A method, system, device and medium for automatically generating road engineering projects

Abstract

The present application provides a method, system, device and medium for automatically generating road engineering projects. The method obtains a real-time engineering data set and multiple historical engineering data sets of a target project, determines multiple similar engineering data sets based on the real-time engineering data set and all the historical engineering data sets, determines the multiple data peak proximity of the target project based on all the similar engineering data sets, and then determines the engineering sensitive data sequence corresponding to all the similar engineering data sets based on all the data peak proximity, determines the engineering difference feature quantity of each engineering sensitive data in the engineering sensitive data sequence, determines the engineering data stage table of the target project based on all the engineering difference feature quantities, and automatically generates the engineering project of the target project based on the engineering data stage table, thereby realizing the automatic generation of engineering projects.

Description

Technical Field

[0001] The present application relates to the technical field of engineering projects, and more specifically, to a method, system, equipment and medium for automatically generating road engineering projects. Background Art

[0002] An engineering project generally refers to a planned, organized, and budgeted series of work carried out to achieve a certain goal or complete a task. Engineering projects can cover a variety of fields, including roads, infrastructure, information technology, and manufacturing.

[0003] The automatic generation of engineering projects is to first collect all necessary information about the engineering projects, including task lists, resource requirements, dependencies, constraints, etc., and then use computer software, algorithms and technologies to input relevant information of the project, set constraints and dependencies between project tasks, and automatically generate reasonable engineering projects, reducing errors in manual operations and making projects more flexible and adaptable. However, in existing technologies, a large number of professionals are often required to evaluate engineering projects and a lot of time is spent on generating engineering projects, resulting in a waste of human resources. Therefore, how to achieve automatic generation of engineering projects has become a difficult problem faced by the industry. Summary of the Invention

[0004] The present application provides a method, system, equipment and medium for automatically generating road engineering projects, which can intelligently realize the automatic generation of engineering projects.

[0005] In a first aspect, the present application provides a method for automatically generating a road engineering project, comprising the following steps:

[0006] Obtain the real-time engineering dataset and multiple historical engineering datasets of the target project;

[0007] determining a plurality of similar engineering data sets based on the real-time engineering data set and all historical engineering data sets;

[0008] Determine the proximity of multiple data peaks of the target project based on all similar project data sets, and then determine the engineering sensitive data sequences corresponding to all similar project data sets based on all data peak proximity;

[0009] Determine an engineering difference feature value of each engineering sensitive data in the engineering sensitive data sequence, and determine an engineering data stage table of the target project based on all the engineering difference feature values;

[0010] Automatically generate engineering projects of the target project based on the engineering data stage table.

[0011] In some embodiments, determining a plurality of similar engineering datasets based on the real-time engineering dataset and all historical engineering datasets specifically includes:

[0012] Select a historical engineering data set, determine the engineering difference of each historical engineering data in the historical engineering data set based on the real-time engineering data set, determine the engineering data similarity coefficient of the historical engineering data set based on all the engineering difference quantities, repeat the above steps to determine the engineering data similarity coefficients of the remaining historical engineering data sets;

[0013] Determine the engineering similarity threshold of the similarity coefficient of all engineering data;

[0014] Selecting a project data similarity coefficient; when the project data similarity coefficient is greater than the project similarity threshold, using the project data similarity coefficient as the project similarity time deviation value; repeating the above steps to judge the remaining project data similarity coefficients to obtain multiple project similarity time deviation values;

[0015] The historical engineering datasets corresponding to similar time deviation values ​​of each project are all used as similar engineering datasets.

[0016] In some embodiments, determining the proximity of multiple data peaks of the target project based on all similar project data sets specifically includes:

[0017] Select a similar engineering data set and determine the engineering compaction factor of each similar engineering data in the similar engineering data set;

[0018] All engineering compaction factors are combined into an engineering compaction sequence of the similar engineering data set;

[0019] Repeat the above steps to determine the engineering compaction sequence of the remaining similar engineering data sets;

[0020] Convert all engineering compaction sequences into engineering compaction matrices;

[0021] The proximity of multiple data peaks of the target project is determined according to the project consolidation matrix.

[0022] In some embodiments, determining the engineering sensitive data sequences corresponding to all similar engineering data sets based on the proximity of all data peaks specifically includes:

[0023] Identify multiple engineering sensitive data based on all data peak proximity and all similar engineering data sets;

[0024] Determine the engineering sensitive data sequences corresponding to all similar engineering data sets based on all engineering sensitive data.

[0025] In some embodiments, determining the engineering difference feature value of each engineering sensitive data in the engineering sensitive data sequence specifically includes:

[0026] Determining the engineering construction data corresponding to each engineering sensitive data in the engineering sensitive data sequence;

[0027] The engineering difference feature value corresponding to each engineering sensitive data in the engineering sensitive data sequence is determined based on all engineering construction data.

