A radar monitoring data storage and calling method

Abstract

The present invention relates to the field of storage call technology, and in particular to a radar monitoring data storage call method. The method comprises the following steps: performing data collection growth analysis per unit time on radar monitoring real-time data to obtain the radar monitoring data time segment growth amount; performing incremental fluctuation trend identification between adjacent segments on the radar monitoring data time segment growth amount, and opening up intersection content pre-storage space to obtain monitoring intersection content pre-storage space; performing application content shortest retrieval path generation on the monitoring intersection content pre-storage space, and performing multi-thread concurrent call optimization at the same time to obtain retrieval path concurrent optimization data; performing storage call firmware design on the retrieval path concurrent optimization data to obtain retrieval storage call firmware; and embedding the retrieval storage call firmware into the radar to execute radar monitoring data storage call. The present invention makes the radar storage call technology more perfect by optimizing the radar storage call technology.

Description

Technical Field

[0001] The present invention relates to the technical field of storage calling, and in particular to a radar monitoring data storage calling method. Background Art

[0002] Modern radar systems are able to acquire multi-dimensional data including speed, distance, azimuth, reflectivity, etc. These data are often characterized by high-frequency acquisition, high precision and large scale. This makes it an important technical challenge to effectively store these huge data sets and quickly retrieve and call them when needed. Problems such as inefficiency, data redundancy and slow retrieval are prone to occur during the storage process. To this end, the storage and call method of radar monitoring data usually adopts a distributed database architecture, combined with efficient indexing algorithms and data compression technology to ensure the optimal use of storage space. In this method, data is organized in categories and tagged according to different radar types, monitoring ranges and time dimensions to facilitate subsequent rapid retrieval. In addition, considering that real-time performance is a core requirement of radar data applications, the storage system needs to support high-speed data writing and reading operations, and be able to extract and call data in a very short time. These technologies not only improve the efficiency of data storage, but also ensure that the system response speed can meet actual needs in data-intensive applications. However, a traditional radar monitoring data storage and call method has the problems of chaotic data cache management structure and long retrieval time. Summary of the invention

[0003] Based on this, it is necessary to provide a radar monitoring data storage and calling method to solve at least one of the above technical problems.

[0004] To achieve the above object, a radar monitoring data storage and calling method is provided, the method comprising the following steps:

[0005] Step S1: Acquire radar monitoring real-time data; perform data collection growth analysis on the radar monitoring real-time data per unit time to obtain the time segment growth of the radar monitoring data;

[0006] Step S2: Identify the incremental fluctuation trend between adjacent time segments of the radar monitoring data time segment growth amount to obtain the incremental fluctuation trend data of each adjacent time segment; open up the intersection content pre-storage space according to the incremental fluctuation trend data of each adjacent time segment to obtain the monitoring intersection content pre-storage space;

[0007] Step S3: Generate the shortest search path for application content in the pre-stored space of the monitored intersection content to obtain the shortest search path data for application content; perform multi-threaded concurrent call optimization on the shortest search path data for application content to obtain concurrent optimization data for the search path;

[0008] Step S4: Design storage call firmware for the retrieval path concurrent optimization data to obtain retrieval storage call firmware; embed the retrieval storage call firmware into the radar to execute radar monitoring data storage call.

[0009] The present invention first obtains real-time data monitored by radar, which usually includes dynamic characteristics such as position information, speed, acceleration, etc. of the target object. After the data is obtained, an appropriate statistical analysis method is used to perform growth analysis on the data in a unit time. This process aims to identify the collection of data in different time periods, and analyze the change trend of these data by calculating the growth amount in each time period. This can not only help evaluate the monitoring performance of the radar system, but also reveal the potential laws of the monitoring data. Through a detailed analysis of the growth amount, the reasons for the surge or decrease of data in a specific time period can be identified, thereby providing a more reliable basis for subsequent monitoring and data processing. In addition, this step can also provide a reference for the formulation of future monitoring strategies to ensure a comprehensive and accurate grasp of the monitored object, thereby optimizing the efficiency and accuracy of radar monitoring. For the growth amount data of each time period obtained from step S1, the incremental fluctuation trend between adjacent time periods is identified. This process involves an in-depth analysis of data fluctuations to determine the laws and trends of data changes in different time periods, such as whether the increment is rising, falling or stable. Through this trend identification, detailed data of incremental fluctuations between adjacent time periods can be obtained, and the impact of these fluctuations on the overall monitoring effect can be understood. On this basis, the identified trend data is used to open up pre-storage space for monitoring intersection content. This means that storage space is allocated in advance in the monitoring system for future data intersection situations, thereby improving the system's responsiveness and processing efficiency when facing emergencies or data surges. The opening of this pre-storage space not only improves the flexibility and adaptability of data storage, but also enhances the overall stability of the system, providing a more solid foundation for subsequent data analysis and decision-making. Through such analysis and preparation, the real-time and reliability of the radar monitoring system in a dynamic environment can be greatly improved. First, the pre-storage space of the monitoring intersection content is analyzed to generate the shortest retrieval path for the application content. This process determines the shortest path from the data source to the required monitoring content through algorithms (such as the Dijkstra algorithm or the A* search algorithm), which helps to improve retrieval efficiency and reduce time consumption. The generation of the shortest retrieval path data makes subsequent data access more efficient and reduces the system's delay in processing data, which is particularly important in radar monitoring systems that require rapid response. Next, based on the generated shortest retrieval path data, the path is optimized using multi-threaded concurrent call technology. This method allows multiple threads to access and retrieve data simultaneously, thereby significantly improving the efficiency and response speed of data processing. In a multi-threaded environment, the system can more effectively utilize computing resources, reduce processing time, and improve user experience. This optimization not only improves the system's concurrent processing capabilities, but also provides strong support for real-time monitoring and analysis, ensuring that the radar system can still operate efficiently when faced with a large number of data requests. The concurrent optimization data of the retrieval path is converted into a retrieval storage call firmware design.This process involves solidifying the optimized retrieval path into firmware so that it has efficient storage and call capabilities. Firmware design must not only take into account the efficiency of the retrieval path, but also ensure the stability and reliability of the system. In the design stage, the storage structure, retrieval algorithm and resource management are fully considered to ensure that the firmware can run efficiently in the radar system. Subsequently, this firmware is embedded in the radar device to perform storage and call of radar monitoring data. This embedded design enables the radar system to quickly retrieve and process monitoring data during operation, greatly improving the real-time performance of data access. By integrating the optimized storage call firmware, the radar system can maintain efficient performance and respond to various monitoring needs in a high-load monitoring environment. In addition, the application of this firmware also enhances the flexibility of the radar system, supports future functional expansion and upgrading, and enables it to adapt to the ever-changing monitoring needs and technological development. Therefore, the present invention is an optimization process made on a traditional radar monitoring data storage and call method, which solves the problems of chaotic data cache management structure and long retrieval time in a traditional radar monitoring data storage and call method, effectively standardizes the data cache management structure and reduces the retrieval time.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Acquire radar monitoring real-time data;

[0012] Step S12: Fill missing values ​​in the radar monitoring real-time data to obtain radar monitoring real-time missing filled data;

[0013] Step S13: Timestamp the radar monitoring real-time missing filling data to obtain radar monitoring time stamp data;

[0014] Step S14: performing data collection growth analysis per unit time on the radar monitoring real-time data according to the radar monitoring time tag data, and obtaining the time segment growth of the radar monitoring data.

[0015] The present invention first obtains the real-time data of radar monitoring, which usually involves the dynamic characteristics of the target such as location information, speed, distance, etc. This step is the basis of the entire monitoring process, ensuring that there is sufficient original information for subsequent data processing and analysis. Through real-time data acquisition, the system can timely grasp the changes of the monitored object and meet the needs of real-time monitoring. The acquired data can be used in various application scenarios, such as traffic monitoring, weather forecasting, and safety monitoring. The effective implementation of this step provides a guarantee for the accuracy and timeliness of the data, and also lays a good foundation for subsequent data processing and analysis. The acquired radar monitoring real-time data is filled with missing values ​​to improve the integrity and reliability of the data. Due to various reasons (such as signal interference or sensor failure), there are missing values ​​in the real-time data, which will affect the subsequent data analysis and decision-making. By adopting technical means such as interpolation, mean filling or regression prediction, the system can effectively fill these missing values ​​and generate radar monitoring real-time missing filling data. The importance of this step is that it not only improves the integrity of the data set, but also enhances the stability and accuracy of the model, providing a more reliable basis for subsequent analysis and decision-making. The radar monitoring data filled with missing values ​​is timestamped. This process involves associating each data record with the corresponding time information to form radar monitoring time-stamped data. The introduction of timestamps is an important part of data processing. It can not only provide a clear time basis for subsequent data analysis, but also help identify and solve potential problems in time series data, such as chaotic time sequence or inconsistent data. Through timestamp marking, the system can effectively track the source and change process of data, ensure that time factors are considered during analysis, and thus improve the accuracy and timeliness of analysis results. According to the marked radar monitoring time data, the real-time data is analyzed for data collection growth per unit time. By analyzing the growth of data in each time period, the system can identify the change trend of data collection in different time periods, and then analyze the operating status and performance of the monitoring system. This process can help monitoring personnel identify the peak and trough periods of data collection, and provide data support for optimizing monitoring strategies. Growth analysis can also reveal potential abnormal situations, such as data surges or decreases caused by monitoring equipment failures or environmental changes. This not only provides a basis for future monitoring decisions, but also provides an important reference for system optimization and resource allocation, ensuring the efficient operation of the radar monitoring system. Through the organic combination of these steps, the availability and accuracy of radar monitoring data are improved as a whole.

[0016] Preferably, step S2 comprises the following steps:

[0017] Step S21: performing data geometric increment analysis on the time segment growth amount of the radar monitoring data in different unit times to obtain the time segment geometric growth amount of the radar monitoring data;

[0018] Step S22: identifying the incremental fluctuation trend between adjacent segments of the radar monitoring data time segment growth amount according to the segmented geometric growth amount of the radar monitoring data, and obtaining the incremental fluctuation trend data of each adjacent time segment;

[0019] Step S23: mapping the monitoring content increment fluctuation between different time segments on the radar monitoring real-time data according to the increment fluctuation trend data of each adjacent time segment, and obtaining the monitoring content increment fluctuation mapping data between different time segments;

[0020] Step S24: opening up a pre-storage space for the intersection content of the monitoring content increment fluctuation mapping data between different time segments to obtain a pre-storage space for the monitoring intersection content.

