Scenic spot edge node data processing method, system, equipment and medium

Abstract

The invention provides a scenic spot edge node data processing method, system and device and a medium, and belongs to the technical field of scenic spot data processing. Scenic spot data acquisition requests for multiple scenic spot edge nodes are received, and scenic spot state data acquired by each scenic spot edge node are called; configuring corresponding scenic spot monitoring data for each scenic spot edge node; generating scenic spot data calling instructions for the corresponding scenic spot edge nodes based on the scenic spot monitoring data, and forming a plurality of scenic spot data calling instructions; configuring the scenic spot data calling instructions with the same execution priority level as the same calling instruction set; obtaining scenic spot target state data; and transmitting the scenic spot target state data to a cloud server through an encrypted communication protocol, and adjusting a scenic spot data preset rule or an execution priority based on a performance monitoring result. According to the method, a closed-loop feedback mechanism is formed from data acquisition, instruction generation to transmission optimization, and the real-time performance, accuracy and reliability of scenic spot data processing are greatly improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of scenic area data processing, and particularly relates to a method, system, device and medium for processing data of edge nodes in a scenic area. Background Art

[0002] In the operation and management control of scenic areas, data processing plays a crucial role. Cameras, sensors, etc. in scenic areas will generate a large amount of real-time data, such as crowd density, tourist behavior, environmental conditions, etc. These data are of great significance for the operation decision-making, resource allocation and tourist experience optimization of scenic areas.

[0003] In the related art, the scenic area operation management and control system uses a fixed instruction template and cannot adjust the acquisition strategy according to the real-time operation status of the scenic area, resulting in a mismatch between the instruction and the actual scenario. The system executes the data retrieval instructions in sequence without distinguishing the task priorities, resulting in emergency data being blocked by ordinary instructions and affecting the response speed. The related art also cannot dynamically adjust according to the changes in edge node resources or network fluctuations, resulting in unstable acquisition efficiency. Summary of the Invention

[0004] The present invention provides a method for processing data of edge nodes in a scenic area. The method forms a closed-loop feedback mechanism from data acquisition, instruction generation to transmission optimization, greatly improving the real-time performance, accuracy and reliability of scenic area data processing.

[0005] The method includes: S101: Receive a scenic area data acquisition request for multiple scenic area edge nodes, and retrieve the scenic area status data obtained by each scenic area edge node; S102: Configure corresponding scenic area monitoring data for each scenic area edge node according to the scenic area status data and the preset scenic area data preset rules; S103: Generate scenic area data retrieval instructions for the corresponding scenic area edge nodes based on the scenic area monitoring data, forming multiple scenic area data retrieval instructions; S104: Configure the scenic area data retrieval instructions with the same execution priority level into the same retrieval instruction set, generating multiple retrieval instructions; S105: Execute the scenic area data retrieval operations in each retrieval instruction set in sequence according to the execution priority level to obtain the scenic area target status data; S106: Transmit the scenic area target status data to the cloud server through an encrypted communication protocol, and adjust the scenic area data preset rules or the execution priority level based on the performance monitoring results.

[0006] Preferably, step S102 specifically includes: Analyze the scenic area status data to obtain the interface address of the corresponding scenic area edge node and the scenic area status data format information; Encapsulate the interface address and the scenic area status data format information based on the preset rules of the scenic area data; Set corresponding retrieval time periods according to the data update frequency of the scenic area edge nodes; Determine the execution priority level based on the retrieval time period.

[0007] Preferably, step S103 specifically includes: extracting features from the scenic area monitoring data to identify key information in the data; Based on the extracted key information, combined with the real-time operation status and historical data patterns of the scenic area, recommend the retrieval instruction template that best matches the current scenario; Evaluate the resource occupancy of the retrieval instruction, and predict the impact of executing this instruction on the computing resources and network bandwidth of the edge node; Dynamically adjust the parameters of the retrieval instruction according to the resource evaluation result and the urgency of the retrieval instruction.

[0008] Preferably, step S104 specifically includes: Initialize multiple retrieval instruction sets, each retrieval instruction set corresponding to one execution priority level; Traverse the scenic area data retrieval instructions, and allocate each scenic area data retrieval instruction to the corresponding retrieval instruction set according to the corresponding execution priority level; Sort each retrieval instruction set according to the execution priority level corresponding to the retrieval instruction set.

[0009] Preferably, step S105 further includes: Collect the retrieval process information of each retrieval instruction set; wherein, the retrieval process information includes retrieval status, response time, and retrieval exception rate; Perform real-time analysis on the retrieval process information based on the preset retrieval status analysis rules; When the retrieval process information meets the preset alarm conditions, generate an alarm message and push the alarm message to the preset alarm channel; Adjust the scenic area data retrieval instructions according to the retrieval process information.

