Intelligent project cost management method and system based on BIM

Abstract

The invention relates to the technical field of project cost management, and particularly discloses an intelligent project cost management method and system based on BIM, and the method comprises the steps: carrying out the SDR signal source layout, carrying out the flight monitoring of an unmanned plane, carrying out the adaptive adjustment of the SDR signal source, analyzing the evaluation indexes of the abnormal features of a monitoring image, judging the permission uploading of a label of the monitoring image, and extracting an abnormal layout point. And secondary monitoring is carried out, it is judged that a secondary monitoring image permits to upload a label, each verification abnormity layout point is determined, early warning information is generated and published, and the upload monitoring image is uploaded to carry out engineering cost management. According to the invention, by reasonably arranging the SDR signal source, the rationality of a monitoring path of the unmanned aerial vehicle is ensured, stable signal transmission is ensured, data loss or errors are reduced, the accuracy of a cost management data basis is improved, monitoring image quality data are analyzed, a secondary monitoring verification mechanism is adopted, abnormal arrangement points are accurately determined and verified, misjudgment is effectively avoided, and the reliability of the system is improved. And the reliability of project cost management data is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of project cost management, and particularly to an intelligent project cost management method and system based on BIM. Background Technique

[0002] In the field of construction projects, as the scale of engineering projects expands day by day and the requirements for cost management increase continuously. Under the traditional mode, the selection of monitoring devices often cannot accurately adapt to specific construction environments and monitoring requirements, making it impossible to flexibly adjust according to real-time interference conditions, which may lead to data loss or errors and reduce the accuracy of the data basis for cost management.

[0003] For example, the invention patent with the publication number CN110473004B discloses a project cost consulting management system and method, including: a cost budget unit that divides the project construction into each construction step and enters the expected building material construction costs of each construction step; an actual project progress unit that inputs the actual building material construction costs in the corresponding construction step; a cost settlement unit that statistically adds up the building material costs required for each construction step to obtain the final expected cost. If there are already prices for some construction units in the actual project progress unit, first add up the actual building material construction costs and then add the remaining expected building material construction costs to obtain the total construction cost; a feedback unit that sends data information to the user.

[0004] For example, the invention patent with the publication number CN116797263B discloses a building project cost accounting management method and system. The method includes: classifying the attribute of the building project construction record data information to obtain the building project attribute node data information; obtaining the project cost influencing factors, which include tender factors, market factors, and construction management factors; determining the project cost accounting rules according to the project cost influencing factors; accounting the building project attribute node data information based on the project cost accounting rules to generate a project cost accounting result; determining an accounting error factor according to the historical cost data processing experience; and adjusting the project cost accounting result based on the accounting error factor to obtain a closed-loop accounting result of the building project cost.

[0005] However, in the process of implementing the embodiments of the present application, it is found that the above technologies have at least the following technical problems: The current project cost management system has many deficiencies in practical applications. Most systems focus on conventional processes such as cost budgeting, project progress accounting, and data feedback. In the actual construction site, the corresponding cost data often has the problems of large volume and high complexity. However, the current data management only for the later processes often fails to accurately adapt to specific construction environments and monitoring requirements, and the selection of monitoring devices lacks flexibility and pertinence. When facing complex and changeable construction sites, such as areas with complex terrain and numerous obstacles, it is difficult to ensure stable signal transmission, and data loss or errors are likely to occur, thus reducing the accuracy of cost management data, resulting in weak overall dynamic adaptability and being unable to provide comprehensive, efficient, and accurate cost management data support for engineering projects. Summary of the Invention

[0006] Aiming at the deficiencies of the existing technology, the present invention provides a BIM-based intelligent project cost management method and system, which can effectively solve the problems involved in the above background technology.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: In the first aspect of the present invention, a BIM-based intelligent project cost management method is provided, including: S1, Collect the status information of the project cost monitoring area, analyze the status characteristic indicators of the project cost monitoring area, and thus arrange the SDR signal sources in the project cost monitoring area to obtain the UAV flight monitoring path, denoted as the UAV monitoring planning path.

[0008] S2, The control center activates the BIM cost management signal, and thus conducts UAV flight monitoring based on the UAV monitoring planning path, collects the signal influence data of the areas corresponding to each SDR signal source arrangement point, analyzes and obtains the sub-region SDR signal adjustment parameters, and performs adaptive adjustment of the SDR signal source based on the sub-region SDR signal adjustment parameters.