[0028] In some embodiments, determining the engineering data stage table of the target engineering project based on all engineering difference feature quantities specifically includes:

[0029] Obtain all engineering construction data;

[0030] All engineering construction data are converted into an engineering data phase table of the target project according to all engineering difference characteristic quantities.

[0031] In some embodiments, the real-time engineering data in the real-time engineering data set is a collection of basic road data in a real-time engineering node in the target project.

[0032] In a second aspect, the present application provides an automatic generation system for road engineering projects, comprising:

[0033] An acquisition module is used to acquire a real-time engineering dataset and multiple historical engineering datasets of a target project;

[0034] a processing module, configured to determine a plurality of similar engineering data sets based on the real-time engineering data set and all historical engineering data sets;

[0035] The processing module is further configured to determine the proximity of multiple data peaks of the target project based on all similar project data sets, and then determine the engineering sensitive data sequences corresponding to all similar project data sets based on the proximity of all data peaks;

[0036] The processing module is further configured to determine an engineering difference feature value of each engineering sensitive data in the engineering sensitive data sequence, and determine an engineering data stage table of a target engineering project based on all the engineering difference feature values;

[0037] An execution module is used to automatically generate engineering items of a target engineering project according to the engineering data stage table.

[0038] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a code, and the processor is configured to obtain the code and execute the above-mentioned method for automatically generating a road engineering project.

[0039] In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program, which implements the above-mentioned method for automatically generating road engineering projects when executed by a processor.

[0040] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0041] The present application provides a method, system, device, and medium for automatically generating road engineering projects. Multiple similar engineering datasets are determined using a real-time engineering dataset and multiple historical engineering datasets of a target project. Multiple historical projects with low similarity to the target project are removed to facilitate the selection of historical projects close to the target project. An engineering data similarity coefficient is calculated for each similar engineering data in each similar engineering dataset. The engineering data similarity coefficient reflects the degree of similarity between each historical engineering node and a real-time engineering node. All the largest engineering data similarity coefficients are combined to form a data peak proximity sequence. Furthermore, based on the data peak proximity sequence, a group of historical road basic data sequences with the greatest similarity to the real-time engineering dataset of the target project are extracted from all similar engineering datasets. Based on this group of historical road basic data sequences, an engineering construction dataset of the target project is predicted. A compensation amount for each engineering construction data in the engineering construction dataset is determined using the real-time engineering dataset. Each compensation amount is used as an engineering difference feature. Each engineering construction data in the engineering construction dataset is compensated based on all the engineering difference features, thereby obtaining an engineering data stage table for the target project. Finally, the engineering data stage table is used to automatically generate engineering projects for the target project, thereby achieving automatic generation of engineering projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is an exemplary flow chart of a method for automatically generating a road engineering project according to some embodiments of the present application;

[0043] Figure 2 is an exemplary flow chart of determining similar engineering data sets according to some embodiments of the present application;

[0044] Figure 3 is an exemplary flow chart of determining data peak proximity according to some embodiments of the present application;

[0045] Figure 4 is a schematic diagram of exemplary hardware and / or software of a system for automatically generating a road engineering project according to some embodiments of the present application;

[0046] Figure 5 It is a structural diagram of a computer device for implementing an automatic generation method of a road engineering project according to some embodiments of the present application. DETAILED DESCRIPTION

[0047] The core of this application is to obtain the real-time engineering data set and multiple historical engineering data sets of the target project, determine multiple similar engineering data sets based on the real-time engineering data set and all historical engineering data sets, determine the multiple data peak proximity of the target project based on all similar engineering data sets, and then determine the engineering sensitive data sequence corresponding to all similar engineering data sets based on all data peak proximity, determine the engineering difference feature quantity of each engineering sensitive data in the engineering sensitive data sequence, determine the engineering data stage table of the target project based on all engineering difference feature quantities, and automatically generate engineering projects of the target project based on the engineering data stage table, so as to realize the automatic generation of engineering projects.

[0048] In order to better understand the above technical solution, the following will be combined with the accompanying drawings and specific implementation methods to describe the above technical solution in detail. Figure 1 , which is an exemplary flow chart of a method for automatically generating a road engineering project according to some embodiments of the present application. The method 100 for automatically generating a road engineering project mainly includes the following steps:

[0049] In step 101 , a real-time engineering dataset and multiple historical engineering datasets of a target project are obtained.

[0050] In specific implementation, the real-time engineering data set of the target project is obtained through the engineering project database of the target project, and the real-time engineering data set is a collection of all real-time engineering data; the project type of the target project is obtained through the engineering construction unit, and multiple historical projects with the same project type are matched from the historical project library through the project type, and the historical engineering data set of each historical project is obtained through the engineering project database, thereby obtaining multiple historical engineering data sets, and the historical engineering data set is a collection of all historical engineering data.