[0010] Preferably, step S106 further includes: Before transmitting the scenic area target status data to the cloud server, encrypt the data and add a data integrity verification flag; Transmit the encrypted scenic area target status data to the cloud server through the preset encrypted communication protocol, and at the same time record the data transmission delay, packet loss rate, and bandwidth occupancy; Analyze the resource load status and data retrieval efficiency of the current edge node based on the metrics collected by the performance monitoring module in real time; Dynamically adjust the parameter thresholds in the preset rules of scenic area data according to the analysis results, or modify the mapping relationship of the retrieval priority levels to optimize the generation strategy of subsequent data retrieval instructions.

[0011] Preferably, after obtaining the target state data of the scenic area, it further includes: Verify the data integrity of the target state data of the scenic area to obtain a scenic area data integrity score; verify the compliance of the data format of the target state data of the scenic area to obtain a scenic area data format compliance score; Perform anomaly recognition on the target state data of the scenic area based on preset business rules to obtain an anomaly recognition score; Calculate the scenic area data status score according to the scenic area data integrity score, the scenic area data format compliance score, and the anomaly recognition score; If the scenic area data status score is lower than the preset threshold, mark the corresponding target state data of the scenic area as data to be processed; for the data to be processed, generate and execute a data adjustment operation.

[0012] This application also provides a scenic area edge node data processing system, which includes: A status acquisition module, configured to receive scenic area data acquisition requests for multiple scenic area edge nodes, and retrieve the scenic area status data acquired by each scenic area edge node; A data configuration module, configured to configure corresponding scenic area monitoring data for each scenic area edge node according to the scenic area status data and the preset scenic area data preset rules; A retrieval instruction generation module, which generates scenic area data retrieval instructions for the corresponding scenic area edge nodes based on the scenic area monitoring data, and forms multiple scenic area data retrieval instructions; An instruction configuration module, configured to configure the scenic area data retrieval instructions with the same execution priority level into the same retrieval instruction set, and generate multiple retrieval instructions; A data retrieval execution module, configured to sequentially execute the scenic area data retrieval operations in each retrieval instruction set according to the execution priority level to obtain the target state data of the scenic area; A data transmission and rule adjustment module, configured to transmit the target state data of the scenic area to the cloud server through an encrypted communication protocol, and adjust the preset rules of the scenic area data or the execution priority level based on the performance monitoring results.

[0013] According to another embodiment of the present application, there is provided an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, it implements the steps of the scenic area edge node data processing method.

[0014] According to another embodiment of the present application, a storage medium is further provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the data processing method for the scenic area edge node are implemented.

[0015] From the above technical solutions, it can be seen that the present invention has the following advantages: The data processing method for the scenic area edge node provided by the present application receives multi-node data requests through a unified interface and collects status data in real time, realizing the centralized aggregation of the whole-region data of the scenic area, and improving the comprehensiveness and timeliness of data acquisition. Dynamically configure monitoring data based on preset rules to make the monitoring indicators of each edge node accurately match the actual requirements. Adjust parameters by predicting the impact of instructions on edge node resources to ensure the stable operation of edge computing devices. Through priority configuration and sorting instruction sets, hierarchical scheduling of tasks is realized to ensure the priority processing of important information such as security warnings and passenger flow peaks. The present application collects and retrieves indicators such as status and response time in real time, discovers and warns of abnormal data collection in a timely manner, and reduces the risk of data loss. Automatically optimize instruction parameters according to the monitoring results to improve the success rate and stability of data acquisition. Based on the analysis of edge node resource load and retrieval efficiency, dynamically optimize preset rules and priority mappings to make the system adapt to the changes in scenic area operations, continuously improve the overall performance, and finally provide more accurate decision-making support for scenic area managers and improve the tourist experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0017] Figure 1 It is a flowchart of the data processing method for the scenic area edge node; Figure 2 It is a schematic diagram of the data processing system for the scenic area edge node; Figure 3 It is a schematic diagram of an electronic device. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] For the data processing method and system for the scenic area edge node involved in the present application, the system architecture has the following hierarchical architecture.

[0019] Multiple scenic area edge nodes are defined. The scenic area edge nodes include devices such as cameras, sensors (temperature and humidity, smoke, vibration), and IoT terminals (intelligent guide screens, turnstiles) deployed in the scenic area as data collection ends. Edge computing nodes: Use lightweight AI boxes integrated with NPU / GPU acceleration chips to support multi-channel video stream processing.

[0020] The cloud server is used for long-term data storage, model iteration, and global scheduling of non-real-time tasks.

[0021] The edge nodes in the scenic area are models optimized for scenic area scenarios (such as crowd counting, behavior recognition, and environmental monitoring), which achieve low resource occupancy through model compression techniques (quantization, pruning, knowledge distillation). Integrate multi-source heterogeneous data (videos, sensors, location information), perform real-time analysis, and trigger responses.

