A distributed cache-based enterprise budget dynamic control method

Abstract

The present application relates to the technical field of distributed storage, in particular to a kind of enterprise budget dynamic control method based on distributed cache. Including parsing budget allocation data and topology change atlas, locate state pointer sequence and quota change value in distributed cache;State pointer sequence is executed bit disassembly, and pre-occupancy offset and cancellation offset are extracted, and are spliced with quota change value to generate concurrent operation input parameter, and displacement calculation is executed to generate state update result;Combined with state update result, generate cluster load characteristics to open background double-write buffer area, use incremental redirection pointer to transfer bottom overwrite instruction to execute lossless live migration.The present application jointly analyzes budget allocation data and topology change atlas, moderately decouples micro-operation link and macro-evolution path, and provides fault-tolerant bottom protection for continuous availability of cache data in complex evolution period.

Description

Technical Field

[0001] This invention relates to the field of distributed storage technology, specifically to a method for dynamic control of enterprise budgets based on distributed caching. Background Technology

[0002] High-concurrency allocation of hierarchical enterprise budget data within a large network architecture is an important current data processing application. Existing technologies generally employ a data update method that combines relational mapping with database exclusive locks. This involves monitoring changes in the data volume of business instructions, and when a set threshold is reached, automatically matching and updating the storage status of related nodes within a pre-established hierarchical topology. This is currently the mainstream technical approach for managing large-scale budget amounts.

[0003] However, existing technical solutions have significant shortcomings in terms of low-level concurrent processing efficiency and topology availability. Their data access mechanisms for maintaining strong consistency suffer from staticity and coarse granularity. Existing mechanisms are mostly based on a single node identifier for independent serialization, and their isolation boundaries cannot be adjusted with changes in node inheritance relationships. This makes it difficult to accurately match complex hierarchical relationship mappings, resulting in blocking deviations in the memory state of low-level concurrent reads and writes. The basic storage mechanism only performs discrete byte overwriting at the moment the request arrives, which cannot achieve high-density atomic state synchronization. It cannot complete the fusion and transformation of multi-dimensional data states within the same instruction cycle, nor can it eliminate the low-level dirty read phenomenon under high load conditions. In addition, there is a coupling problem in the evolution and update of the underlying architecture. That is, when delivering processing results to the access end, a smooth transition is not made according to the carrying characteristics of the underlying physical medium. Only a full-scale blocking shutdown and reconstruction can be adopted, resulting in a lack of continuous availability guarantee for the final generated cached data.

[0004] To address this, a dynamic enterprise budget control method based on distributed caching is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide a dynamic enterprise budget control method based on distributed caching to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a dynamic enterprise budget control method based on distributed caching, comprising: Analyze budget allocation data and topology change map to extract node identifier set and quota change value; perform hash calculation on node identifier set to extract target hash key name; use target hash key name to trigger addressing in distributed cache to generate virtual memory shard; Based on the virtual memory fragmentation, a tree-like topology map is extracted, and the topology node objects and status pointer sequences are located by traversal; the status pointer sequence is decomposed into bits to extract the pre-occupied offset and the cancellation offset, which are then concatenated with the quota change value to generate concurrent operation input parameters; the preset operation logic is run to perform displacement calculations on the concurrent operation input parameters to generate the status update results; Based on the state update results, generate underlying overwrite instructions and execute the underlying overwrite instructions to write to the virtual memory shards; extract the instruction queuing depth of the virtual memory shards and integrate the state update results to generate cluster load characteristics; generate evolution control instructions based on the cluster load characteristics and topology change map, and execute the evolution control instructions to open a background dual write buffer. The topology change graph is loaded in the background dual-write buffer, the incremental redirection pointer of the topology change graph is extracted, the underlying overwrite instruction is transferred to the background dual-write buffer, lossless hot migration is performed, and the underlying resources are pre-expanded.

[0007] Preferably, the specific generation process of the virtual memory shard includes: receiving budget allocation data, triggering the underlying byte stream loading logic, performing byte sequence scanning on the budget allocation data to obtain message header features; extracting the payload from the budget allocation data based on the message header features to generate basic service messages; performing memory dictionary parsing on the basic service messages to obtain key-value pair attributes; separating the key-value pair attributes to generate a node identifier set and a quota change value; extracting the routing alignment label carried by the node identifier set; inputting the routing alignment label into the hash calculation engine to perform underlying hash calculation to generate an initial hash digest; performing bitwise AND mask operations on the initial hash digest to extract a fixed-length sequence; combining the fixed-length sequence with the node identifier set to perform reassembly and splicing to generate a target hash key name; pushing the target hash key name to the addressing routing engine configured in the distributed cache, performing slot mapping calculation on the target hash key name to obtain a hash slot index; using the hash slot index to compare and lock the target memory block in the cluster topology of the distributed cache; and performing boundary parameter encapsulation processing on the target memory block to generate a virtual memory shard.

[0008] Preferably, the specific generation process of the topology node object and the state pointer sequence includes: initiating a slot aggregation read request to the underlying component of the distributed cache based on virtual memory sharding to obtain the same source data block; performing protocol deserialization conversion and restoration on the same source data block to obtain a tree-like topology graph; extracting the root node access credentials of the tree-like topology graph; starting depth-first logic on the tree-like topology graph based on the root node access credentials; performing step-by-step matching and comparison using the node identifier set during the execution of the depth-first logic; locking the target memory block after the matching and comparison is successful and instantiating it to generate a topology node object; extracting the continuous addressing cursor mounted inside the topology node object, and generating a state pointer sequence based on the continuous addressing cursor.

[0009] Preferably, the specific generation process of the concurrent operation input parameters includes: obtaining the underlying binary byte stream mapped by the state pointer sequence; setting a fixed starting position cursor for the underlying binary byte stream; loading a preset memory layout dictionary and extracting the configured high-bit truncation mask and low-bit truncation mask; combining the high-bit truncation mask and the starting position cursor, performing logic and calculation separation on the underlying binary byte stream to obtain a pre-placement offset; combining the low-bit truncation mask and the starting position cursor, performing logic and calculation separation on the underlying binary byte stream to obtain a verification offset; performing memory length alignment conversion on the quota change value to obtain a standard calculation variable; writing the standard calculation variable, the pre-placement offset, and the verification offset in sequence into the underlying continuous buffer area according to a preset continuous arrangement rule, and performing encapsulation processing on the underlying continuous buffer area to generate concurrent operation input parameters.

[0010] Preferably, the specific generation process of the state update result includes: pushing and loading the concurrent operation input parameters into the arithmetic logic unit of the preset operation logic configuration; performing destructuring on the concurrent operation input parameters to extract the quota change value, the pre-occupied offset, and the cancellation offset; extracting the corresponding original pre-occupied data based on the pre-occupied offset; extracting the corresponding original cancellation data based on the cancellation offset; constructing a memory mask variable for the pre-occupied offset; performing a mask shift operation on the original pre-occupied data based on the memory mask variable to isolate the target state identifier; locking the word-aligned memory row to which the target state identifier belongs; performing an integer atomic update in a spin replacement loop using the quota change value to generate new pre-occupied data; constructing a corresponding mask variable for the cancellation offset; performing a mask shift operation on the original cancellation data to isolate the corresponding state identifier; locking the word-aligned memory row to which the corresponding state identifier belongs; performing an integer atomic update in a spin replacement loop using the quota change value to generate new cancellation position data; integrating the new pre-occupied data and the new cancellation position data, and splicing them into a structured carrier to generate the state update result.

[0011] Preferably, the specific process for generating the cluster load characteristics includes: parsing the data byte length attribute contained in the state update result; combining the data byte length attribute with the target hash key name to assemble a low-level overwrite instruction with transaction execution characteristics; delivering the low-level overwrite instruction to persistently save the state update result to the high-speed buffer page corresponding to the virtual memory shard; sending state monitoring probe packets to the input / output queues bound to the virtual memory shards; using the state monitoring probe packets to collect the total amount of unprocessed request packets currently piled up in the input / output queues to generate the instruction queuing depth; performing a normalized mapping calculation on the instruction queuing depth to obtain a dimensionless weight value; and performing a scalar product calculation on the dimensionless weight value and the data byte length attribute to derive the cluster load characteristics.

[0012] Preferably, the specific establishment process of the background dual-write buffer includes: performing scanning calculations from within the topology change graph to extract the relationship evolution path associated with the abnormal nodes; extracting cluster load characteristics and performing comparison and judgment logic in conjunction with a preset capacity baseline; when the cluster load characteristics exceed the capacity baseline, performing resource protection interception based on the relationship evolution path; integrating the relationship evolution path and the target hash key name to encapsulate and compile evolution control instructions; issuing and delivering the evolution control instructions to the cache allocation engine, calling the logical isolation allocation interface to apply for independent logical blocks in the idle storage space; performing ring queue data structure initialization and formatting processing on the independent logical blocks to generate traffic isolation space and establish the background dual-write buffer.