[0051] It should be noted that the real-time engineering data in the real-time engineering data set in this application is a collection of road basic data in a real-time engineering node in the target project, wherein, when the target project is a road, the real-time engineering node may be a section of the road under construction, and the road basic data is the length, width, height or terrain height, slope size, landform type and other basic information of the section. It should be noted that after a real-time engineering node is completed, the next real-time engineering node will be started, that is, the construction of the next section will be started after the construction of the section is completed; the historical engineering data in the historical engineering data set is a collection of historical road basic data in a historical engineering node in the historical project, wherein, when the historical project is a completed road, the historical engineering node may be a completed section of the completed road, and the historical road basic data is the length, width, height or terrain height, slope size, landform type and other basic information of the completed section. It should be noted that one real-time engineering node corresponds to one historical engineering node.

[0052] In step 102 , a plurality of similar engineering data sets are determined based on the real-time engineering data set and all historical engineering data sets.

[0053] In some embodiments, reference Figure 2 As shown in FIG, this figure is a schematic diagram of the process of determining similar engineering data sets in some embodiments of the present application. In this embodiment, determining similar engineering data sets can be implemented using the following steps:

[0054] First, in step 1021, a historical engineering dataset is selected, and the engineering difference of each historical engineering data in the historical engineering dataset is determined based on the real-time engineering dataset. The engineering data similarity coefficient of the historical engineering dataset is determined based on all the engineering difference quantities. The above steps are repeated to determine the engineering data similarity coefficients of the remaining historical engineering datasets.

[0055] Next, in step 1022, the engineering similarity threshold of the similarity coefficients of all engineering data is determined;

[0056] Then, in step 1023, a project data similarity coefficient is selected. When the project data similarity coefficient is greater than the project similarity threshold, the project data similarity coefficient is used as the project similarity time deviation value. The above steps are repeated to determine the remaining project data similarity coefficients to obtain multiple project similarity time deviation values.

[0057] Finally, in step 1024, the historical project data sets corresponding to the similar time deviation values ​​of each project are all used as similar project data sets.

[0058] Among them, in the specific implementation, the engineering difference amount of each historical engineering data in the historical engineering data set is determined according to the real-time engineering data set, that is: select a historical engineering data in the historical engineering data set, subtract the long value of the historical engineering data from the long value of the engineering data corresponding to the historical engineering data in the real-time engineering data set, subtract the wide value of the historical engineering data from the wide value of the engineering data corresponding to the historical engineering data in the real-time engineering data set, and subtract the high value of the historical engineering data from the high value of the engineering data corresponding to the historical engineering data in the real-time engineering data set. The average value of all values ​​obtained by the above subtraction is used as the engineering difference amount of the historical engineering data, and the above steps are repeated to determine the engineering difference amount of the remaining historical engineering data in the historical engineering data set. It should be noted that the engineering difference amount in this application represents the parameter value of the degree of difference between the road basic data in the target project and the road basic data in the historical project, so as to facilitate the identification of historical projects. This is an example to illustrate that other data are similar, so the data here are compared and differentiated with historical data.

[0059] In some embodiments, the engineering data similarity coefficient of the historical engineering data set may be determined based on all engineering difference values ​​according to the following formula:

[0060]

[0061] in, represents the engineering data similarity coefficient of the historical engineering dataset, Indicates the The engineering difference, Represents the average value of all engineering differences, Indicates the maximum engineering difference, Indicates the minimum engineering difference, Indicates the total number of all engineering differences.

[0062] It should be noted that the engineering data similarity coefficient in this application reflects the degree of conformity between the target project and the historical project. The smaller the engineering data similarity coefficient, the higher the degree of conformity between the target project and the historical project.

[0063] Among them, it should be noted that the engineering similarity threshold in this application is a parameter value that reflects the central trend of all engineering data similarity coefficients, and is used to analyze all engineering data similarity coefficients. In some embodiments, the average value of all engineering data similarity coefficients can be used as the engineering similarity threshold; when the engineering data similarity coefficient is greater than the engineering similarity threshold, the engineering data similarity coefficient is used as the engineering similarity time deviation value; if the engineering data similarity coefficient is less than or equal to the engineering similarity threshold, the engineering data similarity coefficient is deleted. The engineering similarity time deviation value represents the parameter value of the degree of deviation between the target project and the historical project.

[0064] It should be noted that in this application, a project similar time deviation value corresponds to a historical project data set. The similar project data set is composed of all similar project data. The similar project data set represents the road basic data set of historical projects close to the target project, so as to be used to predict the road basic data of the target project; all project similar time deviation values ​​are arranged in ascending order, and the arranged sequence is used as the project similar time deviation value sequence; all similar project data sets are arranged in the order of the corresponding project similar time deviation values ​​in the project similar time deviation value sequence, and the arranged sequence is used as the similar project data set sequence.

[0065] In step 103, multiple data peak proximity of the target project is determined based on all similar project data sets, and then the engineering sensitive data sequences corresponding to all similar project data sets are determined based on all data peak proximity.