[0022] The cloud server can provide real-time monitoring of crowd congestion warnings, intelligent services, and management decision-making support.

[0023] The data processing flow of this application is to collect multi-source data in real time based on devices such as cameras and sensors. Perform preprocessing on the data, such as video noise reduction and sensor data filtering. Analyze the data based on lightweight models and output event tags, such as exceeding the limit of the number of people in a smoking behavior. Integrate multi-modal data to generate event reports such as time, location, and type.

[0024] For data transmission, AES-256 encryption can be used for transmission to prevent man-in-the-middle attacks. Communicate with the cloud server using the TLS protocol to ensure the security of data transmission.

[0025] The data processing method of the edge nodes in the scenic area involved in this application will be described in detail below. For the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are proposed to thoroughly understand the embodiments of this application. However, those skilled in the art should clearly understand that this application can also be implemented in other embodiments without these specific details.

[0026] It should be understood that when used in the specification of this application, the term "including" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations. The terms "including", "comprising", "having", and their variants all mean including but not limited to, unless otherwise specifically emphasized in other ways.

[0027] Statements such as "in one embodiment" or "in some embodiments" described in this application mean that the specific features, structures, or characteristics described in that embodiment are included in one or more embodiments of this application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different parts of this application do not necessarily refer to the same embodiment, but mean one or more but not all embodiments, unless otherwise specifically emphasized in other ways.

[0028] In embodiments of the present invention, computer program code for performing the operations of the present disclosure may be written in one or more programming languages or combinations thereof. The foregoing programming languages include, but are not limited to, object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the C language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (exemplarily, connected through the Internet using an Internet service provider).

[0029] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0030] Please refer to Figure 1 The following is a flowchart of a method for processing scenic area edge node data in a specific embodiment. The method includes: Step S101: Receive a scenic area data acquisition request for multiple scenic area edge nodes, and retrieve the scenic area status data acquired by each scenic area edge node.

[0031] In some embodiments, the system receives a scenic area data acquisition request from a management terminal or other data request sources. The scenic area data acquisition request includes parameters such as a specific time, location, data type, etc. The system retrieves the scenic area status data from each scenic area edge node according to the request parameters. These data may involve multi-dimensional information such as the number of tourists, behaviors, environmental conditions, etc.

[0032] Step S102: Configure corresponding scenic area monitoring data for each scenic area edge node according to the scenic area status data and the preset scenic area data preset rules.

[0033] In some embodiments, according to the obtained scenic area status data, combined with the preset scenic area data preset rules, customized scenic area monitoring data is configured for each scenic area edge node. These rules may be based on historical data patterns, scenic area operation strategies, or special event requirements.

[0034] It should be noted that the system has built-in preset rules for scenic area data. When the scenic area status data is input, the rule engine evaluates the data according to the preset conditions. According to the evaluation results, the system adjusts the monitoring parameters of the edge nodes, such as increasing the monitoring frequency of specific areas or expanding the types of data collection, to ensure the pertinence and effectiveness of data collection.

[0035] In some specific embodiments, step S102 specifically includes: parsing the scenic area status data to obtain the interface address of the corresponding scenic area edge node and the scenic area status data format information; encapsulating the interface address and the scenic area status data format information based on the preset rules of the scenic area data; setting the corresponding retrieval period according to the data update frequency of the scenic area edge node; and determining the execution priority level based on the retrieval period.

[0036] In this embodiment, the data parsing module preprocesses the obtained scenic area status data to identify the meta information at the data header. The node configuration database is retrieved according to the meta information to obtain the physical interface address and data format information corresponding to the edge node.

[0037] The preset rules in this embodiment include interface call specifications and data format conversion rules in different scenarios. The system encapsulates the interface address and format information into a standardized instruction parameter package according to the rules. Through the rule-driven parameter encapsulation mechanism, the interfaces and data formats of heterogeneous devices are unified into a standard format recognizable by the system.

[0038] This embodiment can analyze the historical data update rules of each edge node and set a data retrieval period for each node. The retrieval period includes a fixed cycle or a dynamic trigger condition.

[0039] This embodiment presets the mapping relationship between the retrieval period and the priority level. For example, real-time retrieval corresponds to the highest level, and once a day corresponds to the lowest level. According to the retrieval period of each node, the corresponding execution priority level is automatically matched. For example, the data of the security camera updated in real time is set to the emergency level, and the energy consumption data summarized daily is set to the low level. At the same time, manual intervention adjustment is supported. For example, the retrieval period of a node in a certain area is temporarily changed from every hour to every minute, and the priority level is synchronously increased. Through the preset period and priority mapping rules, the time sensitivity is converted into the priority order of data processing.