[0013] Preferably, the specific process of transferring the underlying overwrite instructions to the background dual-write buffer includes: loading the structured object of the topology change graph into the independent memory cache area managed by the background dual-write buffer; traversing and scanning the structured object to locate the abnormal topology nodes whose hierarchical relationship has changed; extracting the continuous memory cursors allocated to the abnormal topology nodes under the new topology structure to generate incremental redirection pointers; performing modification operations on the proxy routing engine of the distributed cache to change the default forwarding path and enable instruction mirroring; using instruction mirroring to perform synchronous dual-path overwrite on the original virtual memory shards and the background dual-write buffer; responding to the old memory data loading completion status, sending a global flow-cutting instruction to the proxy routing engine; using the global flow-cutting instruction to drive the proxy routing engine to close the instruction mirroring and redirect all service requests to the background dual-write buffer; discarding the original virtual memory shards according to the incremental redirection pointers; capturing the legacy overwrite instructions inside the transmission link and performing instruction mirror copying, performing lossless hot migration, and performing tensor calculations based on the cluster load characteristics to trigger pre-expansion.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. By performing bit-by-bit decomposition on the status pointer sequence to extract pre-occupancy offsets and write-off offsets, and concatenating them with quota change values ​​to generate concurrent operation input parameters, the high-frequency fluctuating computational objects are reduced in dimensionality and flattened into a continuous physical memory bit stream. When facing complex hierarchical evolution, large-scale locking and isolation are unnecessary at the macro-level data block level. Through underlying orchestration and sequential fusion concatenation, favorable conditions are provided for the smoothness of computational objects during memory flow, largely avoiding the risk of data flow interruption caused by micro-structural changes, thus laying the foundation for the continuous availability of the final generated cached data.

[0015] 2. By running preset computational logic, the concurrent computation input parameters are shifted to generate state update results. Based on these results, underlying overwrite instructions are generated and written to virtual memory shards, achieving state flipping and quota iteration. This computational mechanism conforms to the carrying characteristics of the underlying physical medium, significantly compressing the complex arithmetic verification cycle. Under the dual pressure of massive requests and dynamic topology adjustments, relying on lightweight instruction execution at the micro-bit level and mask isolation, the underlying binary byte stream can complete the smooth transition of data state in a very short period of time, which is conducive to maintaining the continuity of business data.

[0016] 3. By generating evolution control instructions based on cluster load characteristics and topology change graphs, a dual-write buffer is created in the background. This buffer loads the topology change graph and extracts incremental redirection pointers, transferring the underlying overwrite instructions for lossless hot migration, thus mitigating the physical impact of topology reorganization. Without disrupting the front-end call chain, instruction mirroring and dual-path overwrite facilitate a smooth handover of old and new memory data, ensuring that cached data maintains good continuous availability throughout the entire topology change cycle.

[0017] 4. By jointly analyzing budget allocation data and topology change maps, the micro-computation links and macro-evolution paths are appropriately decoupled, and an isolated yet collaborative overall execution mechanism is built at the underlying level. On the one hand, the state pointer sequence is decomposed and spliced ​​to generate concurrent operation input parameters, and the state update result is obtained independently by relying on bit shift calculation. On the other hand, the instruction queuing depth is extracted and the above results are integrated to generate cluster load characteristics, dynamically generating evolution control instructions, and extracting incremental redirection pointers from the background dual-write buffer to perform independent migration. This loosely coupled logic ensures that quota overwriting and physical topology adjustment do not hinder each other, reduces the binding dependency between memory access links and the underlying storage structure, effectively mitigates the risk of global addressing stagnation caused by local hierarchical changes, avoids the passive processing method of being forced to adopt full-scale blocking shutdown and reconstruction, and provides a more flexible underlying guarantee for the continuous availability of cached data in complex evolution cycles. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a dynamic enterprise budget control method based on distributed caching, as proposed in an embodiment of this invention. Figure 2 This is a flowchart of bit decomposition and concurrent shifting operations proposed in an embodiment of this invention application; Figure 3 This is a flowchart of load sensing and mirror shunt thermal migration proposed in an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figures 1-3 The present invention provides a method for dynamic enterprise budget control based on distributed caching, the specific steps of which are as follows: Analyze budget allocation data and topology change map to extract node identifier set and quota change value; perform hash calculation on node identifier set to extract target hash key name; use target hash key name to trigger addressing in distributed cache to generate virtual memory shard; Based on the virtual memory fragmentation, a tree-like topology map is extracted, and the topology node objects and status pointer sequences are located by traversal; the status pointer sequence is decomposed into bits to extract the pre-occupied offset and the cancellation offset, which are then concatenated with the quota change value to generate concurrent operation input parameters; the preset operation logic is run to perform displacement calculations on the concurrent operation input parameters to generate the status update results; Based on the state update results, generate underlying overwrite instructions and execute the underlying overwrite instructions to write to the virtual memory shards; extract the instruction queuing depth of the virtual memory shards and integrate the state update results to generate cluster load characteristics; generate evolution control instructions based on the cluster load characteristics and topology change map, and execute the evolution control instructions to open a background dual write buffer. The topology change graph is loaded in the background dual-write buffer, the incremental redirection pointer of the topology change graph is extracted, the underlying overwrite instruction is transferred to the background dual-write buffer, lossless hot migration is performed, and the underlying resources are pre-expanded.

[0021] The technical solution of the present invention will be further described in detail below with reference to specific embodiments.

[0022] Example 1

[0023] This application discloses a method for dynamic enterprise budget control based on distributed caching, see below. Figure 1The specific steps proposed in this invention include: S1. Parsing budget allocation data and topology change graphs to extract node identifier sets and quota change values; performing hash calculations on the node identifier sets to extract target hash key names; using the target hash key names to trigger addressing in the distributed cache to generate virtual memory shards; S2. Extracting tree-like topology graphs based on virtual memory shards, locating topology node objects and status pointer sequences through traversal; performing bit-by-bit decomposition on the status pointer sequences to extract pre-occupied offsets and write-off offsets, concatenating them with quota change values ​​to generate concurrent operation input parameters; running preset operation logic for concurrent operation. S3. Calculate the displacement based on the input parameters to generate state update results; S4. Generate underlying overwrite instructions based on the state update results, and execute the underlying overwrite instructions to write to the virtual memory shards; extract the instruction queuing depth of the virtual memory shards, and merge the state update results to generate cluster load characteristics; generate evolution control instructions based on the cluster load characteristics and topology change graph, and execute the evolution control instructions to open a background dual-write buffer; S5. Load the topology change graph in the background dual-write buffer, extract the incremental redirection pointer of the topology change graph, transfer the underlying overwrite instructions to the background dual-write buffer, perform lossless hot migration, and trigger the pre-expansion of underlying resources.

[0024] Further, the budget allocation data and topology change map are analyzed to extract the node identifier set and the quota change value; a hash calculation is performed on the node identifier set to extract the target hash key name; the target hash key name is used to trigger addressing in the distributed cache to generate virtual memory shards; corresponding to step S1 above; the specific implementation process includes: Upon receiving budget allocation data, the underlying byte stream loading logic is triggered. A byte sequence scan is performed on the budget allocation data to obtain header features. Based on the header features, the payload of the budget allocation data is extracted to generate a basic business message. A memory dictionary is parsed on the basic business message to obtain key-value pair attributes. The key-value pair attributes are separated to generate a set of node identifiers and a quota change value. The routing alignment label carried by the node identifier set is extracted. The routing alignment label is input into the hash calculation engine to perform underlying hash calculation to generate an initial hash digest. A bitwise AND mask operation is performed on the initial hash digest to extract a fixed-length sequence. The fixed-length sequence is combined with the node identifier set to reassemble and concatenate to generate the target hash key name. The target hash key name is pushed to the addressing routing engine configured in the distributed cache. Slot mapping calculation is performed on the target hash key name to obtain a hash slot index. The hash slot index is used to compare and lock the target memory block in the cluster topology of the distributed cache. Boundary parameter encapsulation processing is performed on the target memory block to generate a virtual memory fragment.

[0025] Specifically, the process of generating virtual memory fragments is as follows: The network interface controller receives budget allocation data and topology change graphs from upstream services. After the budget allocation data enters the underlying ring network buffer of the node's network interface card (NIC), it is directly mapped to user-space memory, triggering the underlying byte stream loading logic. The processing thread performs a byte sequence scan on the budget allocation data to obtain packet header characteristics. The topology change graph is specifically structured in physical memory as an adjacency list array containing mappings of multiple changed nodes. This adjacency list array is carried by a continuous stream of memory pages, recording the incremental log of node parent-child relationship pointers before and after the change.

[0026] In this embodiment, the source address field contained in the packet header features is extracted and mapped to an independent token bucket state unit for rate limiting verification. If the current remaining amount in the bucket is less than zero, the operation is blocked, the packet is discarded, and stored in a temporary blacklist cache. The source address field contained in the packet header features is obtained and mapped to an independent token bucket state unit in the hash table using this as the key. The token bucket contains two variables: the current timestamp and the current number of tokens. The processing thread calculates and accumulates the number of newly generated tokens by multiplying the difference between the current hardware clock and the timestamp in the bucket by a preset rate constant, and then truncates the tokens using a preset upper limit threshold. The rate constant is selected based on the average request throughput in historical benchmark stress tests; the upper limit threshold is selected based on the maximum burst packet receiving capacity of the underlying physical network interface card. Then, an attempt is made to deduct one token: if the remaining amount after deduction is not less than 0, the timestamp and token count are updated, and the message is allowed to pass; if the remaining amount is less than 0, subsequent operations are blocked, and the source address is stored in a temporary blacklist cache with an expiration time. The expiration time is selected as twice the retransmission timeout of the underlying network transmission protocol standard. The underlying driver compares the blacklist, removes the packet's starting address pointer corresponding to the source address from the receive queue descriptor, performs silent discarding, and thus interrupts the data stream entering the subsequent payload extraction logic. The above process intercepts high-frequency malicious requests in advance, avoids invalid messages consuming memory bandwidth, and improves the security of underlying addressing.

