An engineering survey data management and evaluation system

Abstract

The application discloses an engineering investigation data management and evaluation system, relates to the technical field of data management, and comprises a historical data arrangement module, a data distortion compensation module, a safety risk alarm module and a data security setting module.The historical data arrangement module is used for acquiring historical engineering investigation data and experience description of engineers, and generating an expert decision path according to the historical engineering investigation data and the experience description.The data distortion compensation module is used for collecting engineering investigation data and environmental data, extracting distortion values of the engineering investigation data, generating environmental compensation parameters, and obtaining target engineering investigation data.The safety risk alarm module is used for generating an evaluation result, obtaining a safety risk according to the evaluation result, and alarming staff according to the safety risk.The data security setting module is used for constructing a virtual container, setting a biological feature data lock for the virtual container, and connecting the virtual container with a cloud end.The application has the effects of improving the accuracy, the utilization rate and the safety of engineering investigation data, and reducing the safety risk of investigation personnel.

Description

Technical Field

[0001] The present application relates to the technical field of data management, and in particular to an engineering survey data management and evaluation system. Background Art

[0002] Construction engineering surveys involve mapping, exploring, and testing topographic, geological, and hydrological conditions to meet the needs of project planning, design, construction, operation, and comprehensive management, and to provide relevant results and information. After an engineering survey, a large amount of engineering survey data is generated and presented in a variety of formats, including reports, tables, and images.

[0003] In the existing technology, on the one hand, during the process of engineering surveys, when collecting data, the data needs to be manually recorded and saved, and then the collected data is sent to an external data processing department for data processing. In this process, the data evaluation process requires a lot of time, and it is impossible to conduct a security assessment in a timely manner, which increases the safety risk of the surveyors. At the same time, under the interference of certain environmental conditions, the collected data may be distorted, and the manual recording method will further reduce the credibility of the data. On the other hand, due to technological development, the versions and formats of historical data have become outdated and unusable, and the experience of some old engineers is difficult to digitize, resulting in the waste of effective experience and data. In addition, the existing data is repeatedly modified during use, resulting in version confusion, making it impossible to achieve unified data management. Finally, there are field workers who use private devices when transmitting data, resulting in a lot of sensitive data being recorded on the network, which may lead to leaks and affect the security of the data. Summary of the Invention

[0004] The object of the present invention is to provide an engineering survey data management and evaluation system to solve the problems raised in the above background technology.

[0005] This application provides an engineering survey data management and evaluation system, which includes:

[0006] Historical data collation module: used to obtain historical engineering survey data and experience descriptions of veteran engineers, and generate expert decision paths based on the historical engineering survey data and the experience descriptions;

[0007] Data distortion compensation module: used to collect engineering survey data and environmental data, extract distortion values ​​of the engineering survey data, generate environmental compensation parameters based on the environmental data and the distortion values, and correct the engineering survey data based on the environmental compensation parameters to obtain target engineering survey data;

[0008] Safety risk alarm module: used to perform preliminary analysis and evaluation on the target engineering survey data according to the expert decision path, generate evaluation results, obtain safety risks based on the evaluation results, and alarm staff based on the safety risks;

[0009] Data confidentiality setting module: used to classify the confidentiality level of the target engineering survey data, obtain confidential sensitive data, extract the data security boundary of the confidential sensitive data, and build a virtual container based on the data security boundary. The virtual container is set with a biometric data lock and connected to the cloud.

[0010] Preferably, the steps of obtaining historical engineering survey data and the experience description of veteran engineers, and generating an expert decision path based on the historical engineering survey data and the experience description are specifically as follows:

[0011] Obtain historical engineering survey data and experience descriptions of veteran engineers, digitize the experience descriptions, and generate experience data;

[0012] Screening the historical engineering survey data based on the empirical data to obtain standard data that meets the empirical data, and generating simulation cases based on the standard data;

[0013] Organizing the historical engineering survey data to generate a data time machine, and verifying the results of the simulation case based on the data time machine to obtain a simulation verification result;

[0014] Based on the simulation verification result, a reliability value of each of the experience data is obtained, each of the experience data is weighted according to the reliability value, and an expert decision path is generated according to the weighted experience data.

