Intelligent terminal high-speed communication adjustment method and system based on multi-dimensional mapping

Abstract

The invention relates to the technical field of power system communication, in particular to an intelligent terminal high-speed communication adjustment method and system based on multi-dimensional mapping, and the method comprises the steps: dynamically adjusting the size of a sliding window and retransmission parameters in a TCP layer, and improving the bandwidth utilization rate and fault tolerance in combination with a multiplexing connection pool; in a data cache layer, a Hash and ZSET combined data structure of Redis is adopted to store metadata of the intelligent terminal, a two-stage TTL failure strategy is designed, and a Kafka consumption state is associated to dynamically prolong a cache period; and in the asynchronous processing layer, high-throughput data distribution and load balancing are realized through a Kafka partition strategy, an LZ4 compression algorithm and a consumer parallelism degree self-adaptive mechanism. Meanwhile, a full-link monitoring module is integrated, and a dynamic parameter adjustment rule is fed back and triggered in real time based on Promeheus indexes. Therefore, the problems of high concurrency bottleneck, insufficient real-time performance, poor system expansibility and the like in the prior art are solved.

Description

Technical Field

[0001] The present application relates to the field of power system communication technology, and in particular to a method and system for high-speed communication calibration of intelligent terminals based on multi-dimensional state mapping. Background Art

[0002] With the rapid development of science and technology, the market demand for smart terminals has exploded. Customer requirements for the functions, performance, and appearance of smart terminals are becoming increasingly diverse, resulting in various types of smart terminals displaying unique designs in structure and appearance. Taking ordinary water meters as an example, to meet waterproofing requirements, complex shell structures or full glue filling of the water meter cavity are often used for waterproofing. However, while this design solves the waterproofing problem, it also brings many disadvantages. For example, maintenance becomes significantly more difficult, requiring a lot of manpower and time costs; battery life is shortened, and frequent battery replacement not only increases the cost of use, but also affects the normal operation of the equipment; reported data is not timely, and water meter data cannot be transmitted to the management system in a timely and accurate manner, seriously affecting the real-time and effectiveness of the data, and failing to meet the needs of modern intelligent management.

[0003] However, when it comes to communication between smart terminals and servers, existing technologies mostly rely on a single TCP (Transmission Control Protocol) persistent connection for data transmission. This approach has exposed numerous problems in practical applications. First, faced with the instantaneous influx of massive amounts of data from smart terminals, TCP connections are prone to congestion or timeouts, resulting in packet loss and severely impacting the integrity and accuracy of data transmission. Second, traditional databases are inefficient in storing and querying data, making it difficult to meet the stringent requirements of real-time monitoring for data timeliness and accuracy. This results in significant lags in real-time feedback and responses from smart terminals. Furthermore, centralized processing architectures lack the ability to cope with sudden traffic bursts and lack asynchronous decoupling capabilities. Once traffic spikes occur, the system can easily become paralyzed, making it impossible to guarantee stable operation and service quality. Summary of the Invention

[0004] The present application provides a method and system for high-speed communication calibration of intelligent terminals based on multi-dimensional state mapping to solve the problems of high concurrency bottlenecks, insufficient real-time performance and poor system scalability in the existing technology.

[0005] The first embodiment of the present application provides a method for high-speed communication calibration of intelligent terminals based on multi-dimensional state mapping, comprising the following steps: obtaining multi-dimensional indicator data, wherein the multi-dimensional indicator data includes retransmission rate, hit rate, memory usage, throughput and consumer Lag indicator data; constructing a communication architecture of the TCP communication layer, the Redis cache layer and the Kafka asynchronous processing layer, and establishing a cross-layer monitoring indicator linkage mechanism; performing dynamic parameter tuning at the TCP communication layer, wherein an adaptive window adjustment model is constructed based on the retransmission rate indicator, the window size is dynamically adjusted according to the RTT (Round-TripTime, round-trip time) historical data, a sliding time window is established, a multiplexed connection pool is deployed, and the connection multiplexing strategy is dynamically adjusted according to the hit rate indicator, and based on the network The heartbeat packet interval is dynamically adjusted based on the network status fluctuation coefficient; intelligent data management is performed in the Redis cache layer, wherein the real-time status of the intelligent terminal is stored using a Hash-ZSET composite data structure, a two-level TTL (Time-To-Live) failure strategy is set based on the consumer Lag indicator, and dynamic data elimination is implemented according to the memory usage indicator; load balancing optimization is implemented in the Kafka asynchronous processing layer, and the intelligent terminal ID is hashed and partitioned, the number of partitions is set to 3 times the number of consumers and the LZ4 compression algorithm is enabled, the compression strategy is dynamically adjusted according to the throughput indicator, and an elastic scaling model based on the consumer Lag indicator is constructed; the multi-dimensional indicators are input into the dynamic parameter adjustment rule engine, and the optimal parameter adjustment strategy is generated through the fuzzy logic algorithm.

[0006] Optionally, an adaptive window adjustment model is constructed based on the retransmission rate indicator, including: when the retransmission rate indicator exceeds 5%, the window contraction mechanism is triggered, and the window size is adjusted to 70% of the current BDP (Bandwidth-Delay Product); when the retransmission rate indicator is lower than 2% and remains stable for 10 minutes, the window size is expanded to 120% of the current BDP.

[0007] Optionally, the window size is dynamically adjusted based on RTT historical data to establish a sliding time window, including: when three consecutive RTT fluctuations exceeding the threshold are detected, the window is triggered to shrink by 20%; when the RTT fluctuation is lower than 80% of the threshold for 10 consecutive minutes, the window is expanded by 15%.

[0008] Optionally, a multiplexed connection pool is deployed, and the connection multiplexing strategy is dynamically adjusted according to the hit rate index, including: when the hit rate index is lower than 85%, high-frequency access data is preferentially transmitted through low-load connections; when the hit rate index is higher than 95%, one backup connection is released to reduce resource consumption.

[0009] Optionally, the heartbeat packet interval is dynamically adjusted based on the network status fluctuation coefficient, including: calculating the network status fluctuation coefficient, when the fluctuation coefficient exceeds 0.8, shortening the heartbeat packet interval from the default 10 seconds to 5 seconds; when the fluctuation coefficient is lower than 0.3, extending the heartbeat packet interval to 30 seconds.

[0010] Optionally, a two-level TTL expiration strategy is set based on the consumer Lag indicator, including: when the consumer Lag indicator ≤ 1000, the data TTL is set to 30 seconds; when the consumer Lag indicator > 1000, the corresponding data TTL is automatically extended to 2 minutes, and a data preloading mechanism is started to preload data predicted to be accessed within the next 10 minutes.

[0011] Optionally, dynamic data elimination is implemented according to the memory utilization index, including: when the memory utilization index exceeds 80%, triggering a data elimination mechanism based on the LRU (Least Recently Used) algorithm, and giving priority to clearing the 20% of data with the lowest score value in the ZSET queue; when the memory utilization index is lower than 60%, adjusting the TTL extension threshold from Lag>1000 to Lag>1500 to improve cache utilization.

[0012] Optionally, hash partitioning is performed by smart terminal ID, the number of partitions is set to 3 times the number of consumers and the LZ4 compression algorithm is enabled, the compression strategy is dynamically adjusted according to the throughput indicator, and an elastic scaling model based on the consumer Lag indicator is constructed, including: when the throughput indicator is lower than 50%, switching to no compression to reduce CPU consumption; when the throughput indicator exceeds 80%, switching to ZSTD compression to improve transmission efficiency; when it is detected that the consumer Lag indicator is greater than 1000, triggering consumer instance expansion, and the expansion ratio is 50% of the current number of instances; when the consumer Lag indicator is less than 100 and remains stable for 30 minutes, triggering reduction, and the reduction ratio is 30% of the current number of instances.