[0009] S3, Obtain the UAV flight monitoring images corresponding to each SDR signal source arrangement point, collect the monitoring image quality data of each SDR signal source arrangement point, and analyze the abnormal characteristic evaluation indicators of the monitoring images of each SDR signal source arrangement point.

[0010] S4, Determine the permission upload label of the monitoring image based on the abnormal characteristic evaluation indicators of the monitoring images of each SDR signal source arrangement point, identify and extract the abnormal SDR signal source arrangement points, denoted as each abnormal arrangement point, and synchronously control the UAV to complete the monitoring along the UAV monitoring planning path.

[0011] S5. Based on each abnormal layout point, control the replacement of the drone to conduct secondary monitoring on each abnormal layout point, analyze the secondary monitoring verification indicators of each abnormal layout point, determine the upload permission label of the secondary monitoring image, and simultaneously determine each verified abnormal layout point and generate a release warning message for management reminder.

[0012] S6. Determine the uploadable monitoring images according to the upload permission label of the monitoring image and the upload permission label of the secondary monitoring image, and upload them to the BIM analysis library for engineering cost management.

[0013] The second aspect of the present invention provides a system for an intelligent engineering cost management method based on BIM, including: An SDR signal source layout module, used to collect the status information of the engineering cost monitoring area, analyze the status characteristic indicators of the engineering cost monitoring area, and thus conduct the layout of the SDR signal source in the engineering cost monitoring area; A drone monitoring path planning module, used to obtain the drone flight monitoring path based on the layout result of the SDR signal source in the engineering cost monitoring area, denoted as the drone monitoring planning path.

[0014] An SDR signal source adaptive adjustment module, used to control the center to start the BIM cost management signal, and thus conduct drone flight monitoring based on the drone monitoring planning path, collect the signal influence data of the corresponding areas of each SDR signal source layout point, analyze and obtain the sub-region SDR signal adjustment parameters, and conduct adaptive adjustment of the SDR signal source based on the sub-region SDR signal adjustment parameters.

[0015] A monitoring image anomaly analysis module, used to obtain the drone flight monitoring images corresponding to each SDR signal source layout point, collect the monitoring image quality data of each SDR signal source layout point, and analyze the anomaly characteristic evaluation indicators of the monitoring images of each SDR signal source layout point.

[0016] An abnormal layout point extraction module, used to determine the upload permission label of the monitoring image based on the anomaly characteristic evaluation indicators of the monitoring images of each SDR signal source layout point, identify and extract the abnormal SDR signal source layout points, denoted as each abnormal layout point, and simultaneously control the drone to complete the monitoring with the drone monitoring planning path.

[0017] An abnormal layout point secondary monitoring module, used to based on each abnormal layout point, control the replacement of the drone to conduct secondary monitoring on each abnormal layout point, analyze the secondary monitoring verification indicators of each abnormal layout point, determine the upload permission label of the secondary monitoring image, and simultaneously determine each verified abnormal layout point and generate a release warning message for management reminder.

[0018] The project cost management module is used to determine the uploadable monitoring images according to the monitoring image permission upload tags and the secondary monitoring image permission upload tags, and upload them to the BIM analysis library for project cost management.

[0019] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) By providing an intelligent project cost management method and system based on BIM, the present invention comprehensively collects the status information of the project cost monitoring area, reasonably arranges the SDR signal sources, ensures the rationality and efficiency of the UAV flight monitoring path planning, guarantees the stable signal transmission, reduces data loss or errors, and improves the accuracy of the data basis for cost management. In terms of signal adjustment, based on the in-depth analysis of the sub-region signal influence data to obtain the adjustment parameters, the SDR signal source is adaptively adjusted, which can adapt to the complex and changeable construction site environment, further improve the signal quality, ensure the stable and reliable data transmission, and provide accurate data support for cost management. At the same time, through the careful analysis of the monitoring image quality data and the effective identification and processing of abnormal indicators, and through the secondary monitoring verification mechanism, the abnormal layout points are accurately determined and timely warned, effectively avoiding misjudgment and improving the reliability of the project cost management data.