[0015] Preferably, the steps of arranging the historical engineering survey data and generating a data time machine are specifically as follows:

[0016] Extracting the data type, collection time, and geographical area of ​​the historical engineering survey data, classifying the historical engineering survey data based on the geographical area, and generating engineering survey data sets for multiple different areas;

[0017] sorting and arranging the historical engineering survey data in each engineering survey data set based on the acquisition time and the data type to generate an engineering survey data table;

[0018] Constructing a time axis, substituting the engineering survey data table into the time axis, and extracting data changes of the engineering survey data in the time axis;

[0019] Based on the data changes, the geological changes of the geographical area at different times are obtained, and a data time machine is constructed according to the geological changes and the time axis.

[0020] Preferably, the step of sorting and arranging the historical engineering survey data in each engineering survey data set based on the acquisition time and the data type is specifically as follows:

[0021] Extracting the historical version format of the historical engineering survey data based on the data type, and recording the version format change path of the historical version format;

[0022] Based on the version format change path, the timestamp, positioning data and operator biometrics of the historical engineering survey data when it is modified are recorded, and the time-space coordinates are generated in combination;

[0023] constructing a spatiotemporal three-dimensional tracing system based on the spatiotemporal coordinates, and sorting the historical engineering survey data according to the collection time according to the spatiotemporal three-dimensional tracing system;

[0024] The historical engineering survey data are classified according to the data format according to the spatiotemporal three-dimensional tracing system, and data segmentation is performed on the classified historical engineering survey data.

[0025] Preferably, the steps of extracting the distortion value of the engineering survey data, generating an environmental compensation parameter according to the environmental data and the distortion value, and correcting the engineering survey data according to the environmental compensation parameter to obtain target engineering survey data are specifically as follows:

[0026] Substituting the engineering survey data into the data time machine, performing a reverse simulation evolution test on the engineering survey data, and obtaining an evolution test result;

[0027] Performing a difference comparison between the evolution test result and the historical engineering survey data to obtain a data difference;

[0028] Extracting, based on the environmental data, factors affecting the environment on the data, and obtaining distortion values ​​of the engineering survey data based on the factors and the data difference;

[0029] An environmental compensation parameter is generated based on the influencing factor, and the engineering survey data is corrected according to the compensation parameter and the distortion value to obtain target engineering survey data.

[0030] Preferably, before the step of collecting engineering survey data, the method further includes:

[0031] Acquiring a collection device and a collection method for the engineering survey data, and obtaining accuracy influencing factors based on the collection device and the collection method;

[0032] Collecting surrounding physical field data, performing data extraction on the physical field data according to the accuracy influencing factors, and obtaining temperature and humidity data, air pressure data, and air composition data;

[0033] Evaluate the influence of the temperature and humidity data, the air pressure data, and the air composition data according to the accuracy influencing factors to obtain a temperature and humidity influence value, an air pressure influence value, and an air composition influence value;

[0034] Combining the temperature and humidity impact value, the air pressure impact value, and the air composition impact value to generate a joint correction operation;

[0035] The joint calibration operation is sent to the acquisition device and the staff to perform device calibration on the acquisition device.

[0036] Preferably, the steps of performing preliminary analysis and evaluation on the target engineering survey data according to the expert decision path, generating evaluation results, and obtaining safety risks according to the evaluation results are specifically as follows:

[0037] Extracting path point data on the expert decision path, and extracting the data type range and data volume of the path point data;

[0038] Screening the target engineering survey data according to the data type range and the data volume to obtain key point data;

[0039] Filling the key point data into the expert decision path according to a preset filling method to generate an evaluation result;

[0040] A multi-dimensional risk assessment is performed on the evaluation results to obtain a geological risk assessment, an explosion risk assessment, and a hazardous material risk assessment, and the safety risks are generated in combination.

[0041] Preferably, the steps of performing multi-dimensional risk assessment on the evaluation results to obtain geological risk assessment, explosion risk assessment and hazardous material risk assessment are specifically as follows:

[0042] Obtaining the geological type and geological characteristics of the region based on the target engineering survey data, and obtaining geological derivatives based on the geological type and geological characteristics;

[0043] Obtaining a geological activity framework based on the geological type and the geological characteristics, obtaining a derivative activity framework based on the geological derivatives, and generating a geological safety framework by combining the geological activity framework and the derivative activity framework;

[0044] Obtaining a geological knowledge map, and building a dynamic safety assessment model based on the geological knowledge map and the geological safety framework;

[0045] The evaluation results and the target engineering survey data are substituted into the dynamic safety assessment model to generate a geological risk assessment, an explosion risk assessment, and a hazardous material risk assessment.