[0013] Optionally, the multi-dimensional indicators are input into a dynamic parameter adjustment rule engine, and an optimal parameter adjustment strategy is generated through a fuzzy logic algorithm, including: when it is detected that the retransmission rate indicator is greater than 5% and the hit rate indicator is less than 85%, a first-level response is triggered, wherein the first-level response is to adjust the TCP window size, preheat Redis high-frequency data and optimize Kafka producer parameters; when the throughput indicator exceeds the threshold and the memory utilization indicator is less than 70%, a second-level response is triggered, wherein the second-level response is to expand the Redis cache and Kafka partition and optimize the TCP connection; when the hit rate indicator is less than 80% and the partition water level difference is greater than 30% at the same time, a third-level response is triggered, wherein the third-level response is to start a full-link optimization process, including TCP connection reorganization, Redis data migration and Kafka partition rebalancing, and at the same time generate a performance report and predict load change trends through machine learning to adjust resource configuration in advance.

[0014] The second embodiment of the present application provides a high-speed communication calibration system for intelligent terminals based on multi-dimensional state mapping, including: an acquisition module for acquiring multi-dimensional indicator data, wherein the multi-dimensional indicator data includes retransmission rate, hit rate, memory usage, throughput and consumer Lag indicator data; a construction module for constructing a communication architecture of the TCP communication layer, the Redis cache layer and the Kafka asynchronous processing layer, and establishing a cross-layer monitoring indicator linkage mechanism; a tuning module for performing dynamic parameter tuning at the TCP communication layer, wherein an adaptive window adjustment model is constructed based on the retransmission rate indicator, the window size is dynamically adjusted according to the RTT historical data, a sliding time window is established, a multiplexed connection pool is deployed, and the connection multiplexing strategy is dynamically adjusted according to the hit rate indicator, and the network status fluctuation is based on the network status fluctuation. The dynamic coefficient dynamically adjusts the heartbeat packet interval; a management module is used to perform intelligent data management in the Redis cache layer, wherein a Hash-ZSET composite data structure is used to store the real-time status of the intelligent terminal, a two-level TTL invalidation strategy is set based on the consumer Lag indicator, and dynamic data elimination is implemented according to the memory usage indicator; an optimization module is used to implement load balancing optimization in the Kafka asynchronous processing layer, hash partitioning according to the intelligent terminal ID, setting the number of partitions to 3 times the number of consumers and enabling the LZ4 compression algorithm, dynamically adjusting the compression strategy according to the throughput indicator, and constructing an elastic scaling model based on the consumer Lag indicator; a monitoring feedback module is used to input the multi-dimensional indicators into the dynamic parameter adjustment rule engine, and generate the optimal parameter adjustment strategy through the fuzzy logic algorithm.

[0015] Therefore, this application has at least the following beneficial effects: The embodiment of the present application effectively reduces data transmission delay and packet loss rate by dynamically adjusting the window size based on the retransmission rate and RTT at the TCP communication layer, combined with multiplexing connection pools and dynamic heartbeat packet interval adjustment. For example, when the retransmission rate is too high, the window is shrunk to avoid network congestion and ensure reliable data transmission; and when the network condition is good, the window is expanded to improve transmission efficiency. At the Kafka asynchronous processing layer, reasonable partition settings, dynamic adjustment of compression algorithms, and elastic scaling models based on consumer lags have greatly improved data processing throughput, reduced processing delays, and can quickly process data from a large number of smart terminals to meet communication needs in high-concurrency scenarios. The Redis cache layer uses a Hash-ZSET composite data structure to store data, implements dynamic data elimination based on memory usage, and sets a two-level TTL expiration policy based on consumer lag, effectively managing cache resources. When memory usage is too high, infrequently used data is eliminated to ensure efficient use of cache space. When data processing latency is high, the data cache time is extended to avoid data loss, improve the cache hit rate, and reduce the pressure on the backend storage system. In TCP connection management, the connection reuse strategy is dynamically adjusted based on the hit rate, and network connection resources are rationally allocated to avoid resource waste. A cross-layer monitoring indicator linkage mechanism is established, enabling the system to perceive the operating status of each layer in real time and generate optimal parameter adjustment strategies through a dynamic parameter adjustment rule engine and fuzzy logic algorithms. When abnormal combinations of indicators are detected, such as excessively high retransmission rates and low hit rates, the system automatically triggers corresponding optimization measures, including adjusting the TCP window size, preheating Redis data, and optimizing Kafka producer parameters. This allows the system to quickly adapt to changes in the network environment and business load, maintaining stable and efficient operation. Fuzzy logic algorithms are used to process multi-dimensional indicator data, comprehensively considering multiple factors for decision-making, making parameter adjustment strategies more intelligent and precise. Machine learning is used to predict load trends and proactively adjust resource allocation, further enhancing the system's intelligence. For example, before peak system load, Kafka partitions and Redis caches are automatically expanded, and TCP connection parameters are optimized to ensure stable system performance, reduce manual intervention costs, and enhance the system's automated management capabilities.

[0016] This solves the problems of high concurrency bottleneck, insufficient real-time performance and poor system scalability in the existing technology.

[0017] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which: Figure 1 A flowchart of a method for calibrating high-speed communication of an intelligent terminal based on multi-dimensional state mapping according to an embodiment of the present application; Figure 2 A schematic diagram of data timing in the prior art provided according to one embodiment of the present application; Figure 3 A schematic diagram of data timing in a method for calibrating high-speed communication of an intelligent terminal based on multi-dimensional state mapping according to an embodiment of the present application; Figure 4 A flowchart of a method for calibrating high-speed communication of an intelligent terminal based on multi-dimensional state mapping according to an embodiment of the present application; Figure 5 A flowchart of a dynamic parameter adjustment method for high-speed communication of an intelligent terminal based on multi-dimensional state mapping according to an embodiment of the present application; Figure 6 This is a block diagram of an exemplary device for disaster warning and control based on changes in meteorological element information according to an embodiment of the present application; Figure 7 A schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0019] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0020] The following describes, with reference to the accompanying drawings, a method and system for calibrating high-speed communication of intelligent terminals based on multidimensional state mapping according to an embodiment of the present application. In response to the problem of low communication efficiency mentioned in the above background technology, the present application provides a method for calibrating high-speed communication of intelligent terminals based on multidimensional state mapping. In this method, by obtaining multidimensional indicator data such as retransmission rate and hit rate, a communication architecture consisting of a TCP communication layer, a Redis cache layer, and a Kafka asynchronous processing layer is constructed, and a cross-layer monitoring indicator linkage mechanism is established. At the TCP communication layer, the window size, connection reuse strategy, and heartbeat packet interval are dynamically adjusted based on the retransmission rate, RTT historical data, hit rate, and network status fluctuation coefficient; the Redis cache layer uses the Hash-ZSET composite data structure to store data, and implements a two-level TTL expiration strategy and dynamic data elimination based on consumer lag and memory usage; the Kafka asynchronous processing layer is hashed and partitioned by smart terminal ID, and the compression strategy is dynamically adjusted based on throughput and consumer lag to build an elastic scaling model; finally, multi-dimensional indicators are input into the dynamic parameter adjustment rule engine, and the optimal parameter adjustment strategy is generated through the fuzzy logic algorithm, which comprehensively improves communication performance, optimizes resource utilization, enhances system adaptability and intelligence, and effectively solves the high concurrency, real-time, and scalability problems in smart terminal communications.

[0021] The following describes a method and system for high-speed communication calibration of an intelligent terminal based on multi-dimensional state mapping according to an embodiment of the present application with reference to the accompanying drawings.

[0022] Specifically, Figure 1 A flowchart of a method for calibrating high-speed communication of an intelligent terminal based on multi-dimensional state mapping is provided in an embodiment of the present application.

[0023] like Figure 1 As shown, the intelligent terminal high-speed communication calibration method based on multi-dimensional state mapping includes the following steps: In step S101 , multi-dimensional indicator data is obtained.