[0021] The present invention first performs geometric incremental analysis on the time segment growth of radar monitoring data to evaluate the changing trend of data in different unit time. Geometric incremental analysis can reveal the dynamic characteristics of data growth by calculating the ratio of growth in adjacent time periods, especially when the data growth rate is uneven. This analysis not only helps to identify the growth pattern, but also compares the data performance in different time periods, such as time periods with high growth rate and low growth rate. By obtaining the geometric growth of radar monitoring data segments, the system can better understand the growth behavior of the data and provide strong support for subsequent fluctuation trend identification and monitoring strategy adjustment. This process lays the foundation for subsequent analysis and helps to optimize the responsiveness of the radar monitoring system under various conditions. According to the geometric growth of radar monitoring data segments, the incremental fluctuation trend between adjacent time periods is identified. By analyzing the changes in geometric growth between different time periods, the system can clarify the data fluctuation situation in each adjacent time period. The purpose of incremental fluctuation trend identification is to capture the dynamic changes and patterns of data, so as to understand the operating status of the monitoring system and its potential problems. This process can also help monitoring personnel predict future data change trends, such as incremental growth or decline under specific conditions. This trend identification not only provides a basis for subsequent data analysis and decision-making, but also provides an important reference for the adjustment and optimization of the monitoring system to ensure effective response in different situations. Based on the incremental fluctuation trend data of each adjacent time period, the system maps the incremental fluctuation of the monitoring content between different time segments for the real-time radar monitoring data. This process involves comparing the monitoring content of different time periods and drawing a change map of the incremental fluctuation between each time period. Through the incremental fluctuation mapping of the monitoring content, the system can intuitively understand the changes in the monitoring content between different time periods and identify the root causes and causes of the fluctuations. This mapping can not only reveal the dynamic behavior of the monitoring system, but also provide decision makers with intuitive data display to help make timely adjustments and decisions. By mapping data, the system can also optimize the monitoring strategy to better adapt to the dynamic environment, thereby improving the monitoring effect. For the incremental fluctuation mapping data of the monitoring content between different time segments, the system opens up pre-storage space for the intersection content. This process involves reserving sufficient storage space for the data intersection that occurs, so as to ensure that the monitoring system can still operate efficiently under high load conditions. The opening of pre-storage space enables the system to flexibly respond to future data needs and improve the flexibility and scalability of data management. This approach not only reduces the system's response time in emergencies, but also ensures the security and integrity of monitoring data. By planning and designing the storage space for monitoring intersection content in advance, the system can optimize resource allocation and enhance overall monitoring capabilities, thereby achieving efficient monitoring and data analysis of dynamic environments, and further promoting the intelligent development of radar monitoring systems.

[0022] Preferably, step S22 comprises the following steps:

[0023] Step S221: Calculating the geometric growth variance interval between adjacent time segments on the time segment growth amount of the radar monitoring data based on the segmented geometric growth amount of the radar monitoring data, and obtaining the geometric growth variance interval between adjacent segments;

[0024] Step S222: performing growth rate analysis between adjacent time segments on the time segment growth amount of the radar monitoring data based on the geometric growth variance interval between adjacent segments to obtain segment growth rate data;

[0025] Step S223: performing a rate symmetry analysis between different time segments on the segmented growth rate data to obtain segmented growth rate symmetry data;

[0026] Step S224: performing linear first-order difference processing between different time segments on the time segment growth amount of the radar monitoring data according to the segment growth rate symmetry data and the segment growth rate data, to obtain growth linear first-order difference data;

[0027] Step S225: Identify the incremental fluctuation trend between adjacent segments based on the growth linear first-order difference data to obtain the incremental fluctuation trend data of each adjacent time segment.

[0028] The present invention first calculates the geometric growth variance interval between adjacent time segments based on the segmented geometric growth of radar monitoring data. The core of this calculation is to measure the volatility of data growth, and the geometric growth variance can reveal the unevenness and variation of data growth in different time periods. By comparing the geometric growth variance of adjacent segments, analysts can identify which time periods have relatively stable growth performance and which time periods have large fluctuations. This is crucial for subsequent trend analysis because it helps decision makers understand the behavioral characteristics of data in different time periods and provides a basis for formulating monitoring strategies and response plans. In addition, this process can also identify potential abnormal data changes, thereby improving the sensitivity and responsiveness of the system. Based on the geometric growth variance interval between adjacent segments, the system performs growth rate analysis of the time segment growth of radar monitoring data. By calculating the growth rate of each time period, analysts can more clearly understand the changes in the growth rate of data in different time periods. Growth rate analysis can not only reveal the dynamic changes of monitoring data, but also provide insights into changes in the monitoring environment, such as abnormal growth or decline caused by special events in a certain time period. This step provides important support for identifying and understanding the causes of data changes, making subsequent decisions and adjustments more targeted and effective. Ultimately, the obtained segmented growth rate data will provide a more reliable reference for system optimization and resource allocation. The segmented growth rate data is analyzed for rate symmetry between different time segments. By analyzing the symmetry of the rate, the balance degree of the growth rate in adjacent time periods can be identified, thereby revealing the stability and consistency of the data in time. This symmetry analysis can help monitoring personnel determine whether the growth trend of the data remains consistent, or whether an abnormal growth pattern occurs in a specific time period. If certain time periods show obvious asymmetry, it implies the influence of external factors or abnormal conditions in the internal system. The results of this analysis will provide a basis for subsequent trend prediction and risk assessment, enabling the monitoring system to better adapt to changing environments and needs. Based on the segmented growth rate symmetry data and the segmented growth rate data, the time segment growth of the radar monitoring data is processed by linear first-order difference. This processing removes the trend component in the data by calculating the change in the growth rate between adjacent time periods, focusing on the analysis of short-term fluctuations. The linear first-order difference data of growth can highlight the fluctuation changes between time periods and help identify the dynamic behavior of the data in the short term. In this way, the monitoring system can effectively capture the subtle differences in data changes, providing a basis for the subsequent identification of incremental fluctuation trends. This process not only improves the accuracy of data analysis, but also enables the monitoring system to maintain efficient response in the face of complex environments. Based on the growth linear first-order difference data, the system identifies the incremental fluctuation trend between adjacent segments.By analyzing the differential data, the incremental fluctuation trend information between time periods can be extracted, which provides a clear perspective for understanding the changing characteristics of radar monitoring data in each time period. Incremental fluctuation trend identification can not only reveal the changing pattern of data, but also help identify potential abnormalities, such as sudden fluctuations caused by environmental changes or system failures. Ultimately, by comprehensively analyzing these fluctuation trend data, decision makers can better grasp the dynamic characteristics of the monitoring environment and formulate more accurate and flexible response measures. The effective implementation of this process will greatly enhance the intelligence level and adaptability of the radar monitoring system.

[0029] Preferably, step S24 includes the following steps:

[0030] Step S241: performing intersection content association extraction on monitoring content increment fluctuation mapping data between different time segments to obtain intersection content association data;

[0031] Step S242: defining the intersection range of the intersection content association data according to the monitoring content increment fluctuation mapping data between different time segments to obtain the intersection content association range data;

[0032] Step S243: constructing a subordinate content index network for the intersection content association data according to the intersection content association range data to obtain a subordinate content index network;

[0033] Step S244: performing predictive incremental space allocation logic design on the subordinate content index network according to the monitoring content incremental fluctuation mapping data between different time segments, and obtaining subordinate content incremental space allocation logic data;

[0034] Step S245: opening up the intersection content pre-storage space according to the subordinate content index network and the subordinate content incremental space allocation logic data to obtain the monitoring intersection content pre-storage space.

[0035] The present invention is aimed at the monitoring content incremental fluctuation mapping data between different time segments, and the system performs intersection content association extraction. Through this process, the commonalities and connections between the monitoring contents in different time periods can be identified, and similar data that appear in multiple time periods can be extracted. This operation can not only help monitoring personnel understand the similarity and consistency of monitoring data in different time periods, but also provide important basic data for subsequent analysis. The acquisition of intersection content association data can help identify potential long-term trends and patterns, thereby enhancing the understanding and prediction capabilities of the behavior of the monitored object. This process also provides necessary support for subsequent incremental fluctuation trend analysis, so that the flexibility and adaptability of the entire monitoring system can be improved. Based on the monitoring content incremental fluctuation mapping data between different time segments, the intersection range of the intersection content association data is defined. The purpose of this process is to clarify which intersections of monitoring contents have significant overlaps in space and time, thereby forming clear boundaries. This definition helps analysts determine which data fragments are the core of the intersection and which are edge content, thereby enhancing the understanding of the data. This definition process not only improves the efficiency of data processing, but also provides a more accurate reference basis for subsequent analysis and decision-making. By clarifying the scope of the intersection content, the monitoring system can more effectively perform data aggregation and association analysis, ensuring that valuable information can be effectively extracted in an information-rich environment. According to the intersection content association range data, a subordinate content index network is constructed for the intersection content association data. The core of this construction process is to establish a multi-level index structure that can organize related data in an orderly manner for subsequent rapid query and analysis. By establishing a subordinate content index network, the monitoring system can achieve efficient data management and promote mutual connection and interaction between data. This index network can help analysts quickly locate the required monitoring content, reduce the time and effort of data retrieval, and improve work efficiency. In addition, the construction of the subordinate content index network also lays a good foundation for subsequent prediction and analysis, making the system more efficient and intelligent when processing complex monitoring data. According to the incremental fluctuation mapping data of the monitoring content between different time segments, the predictive incremental space allocation logic design is carried out for the subordinate content index network. Through this design, the system can reasonably configure monitoring resources to ensure that the incremental needs of different time periods and different monitoring contents are effectively met. The design of this spatial allocation logic takes into account the dynamic and diversity of monitoring data, enabling the system to automatically adjust the resource allocation strategy according to real-time changes. This process not only improves the flexibility and response speed of the monitoring system, but also ensures the efficiency of data storage and processing, thereby effectively coping with complex monitoring tasks. Through reasonable space allocation, the system can optimize resource usage and reduce operating costs while ensuring monitoring quality. Based on the subordinate content index network and subordinate content incremental space allocation logic data, the system opens up the intersection content pre-storage space.This process is to apply the analysis results of the previous steps to the actual data storage and management, and to ensure that the intersection content can be processed and analyzed quickly and effectively in future monitoring by reserving and allocating the necessary storage space. The goal of opening up pre-storage space for monitoring intersection content is to improve the storage efficiency of the system and avoid insufficient storage caused by a sharp increase in data volume. This measure can also ensure that the monitoring system can still maintain good performance and stability when facing sudden data fluctuations. Through effective pre-storage space management, the monitoring system can achieve efficient capture, storage and analysis of data, further improving the overall monitoring capability and intelligence level.