[0046] Preferably, the steps of extracting the data security boundary of the confidential sensitive data, constructing a virtual container based on the data security boundary, and setting a biometric data lock on the virtual container and connecting it to the cloud are specifically as follows:

[0047] Extracting the data security boundary of the confidential sensitive data and constructing a virtual container according to the data security boundary;

[0048] Uploading the target engineering survey data to the cloud, performing image processing on the confidential sensitive data to generate image data, and placing the image data in the virtual container;

[0049] constructing a biometric data lock, encrypting the virtual container using the biometric data lock, and monitoring the current state of the virtual container;

[0050] According to the current state, it is determined whether the virtual container is under security threat. If it is determined that the virtual container is under security threat, the virtual container is disconnected from the cloud, so that the image data is self-destructed.

[0051] Preferably, the step of determining whether the virtual container is subject to a security threat according to the current state is specifically:

[0052] According to the current state, determining whether the image data is in a transmission state, a use state, or a disabled state;

[0053] If the image data is in a transmission state, the virtual container is locked and monitors its own attack status. If the virtual container is attacked, it is determined that the virtual container is under security threat;

[0054] If the image data is in use, the virtual container is unlocked, and the usage frequency of the image data is monitored. If the usage frequency is lower than or higher than a preset frequency threshold, it is determined that the virtual container is under a security threat.

[0055] If the image data is in a deactivated state, the virtual container is disconnected from the cloud, and the virtual container actively attracts attacks.

[0056] In summary, this application includes at least one of the following beneficial technical effects:

[0057] Historical engineering survey data and veteran engineers' experience descriptions are digitized and combined to generate an expert decision path. Engineering survey data and environmental data are collected, distortion values ​​are extracted, and environmental compensation parameters are generated based on the environmental data and distortion values. The engineering survey data is then compensated using the environmental compensation parameters to obtain the target engineering survey data. The target engineering survey data is then analyzed and evaluated using the expert decision path to obtain evaluation results. Security risks within the evaluation results are identified and alerted to personnel based on these security risks. The target engineering survey data is then classified according to confidentiality levels to obtain confidential and sensitive data. All target engineering survey data is then uploaded to the cloud. A virtual container is constructed for this data based on its data security boundaries, and a biometric data lock is added to the virtual container. The virtual container is then connected to the cloud. Image processing is performed on the confidential and sensitive data to generate image data, which is then placed in the virtual container. If the virtual container detects an attack or is experiencing abnormal usage, it disconnects from the cloud and the image data self-destructs. This improves the accuracy, usability, and security of engineering survey data and reduces safety risks for surveyors. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is a module block diagram of an engineering survey data management and evaluation system provided in an embodiment of the present application.

[0059] Explanation of the accompanying symbols: 1. Historical data sorting module; 2. Data distortion compensation module; 3. Security risk alarm module; 4. Data confidentiality setting module. DETAILED DESCRIPTION

[0060] The following combination Figure 1 This application is further described in detail, but the embodiments of the present invention are not limited thereto.

[0061] The embodiments of the present application disclose an engineering survey data management and evaluation system.

[0062] In this embodiment, an engineering survey data management and evaluation system includes:

[0063] Historical data collation module 1: used to obtain historical engineering survey data and experience descriptions of veteran engineers, and generate expert decision paths based on these data;

[0064] Data distortion compensation module 2: used to collect engineering survey data and environmental data, extract distortion values ​​of the engineering survey data, generate environmental compensation parameters based on the environmental data and distortion values, and correct the engineering survey data based on the environmental compensation parameters to obtain target engineering survey data;

[0065] Safety risk alarm module 3: used to conduct preliminary analysis and evaluation of target engineering survey data based on the expert decision path, generate evaluation results, obtain safety risks based on the evaluation results, and alarm staff based on the safety risks;

[0066] Data confidentiality setting module 4: used to classify the confidentiality level of target engineering survey data, obtain confidential sensitive data, extract the data security boundary of confidential sensitive data, build a virtual container based on the data security boundary, set a biometric data lock on the virtual container and connect it to the cloud.