[0040] Step S103: Generate scenic area data retrieval instructions for the corresponding scenic area edge nodes based on the scenic area monitoring data, and form a plurality of scenic area data retrieval instructions.

[0041] In some embodiments, based on the configured scenic area monitoring data, the system generates specific scenic area data retrieval instructions for each scenic area edge node. These instructions define in detail the specific requirements for data collection, such as the collection time window, data format, transmission priority, etc., and integrate all the instructions to form multiple sets of scenic area data retrieval instructions to be executed.

[0042] It can be seen that the system creates corresponding data retrieval instructions through the instruction generation module according to the configuration result of the scenic area monitoring data. This module takes into account factors such as the feasibility of data collection, resource occupancy, and data timeliness, generates the most suitable instructions for each edge node, and classifies and organizes these instructions for efficient execution later.

[0043] Step S104: Configure the scenic area data retrieval instructions with the same execution priority level into the same retrieval instruction set to generate multiple retrieval instructions.

[0044] In some embodiments, according to the execution priority level of the instructions, the scenic area data retrieval instructions with the same priority are configured into the same retrieval instruction set. This priority level may be determined based on factors such as the urgency of the data, business importance, or resource allocation strategy. Optimize the execution order of the data collection tasks to ensure that critical data can be collected and processed first, improving the system's response speed and data processing efficiency.

[0045] Step S105: Execute the scenic area data retrieval operations in each retrieval instruction set in sequence according to the execution priority level to obtain the scenic area target status data.

[0046] In some embodiments, in the order of the execution priority level, the scenic area data retrieval instructions are sequentially extracted from each retrieval instruction set and executed. During the execution process, the system communicates with the scenic area edge node and obtains the scenic area target status data according to the instruction requirements. These data are preliminarily processed and encapsulated and are ready to be transmitted to the cloud server. Ensure the orderly execution of the data collection tasks and improve the reliability and stability of data collection.

[0047] Step S106: Transmit the scenic area target status data to the cloud server through an encrypted communication protocol, and adjust the preset rules of the scenic area data or the execution priority level based on the performance monitoring results.

[0048] In some embodiments, the collected scenic area target status data is transmitted to the cloud server through an encrypted communication protocol. At the same time, the system dynamically adjusts the preset rules of the scenic area data or the execution priority level based on the data transmission speed, processing delay, resource occupancy rate, etc. collected by the performance monitoring module to optimize the system performance.

[0049] It should be noted that the data transmission module is responsible for sending the processed data to the cloud server through an encrypted channel to ensure the security of the data during transmission. The performance monitoring module collects the performance indicators of each link of the system in real time and provides this data to the rule adjustment module through a feedback mechanism. The rule adjustment module applies the preset adjustment strategy according to the changes in the performance indicators to optimize the preset rules or task priorities of the data to meet the changing data processing requirements of the scenic area.

[0050] In some specific embodiments, after obtaining the scenic area target status data, it further includes: verifying the data integrity of the scenic area target status data to obtain a scenic area data integrity score; verifying the compliance of the data format of the scenic area target status data to obtain a scenic area data format compliance score.

[0051] In some embodiments, data integrity verification rules are predefined, such as checking whether the traffic flow data contains timestamps and location information, and whether the temperature sensor returns valid values. For each piece of scenic area target status data, compare it with the preset rules one by one, count the proportion of missing or invalid data, and convert it into a scenic area data integrity score from 0 to 100. For example, if the missing rate ≤ 5%, the score is 90 points; if the missing rate > 20%, the score is 60 points. Through the preset field integrity verification rules, check each item of the data content and quantify the degree of missing to generate a score.

[0052] In this embodiment, the abnormal identification of the scenic area target status data is performed based on the preset business rules to obtain an abnormal identification score.

[0053] It should be noted that the system stores the format standards of various types of data. For example, the video stream needs to be in MP4 format, and the environmental data needs to be in JSON format and contain specified fields. During verification, automatically parse the data format and check whether the time format is YYYY-MM-DD and whether the numeric fields contain non-numeric characters. Generate a score according to the number or proportion of format errors. If there are no format errors, the score is 100 points, and 5 points are deducted for each error.

[0054] Furthermore, according to the scenic area data integrity score, the scenic area data format compliance score, and the abnormal identification score, calculate the scenic area data status score.

[0055] This embodiment uses a weighted average algorithm. For example, the integrity score accounts for 40%, the format compliance score accounts for 30%, and the abnormal identification score accounts for 30%. Calculate the three scores comprehensively to obtain a scenic area data status score from 0 to 100. For example, if the integrity score is 85 points, the format compliance score is 90 points, and the abnormal identification score is 80 points, the comprehensive score is 85×0.4 + 90×0.3 + 80×0.3 = 85 points. Through the weighted calculation of multi-dimensional scores, a comprehensive evaluation index of data quality is formed.

