Intelligent land survey data real-time processing system and method based on edge calculation

Abstract

The invention discloses an intelligent territorial survey data real-time processing system and method based on edge calculation, and relates to the technical field of territorial survey data processing. Comprising an intelligent territorial survey data acquisition module, an intelligent territorial survey data evaluation processing module, a comprehensive analysis module and an optimization adjustment module. According to the invention, territorial survey comprehensive data is acquired in real time through edge nodes and sensor networks deployed in remote mountainous areas, and then two-dimensional evaluation is performed on the territorial survey comprehensive data; the method comprises the following steps of: acquiring a two-dimensional evaluation result, judging a data quality risk condition and real-time performance of data processing according to the two-dimensional evaluation result, and finally triggering an intelligent land survey data real-time processing flow according to a judgment result, thereby effectively solving the problem that the traditional land survey data processing is difficult to process in complex environments such as remote mountainous areas and the like. The problems that data acquisition is not timely, data quality is difficult to guarantee, and data processing real-time performance is poor are solved, and the efficiency, accuracy and reliability of land survey data processing are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of land survey data processing, and specifically to a real-time processing system and method for intelligent land survey data based on edge computing. Background Art

[0002] As the land survey work advances to complex areas such as remote mountainous areas, the requirement for real-time processing of intelligent land survey data becomes increasingly urgent. However, the special geographical environment and communication conditions in remote mountainous areas make the existing edge-computing-based processing systems difficult to meet the actual needs.

[0003] For example, the invention patent with the publication number CN119721772A discloses a dynamic perception scheduling method for agricultural data based on the Internet of Things. This method is implemented based on Internet of Things sensors, an edge computing platform, and an edge server, and includes the following steps: deploying Internet of Things sensors including weather stations, soil humidity monitors, and drone inspection systems, and deploying a real-time data processing module on the edge server through the edge computing platform.

[0004] For example, the invention patent with the publication number CN117333343A discloses a digital resettlement intelligent management information release announcement platform, including: a user management module: used to provide user management functions; a permission management module: used to manage user permissions, control the types of information that users can publish, the locations where information is published, and the user groups to which information is published; a user publication management module: used to provide information publication functions within the user's permission range; an edge computing module: used to store and manage geological and terrain data and land resettlement management information through edge computing devices deployed within each resettlement management department; an information management module: used to review and classify the land resettlement management information that needs to be published and uploaded to the cloud server.

[0005] However, in the process of implementing the technical solutions of the present invention in the embodiments of the present application, it is found that the above technologies have at least the following technical problems: in the scenario of remote mountainous areas, there is a problem of insufficient real-time evaluation of intelligent land survey data. Due to the dependence on satellite communication in remote areas, insufficient bandwidth leads to data backhaul delay, real-time feedback fails, 5G network coverage is incomplete, self-organizing network communication is vulnerable to terrain interference, and the data packet loss rate is high. There is a phenomenon of insufficient quality and accuracy of intelligent land survey data. Since multi-modal data fusion requires a large amount of computing resources, the load differences between different edge nodes are relatively large, and some nodes may be overloaded, overheated, or crashed. Running complex deep learning models on edge devices with limited computing power leads to an increase in processing time, and there is a problem of insufficient real-time evaluation of intelligent land survey data. Summary of the Invention

[0006] In view of the deficiencies of the prior art, the present invention provides a real-time processing system and method for intelligent land survey data based on edge computing, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: In the first aspect of the present invention, a real-time processing system for intelligent land survey data based on edge computing is provided, including an intelligent land survey data acquisition module, an intelligent land survey data evaluation and processing module, a comprehensive analysis module, and an optimization and adjustment module: Among them, the intelligent land survey data acquisition module is used to collect comprehensive land survey data in real time through edge nodes and sensor networks deployed in remote mountainous areas. The comprehensive land survey data includes quality risk assessment parameters, real-time performance assessment parameters, comprehensive assessment parameters, and terrain parameters; the intelligent land survey data evaluation and processing module is used to perform a two-dimensional evaluation on the comprehensive land survey data and obtain a two-dimensional evaluation result, which represents the result of quantitatively evaluating the land survey data from the data quality risk dimension and the data real-time performance dimension respectively; the comprehensive analysis module is used to determine the data quality risk status and the real-time performance of data processing for the two-dimensional evaluation result; the optimization and adjustment module is used to trigger the real-time processing process of intelligent land survey data according to the determination result, and the real-time processing process of intelligent land survey data includes a quality optimization and adjustment process and a real-time optimization process.

[0008] As a further method, the quality risk assessment parameters include data transmission rate, edge node overload frequency, and satellite communication bandwidth fluctuation frequency; the real-time performance assessment parameters include multi-modal data fusion time, edge node load difference coefficient, and edge computing model update frequency; the comprehensive assessment parameters include data return delay and self-organizing network communication data packet loss rate; the terrain parameters include slope, altitude, and vegetation coverage rate.

[0009] As a further method, a two-dimensional assessment is performed on the comprehensive land survey data, and a two-dimensional assessment result is obtained. The specific analysis process is as follows: Extract the preset comprehensive land survey data reference values from the intelligent land survey database. The comprehensive land survey data reference values include quality risk assessment reference values, real-time performance assessment reference values, comprehensive assessment reference values, and terrain reference values. The quality risk assessment reference values include the critical data transmission rate, the critical edge node overload frequency, and the critical satellite communication bandwidth fluctuation frequency. The real-time performance assessment reference values include the critical multimodal data fusion time, the critical edge node load difference coefficient, the reference edge computing model update frequency, and the allowable deviation edge computing model update frequency. The comprehensive assessment reference values include the critical data return delay and the critical ad hoc network communication data packet loss rate. The terrain reference values include the maximum slope, the maximum altitude, and the maximum vegetation coverage rate. The terrain interference influence coefficient is obtained by performing a terrain interference ratio approaching influence degree operation on the terrain parameters and the preset terrain reference values. The terrain interference ratio approaching influence degree operation represents the degree of approaching the terrain interference influence coefficient and the preset critical terrain interference influence coefficient by correcting with the preset terrain interference influence coefficient weight factor. The data quality risk is quantitatively determined based on the quality risk assessment parameters, the comprehensive assessment parameters, and the terrain interference influence coefficient, and a data quality risk assessment index is obtained. The data quality risk assessment index is used to quantitatively evaluate the degree of data quality risk. The data real-time performance is quantitatively determined based on the real-time performance assessment parameters and the comprehensive assessment parameters, and a data real-time performance assessment index is obtained. The data real-time performance assessment index is used to quantitatively evaluate the real-time performance of the intelligent land survey data during the processing and feedback process.

[0010] As a further method, the terrain interference influence coefficient is obtained by performing a terrain interference ratio approaching influence degree operation on the terrain parameters and the preset terrain reference values. The specific analysis process is as follows: The slope influence degree is obtained by performing a slope ratio approaching influence degree operation on the slope data and the preset maximum slope. The slope ratio approaching influence degree operation represents the degree of approaching the slope data and the preset maximum slope by correcting with the preset slope weight factor. The altitude influence degree is obtained by performing an altitude ratio approaching influence degree operation on the altitude data and the preset maximum altitude. The altitude ratio approaching influence degree operation represents the degree of approaching the altitude data and the preset maximum altitude by correcting with the preset altitude weight factor. The vegetation coverage rate influence degree is obtained by performing a vegetation coverage rate ratio approaching influence degree operation on the vegetation coverage rate data and the preset maximum vegetation coverage rate. The vegetation coverage rate ratio approaching influence degree operation represents the degree of approaching the vegetation coverage rate data and the preset maximum vegetation coverage rate by correcting with the preset vegetation coverage rate weight factor. The terrain interference influence coefficient is obtained by coupling the slope influence degree, the altitude influence degree, and the vegetation coverage rate influence degree. The terrain interference influence coefficient represents the quantitative data of the terrain interference degree of the slope, altitude, and vegetation coverage rate on the land survey data collection and transmission process.