[0067] It should be noted that the above modules are only basic modules of this embodiment. During the specific implementation process, some modules can be appropriately added, reduced or modified without affecting the overall implementation effect.

[0068] The steps for obtaining historical engineering survey data and the experience descriptions of veteran engineers and generating an expert decision path based on the historical engineering survey data and experience descriptions are as follows:

[0069] Obtain historical engineering survey data and experience descriptions of veteran engineers, digitize the experience descriptions, and generate experience data;

[0070] Based on empirical data, data is screened in historical engineering survey data to obtain standard data that meets the empirical data, and simulation cases are generated based on the standard data;

[0071] Organize historical engineering survey data, generate a data time machine, verify the results of simulation cases based on the data time machine, and obtain simulation verification results;

[0072] Based on the simulation verification results, the reliability value of each empirical data is obtained, each empirical data is weighted according to the reliability value, and an expert decision path is generated based on the weighted empirical data.

[0073] In practice, using a highway construction project as an example, the system acquired historical engineering survey data from the past decade for the area, including geological drilling records, groundwater level reports, and rock strata distribution maps. The system also collected handwritten notes from three veteran engineers, including statements such as "the presence of loose particles in sandy soil layers indicates underground cavity risk" and "the orientation of cracks in limestone areas aligns with the direction of groundwater erosion." The notes were converted into structured data, generating 32 empirical rules, including "loose particle density threshold = 5%" and "risk level increases when crack orientation angle deviation >15°." Based on these rules, the historical data was filtered, identifying 28 drill hole records that met the "loose particles in sandy soil layers >5%" criteria as standard data to generate a "simulated case study of underground cavity development." The system automatically organized the survey data from the past decade, categorized it by year and region, and constructed a data time machine. The simulated case study was validated by running the time machine, finding 23 data points that matched historical collapse records, while 5 data points needed to be excluded due to instrument error. The reliability of each empirical rule was calculated, assigning a weight of 0.9 to the "loose particle rule" and 0.7 to the "crack orientation rule." Finally, an expert path containing 15 decision nodes is generated. For example, node 5 is set to "immediately initiate a level 3 warning when both loose particles exceed the limit and the crack direction is abnormal."

[0074] The steps for organizing historical engineering survey data and generating a data time machine are as follows:

[0075] Extract the data type, collection time, and geographical area of ​​historical engineering survey data, classify the historical engineering survey data based on geographical areas, and generate engineering survey data sets for multiple different regions;

[0076] Sort and organize the historical engineering survey data in each engineering survey data set based on the acquisition time and data type to generate an engineering survey data table;

[0077] Construct a timeline, substitute the engineering survey data table into the timeline, and extract the data changes of the engineering survey data in the timeline;

[0078] Based on the data changes, the geological changes in the geographical area at different times are obtained, and a data time machine is constructed according to the geological changes and the time axis.

[0079] In practice, for example, the system extracts survey data collected from a highway construction project between 2015 and 2023 and divides it into a southern section dataset based on the line stakes K12+300 to K15+800. Within each dataset, data is sorted by acquisition time: separate tables are created for 2015 seismic data, 2017 electromagnetic data, and 2020 drill core data. When constructing the timeline, the data showing the sand layer thickness of 2.3 meters in 2015, 2.1 meters in 2017, and 1.9 meters in 2020 are connected to form a curve. The system found that the average annual erosion thickness in this area was 0.2 meters, and a geological evolution model was established. When newly collected data in 2023 indicated a sudden drop in sand layer thickness to 1.5 meters, the Data Time Machine automatically triggered an anomaly warning and, by comparing historical data, generated an "accelerated erosion warning."

[0080] The steps for sorting and organizing the historical engineering survey data in each engineering survey dataset based on the acquisition time and data type are as follows:

[0081] Based on the data type, the historical version format of the historical engineering survey data is extracted, and the version format change path of the historical version format is recorded;

[0082] Based on the version format change path, the timestamp, positioning data and operator biometrics of historical engineering survey data modifications are recorded and aggregated to generate spatiotemporal coordinates;

[0083] Based on the time and space coordinates, a three-dimensional space-time tracing system is constructed, and historical engineering survey data are sorted according to the collection time according to the three-dimensional space-time tracing system;

[0084] According to the time-space three-dimensional tracing system, the historical engineering survey data are classified according to the data format, and the classified historical engineering survey data are partitioned.

