CN117391863A

CN117391863A - Investment project management analysis method, system, readable storage medium and computer

Info

Publication number: CN117391863A
Application number: CN202311370577.7A
Authority: CN
Inventors: 陆霜霜; 余伟; 张利; 吴限; 陈长松; 邹涛
Original assignee: Jiujiang Digital Industry Development Co ltd
Current assignee: Jiujiang Digital Industry Development Co ltd
Priority date: 2023-10-23
Filing date: 2023-10-23
Publication date: 2024-01-12

Abstract

The invention provides an investment project management analysis method, a system, a readable storage medium and a computer, wherein the method comprises the following steps: performing data detection on remote sensing data selected based on data acquisition requirements to obtain remote sensing optimized data; preprocessing remote sensing optimized data, and performing quality evaluation based on a preprocessing result to obtain accurate data; constructing a data integration rule based on project requirements, integrating data of the accurate data according to the data integration rule, and merging the integrated data to obtain an integrated data set; constructing a data interpretation model, and carrying out data interpretation analysis on the integrated data set by utilizing the data interpretation model to obtain a data interpretation result; and if the checking result of the data interpretation result meets the preset requirement, visually displaying the data interpretation result and generating a report. The invention improves the image processing algorithm and the classification accuracy by adopting the existing remote sensing data, and combines other data sources to realize the update frequency and real-time requirements.

Description

Investment project management analysis method, system, readable storage medium and computer

Technical Field

The present invention relates to the field of image data processing technologies, and in particular, to an investment project management analysis method, an investment project management analysis system, a readable storage medium, and a computer.

Background

In investment project management, satellite remote sensing maps can provide important information and insight for decision makers. By analyzing the remote sensing image, key data such as investment project construction progress, land utilization change, investment project distribution and the like can be obtained, and the method has important significance in project site selection, resource management, market positioning and the like.

In order to better apply the satellite remote sensing map to perform investment project management analysis, further improvement of data processing and interpretation algorithms is required, the quality and instantaneity of data are improved, and the satellite remote sensing technology is combined with other data sources and analysis methods to acquire more comprehensive and accurate information. This will help investors make more informed decisions and optimize the management of investment projects.

First, acquisition and processing of satellite remote sensing data requires specialized equipment and technical support. Purchasing and operating satellite image acquisition equipment and software and hardware equipment that process large amounts of telemetry data is an expensive investment. In addition, the technology for analyzing and processing the remote sensing data also needs to have professional knowledge and skills. This increases the technological threshold and costs, limiting the popularity and applicability of the technology.

Second, the resolution and accuracy of the remote sensing image remain to be improved. Although modern satellites can provide high resolution images, in certain complex terrain and vegetation coverage areas, there is still a problem of inadequate resolution. This may lead to inaccurate classification of features or missing report phenomena, affecting the accuracy and reliability of project management analysis.

Third, the update frequency and real-time nature of satellite telemetry is also a challenge. The acquisition of satellite images generally requires a certain time and the update frequency of the data is limited. For some investment projects needing timely decision, such as rapid market change or emergency occurrence, the real-time performance of the investment projects may not meet the requirements, so that the application range of the satellite remote sensing technology in investment project management is limited.

Fourth, interpretation and analysis of satellite telemetry data also requires specialized knowledge and experience. Although automated image processing and classification algorithms have evolved, manual interpretation and verification is still required for complex feature types and scenes. This may require significant human resources and time costs for large-scale project management analysis.

Disclosure of Invention

Based on this, it is an object of the present invention to provide an investment project management analysis method, system, readable storage medium and computer, which at least solve the above-mentioned drawbacks.

The invention provides an investment project management analysis method, which comprises the following steps:

selecting corresponding remote sensing data based on data acquisition requirements, and performing data detection on the remote sensing data to obtain remote sensing optimized data;

preprocessing the remote sensing optimized data, and performing quality evaluation based on a preprocessing result to obtain corresponding accurate data;

constructing a data integration rule based on project requirements, integrating the data of the accurate data according to the data integration rule, and merging the integrated data to obtain an integrated data set;

constructing a data interpretation model, and carrying out data interpretation analysis on the integrated data set by utilizing the data interpretation model so as to obtain a corresponding data interpretation result;

and checking the data interpretation result, and if the checking result meets the preset requirement, visually displaying the data interpretation result and generating a report.