[0027] During the byte sequence scanning process, the processing thread uses a preset sliding window algorithm to find the pre-defined magic number. The sliding window step size parameter is selected based on the minimum alignment unit of the underlying network protocol stack; in this specific example, a step size of 4 bytes is used. The processing thread slides forward in memory in units of 4 bytes, comparing each byte bit by bit with the target value. When consecutive hexadecimal bytes are detected, it is determined that a valid packet start point has been detected, and then a fixed length is read to generate the packet header features. This fixed length parameter is selected based on the sum of the byte lengths of the four fields in the packet header (i.e., 4-byte magic number + 2-byte version number + 4-byte payload length + 4-byte checksum, totaling 14 bytes).

[0028] The message header features four consecutive fields, all stored in big-endian network byte order and converted to local host byte order by the processing instructions during reading. The first field is the magic number, occupying an offset of 0 to 3 bytes, a 32-bit unsigned integer, representing the message's validity. The second field is the protocol version number, occupying an offset of 4 to 5 bytes, a 16-bit unsigned integer, indicating the interface protocol version. The third field is the payload length, occupying an offset of 6 to 9 bytes, a 32-bit unsigned integer, recording the length of the data body in bytes. The fourth field is the checksum, occupying an offset of 10 to 13 bytes, a 32-bit unsigned integer, serving as the integrity checksum for cyclic redundancy check.

[0029] Based on the payload length recorded in the message header characteristics, the processing thread truncates the budget allocation data according to the corresponding memory boundaries, directly calculates the offset of the memory pointer, removes the padding bytes and headers added by the network protocol stack, and generates the basic business message.

[0030] Because the basic business messages use a compact variable-length encoding format, the processing thread loads a protocol mapping dictionary table pre-loaded in memory. This dictionary table is an array of triples representing "field sequence number-field type-byte length," which is parsed into key-value pairs through deserialization to obtain the key-value pair attributes. The processing thread reads the most significant bit of each field; if it is 1, it indicates that the following bytes belong to the same integer data type; if it is 0, it indicates the end of the current integer data type. Through this bitmask stripping and left-shift concatenation operation, the key-value pair attributes are separated, generating a set of node identifiers and a quota change value. The node identifier set contains a set of strings with hierarchical prefixes, such as fund accounts 01 and 02 under the finance department. The quota change value indicates the specific increase or decrease.

[0031] The processing logic extracts the route alignment tag carried by each node from the generated set of node identifiers. The route alignment tag is the prefix of the node identifier string. The processing unit inputs the route alignment tag into the hardware-accelerated hash calculation engine to perform low-level hash calculation. The hashing process is as follows: the input string is divided into multiple data blocks of 4 bytes each, each data block is multiplied by a preset first constant and then by a preset second constant, then circularly shifted left, and then XORed with the current hash seed; after processing all data blocks, the final result is subjected to an avalanche mixing operation, including right shift XOR, multiplication by a third constant, and then right shift XOR again, outputting a 32-bit initial hash digest. Among them, the first constant, the second constant and the third constant are all preset unencrypted hash magic numbers. The selection method is: based on the collision rate test of the strict avalanche criterion, select a hexadecimal unsigned integer that can cause half of the bits in the output result to change when a single bit is reversed in the input string; the selection method of the specific displacement amount in the circular left shift and right shift XOR operation is: based on the processor's native integer word width, it is evenly set to ensure that the high and low bit data streams are fully orthogonally mixed after the shift operation.

[0032] The control center performs a bitwise AND mask operation on the initial hash digest to extract a fixed-length sequence. The mask variable is selected by combining the binary bit width of the upper limit of the number of logical fragments. During the actual execution, the processing thread performs a bitwise logical AND operation between the previously obtained initial hash digest and the mask variable, and the direct result of this logical operation is output as the fixed-length sequence.

[0033] The processing thread combines the generated fixed-length sequence with the aforementioned set of node identifiers, performs string reassembly and concatenation, allocates a new contiguous byte sequence in memory, and generates the target hash key name. This target hash key name is then pushed to the addressing and routing engine. The addressing and routing engine performs a cyclic redundancy check (CRC) operation on the target hash key name and takes the modulo of the check result with the total number of hash slots in the cluster to obtain the hash slot index. The total number of hash slots in the cluster is selected by allocating them as an integer multiple of the number of physical nodes in the cluster. The hash slot index is then compared in the distributed cache's cluster topology routing table to locate the target memory block on the physical server. The memory management unit then performs boundary parameter encapsulation processing on the target memory block, establishes page table mapping relationships, and generates virtual memory fragments.

[0034] By performing message scanning and memory dictionary parsing to separate node identifiers, and combining hash calculation and bitwise AND mask operations to generate target hash key names, the target memory block is locked by slot mapping in the addressing and routing engine, providing a basis for the continuous construction of virtual memory shards and alleviating the addressing latency caused by invalid parsing.

[0035] Further, based on the virtual memory fragmentation, a tree-like topology graph is extracted, and the topology node objects and state pointer sequences are located through traversal; the state pointer sequence is decomposed into bits to extract the pre-occupied offset and the revocation offset, which are then concatenated with the quota change value to generate concurrent operation input parameters; the preset operation logic is run to perform displacement calculations on the concurrent operation input parameters to generate state update results; this corresponds to step S2 above; see [link to relevant documentation]. Figure 2 The specific implementation process includes: Based on virtual memory sharding, a slot aggregation read request is initiated to the underlying component of the distributed cache to obtain data blocks from the same source; the same source data blocks are deserialized and converted to obtain a tree-like topology graph; the root node access credentials of the tree-like topology graph are extracted; based on the root node access credentials, depth-first logic is initiated on the tree-like topology graph; during the execution of the depth-first logic, a step-by-step matching and comparison is performed using a set of node identifiers; after the matching and comparison are successful, the target memory block is locked and instantiated to generate a topology node object; the continuous addressing cursor mounted inside the topology node object is extracted, and a status pointer sequence is generated based on the continuous addressing cursor.

[0036] Specifically, the generation process of the topology node object and the state pointer sequence is as follows: Based on the physical memory mapping addresses contained in the virtual memory shards, a direct memory access aggregation read request is initiated to the underlying storage engine component of the distributed cache to retrieve data blocks from the same source. Since the memory data exists as a highly compressed adjacency list array, the processing logic performs protocol deserialization on the data blocks from the same source. Specifically, based on the array index and the embedded offset pointer, node objects are reallocated in the heap memory space, and the child node pointers of each node are set to their corresponding memory addresses, thereby reconstructing a tree-like topology graph containing complete parent-child link relationships.

[0037] In this embodiment, the tree-like topology specifically maps to the enterprise's dual-track, multi-level budgeting system. Its main tree branches include a project budget control link consisting of project preliminary estimates (root node), project annual budgets (intermediate layer nodes), and project monthly budgets (leaf nodes), as well as a parallel and decoupled annual expense budget control link. By establishing this topology at the bottom layer, collaborative constraints between budgets at different levels are achieved.

[0038] The access credentials of the root node of the tree topology graph are extracted and their hash digests are compared. After successful verification, the traversal engine initiates depth-first logic for the tree topology graph based on the credentials.

[0039] In this embodiment, because the hierarchical control of enterprise budgets follows a strict bottom-up aggregation constraint (i.e., the total budget of any parent node's subtree must be greater than or equal to the available budget of any of its subordinate child nodes, possessing the principle of numerical monotonicity and boundedness), the total budget of the current topology node's subtree is read in the depth-first logic and compared with the absolute value of the quota change value. If the total subtree budget is smaller (difference less than 0), it indicates that the total budget of all child nodes under this branch, including the target node, is insufficient to meet the current deduction requirement. Even if the target node is found through further traversal, effective business reimbursement cannot be performed. Therefore, pruning will be directly triggered, forcibly backtracking to skip the subsequent node stack traversal. Specifically, an adaptive pruning judgment algorithm is embedded in the explicit stack loop iteration of the depth-first logic. When the processing thread pops the current topology node, it reads the subtree budget total field occupying a fixed-length integer byte space in its object data structure. The arithmetic control unit extracts and obtains the absolute value of the externally input quota change value. The subtree budget total and this absolute value are sent to the comparator to perform a subtraction operation. If the difference is less than 0, the pruning mechanism is triggered. The processing logic directly modifies the pointer to the number of child nodes of the current node, setting it to 0. This operation makes the physical addresses of all the child nodes originally attached to this node unrecognizable by subsequent instructions, thus preventing subordinate child nodes from being pushed onto the stack. The explicit stack structure then skips the current branch, backtracks the stack top pointer, and pops the next sibling node. The entire pruning process only adds one comparison calculation to the original traversal logic, without allocating additional memory or adjusting the basic tree topology graph structure. The above process avoids invalid traversals of branches without budget support, reducing the explicit stack push depth and memory matching comparison latency. If the pruning operation results in the traversal failing to match and lock the target topology node object, the business verification is directly marked as failed, and the process is transferred to the exception handling flow.