[0036] Preferably, constructing a subordinate content index network for the intersection content association data according to the intersection content association range data comprises the following steps:

[0037] Performing subordinate content level recursive verification on the intersection content association data according to the intersection content association range data to obtain subordinate content level recursive data;

[0038] Summarizing the attributes of connected nodes between upper and lower levels for the subordinate content level recursive data to obtain connected node attribute data;

[0039] Performing node content depth / breadth analysis on subordinate content level recursive data between different subordinate levels according to the connected node attribute data to obtain node content depth / breadth data;

[0040] Performing node content association extensibility analysis on the node content depth / breadth data to obtain node content association extensibility data;

[0041] Based on the R-tree and according to the node content association extensibility data and the connected node attribute data, a subordinate content index network is constructed for subordinate content hierarchical recursive data to obtain a subordinate content index network.

[0042] The present invention performs recursive verification of subordinate content levels of intersection content association data based on intersection content association range data. This process is intended to identify and confirm the hierarchical relationship and affiliation between data. Through recursive verification, the data can be structured and the superior-subordinate relationship between each content can be clarified, thereby laying a solid foundation for subsequent analysis. This process can ensure the accuracy and consistency of the data and provide reliable initial data for building an index network. After obtaining the subordinate content level recursive data, the next step is to perform attribute induction on the connected nodes between the upper and lower levels. The core of this step is to analyze the connection between each level, extract common features and key attributes, and form attribute data of the connected nodes. This process can not only help identify the commonality between information, but also provide an important basis for subsequent depth and breadth analysis, ensuring a comprehensive understanding of content relationships. With the help of connected node attribute data, the depth and breadth analysis of node content between different subordinate levels is performed. At this stage, mainly through data mining and analysis means, the details and scope of the node content are deeply explored, and the deep connection and extensive influence between the content are identified. This not only helps to enrich the understanding of the content, but also reveals potential associations and impacts, providing basic data support for subsequent extensibility analysis. The purpose of conducting an associative extensibility analysis on the depth and breadth of node content data is to evaluate the expansion and application of node content in the existing system. This analysis will help identify the ductility, relevance and potential value of information. Especially when dealing with complex data sets, this extensibility analysis can reveal underutilized resources and opportunities, providing strategic support for future decision-making. Finally, based on the R-tree algorithm, combined with the node content associative extensibility data and the connected node attribute data, the subordinate content index network is constructed for the subordinate content hierarchical recursive data. The R-tree is an index structure suitable for multidimensional spatial data, which can effectively improve the efficiency and accuracy of data retrieval. By orderly storing and indexing data at each level, the subordinate content index network finally constructed can quickly respond to query requests, improve the speed and accuracy of information retrieval, and provide an efficient support platform for subsequent data application and analysis.

[0043] Preferably, the predictive incremental space allocation logic design for the subordinate content index network based on the monitored content incremental fluctuation mapping data between different time segments includes the following steps:

[0044] According to the monitoring content increment fluctuation mapping data between different time segments, the periodic growth trend of the subordinate content index network is predicted to obtain the periodic growth trend data of the index network;

[0045] Perform memory pool matching on the subordinate content index network based on the periodic growth trend data of the index network to obtain subordinate content index memory pool matching data;

[0046] Performing memory paging processing on the subordinate content index memory pool matching data to obtain subordinate content index memory paging data;

[0047] According to the subordinate content index memory pool matching data and the subordinate content index memory paging data, a predictive incremental space allocation logic design is performed on the index network periodic growth trend data to obtain the subordinate content incremental space allocation logic data.

[0048] The present invention predicts the periodic growth trend of the subordinate content index network according to the incremental fluctuation mapping data of the monitored content in different time periods. The core of this process is to analyze the changing rules of the monitored data in the time dimension, so as to predict the trend of future data growth. This prediction can identify the seasonality, periodic fluctuation and potential growth mode of the data, and provide a basis for subsequent space allocation. By accurately predicting the growth trend of the index network, the manager can make resource allocation in advance to avoid insufficient storage or performance degradation caused by a sudden increase in data volume, thereby improving the stability and reliability of the entire monitoring system. The system matches the subordinate content index network with the memory pool based on the obtained periodic growth trend data of the index network. This process aims to optimize the use of memory resources, and by matching the predicted growth trend with the actual memory capacity, it ensures that the system can efficiently use the existing resources. The result of this matching is to obtain the subordinate content index memory pool matching data, which helps the system to automatically adjust the memory usage strategy when the data load changes. Through reasonable memory pool management, the system can improve the efficiency of memory use, reduce the performance bottleneck caused by improper memory allocation, and ensure that the system can still maintain smooth operation when facing high concurrency or large amounts of data. After obtaining the subordinate content index memory pool matching data, the system performs memory paging on these data. Memory paging is to divide a large memory block into multiple smaller pages. This process can improve the flexibility and efficiency of data access. Through memory paging, the system can load data on demand, reduce memory fragmentation, and improve the utilization of storage space. This operation can also improve the response speed and processing capacity of the system under high load conditions, ensuring that the required data pages can be quickly retrieved when facing multiple operation requirements. In addition, the implementation of memory paging can make the system more scientific in storage management, which is helpful for subsequent memory usage monitoring and adjustment. According to the subordinate content index memory pool matching data and memory paging data, a predictive incremental space allocation logic design is performed for the periodic growth trend of the index network. The core of this design is to combine the analysis results of the previous steps to formulate a set of scientific space allocation strategies to ensure that resources can be allocated flexibly and efficiently in the case of data increments. Through this logical design, the system can intelligently adjust the allocation of storage space according to the changing trends of real-time monitoring data and historical data, ensuring that various types of data can be quickly accessed and processed when needed. This design not only improves the efficiency of data management, but also significantly reduces the delay in data processing and improves the overall system performance, allowing the monitoring system to maintain efficient and stable operation when facing complex real-time data.

[0049] Preferably, step S3 comprises the following steps:

[0050] Step S31: classifying the content application scenarios of the monitoring intersection content pre-storage space to obtain monitoring intersection content application scenario data;

[0051] Step S32: generating the shortest search path for application content in the pre-stored space of the monitored intersection content according to the application scenario data of the monitored intersection content, and obtaining the shortest search path data for application content;

[0052] Step S33: Optimize the multi-thread concurrent calls of the shortest search path data of the application content to obtain concurrent optimized search path data.

[0053] Step S31 of the present invention classifies the content application scenarios of the pre-stored space of the monitoring intersection content, aiming to classify different types of content into corresponding application scenarios. This classification process can help the system understand the use background and objectives of various types of data, and provide a clear framework for subsequent data retrieval. Through the accurate classification of content, managers can better grasp the characteristics of data, so as to quickly locate the required information in practical applications. This process not only improves the organization and accessibility of information, but also lays the foundation for intelligent analysis and personalized services of data, making subsequent retrieval and processing more efficient. The shortest retrieval path of application content is generated according to the application scenario data of the monitoring intersection content. This process uses an algorithm to analyze the classified content and identify the most effective retrieval path in a specific application scenario. The generation of the shortest retrieval path can significantly shorten the time of data access, improve the system response speed, and ensure that users can obtain the required information in the shortest time. Through this strategy of optimizing the retrieval path, the system can effectively improve the efficiency of data retrieval, reduce the consumption of system resources, and thus improve the user experience and meet the needs of rapid response. The shortest retrieval path data of application content is optimized for multi-threaded concurrent calls. This step aims to use multi-threading technology to improve the concurrent processing capability in the data retrieval process. Through parallel processing, the system can respond to multiple retrieval requests at the same time, greatly improving the throughput and response speed of data access. This optimization can not only effectively utilize the computing power of multi-core processors, but also maintain the stability of the system under high load conditions, ensuring that users can still quickly and accurately obtain information when processing a large number of concurrent requests. Ultimately, concurrent optimization data of the retrieval path will help the system achieve higher efficiency and lower latency when processing complex retrieval tasks.

[0054] Preferably, step S33 includes the following steps:

[0055] Step S331: construct a multi-thread synchronization mechanism for the shortest search path data of the application content to obtain a search path multi-thread synchronization mechanism;

[0056] Step S332: performing load balancing processing on the retrieval path multi-thread synchronization mechanism to obtain synchronization mechanism load balancing data;

[0057] Step S333: performing data concurrent execution and diversion processing on the application content shortest search path data according to the synchronization mechanism load balancing data and the search path multi-thread synchronization mechanism to obtain concurrent execution and diversion data;

[0058] Step S334: Optimize the multi-thread concurrent call of the shortest search path data of the application content according to the concurrent execution diversion data, the synchronization mechanism load balancing data and the search path multi-thread synchronization mechanism to obtain the search path concurrent optimization data.

[0059] The present invention constructs a multi-thread synchronization mechanism for the shortest search path data of application content. The purpose of this mechanism is to ensure that the consistency and integrity of data are maintained when multiple threads perform search operations in parallel. Through this synchronization mechanism, the system can effectively avoid data errors or losses caused by thread competition, thereby ensuring the accuracy of the search results. In addition, the establishment of a multi-thread synchronization mechanism also provides basic support for subsequent concurrent calls, so that the system can efficiently and safely process multi-user requests and improve the stability of the system under high concurrency. The multi-thread synchronization mechanism of the search path is subjected to load balancing processing, aiming to optimize the resource allocation of each thread when performing a search task. Through load balancing, the system can dynamically allocate tasks to different threads, thereby avoiding delays or crashes caused by excessive loads on some threads. This processing method enables the overall system to maintain efficient operation under different workloads, reducing response time and resource waste. By realizing load balancing, the system can improve resource utilization efficiency, ensure that each request can be processed in the shortest time, and improve user experience. According to the synchronization mechanism load balancing data and the search path multi-thread synchronization mechanism, the shortest search path data of the application content is subjected to data concurrent execution diversion processing. The purpose of this process is to reasonably distribute the retrieval tasks to each thread so that each thread can independently process the data assigned to it, thereby improving the overall retrieval speed. Through diversion processing, the system can not only fully utilize the advantages of multi-threading, but also effectively reduce the delay of data processing, ensuring that multiple requests can be processed simultaneously without interfering with each other in the case of high concurrency. This mechanism can significantly improve the concurrent processing capability of the system, so that a smooth user experience can still be maintained under high load conditions. According to the concurrent execution diversion data, the synchronization mechanism load balancing data, and the retrieval path multi-thread synchronization mechanism, the multi-threaded concurrent call optimization is performed on the shortest retrieval path data of the application content. The goal of this step is to further improve the efficiency and response speed of data retrieval by optimizing the call process. Using the synchronization and load balancing mechanism constructed in the previous step, the system can ensure that resources are reasonably utilized when executing multiple retrieval tasks in parallel, thereby reducing the overall delay. This optimization of multi-threaded concurrent calls ensures that user requests can be responded to quickly, while improving the system's ability to handle a large number of concurrent requests, ultimately providing users with more efficient and accurate services.