[0085] During application, taking the highway construction project as an example, when the system detected that the data file format of the southern section in 2018 was converted from .dgn to .revit, it recorded the version change log: June 12, 2018, 15:23, GPS position N32°15'22", operator fingerprint ID007 completed the format conversion. When establishing a 3D traceability model for the 2020 drilling data, it was discovered that the No. 3 drilling data was modified on March 5, 2021. The original core strength value of 35MPa was changed to 32MPa. The device MAC address at the time of modification was 00-1B-63-84-45-E6. The system automatically marked the data as "manually corrected data." After sorting by timestamp, the 2015 original data, the 2018 format conversion data, and the 2021 revised data were stored in isolated storage areas respectively to generate a complete data evolution chain.

[0086] The steps of extracting distortion values ​​of engineering survey data, generating environmental compensation parameters based on environmental data and the distortion values, and correcting the engineering survey data based on the environmental compensation parameters to obtain target engineering survey data are as follows:

[0087] Substitute the engineering survey data into the data time machine, perform reverse simulation evolution test on the engineering survey data, and obtain the evolution test results;

[0088] Based on the difference comparison between the evolution test results and the historical engineering survey data, the data difference is obtained;

[0089] Based on the environmental data, extract the factors that affect the data, and obtain the distortion value of the engineering survey data based on the factors and data differences;

[0090] Environmental compensation parameters are generated based on the influencing factors, and the engineering survey data are corrected according to the compensation parameters and distortion values ​​to obtain the target engineering survey data.

[0091] In practice, for example, a highway construction project involved resistivity data of 85 Ω·m collected in April 2023 for the southern section. The data was compared with data of 100 Ω·m collected at the same location in 2019, revealing a difference of 15 Ω·m. Environmental monitoring showed a humidity of 98% and a temperature of 32°C that day. The system calculated the humidity impact factor on the resistance measurement to be 0.8 Ω·m / %RH, and the temperature impact factor to be 0.5 Ω·m / °C. Compensation calculations were performed: Humidity compensation value = 98% × 0.8 = 78.4 Ω·m, and temperature compensation value = 32 × 0.5 = 16 Ω·m. The corrected resistivity value was 85 + 78.4 + 16 = 179.4 Ω·m. This value matched the historical data trend, and the system determined that the original data was distorted by 38% due to environmental factors. After applying the compensation parameters, valid data was generated.

[0092] Before the steps of collecting engineering survey data, it also includes:

[0093] Obtain the acquisition equipment and methods for collecting engineering survey data, and obtain the factors affecting accuracy based on the acquisition equipment and methods;

[0094] Collect surrounding physical field data, extract the physical field data based on accuracy influencing factors, and obtain temperature and humidity data, air pressure data, and air composition data;

[0095] According to the accuracy influencing factors, the influence degree of temperature and humidity data, air pressure data and air composition data is evaluated respectively, and the temperature and humidity influence value, air pressure influence value and air composition influence value are obtained;

[0096] Combine the temperature and humidity impact values, air pressure impact values, and air composition impact values ​​to generate a joint correction operation;

[0097] The joint calibration operation is sent to the acquisition equipment and staff to perform equipment calibration on the acquisition equipment.

[0098] In practice, taking a highway construction project as an example, when using the TR-600 geological radar at K14+500, the system detected an ambient temperature of 28°C, resulting in a 3dB drop in radar antenna gain. According to the equipment manual, sensitivity decreases by 0.1dB for every 1°C increase in temperature, and the signal-to-noise ratio decreases by 2% for every 10% increase in humidity. The surrounding air pressure data collected was 1013hPa, and the air salt content was 0.3mg / m 3 The system generated a joint correction instruction: increasing the transmit power from 100W to 115W and reducing the scanning speed from 40km / h to 35km / h. After receiving the prompt, the staff adjusted the equipment parameters and re-collected data, and the data quality score improved from 72 to 88.