[0024] Among them, multi-dimensional indicator data includes retransmission rate, hit rate, memory usage, throughput and consumer Lag indicator data.

[0025] It can be understood that the embodiment of the present application obtains multi-dimensional indicator data including retransmission rate, hit rate, memory usage, throughput and consumer Lag indicator data, reflecting the operating status of each key link in the communication process of the smart terminal.

[0026] In step S102, a communication architecture of the TCP communication layer, the Redis cache layer, and the Kafka asynchronous processing layer is constructed, and a cross-layer monitoring indicator linkage mechanism is established.

[0027] Among them, the TCP communication layer is a connection-oriented, reliable, byte stream-based transport layer communication protocol, which is responsible for data storage between smart terminals and servers; the Redis cache layer is an open source, memory-based data storage system that supports multiple data structures such as strings, hashes, lists, sets, and ordered sets, and is responsible for storing metadata and real-time status of smart terminals; Kafka is a distributed stream processing platform that is often used to process high-throughput real-time data, and is mainly responsible for asynchronous batch processing and load balancing of data.

[0028] It can be understood that the TCP communication layer, Redis cache layer and Kafka asynchronous processing layer communication architecture constructed in the embodiment of the present application achieves efficient coordination of data transmission, storage and processing by clarifying the division of labor and collaboration among each layer.

[0029] In step S103, dynamic parameter tuning is performed at the TCP communication layer, wherein an adaptive window adjustment model is constructed based on the retransmission rate indicator, the window size is dynamically adjusted according to the RTT historical data, a sliding time window is established, a multiplexed connection pool is deployed, and the connection multiplexing strategy is dynamically adjusted according to the hit rate indicator, and the heartbeat packet interval is dynamically adjusted based on the network status fluctuation coefficient.

[0030] RTT refers to the time from when the sender starts sending data to when the sender receives confirmation from the receiver.

[0031] It can be understood that the dynamic parameter tuning strategy implemented in the TCP communication layer of the embodiment of the present application significantly improves communication performance and stability through multi-dimensional collaborative optimization. The adaptive window adjustment model constructed based on the retransmission rate can shrink the window in time when the network is congested, reduce the amount of data sent, avoid increased packet loss, and expand the window to fully utilize the bandwidth when the network is unobstructed; combined with RTT historical data and sliding time window, the trend of network delay changes can be accurately grasped, and the window size can be dynamically adjusted to ensure efficient and stable data transmission. The multiplexed connection pool cooperates with the hit rate indicator to adjust the connection reuse strategy, reduce the connection establishment overhead, improve resource utilization, and give priority to low-load connections to transmit high-frequency data when the hit rate is low. When the hit rate is high, the spare connection is released to reduce resource consumption. The heartbeat packet interval is dynamically adjusted based on the network status fluctuation coefficient, the interval is shortened when the network is unstable, and connection anomalies are detected in time. The interval is extended when stable to reduce overhead, ultimately effectively reducing data transmission delay and packet loss rate, enhancing connection stability, and improving the communication efficiency and reliability of smart terminals in complex network environments.

[0032] In an embodiment of the present application, an adaptive window adjustment model is constructed based on the retransmission rate index, including: when the retransmission rate index exceeds 5%, the window contraction mechanism is triggered and the window size is adjusted to 70% of the current BDP; when the retransmission rate index is lower than 2% and remains stable for 10 minutes, the window size is expanded to 120% of the current BDP.

[0033] It can be understood that the adaptive window adjustment model constructed based on the retransmission rate indicator in the embodiment of the present application effectively improves TCP communication performance and network resource utilization by setting clear thresholds and adjustment strategies. When the retransmission rate exceeds 5%, it indicates that the network may be congested, triggering the window to shrink to 70% of the current bandwidth-delay product, reducing the amount of data sent, avoiding increased network congestion, and reducing the packet loss rate; and when the retransmission rate is less than 2% and stable for 10 minutes, it indicates that the network is in good condition, and the window is expanded to 120% of the BDP to fully utilize the network bandwidth and improve data transmission efficiency. This model enables the window size to be dynamically adjusted according to the real-time status of the network, while ensuring reliable data transmission, optimizing transmission efficiency, effectively dealing with network congestion and idle states, balancing transmission stability and efficiency, and improving the integrity of smart terminals in TCP communication.

[0034] It should be noted that the calculation formula for BDP is: BDP=bandwidth*RTT.

[0035] Specifically, if the network bandwidth is 100 Mbps (megabits per second) and the round-trip delay is 50 ms (milliseconds), then BDP = 100 × 10 6 ×50×10 −3 =5×10 6 bits, that is, the bandwidth-delay product of this network connection is 5 megabits.

[0036] The BDP represents the amount of data that can be accommodated in a network within a round-trip time, reflecting the capacity of the network pipeline. In TCP communications, the BDP is crucial for determining the appropriate send window size. The window size should theoretically not exceed the BDP; otherwise, data may accumulate on the network, causing congestion.

[0037] In an embodiment of the present application, the window size is dynamically adjusted according to the RTT historical data to establish a sliding time window, including: when it is detected that the RTT fluctuation exceeds the threshold for three consecutive times, the window is triggered to shrink by 20%; when the RTT fluctuation is lower than 80% of the threshold for 10 consecutive minutes, the window is expanded by 15%.

[0038] The threshold value may be determined according to actual conditions, such as 30ms.

[0039] It can be understood that the embodiment of the present application can quickly respond to changes in network status by analyzing RTT historical data through the establishment of a sliding time window and dynamically adjusting the window size. When the RTT fluctuation exceeds the threshold for three consecutive times, the window is shrunk by 20%, which can effectively deal with sudden network congestion and reduce data backlog and packet loss; when the RTT fluctuation is lower than the threshold of 80% for 10 consecutive minutes, the window is expanded by 15%, which can make full use of the stable network environment to improve transmission efficiency. This mechanism balances transmission stability and efficiency, avoiding transmission interruptions caused by network fluctuations, optimizing bandwidth utilization when the network conditions are good, realizing adaptive tuning of the TCP window, and enhancing the adaptability and robustness of the system to dynamic network environments.

[0040] In an embodiment of the present application, a multiplexed connection pool is deployed, and the connection multiplexing strategy is dynamically adjusted according to the hit rate index, including: when the hit rate index is lower than 85%, high-frequency access data is preferentially transmitted through low-load connections; when the hit rate index is higher than 95%, one backup connection is released to reduce resource consumption.

[0041] It is understandable that in the embodiment of the present application, during the connection initialization phase, 3 TCP connections, 1 primary and 2 backup, are allocated to each smart terminal. The heartbeat packet interval is set to 10 seconds, and the hash consistency algorithm is used to bind the data stream and the connection to ensure the integrity of data transmission. The connection management mechanism provides a basis for policy adjustment by tracking the connection load and monitoring the Redis cache hit rate. When the hit rate is lower than 85%, a low-load connection is selected to transmit high-frequency access data to improve data transmission efficiency; when it is higher than 95%, 1 backup connection is released to improve resource utilization, which not only optimizes system performance and realizes rational resource utilization, but also enhances system stability, reduces data transmission delays, and avoids resource waste.

[0042] In an embodiment of the present application, the heartbeat packet interval is dynamically adjusted based on the network status fluctuation coefficient, including: calculating the network status fluctuation coefficient, when the fluctuation coefficient exceeds 0.8, shortening the heartbeat packet interval from the default 10 seconds to 5 seconds; when the fluctuation coefficient is lower than 0.3, extending the heartbeat packet interval to 30 seconds.

[0043] Among them, the network status fluctuation coefficient is an indicator that quantifies the severity of changes in network status. The heartbeat packet is a special data packet sent periodically between the client and the server in network communication to detect whether the connection is normal and ensure the effectiveness of the communication link.