[0011] As a further method, a quantitative determination of data quality risk is performed according to the quality risk assessment parameter, the comprehensive assessment parameter, and the terrain interference influence coefficient, and a data quality risk assessment index is obtained. The specific analysis process is as follows: Extract the preset critical terrain interference influence coefficient from the intelligent national land survey database; after performing the proportion approach operation on the quality risk assessment parameter and the preset quality risk assessment reference value respectively, the result of the proportion approach operation is weighted by the quality risk assessment factor and then coupled to obtain the quality risk assessment dimension value. The quality risk assessment factor includes the data transmission rate weight factor, the edge node overload frequency weight factor, and the satellite communication bandwidth fluctuation frequency weight factor; after performing the proportion approach operation on the comprehensive assessment parameter and the preset comprehensive assessment reference value respectively, the result of the proportion approach operation is weighted by the comprehensive assessment factor and then coupled to obtain the quality risk comprehensive assessment dimension value. The comprehensive assessment factor includes the quality risk feedback delay weight factor and the quality risk packet loss rate weight factor; the terrain interference influence coefficient influence degree is obtained by performing the terrain interference proportion approach influence degree operation on the terrain interference influence coefficient and the preset critical terrain interference influence coefficient. The terrain interference proportion approach influence degree operation represents the degree of approach between the terrain interference influence coefficient and the preset critical terrain interference influence coefficient corrected by the preset terrain interference influence coefficient weight factor; the data quality risk assessment index is obtained by coupling the obtained quality risk assessment dimension value, the comprehensive assessment dimension value, and the terrain interference influence coefficient influence degree.

[0012] As a further method, a quantitative determination of data real-time efficiency is performed according to the real-time efficiency assessment parameter and the comprehensive assessment parameter, and a data real-time efficiency assessment index is obtained. The specific analysis process is as follows: After performing the proportion approach operation on the real-time efficiency assessment parameter and the preset real-time efficiency assessment dimension reference value respectively, the result of the proportion approach operation is weighted by the real-time efficiency assessment factor and then coupled to obtain the real-time efficiency assessment dimension value. The real-time efficiency assessment factor includes the multi-modal data fusion time weight factor, the edge node load difference coefficient weight factor, and the edge computing model update frequency weight factor; after performing the proportion approach operation on the comprehensive assessment parameter and the preset comprehensive assessment reference value respectively, the result of the proportion approach operation is weighted by the comprehensive assessment factor and then coupled to obtain the real-time efficiency comprehensive assessment dimension value. The real-time efficiency comprehensive assessment factor includes the real-time efficiency feedback delay weight factor and the real-time efficiency packet loss rate weight factor; the data real-time efficiency assessment index is obtained by coupling the obtained real-time efficiency assessment dimension value and the real-time efficiency comprehensive assessment dimension value.

[0013] As a further method, determine the data quality risk status and the real-time performance of data processing for the two-dimensional evaluation results. The specific determination process is as follows: Compare the data quality risk assessment indicators with the preset intelligent land survey data quality risk assessment thresholds in the intelligent land survey database. If the data quality risk assessment indicators are greater than or equal to the preset intelligent land survey data quality risk assessment thresholds, generate a risk alarm report and trigger the quality optimization adjustment process. If the data quality risk assessment indicators are less than the preset intelligent land survey data quality risk assessment thresholds, mark them as the quality compliance status, maintain the current resource allocation, and continuously monitor. Compare the data real-time performance evaluation indicators with the preset intelligent land survey data real-time performance evaluation thresholds in the intelligent land survey database. If the data real-time performance evaluation indicators are greater than or equal to the preset intelligent land survey data real-time performance evaluation thresholds, mark them as the performance compliance status, maintain the current parameter configuration, and continuously monitor. If the data real-time performance evaluation indicators are less than the preset data real-time performance evaluation indicator thresholds, generate a real-time optimization request and trigger the real-time optimization process.

[0014] As a further method, trigger the quality optimization adjustment process. The specific analysis process is as follows: Obtain the quality risk difference by subtracting the preset intelligent land survey data quality risk assessment threshold from the data quality risk assessment indicator. Compare the quality risk difference with the preset quality risk difference range in the intelligent land survey database to obtain the risk difference range. Match the risk difference range with the pre-established quality risk difference - adjustment amount correspondence table in the intelligent land survey database to obtain the corresponding adjustment amount. Increase the number of edge nodes, optimize the investment in ad hoc communication devices, and adjust the data transmission protocol parameters according to the adjustment amount.

[0015] As a further method, trigger the real-time optimization process. The specific analysis process is as follows: Obtain the real-time performance difference by subtracting the preset data real-time performance evaluation indicator threshold from the data real-time performance evaluation indicator. Compare the real-time performance difference with the preset real-time performance difference range in the intelligent land survey database to obtain the performance difference range. Match the performance difference range with the pre-established real-time performance difference - adjustment amount correspondence table in the intelligent land survey database to obtain the corresponding adjustment amount. Improve the optimization level of the multi-modal data fusion algorithm, reduce the update frequency of the edge computing model, and upgrade the network devices according to the adjustment amount.

[0016] The second aspect of the present invention provides a real-time processing method for intelligent land survey data based on edge computing, which is characterized by: including: real-time collection of land survey comprehensive data through edge nodes and sensor networks deployed in remote mountainous areas, the land survey comprehensive data including quality risk assessment parameters, real-time performance assessment parameters, comprehensive assessment parameters and terrain parameters; performing two-dimensional assessment on the comprehensive land survey data, and obtaining two-dimensional assessment results, the two-dimensional assessment results represent the results of quantitative assessment of the land survey data from the data quality risk dimension and the data real-time performance dimension respectively; judging the data quality risk status and the real-time performance of data processing on the two-dimensional assessment results; triggering the real-time processing process of intelligent land survey data according to the judgment result, the real-time processing process of intelligent land survey data including the quality optimization adjustment process and the real-time optimization process.

[0017] Compared with the prior art, the embodiments of the present invention have at least the following beneficial effects:

[0018] (1) The present invention provides an intelligent real-time land survey data processing system and method based on edge computing, and uses edge nodes and sensor networks deployed in remote mountainous areas to collect comprehensive land survey data in real time, including quality risk assessment parameters, real-time performance assessment parameters, comprehensive assessment parameters and terrain parameters. The use of edge computing reduces data transmission delays, improves the real-time nature of land survey data, and provides timely and effective data support for decision-making.

[0019] (2) The present invention performs a two-dimensional assessment on the comprehensive land survey data, comprehensively considers data quality risks and real-time performance, accurately locates problems in various aspects of the data, and thus triggers the optimization process in a targeted manner. Compared with the traditional single-dimensional assessment, the present invention improves the quality and efficiency of data processing and ensures the reliability and availability of land survey data.

[0020] (3) The present invention optimizes the system by establishing a corresponding relationship table between quality risk difference and adjustment amount and real-time performance difference and adjustment amount, and matching the corresponding adjustment amount according to the evaluation results, such as dynamically adjusting the number of edge nodes, optimizing equipment investment, upgrading network equipment, etc., thereby achieving refined control of the data processing process and enhancing the system's adaptability, enabling it to better cope with the complex and changeable land survey environment.

[0021] (4) The present invention effectively balances resource allocation in the data processing process by reasonably adjusting the data transmission protocol parameters according to the risk difference interval in the quality optimization and adjustment module, and improving the optimization level of the multimodal data fusion algorithm and reducing the update frequency of the edge computing model in the real-time optimization process. While improving data quality and real-time performance, it avoids excessive consumption of resources and improves the economy and sustainability of system operation.