[0066] In some embodiments, reference Figure 3 As shown in FIG. 1 , this figure is a schematic diagram of a process for determining the proximity of a data peak in some embodiments of the present application. In this embodiment, determining the proximity of a data peak can be achieved by using the following steps:

[0067] First, in step 1031, a similar engineering data set is selected, and the engineering compaction factor of each similar engineering data in the similar engineering data set is determined;

[0068] Next, in step 1032, all engineering compaction factors are combined into an engineering compaction sequence of the similar engineering data set;

[0069] Then, in step 1033, the above steps are repeated to determine the engineering compaction sequence of the remaining similar engineering data sets;

[0070] Thus, in step 1034, all engineering compaction sequences are converted into engineering compaction matrices;

[0071] Finally, in step 1035 , the proximity of multiple data peaks of the target project is determined based on the project consolidation matrix.

[0072] In some embodiments, determining the engineering compaction factor of each similar engineering data in the similar engineering data set may be achieved by using the following steps:

[0073] Determine the engineering picket coefficient ;

[0074] Get the first of similar engineering data The value of road basic data ;

[0075] Get the real-time engineering data set The first similar engineering data corresponds to the real-time engineering data The value of road basic data ;

[0076] Get the first Project difference ;

[0077] Get the engineering data similarity coefficient of the similar engineering data set ;

[0078] According to the engineering picket coefficient , the similar engineering data set of similar engineering data The value of road basic data , the real-time engineering data set The first similar engineering data corresponds to the real-time engineering data The value of road basic data , the similar engineering data set Project difference The engineering data similarity coefficient of the similar engineering data set Determine the engineering compaction factor of the similar engineering data, wherein the engineering compaction factor can be determined according to the following formula:

[0079]

[0080] in, Indicates the The engineering consolidation factor of similar engineering data, Indicates the number of similar engineering data sets The sum of the values ​​of all road basic data in similar engineering data, Indicates the first The sum of the values ​​of all road basic data in the real-time engineering data corresponding to the similar engineering data. Represents the base-2 logarithm function.

[0081] It should be noted that the engineering correction coefficient in this application represents a parameter for correcting the road basic data in the real-time engineering data. In some embodiments, the engineering correction coefficient can be set by all historical engineering data through the existing cross-validation method. The value range of the engineering correction coefficient is 0-1. In other embodiments, other methods can also be used for setting, which is not limited here.

[0082] It should be noted that in this application The value of can be 1, 2, 3. When 1, Represents the first The longest value in similar engineering data, Indicates the first Similar engineering data corresponds to the longest value in the real-time engineering data. When it is 2, Represents the first The width value in similar engineering data, Indicates the first Similar engineering data corresponds to the widest value in real-time engineering data. It is 3 o'clock, Represents the first The highest value among similar engineering data, Indicates the first The high value in the real-time engineering data corresponds to the similar engineering data; the engineering compaction factor reflects the parameter of the degree of overlap between the road basic data of the real-time engineering and the road basic data of the historical engineering.

[0083] It should be noted that this basic data can be other data, but its significance is to reflect one point: indicating the degree of data overlap.

[0084] Among them, in the specific implementation, all the engineering tamping factors are composed into the engineering tamping sequence of the similar engineering data set, that is: all the engineering tamping factors are arranged in the order of the corresponding engineering nodes, and the sequence obtained by arrangement is used as the engineering tamping sequence of the similar engineering data set; it should be noted that in this application, one engineering tamping factor corresponds to one engineering node; all the engineering tamping sequences are converted into engineering tamping matrices, that is: all the engineering tamping sequences are arranged in the order of the corresponding similar engineering data sets in the similar engineering data set sequence, the first engineering tamping sequence of the sequence obtained by arrangement is selected, and the first engineering tamping factor in the engineering tamping sequence is used as the engineering tamping matrix. The first engineering tamping factor in the first row of the tamping matrix is ​​used as the second engineering tamping factor in the engineering tamping sequence, and so on, until the last engineering tamping factor in the engineering tamping sequence is used as the last engineering tamping factor in the first row of the engineering tamping matrix. Repeat the above steps to determine the remaining engineering tamping factors in the engineering tamping matrix, which is a matrix composed of all engineering tamping factors; determine the multiple data peak proximity of the target project according to the engineering tamping matrix, that is, the maximum engineering tamping factor of each column in the engineering tamping matrix is ​​used as the data peak proximity of the target project.

[0085] It should be noted that the data peak proximity in this application is a parameter value that reflects the maximum similarity between a project node and a corresponding historical project node, so as to select the historical project data with the greatest similarity to the project data of the target project.

[0086] In some embodiments, the following steps may be used to determine the engineering sensitive data sequences corresponding to all similar engineering data sets based on the proximity of all data peaks:

[0087] Identify multiple engineering sensitive data based on all data peak proximity and all similar engineering data sets;

[0088] Determine the engineering sensitive data sequences corresponding to all similar engineering data sets based on all engineering sensitive data.