[0060] Preferably, step S4 comprises the following steps:

[0061] Step S41: performing an execution logic error test on the search path concurrent optimization data to obtain execution logic error test data;

[0062] Step S42: Designing storage calling firmware for the search path concurrent optimization data according to the execution logic error test data to obtain the search storage calling firmware;

[0063] Step S43: embed the retrieval storage call firmware into the radar to execute the radar monitoring data storage call.

[0064] In the embodiment of the present invention, a logical error test is performed on the concurrent optimization data of the retrieval path. This test is intended to identify and analyze logical errors or inconsistencies that exist in the data retrieval and processing process. Through systematic testing, it can be ensured that the optimized data retrieval path can be executed as expected during actual operation, thereby minimizing errors in the operation process. This test not only involves a comprehensive analysis of the data flow, but also includes verification of logical judgments under different concurrent scenarios. Ultimately, the execution logic error test data will provide an important basis for subsequent optimization and firmware design to ensure the stability and reliability of the system. According to the execution logic error test data, the storage call firmware design is performed on the concurrent optimization data of the retrieval path. The main goal of this step is to design a firmware that can efficiently handle data storage and call based on the test results. This firmware will enable the system to better manage and access the data in the storage, thereby improving retrieval efficiency and access speed. During the design process, various actual use scenarios will be taken into account, and flexible interfaces will be provided for different operations to achieve fast call and storage optimization. The retrieval storage call firmware finally obtained will become an important part of the entire system, providing solid support for subsequent applications. The designed retrieval storage call firmware is embedded in the radar device to perform storage call of radar monitoring data. This step not only means that the firmware has been successfully designed and tested, but also means that it will be actually used in real-time monitoring and data processing. Integrating the firmware into the radar system can enable the radar to have higher storage and call efficiency when acquiring monitoring data, thereby achieving rapid data analysis and response. The embedded firmware will improve the overall performance of the radar system, ensure its stable operation in complex environments, and ultimately achieve effective storage and management of monitoring data, enhancing the practicality and reliability of the system.

[0065] The beneficial effect of the present invention is that first, real-time data of radar monitoring is obtained, and these data generally include dynamic characteristics such as position information, speed, acceleration, etc. of the target object. After the data is obtained, the data in the unit time is analyzed for growth using appropriate statistical analysis methods. This process aims to identify the collection of data in different time periods, and analyze the change trend of these data by calculating the growth amount in each time period. This can not only help evaluate the monitoring performance of the radar system, but also reveal the potential laws of the monitoring data. Through a detailed analysis of the growth amount, the reasons for the surge or decrease of data in a specific time period can be identified, thereby providing a more reliable basis for subsequent monitoring and data processing. In addition, this step can also provide a reference for the formulation of future monitoring strategies to ensure a comprehensive and accurate grasp of the monitored objects, thereby optimizing the efficiency and accuracy of radar monitoring. For the growth amount data of each time period obtained from step S1, the incremental fluctuation trend between adjacent time periods is identified. This process involves an in-depth analysis of data fluctuations to determine the laws and trends of data changes in different time periods, such as whether the increment is rising, falling or stable. Through this trend identification, detailed data of incremental fluctuations between adjacent time periods can be obtained, and the impact of these fluctuations on the overall monitoring effect can be understood. On this basis, the identified trend data is used to open up pre-storage space for monitoring intersection content. This means that storage space is allocated in advance in the monitoring system for future data intersection situations, thereby improving the system's responsiveness and processing efficiency when facing emergencies or data surges. The opening of this pre-storage space not only improves the flexibility and adaptability of data storage, but also enhances the overall stability of the system, providing a more solid foundation for subsequent data analysis and decision-making. Through such analysis and preparation, the real-time and reliability of the radar monitoring system in a dynamic environment can be greatly improved. First, the pre-storage space of the monitoring intersection content is analyzed to generate the shortest retrieval path for the application content. This process determines the shortest path from the data source to the required monitoring content through algorithms (such as the Dijkstra algorithm or the A* search algorithm), which helps to improve retrieval efficiency and reduce time consumption. The generation of the shortest retrieval path data makes subsequent data access more efficient and reduces the system's delay in processing data, which is particularly important in radar monitoring systems that require rapid response. Next, based on the generated shortest retrieval path data, the path is optimized using multi-threaded concurrent call technology. This method allows multiple threads to access and retrieve data simultaneously, thereby significantly improving the efficiency and response speed of data processing. In a multi-threaded environment, the system can more effectively utilize computing resources, reduce processing time, and improve user experience. This optimization not only improves the system's concurrent processing capabilities, but also provides strong support for real-time monitoring and analysis, ensuring that the radar system can still operate efficiently when faced with a large number of data requests. The concurrent optimization data of the retrieval path is converted into a retrieval storage call firmware design.This process involves solidifying the optimized retrieval path into firmware so that it has efficient storage and call capabilities. Firmware design must not only take into account the efficiency of the retrieval path, but also ensure the stability and reliability of the system. In the design stage, the storage structure, retrieval algorithm and resource management are fully considered to ensure that the firmware can run efficiently in the radar system. Subsequently, this firmware is embedded in the radar device to perform storage and call of radar monitoring data. This embedded design enables the radar system to quickly retrieve and process monitoring data during operation, greatly improving the real-time performance of data access. By integrating the optimized storage call firmware, the radar system can maintain efficient performance and respond to various monitoring needs in a high-load monitoring environment. In addition, the application of this firmware also enhances the flexibility of the radar system, supports future functional expansion and upgrading, and enables it to adapt to the ever-changing monitoring needs and technological development. Therefore, the present invention is an optimization process made on a traditional radar monitoring data storage and call method, which solves the problems of chaotic data cache management structure and long retrieval time in a traditional radar monitoring data storage and call method, effectively standardizes the data cache management structure and reduces the retrieval time. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 A schematic diagram of the steps of a radar monitoring data storage and calling method;

[0067] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0068] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0069] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0070] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0071] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0072] To achieve this, please refer to Figure 1 to Figure 2 , a radar monitoring data storage and calling method, the method comprising the following steps:

[0073] Step S1: Acquire radar monitoring real-time data; perform data collection growth analysis on the radar monitoring real-time data per unit time to obtain the time segment growth of the radar monitoring data;

[0074] Step S2: Identify the incremental fluctuation trend between adjacent time segments of the radar monitoring data time segment growth amount to obtain the incremental fluctuation trend data of each adjacent time segment; open up the intersection content pre-storage space according to the incremental fluctuation trend data of each adjacent time segment to obtain the monitoring intersection content pre-storage space;

[0075] Step S3: Generate the shortest search path for application content in the pre-stored space of the monitored intersection content to obtain the shortest search path data for application content; perform multi-threaded concurrent call optimization on the shortest search path data for application content to obtain concurrent optimization data for the search path;

[0076] Step S4: Design storage call firmware for the retrieval path concurrent optimization data to obtain retrieval storage call firmware; embed the retrieval storage call firmware into the radar to execute radar monitoring data storage call.

[0077] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of a radar monitoring data storage and calling method of the present invention. In this example, the radar monitoring data storage and calling method includes the following steps:

[0078] Step S1: Acquire radar monitoring real-time data; perform data collection growth analysis on the radar monitoring real-time data per unit time to obtain the time segment growth of the radar monitoring data;

[0079] In the embodiment of the present invention, in order to obtain real-time data of radar monitoring, it is necessary to connect the radar sensor and collect the monitored signals in real time through the data interface. These data are collected in batches by setting a certain time period (such as every minute or every second) to ensure the continuity of the collected radar data in different time periods. In the implementation process, a unit time, such as one minute, is first defined, and the data collected in this minute is recorded as a time segment. When analyzing the data in these time segments, the cumulative sum algorithm is used to calculate the total amount of data per unit time. Then, by analyzing the growth rate of data collection in each time period, the growth rate formula in statistics is used. This formula helps to identify the change in the growth rate per unit time, and finally obtains the "time segment growth amount of radar monitoring data". In order to improve the accuracy and ensure the integrity of the data, the whole process can be used to store and process the data in segments with the help of matrix operations.

[0080] Step S2: Identify the incremental fluctuation trend between adjacent time segments of the radar monitoring data time segment growth amount to obtain the incremental fluctuation trend data of each adjacent time segment; open up the intersection content pre-storage space according to the incremental fluctuation trend data of each adjacent time segment to obtain the monitoring intersection content pre-storage space;

[0081] In the embodiment of the present invention, after obtaining the "time segment growth of radar monitoring data", it is necessary to conduct in-depth analysis of these data. The core of this step is to identify the growth fluctuation between adjacent time segments. By calculating the data difference of adjacent time periods and establishing an incremental fluctuation change matrix. First, a fluctuation interval (such as 5 minutes) is set, and then the incremental fluctuation between adjacent time periods is evaluated using the sliding window technology. For each time period, the growth amount is calculated differently from the growth amount of the previous time period. In this step, data interpolation and curve fitting algorithms are also used to identify the trend line of fluctuation changes. The fluctuation pattern between each adjacent time period is further analyzed by fitting the trend line to obtain "incremental fluctuation trend data". For example, linear regression is used to fit each segment fluctuation to determine whether the fluctuation trend of adjacent segments has a significant increase or decrease. After identifying the fluctuation trend data of adjacent time periods, it is necessary to further use these trend data to prepare for the subsequent data storage space. According to the trend changes in the "incremental fluctuation trend data", combined with the growth pattern of the time segment, the interval partitioning technology is used to group the segments with similar growth amounts or similar trends. These groups will be used to create a pre-storage space for the intersection content. In specific operations, the trend data of each adjacent segment can be processed through logical intersection by constructing an intersection matrix. For example, by applying the intersection operation in set theory, for two time segments A and B, if their fluctuation trends overlap, a special storage space can be opened up in the memory or storage to pre-store the intersection content. In this way, when new data enters in the future, it can be directly connected to the intersection space, reducing redundancy in the data storage process. At the same time, using the memory allocation algorithm, based on the memory pool technology, an appropriate amount of storage space is automatically opened up for each segment to ensure that data can share the common memory space between segments, and obtain the "monitoring intersection content pre-storage space."