[0044] It is understandable that the embodiment of the present application dynamically adjusts the heartbeat packet interval by calculating the network status fluctuation coefficient. When the fluctuation coefficient exceeds 0.8, the heartbeat packet interval is shortened from 10 seconds to 5 seconds, so that the connection can be detected more frequently when the network is unstable, ensuring the continuity of data transmission; when the fluctuation coefficient is lower than 0.3, the interval is extended to 30 seconds, reducing the heartbeat packet's occupation of network bandwidth and system resources in a stable network, thereby improving resource utilization. This dynamic adjustment mechanism can enhance system stability, reduce the risk of failure, better adapt to changes in network status, and ensure reliable system operation.

[0045] Specifically, in a large-scale intelligent logistics park, a large number of intelligent terminal devices are deployed, such as automatic sorting robots, cargo tracking sensors, intelligent warehousing equipment, etc. These devices communicate with the central management system in real time through the network, upload their own status information and task data, and receive instructions from the management system.

[0046] During peak logistics periods, large quantities of goods enter and exit warehouses simultaneously, and numerous smart terminal devices frequently transmit data, leading to network congestion. The calculated network status fluctuation coefficient at this time exceeds 0.8. For example, when sorting goods, automatic sorting robots need to continuously upload cargo information to the central system and receive instructions for the next sorting step. If the default 10-second heartbeat packet interval is used, in cases of network congestion, the connection status may not be detected for a long time, resulting in data transmission interruptions and stalled sorting tasks. By shortening the heartbeat packet interval to 5 seconds, the system can detect connection anomalies more promptly. If a robot's connection is briefly interrupted, the system can detect it within 5 seconds and quickly attempt to reconnect, ensuring that the cargo sorting task is not affected and improving logistics efficiency.

[0047] During the nighttime hours when fewer goods are entering and leaving the logistics park, the network load is low, and the calculated network status fluctuation coefficient is less than 0.3. During this period, smart warehousing equipment primarily uploads small, periodic updates on inventory. Maintaining a 10-second heartbeat packet interval would continue to consume a certain amount of network bandwidth and device resources. Extending the heartbeat packet interval to 30 seconds reduces the number of heartbeat packets sent, while ensuring a stable connection. This frees up network bandwidth, which can be used for other devices that occasionally need to transmit larger amounts of data, such as detailed diagnostic data in the event of a device failure. This reduces the burden on the device to process heartbeat packets, allowing it to perform its tasks more efficiently, extending its lifespan and reducing overall system energy consumption.

[0048] It should be noted that the formula for the network status fluctuation coefficient is: ; in, 、 、 is the weight coefficient, which is used to adjust the importance of different indicators in the calculation of the volatility coefficient, and .

[0049] In step S104, intelligent data management is performed in the Redis cache layer, wherein a Hash-ZSET composite data structure is used to store the real-time status of the intelligent terminal, a two-level TTL expiration strategy is set based on the consumer Lag indicator, and dynamic data elimination is implemented according to the memory usage indicator.

[0050] Among them, Hash is a storage structure that can store multiple key-value pairs in a hash table. When storing the real-time status of smart terminals, Hash is used to store the different attributes and corresponding values of each smart terminal. For example, the real-time data of the smart meter, such as voltage, current, and power, can be stored as different fields in the Hash structure, which is convenient for quickly querying and updating the specific attributes of a smart terminal to efficiently manage the detailed status information of the device. ZSET is used to maintain a priority queue, setting scores for elements according to the urgency of the data or other importance indicators. For example, a higher score is set for equipment fault alarm information that needs to be processed immediately to ensure that these critical data can be processed and paid attention to first. Consumer Lag refers to the message processing progress gap between consumers and producers, that is, the number of messages that consumers have not yet processed; TTL refers to the time that cached data can survive in Redis.

[0051] It is understood that the embodiments of the present application implement intelligent data management at the Redis cache layer, using a Hash-ZSET composite data structure to store the real-time status of smart terminals, quickly querying specific terminal attributes with the help of Hash, and using ZSET to sort data by urgency to improve processing efficiency. At the same time, a two-level TTL expiration strategy is set based on the consumer Lag indicator to extend data retention and ensure data integrity when message processing is delayed. Dynamic data elimination is also performed based on the memory usage indicator to optimize memory resource utilization, avoid insufficient memory affecting system performance, and ensure stable and efficient system operation.

[0052] In an embodiment of the present application, a two-level TTL expiration strategy is set based on the consumer Lag indicator, including: when the consumer Lag indicator ≤ 1000, the data TTL is set to 30 seconds; when the consumer Lag indicator > 1000, the corresponding data TTL is automatically extended to 2 minutes, and the data preloading mechanism is started to preload data predicted to be accessed within the next 10 minutes.

[0053] It is understandable that the embodiment of the present application dynamically adjusts the data TTL and data preloading mechanism based on the consumer Lag indicator. When consumer Lag ≤ 1000, the data TTL is set to 30 seconds to ensure data timeliness; when consumer Lag > 1000, the data TTL is extended to 2 minutes to prevent data loss. At the same time, the data preloading mechanism is activated to preload data predicted to be accessed within the next 10 minutes. This not only ensures the timeliness and integrity of the data, but also reduces data access latency and improves the user experience.

[0054] In an embodiment of the present application, dynamic data elimination is implemented according to the memory utilization index, including: when the memory utilization index exceeds 80%, triggering the data elimination mechanism based on the LRU algorithm, and giving priority to clearing the 20% of data with the lowest score value in the ZSET queue; when the memory utilization index is lower than 60%, adjusting the TTL extension threshold from Lag>1000 to Lag>1500 to improve cache utilization.

[0055] The score value is used to determine the priority of data in the ZSET queue. When data needs to be eliminated, the data with the lowest score value will be cleared first to achieve data management and memory release.

[0056] It can be understood that the embodiments of the present application implement different cache management strategies based on the memory utilization index. When the memory utilization exceeds 80%, the data elimination mechanism is triggered with the help of the LRU algorithm, and the 20% of data with the lowest score value in the ZSET queue is cleared first to free up memory space and ensure that the performance of the Redis cache system is not affected by insufficient memory; when the memory utilization is lower than 60%, the TTL extension threshold is adjusted from Lag>1000 to Lag>1500, allowing more data to be cached when the memory is sufficient, reducing the situation of premature expiration of data, thereby improving cache utilization, realizing intelligent management of the cache according to memory conditions, and ensuring efficient and stable operation of the system.

[0057] In step S105, load balancing optimization is implemented in the Kafka asynchronous processing layer, wherein hash partitioning is performed according to the smart terminal ID, the number of partitions is set to 3 times the number of consumers, and the LZ4 compression algorithm is enabled. The compression strategy is dynamically adjusted according to the throughput indicator, and an elastic scaling model based on the consumer Lag indicator is constructed.

[0058] Among them, the LZ4 compression algorithm is a fast lossless data compression algorithm. This algorithm is enabled in the Kafka asynchronous processing layer to compress messages sent by producers. The elastic scaling model is an intelligent mechanism that dynamically adjusts system resource configuration based on system operation indicators.

[0059] It is understandable that the embodiments of the present application implement load balancing optimization in the Kafka asynchronous processing layer from multiple aspects, including partitioning strategy, compression algorithm, strategy adjustment, and scaling model. Hash partitioning is performed by smart terminal ID, and the number of partitions is set to 3 times the number of consumers to ensure orderly message processing and improve processing efficiency; the LZ4 compression algorithm is enabled to reduce network overhead; the compression strategy is dynamically adjusted based on throughput to flexibly adapt to load changes; and an elastic scaling model is built based on consumer lag to ensure stable system operation and improve resource utilization, effectively responding to data traffic fluctuations in high-concurrency real-time data collection scenarios, and ensuring efficient system operation.

