CN118015196A - Maintenance fund use monitoring and predicting system based on big data - Google Patents
Maintenance fund use monitoring and predicting system based on big data Download PDFInfo
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Abstract
The invention is applicable to the technical field of fund usage monitoring and prediction, and particularly relates to a maintenance fund usage monitoring and prediction system based on big data, which comprises the following steps: the system comprises a display device, a construction device, a creation device, a monitoring device and a prediction device; the display equipment is used for building a data instrument panel and is divided into a front end and a rear end; the construction equipment is used for collecting image data of the maintenance target object and constructing a three-dimensional model; the creation device is configured to obtain usage data of the maintenance funds, create a usage platform in the backend, and receive a usage request submitted by a user, where the usage request at least includes: and correcting the three-dimensional model based on the use request according to the amount and the use mode. According to the invention, through setting up the data instrument panel, the service condition of the maintenance fund can be displayed more intuitively, the map recognition threshold is reduced, the fund abuse is effectively avoided, and the maintenance fund can be ensured to be used in practice.
Description
Technical Field
The invention relates to the technical field of fund usage monitoring and prediction, in particular to a maintenance fund usage monitoring and prediction system based on big data.
Background
The application of big data in the aspect of fund use monitoring is a continuously developed field, particularly in the aspect of maintenance fund use monitoring, the big data technology allows for real-time monitoring of the use condition of maintenance fund, potential problems can be found in time by integrating and analyzing real-time maintenance records, financial data, supply chain information and the like, the use efficiency and transparency of the maintenance fund are improved, the big data analysis can also be used for predicting the maintenance requirement of equipment or assets, and a user is helped to plan the maintenance fund more reasonably; predicting maintenance funds has many benefits, such as being very important for planning and economic management of maintenance projects, being better able to make budget plans, ensuring that there are sufficient funds for maintaining and repairing the assets; in addition, by predicting the need for maintenance funds, the manager can also more efficiently allocate resources, including manpower, materials, and equipment, which helps to increase resource utilization and reduce maintenance costs.
In addition, with the development of society, the transparency of fund use is also higher and higher, so that the specific use condition of maintenance fund is visible to the outside, which is helpful for establishing a good financial question and responsibility system, and also enables benefit relatives to be tracked and overtaken more easily, so that the reasonable use of maintenance fund is ensured through mass supervision, but the conventional public mode of maintenance fund generally only displays financial data simply, which also leads to higher complexity of data and is not fully understood by ordinary people; therefore, "how to show the specific use condition of the maintenance fund by using the three-dimensional model and the chart, and predict the maintenance condition" is the technical problem to be solved by the invention.
Disclosure of Invention
The invention aims to provide a maintenance fund use monitoring and predicting system based on big data, which aims to solve the problem of how to show the specific use condition of the maintenance fund by using a three-dimensional model and a chart and predict the maintenance condition in the background art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a big data based maintenance fund usage monitoring and prediction system, the system comprising:
The system comprises a display device, a construction device, a creation device, a monitoring device and a prediction device;
The display equipment is used for building a data instrument panel and is divided into a front end and a rear end;
the construction equipment is used for collecting image data of the maintenance target object and constructing a three-dimensional model;
the creation device is configured to obtain usage data of the maintenance funds, create a usage platform in the backend, and receive a usage request submitted by a user, where the usage request at least includes: the amount and the use mode are corrected based on the use request;
The monitoring equipment is used for creating a budget area and a live area in the front end, importing the three-dimensional model into the budget area, clustering the amount, determining at least two sectors and inserting item tags into the sectors; integrating all the sectors, drawing a pie chart, transferring the pie chart to the live area, and updating the pie chart by using a preset trigger logic by taking the using platform as a trigger source;
The prediction device is used for positioning key nodes in the three-dimensional model, inserting links with item labels and amounts into the key nodes, inputting the key nodes into the prediction model after training, generating prediction data, and pushing the prediction data into the front end.
Further, the display device includes:
the instrument panel building module is used for building a data instrument panel;
The division module is used for dividing the data instrument panel into a front end and a rear end.
Further, the construction apparatus includes:
the acquisition module is used for acquiring image data of the maintenance target object;
And the processing module is used for constructing a three-dimensional model according to the image data.
