Information data analysis system based on engineering practice

Abstract

The invention discloses an information data analysis system based on engineering practice, which relates to the technical field of engineering data processing, and comprises the following modules: an engineering database establishment module for acquiring design stage data, construction stage data and completion acceptance stage data, establishing an engineering database, establishing a stage data set in the engineering database, and establishing a stage data set in the engineering database; the design stage data, the construction stage data and the completion acceptance stage data are stored in a stage data set in the form of three stage sub-data sets; the data is regularly screened, so that the phenomenon that only one copy of data is stored in a backup data set due to the fact that no data is lost in a stage data set, a structure data set and a change data set when the data is backed up in a singular data backup module is avoided, loss of single data is further avoided, and the data backup efficiency is improved. And meanwhile, more than two duplicated data which are not in the corresponding classification data sets in the engineering database are deleted, so that the storage space of the engineering database is conveniently saved.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering data processing, and specifically to an information data analysis system based on engineering practice. Background Art

[0002] The construction project practice data covers rich information in all stages from project planning, design to construction and acceptance. These data include but are not limited to the performance parameters of building materials, the specific indicators of construction techniques, the time node records of project progress, and the results of quality inspections, etc. By collecting, analyzing and applying these data, the planning and design of construction projects can be carried out more scientifically, the construction plan can be optimized, and the construction efficiency and quality can be improved.

[0003] The construction project has a large scale and a long management cycle, which is one of the characteristics of modern construction projects. Due to the long construction period and numerous data, even if the data clerk uses database storage instead of paper data storage in data management, it is difficult to quickly and accurately locate the information data to be retrieved among the numerous data. At the same time, due to the long storage period of data materials, the personnel changes and weak technology of data clerks and other reasons, the phenomenon of data loss often occurs, thus affecting the accuracy of data analysis in the construction project practice process. For this reason, we propose an information data analysis system based on engineering practice. Summary of the Invention

[0004] (I) Technical Problems to be Solved

[0005] In view of the deficiencies of the prior art, the present invention provides an information data analysis system based on engineering practice to solve the above problems in the prior art.

[0006] (II) Technical Solutions

[0007] To achieve the above object, the present invention is realized through the following technical solutions: An information data analysis system based on engineering practice includes the following modules:

[0008] An engineering database construction module, which obtains the data in the design stage, construction stage and completion acceptance stage, establishes an engineering database, creates a stage data set in the engineering database, and stores the data in the design stage, construction stage and completion acceptance stage in three stage sub-data sets in the stage data set respectively;

[0009] A data structure classification module, which respectively obtains structured data and unstructured data from the stage data set, creates a structure data set in the engineering database, and stores the structured data and unstructured data in three structure sub-data sets in the structure data set respectively;

[0010] The data status classification module establishes a deletion data set, obtains initial data and corrected data according to the deletion data set, establishes a change data set in the engineering database, and stores the initial data and corrected data in the change data set as two change sub-data sets respectively;

[0011] The single data backup module establishes a backup data set, copies the data in the phase data set to the backup data set, deletes the data of the structure data set in the backup data set, and deletes the data of the change data set in the backup data set;

[0012] The data supplement and deletion module obtains missing data, and supplements the missing data and deletes duplicate data based on the missing data through the phase data set, structure data set, change data set and backup data set.

[0013] Preferably, the phase data designed in the engineering database construction module includes topographic and geomorphic data, architectural design data and structural design data of building projects;

[0014] The topographic and geomorphic data includes contour data, elevation point data, site terrain undulation data, drainage data, soil type data, bearing capacity data, and groundwater level data;

[0015] The architectural design data includes architectural floor plans, sectional views, elevation views, renderings, and the specification and performance data of building materials for doors, windows and walls;

[0016] The structural design data includes structural calculation books, structural drawings, and structural material data of concrete strength and steel bar specifications.