[0022] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a schematic diagram of the connection of system modules of the present invention.

[0024] Figure 2 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with 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 the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0026] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover a non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0027] Referring to Figure 1 As shown, in the first aspect of the present invention, a real-time processing system for intelligent national land survey data based on edge computing is provided, including an intelligent national land survey data acquisition module, an intelligent national land survey data evaluation and processing module, a comprehensive analysis module, and an optimization and adjustment module.

[0028] Among them, the intelligent national land survey data acquisition module is used to collect comprehensive national land survey data in real time through edge nodes and sensor networks deployed in remote mountainous areas. The comprehensive national land survey data includes quality risk assessment parameters, real-time performance assessment parameters, comprehensive assessment parameters, and terrain parameters.

[0029] Specifically, the quality risk assessment parameters include data transmission rate, edge node overload frequency, and satellite communication bandwidth fluctuation frequency; the real-time performance assessment parameters include multi-modal data fusion time, edge node load difference coefficient, and edge computing model update frequency; the comprehensive assessment parameters include data backhaul delay and self-organizing network communication data packet loss rate; the terrain parameters include slope, altitude, and vegetation coverage rate.

[0030] Among them, the data transmission rate refers to the amount of data transmitted in the network per unit time, reflecting the speed of data transmission. The edge node overload frequency refers to the frequency of overload situations occurring in edge nodes within a certain period of time. The satellite communication bandwidth fluctuation frequency refers to the fluctuation of the satellite communication bandwidth over time. The multi-modal data fusion time refers to the duration taken to fuse multiple different types of data (such as images, terrain, etc.). The edge node load difference coefficient is used to measure the difference in load between different edge nodes. The edge computing model update frequency refers to the number of times the edge computing model is updated within a certain time period. The data backhaul delay refers to the time taken for data to be transmitted from a collection end such as a remote mountainous area back to the central server. The self-organizing network communication data packet loss rate is the proportion of data packets lost during the self-organizing network communication process. The slope represents the degree of terrain inclination. The altitude refers to the vertical distance of a certain location on the ground above or below sea level. The vegetation coverage rate refers to the proportion of the vegetation-covered area in a certain area to the total area of that region.

[0031] It should be understood that in this embodiment, the data transmission rate is obtained by using Wireshark (a wireshark network analysis tool), Zabbix (a Zabbix monitoring system), or satellite communication device logs; the data backhaul delay is obtained through the network monitoring tools Ping and Traceroute (tracking route); the self-organizing network communication data packet loss rate is obtained through the self-organizing network module logs or Wireshark; the edge node overload frequency is obtained through Prometheus, Grafana, or hardware sensors; the satellite communication bandwidth fluctuation frequency is obtained through satellite communication monitoring devices; the slope and altitude are obtained through drone remote sensing, GIS (Geographic Information System); the vegetation coverage rate is obtained through satellite remote sensing, NDVI index (Normalized Difference Vegetation Index) to determine the terrain interference impact coefficient; a time recording function is embedded in the data fusion algorithm to obtain the multi-modal data fusion time; the CPU utilization rate is obtained through a system performance monitoring tool to determine the edge node load difference coefficient; a counter and a time recording module are set in the edge computing model management system to obtain the edge computing model update frequency.

[0032] The intelligent national land survey data evaluation and processing module is used to perform a two-dimensional evaluation on the comprehensive national land survey data and obtain the two-dimensional evaluation result. The two-dimensional evaluation result represents the result of quantitatively evaluating the national land survey data from the dimensions of data quality risk and data real-time efficiency.

[0033] Specifically, a two-dimensional assessment is performed on the comprehensive land survey data, and a two-dimensional assessment result is obtained. The specific analysis process is as follows: Extract the preset comprehensive land survey data reference values from the intelligent land survey database. The comprehensive land survey data reference values include quality risk assessment reference values, real-time performance assessment reference values, comprehensive assessment reference values, and terrain reference values. The quality risk assessment reference values include the critical data transmission rate, the critical edge node overload frequency, and the critical satellite communication bandwidth fluctuation frequency. The real-time performance assessment reference values include the critical multi-modal data fusion time, the critical edge node load difference coefficient, the reference edge computing model update frequency, and the allowable deviation edge computing model update frequency. The comprehensive assessment reference values include the critical data return delay and the critical ad hoc network communication data packet loss rate. The terrain reference values include the maximum slope, the maximum altitude, and the maximum vegetation coverage rate. The terrain interference influence coefficient is obtained by performing a terrain interference ratio approaching influence degree operation on the terrain parameters and the preset terrain reference values. The terrain interference ratio approaching influence degree operation represents the degree of approaching the terrain interference influence coefficient and the preset critical terrain interference influence coefficient by correcting with the preset terrain interference influence coefficient weight factor. The data quality risk is quantitatively determined based on the quality risk assessment parameters, the comprehensive assessment parameters, and the terrain interference influence coefficient to obtain a data quality risk assessment index, which is used to quantitatively evaluate the degree of data quality risk. The data real-time performance is quantitatively determined based on the real-time performance assessment parameters and the comprehensive assessment parameters to obtain a data real-time performance assessment index, which is used to quantitatively evaluate the real-time performance of the intelligent land survey data during the processing and feedback process.

[0034] Specifically, the terrain interference influence coefficient is obtained by performing a terrain interference ratio approaching influence degree operation on the terrain parameters and the preset terrain reference values. The specific analysis process is as follows: The slope influence degree is obtained by performing a slope ratio approaching influence degree operation on the slope data and the preset maximum slope. The slope ratio approaching influence degree operation represents the degree of approaching the slope data and the preset maximum slope by correcting with the preset slope weight factor. The altitude influence degree is obtained by performing an altitude ratio approaching influence degree operation on the altitude data and the preset maximum altitude. The altitude ratio approaching influence degree operation represents the degree of approaching the altitude data and the preset maximum altitude by correcting with the preset altitude weight factor. The vegetation coverage rate influence degree is obtained by performing a vegetation coverage rate ratio approaching influence degree operation on the vegetation coverage rate data and the preset maximum vegetation coverage rate. The vegetation coverage rate ratio approaching influence degree operation represents the degree of approaching the vegetation coverage rate data and the preset maximum vegetation coverage rate by correcting with the preset vegetation coverage rate weight factor. The terrain interference influence coefficient is obtained by coupling the slope influence degree, the altitude influence degree, and the vegetation coverage rate influence degree. The terrain interference influence coefficient represents the quantitative data of the terrain interference degree of the slope, altitude, and vegetation coverage rate on the land survey data collection and transmission process.

[0035] In a specific embodiment, the method for obtaining the terrain interference influence coefficient is as follows:

[0036]

[0037] τ1 + τ2 + τ3 = 1;

[0038] In the formula, DX represents the terrain interference influence coefficient, and DX PD represents the slope, and DX HB represents the altitude, and DX ZF represents the vegetation coverage rate, represents the maximum slope, represents the maximum altitude, represents the maximum vegetation coverage rate, τ1 represents the preset slope weight factor, τ2 represents the preset altitude weight factor, and τ3 represents the preset vegetation coverage rate weight factor.

[0039] When using τ1, τ2, and τ3, the weight factors corresponding to the slope, altitude, and vegetation coverage rate in the terrain interference influence coefficient can be directly obtained from the intelligent national land survey database, and they respectively represent the numerical values of the influence degrees of the slope, altitude, and vegetation coverage rate on the terrain interference influence coefficient. The slope weight factor, altitude weight factor, and vegetation coverage rate weight factor and the slope, altitude, and vegetation coverage rate can be a preset mapping relationship. For example, by inputting the slope, altitude, and vegetation coverage rate into the corresponding preset mapping sets respectively, the weight factors in the process of obtaining the terrain interference influence coefficient corresponding to the slope, altitude, and vegetation coverage rate can be obtained. The mapping relationship therein can be one-to-one or many-to-one. In this embodiment, the value ranges of the slope weight factor, altitude weight factor, and vegetation coverage rate weight factor are all limited between 0 and 1, and the sum of the slope weight factor, altitude weight factor, and vegetation coverage rate weight factor is 1.