[0020] (2) By arranging the SDR signal sources in the project cost monitoring area, first, the number of SDR signal sources to be arranged is determined according to the status characteristic indicators of the project cost monitoring area, which can be scientifically planned according to the actual complexity of the area, ensure the reasonable number of signal sources, construct a stable and reliable signal environment, enhance the signal coverage and intensity, effectively reduce the adverse effects of external factors on the monitoring accuracy, and guarantee the real-time and accurate transmission of data during UAV flight monitoring, providing comprehensive and accurate data support for intelligent project cost management. Secondly, during the arrangement process, the signal source layout point information is uploaded to the control center, which helps the control center optimize the UAV flight monitoring path based on this, enabling the UAV to efficiently conduct comprehensive flight monitoring of the entire monitoring area, thereby more accurately collecting the signal influence data of each area, providing a data basis for subsequent signal adjustment and project management decision-making, and further improving the accuracy and efficiency of project cost management.

[0021] (3) The present invention adaptively adjusts the SDR signal source based on the SDR signal adjustment parameters of sub-regions. By analyzing the different environmental impact parameters and signal transmission status parameters of each sub-region, the most suitable signal adjustment parameters are accurately analyzed and matched, so as to reasonably optimize the signal transmission power, signal synchronization clock frequency, etc. The influence of interference factors such as complex construction site environment on signal transmission is reduced, enabling the signal to be stably and reliably transmitted under complex and changeable conditions. Ensure that the unmanned aerial vehicle can accurately collect data related to project cost in each sub-region and stably transmit it back to the control center, providing a solid and accurate data basis for subsequent intelligent project cost management and improving the accuracy and reliability of management.

[0022] (4) The present invention manages and reminds by determining each verification abnormal layout point and generating and releasing warning information. Based on the comprehensive analysis of the abnormal indexes of the monitoring images and the abnormal indexes of the secondary monitoring images of each abnormal layout point, the verification abnormal layout points are accurately determined to avoid misjudgment, ensure the accuracy and reliability of the warning information, effectively prevent the deviation of project cost estimation caused by inaccurate data collection, and improve the accuracy of cost management data. Description of the Drawings

[0023] The present invention will be further described with reference to the accompanying drawings. However, the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to the following drawings without creative efforts.

[0024] Figure 1 It is a schematic flow chart of the method steps of the present invention.

[0025] Figure 2 It is a schematic diagram of the connection of system modules of the present invention. Specific Embodiments

[0026] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0027] Refer to Figure 1 As shown, the first aspect of the present invention provides a BIM-based intelligent project cost management method, including: S1, collecting the status information of the project cost monitoring area, analyzing the status characteristic indexes of the project cost monitoring area, thereby arranging the SDR signal source of the project cost monitoring area, so as to obtain the unmanned aerial vehicle flight monitoring path, denoted as the unmanned aerial vehicle monitoring planning path.

[0028] In this embodiment, the state characteristic indexes of the project cost monitoring area are analyzed. The specific analysis process is as follows: During a preset monitoring period, the state information of the project cost monitoring area is collected, including the area of the project cost monitoring area, the standard deviation of altitude, the obstacle density, and the average signal attenuation coefficient.

[0029] It should be understood that the area of the project cost monitoring area can be directly collected from the regional geographic information system.

[0030] The specific method for obtaining the standard deviation of altitude is as follows: The altitude of each point in the project cost monitoring area is collected through the regional geographic information system, and the standard deviation of altitude is calculated according to the standard deviation formula.

[0031] The specific calculation formula is as follows: , where is the standard deviation of altitude, is the altitude of the th point, is the number of each point, , is the number of points.

[0032] The obstacle density can be directly collected from the regional geographic information system. In a specific embodiment, the obstacles can be tree vegetation, bridges, etc.

[0033] The average signal attenuation coefficient is obtained by directly collecting the signal attenuation coefficients of each point through a spectrum analyzer and performing mean processing.

[0034] It should also be noted that the above-mentioned each point is preset in the database and can be directly extracted from the database during use.

[0035] Based on the analysis and processing of the state information of the project cost monitoring area, the state characteristic indexes of the project cost monitoring area are obtained. The state characteristic indexes of the project cost monitoring area are used to characterize the difficulty level of UAV flight monitoring in the project cost monitoring area.

[0036] In a specific embodiment, the method for specifically obtaining the state characteristic indexes of the project cost monitoring area is as follows: , where is the state characteristic index of the project cost monitoring area, is the area of the project cost monitoring area, is the standard deviation of altitude of the project cost monitoring area, is the obstacle density of the project cost monitoring area, is the average signal attenuation coefficient of the project cost monitoring area, is the deviation factor corresponding to the unit area, is the deviation factor corresponding to the standard deviation of the unit altitude, is the deviation factor corresponding to the unit obstacle density, is the deviation factor corresponding to the unit average signal attenuation coefficient, and e is the natural constant.

