A method and system for managing civil engineering survey data

Abstract

The application relates to the technical field of data management, and discloses a civil engineering survey data management method and system, which aims to solve the problem of low survey data quality, and mainly comprises the following steps: determining the data type of survey data, obtaining the use frequency of each data type, and establishing a data format standard according to the use frequency; inputting the obtained survey data into a database for storage, and performing structure format specification on the survey data input into the database; extracting target data related to a survey object in the database, and generating a plurality of target data sets corresponding to the survey object; for each target data set, calculating the portrait difference of target portraits corresponding to any two target data sets, and judging whether the target data in the corresponding target data set is low-quality data according to the portrait difference. The application improves the uniformity and quality of the format of survey data, and is suitable for engineering construction.

Description

Technical Field

[0001] This application relates to the field of data management technology, specifically to a method and system for managing civil engineering survey data. Background Technology

[0002] During engineering construction, it is necessary to conduct site surveys and obtain various survey data of the project area, including topographic data, geological data, hydrological data, measurement data, and material testing data. By collecting relevant data, basic information is provided for engineering design, planning, and construction.

[0003] In existing technologies, during engineering construction, the collected survey data is stored. However, when this survey data needs to be accessed, it is found that different survey data have different structures and formats, making it difficult to use. At the same time, data quality is also very important in the actual use of survey data. High-quality survey data can improve the execution efficiency of civil engineering design, planning and other projects. However, in actual use, a survey object may undergo multiple surveys, and the survey data from different aspects may be of low quality due to factors such as measurement errors, human error or equipment failure, which may not be detected, thus affecting the progress of the project. Summary of the Invention

[0004] This application aims to address the problem of low quality of existing survey data by proposing a method and system for managing civil engineering survey data.

[0005] The technical solution adopted by this application to solve the above-mentioned technical problems is:

[0006] In a first aspect, this application provides a method for managing civil engineering survey data, the method comprising:

[0007] Establish a database and obtain survey data for civil engineering projects;

[0008] Determine the data type of the survey data, obtain the usage frequency of each data type, and establish a data format standard based on the usage frequency;

[0009] The acquired survey data is input into the database for storage, and the structure and format of the survey data input into the database are standardized according to the data format standard.

[0010] The survey object is identified, target data related to the survey object is extracted from the database, and the target data is summarized according to the survey method of the target data to generate multiple target datasets corresponding to the survey object.

[0011] For each target dataset, a profile description of the survey object is created based on the data characteristics of the target data to obtain a target profile. The profile difference between the target profiles corresponding to any two target datasets is calculated, and the target data in the corresponding target dataset is determined as low-quality data based on the profile difference.

[0012] Furthermore, the usage frequency of each data type is obtained, and a data format standard is established based on the usage frequency, specifically including:

[0013] The survey data is processed according to the data type to obtain the survey dataset corresponding to each data type, and the amount of survey data and the frequency of use in each survey dataset are obtained.

[0014] The survey datasets are sorted according to the data volume to obtain a quantity sequence, and the survey data in each survey dataset are sorted according to the usage frequency to obtain a frequency sequence.

[0015] A data format standard is established based on the quantity sequence and frequency sequence.

[0016] Furthermore, a data format standard is established based on the quantity sequence and frequency sequence, specifically including:

[0017] Establish a format standard generation model, determine the project requirements of the corresponding engineering project based on the data type, and generate a weight allocation scheme based on the project requirements;

[0018] The quantity sequence and frequency sequence are input into the format standard generation model, and weights are assigned to the quantity sequence and frequency sequence according to the weight allocation scheme;

[0019] The quantity sequence and frequency sequence are calculated according to the assigned weights to obtain survey data with a standard format, and a data format standard is established based on the data format of the survey data.

[0020] Furthermore, the survey data input into the database is structurally formatted according to the aforementioned data format standard, specifically including:

[0021] A format correction entry point is established based on the data format standard, and the format correction entry point is set at the data entry point of the database to correct the format of the survey data input into the database.

[0022] Furthermore, the survey data input into the database is structurally formatted according to the aforementioned data format standard, specifically including:

[0023] According to the data format standard, a standard data structure is obtained and a structure adjustment screen is established. The structure adjustment screen performs a structure scan on the input survey data and determines whether the survey data conforms to the standard data structure. If it conforms, the survey data is directly saved to the database. If it does not conform, the following steps are performed:

[0024] The structure adjustment screen intercepts and analyzes the initial data structure of the survey data, extracts content from the survey data based on the initial data structure, obtains data content, establishes a target structure according to the content volume of the data content and the standard data structure, re-attaches the data content to the target structure to generate new survey data, and saves the new survey data to the database.