[0040] In this embodiment, the terrain interference influence coefficient is used to quantitatively evaluate the interference degree of terrain factors on the national land survey data link. The higher the slope, or the higher the altitude, or the higher the vegetation coverage rate, the greater the terrain interference influence coefficient, indicating that the interference degree of terrain factors on the national land survey data link is higher.

[0041] The algorithm of this embodiment combines slope, altitude, and vegetation coverage rate, and comprehensively analyzes to obtain the terrain interference influence coefficient. In this formula, slope, altitude, and vegetation coverage rate affect each other. As the altitude rises, the terrain tectonic movement becomes more intense, resulting in greater undulations in the terrain and steeper slopes. Steep slopes are not conducive to soil accumulation and water retention. Rainwater is easily lost quickly on steep slopes and is difficult to penetrate into the soil, resulting in lower soil moisture content, which is not conducive to the rooting and growth of vegetation, and the vegetation coverage rate decreases. Moreover, the soil on steep slopes is more likely to cause geological disasters such as landslides and mudslides under the action of gravity, further damaging the vegetation living environment. In areas with high vegetation coverage, the phenomenon of soil erosion is relatively light, and the change of slope is relatively slow. By comprehensively analyzing slope, altitude, and vegetation coverage rate, the terrain interference influence coefficient can be accurately obtained, which quantitatively reflects the specific degree of interference of terrain factors on the national land survey data link.

[0042] Specifically, according to the quality risk assessment parameter, comprehensive assessment parameter, and terrain interference influence coefficient, a quantitative determination of data quality risk is carried out to obtain the data quality risk assessment index. The specific analysis process is as follows: Extract the preset critical terrain interference influence coefficient from the intelligent national land survey database; After performing the ratio approximation operation on the quality risk assessment parameter and the preset quality risk assessment reference value respectively, the quality risk assessment dimension value is obtained by weighting and coupling the result of the ratio approximation operation through the quality risk assessment factor. The quality risk assessment factor includes the data transmission rate weight factor, the edge node overload frequency weight factor, and the satellite communication bandwidth fluctuation frequency weight factor; After performing the ratio approximation operation on the comprehensive assessment parameter and the preset comprehensive assessment reference value respectively, the quality risk comprehensive assessment dimension value is obtained by weighting and coupling the result of the ratio approximation operation through the comprehensive assessment factor. The comprehensive assessment factor includes the quality risk feedback delay weight factor and the quality risk packet loss rate weight factor; The terrain interference influence coefficient influence degree is obtained by performing the terrain interference ratio approximation influence degree operation on the terrain interference influence coefficient and the preset critical terrain interference influence coefficient. The terrain interference ratio approximation influence degree operation represents the approximation degree of the terrain interference influence coefficient and the preset critical terrain interference influence coefficient corrected by the preset terrain interference influence coefficient weight factor; The data quality risk assessment index is obtained by coupling the obtained quality risk assessment dimension value, comprehensive assessment dimension value, and terrain interference influence coefficient influence degree.

[0043] In a specific embodiment, the acquisition method of the data quality risk assessment index is as follows:

[0044] GT ZF =ZF WD +ZH ZF +DX WD ;

[0045]

[0046] α1 + α2 + α3 + α4 + α5 + α6 = 1;

[0047] Wherein, GT ZF represents the data quality risk assessment index, ZF WD represents the quality risk assessment dimension value, ZH ZF represents the comprehensive quality risk assessment dimension value, DX WD represents the influence degree of the terrain interference influence coefficient, ZG CS represents the data transmission rate, ZG HY represents the data return delay, ZZ DB represents the packet loss rate of the ad hoc network communication data, BJ GP represents the overload frequency of the edge nodes, WX DB represents the satellite communication bandwidth fluctuation frequency, DX represents the terrain interference influence coefficient, represents the preset critical data transmission rate, represents the preset critical data return delay, represents the preset critical packet loss rate of the ad hoc network communication data, represents the preset critical overload frequency of the edge nodes, represents the preset critical satellite communication bandwidth fluctuation frequency, represents the preset critical terrain interference influence coefficient, GZ represents the total number of overload events within the monitoring section, GZ T represents the monitoring overload event duration, μ B represents the bandwidth mean value, σ B represents the bandwidth standard deviation, B i represents the bandwidth at the i-th time point, α1 represents the preset data transmission rate weight factor, α2 represents the preset quality risk return delay weight factor, α3 represents the preset quality risk packet loss rate weight factor, α4 represents the preset edge node overload frequency weight factor, α5 represents the preset satellite communication bandwidth fluctuation frequency weight factor, α6 represents the preset terrain interference influence coefficient weight factor, i represents the number of the time point, i = 1, 2, 3,..., N, N represents the total number of time points.

[0048] When α1, α2, α3, α4, α5, and α6 are used, the weight factors in the data quality risk assessment index corresponding to the data transmission rate, data feedback delay, packet loss rate of ad hoc network communication, overload frequency of edge nodes, satellite communication bandwidth fluctuation frequency, and terrain interference influence coefficient can be directly obtained from the intelligent national land survey database, which respectively represent the numerical values of the influence degrees of the data transmission rate, data feedback delay, packet loss rate of ad hoc network communication, overload frequency of edge nodes, satellite communication bandwidth fluctuation frequency, and terrain interference influence coefficient on the data quality risk assessment index. The weight factor of the data transmission rate, the weight factor of the quality risk feedback delay, the weight factor of the quality risk packet loss rate, the weight factor of the overload frequency of edge nodes, the weight factor of the satellite communication bandwidth fluctuation frequency, and the weight factor of the terrain interference influence coefficient and the data transmission rate, data feedback delay, packet loss rate of ad hoc network communication, overload frequency of edge nodes, satellite communication bandwidth fluctuation frequency, and terrain interference influence coefficient can be a pre-set mapping relationship. For example, the data transmission rate, data feedback delay, packet loss rate of ad hoc network communication, overload frequency of edge nodes, satellite communication bandwidth fluctuation frequency, and terrain interference influence coefficient are respectively input into the corresponding pre-set mapping set to obtain the weight factors in the data quality risk assessment index corresponding to the data transmission rate, data feedback delay, packet loss rate of ad hoc network communication, overload frequency of edge nodes, satellite communication bandwidth fluctuation frequency, and terrain interference influence coefficient, and the mapping relationship therein can be one-to-one or many-to-one. In this embodiment, the value ranges of the weight factor of the data transmission rate, the weight factor of the quality risk feedback delay, the weight factor of the quality risk packet loss rate, the weight factor of the overload frequency of edge nodes, the weight factor of the satellite communication bandwidth fluctuation frequency, and the weight factor of the terrain interference influence coefficient are all limited between 0 and 1, and the sum of the weight factor of the data transmission rate, the weight factor of the quality risk feedback delay, the weight factor of the quality risk packet loss rate, the weight factor of the overload frequency of edge nodes, the weight factor of the satellite communication bandwidth fluctuation frequency, and the weight factor of the terrain interference influence coefficient is 1.

[0049] In this embodiment, the data quality risk assessment index is used to quantitatively evaluate the degree of data quality risk. The slower the data transmission rate, or the higher the data feedback delay, or the higher the packet loss rate of ad hoc network communication, or the higher the overload frequency of edge nodes, or the higher the satellite communication bandwidth fluctuation frequency, or the higher the terrain interference influence coefficient, the greater the data quality risk assessment index, indicating a higher degree of data quality risk.