[0082] Step S3: Generate the shortest search path for application content in the pre-stored space of the monitored intersection content to obtain the shortest search path data for application content; perform multi-threaded concurrent call optimization on the shortest search path data for application content to obtain concurrent optimization data for the search path;

[0083] In the embodiment of the present invention, after the pre-storage space of the monitoring intersection content is created, in order to achieve efficient data retrieval, the shortest retrieval path of the application content must be generated. The goal of this operation is to shorten the data access path to a minimum through algorithm optimization. In the specific implementation process, firstly, by constructing a directed graph model, each data segment in the pre-storage space is regarded as a node, and the weight of the edge is defined according to the data association. Then, the Dijkstra algorithm or the Bellman-Ford algorithm is used to search the directed graph for the shortest path, and the shortest access path from any data segment to the target data segment is found through this process. In order to enhance the retrieval efficiency, the data is quickly indexed in combination with the hash table structure, so that the storage location can be quickly found directly through the key value each time the retrieval is performed, thereby reducing the traversal time. At the same time, in the path generation process, a recursive search strategy is used to generate an optimal access path, which can avoid invalid access and can be automatically adjusted after the data is updated to obtain "application content shortest retrieval path data". After generating the "application content shortest retrieval path data", the path needs to be optimized for multi-threaded concurrent calls to improve the retrieval performance of the system. First, for the generated shortest search path, the thread allocation algorithm is applied to assign different path operations to multiple threads for parallel execution. By using the thread pool technology, the concurrent requests of multiple paths are reasonably scheduled and managed to ensure that each thread can work efficiently and avoid resource contention. In the specific operation, the task decomposition algorithm is used to divide the complex path retrieval task into multiple small tasks, each of which is executed by a thread, and the "task priority queue" is used to ensure that the critical path is executed first. Combined with the synchronization mechanism between threads, it is ensured that multiple threads will not conflict when accessing the same data segment. The concurrent behavior between threads is controlled by the lock mechanism, and the resource preemption strategy is applied to avoid the performance bottleneck caused by the competition of multiple threads for shared resources. Finally, the "retrieval path concurrent optimization data" is obtained to achieve the best concurrent call effect.

[0084] Step S4: Design storage call firmware for the retrieval path concurrent optimization data to obtain retrieval storage call firmware; embed the retrieval storage call firmware into the radar to execute radar monitoring data storage call.

[0085] In the embodiment of the present invention, in order to use the optimized concurrent retrieval path for the storage and call of actual radar monitoring data, a special firmware needs to be designed to complete this task. First, according to the structural characteristics of "retrieval path concurrent optimization data", the basic architecture of the firmware is defined. The core modules of the firmware include data storage module, path management module, multi-thread scheduling module, etc. In the design process, a modular programming method is adopted to independently develop each functional module, and the cooperation between the modules is controlled by register operation. The storage module is responsible for managing the access and update of data, combining the stack storage structure to achieve efficient data call; the path management module relies on the shortest retrieval path for fast data positioning and access to ensure the real-time retrieval; the multi-thread scheduling module performs scheduling management according to the thread call strategy optimized in the previous step. Each module performs low-level control through hardware registers to ensure that the firmware can operate directly at the physical level, and finally obtains "retrieval storage call firmware". After completing the design of "retrieval storage call firmware", the firmware needs to be embedded in the radar system to realize actual data storage and call operations. The specific implementation of this step first requires loading the firmware into the embedded chip of the radar. Through the burning technology, the firmware is written into the memory of the radar motherboard in binary form. After the firmware is burned, start the radar system to ensure that the firmware can work properly. The working principle of the firmware is that when the radar collects monitoring data, the storage module automatically calls the path management module to quickly store the data in the shortest path. After each data storage, the multi-threaded scheduling module quickly processes multiple data call requests according to the concurrency optimization strategy to ensure that the radar can maintain an efficient data call speed in a high-concurrency environment. During the whole process, the firmware interacts directly with the physical hardware of the radar without relying on external software systems to ensure system stability and real-time performance.

[0086] Preferably, step S1 comprises the following steps:

[0087] Step S11: Acquire radar monitoring real-time data;

[0088] Step S12: Fill missing values ​​in the radar monitoring real-time data to obtain radar monitoring real-time missing filled data;

[0089] Step S13: Timestamp the radar monitoring real-time missing filling data to obtain radar monitoring time stamp data;

[0090] Step S14: performing data collection growth analysis per unit time on the radar monitoring real-time data according to the radar monitoring time tag data, and obtaining the time segment growth of the radar monitoring data.

[0091] In the embodiment of the present invention, after the radar system is started, the radar monitoring data of the surrounding environment is obtained in real time through the internal sensors and antenna equipment. The data mainly includes the intensity of the reflected wave, the echo time, and the position and speed of the target object. This data is directly transmitted to the built-in data processing unit through the hardware interface of the radar. In the data acquisition process, high-speed sampling technology is used to ensure the timeliness and accuracy of the data, and hardware clock synchronization is relied on to ensure that the collected real-time data can be recorded in chronological order for the execution of subsequent processing steps. In the transmission and acquisition process of radar monitoring data, some data points are missing due to signal attenuation or external interference. In order to ensure the integrity of the data, it is necessary to perform missing value filling operations. In the specific operation process, an interpolation algorithm (such as linear interpolation or spline interpolation) is used to calculate the missing data points, generate estimated values ​​and insert them into the corresponding positions. For missing data with large intervals, the nearest neighbor algorithm is used to estimate the filling value according to the trend of the adjacent data points. In this process, combined with the anomaly detection algorithm, it is ensured that the filled data is consistent with the change trend of the original data to avoid error accumulation, and obtain "radar monitoring real-time missing filling data". After the missing values ​​are filled, in order to ensure the temporal consistency and traceability of the data, each data record must be accurately timestamped. Each radar monitoring data is assigned a unique timestamp through the clock module inside the hardware. The timestamp format is usually "year-month-day hour: minute: second: millisecond", which can ensure that every instantaneous state of the data is recorded. To achieve this marking, the synchronous clock calibration technology is used to calibrate the time in real time through GPS or internal clock calibration device to ensure the accuracy and consistency of the marking. Finally, the "radar monitoring time stamp data" is generated, which will provide an accurate time reference for subsequent data analysis. After completing the time marking, the radar monitoring data in each time period is collected and analyzed for growth. The purpose of this operation is to evaluate the change trend of data collection within a unit time (such as every second or every minute). First, the radar monitoring time stamp data is grouped by time period, and the number of data and feature changes in each unit time are counted. Then, the data of adjacent time periods are differentiated using the growth rate calculation formula to obtain the growth rate of each time period. In this way, the speed change of data collection can be monitored and abnormal fluctuations in data volume can be detected. At the same time, the statistical regression analysis method is used to model the trend of data changes within a time period, and a dynamic collection growth graph is generated to reflect the change pattern of data over time. The final "time segment growth of radar monitoring data" provides an important basis for the efficiency analysis of radar data storage and call.

[0092] Preferably, step S2 comprises the following steps:

[0093] Step S21: performing data geometric increment analysis on the time segment growth amount of the radar monitoring data in different unit times to obtain the time segment geometric growth amount of the radar monitoring data;

[0094] Step S22: identifying the incremental fluctuation trend between adjacent segments of the radar monitoring data time segment growth amount according to the segmented geometric growth amount of the radar monitoring data, and obtaining the incremental fluctuation trend data of each adjacent time segment;

[0095] Step S23: mapping the monitoring content increment fluctuation between different time segments on the radar monitoring real-time data according to the increment fluctuation trend data of each adjacent time segment, and obtaining the monitoring content increment fluctuation mapping data between different time segments;

[0096] Step S24: opening up a pre-storage space for the intersection content of the monitoring content increment fluctuation mapping data between different time segments to obtain a pre-storage space for the monitoring intersection content.

[0097] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0098] Step S21: performing data geometric increment analysis on the time segment growth amount of the radar monitoring data in different unit times to obtain the time segment geometric growth amount of the radar monitoring data;

[0099] In an embodiment of the present invention, a geometric incremental analysis is first performed based on the previously acquired "time segment growth of radar monitoring data". By dividing into multiple time periods, the growth amplitude of radar monitoring data in different unit times is expressed in the form of a geometric sequence. In a specific operation, based on the starting value and the ending value of each time period, a geometric series formula is used to calculate the data growth ratio in each time period. The geometric incremental analysis determines the geometric growth trend of radar monitoring data by iteratively calculating the product relationship between adjacent time periods, thereby generating a "segmented geometric growth amount of radar monitoring data". This geometric analysis enables the long-term growth trend of monitoring data to be reflected through an exponential relationship, and is particularly suitable for analyzing changes in data growth rate over a large range of time.

[0100] Step S22: identifying the incremental fluctuation trend between adjacent segments of the radar monitoring data time segment growth amount according to the segmented geometric growth amount of the radar monitoring data, and obtaining the incremental fluctuation trend data of each adjacent time segment;

[0101] In an embodiment of the present invention, incremental fluctuation trend identification is performed for each adjacent time period. In the specific implementation process, two adjacent time periods are first compared, and the difference between them is calculated to determine the fluctuation trend. The differential analysis method is used to calculate the fluctuation trend of each pair of adjacent segments, and high-order derivatives are used to characterize the acceleration changes of these incremental fluctuations. By counting the fluctuation amplitude in each time period, a complete trend curve is constructed, and finally the "incremental fluctuation trend data of each adjacent time segment" is generated. This data lays the foundation for the subsequent monitoring content fluctuation mapping, ensuring that the change trend of each time segment can be accurately captured.