[0089] In specific implementation, multiple engineering sensitive data are determined based on all data peak proximities and all similar engineering data sets, that is: all data peak proximities are arranged in the order of corresponding engineering nodes, and the obtained sequence is used as the data peak proximity sequence, and a data peak proximity in the data peak proximity sequence is selected, and the similar engineering data corresponding to the data peak proximity is used as the engineering sensitive data of the data peak proximity, and the above steps are repeated to determine the engineering sensitive data of the remaining data peak proximity; the engineering sensitive data sequence corresponding to all similar engineering data sets is determined based on all engineering sensitive data, that is: all engineering sensitive data are arranged in the order of corresponding data peak proximities in the data peak proximity sequence, and the sequence obtained by arrangement is used as the engineering sensitive data sequence corresponding to all similar engineering data sets.

[0090] It should be noted that the engineering sensitive data in the engineering sensitive data sequence in this application is the data that reflects the greatest similarity with the real-time engineering data in the real-time engineering data set, and is used to predict all engineering data of the target project.

[0091] In step 104, the engineering difference feature of each engineering sensitive data in the engineering sensitive data sequence is determined, and the engineering data stage table of the target project is determined based on all the engineering difference feature values.

[0092] In some embodiments, determining the engineering difference feature of each engineering sensitive data in the engineering sensitive data sequence can be achieved by the following steps;

[0093] Determining the engineering construction data corresponding to each engineering sensitive data in the engineering sensitive data sequence;

[0094] The engineering difference feature value corresponding to each engineering sensitive data in the engineering sensitive data sequence is determined based on all engineering construction data.

[0095] Among them, in the specific implementation, the engineering construction data corresponding to each engineering sensitive data in the engineering sensitive data sequence is determined, that is: the engineering construction data corresponding to each engineering sensitive data in the engineering sensitive data sequence is obtained through the historical engineering construction database, and the engineering construction data is a combination of the start time and end time of the historical engineering node corresponding to the engineering construction data. It should be noted that in this application, one engineering construction data corresponds to one historical engineering node.

[0096] In some embodiments, determining the engineering difference feature value corresponding to each engineering sensitive data in the engineering sensitive data sequence based on all engineering construction data can be achieved by using the following steps:

[0097] Get the first The sum of the values ​​of all road basic data in the sensitive data of the project ;

[0098] Determine the The engineering interval of engineering sensitive data corresponding to engineering construction data ;

[0099] Get the engineering picket coefficient ;

[0100] Get the The engineering consolidation factor corresponding to the engineering sensitive data ;

[0101] According to the engineering sensitive data sequence The sum of the values ​​of all road basic data in the sensitive data of the project 、the said The engineering interval of engineering sensitive data corresponding to engineering construction data , the project progress deviation coefficient and the aforementioned The engineering consolidation factor corresponding to the engineering sensitive data Determine the engineering difference feature of the engineering sensitive data, and then obtain the engineering difference feature corresponding to each engineering sensitive data in the engineering sensitive data sequence, wherein the engineering difference feature can be determined according to the following formula:

[0102]

[0103] in, Indicates the The engineering difference feature quantity of engineering sensitive data, represents the engineering difference equilibrium value, Indicates the first Each engineering sensitive data corresponds to the sum of the values ​​of all road basic data in the real-time engineering data.

[0104] In specific implementation, select an engineering sensitive data in the engineering sensitive data sequence, subtract the long value of the engineering sensitive data from the long value of the real-time engineering data corresponding to the engineering sensitive data in the real-time engineering data set, subtract the wide value of the engineering sensitive data from the wide value of the real-time engineering data corresponding to the engineering sensitive data in the real-time engineering data set, and subtract the high value of the engineering sensitive data from the high value of the real-time engineering data corresponding to the engineering sensitive data in the real-time engineering data set. The sum of all values ​​obtained by the above subtractions is used as the engineering adaptation difference amount of the engineering sensitive data. Repeat the above steps to determine the engineering adaptation difference amounts of the remaining engineering sensitive data in the engineering sensitive data sequence, and use the average value of all engineering adaptation difference amounts as the engineering difference balance value. The engineering difference balance value represents the parameter value for measuring the central trend of the distance difference between the target project and the historical project; the interval time value between the start time and the end time in the engineering construction data is used as the interval amount of the engineering construction data.

[0105] It needs additional explanation. The length, width and height are used here for the best understanding. There is no essential difference if other basic data are used. In principle, the main purpose here is to show the differences.

[0106] It should be noted that the engineering difference characteristic quantity in this application is a parameter value that reflects the degree of compensation for engineering construction data, so as to facilitate the prediction of the progress of the target project.

[0107] In some embodiments, determining the engineering data stage table of the target engineering project based on all engineering difference feature quantities can be achieved by using the following steps:

[0108] Obtain all engineering construction data;

[0109] All engineering construction data are converted into an engineering data phase table of the target project according to all engineering difference characteristic quantities.