[0025] Furthermore, based on the survey methods used for the target data, the target data is summarized to generate multiple target datasets corresponding to the survey objects, specifically including:

[0026] Obtain the data type of the target data, and determine the generation method of the target data based on the data type;

[0027] Based on the generation method, the source of the survey method corresponding to the target data is traced to obtain the survey method;

[0028] Based on the obtained survey methods, the target data is summarized to generate multiple target datasets corresponding to the survey objects.

[0029] Furthermore, determining whether the target data in the corresponding target dataset is low-quality data based on the image difference specifically includes:

[0030] Compare each pair of target images corresponding to each target dataset to obtain multiple image differences. Sort the multiple image differences according to their numerical values ​​and determine whether each image difference exceeds the first threshold.

[0031] If a certain image difference exceeds the first threshold, the comparison image difference between the two target datasets corresponding to the image difference and other target datasets is determined respectively. Based on the comparison image difference, an abnormal target dataset is determined in the two target datasets, and the target data in the abnormal target dataset is judged as low-quality data.

[0032] Furthermore, determining whether the target data in the corresponding target dataset is low-quality data based on the image difference specifically includes:

[0033] Establish a difference table, mark the image differences that do not exceed the first threshold on the difference table, generate punctuation marks, and normalize the punctuation marks to obtain the difference line;

[0034] The distance from each punctuation point to the difference line is determined. If the distance from a punctuation point to the difference line exceeds a second threshold, the target data corresponding to that punctuation point is determined to be low-quality data.

[0035] Furthermore, the method also includes:

[0036] Obtain the survey method corresponding to the low-quality data, determine the reason why the survey method generates low-quality data, and generate a solution based on the reason;

[0037] Based on the solution, the survey object is re-surveyed, and the re-surveyed survey data is entered into the database to replace the corresponding low-quality data.

[0038] Secondly, this application provides a civil engineering survey data management system, the system comprising:

[0039] The data acquisition module is used to acquire survey data for civil engineering projects and input it into the database for storage.

[0040] The data processing module is used to determine the data type of the survey data, obtain the usage frequency of each data type and establish a data format standard based on the usage frequency; and to standardize the structure and format of the survey data input to the database according to the data format standard.

[0041] The data extraction module is used to identify the survey object, extract target data related to the survey object from the database, and summarize the target data according to the survey method of the target data to generate multiple target datasets corresponding to the survey object.

[0042] The data judgment module is used to create a profile of the survey object based on the data characteristics of the target data for each target dataset, obtain a target profile, calculate the profile difference between the target profiles of any two target datasets, and determine whether the target data in the corresponding target dataset is low-quality data based on the profile difference.

[0043] The civil engineering survey data management method and system described in this application have at least one of the following beneficial effects:

[0044] 1. The acquired civil engineering survey data is classified, and the frequency of use of each type of survey data is analyzed. The data format with the highest frequency of use is adopted as the data format standard. The data input into the database is checked according to the data format standard. Survey data that does not meet the standard is modified in format and structured before being stored in the database, thereby improving the uniformity and high quality of the survey data format in the database.

[0045] 2. Generate a weight allocation scheme for quantity and frequency sequences based on the size of the project requirements. Perform weight allocation and calculation on the quantity and frequency sequences in the input format standard generation model to obtain result survey data with a standard format. Establish a data format standard based on the data format of this result survey data, thereby improving the convenience of establishing the data format standard.

[0046] 3. Feature extraction is performed on the target dataset to obtain data features. Based on these features, a profile description of the survey object is created, resulting in a target profile. Multiple target profiles corresponding to the survey object are compared pairwise to obtain profile differences. The profile differences are then sorted, and it is determined whether the profile differences exceed a first threshold. If the first threshold is exceeded, the problematic target datasets in the two target datasets are identified, and the target data in these datasets are marked as low-quality data. If the first threshold is not exceeded, a difference table is created, and the profile differences are marked on the difference table. Normalization is then performed, and the distance from each marked point to the difference line is determined. It is then determined whether this distance exceeds a second threshold. If it does, the target data corresponding to that marked point is judged as low-quality data, thereby improving the comprehensiveness of the quality evaluation of the survey data. Attached Figure Description

[0047] Figure 1 A flowchart illustrating a civil engineering survey data management method provided in this application embodiment;

[0048] Figure 2 A flowchart illustrating a process for establishing a data format standard based on usage frequency, provided in an embodiment of this application;

[0049] Figure 3 This application provides a schematic diagram of a process for establishing a data format standard based on a quantity sequence and a frequency sequence, as illustrated in an embodiment of the present application.