[0102] Step S23: mapping the monitoring content increment fluctuation between different time segments on the radar monitoring real-time data according to the increment fluctuation trend data of each adjacent time segment, and obtaining the monitoring content increment fluctuation mapping data between different time segments;

[0103] In an embodiment of the present invention, the "incremental fluctuation trend data" obtained above is combined with the original radar monitoring real-time data to perform incremental fluctuation mapping of monitoring content between different time segments. The specific operation is to first select the fluctuation trend data of two time periods, and establish a mapping function based on their differences to associate the content increment of each time period with the corresponding fluctuation trend. Through the mapping operation, the data change relationship between different time periods can be intuitively reflected. In order to ensure the accuracy of the mapping, the mapping matrix method is used to arrange each incremental fluctuation trend data in time series, and combine the actual content of the radar monitoring real-time data to generate "incremental fluctuation mapping data of monitoring content between different time segments". This process can better present the dynamic changes of monitoring data in the time dimension.

[0104] Step S24: opening up a pre-storage space for the intersection content of the monitoring content increment fluctuation mapping data between different time segments to obtain a pre-storage space for the monitoring intersection content.

[0105] In an embodiment of the present invention, it is necessary to analyze the "incremental fluctuation mapping data of monitoring content between different time segments", extract its intersection content and open up pre-storage space. The extraction of intersection content is mainly through finding the overlapping areas of data fluctuations in different time segments, and identifying the key data points that coexist in multiple time periods. Using the interval merging algorithm, the data in each time segment is sorted and merged to identify the intersection area of ​​the data in each time period. After the intersection area is confirmed, in order to ensure the efficiency of subsequent data storage and call, pre-storage space is opened up through hardware-level storage management operations, and the intersection content is stored in the cache to generate "monitoring intersection content pre-storage space". The division and management of this space ensures that the system can quickly access these common contents when calling data, thereby improving the overall efficiency of storage calls.

[0106] Preferably, step S22 comprises the following steps:

[0107] Step S221: Calculating the geometric growth variance interval between adjacent time segments on the time segment growth amount of the radar monitoring data based on the segmented geometric growth amount of the radar monitoring data, and obtaining the geometric growth variance interval between adjacent segments;

[0108] Step S222: performing growth rate analysis between adjacent time segments on the time segment growth amount of the radar monitoring data based on the geometric growth variance interval between adjacent segments to obtain segment growth rate data;

[0109] Step S223: performing a rate symmetry analysis between different time segments on the segmented growth rate data to obtain segmented growth rate symmetry data;

[0110] Step S224: performing linear first-order difference processing between different time segments on the time segment growth amount of the radar monitoring data according to the segment growth rate symmetry data and the segment growth rate data, to obtain growth linear first-order difference data;

[0111] Step S225: Identify the incremental fluctuation trend between adjacent segments based on the growth linear first-order difference data to obtain the incremental fluctuation trend data of each adjacent time segment.

[0112] In the embodiment of the present invention, for the segmented geometric growth of radar monitoring data, a variance calculation method is used to calculate the geometric growth variance interval for the data changes between adjacent time segments. In the specific operation, by constructing a variance calculation formula, the growth amplitude of each adjacent time segment is first determined, and then the geometric growth variance of the adjacent segments is calculated. The variance interval is used to evaluate the stability of the fluctuation of the data of adjacent time segments and the distribution breadth of the data, and can effectively reflect the growth fluctuation range of the monitoring data in each time segment. The obtained "geometric growth variance interval between adjacent segments" will provide an accurate reference for the subsequent incremental fluctuation trend identification. According to the "geometric growth variance interval between adjacent segments" obtained in the previous step, this step uses the growth rate analysis method to determine the actual growth rate of each time segment. The growth rate in each segment is calculated by the rate function, and the law of rate change between adjacent time segments is analyzed. During the rate analysis, the linear regression method is used, combined with the variance change in each adjacent segment, to calculate the rate change rate of the adjacent time segment to obtain the growth rate of the segment. This process can identify the trend of data change and abnormal growth, and finally generate "segmented growth rate data", laying the foundation for subsequent symmetry analysis. In this step, the rate symmetry analysis is performed on the previously obtained "segmented growth rate data". This process compares the growth rates of adjacent segments to determine their symmetry and balance. In specific operations, based on the symmetry analysis method in mathematical statistics, the degree of deviation of the growth rate in adjacent time segments is calculated, and the balance point of the rate change in adjacent time periods is identified by constructing a symmetry function. This analysis can effectively identify the excessively fast or slow fluctuations in the data growth rate in certain time periods, thereby obtaining "segmented growth rate symmetry data", which provides an important reference for subsequent linear first-order difference processing. In this step, according to the "segmented growth rate symmetry data" and "segmented growth rate data", the data change trend between different time segments is analyzed through the linear first-order difference processing technology. First, the growth rate of each time segment is differentially operated, and the change amplitude within the time segment is calculated through the first-order difference function, and the growth rate difference between the segments is identified. The main function of this process is to smooth the rate fluctuations in different time segments, making the growth curve tend to be linear. Linear first-order difference processing can effectively eliminate noise or extreme change points, ensure the accuracy of subsequent incremental fluctuation trend identification, and the generated "growth linear first-order difference data" is the input of subsequent steps.

[0113] Preferably, step S24 comprises the following steps:

[0114] Step S241: performing intersection content association extraction on monitoring content increment fluctuation mapping data between different time segments to obtain intersection content association data;

[0115] Step S242: defining the intersection range of the intersection content association data according to the monitoring content increment fluctuation mapping data between different time segments to obtain the intersection content association range data;

[0116] Step S243: constructing a subordinate content index network for the intersection content association data according to the intersection content association range data to obtain a subordinate content index network;

[0117] Step S244: performing predictive incremental space allocation logic design on the subordinate content index network according to the monitoring content incremental fluctuation mapping data between different time segments, and obtaining subordinate content incremental space allocation logic data;

[0118] Step S245: opening up the intersection content pre-storage space according to the subordinate content index network and the subordinate content incremental space allocation logic data to obtain the monitoring intersection content pre-storage space.

[0119] In the embodiment of the present invention, by analyzing the "monitoring content incremental fluctuation mapping data", an intersection extraction algorithm is used to identify the intersection content of data changes between different time segments. In the specific operation process, the data sets of different time periods are gradually compared to extract the intersection parts of the same data. This process requires the construction of an intersection association matrix, the use of Boolean operations for intersection determination, and the accuracy of association extraction to be ensured. The extracted "intersection content association data" reflects the shared or related monitoring content between different time segments, providing a basis for the subsequent intersection range definition. Based on the "intersection content association data" and the "monitoring content incremental fluctuation mapping data", the interval analysis method is used to define the specific range of the intersection. During the operation, by constructing an intersection range function, the data in each time segment is compared with the adjacent segment to determine the boundary and coverage of the intersection data. This process requires quantitative analysis of data changes, and the use of segmented statistical methods to ensure that the range of each intersection content is clear and definite. The defined "intersection content association range data" will lay a data foundation for building a subordinate content index network. The "intersection content association range data" is used to further process the "intersection content association data" to construct a subordinate content index network. In the specific operation, the association network construction algorithm is used to map the relationship between nodes and edges to establish an index network for monitoring content affiliation. Each intersection data is defined as a node in the network, and the affiliation between these nodes is represented by edges. Through this step, the constructed "subordinate content index network" can clearly present the hierarchical structure of the intersection content, providing a basis for predictive incremental space allocation. Combining the "subordinate content index network" with the "monitoring content incremental fluctuation mapping data", a predictive incremental space allocation logic is designed. By analyzing the data change trend in each time period, an incremental prediction model is established to determine the path of data growth. Through logical reasoning and space allocation algorithms, the relationship between different nodes and the spatial location of future data growth are calculated. In this process, linear regression or trend analysis technology is used to ensure the accuracy and rationality of the allocation logic, and the "subordinate content incremental space allocation logic data" is generated to provide a basis for the development of pre-stored space. Based on the "subordinate content index network" and "subordinate content incremental space allocation logic data", the pre-storage space for intersection content is opened up through space planning methods. In the specific operation process, the size and layout of the storage space are determined by analyzing the allocation logic, and the pre-storage space is reasonably divided using the space allocation algorithm. The focus of this step is to ensure efficient storage of data and convenience for future calls. The division of pre-storage space follows the hierarchical and spatial distribution characteristics of the intersection data, and the final generated "monitoring intersection content pre-storage space" ensures the sustainable call of radar monitoring data.

[0120] Preferably, constructing a subordinate content index network for the intersection content association data according to the intersection content association range data comprises the following steps:

[0121] Performing subordinate content level recursive verification on the intersection content association data according to the intersection content association range data to obtain subordinate content level recursive data;

[0122] Summarizing the attributes of connected nodes between upper and lower levels for the subordinate content level recursive data to obtain connected node attribute data;

[0123] Performing node content depth / breadth analysis on subordinate content level recursive data between different subordinate levels according to the connected node attribute data to obtain node content depth / breadth data;

[0124] Performing node content association extensibility analysis on the node content depth / breadth data to obtain node content association extensibility data;

[0125] Based on the R-tree and according to the node content association extensibility data and the connected node attribute data, a subordinate content index network is constructed for subordinate content hierarchical recursive data to obtain a subordinate content index network.