Further, the step of selecting corresponding remote sensing data based on the data acquisition requirement and performing data detection on the remote sensing data to obtain remote sensing optimized data includes:

screening a target supplier from a plurality of suppliers based on supplier screening conditions, and acquiring corresponding remote sensing data from the target supplier according to data acquisition requirements;

And performing quality inspection on the remote sensing data, and establishing a data index according to the quality inspection result to obtain remote sensing optimized data.

Further, the steps of preprocessing the remote sensing optimized data and performing quality evaluation based on the preprocessing result to obtain corresponding accurate data include:

respectively carrying out normalization processing, geometric position correction and radiation signal correction on the remote sensing optimized data, and carrying out enhancement processing on the remote sensing optimized data through contrast pulling, filtering and frequency domain transformation to obtain remote sensing preprocessing data;

respectively carrying out data filtering and data fusion on the remote sensing preprocessing data to obtain remote sensing fusion data;

and sequentially carrying out data classification and change detection on the remote sensing fusion data, and carrying out quality assessment based on a change detection result so as to obtain corresponding accurate data.

Further, the step of integrating the accurate data according to the data integration rule and merging the integrated data to obtain an integrated data set includes:

extracting corresponding data from the data source of the accurate data according to the data integration rule to obtain a plurality of extracted data with different formats and resolutions;

And converting the extracted data with different formats and resolutions into data with uniform formats and resolutions, and combining the data with the uniform formats and resolutions to obtain corresponding integrated data sets.

Further, the steps of constructing a data interpretation model, and performing data interpretation analysis on the integrated data set by using the data interpretation model to obtain a corresponding data interpretation result include:

selecting a corresponding automatic interpretation and analysis method according to the data type and the interpretation target, and constructing a data interpretation model according to the automatic interpretation and analysis method;

and inputting the integrated data set into the data interpretation model for data interpretation analysis to obtain a data interpretation result of the integrated data set.

The invention also provides an investment project management analysis system, which comprises:

the data detection module is used for selecting corresponding remote sensing data based on data acquisition requirements and carrying out data detection on the remote sensing data to obtain remote sensing optimized data;

the data preprocessing module is used for preprocessing the remote sensing optimized data and carrying out quality evaluation based on a preprocessing result so as to obtain corresponding accurate data;

The data integration module is used for constructing a data integration rule based on project requirements, integrating the data of the accurate data according to the data integration rule, and combining the integrated data to obtain an integrated data set;

the data interpretation module is used for constructing a data interpretation model, and carrying out data interpretation analysis on the integrated data set by utilizing the data interpretation model so as to obtain a corresponding data interpretation result;

and the data checking module is used for checking the data interpretation result, and if the checking result meets the preset requirement, the data interpretation result is visually displayed and a report is generated.

Further, the data detection module includes:

the data acquisition unit is used for screening a target supplier from a plurality of suppliers based on supplier screening conditions and acquiring corresponding remote sensing data from the target supplier according to data acquisition requirements;

and the quality inspection unit is used for performing quality inspection on the remote sensing data and establishing a data index according to the quality inspection result so as to obtain remote sensing optimized data.

Further, the data preprocessing module includes:

the data preprocessing unit is used for respectively carrying out normalization processing, geometric position correction and radiation signal correction on the remote sensing optimized data, and carrying out enhancement processing on the remote sensing optimized data through contrast pulling, filtering and frequency domain transformation so as to obtain remote sensing preprocessed data;

The data fusion unit is used for respectively carrying out data filtering and data fusion on the remote sensing pretreatment data so as to obtain remote sensing fusion data;

and the data quality evaluation unit is used for sequentially carrying out data classification and change detection on the remote sensing fusion data and carrying out quality evaluation based on a change detection result so as to obtain corresponding accurate data.

Further, the data integration module includes:

the data extraction unit is used for extracting corresponding data from the data source of the accurate data according to the data integration rule so as to obtain a plurality of extracted data with different formats and resolutions;

the data merging unit is used for converting the extracted data with different formats and resolutions into data with uniform formats and resolutions, and merging the data with the uniform formats and resolutions to obtain corresponding integrated data sets.

Further, the data interpretation module includes:

the model construction unit is used for selecting a corresponding automatic interpretation and analysis method according to the data type and the interpretation target, and constructing a data interpretation model according to the automatic interpretation and analysis method;

and the interpretation analysis unit is used for inputting the integrated data set into the data interpretation model to perform data interpretation analysis so as to obtain a data interpretation result of the integrated data set.