[0017] Preferably, the construction phase data in the engineering database construction module includes progress data, quality data, safety data, cost data and personnel and equipment data;

[0018] The progress data includes the progress plan and actual progress records of building construction;

[0019] The quality data includes material inspection reports, construction process inspection data and quality acceptance records;

[0020] The safety data includes safety inspection records, accident reports and safety training records;

[0021] The cost data includes budget documents, cost accounting records and project change visa records;

[0022] The personnel and equipment data includes the list of construction personnel, the qualification certificates of construction personnel, the equipment list and the equipment operation records.

[0023] Preferably, the data in the completion acceptance stage in the engineering database construction module includes completion drawings, quality inspection reports, completion acceptance reports and project settlement data.

[0024] Preferably, structured data and unstructured data are respectively obtained from the phase dataset in the data structure classification module, specifically as follows:

[0025] Step 1: Obtain all numerical data and categorical data from the three-phase sub-datasets of the phase dataset respectively, and delete the duplicate data in all numerical data and categorical data to obtain structured data;

[0026] Step 2: Obtain all descriptive text data, image data, audio data, and video data from the three-phase sub-datasets of the phase dataset respectively, and delete the duplicate data in all text data, image data, audio data, and video data to obtain unstructured data.

[0027] Preferably, a deletion dataset is established in the data status classification module, and initial data and corrected data are obtained according to the deletion dataset, specifically as follows:

[0028] Step 1: Establish a deletion dataset. When data is deleted, transfer the deleted data to the deletion dataset, set the automatic emptying and destruction time of the deletion dataset, empty and delete the data in the deletion dataset according to the automatic emptying and destruction time of the deletion dataset, obtain the data deletion record time, set the correction cycle duration, and obtain the supplementary recording time range by summing the data deletion record time and the correction cycle duration, and obtain the supplementary recording data within the supplementary recording time range;

[0029] Step 2: Respectively convert the data in the deletion dataset and the corresponding supplementary recording data into hash values through the hash algorithm according to the supplementary recording time range, and record them as deletion hash values and supplementary recording hash values;

[0030] Step 3: Determine whether the deletion hash value is the same as the supplementary recording hash value. If the deletion hash value is different from the supplementary recording hash value, define the supplementary recording data corresponding to the supplementary recording hash value as corrected data. If the deletion hash value is the same as the supplementary recording hash value, define the deletion data corresponding to the deletion hash value as initial data.

[0031] Preferably, the single data backup module is specifically as follows:

[0032] Step 1: Obtain all the data in the phase dataset, obtain all the data in the structure dataset, obtain all the data in the change dataset, establish a backup dataset, and copy all the data in the phase dataset into the backup dataset for storage;

[0033] Step 2: Copy all the data in the phase dataset, the structure dataset, and the change dataset and convert them into hash values through the hash algorithm, and record them as phase hash values, structure hash values, and change hash values;

[0034] Step 3: Capture the data in the phase hash value that is the same as the structure hash value, denoted as the structure identical hash value, and capture the data in the phase hash value that is the same as the change hash value, denoted as the change identical hash value;

[0035] Step 4: Delete the data corresponding to the structure identical hash value in the backup dataset, and delete the data corresponding to the change identical hash value in the backup dataset.

[0036] Preferably, the data supplement and deletion module is specifically as follows:

[0037] Step 1: Set a data missing judgment period, perform periodic data missing judgment on the data in the engineering database according to the data missing judgment period, establish a missing judgment dataset, and copy all the data in the phase dataset, structure dataset, change dataset, and backup dataset and incorporate them into the missing judgment dataset;

[0038] Step 2: Convert all the data in the missing judgment dataset into hash values through a hash algorithm and denote them as judgment hash values. Determine whether there are non-repeated hash values among all the judgment hash values in the missing judgment dataset. If there are non-repeated hash values, copy the data corresponding to the non-repeated hash values and store them in the backup dataset. If there are no non-repeated hash values, execute Step 3;