[0050] The algorithm of this embodiment combines the data transmission rate, data return delay, data packet loss rate of ad-hoc network communication, edge node overload frequency, satellite communication bandwidth fluctuation frequency, and terrain interference influence coefficient, and comprehensively analyzes to obtain the data quality risk assessment index. In this formula, the data transmission rate, data return delay, data packet loss rate of ad-hoc network communication, edge node overload frequency, satellite communication bandwidth fluctuation frequency, and terrain interference influence coefficient affect each other. The slower the data transmission rate, the higher the data return delay. When the data transmission rate decreases, the amount of data transmitted per unit time decreases, and the time spent in the transmission process increases, resulting in an increase in the return delay. For example, in the case of limited network bandwidth, a large amount of data queues up for transmission, the transmission rate drops, and the return delay rises. When the data transmission rate is too high and exceeds the processing capacity of ad-hoc network communication devices, data packet loss is likely to occur. For example, when the performance of ad-hoc network communication devices is limited, if the data transmission rate is forcibly increased, some data may not be processed in time and will be lost. The change in the data transmission rate will affect the load of edge nodes. If the transmission rate is too fast, the amount of data that edge nodes need to process will increase significantly, resulting in an increase in the edge node overload frequency. For example, when a large amount of data floods into edge nodes quickly, the computing and storage resources of the nodes may not be able to process it in time, and thus overload occurs. A high data return delay means that the data stays at the edge node for a longer time, which will increase the processing burden of the edge node and lead to an increase in the edge node overload frequency. For example, data that has not been transmitted in time for a long time accumulates at the edge node, keeping the node in a high-load state continuously and increasing the overload risk. A high data packet loss rate of ad-hoc network communication means that edge nodes need to retransmit the lost data, which additionally increases the load of edge nodes and leads to an increase in the edge node overload frequency. For example, in a network environment with serious packet loss, edge nodes keep retransmitting data, consuming a large amount of resources and easily causing overload. The higher the satellite communication bandwidth fluctuation frequency, the worse the stability of the data transmission rate. When the bandwidth fluctuates greatly, the data transmission rate will fluctuate accordingly, and even the transmission rate will drop. For example, when the satellite communication bandwidth suddenly becomes narrower, the data transmission rate will decrease significantly. By comprehensively analyzing the data transmission rate, data return delay, data packet loss rate of ad-hoc network communication, edge node overload frequency, satellite communication bandwidth fluctuation frequency, and terrain interference influence coefficient, the data quality risk assessment index can be accurately obtained, which quantitatively reflects the degree to which the data quality deviates from the ideal state caused by various factors.

[0051] Specifically, the quantitative determination of data real-time performance is carried out according to the real-time performance evaluation parameters and the comprehensive evaluation parameters to obtain the data real-time performance evaluation index. The specific analysis process is as follows: After performing the ratio approximation operation on the real-time performance evaluation parameters and the preset reference values of the real-time performance evaluation dimensions respectively, the real-time performance evaluation dimension value is obtained by coupling and weighting the results of the ratio approximation operation through the real-time performance evaluation factor. The real-time performance evaluation factor includes the multi-modal data fusion time weight factor, the edge node load difference coefficient weight factor, and the edge computing model update frequency weight factor; After performing the ratio approximation operation on the comprehensive evaluation parameters and the preset comprehensive evaluation reference values respectively, the real-time performance comprehensive evaluation dimension value is obtained by coupling and weighting the results of the ratio approximation operation through the comprehensive evaluation factor. The real-time performance comprehensive evaluation factor includes the real-time performance feedback delay weight factor and the real-time performance packet loss rate weight factor; The data real-time performance evaluation index is obtained by coupling the obtained real-time performance evaluation dimension value and the real-time performance comprehensive evaluation dimension value.

[0052] In a specific embodiment, the acquisition method of the data real-time performance evaluation index is as follows:

[0053]

[0054] DS T =SR E -SR S ;

[0055]

[0056] β1 + β2 + β3 + β4 + β5 = 1;

[0057] In the formula, GT ZF represents the data real-time performance evaluation index, ZH SX represents the real-time performance comprehensive evaluation dimension value, SX WD represents the real-time performance evaluation dimension value, ZG HY represents the data feedback delay, ZZ DB represents the ad-hoc network communication data packet loss rate, DS T represents the multi-modal data fusion time, BJ FC represents the edge node load difference coefficient, BM GP represents the edge computing model update frequency, represents the preset reference edge computing model update frequency, represents the preset critical data feedback delay, represents the preset critical ad-hoc network communication data packet loss rate, represents the preset critical multi-modal data fusion time, represents the preset critical edge node load difference coefficient, Denote the preset critical edge computing model update frequency, ΔBM GP Denote the preset allowable deviation edge computing model update frequency, SR S Denote the data fusion start time, SR E Denote the data fusion end time, μ U Denote the average CPU utilization rate, σ U Denote the standard deviation of CPU utilization rate, U t Denote the CPU utilization rate of the t-th node, GP denotes the total number of model updates during the monitoring period, GP T Denote the monitoring model update duration, β1 denotes the preset real-time performance feedback delay weight factor, β2 denotes the preset real-time performance packet loss rate weight factor, β3 denotes the preset multi-modal data fusion time weight factor, β4 denotes the preset edge node load difference coefficient weight factor, β5 denotes the preset edge computing model update frequency weight factor, t denotes the edge node number, t = 1, 2, 3,..., K, K denotes the total number of edge nodes.

[0058] When β1, β2, β3, β4, and β5 are used, the weight factors in the data quality risk assessment index process corresponding to the data return delay, the packet loss rate of the ad hoc network communication data, the multi-modal data fusion time, the edge node load difference coefficient, and the edge computing model update frequency can be directly obtained from the intelligent national land survey database, respectively representing the numerical values of the influence degrees of the data return delay, the packet loss rate of the ad hoc network communication data, the multi-modal data fusion time, the edge node load difference coefficient, and the edge computing model update frequency on the data quality risk assessment index. The real-time performance data return delay weight factor, the real-time performance packet loss rate weight factor, the multi-modal data fusion time weight factor, the edge node load difference coefficient weight factor, and the edge computing model update frequency weight factor and the data return delay, the packet loss rate of the ad hoc network communication data, the multi-modal data fusion time, the edge node load difference coefficient, and the edge computing model update frequency can be a preset mapping relationship. For example, by inputting the data return delay, the packet loss rate of the ad hoc network communication data, the multi-modal data fusion time, the edge node load difference coefficient, and the edge computing model update frequency into the corresponding preset mapping sets respectively, the weight factors in the data quality risk assessment index process corresponding to the data return delay, the packet loss rate of the ad hoc network communication data, the multi-modal data fusion time, the edge node load difference coefficient, and the edge computing model update frequency are obtained, and the mapping relationship therein can be a one-to-one correspondence or a many-to-one relationship. In this embodiment, the value ranges of the real-time performance data return delay weight factor, the real-time performance packet loss rate weight factor, the multi-modal data fusion time weight factor, the edge node load difference coefficient weight factor, and the edge computing model update frequency weight factor are all limited between 0 and 1, and the sum of the real-time performance data return delay weight factor, the real-time performance packet loss rate weight factor, the multi-modal data fusion time weight factor, the edge node load difference coefficient weight factor, and the edge computing model update frequency weight factor is 1.

[0059] In this embodiment, the data real-time performance evaluation index is used to quantitatively evaluate the real-time performance of the intelligent national land survey data in the processing and feedback process. The lower the data return delay, or the lower the packet loss rate of the ad hoc network communication data, or the shorter the multi-modal data fusion time, or the smaller the edge node load difference coefficient, or the smaller the deviation of the edge computing model update frequency from the reference edge computing model update frequency, the larger the intelligent national land survey data real-time performance evaluation index, indicating that the real-time performance of the intelligent national land survey data in the processing and feedback process is better.