The present invention also proposes a readable storage medium having stored thereon a computer program which when executed by a processor implements the above described investment project management analysis method.

The invention also provides a computer, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the investment project management analysis method when executing the computer program.

The investment project management analysis method, system, readable storage medium and computer in the invention utilize the existing remote sensing data to reduce the equipment cost and software cost in the early stage, thereby reducing the overall cost of investment project management analysis, adopting image processing and classification algorithm, combining artificial intelligence and machine learning technology, improving the resolution of remote sensing image and accuracy of feature classification, providing project management analysis result with more details and accuracy through fine feature classification and accurate data extraction, combining with other data sources updated immediately, realizing more timely data update and project management analysis, providing users with the capability of responding market change and risk event more quickly, making more timely decision, introducing intelligent algorithm and tool, realizing the functions of automatically explaining and extracting important information of remote sensing data, reducing the dependence on manpower resources and time cost, and improving the efficiency and accuracy of project management.

Drawings

FIG. 1 is a flow chart of an investment project management analysis method in a first embodiment of the invention;

FIG. 2 is a detailed flowchart of step S101 in FIG. 1;

FIG. 3 is a detailed flowchart of step S102 in FIG. 1;

fig. 4 is a detailed flowchart of step S103 in fig. 1;

FIG. 5 is a detailed flowchart of step S104 in FIG. 1;

FIG. 6 is a block diagram of an investment project management analysis system in a second embodiment of the present invention;

fig. 7 is a block diagram showing a structure of a computer according to a third embodiment of the present invention.

The invention will be further described in the following detailed description in conjunction with the above-described figures.

Detailed Description

In order that the invention may be readily understood, a more complete description of the invention will be rendered by reference to the appended drawings. Several embodiments of the invention are presented in the figures. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.

Example 1

Referring to fig. 1, there is shown an investment project management analysis method according to a first embodiment of the present invention, which specifically includes steps S101 to S106:

s101, selecting corresponding remote sensing data based on data acquisition requirements, and performing data detection on the remote sensing data to obtain remote sensing optimized data;

further, referring to fig. 2, the step S101 specifically includes steps S1011 to S1012:

s1011, screening out target suppliers from a plurality of suppliers based on supplier screening conditions, and acquiring corresponding remote sensing data from the target suppliers according to data acquisition requirements;

and S1012, performing quality inspection on the remote sensing data, and establishing a data index according to the quality inspection result to obtain remote sensing optimized data.

In the implementation, in the process of acquiring investment project data, related data needs to be acquired from domestic satellite remote sensing data suppliers, and the following implementation process is as follows:

1. selecting an appropriate domestic satellite remote sensing data provider

Before data acquisition can be performed, a suitable domestic satellite remote sensing data provider needs to be selected. In selecting a vendor, the following factors need to be considered:

(1) Creditworthiness and data quality of the vendor. A provider should be selected that has good credibility and provides high quality data.

(2) Data coverage and resolution. Consideration needs to be given to whether the remote sensing data coverage and resolution provided by the provider meets our needs.

(3) Frequency and time delay of data update. The frequency and time delay of data updates provided by the provider are also important factors to consider.

(4) Data price and payment means. A supplier with reasonable price and flexible payment mode needs to be selected.

After comprehensively considering the factors, a provider with higher reputation in the domestic satellite remote sensing data field is finally selected.

2. Determining data acquisition requirements

After selecting the appropriate vendor, it is necessary to ascertain its own data acquisition requirements. The method comprises the following specific steps:

(1) The type and format of the data required. It is necessary to determine which types of remote sensing data are required by the user, such as high-resolution satellite remote sensing images, multispectral satellite remote sensing images, and the like, and determine the format of the required data.

(2) Temporal and spatial extent of data. Specific time and space ranges of the required data need to be specified in order for the provider to be able to provide the data to meet the demand.

(3) Frequency and period of data acquisition. It is necessary to ascertain the frequency and period of acquisition of the required data so that the provider can provide timely updated data.

(4) Preprocessing and post-processing of data. It is necessary to specify its own pre-processing and post-processing requirements for the data so that the provider can provide satisfactory data.

3. Provisioning data acquisition agreements with suppliers

After specifying its own data acquisition requirements, it is necessary to sign a data acquisition protocol with the vendor. The method comprises the following specific steps:

(1) Rights and obligations of both parties. The agreement should clarify the rights and obligations of both parties, including obligations that need to be fulfilled as a customer, such as payment, protection of data security, etc., and obligations that need to be fulfilled by the provider, such as providing satisfactory data, etc.