[0110] In the specific implementation, all engineering construction data are converted into an engineering data stage table according to all engineering difference characteristics, that is: select an engineering difference characteristic, add the engineering difference characteristic with the end time of the engineering construction data corresponding to the engineering difference characteristic, and use the combination of the end time after the addition and the start time of the engineering construction data corresponding to the engineering difference characteristic as the engineering progress solidified data, repeat the above steps to determine the remaining engineering progress solidified data, and make all the engineering progress solidified data into a table through the third-party library openpyxl of the existing python software, and use the obtained table as the engineering data stage table of the target project.

[0111] It should be noted that the solidified project progress data in this application is the progress data after adjusting the project construction data, which is used to predict the progress of the target project; the project data stage table is a table that predicts the construction progress of each project node of the target project.

[0112] In step 105, engineering items of the target project are automatically generated according to the engineering data stage table.

[0113] In specific implementation, the engineering data stage table is used as the engineering project construction table of the target project, and the engineering project construction table and the construction configuration file of the target project are used to generate the engineering project of the target project through an engineering project management tool, such as procore software. Other methods can be used to implement it in other embodiments, which are not limited here.

[0114] It should be noted that the construction configuration file of the target project in this application includes but is not limited to construction site design documents, construction foundation engineering tables, overall project structure configuration files and construction site layout files, etc. The construction configuration file of the target project can be obtained through the construction configuration system of the target project, which will not be repeated here.

[0115] It should be noted that in this application, multiple historical projects of the same project type as the target project are matched, and the historical project data set of each historical project is obtained. By comparing the similarity between the historical project data and the real-time project data, the historical project data of each historical project node with the greatest similarity is screened out, and then the project sensitive data sequence is obtained. All the engineering construction data of the target project are predicted through the engineering sensitive data sequence, and corresponding adjustments are made to the predicted engineering construction data. Then, an engineering data stage table is generated through all the adjusted engineering construction data, and the engineering data stage table is used as the engineering project construction table of the target project.

[0116] In addition, in another aspect of the present application, in some embodiments, the present application provides an automatic generation system for road engineering projects, referring to Figure 4 , which is a schematic diagram of exemplary hardware and / or software of a system for automatically generating road engineering projects according to some embodiments of the present application. The system 400 for automatically generating road engineering projects includes: an acquisition module 401, a processing module 402, and an execution module 403, which are described as follows:

[0117] Acquisition module 401, in this application, the historical engineering data set acquisition module 401 is mainly used to acquire the real-time engineering data set of the target project and multiple historical engineering data sets;

[0118] Processing module 402, in this application, the processing module 402 is used to determine multiple similar engineering data sets based on the real-time engineering data set and all historical engineering data sets;

[0119] The processing module 402 in the present application is further configured to determine the proximity of multiple data peaks of the target project based on all similar project data sets, and then determine the engineering sensitive data sequences corresponding to all similar project data sets based on the proximity of all data peaks;

[0120] The processing module 402 in this application is further configured to determine an engineering difference feature value for each engineering sensitive data in the engineering sensitive data sequence, and determine an engineering data stage table for the target engineering based on all the engineering difference feature values;

[0121] Execution module 403, in this application, execution module 403 is mainly used to automatically generate engineering projects of the target engineering according to the engineering data stage table.

[0122] In addition, the present application also provides a computer device, which includes a memory and a processor, wherein the memory stores code, and the processor is configured to obtain the code and execute the above-mentioned method for automatically generating road engineering projects.

[0123] In some embodiments, reference Figure 5 , which is a schematic diagram of the structure of a computer device according to some embodiments of the present application using the method for automatically generating road engineering projects. The method for automatically generating road engineering projects in the above embodiments can be Figure 5 The computer device 500 shown in FIG. 5 is implemented as shown in FIG. 5 . The computer device 500 includes at least one processor 501 , a communication bus 502 , a memory 503 , and at least one communication interface 504 .

[0124] The processor 501 may be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more processors for controlling the execution of the method for automatically generating road engineering projects in the present application.

[0125] The communication bus 502 may include a pathway for transmitting information between the aforementioned components.

[0126] The memory 503 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, a random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto. The memory 503 may be independent and connected to the processor 501 via the communication bus 502. The memory 503 may also be integrated with the processor 501.

[0127] The memory 503 is used to store program code for executing the solution of the present application, and is controlled by the processor 501 for execution. The processor 501 is used to execute the program code stored in the memory 503. The program code may include one or more software modules. The determination of similar engineering data sets in the above embodiment can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0128] The communication interface 504 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area network (WLAN), etc.

[0129] In a specific implementation, as an example, a computer device may include multiple processors, each of which may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. A processor herein may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0130] The aforementioned computer device can be a general-purpose computer device or a dedicated computer device. In a specific implementation, the computer device can be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. The embodiments of this application do not limit the type of computer device.

[0131] In addition, the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned method for automatically generating road engineering projects.