[0050] Figure 4 A flowchart illustrating a structural format specification method provided in an embodiment of this application;

[0051] Figure 5 A schematic flowchart illustrating a method for generating a target dataset provided in an embodiment of this application;

[0052] Figure 6 This is a schematic flowchart of a method for judging low-quality data provided in an embodiment of this application;

[0053] Figure 7 This is a schematic diagram of the structure of a civil engineering survey data management system provided in an embodiment of this application. Detailed Implementation

[0054] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0055] In some of the processes described in the specification and accompanying drawings of this application, multiple operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or they may be executed in parallel. The sequence numbers of the operations, such as 100, 200, etc., are merely used to distinguish different operations and do not represent any execution order in themselves. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel.

[0056] The technical solutions of this application are applicable to application scenarios that require the management of survey data during the engineering construction process.

[0057] Because the surveyed objects have undergone multiple surveys, the survey data from different aspects may be affected by factors such as measurement errors, human error, or equipment failure. Furthermore, different survey data have different structures and formats, making the survey data difficult to use. At the same time, it is difficult to identify low-quality survey data during the actual use of these survey data, thus affecting the progress of the project.

[0058] To facilitate unified management of survey data and identify low-quality data, thereby improving data quality, this application proposes a technical solution. In this embodiment, a data format standard is established based on the usage frequency of the survey data. The survey data input into the database is then checked against this standard. Data that does not conform to the standard is modified in format and structure before being stored in the database, thus improving the uniformity and high quality of the survey data in the database. Simultaneously, for multiple target datasets of the survey object, a profile description of the survey object is created based on the data characteristics of the target data, obtaining a target profile. The profile difference between any two target datasets corresponding to the target profiles is calculated. Based on this profile difference, it is determined whether the target data in the corresponding target dataset is low-quality data, thus enabling the identification and replacement of low-quality data and improving the overall quality of the survey data.

[0059] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0060] Figure 1 A flowchart illustrating a civil engineering survey data management method provided in this application embodiment, the method comprising the following steps:

[0061] Step 100: Establish a database and obtain survey data for civil engineering projects;

[0062] In practical applications, survey data can include topographic data, geological data, hydrological data, measurement data, and material testing data, which are used to provide basic information for engineering design, planning, and construction.

[0063] Step 200: Determine the data type of the survey data, obtain the usage frequency of each data type, and establish a data format standard based on the usage frequency;

[0064] Please see Figure 2 In this embodiment of the application, step 200 specifically includes:

[0065] Step 201: Perform collection processing on the survey data according to the data type of the survey data to obtain the survey dataset corresponding to each data type, and obtain the data volume and usage frequency of the survey data in each survey dataset;

[0066] Step 202: Sort the survey dataset according to the data volume to obtain a quantity sequence, and sort the survey data in each survey dataset according to the usage frequency to obtain a frequency sequence;

[0067] Step 203: Establish a data format standard based on the quantity sequence and frequency sequence.

[0068] In practical applications, civil engineering survey data is acquired, its data type and volume are determined, datasets are established, and the frequency of its use is assessed. A standard format is then determined, and a data format standard is established. For example, if a batch of survey data is acquired and processed, it is found that it contains data types 1, 2, and 3, with the data volume of type 1 being greater than that of type 2, which is greater than that of type 3. Based on these types, three survey datasets are established. The frequency of their use is determined, and it is found that type 1 is greater than type 2, which is greater than type 3. A frequency sequence is obtained, and then a quantity sequence is obtained based on the data volume. Using the frequency and quantity sequences, the type of survey data (type 1) is calculated and used as the standard format, thus establishing a data format standard.

[0069] Please see Figure 3 In this embodiment of the application, step 203 specifically includes:

[0070] Step 2031: Establish a format standard generation model, determine the project requirements of the corresponding engineering project based on the data type, and generate a weight allocation scheme based on the project requirements.

[0071] Step 2032: Input the quantity sequence and frequency sequence into the format standard generation model, and assign weights to the quantity sequence and frequency sequence according to the weight allocation scheme;

[0072] Step 2033: Calculate the quantity sequence and frequency sequence according to the assigned weights to obtain the result survey data with a standard format, and establish a data format standard based on the data format of the result survey data.