[0060] In an embodiment of the present application, hash partitioning is performed according to the smart terminal ID, the number of partitions is set to 3 times the number of consumers and the LZ4 compression algorithm is enabled, the compression strategy is dynamically adjusted according to the throughput index, and an elastic scaling model based on the consumer Lag index is constructed, including: when the throughput index is lower than 50%, switching to no compression to reduce CPU consumption; when the throughput index exceeds 80%, switching to ZSTD compression to improve transmission efficiency; when it is detected that the consumer Lag index is greater than 1000, triggering consumer instance expansion, the expansion ratio is 50% of the current number of instances; when the consumer Lag index is less than 100 and remains stable for 30 minutes, triggering contraction, the contraction ratio is 30% of the current number of instances.

[0061] It can be understood that the embodiment of the present application effectively optimizes system performance and resource utilization efficiency by implementing a dynamic adjustment strategy based on the throughput index and the consumer Lag index. When the throughput is lower than 50%, it switches to no compression, which can reduce the CPU's unnecessary compression calculation overhead and allow system resources to be allocated more to other key tasks; when the throughput exceeds 80%, ZSTD compression is used to improve the data transmission speed during the network transmission stage and ensure the timeliness of data processing. The elastic scaling mechanism based on the consumer Lag index expands the consumer instance by 50% when Lag>1000, quickly improves the message processing capability, and avoids a large backlog of messages; when Lag<100 and remains stable for 30 minutes, it shrinks by 30%, promptly releasing idle resources and reducing system operating costs.

[0062] Specifically, the real-time order processing system of a large e-commerce platform is responsible for asynchronously processing order messages from the front end in Kafka.

[0063] During normal, non-promotional periods, system throughput is low, reaching, for example, only 30% of total processing capacity. At this point, according to the optimization strategy, the system switches to uncompressed mode. Since order message data is relatively small and network bandwidth is ample, uncompressed mode reduces the CPU overhead of compressing data, freeing up server resources for processing order business logic, such as inventory queries and price calculations. This ensures efficient order processing while reducing server resource consumption.

[0064] During major promotional events like Singles' Day (Singles' Day), system throughput surges, exceeding 80%. This influx of order messages places increased pressure on network transmission. At this point, the system automatically switches to the ZSTD compression algorithm. This significantly reduces the size of order messages. Data that previously required significant network bandwidth can now be transmitted much more quickly, significantly improving data transmission efficiency and avoiding order processing delays caused by network congestion, ensuring a quick response to user orders.

[0065] At the start of a promotion, due to sudden surges in traffic, the consumer Lag indicator quickly exceeded 1000. This meant that consumers were processing order messages much slower than producers were generating them, resulting in a large backlog of order messages. At this point, the system triggered the consumer instance expansion mechanism, increasing capacity by 50% of the current number of instances. For example, if there were originally 20 consumer instances, adding 10 would significantly improve the system's order message processing capacity, allowing for faster processing of accumulated order messages. This prevented users from waiting too long or even losing orders due to a prolonged message backlog.

[0066] As the promotion draws to a close, order volume gradually decreases. When the consumer Lag indicator remains below 100 and remains stable for 30 minutes, it indicates that the system's message processing capacity is excessive. The system triggers a scaling-down mechanism, reducing the number of instances by 30%. For example, if there are 30 consumer instances at this time, the number will be reduced to 21 after scaling down. The freed-up server resources can be allocated to other businesses, avoiding resource waste, reducing operating costs, and ensuring that the system always operates efficiently and economically.

[0067] It should be noted that if Figure 2As shown in the figure, in the traditional meter data processing solution, the meter concentrator sends data to the server through the TCP protocol. The server processes the data in a single-threaded manner and then writes the data to the database. During this process, the operation of writing to the database will block subsequent processes, resulting in low overall processing efficiency. Judging from the performance data, its throughput is only 30,000 items / second, the average delay is as high as 120ms, and the TCP retransmission rate reaches 8%. The single-threaded processing method limits the data processing capability, the blocking write to the database further increases the delay, and the high retransmission rate also reflects the poor stability of network transmission. These factors together make it difficult for traditional solutions to cope with large-scale, high-concurrency data processing needs. However, as Figure 3 As shown, in the embodiment of the present application, the meter concentrator compresses the data in batches through the TCP protocol combined with the LZ4 compression algorithm and sends it to the Kafka producer. The Kafka producer asynchronously caches the data in the Redis cache, and the caching takes only 5ms. Subsequently, the Kafka consumer consumes the data in parallel. This solution fully utilizes the advantages of asynchronous caching and parallel consumption, greatly improving processing efficiency. Performance data shows that the throughput of the solution of the present invention is increased to 100,000 items / second, the average delay is greatly reduced to 38ms, and the TCP retransmission rate is also reduced to 0.7%. Asynchronous caching avoids blocking and waiting, parallel consumption improves the concurrency of data processing, and the compression algorithm reduces network transmission volume, thereby comprehensively improving system performance and being able to better adapt to the real-time processing requirements of massive meter data in scenarios such as smart grids.

[0068] In step S106, the multi-dimensional indicators are input into the dynamic parameter adjustment rule engine, and the optimal parameter adjustment strategy is generated through the fuzzy logic algorithm.

[0069] Among them, fuzzy logic algorithm is an algorithm based on fuzzy set theory, which allows the algorithm to process imprecise or ambiguous information.

[0070] It can be understood that the embodiment of the present application inputs multi-dimensional indicators into the dynamic parameter adjustment rule engine, combines the fuzzy logic algorithm to generate the optimal parameter adjustment strategy, and breaks through the limitations of traditional fixed parameters by comprehensively capturing various status information of system operation, thereby realizing dynamic and precise adjustment of system parameters.

[0071] In an embodiment of the present application, multi-dimensional indicators are input into a dynamic parameter adjustment rule engine, and an optimal parameter adjustment strategy is generated through a fuzzy logic algorithm, including: when it is detected that the retransmission rate indicator is greater than 5% and the hit rate indicator is less than 85%, a first-level response is triggered, wherein the first-level response is to adjust the TCP window size, preheat Redis high-frequency data, and optimize the Kafka producer parameters; when the throughput indicator exceeds the threshold and the memory utilization indicator is less than 70%, a second-level response is triggered, wherein the second-level response is to expand the Redis cache and Kafka partition and optimize the TCP connection; when the hit rate indicator is less than 80% and the partition water level difference is greater than 30% at the same time, a third-level response is triggered, wherein the third-level response is to start a full-link optimization process, including TCP connection reorganization, Redis data migration, and Kafka partition rebalancing, while generating a performance report and predicting load change trends through machine learning to adjust resource configuration in advance.

[0072] It is understandable that the embodiments of this application construct a three-level response mechanism based on different indicator combinations to accurately locate system performance bottlenecks. The first-level response focuses on basic optimization of network transmission and data caching to quickly resolve problems such as high retransmission rate and low hit rate; the second-level response targets traffic growth and improves data processing capabilities through capacity expansion and parameter optimization; the third-level response initiates full-link deep optimization when serious performance issues occur in the system, combining performance reports with machine learning predictions to adjust resource allocation in advance.

[0073] Specifically, at the beginning of a major e-commerce promotion, a sudden influx of user requests increased network transmission pressure, causing the data retransmission rate to rise to 7% and the Redis cache hit rate to drop to 82%. Upon detecting these changes, the system immediately triggered a Level 1 response. By adjusting the TCP window size, data transmission latency and packet loss were reduced. High-frequency data in Redis was pre-loaded into the cache, preloading frequently used data such as product detail pages and shopping carts for quick access when users accessed the data. Kafka producer parameters were optimized to more efficiently send order messages to the message queue, ensuring the stability of the transaction process and preventing network and cache issues from impacting user orders.

