platform office equipment resource dynamic scheduling and optimization method as described in claim 1, characterized in that: The generation of the fragmented data set F includes: Build a status log list containing all office equipment. ,in, ~ For the device nodes to be synchronized, ~ The timestamp of the most recent synchronization record for the corresponding node. For device nodes; By device node ~ Time stamp in the state buffer ~ For newly added target states, extract the data set A that needs to be synchronized, and attach the original node identifier and collection time to each data item; Data set A is initially aggregated according to preset content type and node priority dimension, and then sharded according to source node and data generation time sequence to generate sharded data set F; Add a source node identifier and the time difference Δt between the previous time stamp and each fragment in the fragmented data set F.

3. The cross-platform office equipment resource dynamic scheduling and optimization method as described in claim 2, characterized in that: The forwarding pressure weight coefficient It is inversely proportional to the frequency of overload.

4. The cross-platform office equipment resource dynamic scheduling and optimization method as described in claim 1, characterized in that: The parameter L is calculated as follows: For each fragment in the recoded fragmented data set F′, the relay node set of the synchronization path and the corresponding node pressure weight vector are called, combined with the proximity of each node. The average time for unit data forwarding within a period is used to construct the basic forwarding time sequence for each path; By combining the time difference Δt associated with the fragment, the basic forwarding time sequence is weighted and accumulated according to the node order to obtain the expected synchronization delay parameter L of the corresponding fragment; The prediction of the synchronization status result B includes: based on the parameter L value corresponding to each segment, combined with the current network bandwidth status and queuing buffer parameters of the target device, comprehensively evaluating the estimated time for segment synchronization to be completed, and combining the waiting backlog degree represented by Δt, judging the synchronization status of each segment to form the synchronization status result B.

5. The cross-platform office equipment resource dynamic scheduling and optimization method as described in claim 1, characterized in that: The update of the node overload frequency parameter W and the adjustment of subsequent fragment recoding rules include: If the delay deviation exceeds the preset threshold, the relay node number in the corresponding path is retrieved and recorded as a set of nodes with abnormal performance; the frequency of abnormal occurrence of each node in the set of nodes with abnormal performance is counted, and the node overload frequency parameter W is updated to complete a historical performance status correction. Based on the updated W, recalculate the forwarding pressure weight coefficient. Correct the node weight vector; Based on the corrected node weight vector, the recoding rules of subsequent sharded data sets are adjusted to ensure that high-frequency shards avoid unstable nodes.

6. A cross-platform office equipment resource dynamic scheduling and optimization system, based on the cross-platform office equipment resource dynamic scheduling and optimization method according to any one of claims 1 to 5, characterized in that: Also includes: The status acquisition module is used to collect target status data A that needs to be synchronized between multiple office devices, and divide it into fragmented data sets F according to preset fragmentation rules; The recoding module is used to extract the node overload frequency parameter W recorded in the historical state data and dynamically recode the fragmented data set F to generate the recoded fragmented data set F′. The delay prediction module is used to calculate the parameter L of the expected synchronization delay of the recoded fragment data set F′ on the current data stream path, and predict the synchronization status result B based on the parameter L; The synchronization execution module is used to synchronize the recoded fragmented data set F′ across cross-platform devices and record the synchronization status data C in real time. The deviation adjustment module is used to compare the synchronization status data C with the synchronization status result B. If the delay deviation exceeds a preset threshold, the node overload frequency parameter W is updated and the subsequent fragment recoding rules are adjusted.

7. A computer device, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the cross-platform office equipment resource dynamic scheduling and optimization method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the cross-platform office equipment resource dynamic scheduling and optimization method according to any one of claims 1 to 5.

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