[0073] In practical applications, weight allocation schemes can be generated based on the project requirements of different types of survey data. The quantity and frequency sequences are input into the model, and the results are calculated according to the weight allocation scheme. For example, if a batch of survey data contains three data types (Type 1, Type 2, and Type 3), and the project requirements for these three types of survey data are calculated, it can be determined that Type 1 survey data accounts for 80% of the project requirements during the project construction process. Therefore, the usage frequency of Type 1 survey data is very high, and the weight allocation for the frequency sequence is 70%, while the weight allocation for the quantity sequence is 30%. Substituting these values ​​into the format standard generation model, the calculated survey data is Type 1 survey data. Therefore, Type 1 is used as the standard format to establish a data format standard.

[0074] Step 300: Input the acquired survey data into the database for storage, and at the same time, standardize the structure and format of the survey data input into the database according to the data format standard.

[0075] Please see Figure 4 In this embodiment of the application, the step of standardizing the structure and format of the survey data input into the database specifically includes:

[0076] Step 301: Establish a format correction entry point according to the data format standard, and set the format correction entry point at the data entry point of the database to correct the format of the survey data input into the database;

[0077] Step 302: Obtain the standard data structure according to the data format standard and establish a structure adjustment screen. The structure adjustment screen performs a structure scan on the input survey data and determines whether the survey data conforms to the standard data structure. If it does, the survey data is directly saved to the database. If it does not, proceed to step 303.

[0078] Step 303: The structure adjustment screen intercepts and analyzes the initial data structure of the survey data, extracts the content of the survey data based on the initial data structure, obtains the data content, establishes a target structure according to the content volume of the data content and the standard data structure, re-attaches the data content on the target structure to generate new survey data, and saves the new survey data to the database.

[0079] In practical applications, a format correction entry and structure adjustment screen can be established in the database according to the data format standard to modify the input survey data. For example, if the data standard format is Type 1, and all the survey data is Type 2 when inputting data into the database, the format of the Type 2 data will be adjusted. At the same time, the data structure of Type 2 will be scanned and determined to be non-compliant with the standard. The data content of the Type 2 survey data will then be extracted, and a new data structure will be built based on Type 1. The data content of the Type 2 survey data will be pasted onto the newly built data structure and then input into the database.

[0080] Step 400: Determine the survey object, extract target data related to the survey object from the database, and summarize the target data according to the survey method of the target data to generate multiple target datasets corresponding to the survey object;

[0081] Please see Figure 5 In this embodiment of the application, the method for generating the target dataset includes the following steps:

[0082] Step 401: Obtain the data type of the target data, and determine the generation method of the target data based on the data type;

[0083] Step 402: Based on the generation method, trace the source of the survey method corresponding to the target data to obtain the survey method;

[0084] Step 403: Based on the obtained survey method, summarize the target data to generate multiple target datasets corresponding to the survey object.

[0085] In practical applications, based on the acquired target data, the generation method is determined, and the source of the surveying method is traced to establish the target dataset. For example, if a batch of surveying data contains two types of data, Type 1 and Type 2, tracing the source of the surveying method reveals that Type 1 is from manual measurement and Type 2 is from remote sensing measurement. Therefore, the data of Type 1 and Type 2 are summarized based on the manual measurement and remote sensing measurement to generate the target dataset.

[0086] Step 500: For each target dataset, create a profile of the survey object based on the data characteristics of the target data to obtain a target profile, and calculate the profile difference between the target profiles corresponding to any two target datasets. Based on the profile difference, determine whether the target data in the corresponding target dataset is low-quality data.

[0087] Please see Figure 6 In this embodiment of the application, determining whether the target data in the corresponding target dataset is low-quality data based on the image difference specifically includes the following steps:

[0088] Step 501: Compare each pair of target images corresponding to each target dataset to obtain multiple image differences. Sort the multiple image differences according to their numerical values ​​and determine whether each image difference exceeds the first threshold.

[0089] Step 502: If a certain image difference exceeds the first threshold, then determine the comparison image difference between the two target datasets corresponding to the image difference and other target datasets respectively, determine the abnormal target dataset in the two target datasets based on the comparison image difference, and determine the target data in the abnormal target dataset as low-quality data.

[0090] Step 503: Establish a difference table, mark the image differences that do not exceed the first threshold on the difference table, generate punctuation marks, and normalize the punctuation marks to obtain the difference line;

[0091] Step 504: Determine the distance from each punctuation point to the difference line. If the distance from a punctuation point to the difference line exceeds the second threshold, the target data corresponding to that punctuation point is determined to be low-quality data.