[0099] Conduct preliminary analysis and evaluation of the target engineering survey data according to the expert decision path, generate evaluation results, and determine the safety risk based on the evaluation results. The specific steps are as follows:

[0100] Extract the path point data on the expert decision path, and extract the data type range and data volume of the path point data;

[0101] Screen the target engineering survey data according to the data type range and data volume to obtain key point data;

[0102] Fill key point data into the expert decision path according to the preset filling method to generate evaluation results;

[0103] A multi-dimensional risk assessment is conducted on the evaluation results to obtain geological risk assessment, explosion risk assessment and hazardous material risk assessment, and the safety risks are generated in combination.

[0104] In practice, using a highway construction project as an example, the expert path requires focused monitoring of five data types, including "groundwater depth" and "rock integrity coefficient." The system screened 200 newly collected data sets, selecting 38 sets of water level data and 42 sets of integrity coefficient data. The water level data of 2.3m was added to path node 3, and the integrity coefficient of 0.55 was added to node 5. The generated evaluation report showed that node 3 triggered an "abnormal water level drop" warning (historical average 3.1m), and node 5 triggered a "rock fragmentation" alarm (coefficient <0.6). The system's comprehensive assessment of the safety risk level was orange, corresponding to the "carry out support operations within 24 hours" response.

[0105] Conduct multi-dimensional risk assessment on the evaluation results to obtain the steps of geological risk assessment, explosion risk assessment and hazardous material risk assessment, specifically:

[0106] Obtain the geological type and geological characteristics of the region based on the target engineering survey data, and obtain geological derivatives based on the geological type and geological characteristics;

[0107] Based on the geological type and geological characteristics, a geological activity framework is obtained, and based on the geological derivatives, a derivative activity framework is obtained. Then, a geological safety framework is generated by combining the geological activity framework and the derivative activity framework.

[0108] Obtain a geological knowledge map and build a dynamic safety assessment model based on the geological knowledge map and geological safety framework;

[0109] Substitute the evaluation results and target engineering survey data into the dynamic safety assessment model to generate geological risk assessment, explosion risk assessment and hazardous material risk assessment.

[0110] In application, taking the highway construction project as an example, in the K15+200 limestone area, the system identified the karst development characteristics. Combined with the geological knowledge base, a safety framework including cave distribution, groundwater runoff, and roof thickness was established. Input the current monitoring data: cave diameter 4.5m, roof thickness 2.3m, groundwater flow rate 0.8m / s. The dynamic model calculated that the collapse probability is 42%, the explosion risk (methane concentration 0%) is rated green, and the harmful substances (hydrogen sulfide concentration 5ppm) are rated yellow. The generated three-dimensional risk map shows that the red collapse area is 185m 2 The yellow gas zone is 36m 2 .

[0111] The steps for extracting the data security boundary of confidential sensitive data, building a virtual container based on the data security boundary, setting a biometric data lock on the virtual container and connecting it to the cloud are as follows:

[0112] Extract the data security boundary of confidential and sensitive data and build a virtual container based on the data security boundary;

[0113] Upload the target engineering survey data to the cloud, perform image processing on the confidential sensitive data to generate image data, and place the image data in a virtual container;

[0114] Build a biometric data lock, use the biometric data lock to encrypt the virtual container, and monitor the current status of the virtual container;

[0115] Based on the current status, it is determined whether the virtual container is under security threat. If it is determined that the virtual container is under security threat, the virtual container is disconnected from the cloud, causing the image data to self-destruct.

[0116] During operation, taking a highway construction project as an example, the system identified newly collected underground pipeline data in a military zone as confidential. A data security boundary with a radius of 200 meters and centered at stake number K13+700 was defined. When constructing a virtual container, the pipeline coordinates (325461, 4456789) were converted into a blurred image (325*, 445**). Two-factor authentication was set up: fingerprint ID 008 + iris scan was required for access. When an overseas IP access attempt was detected on May 6, 2023, the container immediately disconnected from the cloud, the image data self-destructed, and only a prompt message "Sensitive facilities exist in this area" was retained on the cloud.