[0040] To prevent stack overflow caused by excessive recursion depth, depth-first traversal uses a self-built explicit stack structure for iterative looping in heap memory. During the traversal, identifiers are continuously extracted from the previously generated node identifier set and compared byte-by-byte with the identifier string carried by the currently popped tree node. If a match is found, the target memory block corresponding to that node is locked, and a topology node object is instantiated.

[0041] The execution unit extracts the contiguous addressing cursor mounted inside the topology node object. This cursor is essentially a set of 64-bit absolute physical pointers to memory. Based on this contiguous addressing cursor, a status pointer sequence is generated. The status pointer sequence points to the underlying physical memory units that store the pre-allocated quota and the write-off quota.

[0042] Each pointer element in the state pointer sequence is uniformly configured as a standard 64-bit wide memory structure, which includes a state flag bit for indicating the locked state at the highest bit, and a numerical area for storing the absolute physical offset of the memory in the remaining bits.

[0043] By initiating slot aggregation reading to obtain data blocks from the same source, and performing deserialization to restore the tree topology, the depth-first logic is used to compare the node identifier level by level to instantiate the topology node object, so that the topology relationship at the logical level can be restored more realistically in memory, creating favorable structural conditions for the smooth extraction of the subsequent state pointer sequence.

[0044] Obtain the underlying binary byte stream mapped by the status pointer sequence; set a fixed starting position cursor for the underlying binary byte stream; load the preset memory layout dictionary and extract the configured high-order and low-order truncation masks; combine the high-order truncation mask and the starting position cursor to perform logic and calculation separation on the underlying binary byte stream to obtain the pre-placement offset; combine the low-order truncation mask and the starting position cursor to perform logic and calculation separation on the underlying binary byte stream to obtain the verification offset; perform memory length alignment conversion on the quota change value to obtain the standard calculation variable; write the standard calculation variable, the pre-placement offset, and the verification offset in sequence into the underlying continuous buffer area according to the preset continuous arrangement rules, and perform encapsulation processing on the underlying continuous buffer area to generate concurrent operation input parameters.

[0045] Specifically, the process of generating input parameters for concurrent operations is as follows: The processing logic obtains the underlying binary byte stream mapped from the state pointer sequence. A fixed starting position cursor is set for the underlying binary byte stream. The starting position cursor is selected by forcibly aligning it to the starting address of the current memory block. The processing unit loads a preset memory layout dictionary and extracts the high-order and low-order truncation masks.

[0046] The high-order truncation mask is used to separate the offset position of pre-placement data from the underlying byte stream. It occupies the high 16 bits of data space, and the selection method of the high-order 16 bits of the truncation mask is based on the memory address high 16-bit reserved area rules of the 64-bit operating system. The low-order truncation mask is used to separate the offset position of checksum data from the underlying byte stream. It occupies the second-highest 16 bits of data space, and the selection method of the low-order truncation mask is determined based on the alignment offset of the second-highest 16-bit field in the data structure.

[0047] Because the underlying data evolution of enterprise budgeting must adhere to a strict two-stage state machine constraint of "pre-allocation (locking the amount) followed by write-off (actual expenditure or release)" to prevent over-issuance of funds or phantom reads, the state pointer sequence is decomposed into bits to extract the pre-allocation offset and write-off offset, simultaneously anchoring the physical memory boundaries of both stages within a single 64-bit word length. In the four-stage flow control of enterprise budgeting, the pre-allocation offset is specifically used to anchor the memory state area of ​​the committed amount in the business flow, covering the underlying data operations of the three pre-locking stages: purchase application locking, contract drafting locking, and payment in transit freezing; the write-off offset is specifically used to anchor the memory state area of ​​the actual amount after payment is completed, enabling the budget status data of the four independent dimensions—estimate, annual project, monthly, and annual expenses—to operate independently in a distributed cache without interference.

[0048] Combining the high-order truncation mask and the starting position cursor, the arithmetic logic performs a bitwise AND operation on the actual data content loaded within the physical memory unit corresponding to the underlying binary byte stream, clearing all irrelevant low-order bits to obtain the pre-placement offset. Since the pre-placement data extracted from the high-order truncation mask is naturally located in the high-aligned region of the register, it can be directly used as the high-order index parameter of the memory base address without shifting. Conversely, combining the low-order truncation mask and the starting position cursor, the arithmetic logic performs a bitwise AND calculation on the address corresponding to the underlying binary byte stream. Because this field is truncated from the middle bit, a rightward logical shift operation needs to be performed to align it to the least significant bit of the register, converting it into a standard scalar to obtain the offset. This shift amount is selected based on the absolute offset position of the least significant bit of the low-order truncation mask to ensure that the extracted value is right-aligned to the least significant bit of memory.

[0049] For the input quota change value, a memory length alignment conversion is performed to obtain the standard calculation variable. The type standard selection method used in this alignment conversion process is determined according to the maximum integer word width natively supported by the arithmetic logic unit. In this embodiment, it is uniformly forcibly converted to a 64-bit signed long integer. In this conversion process, in order to meet the physical constraints of the strict requirement of accuracy to the cent for financial budget data and the intolerance of floating-point cumulative errors, the quota change value with decimal features is uniformly multiplied by 100 to a fixed ratio amplification operation, forcibly mapping it to fixed-point machine code containing only the integer part, and then byte padding and alignment are performed on the 64-bit signed long integer, thereby completely avoiding the precision truncation risk of the floating-point coprocessor during low-level concurrent operations.

[0050] Based on the preset continuous arrangement rules, in order to cope with the false sharing effect of cache lines caused by concurrent modification by multiple threads, 64 bytes of blank padding bits are reserved as a buffer. The standard calculation variables, pre-placement offsets and check offsets are written sequentially into a continuous buffer area aligned to the 64-byte boundary allocated by the underlying layer. The starting address and total length of the buffer area are encapsulated to generate concurrent operation input parameters.

[0051] By setting a starting position cursor to perform high and low bit mask calculations to separate the offset, and writing the calculated variables, aligned with the memory length, into a continuous buffer area in sequence, the fusion and splicing is achieved. This reduces the probability of conflicts when microstructures are joined, and provides support for the smooth flow of low-level operation parameters between memory.

[0052] The concurrent operation input parameters are pushed and loaded into the arithmetic logic unit of the preset operation logic configuration. The concurrent operation input parameters are destructured to extract the quota change value, the pre-occupancy offset, and the cancellation offset. The corresponding original pre-occupancy data is extracted based on the pre-occupancy offset. The corresponding original cancellation data is extracted based on the cancellation offset. A memory mask variable is constructed for the pre-occupancy offset. The target status identifier is isolated by performing a mask shift operation on the original pre-occupancy data based on the memory mask variable. The word-aligned memory row to which the target status identifier belongs is locked. The integer atomic update is performed in the spin replacement loop using the quota change value to generate new pre-occupancy data. A corresponding mask variable is constructed for the cancellation offset. The mask shift operation is performed on the original cancellation data to isolate the corresponding status identifier. The word-aligned memory row to which the corresponding status identifier belongs is locked. The integer atomic update is performed in the spin replacement loop using the quota change value to generate new cancellation data. The new pre-occupancy data and the new cancellation data are integrated and spliced ​​into a structured carrier to generate the status update result.

[0053] Specifically, the process of generating the state update result is as follows: The concurrent operation input parameters are pushed and loaded into the processor's arithmetic logic unit (ALU). The ALU directly reads general-purpose registers and destructures the concurrent operation input parameters, extracting a 64-bit quota change value, a pre-occupancy offset, and a cancellation offset. Hardware-level memory addressing is initiated based on the pre-occupancy offset to retrieve the original pre-occupancy data. The original cancellation data is retrieved based on the cancellation offset.

[0054] The mask calculation unit constructs a memory mask variable based on the pre-occupied offset. The memory mask variable is selected by specifying a particular bit segment where the target status identifier is located. In this embodiment, the most significant bit of the data structure is extracted as an independent status identifier bit, while the remaining lower bits are reserved as a pure numerical data area. The arithmetic unit performs mask shifting operations on the original pre-occupied data based on this mask variable to isolate the target status identifier. This identifier has a value of 0 when in an idle state.