[0126] In the embodiment of the present invention, the hierarchical recursive verification of subordinate content is performed by performing a detailed analysis of the "intersection content association range data". In the specific operation, a recursive algorithm is used to traverse and verify the multi-level structure of the intersection content to ensure that the relationship between the content in each level in different time segments conforms to the subordinate rule. Through the hierarchical tree construction of the subordinate relationship, the dependency relationship between the nodes of each layer is recursively analyzed upward or downward in turn, and a strict hierarchical verification is performed to finally generate "subordinate content hierarchical recursive data". This process ensures the logical consistency of the hierarchical structure and reduces the redundancy and repeated verification of the subordinate data. Based on the "subordinate content hierarchical recursive data", the attributes of the connected nodes between the upper and lower levels are summarized. In the specific operation, the node content of each level and its association characteristics with the upper and lower levels are analyzed through the attribute induction algorithm, and the key attribute values ​​of the connected nodes are extracted. The induction method adopts hierarchical clustering technology to generate "connected node attribute data" by calculating the attribute similarity and correlation of the nodes. The purpose of this step is to ensure that the attributes of the connected nodes between the levels are clear and clear, and to provide accurate basic data for subsequent depth / breadth analysis. Through further analysis of the "connected node attribute data", the depth-first and breadth-first search algorithms are combined to conduct a comprehensive analysis of the depth and breadth of the node content at each level in the "subordinate content hierarchical recursive data". In the specific operation, relying on the depth-first search (DFS) and breadth-first search (BFS) techniques of graph theory, the node content is traversed layer by layer, and its relative depth and breadth in the entire hierarchical structure are evaluated, and finally the "node content depth / breadth data" is generated. This step ensures that the hierarchical expansion of subordinate content can be fully grasped, and provides data support for the subsequent node content association scalability analysis. Based on the "node content depth / breadth data", the scalability of node content at different levels is evaluated through the association expansion analysis algorithm. In the specific operation, the association analysis technology is used to calculate the association between nodes, evaluate the expansion potential of nodes and the degree of association with adjacent nodes. Combining the analysis results of depth and breadth, it is determined which nodes have strong expansion capabilities, so as to predict the future direction of association expansion. Finally, we get "node content associated extensibility data". This step ensures that nodes that will expand or change in the future can be identified, supporting the efficient construction of subsequent index networks. In this step, the "node content associated extensibility data" and "connected node attribute data" are combined to construct a subordinate content index network for the "subordinate content hierarchical recursive data" through the R-tree spatial index structure. The R-tree is an efficient spatial data indexing technology that can effectively handle data distribution problems in multidimensional space through its hierarchical node structure. In actual operation, the R-tree is used to model the spatial distribution and extensibility of each node, and the node association is mapped to the hierarchical structure of the R-tree to ensure the hierarchical, associative, and spatial construction of the index network, and finally obtain the "subordinate content index network".This step ensures that the space allocation of the index network is reasonable and provides an efficient storage and indexing basis for subsequent data calls.

[0127] Preferably, the predictive incremental space allocation logic design for the subordinate content index network based on the monitored content incremental fluctuation mapping data between different time segments includes the following steps:

[0128] According to the monitoring content increment fluctuation mapping data between different time segments, the periodic growth trend of the subordinate content index network is predicted to obtain the periodic growth trend data of the index network;

[0129] Perform memory pool matching on the subordinate content index network based on the periodic growth trend data of the index network to obtain subordinate content index memory pool matching data;

[0130] Performing memory paging processing on the subordinate content index memory pool matching data to obtain the subordinate content index memory paging data;

[0131] According to the subordinate content index memory pool matching data and the subordinate content index memory paging data, a predictive incremental space allocation logic design is performed on the index network periodic growth trend data to obtain the subordinate content incremental space allocation logic data.

[0132] In the embodiment of the present invention, by analyzing the "monitoring content incremental fluctuation mapping data" between different time segments, the growth trend of the subordinate content index network is predicted by using a periodic fluctuation analysis algorithm. In specific operation, based on the time series analysis theory, the incremental changes of each time segment are analyzed for periodic trends to extract regular growth patterns. The incremental fluctuations of the time segment are analyzed in the frequency domain using Fourier transform to identify the periodic growth law, and finally the "index network periodic growth trend data" is obtained. This step ensures that the growth of the index network in different time segments can be accurately predicted, thereby providing a basis for subsequent memory pool matching. The memory pool is reasonably allocated to the subordinate content index network based on the "index network periodic growth trend data". In specific operation, the memory management algorithm is combined to analyze the memory requirements of the index network in periodic growth. The memory pool allocation strategy is used to dynamically match the memory space in the index network growth process to ensure that the index network has sufficient memory support in the growth process. In this process, the first fit algorithm (FirstFit) or the best fit algorithm (Best Fit) is used to optimize the allocation of memory space, and finally generate "subordinate content index memory pool matching data". This step ensures efficient use of memory resources and lays the foundation for subsequent memory paging processing. Based on the "subordinate content index memory pool matching data", the memory pool is paging processed. In specific operations, the paging algorithm is used to divide the memory pool into fixed-size pages to optimize memory utilization and management efficiency. By calculating the actual content size of each page, the page mapping table is used to record the correspondence between the page and each part of the index network to avoid memory waste and conflict. The two-level paging algorithm is used to divide the large memory pool into small pages, while retaining the multi-level index table to manage different memory pages, and finally the "subordinate content index memory paging data" is obtained. This step ensures the flexibility and scalability of memory management and adapts to the dynamic growth of the index network. By combining the "subordinate content index memory pool matching data" and the "subordinate content index memory paging data", the predictive incremental space allocation logic design is performed for the periodic growth trend of the index network. In specific operations, the linear programming method is used to analyze the space usage in the memory pool matching data and the paging data, and predict the memory space increment required for the future index network when it grows. By analyzing the memory usage efficiency and growth rate, a reasonable space allocation scheme is designed to ensure that the memory space can be dynamically adjusted according to the growth of the index network. Finally, the "slave content incremental space allocation logic data" is generated. The core of this step is to ensure that the dynamic allocation of memory space can keep up with the growth needs of the index network, thereby optimizing memory utilization efficiency.

[0133] Preferably, step S3 comprises the following steps:

[0134] Step S31: classifying the content application scenarios of the monitoring intersection content pre-storage space to obtain monitoring intersection content application scenario data;

[0135] Step S32: generating the shortest search path for application content in the pre-stored space of the monitored intersection content according to the application scenario data of the monitored intersection content, and obtaining the shortest search path data for application content;

[0136] Step S33: Optimize the multi-thread concurrent calls of the shortest search path data of the application content to obtain concurrent optimized search path data.

[0137] In the embodiment of the present invention, the content of the pre-storage space of the monitoring intersection content first needs to be carefully classified into application scenarios. In the specific implementation process, a clustering algorithm is used to analyze the data in the pre-storage space to identify different application scenarios. First, the data is converted into numerical features, and the K-means clustering algorithm is used to group similar monitoring contents. An appropriate K value is set to distinguish the main application scenarios, such as traffic flow monitoring, weather change monitoring, and environmental quality monitoring. Each clustering result represents a specific application scenario, and generates "monitoring intersection content application scenario data" for subsequent retrieval and application. This process ensures the orderly classification of monitoring data and improves the efficiency and accuracy of subsequent data processing. Based on the "monitoring intersection content application scenario data", the shortest retrieval path generation of the application content of the pre-storage space of the monitoring intersection content is implemented. First, a graph structure is constructed, in which the nodes represent the monitoring content and the edges represent the similarity or connection relationship between the content. The Dijkstra algorithm is used to find the shortest path, and the system identifies the shortest path from the source content to the target content by calculating the connection weights between different contents. This path can guide the optimal access sequence in the subsequent retrieval process, and finally generate the "application content shortest retrieval path data". This step effectively reduces data retrieval latency and improves data access efficiency by optimizing the retrieval path. Based on the "shortest retrieval path data of application content", multi-threaded concurrent call optimization is implemented. First, the data of the shortest retrieval path is decomposed into multiple subtasks to achieve parallel processing. Using thread pool technology, multiple working threads are created and assigned different subtasks, and each thread performs retrieval operations independently. The lock mechanism is used to ensure secure access to shared resources and avoid data conflicts and inconsistencies. At the same time, through the load balancing algorithm, tasks are dynamically allocated according to the processing power of each thread to optimize the overall processing efficiency. Finally, the "retrieval path concurrent optimization data" is generated to ensure that the system can respond to user requests efficiently and quickly during the concurrent retrieval process, significantly improving the overall performance of data calls.

[0138] Preferably, step S33 includes the following steps:

[0139] Step S331: construct a multi-thread synchronization mechanism for the shortest search path data of the application content to obtain a search path multi-thread synchronization mechanism;

[0140] Step S332: performing load balancing processing on the retrieval path multi-thread synchronization mechanism to obtain synchronization mechanism load balancing data;

[0141] Step S333: performing data concurrent execution and diversion processing on the application content shortest search path data according to the synchronization mechanism load balancing data and the search path multi-thread synchronization mechanism to obtain concurrent execution and diversion data;

[0142] Step S334: Optimize the multi-thread concurrent call of the shortest search path data of the application content according to the concurrent execution diversion data, the synchronization mechanism load balancing data and the search path multi-thread synchronization mechanism to obtain the search path concurrent optimization data.

[0143] In the embodiment of the present invention, in this step, a multi-thread synchronization mechanism for the shortest search path data of the application content is constructed. First, the basic framework of the synchronization mechanism is defined, and semaphore and mutex lock technology are used to ensure that multiple threads do not interfere with each other when performing retrieval. A scheduler is designed to manage the access of different threads to the retrieval path data. Before each thread starts to retrieve, it first obtains a mutex lock to ensure that the thread can exclusively access the path data. After the retrieval is completed, the lock is released so that other threads can access the same data. This synchronization mechanism can not only effectively avoid data competition and inconsistency problems, but also ensure the security and efficiency of multi-thread operations, lay the foundation for subsequent concurrent execution, and finally obtain a "retrieval path multi-thread synchronization mechanism". In this step, load balancing processing of the retrieval path multi-thread synchronization mechanism is implemented. First, the workload of each thread is monitored, and a dynamic load balancing algorithm is used to evaluate the task processing speed and current load of each thread. The load information is summarized and the overall load status of the current system is calculated accordingly. Based on this data, a scheduling strategy is used to dynamically adjust the task allocation of each thread to ensure that the load of each thread is as even as possible. When implementing this processing process, a weighted polling algorithm is used to balance the request processing volume of each thread, thereby effectively reducing the risk of overload of a certain thread, ensuring the optimization of the response time and processing efficiency of the entire system, and finally obtaining the "synchronization mechanism load balancing data". In this step, based on the "synchronization mechanism load balancing data" and the "search path multi-thread synchronization mechanism", the concurrent execution diversion processing of the shortest search path data of the application content is implemented. First, according to the load balancing data, the execution capability and idle state of each thread are determined, and the search path data is reasonably diverted. By implementing a diversion algorithm based on a distributed hash table, the search path data is divided according to the task characteristics to ensure that each thread can obtain a relatively balanced task volume. Each thread independently performs the retrieval operation according to the data fragment it obtains, reducing the competition between threads. This process can realize efficient task parallel execution and finally generate "concurrent execution diversion data" to provide support for subsequent data calls. In this step, based on the "concurrent execution diversion data", "synchronization mechanism load balancing data" and "search path multi-thread synchronization mechanism", the multi-thread concurrent call optimization of the shortest search path data of the application content is implemented. First, the collected diversion data is analyzed to evaluate the current processing status and response time of each thread. Based on this information, an adaptive scheduling algorithm is used to adjust the task allocation of each thread to ensure that threads with faster response times have priority in processing more requests. In the implementation process, a queue management mechanism is used to distribute requests to different threads, while monitoring the execution status of each thread and dynamically adjusting the allocation ratio of requests. Through this optimization measure, the efficiency of data calls can be significantly improved, latency can be reduced, and finally "retrieval path concurrent optimization data" can be obtained, providing efficient guarantee for the storage and call of radar monitoring data.