[0037] It should be noted that the deviation factor corresponding to the unit area, the deviation factor corresponding to the standard deviation of the unit altitude, the deviation factor corresponding to the unit obstacle density, and the deviation factor corresponding to the unit average signal attenuation coefficient can be directly extracted from the database. For example, the state information of the project cost monitoring area is respectively mapped to its corresponding unit deviation factor one by one to construct a mapping set. When in use, the real-time collected state information of the project cost monitoring area is input into the constructed mapping set, and the corresponding unit deviation factor can be extracted therefrom.

[0038] It should also be noted that the state characteristic index of the project cost monitoring area is obtained by analyzing and processing the state information of the project cost monitoring area, taking into account the mutual influence between these parameters. For example, when the area of the region increases, it may cover more different terrains, such as mountains or hills, which will lead to an increase in the standard deviation of the altitude, because a larger area is more likely to contain larger terrain undulations. A larger area may have more buildings, natural obstacles, etc., resulting in an increase in the obstacle density. The increase in obstacles and complex terrain will make the signal propagation path longer and the signal blocking situation more severe, thereby leading to an increase in the average signal attenuation coefficient. If the standard deviation of the altitude becomes larger, it means that the terrain undulation intensifies. The complex terrain will increase the signal reflection, scattering, and blocking phenomena, resulting in a more complex signal transmission path, and the average signal attenuation coefficient will also increase accordingly. As the obstacle density increases, the signal propagation will be more obstructed. The signal needs to bypass or penetrate obstacles, increasing the propagation distance and energy loss, making the average signal attenuation coefficient increase.

[0039] In this embodiment, the SDR signal source of the project cost monitoring area is arranged accordingly to obtain the UAV flight monitoring path. The specific analysis process is as follows: It should be noted that the SDR signal source can stably transmit signals in the project cost monitoring areas with different terrains and landforms (such as mountains, hills, plains, etc.) and meteorological conditions (sunny, rainy, foggy, etc.), providing a reliable communication link for the UAV to ensure that the UAV can accurately receive control commands and transmit monitoring data in real time during flight.

[0040] It should be noted that the English full name of SDR is "Software Defined Radio", and the Chinese name is "Software Defined Radio".

[0041] Extract the number of SDR signal sources corresponding to each state characteristic index interval stored in the database, and map and extract the number of SDR signal sources corresponding to the interval where the state characteristic index of the project cost monitoring area is located, which is denoted as the number of SDR signal sources in the project cost monitoring area.

[0042] It should be noted that the larger the state characteristic index of the project cost monitoring area, the greater the difficulty of UAV flight monitoring in the project cost monitoring area. To ensure the accuracy of UAV flight monitoring, the corresponding number of SDR signal sources is larger. By extracting the number of SDR signal sources in the project cost monitoring area based on the state characteristic index of the project cost monitoring area, the required number of signal sources can be scientifically and reasonably determined according to the actual complexity of the project cost monitoring area, so as to build a stable and reliable signal environment for UAV flight monitoring. More signal sources can effectively enhance the signal coverage and intensity, reduce the influence of external factors such as terrain, obstacles, and signal attenuation on the monitoring accuracy, ensure that the UAV can receive and transmit data in real time and accurately during flight, provide comprehensive and accurate data support for intelligent project cost management, and then improve the scientificity, accuracy, and efficiency of the entire project cost management work.

[0043] Based on the number of SDR signal sources in the project cost monitoring area, conduct SDR signal source layout for the project cost monitoring area, and synchronously upload the layout point information of the SDR signal sources to the control center.

[0044] It should be understood that in a specific embodiment, for example, assume that after comprehensive analysis of a large project cost monitoring area with an area of about 100,000 square meters, it is determined that 20 SDR signal sources need to be deployed to ensure that the signal can fully cover the entire project cost monitoring area and meet the requirements of the monitoring task. Then the layout rule can be: regard the project cost monitoring area as a whole, calculate the approximate ratio in the length and width directions according to its shape (assumed to be approximately rectangular). Then, evenly divide 5 intervals in the length direction and 4 intervals in the width direction, so that the entire project cost monitoring area is divided into 20 sub-areas with similar sizes. Deploy an SDR signal source at the center position or approximate center position of each sub-area to ensure that the signal sources are relatively evenly distributed in the project cost monitoring area. For example, for a project cost monitoring area with a length of 5000 meters and a width of 2000 meters, each interval in the length direction is about 1000 meters, and each interval in the width direction is about 500 meters, and a signal source is set at the center of each 500-meter × 1000-meter sub-area.