(2) Specific requirements for data acquisition. The protocol should specify the specific requirements of the type of data, format, time range, spatial range, acquisition frequency and period required so that the provider can provide the data according to our requirements.

(3) The manner and time of data delivery. The protocol should make clear the way and time the provider delivers the data to ensure that the required data is available in time.

(4) Privacy protocols and intellectual property protection. The agreement should contain a privacy agreement and intellectual property protection clauses to protect our and vendor's legal rights.

4. Acquiring data

After signing the data acquisition protocol, the provider will provide the telemetry data in accordance with the time and manner agreed upon by the protocol. The method comprises the following specific steps:

(1) Pay in time and confirm receipt. The receipt should be confirmed in time after the remote sensing data provided by the provider is received and the payment should be paid according to the agreement.

(2) And (5) checking data quality. The received data needs to be checked for quality, including integrity, accuracy, sharpness, etc. of the data to ensure that the obtained data meets our requirements.

(3) Data storage and management. The acquired telemetry data is stored and managed in a specialized database or data warehouse for subsequent data processing and analysis. At the same time, a data indexing and querying mechanism needs to be established to quickly retrieve and query the required data.

S102, preprocessing the remote sensing optimization data, and performing quality evaluation based on a preprocessing result to obtain corresponding accurate data;

further, referring to fig. 3, the step S102 specifically includes steps S1021 to S1023:

S1021, respectively carrying out normalization processing, geometric position correction and radiation signal correction on the remote sensing optimization data, and carrying out enhancement processing on the remote sensing optimization data through contrast pulling, filtering and frequency domain transformation to obtain remote sensing preprocessing data;

s1022, respectively performing data filtering and data fusion on the remote sensing preprocessing data to obtain remote sensing fusion data;

s1023, sequentially carrying out data classification and change detection on the remote sensing fusion data, and carrying out quality evaluation based on a change detection result so as to obtain corresponding accurate data.

In the specific implementation, the acquired data often needs to be processed to meet the requirements of subsequent analysis and application. In the investment project data processing stage, preprocessing, screening, classifying and other operations are carried out on the data acquired from domestic satellite remote sensing data suppliers so as to ensure the quality and reliability of the data and meet the precision and efficiency requirements of subsequent analysis. The following is a specific functional implementation description:

1. data preprocessing

The data preprocessing is an important link in the investment project data processing process, and aims to eliminate errors, noise and redundant information possibly existing in the original data and improve the accuracy and reliability of the data. The method comprises the following specific steps:

(1) Radiation calibration: and normalizing the radiance or reflectivity of the original data to eliminate the influence of factors such as sensor performance difference, atmospheric conditions and the like, and improve the comparability among different data sources.

(2) Geometric correction: and correcting the geometric position of the original data to eliminate the influence of factors such as earth rotation, earth curvature, atmospheric refraction and the like at the shooting moment and improve the positioning accuracy of the data.

(3) Atmospheric correction: and correcting the radiation signal of the original data to eliminate the influence of factors such as atmospheric absorption, scattering, radiation and the like, and improving the credibility and the accuracy of the data.

(4) Image enhancement: the original data is enhanced by means of contrast stretching, filtering, frequency domain transformation and the like so as to highlight target information in the image and improve the visibility and definition of the data.

2. Data screening

In the data screening stage, the preprocessed data is further screened and processed to remove invalid data and improve the accuracy and reliability of the data. The method comprises the following specific steps:

(1) And (3) data filtering: and filtering the data according to certain rules and standards to remove invalid data and data which do not meet the requirements. For example, low resolution, low signal to noise ratio, outliers, etc. data may be filtered out.

(2) Data fusion: for multi-source data, a fusion algorithm is adopted to fuse the data of different data sources so as to improve the accuracy and the credibility of the data. Common fusion algorithms include pixel-based fusion, feature-based fusion, model-based fusion, and the like.

3. Data classification

In the data classification stage, the processed data are classified and organized so as to facilitate subsequent analysis and application. The method comprises the following specific steps:

(1) Classification of ground objects: the images are divided into different ground object types, such as water bodies, vegetation, buildings and the like, according to the ground object characteristics in the remote sensing images. Classification may be performed by supervised classification, unsupervised classification, or semi-supervised classification.