[0092] In practical applications, target datasets are used to create profiles of the surveyed objects, and the differences between different target profiles are assessed to determine the presence of low-quality data. For example, if there are four target datasets, data features are extracted from these four datasets, and profiles are created to describe the surveyed objects, resulting in four target profiles. These four profiles are then compared. If profile number 3 shows a significant difference from the other profiles, the target data in the dataset corresponding to profile number 3 is considered low-quality data. If the differences between the four profiles are not significant, a difference table is created, and the differences between the profiles are marked and processed. Normalization is then performed to generate a straight line. The point furthest from the line is considered low-quality data if it is extremely far from the line.

[0093] Please see Figure 7 In this embodiment of the application, the civil engineering survey data management method further includes the following steps:

[0094] Step 511: Obtain the survey method corresponding to the low-quality data, determine the reason why the survey method generates low-quality data, and generate a solution based on the reason;

[0095] Step 512: Based on the solution, re-survey the survey object and input the re-surveyed survey data into the database to replace the corresponding low-quality data.

[0096] In practical applications, the survey methods corresponding to low-quality data are examined to determine the cause of the low data quality. New survey data is then acquired to replace the original low-quality data. For example, if a batch of low-quality data A exists in the database, the survey method for A is examined. If it is found that a certain step in the survey process resulted in low data quality due to human error, that step is improved to eliminate the human error. New survey data B is then acquired and entered into the database to replace the original data A.

[0097] In summary, the civil engineering survey data management method provided in this application generates a weighted allocation scheme for quantity and frequency sequences based on the size of project requirements. It then assigns weights to and calculates these sequences in the input format standard generation model, obtaining survey data with a standard format. This standard format is used to establish a data format standard, thereby improving the ease of establishing the standard. The method checks the format of data input into the database according to the established standard, modifying the format and structure of survey data that does not meet the standard before storing it in the database, thus improving the uniformity and high quality of the survey data in the database. Finally, it compares multiple target profiles corresponding to the survey object pairwise to obtain profile differences. Based on these differences, it determines whether the target data in the corresponding target dataset is low-quality data, thus enabling the identification and replacement of low-quality data and improving the overall quality of the survey data.

[0098] Based on the above technical solution, this embodiment also proposes a civil engineering survey data management system. Please refer to [link / reference]. Figure 7 The system includes:

[0099] The data acquisition module is used to acquire survey data for civil engineering projects and input it into the database for storage.

[0100] The data processing module is used to determine the data type of the survey data, obtain the usage frequency of each data type and establish a data format standard based on the usage frequency; and to standardize the structure and format of the survey data input to the database according to the data format standard.

[0101] The data extraction module is used to identify the survey object, extract target data related to the survey object from the database, and summarize the target data according to the survey method of the target data to generate multiple target datasets corresponding to the survey object.

[0102] The data judgment module is used to create a profile of the survey object based on the data characteristics of the target data for each target dataset, obtain a target profile, calculate the profile difference between the target profiles of any two target datasets, and determine whether the target data in the corresponding target dataset is low-quality data based on the profile difference.

[0103] It is understood that since the civil engineering survey data management system described in this application embodiment is a system for implementing the civil engineering survey data management method described in the embodiment, the system disclosed in the embodiment is relatively simple to describe because it corresponds to the method disclosed in the embodiment. For relevant parts, please refer to the description of the method, and it will not be repeated here.

Claims

1. A method for managing civil engineering survey data, characterized in that, The method includes: Establish a database and obtain survey data for civil engineering projects; Determine the data type of the survey data, obtain the usage frequency of each data type, and establish a data format standard based on the usage frequency; The acquired survey data is input into the database for storage, and the structure and format of the survey data input into the database are standardized according to the data format standard. The survey object is identified, target data related to the survey object is extracted from the database, and the target data is summarized according to the survey method of the target data to generate multiple target datasets corresponding to the survey object. For each target dataset, a profile description of the survey object is created based on the data characteristics of the target data to obtain a target profile. The profile difference between the target profiles corresponding to any two target datasets is calculated, and the target data in the corresponding target dataset is determined as low-quality data based on the profile difference.