[0117] Based on the current status, determine whether the virtual container is under security threat. Specifically:

[0118] According to the current state, whether the image data is in the transmission state, the use state or the disabled state is obtained;

[0119] If the image data is in the transmission state, the virtual container is in the locked state and monitors its own attack status. If the virtual container is attacked, it is judged that the virtual container is under security threat;

[0120] If the image data is in use, the virtual container is unlocked and the usage frequency of the image data is monitored. If the usage frequency is lower or higher than the preset frequency threshold, it is determined that the virtual container is under security threat.

[0121] If the image data is in a deactivated state, the virtual container is disconnected from the cloud, and the virtual container actively attracts attacks.

[0122] In practice, for example, in a highway construction project, a virtual container detected confidential data being accessed outside of business hours (2:15 AM). The system checked the status: the image data was in use, with 27 accesses within 24 hours (the threshold is 10). Upon identifying the anomaly, the container was immediately locked, and a disguised program was activated to generate fake pipeline coordinates (325000, 4456000) to trigger an attack. An alert was also sent to the security center. Logs revealed that the 1.2GB of data the attacker attempted to download was actually an encrypted blank file, effectively protecting the real data.

[0123] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. An engineering survey data management and evaluation system, characterized in that: include: Historical data collation module: used to obtain historical engineering survey data and engineers' experience descriptions, digitize the experience descriptions, and generate experience data; Screening the historical engineering survey data based on the empirical data to obtain standard data that meets the empirical data, and generating simulation cases based on the standard data; Organizing the historical engineering survey data to generate a data time machine, and verifying the results of the simulation case based on the data time machine to obtain a simulation verification result; Based on the simulation verification results, a reliability value of each of the empirical data is obtained, each of the empirical data is weighted according to the reliability value, and an expert decision path is generated according to the weighted empirical data; Data distortion compensation module: used to collect engineering survey data and environmental data, extract distortion values ​​of the engineering survey data, generate environmental compensation parameters based on the environmental data and the distortion values, and correct the engineering survey data based on the environmental compensation parameters to obtain target engineering survey data; Safety risk alarm module: used to perform preliminary analysis and evaluation on the target engineering survey data according to the expert decision path, generate evaluation results, obtain safety risks based on the evaluation results, and alarm staff based on the safety risks; Data confidentiality setting module: used to classify the confidentiality level of the target engineering survey data, obtain confidential sensitive data, extract the data security boundary of the confidential sensitive data, and build a virtual container based on the data security boundary. The virtual container is set with a biometric data lock and connected to the cloud.

2. The engineering survey data management and evaluation system according to claim 1, characterized in that: The steps of organizing the historical engineering survey data and generating a data time machine are specifically as follows: Extracting the data type, collection time, and geographical area of ​​the historical engineering survey data, classifying the historical engineering survey data based on the geographical area, and generating engineering survey data sets for multiple different areas; sorting and arranging the historical engineering survey data in each engineering survey data set based on the acquisition time and the data type to generate an engineering survey data table; Constructing a time axis, substituting the engineering survey data table into the time axis, and extracting data changes of the engineering survey data in the time axis; Based on the data changes, the geological changes of the geographical area at different times are obtained, and a data time machine is constructed according to the geological changes and the time axis.

3. The engineering survey data management and evaluation system according to claim 2, characterized in that: The step of sorting and arranging the historical engineering survey data in each engineering survey data set based on the acquisition time and the data type is specifically as follows: Extracting the historical version format of the historical engineering survey data based on the data type, and recording the version format change path of the historical version format; Based on the version format change path, the timestamp, positioning data and operator biometrics of the historical engineering survey data when it is modified are recorded, and the time-space coordinates are generated in combination; constructing a spatiotemporal three-dimensional tracing system based on the spatiotemporal coordinates, and sorting the historical engineering survey data according to the collection time according to the spatiotemporal three-dimensional tracing system; The historical engineering survey data are classified according to the data format according to the spatiotemporal three-dimensional tracing system, and data segmentation is performed on the classified historical engineering survey data.