[0132] In summary, in the automatic generation method, system, device and storable medium of road engineering projects disclosed in the embodiments of the present application, first, a real-time engineering data set and multiple historical engineering data sets of the target project are obtained, multiple similar engineering data sets are determined based on the real-time engineering data set and all historical engineering data sets, multiple data peak proximity of the target project is determined based on all similar engineering data sets, and then the engineering sensitive data sequence corresponding to all similar engineering data sets is determined based on all data peak proximity, the engineering difference feature quantity of each engineering sensitive data in the engineering sensitive data sequence is determined, the engineering data stage table of the target project is determined based on all engineering difference feature quantities, and the engineering project of the target project is automatically generated based on the engineering data stage table, thereby realizing the automatic generation of engineering projects.

[0133] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0134] Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present application fall within the scope of the claims and their equivalents, the present application is intended to include such modifications and variations.

Claims

1. A method for automatically generating a road engineering project, characterized in that: The steps include: Obtain the real-time engineering dataset and multiple historical engineering datasets of the target project; determining a plurality of similar engineering data sets based on the real-time engineering data set and all historical engineering data sets; Determine the proximity of multiple data peaks of the target project based on all similar project data sets, and then determine the engineering sensitive data sequences corresponding to all similar project data sets based on all data peak proximity; Determine an engineering difference feature value of each engineering sensitive data in the engineering sensitive data sequence, and determine an engineering data stage table of the target project based on all the engineering difference feature values; Automatically generate engineering items of the target project based on the engineering data stage table; Determine multiple similar engineering datasets based on the real-time engineering dataset and all historical engineering datasets, including: Select a historical engineering data set, determine the engineering difference of each historical engineering data in the historical engineering data set based on the real-time engineering data set, determine the engineering data similarity coefficient of the historical engineering data set based on all the engineering difference quantities, repeat the above steps to determine the engineering data similarity coefficients of the remaining historical engineering data sets; Determine the engineering similarity threshold of the similarity coefficient of all engineering data; Selecting a project data similarity coefficient; when the project data similarity coefficient is greater than the project similarity threshold, using the project data similarity coefficient as the project similarity time deviation value; repeating the above steps to judge the remaining project data similarity coefficients to obtain multiple project similarity time deviation values; The historical engineering data sets corresponding to similar time deviation values ​​of each project are all used as similar engineering data sets; Determining the proximity of multiple data peaks of the target project based on all similar project data sets specifically includes: Select a similar engineering data set and determine the engineering compaction factor of each similar engineering data in the similar engineering data set; All engineering compaction factors are combined into an engineering compaction sequence of the similar engineering data set; Repeat the above steps to determine the engineering compaction sequence of the remaining similar engineering data sets; Convert all engineering compaction sequences into engineering compaction matrices; Determining proximity of multiple data peaks of the target project according to the project consolidation matrix; The maximum engineering consolidation factor of each column in the engineering consolidation matrix is ​​used as the data peak proximity of the target project; The engineering compaction factor can be determined according to the following formula: in, Indicates the The engineering consolidation factor of similar engineering data, Indicates the number of similar engineering data sets The sum of the values ​​of all road basic data in similar engineering data, Indicates the first The sum of the values ​​of all road basic data in the real-time engineering data corresponding to the similar engineering data. represents the logarithmic function with base 2; represents the engineering picket coefficient, Represents the first of similar engineering data The value of the road basic data, Indicates the first The first similar engineering data corresponds to the real-time engineering data The value of the road basic data, Represents the first The engineering difference, The engineering data similarity coefficient representing similar engineering data sets; The engineering sensitive data sequences corresponding to all similar engineering data sets are determined by the proximity of all data peaks, including: Identify multiple engineering sensitive data based on all data peak proximity and all similar engineering data sets; Determine the engineering sensitive data sequences corresponding to all similar engineering data sets based on all engineering sensitive data; In a specific implementation, multiple engineering sensitive data are determined based on all data peak proximity and all similar engineering data sets, including arranging all data peak proximities according to the order of corresponding engineering nodes, using the obtained sequence as a data peak proximity sequence, selecting a data peak proximity in the data peak proximity sequence, and using the similar engineering data corresponding to the data peak proximity as the engineering sensitive data of the data peak proximity, repeating the above steps to determine the engineering sensitive data of the remaining data peak proximity; determining the engineering sensitive data sequence corresponding to all similar engineering data sets based on all engineering sensitive data, including arranging all engineering sensitive data according to the order of corresponding data peak proximities in the data peak proximity sequence, and using the sequence obtained by arrangement as the engineering sensitive data sequence corresponding to all similar engineering data sets; Data peak proximity is a parameter value that reflects the maximum similarity between a project node and the corresponding historical project node; The engineering sensitive data in the engineering sensitive data sequence is the data that reflects the greatest similarity with the real-time engineering data in the real-time engineering data set; The engineering compaction factor is a parameter that reflects the degree of overlap between the road foundation data of the real-time engineering and the road foundation data of the historical engineering.

2. The method according to claim 1, wherein Determining the engineering difference feature value of each engineering sensitive data in the engineering sensitive data sequence specifically includes: Determining the engineering construction data corresponding to each engineering sensitive data in the engineering sensitive data sequence; The engineering difference feature value corresponding to each engineering sensitive data in the engineering sensitive data sequence is determined based on all engineering construction data.