[0074] As the promotion continued, system throughput continued to climb, far exceeding normal thresholds, reaching three times the usual rate. However, memory utilization remained at 65%. Based on this combination of metrics, the system triggered a secondary response, expanding the Redis cache and Kafka partitions. The expanded Redis cache allowed for more temporary user data, such as browsing history and pending order information. The expanded Kafka partitions increased message processing parallelism, accelerating order processing. Furthermore, optimizing TCP connections further improved network transmission efficiency, enabling the system to handle high traffic volumes and smoothly process large orders, avoiding system stalls or crashes due to insufficient resources.

[0075] As the promotion drew to a close, uneven load on some servers caused the hit rate to drop to 78%, and the water level difference among Kafka partitions reached 35%. The system immediately triggered a Level 3 response and initiated a full-link optimization process. TCP connections were reorganized, data transmission paths were replanned, and network stability was improved. Redis data was migrated to evenly distribute data across cache nodes, improving overall cache performance. Kafka partitions were rebalanced to ensure even message processing pressure across partitions. Performance reports were also generated to help operations personnel fully understand the system's performance and existing issues during the promotion. Machine learning algorithms were used to predict subsequent load trends, such as user visits and order volume, allowing resource allocation to be adjusted in advance to prepare for the next promotion or business peak, ensuring long-term stable system operation.

[0076] According to the embodiment of the present application, a high-speed communication calibration method for smart terminals based on multi-dimensional state mapping is proposed. By acquiring multi-dimensional indicator data such as retransmission rate and hit rate, a communication architecture consisting of a TCP communication layer, a Redis cache layer and a Kafka asynchronous processing layer is constructed, and a cross-layer monitoring indicator linkage mechanism is established. In the TCP communication layer, the window size, connection reuse strategy and heartbeat packet interval are dynamically adjusted according to the retransmission rate, RTT historical data, hit rate and network status fluctuation coefficient; the Redis cache layer uses a Hash-ZSET composite data structure to store data, and implements a two-level TTL invalidation strategy and dynamic data elimination according to consumer Lag and memory usage; the Kafka asynchronous processing layer is hash partitioned by smart terminal ID, and dynamically adjusts the compression strategy based on throughput and consumer Lag and constructs an elastic scaling model; finally, the multi-dimensional indicators are input into the dynamic parameter adjustment rule engine, and the optimal parameter adjustment strategy is generated by the fuzzy logic algorithm, which comprehensively improves the communication performance, optimizes resource utilization, enhances the system adaptability and intelligence level, and effectively solves the high concurrency, real-time and scalability problems in smart terminal communications.

[0077] The following will describe the intelligent terminal high-speed communication calibration method based on multi-dimensional state mapping through a specific embodiment. Figure 4 As shown, taking the smart grid real-time monitoring system as an example, the specific contents are as follows: In a smart grid, millions of smart meters distributed throughout a city need to upload real-time monitoring data, such as voltage and current. Relying on a monitoring and feedback system (using Prometheus to collect metrics and monitor key data such as retransmission rates), a dynamic parameter adjustment engine (including a rules engine and parameter update module) regulates communication parameters. Three TCP connections (one primary and two backup) are allocated to each smart meter. Hash-consistent routing (implemented in the server communication gateway) binds data streams and connections to ensure stable and reliable data transmission. The heartbeat packet interval has been shortened from the default 60 seconds to 10 seconds (the heartbeat packet interval in the TCP connection pool is set to 10 seconds) to quickly detect connection anomalies. A dynamic sliding window optimization strategy is employed, with the initial window size set to twice the bandwidth-delay product (BDP).

[0078] The system continuously records the RTT (round-trip time) history of data transmission from each smart meter. When the monitoring and feedback system detects a sudden increase in the RTT of a meter, it triggers the dynamic parameter adjustment engine through indicator feedback, automatically reducing the TCP window size (implemented by the dynamic window module in the TCP connection pool) to avoid network congestion.

[0079] like Figure 5 As shown in the figure, when Prometheus detects a TCP retransmission rate greater than 5%, the rules engine in the dynamic parameter adjustment engine is triggered. The system automatically reduces the sliding window by 20% and switches to a backup connection (executed by the dynamic window module in the TCP connection pool) to avoid network congestion. For example, when the TCP retransmission rate reaches 6% during data transmission at a certain electricity meter, the system responds quickly, adjusting the window size and switching the connection to ensure stable data transmission.

[0080] A Redis cluster is used to store data related to smart meters. At the cache layer, a hash data structure is used to store real-time status information of smart meters. ZSET is also used to maintain a priority queue, sorting data based on its urgency (for example, voltage anomaly alarm data takes precedence over normal monitoring data), facilitating the prioritized processing of important data.

[0081] The cache expiration strategy uses a two-level TTL design: the default TTL is 30 seconds. When Kafka consumption latency exceeds a preset threshold (e.g., 1,000 unprocessed messages, monitored by the Lag Threshold Control module in the consumer group), the TTL of the corresponding meter data is automatically extended to 2 minutes (implemented through the dynamic TTL management module in the Redis cluster), ensuring that data does not expire before being processed by the Kafka consumer. Detecting abnormalities in Redis-related metrics triggers the dynamic parameter adjustment engine to perform operations such as expanding the Redis cluster's nodes.

[0082] like Figure 5As shown, when Prometheus detects a Redis hit rate of less than 85%, the rules engine is triggered, and the system automatically performs operations such as preheating high-frequency data and expanding Redis nodes (implemented in the Redis cluster). The cache invalidation strategy uses a two-level TTL design: the default TTL is 30 seconds. When the Kafka consumption latency exceeds a preset threshold (such as 1000 unprocessed messages, monitored by the Lag Threshold Control module in the consumer group), the TTL of the corresponding meter data is automatically extended to 2 minutes (implemented by the dynamic TTL management module in the Redis cluster), ensuring that the data does not expire before being processed by the Kafka consumer.

[0083] Kafka message queues are responsible for processing massive amounts of meter data. Hash partitioning is performed by smart concentrator ID (partitioning the Kafka message queue by concentrator ID) to ensure that data from meters associated with the same concentrator is processed in the same partition. The number of partitions is set to three times the number of consumers to achieve load balancing.

[0084] On the data sending side, the producer enables the LZ4 compression algorithm (in the LZ4 compression batch sending module in the Kafka message queue) and sets a sending policy: data sending is triggered when the message batch size reaches 1MB or the waiting time exceeds 50ms. This policy effectively reduces network I / O times and improves data transmission efficiency.

[0085] Monitor consumer lag in real time (number of unprocessed messages, monitored by the lag threshold control module in the consumer group), such as Figure 5 As shown, when Prometheus detects a Lag value greater than 1000, it automatically scales up consumer instances (using the adaptive scalability module within the consumer group) to enhance message processing capabilities. When the Lag value is less than 100, it scales down consumer instances to conserve system resources. For example, during peak hours, the system automatically increases the number of consumer instances to ensure timely data processing. When the monitoring and feedback system detects abnormalities in Kafka-related metrics, it can use the dynamic parameter adjustment engine to adjust the number of instances in the Kafka consumer group.

[0086] Key indicators, including TCP retransmission rate, Redis cache hit rate, Kafka partition lag, etc., are collected through Prometheus and fed back to the monitoring and feedback system. Based on the indicator conditions, the monitoring and feedback system triggers the dynamic parameter tuning engine to execute predefined tuning rules: like Figure 5 As shown, if the indicator is normal, the process returns to the indicator collection stage; if the indicator is abnormal, the rule engine is triggered.

[0087] The rule engine performs corresponding actions based on different abnormal indicators: when the TCP retransmission rate is greater than 5%, the sliding window is reduced by 20% and the backup connection is switched; when the Redis hit rate is less than 85%, high-frequency data is preheated and the Redis node is expanded; when KafkaLag is greater than 1000, consumer instances are added; when other abnormal situations are encountered, logs are recorded.