Further, the creation device includes:
the acquisition module is used for acquiring the use data of the maintenance funds and creating a use platform based on the use data;
And the correction module is used for receiving a use request submitted by a user and correcting the three-dimensional model based on the use request.
Further, the monitoring device includes:
an importing module for creating a budget area and a live area, importing the three-dimensional model into the budget area;
The drawing module is used for clustering the amount, determining at least two sectors, inserting item labels into the sectors, integrating all the sectors, drawing a pie chart, and transferring the pie chart to the live area;
And the updating module is used for updating the pie chart by using the using platform as a trigger source and utilizing preset trigger logic.
Further, the prediction apparatus includes:
The link insertion module is used for positioning key nodes of the three-dimensional model, generating links pointing to item labels and money, and inserting the links into the key nodes;
and the prediction generation module is used for inputting the key nodes into the trained prediction model, generating prediction data and pushing the prediction data to the front end.
Further, the dividing module includes:
a building unit 1121, configured to determine a dashboard architecture, and build a data dashboard;
and the building unit is used for determining the technical stack and the communication mode of the instrument panel framework, building the front end and the back end and integrating the front end and the back end into the instrument panel framework.
Further, the processing module includes:
the feature extraction unit is used for carrying out feature extraction on the image data, obtaining feature points, superposing depth information into the feature points, and generating point cloud data, wherein the point cloud data is used for representing coordinates of the feature points in a three-dimensional space;
the reconstruction unit can preprocess the point cloud data and reconstruct a three-dimensional model by utilizing the preprocessed point cloud data.
Further, the updating module includes:
the selecting unit is used for selecting a data set and testing trigger logic by utilizing the data set;
and the pie chart updating unit is used for updating the pie chart by using trigger logic.
Further, the link insertion module includes:
The key node positioning unit is used for traversing the three-dimensional model and positioning key nodes;
and the link inserting unit is used for inserting links into the key nodes and taking the item labels and the amounts as pointing targets of the links.
Compared with the prior art, the invention has the beneficial effects that:
1. The method has the advantages that the service condition of the maintenance funds can be displayed more intuitively by building the data instrument panel, the graph recognition threshold is reduced, the funds are effectively avoided from being abused, the maintenance progress can be displayed by building the three-dimensional model, meanwhile, the condition of the maintained target object can be previewed, the maintenance funds can be better managed by receiving the use request submitted by a user, the maintenance funds can be reasonably used, the accuracy of the three-dimensional model can be ensured by timely updating the three-dimensional model by utilizing the use request, the display effect is improved, the details of the maintenance funds can be shown by drawing the graph, the detailed service condition of the maintenance funds can be better displayed by combining with the three-dimensional model, the transparency of the use of the maintenance funds is greatly improved, and the reasonable and standard use of the maintenance funds is ensured.
2. By constructing the prediction model, the use amount of the maintenance funds can be predicted, so that the maintenance of the target object can be planned better, the financial stability is improved, and the normal development of maintenance engineering is ensured.
Drawings
FIG. 1 is a block diagram of a large data based maintenance fund usage monitoring and prediction system provided by an embodiment of the present invention;
FIG. 2 is a block diagram of the components of a display device in a big data based maintenance fund usage monitoring and prediction system provided by an embodiment of the present invention;
FIG. 3 is a block diagram of the construction equipment in the big data based maintenance fund usage monitoring and prediction system provided by an embodiment of the present invention;
FIG. 4 is a block diagram of the creation device in the big data based maintenance fund usage monitoring and prediction system provided by an embodiment of the present invention;
FIG. 5 is a block diagram of the components of the monitoring device in the big data based maintenance fund usage monitoring and prediction system provided by the embodiment of the present invention;
FIG. 6 is a block diagram of a predictive device in a big data based maintenance fund usage monitoring and prediction system provided by an embodiment of the present invention;
FIG. 7 is a block diagram illustrating the partitioning modules of the big data based maintenance fund usage monitoring and prediction system according to an embodiment of the present invention;
FIG. 8 is a block diagram of a processing module in a big data based maintenance fund usage monitoring and prediction system according to an embodiment of the present invention;
FIG. 9 is a block diagram of an update module in a big data based maintenance fund usage monitoring and prediction system according to an embodiment of the present invention;
FIG. 10 is a block diagram illustrating the components of a link insertion module in a big data based maintenance fund usage monitoring and prediction system according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In embodiment 1, fig. 1 shows a flow of implementation of a large data-based maintenance fund usage monitoring and predicting system 1 according to an embodiment of the present invention, which is described in detail below:
the display device 11 is used for building a data instrument panel and is divided into a front end and a rear end;
The method comprises the steps of building a data instrument panel, dividing the data instrument panel into a front end and a rear end, wherein the front end is mainly responsible for interaction with a user and visual display of data, and the rear end is mainly responsible for displaying the data.