[0056] Furthermore, if the scenic area data status score is lower than the preset threshold, mark the corresponding scenic area target status data as data to be processed; for the data to be processed, generate and execute a data adjustment operation.

[0057] The system presets a data quality threshold. When the comprehensive score is lower than the threshold, automatically add a to-be-processed label to the data and record the problem type. The marked data enters the to-be-processed queue, triggering the subsequent correction process. By comparing with the preset threshold, the data with unqualified quality is automatically screened out, realizing the rapid sorting and marking of data quality problems.

[0058] This embodiment generates corresponding adjustment operations according to the data problem type: If it is data loss, automatically trigger the supplementary collection process, or fill in the temperature blank value with the historical average value; If it is a format error, call a data conversion tool to correct the format; If it is an abnormal event, generate a warning message and push it to the scenic area management terminal, and at the same time trigger an emergency plan, such as dispatching staff to the scene.

[0059] The following uses a specific example to illustrate: Taking a certain scenic area as an example, a large number of scenic area target status data are generated daily by deploying a passenger flow sensor, water quality monitoring equipment, and surveillance cameras at its edge nodes.

[0060] The system discovers that the field of tourist age distribution in the passenger flow data of a certain beach area is missing during a certain period. According to the rule, the missing rate is determined to be 15%, the integrity score is 80 points out of a full score of 100 points. If the missing rate ≤ 10%, the score is 90 points, and 1 point is deducted for every 1% exceeded.

[0061] A certain water quality monitoring data is transmitted in a non-standard JSON format. For example, the time field is 2023 / 7 / 1 instead of 2023-07-01, and there are 3 format errors. The format compliance score is 85 points, and 5 points are deducted for each error.

[0062] The surveillance camera captures that the number of tourists gathering in a certain area exceeds 50 people and lasts for 15 minutes, triggering the crowd density warning rule, and the abnormal recognition score is 70 points. 30 points are deducted for major abnormalities.

[0063] The integrity is 80 points (weight 40%), the format compliance is 85 points (weight 30%), and the abnormal recognition is 70 points (weight 30%). The comprehensive score is 80×0.4 + 85×0.3 + 70×0.3 = 78.5 points, which is lower than the preset threshold of 80 points, and the data is marked as to be processed.

[0064] Data adjustment operation: In case of data missing, the system automatically requests the tourist age distribution data from the edge node again. After successful supplementary collection, it is filled into the original record; in case of format error, the data conversion tool is called to correct the time field to the standard format; in case of abnormal crowd gathering, a warning message is generated and pushed to the scenic area security department, and the security personnel immediately go to the scene to divert tourists to avoid congestion risks.

[0065] Through the above process, the data quality of the scenic area is effectively guaranteed, and low-quality data is identified and corrected in a timely manner, ensuring that the cloud management system makes decisions such as passenger flow regulation and safety guarantee based on reliable data, and improving the operation efficiency of the scenic area and the tourist experience.

[0066] In an embodiment of the present invention, based on step S301, a possible embodiment will be given below to non-restrictively elaborate on its specific implementation scheme.

[0067] S301: Extract features from the scenic area monitoring data to identify key information in the data; S302: Based on the extracted key information, combined with the real-time operation status and historical data pattern of the scenic area, recommend the retrieval instruction template that best matches the current scenario; In some embodiments, a retrieval instruction template library is configured, which contains preset instructions in different scenarios, such as high-frequency collection templates for the number of people during peak hours and low-frequency templates for night environment monitoring. By matching the key information with the real-time operation status and historical data pattern, calculate the matching degree scores of each template, and recommend the template with the highest score.

[0068] For example, if the key information shows that the number of people at a certain scenic area entrance reaches 85% of the historical peak, and the real-time status is weekend morning, the system recommends the real-time monitoring template for the entrance area, which includes high-frequency video retrieval and people flow statistics instructions. Through multi-dimensional matching of data characteristics, real-time status, and historical rules, intelligent recommendation of templates is achieved using pattern recognition algorithms.

[0069] S303: Evaluate the resource occupancy of the retrieval instruction and predict the impact of executing this instruction on the computing resources and network bandwidth of the edge node.

[0070] In some embodiments, the system has a built-in resource evaluation model, which calculates indicators such as CPU occupancy rate, memory consumption, and network traffic required for executing the instruction according to the type, frequency, and data volume of the retrieval instruction.

[0071] S304: Dynamically adjust the parameters of the retrieval instruction according to the resource evaluation result and the urgency of the retrieval instruction.