[0060] The algorithm of this embodiment combines data transmission delay, packet loss rate of ad-hoc network communication data, multi-modal data fusion time, edge node load difference coefficient, and edge computing model update frequency, and comprehensively analyzes to obtain the data real-time performance evaluation index. In this formula, data transmission delay, packet loss rate of ad-hoc network communication data, multi-modal data fusion time, edge node load difference coefficient, and edge computing model update frequency affect each other. The longer the data transmission delay, the more unstable factors there are in the network transmission process, increasing the probability of packet loss in ad-hoc network communication data. For example, a long transmission time results in more interference to the signal, causing packet loss. Data transmission delay affects the multi-modal data fusion time. When data is not transmitted in a timely manner, multi-modal data fusion can only be carried out after all data arrives, resulting in a longer multi-modal data fusion time. Data transmission delay causes changes in the edge node load difference coefficient. The greater the data transmission delay, the more data accumulated for waiting to be processed by some nodes, and the load of these nodes increases accordingly, thus increasing the edge node load difference coefficient. The change in the edge node load difference coefficient also affects the data transmission delay. For example, nodes with high load may give priority to processing local tasks, further exacerbating the data transmission delay. Data transmission delay affects the edge computing model update frequency. If data is not transmitted in a timely manner, the edge computing model cannot obtain the latest data for updating in a timely manner, increasing the deviation between its update frequency and the reference edge computing model update frequency. When the packet loss rate of ad-hoc network communication data is high, it will lead to an extension of the multi-modal data fusion time because packet loss makes the edge computing model unable to obtain complete data for updating, further increasing the deviation between the update frequency and the reference frequency. The longer the multi-modal data fusion time, the more resources and time some edge nodes occupy during the fusion process, resulting in an increase in their load and a greater load difference from other nodes. The longer the multi-modal data fusion time will delay the time for the edge computing model to obtain new data, thus affecting the update frequency and increasing the deviation. The greater the edge node load difference coefficient, the higher the load nodes cannot process the edge computing model update task in a timely manner, resulting in a decrease in the update frequency and an increase in the deviation from the reference update frequency. By comprehensively analyzing data transmission delay, packet loss rate of ad-hoc network communication data, multi-modal data fusion time, edge node load difference coefficient, and edge computing model update frequency, the data real-time performance evaluation index can be accurately obtained, which quantitatively reflects the real-time performance of intelligent land survey data during the processing and feedback process.

[0061] The comprehensive analysis module is used to determine the data quality risk status and the real-time performance of data processing for the two-dimensional evaluation results.

[0062] Specifically, the determination of the data quality risk status and the real-time performance of data processing for the two-dimensional evaluation results is as follows. The specific determination process is as follows: Compare the data quality risk assessment indicators with the preset intelligent land survey data quality risk assessment thresholds in the intelligent land survey database. If the data quality risk assessment indicators are greater than or equal to the preset intelligent land survey data quality risk assessment thresholds, a risk alert report is generated, and the quality optimization adjustment process is triggered. If the data quality risk assessment indicators are less than the preset intelligent land survey data quality risk assessment thresholds, it is marked as a quality compliance status, and the current resource allocation is maintained and continuously monitored. Compare the data real-time performance evaluation indicators with the preset intelligent land survey data real-time performance evaluation thresholds in the intelligent land survey database. If the data real-time performance evaluation indicators are greater than or equal to the preset intelligent land survey data real-time performance evaluation thresholds, it is marked as a performance compliance status, and the current parameter configuration is maintained and continuously monitored. If the data real-time performance evaluation indicators are less than the preset data real-time performance evaluation indicator thresholds, a real-time optimization request is generated, and the real-time optimization process is triggered.

[0063] In this embodiment, when the data quality risk assessment indicators are greater than or equal to the preset intelligent land survey data quality risk assessment thresholds, it indicates that the quality risks faced by the current land survey data have exceeded the system's expected security range. To avoid the serious impact of these quality problems on subsequent land survey work, the system will immediately generate a risk alert report. This report details the relevant information on data quality risks, such as risk types, impact ranges, possible causes, etc. At the same time, the quality optimization adjustment process is automatically triggered, and a series of targeted optimization measures are initiated to reduce data quality risks and improve data quality. If the data real-time performance evaluation indicators are greater than or equal to the preset intelligent land survey data real-time performance evaluation thresholds, it indicates that the current data quality risks are at an acceptable level and are marked as a quality compliance status. In this case, to maintain the stability of data quality, the system will maintain the current resource allocation without large-scale adjustments, and continuously monitor the data quality risk status to promptly detect possible changes in quality risks.

[0064] In this embodiment, if the data real-time performance evaluation indicators are greater than or equal to the preset intelligent land survey data real-time performance evaluation thresholds, it means that the real-time performance of data during processing and feedback is good and meets the system's set standards, and it is marked as a performance compliance status. At this time, the system will maintain the current parameter configuration to ensure the stability of data real-time processing and continuously monitor the real-time performance to prevent real-time problems from occurring. If the data real-time performance evaluation indicators are less than the preset data real-time performance evaluation indicator thresholds, it indicates that there are deficiencies in the real-time performance of data processing and it cannot meet the business requirements. The system will generate a real-time optimization request and trigger the real-time optimization process.

[0065] Specifically, a quality optimization adjustment process is triggered, and the specific analysis process is as follows: By subtracting the data quality risk assessment index from the preset intelligent land survey data quality risk assessment threshold, a quality risk difference is obtained; by comparing the quality risk difference with the preset quality risk difference interval in the intelligent land survey database, a risk difference interval is obtained; according to the risk difference interval, a corresponding adjustment amount is matched by matching with the pre-established quality risk difference - adjustment amount correspondence table in the intelligent land survey database; according to the adjustment amount, the number of edge nodes is increased, the optimization investment in ad hoc communication devices is increased, and the data transmission protocol parameters are adjusted.

[0066] In this embodiment, for example, the data quality risk assessment index of a certain area is 75, the preset data quality risk assessment threshold is 50, and the quality risk difference is 25. If the preset first risk interval is (0, 15], the second risk interval is (15, 30], and the third risk interval is (30, +∞), then this area is in the second risk interval. Corresponding to the second risk interval, the adjustment amount matched from the relationship table is: the number of edge nodes is increased by 15%, the optimization investment in ad hoc communication devices is increased by 20%, and the transmission rate in the data transmission protocol is increased by 10%. Originally, there were 200 edge nodes in this area, and after the increase, it becomes 230; the original transmission rate was 100 Mbps, and after the increase, it reaches 110 Mbps. Through these adjustments, continuously monitor the data quality risk assessment index in the follow-up. If the expected value is still not reached, start this optimization process again.

[0067] It should be understood that in this embodiment, the elastic adjustment scheme is automatically matched through the quality risk difference-adjustment amount correspondence table (dynamically calculated based on the real-time resource status and historical optimization effects). The adjustment of the number of edge nodes is calculated according to the node load balancing algorithm (such as consistent hashing) and the pre-deployed resource pool, and it is necessary to enhance the acquisition coverage range, and increase elastically by 10%-15% (take the median of the optimal solutions in the same historical interval, which is 15%). For example, there were originally 200 edge nodes in this area. Through the pre-deployed modular devices (lightweight terminals supporting plug-and-play, including solar power supply and 4G modules), 30 nodes (200×15%) were added within 48 hours, and the total number reached 230. The adjustment of the data transmission protocol parameters is based on the real-time monitoring of the network bandwidth (the current utilization rate is 70%), and a 10%-15% rate increase interval is determined (take the safety margin of 10% to avoid congestion). For example, at the hardware level, a gigabit switch is deployed between the edge node and the aggregation node to replace the original 100M device; in the wireless transmission scenario, the frequency band is switched from 2.4GHz to 5GHz to reduce co-channel interference. At the software level, the TCP protocol parameters are optimized. The sliding window is expanded from 64KB to 256KB to reduce the number of ACK confirmations; the BBR congestion control algorithm is enabled to dynamically detect network bottlenecks, and the measured transmission rate is increased by 12%. The UDP packet size is adjusted. For remote sensing image data, the MTU is adjusted from 1500 bytes to 9000 bytes (Jumbo Frame mode), the amount of data transmitted in a single time is increased by 6 times, and the fragmentation overhead is reduced by 80%. After the adjustment, the indicators are continuously monitored. If the expected value is still not reached, the optimization process is started again to trigger secondary optimization, and an 8-time optimization upper limit or a 48-hour time window is set. If the standard is still not met, a "Manual Intervention Work Order" is automatically generated, attached with optimization records and abnormal data analysis (such as whether there are sudden changes in terrain parameters).