[0039] Step 3: Respectively determine whether the repetition count of all the judgment hash values is two. If the repetition count of all the judgment hash values is two, end the missing data supplement. If the repetition count of the judgment hash values is not two, obtain the data corresponding to the judgment hash value and mark it as data to be deleted. Respectively determine whether the data to be deleted in the phase dataset, structure dataset, change dataset, and backup dataset is greater than one. If the data to be deleted is greater than one, delete the repeated data to be deleted in the dataset. If the data to be deleted is less than or equal to one, execute Step 4;

[0040] Step 4: Determine whether the data to be deleted in the structure dataset is structured data or unstructured data. If the data to be deleted is not structured data or unstructured data, delete the data to be deleted in the structure dataset. Determine whether the data to be deleted in the change dataset is initial data or corrected data. If the data to be deleted is not initial data or corrected data, delete the data to be deleted in the change dataset. Determine whether the data to be deleted is stored in the backup dataset. If the data to be deleted is stored in the backup dataset, delete the data to be deleted in the backup dataset.

[0041] (3) Beneficial effects

[0042] The present invention provides an information data analysis system based on engineering practice, having the following beneficial effects:

[0043] (1) In this solution, in the single data backup module, by identifying the single-stored data in the phase dataset, structure dataset, and change dataset, and then backing up and storing the single-stored data, it is ensured that all the data in the engineering database has two copies and is classified by different types. This not only facilitates the data clerk to find the data but also prevents the phenomenon of permanent loss of data caused by the loss of a single piece of data, thereby benefiting to avoid affecting the accuracy of data analysis in the construction project practice process.

[0044] (2) In this solution, in the data entry and deletion module, by regularly screening the data, it is beneficial to avoid the situation where there is no lost data in the phase dataset, structure dataset, and change dataset during data backup in the single data backup module, and the data only exists in one copy in the backup dataset. This further avoids the loss of a single piece of data. At the same time, duplicate data in the engineering database that is more than two copies and not in the corresponding classification dataset is deleted, thus facilitating the saving of the storage space of the engineering database. Description of the Drawings

[0045] Figure 1 It is a schematic structural diagram of an information data analysis system based on engineering practice of the present invention;

[0046] Figure 2 It is a flow chart of an information data analysis system based on engineering practice of the present invention. Detailed Embodiment

[0047] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the 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.

[0048] Please refer to Figure 1 - Figure 2 , the present invention provides an information data analysis system based on engineering practice, including the following modules:

[0049] Engineering database construction module, which obtains the data in the design phase, construction phase, and completion acceptance phase, establishes an engineering database, and establishes a phase dataset in the engineering database. The data in the design phase, construction phase, and completion acceptance phase are respectively stored in the phase dataset as three phase sub-datasets;

[0050] Data structure classification module, which respectively obtains the structured data and unstructured data from the phase dataset, establishes a structure dataset in the engineering database, and stores the structured data and unstructured data in the structure dataset as three structure sub-datasets;

[0051] A data status classification module creates a deletion data set, obtains initial data and corrected data based on the deletion data set, creates a change data set in the engineering database, and stores the initial data and corrected data in the change data set as two change sub-data sets respectively.

[0052] A single data backup module creates a backup data set, copies the data in the stage data set to the backup data set, deletes the data in the structure data set in the backup data set, and deletes the data in the change data set in the backup data set.

[0053] A data supplement and deletion module obtains missing data, and supplements the missing data and deletes duplicate data based on the missing data through the stage data set, structure data set, change data set and backup data set.

[0054] In the engineering database construction module, the designed stage data includes topographic and geomorphic data, architectural design data and structural design data of the construction project.

[0055] The topographic and geomorphic data includes contour data, elevation point data, site terrain undulation data, drainage data, soil type data, bearing capacity data, and groundwater level data.

[0056] The architectural design data includes architectural floor plans, sectional views, elevation views, renderings, and building material specification and performance data of doors, windows and walls.

[0057] The structural design data includes structural calculation books, structural drawings, and structural material data of concrete strength and steel bar specifications.