2. The method for managing civil engineering survey data according to claim 1, characterized in that, Obtain the usage frequency of each data type and establish a data format standard based on the usage frequency, specifically including: The survey data is processed according to the data type to obtain the survey dataset corresponding to each data type, and the amount of survey data and the frequency of use in each survey dataset are obtained. The survey datasets are sorted according to the data volume to obtain a quantity sequence, and the survey data in each survey dataset are sorted according to the usage frequency to obtain a frequency sequence. A data format standard is established based on the quantity sequence and frequency sequence.

3. The method for managing civil engineering survey data according to claim 2, characterized in that, A data format standard is established based on the quantity sequence and frequency sequence, specifically including: Establish a format standard generation model, determine the project requirements of the corresponding engineering project based on the data type, and generate a weight allocation scheme based on the project requirements; The quantity sequence and frequency sequence are input into the format standard generation model, and weights are assigned to the quantity sequence and frequency sequence according to the weight allocation scheme; The quantity sequence and frequency sequence are calculated according to the assigned weights to obtain survey data with a standard format, and a data format standard is established based on the data format of the survey data.

4. The method for managing civil engineering survey data according to claim 1, characterized in that, The survey data input into the database is structured and formatted according to the aforementioned data format standard, specifically including: A format correction entry point is established based on the data format standard, and the format correction entry point is set at the data entry point of the database to correct the format of the survey data input into the database.

5. The method for managing civil engineering survey data according to claim 4, characterized in that, The survey data input into the database is structured and formatted according to the aforementioned data format standard, specifically including: According to the data format standard, a standard data structure is obtained and a structure adjustment screen is established. The structure adjustment screen performs a structure scan on the input survey data and determines whether the survey data conforms to the standard data structure. If it conforms, the survey data is directly saved to the database. If it does not conform, the following steps are performed: The structure adjustment screen intercepts and analyzes the initial data structure of the survey data, extracts content from the survey data based on the initial data structure, obtains data content, establishes a target structure according to the content volume of the data content and the standard data structure, re-attaches the data content to the target structure to generate new survey data, and saves the new survey data to the database.

6. The method for managing civil engineering survey data according to claim 1, characterized in that, Based on the survey methods used for the target data, the target data is summarized to generate multiple target datasets corresponding to the survey objects, specifically including: Obtain the data type of the target data, and determine the generation method of the target data based on the data type; Based on the generation method, the source of the survey method corresponding to the target data is traced to obtain the survey method; Based on the obtained survey methods, the target data is summarized to generate multiple target datasets corresponding to the survey objects.

7. The method for managing civil engineering survey data according to claim 1, characterized in that, Determining whether the target data in the corresponding target dataset is low-quality data based on the image difference specifically includes: Compare each pair of target images corresponding to each target dataset to obtain multiple image differences. Sort the multiple image differences according to their numerical values ​​and determine whether each image difference exceeds the first threshold. If a certain image difference exceeds the first threshold, the comparison image difference between the two target datasets corresponding to the image difference and other target datasets is determined respectively. Based on the comparison image difference, an abnormal target dataset is determined in the two target datasets, and the target data in the abnormal target dataset is judged as low-quality data.

8. The method for managing civil engineering survey data according to claim 7, characterized in that, Determining whether the target data in the corresponding target dataset is low-quality data based on the image difference specifically includes: Establish a difference table, mark the image differences that do not exceed the first threshold on the difference table, generate punctuation marks, and normalize the punctuation marks to obtain the difference line; The distance from each punctuation point to the difference line is determined. If the distance from a punctuation point to the difference line exceeds a second threshold, the target data corresponding to that punctuation point is judged as low-quality data.

9. The method for managing civil engineering survey data according to claim 1, characterized in that, The method further includes: Obtain the survey method corresponding to the low-quality data, determine the reason why the survey method generates low-quality data, and generate a solution based on the reason; Based on the solution, the survey object is re-surveyed, and the re-surveyed survey data is entered into the database to replace the corresponding low-quality data.

10. A civil engineering survey data management system, characterized in that, The system includes: The data acquisition module is used to acquire survey data for civil engineering projects and input it into the database for storage. The data processing module is used to determine the data type of the survey data, obtain the usage frequency of each data type and establish a data format standard based on the usage frequency; and to standardize the structure and format of the survey data input to the database according to the data format standard. The data extraction module is used to identify the survey object, extract target data related to the survey object from the database, and summarize the target data according to the survey method of the target data to generate multiple target datasets corresponding to the survey object. The data judgment module is used to create a profile of the survey object based on the data characteristics of the target data for each target dataset, obtain a target profile, calculate the profile difference between the target profiles of any two target datasets, and determine whether the target data in the corresponding target dataset is low-quality data based on the profile difference.

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