[0068] Specifically, the real-time optimization process is triggered, and the specific analysis process is as follows: By subtracting the data real-time performance evaluation index from the preset data real-time performance evaluation index threshold, the real-time performance difference is obtained; by comparing the real-time performance difference with the preset real-time performance difference interval in the intelligent national land survey database, the performance difference interval is obtained; according to the performance difference interval and the real-time performance difference-adjustment amount correspondence table pre-established in the intelligent national land survey database, the corresponding adjustment amount is matched: According to the adjustment amount, the optimization level of the multi-modal data fusion algorithm is improved, the update frequency of the edge computing model is reduced, and the network device is upgraded.

[0069] In this embodiment, for example, the real-time performance evaluation index of a certain area is 50, the preset real-time performance evaluation threshold is 80, and the real-time performance difference is -30. If the preset first performance difference interval is [-15, 0), the second performance difference interval is [-30, -15), and the third performance difference interval is (-∞, -30), then this area is in the second performance difference interval, and the corresponding adjustment amount is as follows: the multi-modal data fusion algorithm is upgraded from ordinary optimization to deep optimization, such as changing from a basic algorithm to a deep learning algorithm; the update frequency of the edge computing model is reduced from once every 2 hours to once every 3 hours; the network device is upgraded from 100 Mbps to 1 Gbps. After this adjustment, continuously monitor the real-time index subsequently. If it does not meet the standard, start this optimization process again.

[0070] The optimization adjustment module is used to trigger the real-time processing process of intelligent land survey data according to the determination result. The real-time processing process of intelligent land survey data includes a quality optimization adjustment process and a real-time optimization process.

[0071] Refer to Figure 2 As shown, the second aspect of the present invention provides a real-time processing method for intelligent land survey data based on edge computing, which is characterized in that it includes: real-time collecting comprehensive land survey data through edge nodes and sensor networks deployed in remote mountainous areas. The comprehensive land survey data includes quality risk assessment parameters, real-time performance evaluation parameters, comprehensive evaluation parameters, and terrain parameters; performing a two-dimensional evaluation on the comprehensive land survey data and obtaining a two-dimensional evaluation result, where the two-dimensional evaluation result represents the result of quantitatively evaluating the land survey data from the data quality risk dimension and the data real-time performance dimension respectively; determining the data quality risk status and the real-time performance of data processing for the two-dimensional evaluation result; triggering the real-time processing process of intelligent land survey data according to the determination result. The real-time processing process of intelligent land survey data includes a quality optimization adjustment process and a real-time optimization process.

[0072] The intelligent land survey database is used to store various types of data closely related to intelligent land survey, covering information such as the critical data transmission rate, the critical edge node overload frequency, the critical satellite communication bandwidth fluctuation frequency, the critical multi-modal data fusion time, the critical edge node load difference coefficient, the reference edge computing model update frequency, the allowable deviation edge computing model update frequency, the critical data backhaul delay, the critical ad-hoc network communication data packet loss rate, the maximum slope, the maximum altitude, and the maximum vegetation coverage rate. The data in the intelligent land survey database can be obtained from channels such as network analysis and monitoring tools, the operation logs of satellite communication devices, and hardware sensors.

[0073] The real-time processing system and method for intelligent land survey data based on edge computing processes the whole process from the collection of land survey data to its conversion into available information. Multi-source data is collected in real time through edge nodes and sensor networks, and a two-dimensional evaluation system is used to analyze the data quality risk and real-time efficiency. According to the evaluation results, each link of data processing is optimized and adjusted according to the preset corresponding relation table to meet the business requirements of land survey.

[0074] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and changes can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the relevant technical fields can understand and utilize the present invention well. As long as it does not deviate from the structure of the present invention or exceed the scope defined by the present invention, it should fall within the protection scope of the present invention.

Claims

1. A real-time processing system for intelligent land survey data based on edge computing, characterized in that: It includes an intelligent land survey data acquisition module, an intelligent land survey data evaluation and processing module, a comprehensive analysis module, and an optimization and adjustment module: Among them, the intelligent land survey data acquisition module is used to collect comprehensive land survey data in real time through edge nodes and sensor networks deployed in remote mountainous areas. The comprehensive land survey data includes quality risk assessment parameters, real-time performance evaluation parameters, comprehensive evaluation parameters, and terrain parameters; The intelligent land survey data evaluation and processing module is used to perform a two-dimensional evaluation on the comprehensive land survey data and obtain a two-dimensional evaluation result. The two-dimensional evaluation result represents the result of quantitatively evaluating the land survey data from the data quality risk dimension and the data real-time performance dimension; The comprehensive analysis module is used to determine the data quality risk status and the real-time performance of data processing for the two-dimensional evaluation result; The optimization and adjustment module is used to trigger the real-time processing process of intelligent land survey data according to the determination result. The real-time processing process of intelligent land survey data includes a quality optimization and adjustment process and a real-time performance optimization process.

2. The real-time data processing system for intelligent national land survey based on edge computing according to claim 1, wherein: The quality risk assessment parameters include data transmission rate, edge node overload frequency, and satellite communication bandwidth fluctuation frequency; The real-time performance evaluation parameters include multi-modal data fusion time, edge node load difference coefficient, and edge computing model update frequency; The comprehensive evaluation parameters include data return delay and ad hoc network communication data packet loss rate; The terrain parameters include slope, altitude, and vegetation coverage rate.

3. The real-time data processing system for intelligent national land survey based on edge computing according to claim 1, wherein: The specific analysis process for performing a two-dimensional evaluation on the comprehensive land survey data and obtaining a two-dimensional evaluation result is as follows: Extract the preset comprehensive land survey data reference values from the intelligent land survey database. The comprehensive land survey data reference values include quality risk assessment reference values, real-time performance evaluation reference values, comprehensive evaluation reference values, and terrain reference values; The quality risk assessment reference values include critical data transmission rate, critical edge node overload frequency, and critical satellite communication bandwidth fluctuation frequency; The real-time performance evaluation reference values include critical multi-modal data fusion time, critical edge node load difference coefficient, reference edge computing model update frequency, and allowable deviation edge computing model update frequency; The comprehensive evaluation reference values include critical data return delay and critical ad hoc network communication data packet loss rate; The terrain reference values include maximum slope, maximum altitude, and maximum vegetation coverage rate; Obtain a terrain interference influence coefficient through a terrain interference ratio approaching influence degree operation on the terrain parameters and the preset terrain reference values. The terrain interference ratio approaching influence degree operation represents the degree of approaching the terrain interference influence coefficient and the preset critical terrain interference influence coefficient by correcting with a preset terrain interference influence coefficient weight factor; Perform a quantitative determination of data quality risk based on the quality risk assessment parameters, comprehensive evaluation parameters, and terrain interference influence coefficient to obtain a data quality risk assessment index. The data quality risk assessment index is used to quantitatively evaluate the degree of data quality risk; Quantitatively determine the real-time performance of data based on real-time performance evaluation parameters and comprehensive evaluation parameters to obtain real-time performance evaluation indicators for data, which are used to quantitatively evaluate the real-time performance of intelligent national land survey data during processing and feedback.