[0058] In the engineering database construction module, the construction stage data includes progress data, quality data, safety data, cost data and personnel and equipment data.

[0059] The progress data includes the construction progress plan and actual progress records of the building construction.

[0060] The quality data includes material inspection reports, construction process inspection data and quality acceptance records.

[0061] The safety data includes safety inspection records, accident reports and safety training records.

[0062] The cost data includes budget documents, cost accounting records and project change visa records.

[0063] The personnel and equipment data includes the list of construction personnel, construction personnel qualification certificates, equipment list and equipment operation records.

[0064] In the engineering database construction module, the completion acceptance stage data includes as-built drawings, quality inspection reports, completion acceptance reports and project settlement data.

[0065] In this embodiment, in the engineering database construction module, the data of construction engineering practice is classified by stage according to the data obtained in different stages and stored in the stage data set of the engineering database, so as to facilitate the data clerk to find the data to be retrieved according to the engineering stage, and thus facilitate the data clerk to quickly retrieve the data;

[0066] In the data structure classification module of this solution, by establishing a structure data set parallel to the stage data set, the structured data and unstructured data in the engineering database are extracted and classified, so as to facilitate the data clerk to find the data to be retrieved according to the structure type of the data, and thus facilitate the data clerk to quickly retrieve the data;

[0067] In the data status classification module of this solution, by establishing a deletion data set to identify the deleted and modified data in the stage data set, and by establishing a change data set parallel to the stage data set and the structure data set, the initial data and corrected data in the engineering database are extracted and classified, so as to facilitate the data clerk to find the data to be retrieved according to the data replacement status, and thus facilitate the data clerk to quickly retrieve the data;

[0068] In the single data backup module of this solution, by identifying the single stored data in the stage data set, the structure data set and the change data set, and then backing up and storing the single stored data, it is ensured that all the data in the engineering database have two copies and are classified according to different types. Furthermore, it is convenient for the data clerk to find the data while preventing the phenomenon of permanent loss of data caused by the loss of a single data, and thus it is beneficial to avoid affecting the accuracy of data analysis in the process of construction engineering practice;

[0069] In the data supplement and deletion module of this solution, by regularly screening the data, it is beneficial to avoid the phenomenon that there is no lost data in the stage data set, the structure data set and the change data set when backing up the data in the single data backup module, and the data only exists in one copy in the backup data set, so as to further avoid the loss of a single data. At the same time, the duplicate data in the engineering database that is more than two copies and not in the corresponding classification data set is deleted, so as to facilitate saving the storage space of the engineering database;

[0070] It is worth mentioning that the structured data and unstructured data in the structure data set cannot cover all the data in the stage data set. For example, the semi-structured data combining numerical data and image data stored in XML or JSON files is stored in the stage data set but not in the structure data set. Similarly, the initial data and corrected data in the change data set also cannot cover all the data in the stage data set. For example, the data such as safety inspection records that are dynamically updated without deleting historical data are in the stage data set but not in the change data set.

[0071] In the data structure classification module, structured data and unstructured data are obtained from the phase dataset respectively, specifically as follows:

[0072] Step 1: Obtain all numerical data and categorical data from the three phase sub-datasets of the phase dataset respectively, and delete the duplicate data in all numerical data and categorical data to obtain structured data;

[0073] Step 2: Obtain all descriptive text data, image data, audio data and video data from the three phase sub-datasets of the phase dataset respectively, and delete the duplicate data in all text data, image data, audio data and video data to obtain unstructured data.

[0074] In this embodiment, since numerical data refers to data with clear numerical values and units such as dimensions, strengths, weights, etc., categorical data refers to data of different types such as material types, equipment status, etc., and text data refers to data such as engineering reports, fault descriptions, etc., the data is classified and stored according to the data structure type, which can greatly improve the efficiency of data retrieval by data clerks whether manually searching or through program search.