[0055] The target status identifier is locked to the word-aligned memory row. Using the deconstructed quota change value, an atomic update of the entire word is performed within the underlying spin-replacement loop. The maximum number of retries for the spin-replacement loop is selected based on the average contention latency of the bus arbitration under the CPU architecture. During this loop, the underlying assembly instruction issues a compare-and-swap opcode with a locking prefix. This instruction blocks bus operations at the corresponding memory address at the hardware level. To prevent memory misjudgment in high-concurrency environments, this embodiment forcibly binds a contiguous 32-bit version sequence stamp to the status pointer in the memory layout. Specifically, the underlying assembly instruction issues a double-word compare-and-swap opcode with a locking prefix. This instruction compares the current memory value (including the current value and the current version sequence stamp) with the original pre-occupied data cached in the register (including the original value and the original version stamp). Only when both the value and the version stamp match is it updated to the new value of the original data plus the quota change value, and simultaneously the version sequence stamp is incremented. During the addition operation, a bitmask boundary truncation operation is performed. Specifically, the addition result is bitwise ANDed with the mask that masks the most significant bit to prevent the value from overflowing and polluting the status flag of the most significant bit. Once the write is successful, the loop is exited and new pre-placement data is generated.

[0056] For the offset of the write-off bit, a corresponding mask variable is constructed. The selection method for the corresponding mask variable is also to specify the specific bit position segment where the field is located. The original write-off data is subjected to a mask shift operation to isolate the corresponding status identifier. The word-aligned memory line to which the corresponding status identifier belongs is locked. The quota change value is used to emit a comparison and swap instruction of the locked prefix in the spin replacement loop to perform an integer atomic update (this process also includes the above-mentioned anti-overflow boundary truncation processing) to generate new write-off bit data.

[0057] The new pre-placement data and the new checkout data are combined and concatenated by performing a register-level logical OR operation to generate a state update result in a structured carrier format that occupies 128 bits of physical space.

[0058] Furthermore, after the spin replacement loop successfully generates new write-off bit data, a 1 is written to the most significant bit (i.e., the status flag) of the word-aligned memory row, indicating that the current underlying memory structure is in a budget write-off locked state; and when a business completion or rollback reversal instruction is received subsequently, this status flag is reset to 0. After the new pre-occupied bit data or new write-off bit data of the current leaf topology node completes the spin atomic update in physical memory, the tree graph control engine triggers the synchronous backtracking update mechanism. The processing thread extracts the parent node mounting pointer recorded in the current leaf node data structure and traverses the topology graph from bottom to top level by level in reverse. For each ancestor node on the path, the same comparison and exchange atomic instruction is used to synchronously deduct or increase the corresponding quota change value in the budget total field of its internally mounted subtree, avoiding misjudgment due to reading old dirty data of ancestor nodes when subsequent concurrent requests are accessed.

[0059] This bottom-up synchronous backtracking update mechanism specifically realizes closed-loop business collaboration between multi-level budgets. For example, when actual payment deductions are executed, changes in the write-off position of the underlying project's monthly budget (leaf node) will be synchronously accumulated to the project's annual budget (parent node) through this mechanism, relying on underlying distributed atomic instructions, thus enhancing data consistency when summarizing budgets across levels.

[0060] By deconstructing the input parameters of concurrent operations within the arithmetic logic unit and isolating the state flags using mask shifting, the word-aligned memory rows are locked and integer atomic updates are performed within the spin replacement loop. This lightweight integer iteration follows the processing rhythm of the underlying registers, significantly compressing the state flip cycle and helping to maintain the continuous availability of concurrent operations.

[0061] Further, based on the state update results, low-level overwrite instructions are generated and executed to write to virtual memory shards; the instruction queuing depth of the virtual memory shards is extracted, and the state update results are fused to generate cluster load characteristics; based on the cluster load characteristics and topology change map, evolution control instructions are generated and executed to open a background dual-write buffer; corresponding to the above S3 steps; the specific implementation process includes: The process involves parsing the data byte length attribute in the state update result; combining the data byte length attribute with the target hash key name to assemble a low-level overwrite instruction with transaction execution characteristics; delivering the low-level overwrite instruction to persistently save the state update result to the high-speed cache page corresponding to the virtual memory shard; sending state monitoring probe packets to the input / output queues bound to the virtual memory shard; using the state monitoring probe packets to collect the total number of currently accumulated unprocessed request packets in the input / output queues to generate the instruction queuing depth; performing a normalized mapping calculation on the instruction queuing depth to obtain a dimensionless weight value; and performing a scalar product calculation between the dimensionless weight value and the data byte length attribute to derive the cluster load characteristics.

[0062] Specifically, the process for generating cluster load characteristics is as follows: The state update result is parsed, its data byte length attribute is extracted, and combined with this data byte length attribute and the target hash key name, the memory address and length information are encapsulated in a fixed-format record block. This block is then assembled to generate a low-level overwrite instruction containing a unique transaction identifier and a rollback data pointer. The input / output control unit delivers this low-level overwrite instruction, persistently saving the state update result to the non-volatile cache page corresponding to the virtual memory slice using direct input / output technology.

[0063] The underlying monitoring probe packet is sent to the remote direct memory access receive queue bound to the virtual memory shard. The status monitoring probe packet directly reads the memory mapping address of the queue head and tail pointer registers, subtracts the head pointer from the tail pointer to obtain the number of currently unprocessed requests, and thus collects the total number of unprocessed request packets accumulated in the queue to generate the instruction queuing depth.

[0064] In this embodiment, a first-order exponential moving average filter is introduced into the collected instruction queuing depth to calculate the queuing depth. The historical smoothed value is fused with the current collected value to eliminate transient traffic spikes, and the newly calculated smoothed result is used as the final queuing depth. Specifically, after obtaining the discrete collected values ​​of the instruction queuing depth, the processing logic calls a preset first-order exponential moving average filter function for numerical calculation. A double-precision floating-point register is allocated in physical memory to store the smoothed output value of the previous time step. The calculation formula is defined as: the current smoothed value equals the preset smoothing coefficient multiplied by the current collected value, plus a difference of the smoothing coefficient multiplied by the historical smoothed value in the register. The smoothing coefficient is an asymmetric adaptive dynamic parameter based on the queuing depth change rate. The derivative (difference) between the current collected value and the historical smoothed value is calculated: when the difference is positive and exceeds a preset surge threshold, it indicates that the cluster is under real burst pressure; in this case, it is set to 0.8 to enhance the weight and enable the model to respond quickly and avoid scaling lag; when the difference is negative or the fluctuation does not exceed the surge threshold, it indicates that it is in a transient spike or normal network jitter fallback period; in this case, it is set to 0.2 to enhance smoothing damping. The arithmetic unit utilizes the Single Instruction Multiple Data (SID) mechanism to perform multiplication and addition operations in parallel at the register level, deriving a smoothed floating-point value for the current cycle. This floating-point value is then overwritten into the aforementioned register to update the historical state, and a floor operation is performed on it to convert it into an unsigned integer variable. This variable is output as the final basis for the instruction queuing depth. This process filters out pseudo-high-bit fluctuations in queue length caused by transient bursts of traffic, preventing erroneous resource expansion.

[0065] Perform a normalized mapping calculation on the instruction queuing depth to obtain the dimensionless weight value.

[0066] In the specific mathematical calculation steps, the mathematical operation logic first subtracts the currently acquired total number of unprocessed request packets (i.e., instruction queuing depth) from the set minimum instruction queuing depth threshold to obtain a first difference; then, it subtracts the set maximum instruction queuing depth threshold from the minimum instruction queuing depth threshold to obtain a second difference; next, it divides the first difference by the second difference, and the calculated division quotient is the dimensionless weight value. To prevent the first difference from becoming negative or the division quotient from overflowing due to transient abnormal accumulation, boundary clamping truncation processing is performed on the division quotient. Specifically, the judgment constraint logic for the dimensionless weight value is set as follows: when the division quotient is less than 0, the operation unit forces the dimensionless weight value to be 0; when the division quotient is greater than 1, the operation unit forces the dimensionless weight value to be 1; if the division quotient is between 0 and 1, the calculated value remains unchanged. Through the above truncation processing, it is ensured that the weight coefficients participating in the subsequent scalar product calculation are strictly constrained within the closed interval of 0 to 1.

[0067] The obtained dimensionless weight value is directly multiplied by the specific numerical value represented by the data byte length attribute, and the result of the product operation is exported as the weighted traffic feature. Subsequently, the total number of currently allocated physical memory pages in the distributed cache is extracted by operating the underlying interface. The weighted traffic feature is used as an incremental parameter and fused with the total number of allocated physical memory pages for calculation. Using a preset page size constant, the traffic feature is converted into an estimated memory page usage, and then added to the current total number of pages, thereby deriving the cluster load feature. The page size constant is selected based on the default physical page frame size natively configured by the underlying operating system's memory management unit.

[0068] The minimum instruction queuing depth threshold is determined by averaging samples taken over one hour during a period when the underlying architecture is idle and has no service access; an example value of 0 is used. The maximum instruction queuing depth threshold is calculated by dividing the total memory capacity allocated to the physical queue in the kernel by the maximum transmission unit size of a single data packet; an example value of 10000 is used.

[0069] By assembling the underlying overwrite instructions, the update results are persisted to the cache page, and the total backlog of the input and output queues is collected using probes. Then, normalization and scalar product calculations are performed to derive the cluster load characteristics, providing a reliable data reference for subsequent physical layer isolation evolution and traffic routing.