The construction device 12 is used for acquiring image data of the maintenance target object and constructing a three-dimensional model;
the object to be repaired is referred to herein as a repair object, and image data of the repair object is collected, wherein the image data mainly includes size, position, connection mode, etc., and a three-dimensional model of the repair object is constructed by using professional building modeling software such as SketchUp, revit.
The creation device 13 is configured to obtain usage data of the maintenance funds, create a usage platform in the backend, and receive a usage request submitted by a user, where the usage request at least includes: the amount and the use mode are corrected based on the use request;
Obtaining service data of the maintenance funds from a maintenance project manager, wherein the service data is detailed service conditions of the maintenance funds, creating a service platform of the maintenance funds in the rear end, when a user needs to use the maintenance funds, issuing an application in the service platform, namely a service request, wherein the service request should include a specific amount of the applied funds, a service mode and the like, correcting the three-dimensional model according to the service request, for example, 10 ten thousand yuan of the application A, for replacing A in a target object, the amount of money is 10 ten thousand yuan, replacing A in the service mode, and correcting A in the three-dimensional model.
The monitoring device 14 is configured to create a budget area and a live area in the front end, import the three-dimensional model into the budget area, cluster the amounts, determine at least two sectors, and insert item tags into the sectors; integrating all the sectors, drawing a pie chart, transferring the pie chart to the live area, and updating the pie chart by using a preset trigger logic by taking the using platform as a trigger source;
In the display part of the front end, a budget area and a live area are created, wherein the budget area is a three-dimensional model, the maintenance part and the maintenance progress can be visually displayed, and the live area displays real-time use condition of funds.
Clustering the collected usage data, wherein the clustering standard can be multiple, for example, the usage data can be divided into personnel and materials, or can be specifically refined, the usage data can be divided into labor cost, equipment lease cost, material cost, professional service cost and the like, each category corresponds to one sector, and all the sectors are integrated to obtain a pie chart; of course, the data processing can be further performed on the pie chart, different colors or labels are added to the pie chart, meanwhile, the use platform is used as a trigger source, after the use platform receives a user use request, the pie chart is updated according to preset trigger logic, and the preset trigger logic is to adjust the sector by using the use request, so that the pie chart is updated.
The project label is the project name of the sector, for example in a labor cost sector, the project label should be personnel cost.
The prediction device 15 is configured to locate a key node in the three-dimensional model, insert a link with an item tag and an amount into the key node, input the key node into a trained prediction model, generate prediction data, and push the prediction data into a front end.
According to the specific structure of the target object, determining the important part of the target object in maintenance, finding out the key node corresponding to the important part in the three-dimensional model, and generating a link by utilizing the project label and the amount.
For example, after determining the key node a, the link on a is unfolded, so that the maintenance process of the a is referred to in the maintenance process, and of course, the a may not need to be maintained, and no link exists.
The key nodes and corresponding links are input into a predictive model to generate predictive data, wherein the predictive data may include a total amount of use of maintenance funds, a total budget, etc., and the predictive data is pushed into the front end and presented.
In embodiment 2, fig. 2 shows a flow implemented by the big data based maintenance fund usage monitoring and predicting system according to an embodiment of the present invention, and the exhibition device 11 includes:
a dashboard building module 111, configured to build a data dashboard;
The dividing module 112 is configured to divide the data dashboard into a front end and a back end.
The data dashboard is similar to the structure of the data visual analysis dashboard in the market, the data dashboard is divided into a front end and a back end, the front end is responsible for the visualization of user interfaces and data, the back end is responsible for processing data, executing business logic, interacting with databases or other data sources, and the like, and the display part in the front end is mainly divided into a prediction area and a live area.