4. The engineering survey data management and evaluation system according to claim 3, characterized in that: The steps of extracting the distortion value of the engineering survey data, generating an environmental compensation parameter according to the environmental data and the distortion value, and correcting the engineering survey data according to the environmental compensation parameter to obtain target engineering survey data are specifically as follows: Substituting the engineering survey data into the data time machine, performing a reverse simulation evolution test on the engineering survey data, and obtaining an evolution test result; Performing a difference comparison between the evolution test result and the historical engineering survey data to obtain a data difference; Extracting, based on the environmental data, factors affecting the environment on the data, and obtaining distortion values ​​of the engineering survey data based on the factors and the data difference; An environmental compensation parameter is generated based on the influencing factor, and the engineering survey data is corrected according to the compensation parameter and the distortion value to obtain target engineering survey data.

5. The engineering survey data management and evaluation system according to claim 4, characterized in that: Before the steps of collecting engineering survey data, it also includes: Acquiring a collection device and a collection method for the engineering survey data, and obtaining accuracy influencing factors based on the collection device and the collection method; Collecting surrounding physical field data, performing data extraction on the physical field data according to the accuracy influencing factors, and obtaining temperature and humidity data, air pressure data, and air composition data; Evaluate the influence of the temperature and humidity data, the air pressure data, and the air composition data according to the accuracy influencing factors to obtain a temperature and humidity influence value, an air pressure influence value, and an air composition influence value; Combining the temperature and humidity impact value, the air pressure impact value, and the air composition impact value to generate a joint correction operation; The joint calibration operation is sent to the acquisition device and the staff to perform device calibration on the acquisition device.

6. The engineering survey data management and evaluation system according to claim 5, characterized in that: The steps of performing preliminary analysis and evaluation on the target engineering survey data according to the expert decision path, generating evaluation results, and obtaining safety risks based on the evaluation results are specifically as follows: Extracting path point data on the expert decision path, and extracting the data type range and data volume of the path point data; Screening the target engineering survey data according to the data type range and the data volume to obtain key point data; Filling the key point data into the expert decision path according to a preset filling method to generate an evaluation result; A multi-dimensional risk assessment is performed on the evaluation results to obtain a geological risk assessment, an explosion risk assessment, and a hazardous material risk assessment, and the safety risks are generated in combination.

7. The engineering survey data management and evaluation system according to claim 6, characterized in that: The steps of performing multi-dimensional risk assessment on the evaluation results to obtain geological risk assessment, explosion risk assessment and hazardous material risk assessment are as follows: Obtaining the geological type and geological characteristics of the region based on the target engineering survey data, and obtaining geological derivatives based on the geological type and geological characteristics; Obtaining a geological activity framework based on the geological type and the geological characteristics, obtaining a derivative activity framework based on the geological derivatives, and generating a geological safety framework by combining the geological activity framework and the derivative activity framework; Obtaining a geological knowledge map, and building a dynamic safety assessment model based on the geological knowledge map and the geological safety framework; The evaluation results and the target engineering survey data are substituted into the dynamic safety assessment model to generate a geological risk assessment, an explosion risk assessment, and a hazardous material risk assessment.

8. The engineering survey data management and evaluation system according to claim 7, characterized in that: The steps of extracting the data security boundary of the confidential sensitive data, constructing a virtual container based on the data security boundary, setting a biometric data lock on the virtual container and connecting it to the cloud are specifically as follows: Extracting the data security boundary of the confidential sensitive data and constructing a virtual container according to the data security boundary; Uploading the target engineering survey data to the cloud, performing image processing on the confidential sensitive data to generate image data, and placing the image data in the virtual container; constructing a biometric data lock, encrypting the virtual container using the biometric data lock, and monitoring the current state of the virtual container; According to the current state, it is determined whether the virtual container is under security threat. If it is determined that the virtual container is under security threat, the virtual container is disconnected from the cloud, so that the image data is self-destructed.

9. The engineering survey data management and evaluation system according to claim 8, characterized in that: The step of determining whether the virtual container is subject to a security threat according to the current state is specifically: According to the current state, determining whether the image data is in a transmission state, a use state, or a disabled state; If the image data is in a transmission state, the virtual container is locked and monitors its own attack status. If the virtual container is attacked, it is determined that the virtual container is under security threat; If the image data is in use, the virtual container is unlocked, and the usage frequency of the image data is monitored. If the usage frequency is lower than or higher than a preset frequency threshold, it is determined that the virtual container is under a security threat. If the image data is in a deactivated state, the virtual container is disconnected from the cloud, and the virtual container actively attracts attacks.

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