[0070] The system performs scanning calculations within the topology change graph to extract the relationship evolution paths associated with abnormal nodes; it extracts cluster load characteristics and performs comparison and judgment logic based on a preset capacity baseline; when the cluster load characteristics exceed the capacity baseline, it performs resource protection interception based on the relationship evolution paths; it integrates the relationship evolution paths with the target hash key name, encapsulates and compiles them to export evolution control instructions; it sends the evolution control instructions to the cache allocation engine, calls the logical isolation allocation interface to apply for independent logical blocks in idle storage space; it performs ring queue data structure initialization and formatting processing on the independent logical blocks to generate traffic isolation space and establish a background dual-write buffer.

[0071] Specifically, the process of establishing the background dual write buffer is as follows: The scanning engine performs scanning calculations from within the topology change graph, compares the changes in the pointers attached to child nodes in the two graphs before and after, and extracts the relationship evolution path of the nodes that have undergone pointer transfer.

[0072] The cluster load characteristics derived from scalar product calculations are extracted and compared with a preset capacity baseline for judgment. The capacity baseline is selected by setting 85% of the critical value for physical memory overflow in historical benchmark stress tests as the red line. When the cluster load characteristics exceed the capacity baseline, resource protection interception is performed according to the relationship evolution path, blocking read and write access to the original memory pointer by configuring access control lists.

[0073] The compilation unit integrates the evolution path of the relationship and the target hash key name, and encapsulates and compiles the evolution control instructions according to the control plane protocol. The scheduling center issues the evolution control instructions to the cache allocation engine, calls the namespace isolation allocation interface of the underlying operating system, and requests an independent logical block with an independent file descriptor in an idle physical page frame.

[0074] The independent logical blocks are initialized with a circular queue data structure, and contiguous memory blocks are allocated and read / write cursors are set. When the write cursor reaches the end of the memory block, it automatically wraps back to the beginning address. This formatting process generates a traffic isolation space and establishes a background dual write buffer.

[0075] By comparing the dimensionless weight value with the capacity baseline defense line to trigger the evolution analysis, the evolution control instructions are compiled and exported, thereby driving the cache allocation engine to apply for independent logical blocks to establish a background dual-write buffer. This mitigates the structural impact of direct expansion and contraction on the underlying media, creating a prerequisite for traffic differentiation and seamless connection.

[0076] Furthermore, the topology change graph is loaded into the background dual-write buffer, the incremental redirection pointer of the topology change graph is extracted, the underlying overwrite instruction is transferred to the background dual-write buffer, lossless hot migration is performed, and the underlying resources are pre-expanded; this corresponds to step S4 above; see [link / reference]. Figure 3 The specific implementation process includes: The process involves: loading the structured object of the topology change graph into an independent memory cache area managed by the backend dual-write buffer; traversing and scanning the structured object to locate the abnormal topology nodes whose hierarchical relationships have changed; extracting the continuous memory cursors allocated to the abnormal topology nodes under the new topology structure to generate incremental redirection pointers; modifying the proxy routing engine external to the distributed cache to change the default forwarding path and enable instruction mirroring; using instruction mirroring to perform synchronous dual-path overwrite on the original virtual memory shards and the backend dual-write buffer; responding to the completion of loading the old memory data, sending a global flow-cutting instruction to the proxy routing engine; using the global flow-cutting instruction to drive the proxy routing engine to disable instruction mirroring and redirect all business requests to the backend dual-write buffer; discarding the original virtual memory shards based on the incremental redirection pointers; capturing legacy overwrite instructions inside the transmission link and performing instruction mirror copying for lossless hot migration; and triggering pre-expansion by performing tensor calculations based on cluster load characteristics.

[0077] Specifically, the process of transferring the underlying overwrite instruction to the background double write buffer is as follows: The migration center loads the structured object data stream of the topology change graph into an independent memory cache area managed by a background dual-write buffer. The addressing engine traverses and scans this structured object, extracts the object's memory offset attribute, and accurately locates the aberrant topology nodes whose hierarchical relationships have changed. It extracts the contiguous absolute memory address cursors allocated to these aberrant topology nodes under the new tree structure and generates incremental redirection pointers for subsequent traffic routing.

[0078] The routing controller modifies the default forwarding path of the external proxy routing engine of the distributed cache, assigns a globally monotonically increasing transaction sequence number to each underlying overwrite instruction, and enables instruction mirroring. Using instruction mirroring, it performs synchronous dual-path overwrite of the address corresponding to the original virtual memory slice and the newly created background dual-write buffer. In response to the completion of loading the old memory data, it sends a ready signal to the upper-layer routing controller via the internal notification bus; the routing controller then sends a global flow-switching instruction to the proxy routing engine. This global flow-switching instruction drives the proxy routing engine to disable instruction mirroring, directly rewrite the kernel routing table entries, and redirect all service requests to the physical memory address of the background dual-write buffer. The garbage collection unit discards the original virtual memory slice based on the incremental redirection pointer, marking it as a reclaimable block for cleanup.

[0079] Meanwhile, for requests that are still in transit and have not yet been completed, the underlying send buffer queue or receive acknowledgment queue of the proxy routing engine is monitored. By scanning the above queues, any legacy overwrite instructions that have been sent but have not yet received downstream acknowledgment responses are captured in the proxy routing buffer queue, and the instructions are mirrored and copied to the background double write buffer according to their attached transaction sequence numbers.

[0080] Subsequently, the background dual-write buffer executes strict sequence number continuity and consistency verification logic: it reads the latest transaction sequence number written to disk from the dual-write buffer and compares it with the sequence number of the intercepted instruction to remove duplicates; if a gap or missing sequence number is detected, a retransmission request is initiated to the proxy routing engine based on the rollback data pointer attached to the instruction; if a duplicate sequence number is detected, it is silently discarded according to the idempotency principle. Finally, the migration and handover of the old and new memory data is completed.

[0081] The deep learning inference engine performs tensor numerical calculations based on cluster load characteristics to predict the concurrent pressure fluctuations caused by the reallocation of financial funds, triggering the pre-expansion of underlying data resource nodes. In terms of model feature extraction and network structure setting, the cluster load characteristics aggregated within the time series and the frequency of network-level topology changes are extracted as input variables, and a multi-dimensional continuous array is allocated in the GPU memory to hold the data.

[0082] The input layer receives 3D tensor data, including batch size, time window, and number of features. The batch size is configured as 64; the time window parameter is 24 steps; and the number of features is 2. The specific construction and alignment of the 2 features is as follows: within a fixed, uniform sampling period, the average of multiple discrete cluster load feature values ​​generated by event-driven processes within that period is used as the first feature; simultaneously, the cumulative count of topology change map changes within that period is used as the second feature. The aligned first and second features are concatenated to form a 2D feature vector for the current time step. This concatenation is performed sequentially for 24 periods, forming an input sample with a time window of 24 steps. The above assembly process maintains a circular sliding window cache with a capacity of 24 in independent background memory. Regardless of whether a topology change occurs, a 2D feature vector is continuously generated at a sampling period of one minute and pushed into this circular queue, while the oldest data is removed using an overwrite pointer. When the hot migration mechanism responds to a high-load surge and instantly triggers the deep learning inference mechanism, it directly uses memory copy instructions to read the historical time window data of the most recent 24 cycles that are ready in the current circular cache, thus completing the construction of the input sample.

[0083] Configure forward and backward Long Short-Term Memory (LSTM) network hidden layers, with the number of unidirectional hidden units set to 64. Set the inter-layer dropout rate to 0.2. After concatenating the forward and backward states, connect the complete hidden state sequence matrix (with a matrix dimension of 24 time steps multiplied by 128 hidden units) output from the bidirectional LSM network hidden layers to the self-attention mechanism layer. In the attention mechanism layer, multiply this hidden state sequence matrix by three learnable independent weight matrices to generate a query matrix, a key matrix, and a value matrix.

[0084] Subsequently, scaled dot product attention calculation is performed. The specific steps are as follows: First, the transpose of the query matrix and the key matrix is ​​multiplied by a dot product. Next, the dot product result is divided by the square root of the key vector dimension (i.e., by the square root of 128) to perform scaling and prevent overflow. Then, the scaled result is substituted into the Softmax function to calculate the attention weight distribution for each time step. Finally, the weight distribution matrix is ​​multiplied by the value matrix to obtain a weighted summed context vector. This context vector aggregates the most important temporal features for bursty loads across 24 time steps. This context feature vector is then fed into a fully connected output layer, and after linear mapping, a continuous prediction scalar tensor is output.

[0085] In the forward propagation process of the hidden layer, the forward propagation execution logic obtains the forget gate output vector of the current time step through matrix multiplication and addition operations. The specific computation trajectory is as follows: The tensor concatenation unit first concatenates the hidden state vector of the previous time step with the input vector of the current time step in the continuous space of the video memory to construct a joint state input matrix; it calls the general matrix multiplication and addition function of the underlying basic linear algebra subroutine to perform a dot product operation on the joint state input matrix and the pre-loaded forget gate weight matrix; then it performs an element-wise addition operation on the product matrix obtained by the dot product and the forget gate bias term; finally, it extracts the sum one by one and substitutes it into the logistic activation function to perform nonlinear mapping processing. The result after the exponential operation mapping by this function is determined as the forget gate output vector of the current time step.