In embodiment 3, fig. 3 shows a flow implemented by the big data based maintenance fund usage monitoring and predicting system provided in an embodiment of the present invention, and the construction device 12 includes:
an acquisition module 121 for acquiring image data of a maintenance target;
and the processing module 122 is used for constructing a three-dimensional model according to the image data.
Image data of a maintenance target object is collected from an existing database or by using measuring equipment such as a range finder, a level meter and the like, wherein the maintenance target object is an object to be maintained, and a three-dimensional model is constructed by using the collected image data.
In embodiment 4, fig. 4 shows a creation device in a large data-based maintenance fund usage monitoring and predicting system provided by an embodiment of the present invention, the creation device 13 includes:
an obtaining module 131, configured to obtain usage data of maintenance funds, and create a usage platform based on the usage data;
And the correction module 132 is used for receiving a use request submitted by a user and correcting the three-dimensional model based on the use request.
Specific use data of maintenance funds are obtained from a maintenance engineering manager, and a use platform is created, but the creation process of the use platform also needs to consider the problems of technical stacks, database development and the like; when the maintenance fund service system is used, a user uploads a maintenance fund service request to a service platform, and a manager approves the service request.
In actual use, for example, constructors apply for a payment for maintaining the target structure A, upload data such as specific amount of the payment and maintenance of the structure A to a use platform, and update A in the three-dimensional model after approval is completed.
In embodiment 5, fig. 5 shows a flow implemented by the big data based maintenance fund usage monitoring and predicting system according to an embodiment of the present invention, where the monitoring device 14 includes:
an importing module 141 for creating a budget area and a live area, importing the three-dimensional model into the budget area;
a drawing module 142, configured to cluster the amounts, determine at least two sectors, insert item tags into the sectors, integrate all the sectors, draw a pie chart, and transfer the pie chart to the live area;
And an updating module 143, configured to update the pie chart with a preset trigger logic by using the usage platform as a trigger source.
In the front end, a budget area and a live area are created, wherein the budget area is mainly used for displaying a three-dimensional model, the live area is mainly used for displaying a pie chart, maintenance funds are classified, different fund item names are determined, different sectors are generated according to the names and corresponding maintenance fund data, all the sectors are integrated together, the pie chart is drawn, and the pie chart is transferred into the live area; and taking the data in the using platform as a trigger source, and updating the pie chart according to the change item when the data change is received in the using platform, wherein the preset trigger logic is that when the data change occurs in the using platform, the trigger is generated, and the pie chart is updated.
In embodiment 6, fig. 6 shows a prediction apparatus in a large data-based maintenance fund usage monitoring and prediction system provided by an embodiment of the present invention, the prediction apparatus 15 includes:
A link inserting module 151, configured to locate a key node of the three-dimensional model, generate a link pointing to a project label and an amount, and insert the link into the key node;
the prediction generating module 152 is configured to input the key node into a trained prediction model, generate prediction data, and push the prediction data into the front end.
And finding out key nodes in the three-dimensional model, wherein the key nodes are nodes needing maintenance, generating item labels of the key nodes in maintenance engineering, integrating the item labels and the money into one link, and inserting the link into the key nodes.
For example, in the key node A of the three-dimensional model, in the maintenance engineering, when the A structure in the A needs to be maintained, a link is inserted into the A, the link after the jump comprises the amount of the item tag, and the item tag should be more detailed, and at least the specific condition of maintaining the A should be included.
The method comprises the steps of constructing a prediction model, wherein the prediction model is mainly used for outputting prediction data, budget data comprise total use amount of maintenance funds, total budget and the like, training the prediction model by utilizing an existing data set or public data, inputting key nodes and corresponding links and the like into the training model, outputting the prediction data, and pushing the prediction data into a front end.
In embodiment 7, fig. 7 shows a partitioning module 112 in the big data based maintenance fund usage monitoring and predicting system provided in an embodiment of the present invention, the partitioning module includes:
and a building unit 1121, configured to determine a dashboard architecture, and build a data dashboard.
The building unit 1122 is configured to determine a technology stack and a communication manner of the instrument panel architecture, build a front end and a back end, and integrate the front end and the back end into the instrument panel architecture.