(2) Land utilization classification: according to the present situation of land use and the future development needs, the land is divided into different use types, such as agricultural land, construction land, forest land, etc. Classification can be performed by using a classification regression tree, a support vector machine, deep learning and other methods.

(3) And (3) detecting change: by comparing the remote sensing images of different time periods, the change condition of the land feature or land utilization type, such as vegetation change, building increase and decrease, and the like, is detected. The change detection may be performed using pixel level comparison, feature level comparison, or model level comparison, among other methods.

4. Data processing result inspection

In the data processing result checking stage, quality evaluation and verification are carried out on the processed data so as to ensure the accuracy and reliability of the data. The method comprises the following specific steps:

(1) Precision evaluation: and performing precision evaluation by comparing the processed data with reference data, for example, performing evaluation by adopting indexes such as confusion matrix, kappa coefficient and the like.

(2) Error analysis: quantitative analysis is performed on errors generated during data processing, such as analysis of errors generated during radiometric calibration, geometric correction, and the like.

(3) Visualization of data processing results: and presenting the processed data through a visualization technology so as to observe the quality and effect of the data processing result.

S103, constructing a data integration rule based on project requirements, integrating the data of the accurate data according to the data integration rule, and combining the integrated data to obtain an integrated data set;

further, referring to fig. 4, the step S103 specifically includes steps S1031 to S1032:

s1031, extracting corresponding data from a data source of the accurate data according to the data integration rule to obtain extracted data with different formats and resolutions;

S1032, converting the extracted data with different formats and resolutions into data with uniform formats and resolutions, and merging the data with uniform formats and resolutions to obtain corresponding integrated data sets.

In practice, after the data processing is completed, it is often necessary to integrate data from different sources, different time scales, and different resolutions for subsequent analysis and visual presentation.

1. Determining an integration target:

the primary step in targeting data integration is the explicit project need, for example, the integration of multiple different sources of remote sensing data may be required for more comprehensive land utilization classification or resource distribution analysis. In addition, there is a need for a time node that defines data integration, such as integration after data preprocessing is complete, to ensure that all data processing is performed on a unified data set.

2. Making an integration plan:

in the stage of planning an integration plan, specific steps of data integration are designed according to project requirements and targets. This may include determining an integrated data source, data transfer means, data processing flow, data storage means, etc. In addition, it is also necessary to consider problems that may be encountered, such as data format inconsistency, data missing, etc., and to make coping strategies in advance.

The method comprises the following specific steps:

1) Analyzing the data source: the data type, data accuracy, coverage, etc. information of each data source is analyzed to determine the most appropriate data source.

2) Determining a transmission mode: depending on the amount of data and the distance of the data source, a suitable transmission mode is selected, such as network transmission or direct copying.

3) Designing a data processing flow: corresponding data processing flows, such as format conversion, coordinate system conversion and the like, are designed for different data sources and data processing requirements.

4) Determining a storage strategy: the appropriate storage and indexing modes are determined according to the project requirements and the requirements of subsequent use.

3. Data integration:

in the data integration stage, data integration is carried out according to a preset plan through a programming language or a professional tool. The method comprises the following specific steps:

1) And (3) data extraction: the required data is extracted from the various data sources.

2) Data conversion: data of different formats and different resolutions are converted into a unified format and resolution.

3) Data merging: data from different data sources are combined to form a large and complete data set.

4. And (3) checking an integration result:

after data integration is completed, the integration result needs to be checked to ensure the integrity and accuracy of the data. The method comprises the following specific steps:

1) Data integrity verification: it is checked whether the integrated data is complete or whether all the required data has been integrated.

2) And (3) checking data accuracy: the accuracy of the integrated data is checked by comparison with known data or by using statistical verification methods.

3) And (3) checking data processing effect: and visually displaying the integrated data processing result so as to intuitively evaluate the effect of the data processing.

S104, constructing a data interpretation model, and carrying out data interpretation analysis on the integrated data set by utilizing the data interpretation model so as to obtain a corresponding data interpretation result;

further, referring to fig. 5, the step S104 specifically includes steps S1041 to S1042:

s1041, selecting a corresponding automatic interpretation and analysis method according to the data type and the interpretation target, and constructing a data interpretation model according to the automatic interpretation and analysis method;

s1042, inputting the integrated data set into the data interpretation model for data interpretation analysis to obtain the data interpretation result of the integrated data set.