[0144] Preferably, step S4 comprises the following steps:

[0145] Step S41: performing an execution logic error test on the search path concurrent optimization data to obtain execution logic error test data;

[0146] Step S42: Designing storage calling firmware for the search path concurrent optimization data according to the execution logic error test data to obtain the search storage calling firmware;

[0147] Step S43: embed the retrieval storage call firmware into the radar to execute the radar monitoring data storage call.

[0148] In the embodiment of the present invention, the execution logic error test is performed on the "retrieval path concurrent optimization data" to verify whether the operation logic of the system in a multi-threaded environment meets expectations. First, a set of test cases is constructed, which cover a variety of concurrent scenarios, including normal operation, abnormal operation and extreme load conditions. During the test, the execution results, processing time and final return data of each thread are recorded in logs. A step-by-step comparative analysis is implemented to compare the test results with the expected correct output to identify logical errors and data inconsistency problems in the execution process. In this process, statistical methods are used to analyze the source of errors, and charts are used to intuitively display the performance of each thread under different test conditions. Finally, the "execution logic error test data" is obtained to provide a basis for subsequent firmware design. Based on the "execution logic error test data", the storage call firmware design of the "retrieval path concurrent optimization data" is performed. First, the error test data is analyzed to determine the logical loopholes and performance bottlenecks in the system. Then, the modular design method is used to divide the storage call firmware into multiple functional modules, including a data preprocessing module, a request scheduling module and an error handling module. Each module is designed to ensure high cohesion and low coupling of functions to facilitate subsequent maintenance and updates. In the implementation process, the state machine design concept is adopted to dynamically manage the state transfer of each module and improve the flexibility of the system. At the same time, in order to ensure the efficiency of the firmware, efficient data structures such as hash tables and priority queues are used to optimize the storage and access efficiency of data. The final "retrieval storage call firmware" has high scalability and stability, and can effectively support the rapid access of radar monitoring data. The designed "retrieval storage call firmware" is embedded in the radar system to realize the storage call of radar monitoring data. First, the firmware is integrated on the radar hardware platform to ensure that the firmware can seamlessly connect with the radar's sensor and data processing unit. In the specific implementation, the firmware is compiled into executable machine code by using embedded programming technology and uploaded to the radar's memory. During the startup process of the firmware, the initialization settings are first performed, including system parameter configuration and connection status check. Then, the firmware generates a storage request based on the real-time monitoring data, interacts with the data storage module through the internal communication interface, and realizes efficient writing and reading of data. During the execution process, the firmware will continuously monitor the operating status of the system, capture any abnormalities, and promptly feedback to the system to ensure the stability and reliability of the data storage call process. Ultimately, radar monitoring data can be stored and called quickly and accurately to meet the needs of real-time monitoring.

[0149] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0150] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A radar monitoring data storage and calling method, characterized in that: The following steps are involved: Step S1: Acquire radar monitoring real-time data; Perform data collection growth analysis on real-time radar monitoring data per unit time to obtain the time segment growth of radar monitoring data; Step S2: Identify the incremental fluctuation trend between adjacent time segments of the radar monitoring data time segment growth amount to obtain the incremental fluctuation trend data of each adjacent time segment; open up the intersection content pre-storage space according to the incremental fluctuation trend data of each adjacent time segment to obtain the monitoring intersection content pre-storage space; Step S3: Generate the shortest search path for application content in the pre-stored space of the monitored intersection content to obtain the shortest search path data for application content; perform multi-threaded concurrent call optimization on the shortest search path data for application content to obtain concurrent optimization data for the search path; Step S4: Designing storage calling firmware for the concurrent optimization data of the retrieval path to obtain retrieval storage calling firmware; The retrieval storage call firmware is embedded into the radar to perform radar monitoring data storage call.

2. The radar monitoring data storage and calling method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire radar monitoring real-time data; Step S12: Fill missing values ​​in the radar monitoring real-time data to obtain radar monitoring real-time missing filled data; Step S13: Timestamp the radar monitoring real-time missing filling data to obtain radar monitoring time stamp data; Step S14: performing data collection growth analysis per unit time on the radar monitoring real-time data according to the radar monitoring time tag data, and obtaining the time segment growth of the radar monitoring data.

3. The radar monitoring data storage and calling method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing data geometric increment analysis on the time segment growth amount of the radar monitoring data in different unit times to obtain the time segment geometric growth amount of the radar monitoring data; Step S22: identifying the incremental fluctuation trend between adjacent segments of the radar monitoring data time segment growth amount according to the segmented geometric growth amount of the radar monitoring data, and obtaining the incremental fluctuation trend data of each adjacent time segment; Step S23: mapping the monitoring content increment fluctuation between different time segments on the radar monitoring real-time data according to the increment fluctuation trend data of each adjacent time segment, and obtaining the monitoring content increment fluctuation mapping data between different time segments; Step S24: opening up a pre-storage space for the intersection content of the monitoring content increment fluctuation mapping data between different time segments to obtain a pre-storage space for the monitoring intersection content.

4. The radar monitoring data storage and calling method according to claim 3 is characterized in that: Step S22 includes the following steps: Step S221: Calculating the geometric growth variance interval between adjacent time segments for the time segment growth amount of the radar monitoring data according to the segmented geometric growth amount of the radar monitoring data, and obtaining the geometric growth variance interval between adjacent segments; Step S222: performing growth rate analysis between adjacent time segments on the time segment growth amount of the radar monitoring data based on the geometric growth variance interval between adjacent segments to obtain segment growth rate data; Step S223: performing a rate symmetry analysis between different time segments on the segmented growth rate data to obtain segmented growth rate symmetry data; Step S224: performing linear first-order difference processing between different time segments on the time segment growth amount of the radar monitoring data according to the segment growth rate symmetry data and the segment growth rate data, to obtain growth linear first-order difference data; Step S225: Identify the incremental fluctuation trend between adjacent segments based on the growth linear first-order difference data to obtain the incremental fluctuation trend data of each adjacent time segment.

5. The radar monitoring data storage and calling method according to claim 3 is characterized in that: Step S24 includes the following steps: Step S241: performing intersection content association extraction on monitoring content increment fluctuation mapping data between different time segments to obtain intersection content association data; Step S242: defining the intersection range of the intersection content association data according to the monitoring content increment fluctuation mapping data between different time segments to obtain the intersection content association range data; Step S243: constructing a subordinate content index network for the intersection content association data according to the intersection content association range data to obtain a subordinate content index network; Step S244: performing predictive incremental space allocation logic design on the subordinate content index network according to the monitoring content incremental fluctuation mapping data between different time segments, and obtaining subordinate content incremental space allocation logic data; Step S245: opening up the intersection content pre-storage space according to the subordinate content index network and the subordinate content incremental space allocation logic data to obtain the monitoring intersection content pre-storage space.

6. The radar monitoring data storage and calling method according to claim 5, characterized in that: Constructing a subordinate content index network for the intersection content association data according to the intersection content association range data includes the following steps: Performing subordinate content level recursive verification on the intersection content association data according to the intersection content association range data to obtain subordinate content level recursive data; Summarizing the attributes of connected nodes between upper and lower levels for the subordinate content level recursive data to obtain connected node attribute data; Performing node content depth / breadth analysis on subordinate content level recursive data between different subordinate levels according to the connected node attribute data to obtain node content depth / breadth data; Performing node content association extensibility analysis on the node content depth / breadth data to obtain node content association extensibility data; Based on the R-tree and according to the node content association extensibility data and the connected node attribute data, a subordinate content index network is constructed for subordinate content hierarchical recursive data to obtain a subordinate content index network.

7. The radar monitoring data storage and calling method according to claim 5, characterized in that: The logical design of predictive incremental space allocation for the subordinate content index network based on the monitoring content incremental fluctuation mapping data between different time segments includes the following steps: According to the monitoring content increment fluctuation mapping data between different time segments, the periodic growth trend of the subordinate content index network is predicted to obtain the periodic growth trend data of the index network; Perform memory pool matching on the subordinate content index network based on the periodic growth trend data of the index network to obtain subordinate content index memory pool matching data; Performing memory paging processing on the subordinate content index memory pool matching data to obtain the subordinate content index memory paging data; According to the subordinate content index memory pool matching data and the subordinate content index memory paging data, a predictive incremental space allocation logic design is performed on the index network periodic growth trend data to obtain the subordinate content incremental space allocation logic data.

8. The radar monitoring data storage and calling method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: classifying the content application scenarios of the monitoring intersection content pre-storage space to obtain monitoring intersection content application scenario data; Step S32: generating the shortest search path for application content in the pre-stored space of the monitored intersection content according to the application scenario data of the monitored intersection content, and obtaining the shortest search path data for application content; Step S33: Optimize the multi-thread concurrent calls of the shortest search path data of the application content to obtain concurrent optimized search path data.

9. The radar monitoring data storage and calling method according to claim 8, characterized in that: Step S33 includes the following steps: Step S331: construct a multi-thread synchronization mechanism for the shortest search path data of the application content to obtain a search path multi-thread synchronization mechanism; Step S332: performing load balancing processing on the retrieval path multi-thread synchronization mechanism to obtain synchronization mechanism load balancing data; Step S333: performing data concurrent execution and diversion processing on the application content shortest search path data according to the synchronization mechanism load balancing data and the search path multi-thread synchronization mechanism to obtain concurrent execution and diversion data; Step S334: Optimize the multi-thread concurrent call of the shortest search path data of the application content according to the concurrent execution diversion data, the synchronization mechanism load balancing data and the search path multi-thread synchronization mechanism to obtain the search path concurrent optimization data.

10. The radar monitoring data storage and calling method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing an execution logic error test on the search path concurrent optimization data to obtain execution logic error test data; Step S42: Designing storage calling firmware for the search path concurrent optimization data according to the execution logic error test data to obtain the search storage calling firmware; Step S43: embed the retrieval storage call firmware into the radar to execute the radar monitoring data storage call.

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