[0075] In the data status classification module, a deletion dataset is established, and initial data and corrected data are obtained according to the deletion dataset, specifically as follows:

[0076] Step 1: Establish a deletion dataset. When deleting data, transfer the deleted data to the deletion dataset, set the automatic emptying and destruction time of the deletion dataset, empty and delete the data in the deletion dataset according to the automatic emptying and destruction time of the deletion dataset, obtain the data deletion record time, set the correction cycle duration, and obtain the supplementary recording time range by summing the data deletion record time and the correction cycle duration, and obtain the supplementary recording data within the supplementary recording time range;

[0077] Step 2: Respectively convert and represent the data in the deletion dataset and the corresponding supplementary recording data into hash values through the hash algorithm, denoted as the deletion hash value and the supplementary recording hash value;

[0078] Step 3: Determine whether the deletion hash value is the same as the supplementary recording hash value. If the deletion hash value is different from the supplementary recording hash value, define the supplementary recording data corresponding to the supplementary recording hash value as the corrected data. If the deletion hash value is the same as the supplementary recording hash value, define the deletion data corresponding to the deletion hash value as the initial data.

[0079] In this embodiment, by transferring the deleted data to the deletion data set, it is convenient to conduct supplementary recording confirmation tracking on the deleted data, thereby facilitating the judgment of whether the deleted data is replacement corrected data, and further facilitating the identification of initial data and corrected data in the phase data set. The data is converted into a hash value representation through the hash algorithm, which is convenient for comparing the data, and further facilitates the judgment of data consistency. It should be noted that the hash algorithm is a prior art, and the specific conversion steps are not elaborated here.

[0080] The single data backup module is specifically as follows:

[0081] Step 1: Obtain all the data in the phase data set, obtain all the data in the structure data set, obtain all the data in the change data set, establish a backup data set, and copy all the data in the phase data set into the backup data set for storage;

[0082] Step 2: Copy all the data in the phase data set, structure data set, and change data set and convert them into hash value representations through the hash algorithm, denoted as the phase hash value, structure hash value, and change hash value;

[0083] Step 3: Capture the data in the phase hash value that is the same as the structure hash value, denoted as the structure-identical hash value, and capture the data in the phase hash value that is the same as the change hash value, denoted as the change-identical hash value;

[0084] Step 4: Delete the data corresponding to the structure-identical hash value in the backup data set, and delete the data corresponding to the change-identical hash value in the backup data set.

[0085] In this embodiment, the data in the phase data set is copied to the backup data set, the data in the structure data set in the backup data set is deleted, and the data in the change data set in the backup data set is deleted, so as to ensure that there are at least two identical copies of the data in the engineering database, which is beneficial to avoiding the phenomenon of permanent loss of data caused by the loss of a single piece of data, and further beneficial to avoiding affecting the accuracy of data analysis in the process of building engineering practice.

[0086] The data supplementary recording and deletion module is specifically as follows:

[0087] Step 1: Set a data missing judgment period, conduct periodic data missing judgment on the data in the engineering database according to the data missing judgment period, establish a missing judgment data set, and copy all the data in the phase data set, structure data set, change data set, and backup data set and incorporate them into the missing judgment data set;

[0088] Step 2: Convert all the data in the missing judgment dataset into hash values through a hash algorithm and denote them as judgment hash values. Determine whether there are non-repeated hash values among all the judgment hash values in the missing judgment dataset. If there are non-repeated hash values, copy the data corresponding to the non-repeated hash values and store them in the backup dataset. If there are no non-repeated hash values, execute Step 3;

[0089] Step 3: Determine whether the number of repetitions of all the judgment hash values is two respectively. If the number of repetitions of all the judgment hash values is two, end the missing data supplementation. If the number of repetitions of the judgment hash values is not two, obtain the data corresponding to the judgment hash value and mark it as data to be deleted. Determine whether the data to be deleted in the phase dataset, structure dataset, change dataset, and backup dataset is greater than one respectively. If the data to be deleted is greater than one, delete the duplicate data to be deleted in the dataset. If the data to be deleted is less than or equal to one, execute Step 4;

[0090] Step 4: Determine whether the data to be deleted in the structure dataset is structured data or unstructured data. If the data to be deleted is not structured data or unstructured data, delete the data to be deleted in the structure dataset. Determine whether the data to be deleted in the change dataset is initial data or corrected data. If the data to be deleted is not initial data or corrected data, delete the data to be deleted in the change dataset. Determine whether the data to be deleted is stored in the backup dataset. If the data to be deleted is stored in the backup dataset, delete the data to be deleted in the backup dataset.