4. The real-time processing system for intelligent national land survey data based on edge computing according to claim 3, characterized in that: The terrain interference influence coefficient is obtained by performing a terrain interference proportion approximation influence degree operation on terrain parameters and preset terrain reference values. The specific analysis process is as follows: The slope influence degree is obtained by performing a slope proportion approximation influence degree operation on slope data and a preset maximum slope. The slope proportion approximation influence degree operation represents the degree of approximation of the slope data to the preset maximum slope corrected by a preset slope weight factor. The altitude influence degree is obtained by performing an altitude proportion approximation influence degree operation on altitude data and a preset maximum altitude. The altitude proportion approximation influence degree operation represents the degree of approximation of the altitude data to the preset maximum altitude corrected by a preset altitude weight factor. The vegetation coverage influence degree is obtained by performing a vegetation coverage proportion approximation influence degree operation on vegetation coverage data and a preset maximum vegetation coverage. The vegetation coverage proportion approximation influence degree operation represents the degree of approximation of the vegetation coverage data to the preset maximum vegetation coverage corrected by a preset vegetation coverage weight factor. The terrain interference influence coefficient is obtained by coupling the slope influence degree, altitude influence degree, and vegetation coverage influence degree. The terrain interference influence coefficient represents the quantitative data of the degree of terrain interference of slope, altitude, and vegetation coverage during the acquisition and transmission of national land survey data.

5. The real-time data processing system for intelligent national land survey based on edge computing according to claim 3, characterized in that: Quantitatively determine the data quality risk based on the quality risk assessment parameters, comprehensive evaluation parameters, and terrain interference influence coefficient to obtain the data quality risk assessment indicator. The specific analysis process is as follows: Extract the preset critical terrain interference influence coefficient from the intelligent national land survey database. After performing proportion approximation degree operations on the quality risk assessment parameters and preset quality risk assessment reference values respectively, the quality risk assessment dimension value is obtained by weighting the results of the proportion approximation degree operations through quality risk assessment factors and then coupling them. The quality risk assessment factors include a data transmission rate weight factor, an edge node overload frequency weight factor, and a satellite communication bandwidth fluctuation frequency weight factor. After performing proportion approximation degree operations on the comprehensive evaluation parameters and preset comprehensive evaluation reference values respectively, the quality risk comprehensive evaluation dimension value is obtained by weighting the results of the proportion approximation degree operations through comprehensive evaluation factors and then coupling them. The comprehensive evaluation factors include a quality risk feedback delay weight factor and a quality risk packet loss rate weight factor. The terrain interference influence coefficient influence degree is obtained by performing a terrain interference proportion approximation influence degree operation on the terrain interference influence coefficient and the preset critical terrain interference influence coefficient. The terrain interference proportion approximation influence degree operation represents the degree of approximation of the terrain interference influence coefficient to the preset critical terrain interference influence coefficient corrected by a preset terrain interference influence coefficient weight factor. The data quality risk assessment indicator is obtained by coupling the obtained quality risk assessment dimension value, comprehensive evaluation dimension value, and terrain interference influence coefficient influence degree.

6. The real-time data processing system for intelligent national land survey based on edge computing according to claim 3, wherein: Quantitatively determine the real-time performance of data based on real-time performance evaluation parameters and comprehensive evaluation parameters to obtain real-time performance evaluation indicators for data. The specific analysis process is as follows: After performing ratio approximation operations on the real-time performance evaluation parameters and the preset reference values of real-time performance evaluation dimensions respectively, the real-time performance evaluation dimension values are obtained by weighting and coupling the results of the ratio approximation operations through real-time performance evaluation factors. The real-time performance evaluation factors include multi-modal data fusion time weight factor, edge node load difference coefficient weight factor, and edge computing model update frequency weight factor; After performing ratio approximation operations on the comprehensive evaluation parameters and the preset comprehensive evaluation reference values respectively, the real-time performance comprehensive evaluation dimension values are obtained by weighting and coupling the results of the ratio approximation operations through comprehensive evaluation factors. The real-time performance comprehensive evaluation factors include real-time performance feedback delay weight factor and real-time performance packet loss rate weight factor; Couple the obtained real-time performance evaluation dimension values and real-time performance comprehensive evaluation dimension values to obtain real-time performance evaluation indicators for data.

7. The real-time processing system for intelligent national land survey data based on edge computing according to claim 3, wherein: Determine the data quality risk status and real-time performance of data processing for the two-dimensional evaluation results. The specific determination process is as follows: Compare the data quality risk assessment indicator with the preset intelligent land survey data quality risk assessment threshold in the intelligent land survey database: If the data quality risk assessment indicator is greater than or equal to the preset intelligent land survey data quality risk assessment threshold, generate a risk alarm report and trigger the quality optimization adjustment process; If the data quality risk assessment indicator is less than the preset intelligent land survey data quality risk assessment threshold, mark it as a quality compliance status, maintain the current resource configuration and continue to monitor; Compare the real-time performance evaluation indicator for data with the preset intelligent land survey data real-time performance evaluation threshold in the intelligent land survey database: If the real-time performance evaluation indicator for data is greater than or equal to the preset intelligent land survey data real-time performance evaluation threshold, mark it as an efficiency compliance status, maintain the current parameter configuration and continue to monitor; If the real-time performance evaluation indicator for data is less than the preset real-time performance evaluation indicator threshold for data, generate a real-time optimization request and trigger the real-time optimization process.

8. The real-time data processing system for intelligent national land survey based on edge computing according to claim 7, characterized in that: The specific process of triggering the quality optimization adjustment process is as follows: Obtain the quality risk difference by subtracting the data quality risk assessment indicator from the preset intelligent land survey data quality risk assessment threshold; Obtain the risk difference interval by comparing the quality risk difference with the preset quality risk difference interval in the intelligent land survey database; Match according to the risk difference interval and the pre-established quality risk difference-adjustment amount correspondence table in the intelligent land survey database to obtain the corresponding adjustment amount; Increase the number of edge nodes, the optimization investment in ad hoc communication devices, and adjust the data transmission protocol parameters according to the adjustment amount.

9. The real-time processing system for intelligent national land survey data based on edge computing according to claim 7, characterized in that: The specific process of triggering the real-time optimization process is as follows: Obtain the real-time performance difference by subtracting the real-time performance evaluation indicator for data from the preset real-time performance evaluation indicator threshold for data; By comparing the real-time performance difference with the preset real-time performance difference range in the intelligent national land survey database, a performance difference range is obtained; According to the performance difference range, a matching is made with the pre-established real-time performance difference - adjustment amount correspondence table in the intelligent national land survey database, and the corresponding adjustment amount is obtained through the matching: According to the adjustment amount, the optimization level of the multi-modal data fusion algorithm is improved, the update frequency of the edge computing model is reduced, and the network device is upgraded.

10. A real-time processing method for intelligent national land survey data based on edge computing, characterized in that: Including: Through the edge nodes and sensor networks deployed in remote mountainous areas, comprehensive national land survey data is collected in real time, and the comprehensive national land survey data includes quality risk assessment parameters, real-time performance assessment parameters, comprehensive assessment parameters, and terrain parameters; Perform a two-dimensional evaluation on the comprehensive national land survey data and obtain a two-dimensional evaluation result, where the two-dimensional evaluation result represents the result of quantitatively evaluating the national land survey data from the data quality risk dimension and the data real-time performance dimension respectively; Judge the data quality risk status and the real-time performance of data processing for the two-dimensional evaluation result; According to the judgment result, trigger the real-time processing process of intelligent national land survey data, and the real-time processing process of intelligent national land survey data includes a quality optimization adjustment process and a real-time optimization process.

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