In practice, after data acquisition, processing and integration are completed, automated interpretation and analysis is often required in order to mine the deep value of the data. The interpretation and analysis process is as follows:

1. Selecting a suitable automated interpretation and analysis method: depending on the data type and interpretation objectives, a suitable automated interpretation and analysis method is selected. For example, techniques such as machine learning, image classification, feature recognition, etc. may be employed.

2. Establishing an interpretation model: and establishing an interpretation model by using the selected automatic interpretation and analysis method. The interpretation model should be based on the deep understanding of the knowledge in the field and the accumulation of a large amount of training data to ensure accuracy and reliability.

3. Automated interpretation and analysis of the data: and inputting the integrated data into an interpretation model for automatic interpretation and analysis to obtain a related result.

4. Result inspection and optimization: and (3) checking the results of automatic interpretation and analysis, and if deviation exists, adjusting and optimizing the interpretation model.

S105, checking the data interpretation result, and if the checking result meets the preset requirement, visually displaying the data interpretation result and generating a report.

In practice, to better convey interpretation and analysis results of investment projects, it is often necessary to create intuitive, attractive displays with a variety of tools and techniques.

1. Selecting an appropriate visual presentation tool

First, a suitable visual presentation tool needs to be selected. Such tools typically include various functions that can create various types of charts, graphs, and other visualization elements. The selection of the tool takes into account the specific requirements of the project, such as the type of data to be presented, the complexity of the data, the interactivity of the presentation, etc. Some common visual presentation tools include Tableau, powerBI, excel, etc.

2. Making visual charts

After selecting the appropriate tool, the creation of the visual chart may begin. The method comprises the following specific steps:

1) Data cleaning and preparation: firstly, data needs to be acquired and cleaned, so that the data is ensured to be accurate, and then necessary preprocessing, such as data conversion, data aggregation and the like, is carried out on the data so as to adapt to the visual requirements.

2) Creating a visualization element: various visual elements, such as bar charts, line charts, pie charts, maps, etc., are created with the selected tools according to the requirements of the project.

3) Configuration data and style: the cleaned and prepared data is mapped onto the visualization elements and various styles and colors are configured to convey information more clearly.

4) Adding interaction functions: to enhance the user experience, various interactive functions, such as zoom in/out, drag, screen, etc., may be added to the chart.

3. Generating reports

The already-made visual chart may be exported into various formats, such as pictures, PDFs, PPTs, etc., before the report is generated. The content of the report may then be organized using elements such as text, tables, and charts. Some common report generation tools include Microsoft Word, powerPoint, laTeX, and the like. In this process, templates or frameworks can be used to help layout and design report formats, making reports more specialized and attractive.

4. Issuing reports

After the report is generated, it may be distributed to various platforms or mailed to the relevant personnel. In order to ensure the reliability and safety of the report, the following points need to be noted:

1) Confirm the accuracy and integrity of the report: the content of the report is checked again to ensure that there is no error or missing information.

2) Selecting a proper release platform: and selecting a proper release platform according to the requirements. This may include corporate websites, social media, email, or other communication channels.

3) Maintaining communication and feedback: after the report is issued, the report is effectively communicated with related personnel, the problems or doubts of the related personnel are solved, and the report is adjusted and optimized according to feedback.

5. Result feedback and optimization

After the report is issued, it is important to collect feedback from the relevant personnel, knowing the acceptance and advice of the report. The method comprises the following specific steps:

1) Collecting feedback: and collecting feedback comments of related personnel on the report by means of investigation, interview or an online feedback system and the like.

2) Analysis feedback: and analyzing the collected feedback, and finishing out constructive suggestions and criticisms.

3) Improvement and optimization: and (3) improving and optimizing the visual display and report according to the feedback analysis result, and improving the quality and effect.

4) Update and release new reports: after improvement and optimization, new reports can be updated and issued to meet the needs of the relevant personnel.

In summary, the investment project management analysis method in the above embodiment of the present invention utilizes the existing remote sensing data to reduce the equipment cost and the software cost in the early stage, thereby reducing the overall cost of the investment project management analysis, adopting image processing and classification algorithm, combining with artificial intelligence and machine learning technology, improving the resolution of remote sensing image and the accuracy of feature classification, providing project management analysis results with more details and accuracy through fine feature classification and accurate data extraction, combining with other data sources updated in real time, realizing more timely data update and project management analysis, providing users with the capability of more quickly responding market change and risk event to make more timely decisions, introducing intelligent algorithm and tool, realizing the functions of automatically explaining and extracting important information of remote sensing data, reducing the dependence on manual interpretation, saving manpower resources and time cost, and improving the efficiency and accuracy of project management.