The instrument panel framework comprises a front end, a rear end, a data layer, a buffer layer and other parts, the data instrument panel is built according to the instrument panel framework, the technical stack and the communication mode in the instrument panel framework are determined, the front end and the rear end are respectively built, and then the front end and the rear end are integrated into the data instrument panel framework to complete the building of the data instrument panel.
In embodiment 8, fig. 8 shows a processing module 122 in a big data based maintenance fund usage monitoring and predicting system provided by an embodiment of the present invention, the processing module 122 includes:
a feature extraction unit 1221, configured to perform feature extraction on the image data, obtain feature points, and superimpose depth information on the feature points to generate point cloud data, where the point cloud data is used to characterize coordinates of the feature points in a three-dimensional space;
And a reconstruction unit 1222 capable of preprocessing the point cloud data and reconstructing a three-dimensional model using the preprocessed point cloud data.
And performing feature extraction on the image data, taking the extracted points as feature points, adding depth information into the feature points to generate point cloud data, processing the point cloud data, such as point cloud filtering, resampling, outlier removal and the like, and finally reconstructing a three-dimensional model by using the processed point cloud data.
In embodiment 9, fig. 9 shows an update module 143 in a large data-based maintenance fund usage monitoring and predicting system provided by an embodiment of the present invention, where the update module 143 includes:
a selection unit 1431 for selecting a data set and testing the trigger logic using the data set;
a pie chart updating unit 1432 for updating the pie chart with trigger logic.
And selecting a data set, testing trigger logic by using the data set, and updating the pie chart according to the trigger logic.
In embodiment 10, fig. 10 shows a link insertion module 151 in the big data based maintenance fund usage monitoring and predicting system provided by the embodiment of the present invention, the link insertion module includes:
The key node positioning unit 1511 is used for traversing the three-dimensional model and positioning key nodes;
And a link inserting unit 1512 for inserting a link into the key node, and taking the item tag and the amount as pointing targets of the link.
Traversing the three-dimensional model, finding out key nodes in the three-dimensional model, inserting links into the three-dimensional model, and taking data with item labels and money as a linked target.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
The functions that the big data based maintenance fund usage monitoring and predicting system can achieve are all completed by computer equipment, the computer equipment comprises one or more processors and one or more memories, at least one program code is stored in the one or more memories, and the program code is loaded and executed by the one or more processors to achieve the functions of the big data based maintenance fund usage monitoring and predicting system.
The processor takes out instructions from the memory one by one, analyzes the instructions, then completes corresponding operation according to the instruction requirement, generates a series of control commands, enables all parts of the computer to automatically, continuously and cooperatively act to form an organic whole, realizes the input of programs, the input of data, the operation and the output of results, and the arithmetic operation or the logic operation generated in the process is completed by the arithmetic unit; the Memory comprises a Read-Only Memory (ROM) for storing a computer program, and a protection device is arranged outside the Memory.
For example, a computer program may be split into one or more modules, one or more modules stored in memory and executed by a processor to perform the present invention. One or more of the modules may be a series of computer program instruction segments capable of performing specific functions for describing the execution of the computer program in the terminal device.
It will be appreciated by those skilled in the art that the foregoing description of the service device is merely an example and is not meant to be limiting, and may include more or fewer components than the foregoing description, or may combine certain components, or different components, such as may include input-output devices, network access devices, buses, etc.
The Processor may be a central processing unit (Central Processing Unit, CPU), other general purpose Processor, digital signal Processor (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is the control center of the terminal device described above, and which connects the various parts of the entire user terminal using various interfaces and lines.