3. The method according to claim 1, wherein The engineering data phase table of the target project is determined based on all engineering difference characteristics, including: Obtain all engineering construction data; All engineering construction data are converted into an engineering data phase table of the target project according to all engineering difference characteristic quantities.

4. The method according to claim 1, wherein The real-time engineering data in the real-time engineering data set is a collection of basic road data in a real-time engineering node in the target project.

5. An automatic generation system for road engineering projects, characterized in that: include: An acquisition module is used to acquire a real-time engineering dataset and multiple historical engineering datasets of a target project; a processing module, configured to determine a plurality of similar engineering data sets based on the real-time engineering data set and all historical engineering data sets; The processing module is further configured to determine the proximity of multiple data peaks of the target project based on all similar project data sets, and then determine the engineering sensitive data sequences corresponding to all similar project data sets based on the proximity of all data peaks; The processing module is further configured to determine an engineering difference feature value of each engineering sensitive data in the engineering sensitive data sequence, and determine an engineering data stage table of a target engineering project based on all the engineering difference feature values; An execution module, configured to automatically generate engineering items of a target engineering project according to the engineering data stage table; Determine multiple similar engineering datasets based on the real-time engineering dataset and all historical engineering datasets, including: Select a historical engineering data set, determine the engineering difference of each historical engineering data in the historical engineering data set based on the real-time engineering data set, determine the engineering data similarity coefficient of the historical engineering data set based on all the engineering difference quantities, repeat the above steps to determine the engineering data similarity coefficients of the remaining historical engineering data sets; Determine the engineering similarity threshold of the similarity coefficient of all engineering data; Selecting a project data similarity coefficient; when the project data similarity coefficient is greater than the project similarity threshold, using the project data similarity coefficient as the project similarity time deviation value; repeating the above steps to judge the remaining project data similarity coefficients to obtain multiple project similarity time deviation values; The historical engineering data sets corresponding to similar time deviation values ​​of each project are all used as similar engineering data sets; Determining the proximity of multiple data peaks of the target project based on all similar project data sets specifically includes: Select a similar engineering data set and determine the engineering compaction factor of each similar engineering data in the similar engineering data set; All engineering compaction factors are combined into an engineering compaction sequence of the similar engineering data set; Repeat the above steps to determine the engineering compaction sequence of the remaining similar engineering data sets; Convert all engineering compaction sequences into engineering compaction matrices; Determining proximity of multiple data peaks of the target project according to the project consolidation matrix; The maximum engineering consolidation factor of each column in the engineering consolidation matrix is ​​used as the data peak proximity of the target project; The engineering compaction factor can be determined according to the following formula: in, Indicates the The engineering consolidation factor of similar engineering data, Indicates the number of similar engineering data sets The sum of the values ​​of all road basic data in similar engineering data, Indicates the first The sum of the values ​​of all road basic data in the real-time engineering data corresponding to the similar engineering data. represents the logarithmic function with base 2; represents the engineering picket coefficient, Represents the first of similar engineering data The value of the road basic data, Indicates the first The first similar engineering data corresponds to the real-time engineering data The value of the road basic data, Represents the first The engineering difference, The engineering data similarity coefficient representing similar engineering data sets; The engineering sensitive data sequences corresponding to all similar engineering data sets are determined by the proximity of all data peaks, including: Identify multiple engineering sensitive data based on all data peak proximity and all similar engineering data sets; Determine the engineering sensitive data sequences corresponding to all similar engineering data sets based on all engineering sensitive data; In a specific implementation, multiple engineering sensitive data are determined based on all data peak proximity and all similar engineering data sets, including arranging all data peak proximities according to the order of corresponding engineering nodes, using the obtained sequence as a data peak proximity sequence, selecting a data peak proximity in the data peak proximity sequence, and using the similar engineering data corresponding to the data peak proximity as the engineering sensitive data of the data peak proximity, repeating the above steps to determine the engineering sensitive data of the remaining data peak proximity; determining the engineering sensitive data sequence corresponding to all similar engineering data sets based on all engineering sensitive data, including arranging all engineering sensitive data according to the order of corresponding data peak proximities in the data peak proximity sequence, and using the sequence obtained by arrangement as the engineering sensitive data sequence corresponding to all similar engineering data sets; Data peak proximity is a parameter value that reflects the maximum similarity between a project node and the corresponding historical project node; The engineering sensitive data in the engineering sensitive data sequence is the data that reflects the greatest similarity with the real-time engineering data in the real-time engineering data set; The engineering compaction factor is a parameter that reflects the degree of overlap between the road foundation data of the real-time engineering and the road foundation data of the historical engineering.

6. A computer device, characterized in that: The computer device includes a memory and a processor, wherein the memory stores codes, and the processor is configured to obtain the codes and execute the method for automatically generating a road engineering project according to any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for automatically generating a road engineering project as claimed in any one of claims 1 to 4 is implemented.

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