Example two

In another aspect, referring to fig. 6, an investment project management and analysis system according to a second embodiment of the present invention is shown, the system includes:

the data detection module 11 is configured to select corresponding remote sensing data based on a data acquisition requirement, and perform data detection on the remote sensing data to obtain remote sensing optimized data;

further, the data detection module 11 includes:

The data preprocessing module 12 is configured to preprocess the remote sensing optimized data, and perform quality evaluation based on a preprocessing result, so as to obtain corresponding accurate data;

further, the data preprocessing module 12 includes:

The data integration module 13 is configured to construct a data integration rule based on project requirements, integrate the accurate data according to the data integration rule, and combine the integrated data to obtain an integrated data set;

further, the data integration module 13 includes:

The data interpretation module 14 is configured to construct a data interpretation model, and perform data interpretation analysis on the integrated data set by using the data interpretation model to obtain a corresponding data interpretation result;

Further, the data interpretation module 14 includes:

The data checking module 15 is configured to check the data interpretation result, and if the check result meets a preset requirement, visually display the data interpretation result and generate a report.

The functions or operation steps implemented when the above modules and units are executed are substantially the same as those in the above method embodiments, and are not described herein again.

The investment project management analysis system provided by the embodiment of the invention has the same implementation principle and technical effects as those of the embodiment of the method, and for the sake of brevity, the corresponding contents in the embodiment of the method can be referred to for the parts of the system that are not mentioned.

Example III

The present invention also proposes a computer, referring to fig. 7, which shows a computer according to a third embodiment of the present invention, including a memory 10, a processor 20, and a computer program 30 stored in the memory 10 and executable on the processor 20, wherein the processor 20 implements the above-mentioned investment project management analysis method when executing the computer program 30.

The memory 10 includes at least one type of readable storage medium including flash memory, a hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. Memory 10 may in some embodiments be an internal storage unit of a computer, such as a hard disk of the computer. The memory 10 may also be an external storage device in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card), etc. Further, the memory 10 may also include both internal storage units and external storage devices of the computer. The memory 10 may be used not only for storing application software installed in a computer and various types of data, but also for temporarily storing data that has been output or is to be output.

The processor 20 may be, in some embodiments, an electronic control unit (Electronic Control Unit, ECU), a central processing unit (Central Processing Unit, CPU), a controller, a microcontroller, a microprocessor, or other data processing chip, for executing program codes or processing data stored in the memory 10, such as executing an access restriction program, or the like.

It should be noted that the structure shown in fig. 7 is not limiting of the computer, and in other embodiments, the computer may include fewer or more components than shown, or may combine certain components, or may have a different arrangement of components.

The embodiment of the invention also provides a readable storage medium, on which a computer program is stored, which program, when being executed by a processor, implements the investment project management analysis method as described above.

Those of skill in the art will appreciate that the logic and/or steps represented in the flow diagrams or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.

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 above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.

Claims

1. A method of investment project management analysis, comprising:

2. The investment project management analysis method of claim 1, wherein the steps of selecting corresponding remote sensing data based on data acquisition requirements and performing data detection on the remote sensing data to obtain remote sensing optimization data comprise:

3. The investment project management analysis method of claim 1, wherein the steps of preprocessing the remote sensing optimization data and performing quality assessment based on the preprocessing result to obtain the corresponding accurate data comprise:

4. The investment project management analysis method of claim 1, wherein the step of integrating the accurate data according to the data integration rule and merging the integrated data to obtain an integrated data set comprises:

5. The investment project management analysis method of claim 1, wherein the steps of constructing a data interpretation model and performing data interpretation analysis on the integrated data set using the data interpretation model to obtain a corresponding data interpretation result comprise:

6. An investment project management analysis system, comprising:

7. The investment project management analysis system of claim 6, wherein the data detection module comprises:

8. The investment project management analysis system of claim 6, wherein the data preprocessing module comprises:

9. A readable storage medium having stored thereon a computer program, characterized in that the program, when executed by a processor, implements the investment project management analysis method of any one of claims 1 to 5.

10. A computer comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the investment project management analysis method of any one of claims 1 to 5 when the computer program is executed.