[0086] After calculating the forget gate output vector, the forward propagation logic continues to calculate the input gate and candidate cell states. Specifically, it extracts the joint state input matrix, which consists of the hidden state from the previous time step and the input from the current time step. This matrix is ​​then multiplied by the input gate weight matrix and the cell state weight matrix, and the corresponding bias terms are added. The input gate result is then substituted into the logistic activation function, and the candidate cell states are substituted into the hyperbolic tangent activation function.

[0087] Next, the cell state is updated. The specific operation logic is as follows: the output vector of the forget gate is multiplied element-wise with the cell state of the previous time step, and the output vector of the input gate is multiplied element-wise with the candidate cell state. Finally, the results of the two multiplications are summed to obtain the updated cell state of the current time step.

[0088] Finally, the output gate is calculated (i.e., the output gate weight matrix is ​​multiplied by the aforementioned joint input matrix and a bias is added before the logistic activation function is applied), and an element-wise multiplication operation is performed on the current time step cell state after processing by the hyperbolic tangent activation function based on the output gate, ultimately generating the hidden state vector of the current time step.

[0089] During the model parameter iteration phase, i.e., the offline pre-training process executed independently in the background, a training dataset needs to be constructed. Historical cluster load characteristics and topology change frequency are extracted from the historical operation logs of the cloud management platform at a fixed, uniform sampling period (e.g., once every 1 minute). A sliding window with a length of 24 time steps and a sliding step size of 1 is used to slice the extracted time series, forming historical input feature samples. For each input sample, when constructing the true supervision label, the absolute timestamp of the new expansion container instance completing the ready state change in the cluster registry center is recorded in the historical operation log. This absolute timestamp is then offset forward by a preset physical delay constant for container startup to determine the theoretical trigger time of the expansion command. Data from the 24 consecutive sampling periods preceding this theoretical trigger time point are extracted to form the corresponding historical input feature sample, and the actual integer number of nodes in this expansion is used as the supervision label. The constructed historical dataset is divided into a training set and a validation set in an 8:2 ratio according to chronological order to prevent future data leakage of time series information. The loss function for model training uses mean squared error, and the optimization objective is to minimize the error between the predicted number of expansion nodes and the actual number of expansion nodes. The selection method for the physical delay constant of container startup is as follows: statistically analyze the actual time taken for all newly added container instances in the cloud management platform cluster from receiving scheduling instructions to pulling images and finally completing the service port readiness within a continuous 30-day period, and construct a cumulative probability distribution function for the time consumption distribution; extract the long-tailed high probability quantile that can cover the time consumption of most normal scheduling scenarios in the cluster and remove extreme timeout extremes such as network anomalies, and use the corresponding time span as the constant value.

[0090] The initial learning rate was set to 0.001; the momentum decay hyperparameters were set to 0.9 and 0.999, respectively. The total training epochs were set to 100; the continuous termination epochs were set to 10. After obtaining the converged model weight matrix through the above offline pre-training, it was loaded into the current online hot transfer deep learning inference engine to perform forward propagation calculations.

[0091] After the deep neural network inference computation is completed, the fully connected layer outputs a predicted scalar tensor representing future computing resource requirements. This predicted scalar tensor is rounded up to convert it into the integer number of underlying container instances that need to be pre-allocated for expansion under the new topology. The resource scheduling application programming interface sends an expansion request to the cloud management platform based on this predicted scalar tensor to support the high-concurrency write computation overhead caused by the reallocation of financial funds, triggering the pre-expansion of underlying computing resources. This ensures the safe transfer of underlying overwrite instructions to the backend dual-write buffer while maintaining cluster computing power redundancy and business continuity under the new topology.

[0092] By extracting the incremental redirection pointer generated by the abnormal node, modifying the external proxy routing engine to enable instruction mirroring and synchronous dual-path overwriting, and discarding the original fragment and capturing the legacy instructions after loading, a consistent and smooth handover of the old and new memory data is facilitated, maintaining the continuous availability of the global cache.

[0093] This invention provides a dynamic enterprise budget control method based on distributed caching. By parsing budget allocation data and topology change maps, it triggers addressing and locating state pointer sequences in the distributed cache, then performs bit-level splitting and fusion to generate concurrent computation input parameters. It generates state update results based on preset computational logic for displacement calculation and overwrites virtual memory shards. Simultaneously, it integrates queuing depth and state update results to generate cluster load characteristics, then opens a background dual-write buffer and uses incremental redirection pointers to perform lossless hot migration. This overall mechanism aligns with the carrying characteristics of the underlying physical medium when facing heterogeneous loads and topology evolution, appropriately decoupling micro-displacement operations from macro-flow guidance. It mitigates the risk of global addressing stagnation caused by long-cycle arithmetic blocking or architectural changes, changing the passive approach of full-scale shutdown and reconstruction. It provides favorable conditions for the continuous flow and smooth transition of memory data during complex evolution cycles, thus better maintaining the continuous availability of the final generated cached data.

[0094] Example 2

[0095] This embodiment illustrates an application of a distributed caching-based dynamic budget control method to the high-concurrency closed-loop management of multi-level budgets in a large enterprise. In the complex procurement, contract drafting, and payment settlement processes of large enterprises, frequent cross-dimensional budget adjustments and state transitions are involved.

[0096] First, the network interface receives messages containing branch allocation data such as procurement requests, contract drafts, or payment freeze instructions. It performs a sliding scan in 4-byte increments, and after matching a valid magic number, extracts a 14-byte message header. After removing the additional header and deserializing, it separates the branch identifier set (e.g., budget subject object identifier) ​​and the budget change value. A hash calculation is performed on the branch identifier prefix to generate a 32-bit initial digest, and a bitwise AND operation is performed on the lower 16-bit mask to extract a fixed-length sequence. This sequence is reconstructed with the branch identifier to form the target key name. Through addressing routing, the modulo operation is performed on 16384 cluster slots to lock the target memory block of a specific physical server and generate a virtual memory shard.

[0097] Secondly, based on the memory sharding, data blocks from the same source are obtained, and a tree-like topology graph with project estimates, annual project budgets, and monthly budget attribution links is reconstructed in the heap memory. After verifying the root node credentials, a depth-first traversal algorithm is used to compare the network node identifier with each node in the graph. After locking the topology node object corresponding to the network node, the 64-bit continuously addressing cursor mounted inside is extracted, and a sequence of state pointers is generated pointing to the physical units of the pre-allocated budget (i.e., committed amount) and the reimbursed budget (i.e., actual amount) of the above four independent budget objects.

[0098] Next, for the underlying byte stream mapped by the pointer sequence, starting with a cursor of 0, offset calculations are performed using the high 16 bits and the second-highest 16 bits of the truncation mask to obtain the pre-occupied offset and the write-off offset. The budget change value is converted into a 64-bit signed long integer standard variable. In contiguous memory with a reserved 64-byte buffer, it is concatenated with the two offsets in sequence to generate concurrent operation input parameters. The arithmetic logic unit deconstructs this input parameter, addresses and extracts the original data, and constructs a mask variable isolation status flag for the highest sign bit. After confirming that the status is idle, the locked word is aligned with the memory line, and the integer atomic update of the new budget is completed in a spin loop using a compare-and-swap instruction. Finally, a 128-bit structured status update result is generated. By performing this atomic update in a closed loop within the arithmetic logic unit, in business scenarios where multiple users simultaneously initiate payment applications, exclusive deduction calculations can be achieved based on the current remaining budget amount (i.e., the total budget minus the actual amount of the write-off and the committed amount of the pre-occupied portion).

[0099] Subsequently, the delivery command updates the results and persists them. The total number of unprocessed requests in the receive queue is collected using probe packets. Normalized division is then performed using the set minimum and maximum thresholds, and multiplied by the byte length to derive the cluster load characteristics. The evolution path of network point ownership changes is extracted by scanning the old and new network maps. When the dimensionless load weight is greater than 0.85, and the cluster load characteristics derived from this exceed the capacity baseline, resource protection interception is executed, and a circular queue is initialized in an idle physical page frame, establishing a background dual-write buffer.

[0100] Finally, the structured objects are imported into the double-write buffer to generate incremental redirection pointers to the new network node's memory. The proxy routing table is modified to enable instruction mirroring and traffic splitting, synchronizing the double-write between the original shard and the new buffer. After the old data catches up, a global traffic switching instruction is delivered, redirecting all services to the new buffer and discarding the original memory shard. Simultaneously, using load characteristics and topology change frequency as input, a Long Short-Term Memory (LSTM) network model with 128 hidden units and a drop rate of 0.2 is used. After 100 training cycles offline, prediction is performed to infer the scalar of computing power demand representing the number of new computing nodes. A request is sent to the cloud management platform to trigger the pre-expansion of underlying computing resources, ensuring high-concurrency operation under high-frequency allocation of promotional budgets.