[0072] In some embodiments, if the resource assessment shows that executing an instruction will cause node resource overload, the system makes differential adjustments according to the urgency of the instruction: for urgent instructions, the original parameters are maintained, but a resource preemption mechanism is triggered to pause non-critical tasks to release resources; for high-priority instructions, the data collection frequency can be reduced; for medium and low-priority instructions, execution is delayed or they enter a queue. After the adjustment, the resource occupancy is re-evaluated until the execution conditions are met.

[0073] In an embodiment of the present invention, based on step S104, a possible embodiment will be given below to non-restrictively elaborate on its specific implementation scheme.

[0074] Step S104 specifically includes: Step S1041 initializes multiple said retrieval instruction sets, and each said retrieval instruction set corresponds to one said execution priority level; Step S1042 traverses the scenic area data retrieval instructions, and assigns each said scenic area data retrieval instruction to the corresponding said retrieval instruction set according to the corresponding said execution priority level; Step S1043 sorts each said retrieval instruction set according to the said execution priority level corresponding to the said retrieval instruction set.

[0075] In this embodiment, the system will pre-set multiple retrieval instruction sets, and each instruction set corresponds to a specific execution priority level. These priority levels are usually determined according to factors such as the urgency of the data, the importance of the business, or the resource allocation strategy. The system checks each scenic area data retrieval instruction one by one and assigns it to the corresponding retrieval instruction set according to the execution priority level of the instruction. This process involves evaluating the attributes of each instruction to determine which priority queue it should belong to. All the retrieval instruction sets are sorted according to the high and low of the execution priority level. In this way, during the execution stage, the system can process the instructions in each instruction set in the sorted order in turn, ensuring that high-priority instructions are executed first. The sorted retrieval instruction sets enable the system to process data retrieval tasks in an orderly manner, prioritize the processing of key data, and thus maximize the satisfaction of business requirements under limited resource conditions, improving the reliability and timeliness of data processing.

[0076] This mechanism not only improves the efficiency and response speed of data processing, but also enhances the stability and reliability of the system, making the data processing of the scenic area edge node more efficient and orderly. At the same time, it also facilitates the system to manage resources and schedule tasks, optimizes the overall performance, and ensures that the scenic area data can be collected and processed in a timely and accurate manner.

[0077] In an embodiment of the present invention, based on step S105, a possible embodiment will be given below to non-restrictively elaborate on its specific implementation scheme.

[0078] Step S105 further includes: Step S1051: Collect the retrieval process information of each of the retrieval instruction sets; wherein, the retrieval process information includes retrieval status, response time, and retrieval exception rate; Step S1052: Based on a preset retrieval status analysis rule, perform real-time analysis on the retrieval process information; Step S1053: When the retrieval process information meets a preset warning condition, generate a warning message and push the warning message to a preset warning channel; Step S1054: Adjust the scenic area data retrieval instruction according to the retrieval process information.

[0079] In the process of executing the retrieval instruction set in this embodiment, the retrieval process information related to each instruction set is collected in real time. This information includes retrieval statuses such as success, failure, and in progress, and also involves the time from instruction issuance to data reception and the proportion of abnormal situations occurring during the retrieval process.

[0080] Based on a preset retrieval status analysis rule, perform real-time analysis on the collected retrieval process information. The retrieval status analysis rule includes setting a threshold for the response time, a tolerance for the retrieval exception rate, etc. Through analysis, potential problems or abnormal situations can be identified, such as too long response time, too high retrieval exception rate, or abnormal retrieval status.

[0081] If the retrieval process information violates a preset warning condition, such as the response time exceeding the threshold, the retrieval exception rate exceeding the tolerance, etc., a warning message is automatically generated.

[0082] In this embodiment, according to the analysis result of the retrieval process information, the scenic area data retrieval instruction is dynamically adjusted. The adjustment includes modifying the retrieval frequency, adjusting the retrieval priority, optimizing the data transmission method, etc. For example, if the response time of a certain retrieval instruction is too long, the system may reduce its retrieval frequency or increase its priority to improve the efficiency of data retrieval. In this way, problems occurring in the data retrieval process can be timely discovered and solved, data loss or delay can be avoided, and the overall performance and stability of the system can be improved. At the same time, the timely warning mechanism also enhances the manageability of the system, enabling managers to quickly respond to abnormal situations and ensuring the timely and accurate acquisition of scenic area data.

[0083] In an embodiment of the present invention, based on step S106, a possible embodiment will be given below to non-restrictively elaborate on its specific implementation scheme. Step S106 further includes: Step S601: Before transmitting the scenic area target status data to the cloud server, encrypt the data and add a data integrity verification identifier; Step S602: Transmit the encrypted scenic area target status data to the cloud server through a preset encrypted communication protocol, and record the data transmission delay, packet loss rate, and bandwidth occupancy at the same time; Step S603: Analyze the resource load status and data retrieval efficiency of the current edge node based on the metrics collected by the performance monitoring module in real time; Step S604: Dynamically adjust the parameter thresholds in the preset rules of the scenic area data according to the analysis results, or modify the mapping relationship of the retrieval priority levels to optimize the generation strategy of subsequent data retrieval instructions.