[0088] After executing the corresponding operations, a parameter hot update is issued through the configuration center. Next, the system verifies that the indicators have returned to normal. If so, the system returns to the indicator collection phase and continues monitoring. If not, an alarm is triggered, notifying operations and maintenance personnel for manual intervention and inspection. Through the collaborative operation of these layers and modules (the interaction between modules and the monitoring and optimization process are shown in the flowchart), the smart grid real-time monitoring system can efficiently and stably process massive amounts of meter data, ensuring real-time monitoring of the grid's operating status and providing early warning of abnormalities.

[0089] In summary, smart meter data is first collected by the electricity concentrator cluster (following the Modbus / DLMS protocol), multiplexed and transmitted through the TCP connection pool (multiplexing is implemented in the server communication gateway), asynchronously distributed to the Kafka message queue, and then consumed in parallel by the consumer group. Some metadata is temporarily stored in the Redis cluster. The smart grid real-time monitoring system can efficiently and stably process massive amounts of meter data, ensuring real-time monitoring of the grid operation status and abnormal warnings.

[0090] Specifically, the TCP communication layer dynamically adjusts the window and retransmission parameters based on the network status, combines multiplexing to improve bandwidth utilization, reduces the retransmission rate to below 1%, and automatically restores the connection within 30 seconds after a network interruption to ensure reliable transmission. Redis builds a key-value storage model to cache metadata, with a hit rate exceeding 95% and query response within 5ms, achieving millisecond-level real-time response. Kafka uses partitioning strategies and consumer groups to implement asynchronous batch processing and load balancing. Through batch compression, the single-node throughput reaches 100,000 items per second, solving the problem of high throughput and system decoupling. The full-link monitoring module collects indicators in real time and dynamically feeds back parameters for adjustment, ultimately achieving a significant increase in throughput, reduced latency, and enhanced reliability.

[0091] The embodiments of the present application significantly improve communication throughput (up to 100,000 messages per second for a single node), reduce query latency to below 5ms, and achieve a TCP retransmission rate of less than 1% under network fluctuation scenarios. It is suitable for high-concurrency real-time data collection scenarios with millions of devices, such as smart grids.

[0092] Next, a high-speed communication calibration system for intelligent terminals based on multi-dimensional state mapping proposed in accordance with an embodiment of the present application will be described with reference to the accompanying drawings.

[0093] Figure 6It is a block diagram of an intelligent terminal high-speed communication calibration system based on multi-dimensional state mapping according to an embodiment of the present application.

[0094] like Figure 6 As shown, the intelligent terminal high-speed communication calibration system 10 based on multi-dimensional state mapping includes: an acquisition module 100, a construction module 200, a tuning module 300, a management module 400, an optimization module 500 and a monitoring feedback module 600.

[0095] Among them, the acquisition module 100 is used to obtain multi-dimensional indicator data, wherein the multi-dimensional indicator data includes retransmission rate, hit rate, memory usage, throughput and consumer Lag indicator data; the construction module 200 is used to build the communication architecture of the TCP communication layer, Redis cache layer and Kafka asynchronous processing layer, and establish a cross-layer monitoring indicator linkage mechanism; the tuning module 300 is used to perform dynamic parameter tuning at the TCP communication layer, wherein an adaptive window adjustment model is constructed based on the retransmission rate indicator, the window size is dynamically adjusted according to the RTT historical data, a sliding time window is established, a multiplexed connection pool is deployed, and the connection multiplexing strategy is dynamically adjusted according to the hit rate indicator, and the heartbeat packet interval is dynamically adjusted based on the network status fluctuation coefficient; the management module 300 is used to build a multi-dimensional indicator data, wherein the multi-dimensional indicator data includes retransmission rate, hit rate, memory usage, throughput and consumer Lag indicator data; the construction module 200 is used to build the communication architecture of the TCP communication layer, Redis cache layer and Kafka asynchronous processing layer, and establish a cross-layer monitoring indicator linkage mechanism; the tuning module 300 is used to perform dynamic parameter tuning at the TCP communication layer, wherein an adaptive window adjustment model is constructed based on the retransmission rate indicator, the window size is dynamically adjusted according to the RTT historical data, a sliding time window is established, a multiplexed connection pool is deployed, and the connection multiplexing strategy is dynamically adjusted according to the hit rate indicator, and the heartbeat packet interval is dynamically adjusted based on the network status fluctuation coefficient; The management module 400 is used to perform intelligent data management in the Redis cache layer, in which a Hash-ZSET composite data structure is used to store the real-time status of smart terminals, a two-level TTL expiration strategy is set based on the consumer Lag indicator, and dynamic data elimination is implemented according to the memory usage indicator; the optimization module 500 is used to implement load balancing optimization in the Kafka asynchronous processing layer, hash partitioning by smart terminal ID, setting the number of partitions to 3 times the number of consumers and enabling the LZ4 compression algorithm, dynamically adjusting the compression strategy according to the throughput indicator, and building an elastic scaling model based on the consumer Lag indicator; the monitoring feedback module 600 is used to input multi-dimensional indicators into the dynamic parameter adjustment rule engine, and generate the optimal parameter adjustment strategy through the fuzzy logic algorithm.

[0096] It should be noted that the above explanation of the embodiment of the intelligent terminal high-speed communication calibration method based on multi-dimensional state mapping is also applicable to the intelligent terminal high-speed communication calibration system based on multi-dimensional state mapping in this embodiment, and will not be repeated here.

[0097] According to the intelligent terminal high-speed communication calibration system based on multi-dimensional state mapping proposed in the embodiment of the present application, by obtaining multi-dimensional indicator data such as retransmission rate and hit rate, a communication architecture consisting of a TCP communication layer, a Redis cache layer and a Kafka asynchronous processing layer is constructed, and a cross-layer monitoring indicator linkage mechanism is established. In the TCP communication layer, the window size, connection reuse strategy and heartbeat packet interval are dynamically adjusted according to the retransmission rate, RTT historical data, hit rate and network status fluctuation coefficient; the Redis cache layer uses a Hash-ZSET composite data structure to store data, and implements a two-level TTL invalidation strategy and dynamic data elimination according to consumer Lag and memory usage; the Kafka asynchronous processing layer is hash partitioned by the intelligent terminal ID, and dynamically adjusts the compression strategy based on throughput and consumer Lag and constructs an elastic scaling model; finally, the multi-dimensional indicators are input into the dynamic parameter adjustment rule engine, and the optimal parameter adjustment strategy is generated by the fuzzy logic algorithm, which comprehensively improves the communication performance, optimizes resource utilization, enhances the system adaptability and intelligence level, and effectively solves the high concurrency, real-time and scalability problems in intelligent terminal communication.

[0098] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include: Memory 701 , processor 702 , and computer programs stored in the memory 701 and executable on the processor 702 .

[0099] When the processor 702 executes the program, the intelligent terminal high-speed communication calibration method based on multi-dimensional state mapping provided in the above embodiment is implemented.

[0100] Furthermore, the electronic device further includes: The communication interface 703 is used for communication between the memory 701 and the processor 702 .

[0101] The memory 701 is used to store computer programs that can be run on the processor 702 .

[0102] The memory 701 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0103] If the memory 701, processor 702, and communication interface 703 are implemented independently, the communication interface 703, memory 701, and processor 702 can be interconnected via a bus to communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 7 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0104] Optionally, in a specific implementation, if the memory 701, the processor 702 and the communication interface 703 are integrated on a chip, the memory 701, the processor 702 and the communication interface 703 can communicate with each other through an internal interface.