[0091] In this embodiment, by periodically judging the missing situation of the data in the engineering database, it is convenient to supplement the missing data before both copies of the same data are missing. By judging whether the number of duplicate data is greater than two, it is convenient to delete the duplicate and useless data in the engineering database, thereby saving the storage space of the engineering database. After the data supplementation and deletion are completed, the established missing judgment dataset is deleted, thereby further saving the storage space of the engineering database.

[0092] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution.

[0093] The unit described as a separation component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place or may be distributed over multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0094] As described above, the above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application.

Claims

1. An information data analysis system based on engineering practice, characterized in that, It includes the following modules: The engineering database construction module obtains the data in the design stage, construction stage, and completion acceptance stage, establishes an engineering database, creates a stage dataset in the engineering database, and stores the design stage data, construction stage data, and completion acceptance stage data in the stage dataset as three stage sub-datasets respectively; The data structure classification module respectively obtains structured data and unstructured data from the stage dataset, creates a structure dataset in the engineering database, and stores the structured data and unstructured data in the structure dataset as three structure sub-datasets respectively; The data status classification module creates a deletion dataset, obtains the initial data and corrected data according to the deletion dataset, creates a change dataset in the engineering database, and stores the initial data and corrected data in the change dataset as two change sub-datasets respectively; The single data backup module creates a backup dataset, copies the data in the stage dataset to the backup dataset, deletes the data in the structure dataset in the backup dataset, and deletes the data in the change dataset in the backup dataset; The data supplement and deletion module obtains the missing data, and performs data supplementation for the missing data and deletion for the duplicate data based on the missing data through the stage dataset, structure dataset, change dataset, and backup dataset.

2. The information data analysis system based on engineering practice according to claim 1, characterized in that: In the engineering database construction module, the design stage data includes the topographic and geomorphic data, building design data, and structural design data of the construction project; The topographic and geomorphic data includes contour data, elevation point data, site terrain undulation data, drainage data, soil type data, bearing capacity data, and groundwater level data; The building design data includes building floor plans, sectional views, elevation views, renderings, and the building material specification and performance data of doors, windows, and walls; The structural design data includes structural calculation books, structural drawings, and structural material data of concrete strength and steel bar specifications.

3. An information data analysis system based on engineering practice according to claim 1, characterized in that: In the engineering database construction module, the construction stage data includes progress data, quality data, safety data, cost data, and personnel and equipment data; The progress data includes the construction progress plan and actual progress records of the building construction; The quality data includes material inspection reports, construction process inspection data, and quality acceptance records; The safety data includes safety inspection records, accident reports, and safety training records; The cost data includes budget documents, cost accounting records, and project change visa records; The personnel and equipment data includes the list of construction personnel, the qualification certificates of construction personnel, equipment lists, and equipment operation records.

4. An information data analysis system based on engineering practice according to claim 1, characterized in that: In the engineering database construction module, the completion acceptance stage data includes as-built drawings, quality inspection reports, completion acceptance reports, and project settlement data.

5. An information data analysis system based on engineering practice according to claim 1, characterized in that: In the data structure classification module, structured data and unstructured data are respectively obtained from the stage dataset. Specifically: Step 1: Obtain all numerical data and categorical data from the three stage sub-datasets of the stage dataset respectively, and delete the duplicate data in all numerical data and categorical data to obtain structured data; Step 2: Obtain all the descriptive text data, image data, audio data, and video data from the three phase sub-datasets of the phase dataset, and delete the duplicate data in all the text data, image data, audio data, and video data to obtain unstructured data.