[0084] In some specific embodiments, when executing step S106, the scenic area target status data is preprocessed through step S601. The encryption process uses a high-strength symmetric encryption algorithm such as AES-256 to encrypt the data content byte by byte, and at the same time generates a data integrity check identifier based on the hash algorithm, which is attached to the data header for verifying whether the data has been tampered with when received by the cloud.

[0085] In step S602, a secure transmission channel is established through a preconfigured encrypted communication protocol, and the encrypted data and check identifier are transmitted to the cloud server. Step S603 uses the performance monitoring module to continuously collect resource load metrics such as the CPU utilization rate, memory usage, and storage read / write speed of the edge node, as well as metrics related to data retrieval efficiency. By analyzing these metrics, it is judged whether the edge node is in a high-load state. Step S604 dynamically adjusts the preset rules of the scenic area data. Real-time monitoring of metrics such as transmission delay and packet loss rate enables the system to quantitatively evaluate the stability of data transmission, timely detect network link problems, and provide a basis for network optimization.

[0086] The following is an embodiment of the scenic area edge node data processing system provided by the embodiments of the present disclosure. This system belongs to the same inventive concept as the scenic area edge node data processing method of the above embodiments. For the details not described in detail in the embodiment of the scenic area edge node data processing system, reference can be made to the embodiment of the scenic area edge node data processing method.

[0087] As Figure 2 shown, the system includes: A status acquisition module, configured to receive scenic area data acquisition requests for multiple scenic area edge nodes, and retrieve the scenic area status data obtained by each scenic area edge node.

[0088] A data configuration module, configured to configure corresponding scenic area monitoring data for each scenic area edge node according to the scenic area status data and the preset scenic area data preset rules.

[0089] The retrieval instruction generation module generates scenic area data retrieval instructions for corresponding scenic area edge nodes based on the scenic area monitoring data, forming multiple scenic area data retrieval instructions.

[0090] The instruction configuration module is used to configure the scenic area data retrieval instructions with the same execution priority level into the same retrieval instruction set, generating multiple retrieval instructions.

[0091] The data retrieval execution module is used to sequentially execute the scenic area data retrieval operations in each retrieval instruction set according to the execution priority level to obtain the scenic area target status data.

[0092] The data transmission and rule adjustment module is used to transmit the scenic area target status data to the cloud server through an encrypted communication protocol, and adjust the preset rules or execution priority level of the scenic area data based on the performance monitoring results.

[0093] As Figure 3 shown, the present application further provides an electronic device, including a display module 103, a memory 102, a processor 101, and a computer program stored on the memory and executable on the processor 101. When the processor 101 executes the program, the steps of the scenic area edge node data processing method are implemented.

[0094] In the embodiments of the present invention, the electronic device includes, but is not limited to, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, a personal digital processor, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the embodiments of the present application described herein and / or required herein.

[0095] In the embodiments of the present application, the processor 101 can be implemented by using at least one of an application specific integrated circuit, a programmable logic device, a field programmable gate array, a processor, a controller, a microcontroller, a microprocessor, and an electronic unit designed to execute the functions described herein. In some cases, such an implementation can be implemented in a controller. For a software implementation, an implementation of a process or function can be implemented with a separate software module that allows the execution of at least one function or operation. The software code can be implemented by a software application (or program) written in any suitable programming language. The software code can be stored in the memory and executed by the controller.

[0096] The display module 103 is used to display the information input by the user or the information provided to the user. The display module 103 may include a display panel, and the display panel can be configured in the form of a liquid crystal display, an organic light emitting diode, etc.

[0097] The memory 102 can be used to store software programs and various data. The memory 102 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0098] This application also provides a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the data processing method for the scenic area edge node are implemented.

[0099] The storage medium may adopt any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may, for example, but not be limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the readable storage medium (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0100] In the storage medium, the readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which the readable program code is carried. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable signal medium may also be any readable medium other than the readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0101] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can 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 is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for processing data of scenic area edge nodes, characterized in that The method includes: S101: Receive a scenic area data acquisition request for multiple scenic area edge nodes, and retrieve the scenic area status data obtained by each scenic area edge node; S102: Configure corresponding scenic area monitoring data for each scenic area edge node according to the scenic area status data and the preset scenic area data preset rules; S103: Generate a scenic area data retrieval instruction for the corresponding scenic area edge node based on the scenic area monitoring data, forming multiple scenic area data retrieval instructions; S104: Configure the scenic area data retrieval instructions with the same execution priority level into the same retrieval instruction set, generating multiple retrieval instructions; S105: Execute the scenic area data retrieval operations in each retrieval instruction set in sequence according to the execution priority level to obtain the scenic area target status data; S106: Transmit the scenic area target status data to the cloud server through an encrypted communication protocol, and adjust the scenic area data preset rules or the execution priority level based on the performance monitoring results.