[0101] Furthermore, in the business execution chain, the aforementioned status update result generation mechanism, combined with externally preset threshold configuration, can dynamically execute imperceptible weak control release (only recording alarm logs) or trigger strong control interception instructions (blocking offset writes) at the underlying level according to the business type (such as project payment or expense reimbursement). At the same time, when processing cross-year contract performance or contract amount changes, the single-threaded exclusive write feature of this underlying cache is used to achieve security control of single changes in transit, avoiding underlying memory locking / release state jitter caused by frequent additions and subtractions.

[0102] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for dynamic enterprise budget control based on distributed caching, characterized in that, include: Analyze budget allocation data and topology change map to extract node identifier sets and quota change values; Perform hash calculation on the set of node identifiers to extract the target hash key name; Virtual memory shards are generated by triggering addressing in the distributed cache using the target hash key name; the topology change graph is specifically structured in physical memory as an adjacency list array containing multiple abnormal node mapping relationships. The adjacency list array is carried by a continuous memory page stream and records the incremental log of the parent-child relationship pointers of the nodes before and after the change. Based on virtual memory shards, a tree-like topology graph is extracted. Specifically, based on the physical memory mapping addresses contained in the virtual memory shards, a direct memory access aggregation read request is initiated to the underlying storage engine component of the distributed cache to obtain data blocks from the same source. Protocol deserialization is then performed on these data blocks. Specifically, based on array indices and embedded offset pointers, node objects are reallocated in the heap memory space, and the child node pointers of each node are set to their corresponding memory addresses, thus restoring a tree-like topology graph containing complete parent-child link relationships. This tree-like topology graph specifically maps to enterprise dual... The parallel multi-level budget preparation system has a main tree-like branch consisting of a project budget control link composed of project preliminary budget (root node), project annual budget (intermediate layer node), and project monthly budget (leaf node), as well as a parallel and decoupled annual expense budget control link. The system traverses and locates topology node objects and status pointer sequences. It then performs bit-by-bit decomposition on the status pointer sequence to extract pre-occupied offsets and write-off offsets, concatenates these with the quota change values, and generates concurrent operation input parameters. Finally, it runs preset operation logic to perform displacement calculations on the concurrent operation input parameters, generating status update results. Based on the state update results, generate underlying overwrite instructions and execute the underlying overwrite instructions to write to the virtual memory slices; Extract the instruction queuing depth of virtual memory shards and merge the state update results to generate cluster load characteristics; Evolution control commands are generated based on cluster load characteristics and topology change map, and the execution of evolution control commands opens up a background dual-write buffer. The topology change graph is loaded in the background dual-write buffer, the incremental redirection pointer of the topology change graph is extracted, the underlying overwrite instruction is transferred to the background dual-write buffer, lossless hot migration is performed, and the underlying resources are pre-expanded.

2. The enterprise budget dynamic control method based on distributed caching according to claim 1, characterized in that, The specific generation process of the virtual memory shard includes: receiving budget allocation data, triggering the underlying byte stream loading logic, performing byte sequence scanning on the budget allocation data to obtain message header features; extracting the payload from the budget allocation data based on the message header features to generate basic business messages; performing memory dictionary parsing on the basic business messages to obtain key-value pair attributes; separating the key-value pair attributes to generate a node identifier set and a quota change value; extracting the routing alignment label carried by the node identifier set; inputting the routing alignment label into the hash calculation engine to perform underlying hash calculation to generate an initial hash digest; performing bitwise AND mask operations on the initial hash digest to extract a fixed-length sequence; combining the fixed-length sequence with the node identifier set to perform reassembly and splicing to generate a target hash key name; pushing the target hash key name to the addressing routing engine configured in the distributed cache, performing slot mapping calculation on the target hash key name to obtain a hash slot index; using the hash slot index to compare and lock the target memory block in the cluster topology of the distributed cache; and performing boundary parameter encapsulation processing on the target memory block to generate a virtual memory shard.

3. The enterprise budget dynamic control method based on distributed caching according to claim 1, characterized in that, The specific generation process of the topology node object and the state pointer sequence includes: initiating a slot aggregation read request to the underlying component of the distributed cache based on virtual memory sharding to obtain the same source data block; performing protocol deserialization conversion and restoration on the same source data block to obtain a tree-like topology graph; extracting the root node access credentials of the tree-like topology graph; starting the depth-first logic for the tree-like topology graph based on the root node access credentials; performing step-by-step matching and comparison using the node identifier set during the execution of the depth-first logic; locking the target memory block after the matching and comparison is successful and instantiating it to generate a topology node object; extracting the continuous addressing cursor mounted inside the topology node object, and generating a state pointer sequence based on the continuous addressing cursor.

4. The enterprise budget dynamic control method based on distributed caching according to claim 1, characterized in that, The specific generation process of the concurrent operation input parameters includes: obtaining the underlying binary byte stream mapped by the state pointer sequence; setting a fixed starting position cursor for the underlying binary byte stream; loading a preset memory layout dictionary and extracting the configured high-order truncation mask and low-order truncation mask; combining the high-order truncation mask and the starting position cursor, performing logic and calculation separation on the underlying binary byte stream to obtain the pre-placement offset; combining the low-order truncation mask and the starting position cursor, performing logic and calculation separation on the underlying binary byte stream to obtain the verification offset; performing memory length alignment conversion on the quota change value to obtain the standard calculation variable; writing the standard calculation variable, the pre-placement offset, and the verification offset in sequence into the underlying continuous buffer area according to the preset continuous arrangement rules, and performing encapsulation processing on the underlying continuous buffer area to generate the concurrent operation input parameters.

5. The enterprise budget dynamic control method based on distributed caching according to claim 4, characterized in that, The specific generation process of the state update result includes: pushing and loading the concurrent operation input parameters into the arithmetic logic unit of the preset operation logic configuration; destructuring the concurrent operation input parameters to extract the quota change value, the pre-occupied offset, and the write-off offset; extracting the corresponding original pre-occupied data based on the pre-occupied offset; extracting the corresponding original write-off data based on the write-off offset; constructing a memory mask variable for the pre-occupied offset; performing a mask shift operation on the original pre-occupied data based on the memory mask variable to isolate the target state identifier; locking the word-aligned memory row to which the target state identifier belongs; using the quota change value to perform an integer atomic update in a spin replacement loop to generate new pre-occupied data; constructing a corresponding mask variable for the write-off offset; performing a mask shift operation on the original write-off data to isolate the corresponding state identifier; locking the word-aligned memory row to which the corresponding state identifier belongs; using the quota change value to perform an integer atomic update in a spin replacement loop to generate new write-off data; integrating the new pre-occupied data and the new write-off data, and splicing them into a structured carrier to generate the state update result.

6. The enterprise budget dynamic control method based on distributed caching according to claim 1, characterized in that, The specific process for generating the cluster load characteristics includes: parsing the data byte length attribute contained in the state update result; assembling a low-level overwrite instruction with transaction execution characteristics by combining the data byte length attribute with the target hash key name; delivering the low-level overwrite instruction to persistently save the state update result to the high-speed buffer page corresponding to the virtual memory shard; sending state monitoring probe packets to the input / output queues bound to the virtual memory shards; using the state monitoring probe packets to collect the total amount of unprocessed request packets currently piled up in the input / output queues to generate the instruction queuing depth; performing a normalized mapping calculation on the instruction queuing depth to obtain a dimensionless weight value; and performing a scalar product calculation between the dimensionless weight value and the data byte length attribute to derive the cluster load characteristics.

7. The enterprise budget dynamic control method based on distributed caching according to claim 6, characterized in that, The specific process of establishing the background dual-write buffer includes: performing scanning calculations from within the topology change graph to extract the relationship evolution path associated with abnormal nodes; extracting cluster load characteristics and performing comparison and judgment logic in conjunction with a preset capacity baseline; when the cluster load characteristics exceed the capacity baseline, performing resource protection interception based on the relationship evolution path; integrating the relationship evolution path and the target hash key name to encapsulate and compile evolution control instructions; issuing and delivering the evolution control instructions to the cache allocation engine, calling the logical isolation allocation interface to apply for independent logical blocks in idle storage space; performing ring queue data structure initialization and formatting processing on the independent logical blocks to generate traffic isolation space and establish the background dual-write buffer.

8. The enterprise budget dynamic control method based on distributed caching according to claim 7, characterized in that, The specific process of transferring the underlying overwrite instructions to the background dual-write buffer includes: loading the structured object of the topology change graph into the independent memory cache area managed by the background dual-write buffer; traversing and scanning the structured object to locate the abnormal topology nodes whose hierarchical relationship has changed; extracting the continuous memory cursors allocated by the abnormal topology nodes under the new topology structure to generate incremental redirection pointers; performing modification operations on the proxy routing engine of the distributed cache to change the default forwarding path and enable instruction mirroring; using instruction mirroring to perform synchronous dual-path overwrite on the original virtual memory shards and the background dual-write buffer; responding to the old memory data loading completion status, sending a global flow-cutting instruction to the proxy routing engine; using the global flow-cutting instruction to drive the proxy routing engine to close the instruction mirroring and redirect all service requests to the background dual-write buffer; discarding the original virtual memory shards according to the incremental redirection pointers; capturing the legacy overwrite instructions inside the transmission link and performing instruction mirror copying for lossless hot migration; and performing tensor calculations based on the cluster load characteristics to trigger pre-expansion.

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