The memory may be used for storing computer programs and/or modules, and the processor may implement various functions of the terminal device by running or executing the computer programs and/or modules stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function (such as an information acquisition template display function, a product information release function, etc.), and the like; the storage data area may store data created according to the use of the berth status display system (e.g., product information acquisition templates corresponding to different product types, product information required to be released by different product providers, etc.), and so on. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart memory card (SMART MEDIA CARD, SMC), secure Digital (SD) card, flash memory card (FLASH CARD), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
The modules/units integrated in the terminal device may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on this understanding, the present invention may implement all or part of the modules/units in the system of the above-described embodiments, or may be implemented by instructing the relevant hardware by a computer program, which may be stored in a computer-readable storage medium, and which, when executed by a processor, may implement the functions of the respective system embodiments described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (10)
1. A big data based maintenance fund usage monitoring and prediction system, comprising: the system comprises a display device, a construction device, a creation device, a monitoring device and a prediction device;
The display equipment is used for building a data instrument panel and is divided into a front end and a rear end;
the construction equipment is used for collecting image data of the maintenance target object and constructing a three-dimensional model;
the creation device is configured to obtain usage data of the maintenance funds, create a usage platform in the backend, and receive a usage request submitted by a user, where the usage request at least includes: the amount and the use mode are corrected based on the use request;
The monitoring equipment is used for creating a budget area and a live area in the front end, importing the three-dimensional model into the budget area, clustering the amount of money, determining at least two sectors, inserting item labels into the sectors, integrating all the sectors, drawing a pie chart, transferring the pie chart into the live area, and updating the pie chart by using the using platform as a trigger source and using preset trigger logic;
The prediction device is used for positioning key nodes in the three-dimensional model, inserting links with item labels and amounts into the key nodes, inputting the key nodes into the prediction model after training, generating prediction data, and pushing the prediction data into the front end.
2. The big data based maintenance fund usage monitoring and prediction system of claim 1, wherein the presentation device comprises:
the instrument panel building module is used for building a data instrument panel;
The division module is used for dividing the data instrument panel into a front end and a rear end.
3. The big data based maintenance fund usage monitoring and prediction system of claim 1, wherein the construction equipment comprises:
the acquisition module is used for acquiring image data of the maintenance target object;
And the processing module is used for constructing a three-dimensional model according to the image data.
4. The big data based maintenance fund usage monitoring and prediction system of claim 3, wherein said creation device comprises:
the acquisition module is used for acquiring the use data of the maintenance funds and creating a use platform based on the use data;
And the correction module is used for receiving a use request submitted by a user and correcting the three-dimensional model based on the use request.
5. The big data based maintenance fund usage monitoring and prediction system of claim 4, wherein the monitoring device comprises:
an importing module for creating a budget area and a live area, importing the three-dimensional model into the budget area;
The drawing module is used for clustering the amount, determining at least two sectors, inserting item labels into the sectors, integrating all the sectors, drawing a pie chart, and transferring the pie chart to the live area;
And the updating module is used for updating the pie chart by using the using platform as a trigger source and utilizing preset trigger logic.
6. The big data based maintenance fund usage monitoring and prediction system of claim 5, wherein the prediction device comprises:
The link insertion module is used for positioning key nodes of the three-dimensional model, generating links pointing to item labels and money, and inserting the links into the key nodes;
and the prediction generation module is used for inputting the key nodes into the trained prediction model, generating prediction data and pushing the prediction data to the front end.
7. The big data based maintenance fund usage monitoring and prediction system of claim 2, wherein the partitioning module comprises:
the building unit is used for determining an instrument panel framework and building the data instrument panel;
and the building unit is used for determining the technical stack and the communication mode of the instrument panel framework, building the front end and the back end and integrating the front end and the back end into the instrument panel framework.
8. The big data based maintenance fund usage monitoring and prediction system of claim 3, wherein said processing module comprises:
the feature extraction unit is used for carrying out feature extraction on the image data, obtaining feature points, superposing depth information into the feature points, and generating point cloud data, wherein the point cloud data is used for representing coordinates of the feature points in a three-dimensional space;
the reconstruction unit can preprocess the point cloud data and reconstruct a three-dimensional model by utilizing the preprocessed point cloud data.
9. The big data based maintenance fund usage monitoring and prediction system of claim 5, wherein the update module comprises:
the selecting unit is used for selecting a data set and testing trigger logic by utilizing the data set;
and the pie chart updating unit is used for updating the pie chart by using trigger logic.
10. The big data based maintenance fund usage monitoring and prediction system of claim 8, wherein the link insertion module comprises:
The key node positioning unit is used for traversing the three-dimensional model and positioning key nodes;
and the link inserting unit is used for inserting links into the key nodes and taking the item labels and the amounts as pointing targets of the links.
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