2. The method for processing scenic area edge node data according to claim 1, wherein, Step S102 specifically includes: Parse the scenic area status data to obtain the interface address of the corresponding scenic area edge node and the scenic area status data format information; Package the interface address and the scenic area status data format information based on the scenic area data preset rules; Set the corresponding retrieval time period according to the data update frequency of the scenic area edge node; Determine the execution priority level based on the retrieval time period.

3. The method for processing scenic area edge node data according to claim 1, wherein Step S103 specifically includes: Extract features from the scenic area monitoring data to identify the key information in the data; Based on the extracted key information, combined with the real-time operation status and historical data pattern of the scenic area, recommend the retrieval instruction template that best matches the current scenario; Evaluate the resource occupancy of the retrieval instruction, and predict the impact of executing this instruction on the computing resources and network bandwidth of the edge node; Dynamically adjust the parameters of the retrieval instruction according to the resource evaluation results and the urgency of the retrieval instruction.

4. The method for processing scenic area edge node data according to claim 1, wherein Step S104 specifically includes: Initialize multiple of the retrieval instruction sets, with each retrieval instruction set corresponding to one of the execution priority levels; Traverse the scenic area data retrieval instructions, and allocate each scenic area data retrieval instruction to the corresponding retrieval instruction set according to the corresponding execution priority level; Sort each of the retrieval instruction sets according to the execution priority level corresponding to the retrieval instruction set.

5. The scenic area edge node data processing method according to claim 1, wherein, Step S105 further includes: Collect the retrieval process information of each of the retrieval instruction sets; wherein, the retrieval process information includes the retrieval status, response time, and retrieval exception rate; Perform real-time analysis on the retrieval process information based on the preset retrieval status analysis rules; When the retrieval process information meets the preset alarm conditions, generate an alarm message and push the alarm message to the preset alarm channel; Adjust the scenic area data retrieval instruction according to the retrieval process information.

6. The method for processing scenic area edge node data according to claim 1, characterized in that, Step S106 further includes: Before transmitting the scenic area target status data to the cloud server, encrypt the data and add a data integrity verification identifier; Transmit the encrypted scenic area target status data to the cloud server through the preset encrypted communication protocol, and record the data transmission delay, packet loss rate, and bandwidth occupancy at the same time. Analyze the resource load status and data retrieval efficiency of the current edge node based on the metrics collected in real time by the performance monitoring module; Dynamically adjust the parameter thresholds in the preset rules of scenic area data or modify the mapping relationship of retrieval priority levels according to the analysis results to optimize the generation strategy of subsequent data retrieval instructions.

7. The method for processing scenic area edge node data according to claim 1, wherein After obtaining the target status data of the scenic area, it further includes: Verify the data integrity of the target status data of the scenic area to obtain a scenic area data integrity score; verify the compliance of the data format of the target status data of the scenic area to obtain a scenic area data format compliance score; Perform anomaly recognition on the target status data of the scenic area based on preset business rules to obtain an anomaly recognition score; Calculate the scenic area data status score according to the scenic area data integrity score, the scenic area data format compliance score, and the anomaly recognition score; If the scenic area data status score is lower than the preset threshold, mark the corresponding target status data of the scenic area as data to be processed; for the data to be processed, generate and execute a data adjustment operation.

8. A scenic area edge node data processing system, characterized in that The system is used to implement the scenic area edge node data processing method according to any one of claims 1 to 7; The system includes: A status acquisition module, configured to receive scenic area data acquisition requests for multiple scenic area edge nodes and retrieve the scenic area status data obtained by each scenic area edge node; A data configuration module, configured to configure corresponding scenic area monitoring data for each scenic area edge node according to the scenic area status data and the preset scenic area data preset rules; A retrieval instruction generation module, configured to generate scenic area data retrieval instructions for the corresponding scenic area edge nodes based on the scenic area monitoring data, forming multiple scenic area data retrieval instructions; An instruction configuration module, configured to configure the scenic area data retrieval instructions with the same execution priority level into the same retrieval instruction set, generating multiple retrieval instructions; A data retrieval execution module, configured to sequentially execute the scenic area data retrieval operations in each retrieval instruction set according to the execution priority level to obtain the target status data of the scenic area; A data transmission and rule adjustment module, configured to transmit the target status data of the scenic area to the cloud server through an encrypted communication protocol, and adjust the preset rules of the scenic area data or the execution priority level based on the performance monitoring results.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the scenic area edge node data processing method according to any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the scenic area edge node data processing method according to any one of claims 1 to 7.

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