[0105] The processor 702 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0106] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0107] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this application, "N" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0108] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing a custom logical function or process step, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed in a different order than shown or discussed, including performing functions in a substantially simultaneous manner or in a reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0109] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, it can be implemented using any one or a combination of the following technologies known in the art: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0110] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

Claims

1. A method for calibrating high-speed communication of intelligent terminals based on multi-dimensional state mapping, characterized in that: The following steps are involved: Acquire multi-dimensional indicator data, wherein the multi-dimensional indicator data includes retransmission rate, hit rate, memory usage, throughput, and consumer lag indicator data; Build a communication architecture for the TCP communication layer, Redis cache layer, and Kafka asynchronous processing layer, and establish a cross-layer monitoring indicator linkage mechanism; Dynamic parameter tuning is performed at the TCP communication layer, wherein an adaptive window adjustment model is constructed based on the retransmission rate indicator, the window size is dynamically adjusted according to RTT historical data, a sliding time window is established, a multiplexed connection pool is deployed, and a connection multiplexing strategy is dynamically adjusted according to the hit rate indicator, and a heartbeat packet interval is dynamically adjusted based on a network status fluctuation coefficient; Intelligent data management is performed in the Redis cache layer, wherein a Hash-ZSET composite data structure is used to store the real-time status of smart terminals, a two-level TTL expiration strategy is set based on the consumer Lag indicator, and dynamic data elimination is implemented according to the memory usage indicator; Implement load balancing optimization in the Kafka asynchronous processing layer, where hash partitioning is performed based on smart terminal IDs, the number of partitions is set to three times the number of consumers, and the LZ4 compression algorithm is enabled. The compression strategy is dynamically adjusted based on the throughput metric, and an elastic scaling model based on the consumer Lag metric is constructed. The multi-dimensional indicators are input into the dynamic parameter adjustment rule engine, and the optimal parameter adjustment strategy is generated through the fuzzy logic algorithm.

2. The intelligent terminal high-speed communication calibration method based on multi-dimensional state mapping according to claim 1 is characterized in that: An adaptive window adjustment model is constructed based on the retransmission rate indicator, including: when the retransmission rate indicator exceeds 5%, the window contraction mechanism is triggered and the window size is adjusted to 70% of the current BDP; when the retransmission rate indicator is lower than 2% and remains stable for 10 minutes, the window size is expanded to 120% of the current BDP.

3. The intelligent terminal high-speed communication calibration method based on multi-dimensional state mapping according to claim 1 is characterized in that: The window size is dynamically adjusted based on historical RTT data to establish a sliding time window. This includes: when three consecutive RTT fluctuations exceeding the threshold are detected, the window is triggered to shrink by 20%; when the RTT fluctuation is lower than 80% of the threshold for 10 consecutive minutes, the window is expanded by 15%.

4. The intelligent terminal high-speed communication calibration method based on multi-dimensional state mapping according to claim 1 is characterized in that: Deploy a multiplexed connection pool and dynamically adjust the connection multiplexing strategy based on the hit rate indicator, including: when the hit rate indicator is lower than 85%, give priority to transmitting high-frequency access data through low-load connections; when the hit rate indicator is higher than 95%, release one standby connection to reduce resource consumption.

5. The intelligent terminal high-speed communication calibration method based on multi-dimensional state mapping according to claim 1 is characterized in that: Dynamically adjust the heartbeat packet interval based on the network status fluctuation coefficient, including: calculating the network status fluctuation coefficient, when the fluctuation coefficient exceeds 0.8, shortening the heartbeat packet interval from the default 10 seconds to 5 seconds; when the fluctuation coefficient is lower than 0.3, extending the heartbeat packet interval to 30 seconds.

6. The intelligent terminal high-speed communication calibration method based on multi-dimensional state mapping according to claim 1 is characterized in that: A two-level TTL expiration strategy is set based on the consumer Lag indicator, including: when the consumer Lag indicator is ≤1000, the data TTL is set to 30 seconds; when the consumer Lag indicator is >1000, the corresponding data TTL is automatically extended to 2 minutes, and a data preloading mechanism is started to preload data predicted to be accessed within the next 10 minutes.

7. The intelligent terminal high-speed communication calibration method based on multi-dimensional state mapping according to claim 1 is characterized in that: Dynamic data elimination is implemented according to the memory utilization index, including: when the memory utilization index exceeds 80%, a data elimination mechanism based on the LRU algorithm is triggered, and the 20% data with the lowest score value in the ZSET queue is cleared first; when the memory utilization index is lower than 60%, the TTL extension threshold is adjusted from Lag>1000 to Lag>1500 to improve cache utilization.

8. The intelligent terminal high-speed communication calibration method based on multi-dimensional state mapping according to claim 1 is characterized in that: Hash partitioning is performed by smart terminal ID. The number of partitions is set to three times the number of consumers and the LZ4 compression algorithm is enabled. The compression strategy is dynamically adjusted based on the throughput metric. An elastic scaling model based on the consumer Lag metric is constructed, including: When the throughput index is lower than 50%, it switches to no compression to reduce CPU consumption; when the throughput index exceeds 80%, it switches to ZSTD compression to improve transmission efficiency; When it is detected that the consumer Lag indicator is greater than 1000, the consumer instance expansion is triggered, and the expansion ratio is 50% of the current number of instances; when the consumer Lag indicator is less than 100 and remains stable for 30 minutes, the consumer instance reduction is triggered, and the reduction ratio is 30% of the current number of instances.

9. The intelligent terminal high-speed communication calibration method based on multi-dimensional state mapping according to claim 1 is characterized in that: The multi-dimensional indicators are input into the dynamic parameter adjustment rule engine, and the optimal parameter adjustment strategy is generated through the fuzzy logic algorithm, including: When it is detected that the retransmission rate index is greater than 5% and the hit rate index is less than 85%, a first-level response is triggered, wherein the first-level response is to adjust the TCP window size, preheat Redis high-frequency data, and optimize Kafka producer parameters; When the throughput indicator exceeds the threshold and the memory usage indicator is less than 70%, a secondary response is triggered, wherein the secondary response is to expand the Redis cache and Kafka partition and optimize the TCP connection; When the hit rate indicator is <80% and the partition water level difference is >30% at the same time, a third-level response is triggered. The third-level response is to start the full-link optimization process, including TCP connection reorganization, Redis data migration and Kafka partition rebalancing, while generating a performance report and predicting load change trends through machine learning to adjust resource configuration in advance.

10. A high-speed communication calibration system for intelligent terminals based on multi-dimensional state mapping, characterized in that: include: An acquisition module is used to acquire multi-dimensional indicator data, wherein the multi-dimensional indicator data includes retransmission rate, hit rate, memory usage, throughput and consumer lag indicator data; Build a module to construct the communication architecture of the TCP communication layer, Redis cache layer, and Kafka asynchronous processing layer, and establish a cross-layer monitoring indicator linkage mechanism; A tuning module for dynamically tuning parameters at the TCP communication layer, wherein an adaptive window adjustment model is constructed based on the retransmission rate indicator, the window size is dynamically adjusted according to RTT historical data, a sliding time window is established, a multiplexed connection pool is deployed, and a connection multiplexing strategy is dynamically adjusted according to the hit rate indicator, and a heartbeat packet interval is dynamically adjusted based on a network status fluctuation coefficient; A management module is used to perform intelligent data management in the Redis cache layer, wherein a Hash-ZSET composite data structure is used to store the real-time status of smart terminals, a two-level TTL expiration strategy is set based on the consumer Lag indicator, and dynamic data elimination is implemented according to the memory usage indicator; An optimization module, configured to implement load balancing optimization in the Kafka asynchronous processing layer, wherein the module performs hash partitioning based on smart terminal IDs, sets the number of partitions to three times the number of consumers, enables the LZ4 compression algorithm, dynamically adjusts the compression strategy based on the throughput metric, and builds an elastic scaling model based on the consumer Lag metric; The monitoring feedback module is used to input the multi-dimensional indicators into the dynamic parameter adjustment rule engine and generate the optimal parameter adjustment strategy through the fuzzy logic algorithm.

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