6. The information data analysis system based on engineering practice according to claim 1, characterized in that: Establish a deletion dataset in the data status classification module, and obtain the initial data and corrected data according to the deletion dataset. Specifically: Step 1: Establish a deletion dataset. When deleting data, transfer the deleted data to the deletion dataset, set the automatic emptying and destruction time of the deletion dataset, empty and delete the data in the deletion dataset according to the automatic emptying and destruction time of the deletion dataset, obtain the data deletion record time, set the correction cycle duration, and obtain the supplementary recording time range by summing the data deletion record time and the correction cycle duration, and obtain the supplementary recording data within the supplementary recording time range; Step 2: Respectively convert the data in the deletion dataset and the corresponding supplementary recording data into hash values through the hash algorithm according to the supplementary recording time range, and record them as the deletion hash value and the supplementary recording hash value; Step 3: Determine whether the deletion hash value is the same as the supplementary recording hash value. If the deletion hash value is different from the supplementary recording hash value, define the supplementary recording data corresponding to the supplementary recording hash value as the corrected data. If the deletion hash value is the same as the supplementary recording hash value, define the deletion data corresponding to the deletion hash value as the initial data.

7. An information data analysis system based on engineering practice according to claim 1, characterized in that: Single data backup module, specifically: Step 1: Obtain all the data in the phase dataset, obtain all the data in the structure dataset, obtain all the data in the change dataset, establish a backup dataset, and copy all the data in the phase dataset into the backup dataset for storage; Step 2: Copy all the data in the phase dataset, structure dataset, and change dataset and convert them into hash values through the hash algorithm, and record them as the phase hash value, structure hash value, and change hash value; Step 3: Capture the data in the phase hash value that is the same as the structure hash value, and record it as the structure same hash value. Capture the data in the phase hash value that is the same as the change hash value, and record it as the change same hash value; Step 4: Delete the data corresponding to the structure same hash value in the backup dataset, and delete the data corresponding to the change same hash value in the backup dataset.

8. An information data analysis system based on engineering practice according to claim 1, characterized in that: Data supplementary recording and deletion module, specifically: Step 1: Set the data missing judgment cycle, perform periodic data missing judgment on the data in the engineering database according to the data missing judgment cycle, establish a missing judgment dataset, and copy all the data in the phase dataset, structure dataset, change dataset, and backup dataset and incorporate them into the missing judgment dataset; Step 2: Convert all the data in the missing judgment dataset into hash values through the hash algorithm, and record them as the judgment hash value. Determine whether there are non-repeated hash values among all the judgment hash values in the missing judgment dataset. If there are non-repeated hash values, copy the data corresponding to the non-repeated hash values and incorporate them into the backup dataset for storage. If there are no non-repeated hash values, execute Step 3; Step 3: Determine whether the number of duplicates of all the determined hash values is two respectively. If the number of duplicates of all the determined hash values is two, then end the missing data supplement. If the number of duplicates of the determined hash values is not two, then obtain the data corresponding to the determined hash value and mark it as data to be deleted. Determine whether the data to be deleted in the phase dataset, the structure dataset, the change dataset, and the backup dataset is greater than one respectively. If the data to be deleted is greater than one, then delete the duplicate data to be deleted in the dataset. If the data to be deleted is less than or equal to one, then execute Step 4; Step 4: Determine whether the data to be deleted in the structure dataset is structured data or unstructured data. If the data to be deleted is not structured data or unstructured data, then delete the data to be deleted in the structure dataset. Determine whether the data to be deleted in the change dataset is initial data or revised data. If the data to be deleted is not initial data or revised data, then delete the data to be deleted in the change dataset. Determine whether the data to be deleted is stored in the backup dataset. If the data to be deleted is stored in the backup dataset, then delete the data to be deleted in the backup dataset.

Images