[0045] It should be noted that during the layout process, the installation height of the signal sources needs to be kept consistent (such as a unified height of 5 meters above the ground as set in the embodiment).

[0046] After the installation of each SDR signal source is completed, synchronously upload the layout point information thereof to the control center. For each signal source, upload the number of the sub-region where it is located and the relative position coordinates of the signal source in the coordinate system of the entire project cost monitoring area.

[0047] The control center obtains the UAV flight monitoring path based on the analysis and processing of the layout points of the SDR signal sources.

[0048] In a specific embodiment, for example, it is set that there are 20 layout points of SDR signal sources. According to the order of the sub-region numbers, connect the coordinate points of each signal source in sequence to form a continuous broken line, which is used as the UAV flight monitoring path. That is, starting from the coordinate point of the signal source numbered 1, connect to the coordinate point of the signal source numbered 2, and then connect to the coordinate point of the signal source numbered 3, and so on, until all 20 coordinate points of the signal sources are connected, thus obtaining a complete UAV flight monitoring path. Such path planning can ensure that the UAV flies over each layout point of the SDR signal source in sequence, conducts comprehensive flight monitoring on the entire project cost monitoring area, and thus collects accurate signal impact data of the corresponding areas of each layout point of the SDR signal source, providing a basic guarantee for subsequent analysis and adjustment work.

[0049] S2, the control center activates the BIM cost management signal, and thus conducts UAV flight monitoring based on the UAV monitoring planned path, collects the signal impact data of the corresponding areas of each layout point of the SDR signal source, analyzes and obtains the SDR signal adjustment parameters of the sub-region, and conducts adaptive adjustment of the SDR signal source based on the SDR signal adjustment parameters of the sub-region.

[0050] In this embodiment, the specific analysis process for conducting adaptive adjustment of the SDR signal source based on the SDR signal adjustment parameters of the sub-region is as follows: Collect the signal impact data of the corresponding areas of each layout point of the SDR signal source, and record the corresponding areas of each layout point of the SDR signal source as each sub-region, and thus count the signal impact data of each sub-region.

[0051] Based on the analysis and processing of the signal impact data of each sub-region, obtain the SDR signal adjustment requirement indicators of each sub-region.

[0052] In this embodiment, the specific analysis process for the SDR signal adjustment requirement indicators of each sub-region is as follows: The signal impact data of each sub-region includes environmental impact parameters and signal transmission status parameters.

[0053] The environmental impact parameters include the dew point temperature, water vapor pressure, light intensity, and electromagnetic interference intensity of each sub-region.

[0054] It should be noted that the dew point temperature can be obtained by collecting with a dew point meter, the water vapor pressure can be obtained by collecting with a humidity sensor, the light intensity can be obtained by collecting with a light intensity sensor, and the electromagnetic interference intensity can be obtained by collecting with an electromagnetic interference detector.

[0055] It also should be noted that the dew point temperature is closely related to the water vapor content in the air. When the dew point temperature rises close to or exceeds the ambient temperature, the air humidity will increase, and water vapor is likely to condense into small water droplets on the surface of objects in the signal transmission path. These small water droplets will change the dielectric constant of the air, causing the signal to scatter and absorb during transmission, thereby increasing the signal attenuation. For example, in a humid environment, the propagation distance of wireless signals may be significantly shortened, and the signal strength is weakened, affecting the communication quality between the signal source and the drone.

[0056] The increase in water vapor pressure means an increase in water vapor molecules in the air. Water vapor molecules are polar molecules that can interact with electromagnetic waves and absorb the energy of the signal. When the signal propagates in an environment with high water vapor pressure, part of the energy will be absorbed by the water vapor molecules, resulting in a weakening of the signal strength. Especially in the high-frequency band, such as the microwave band used for drone communication, the absorption of water vapor by the signal is more obvious. For example, in humid weather conditions, the signal strength received by the drone may be significantly lower than that in dry weather, affecting the reliability and transmission distance of communication.

[0057] The signal transmission status parameters include the signal strength of each sub-region, the signal transmission delay duration, the signal frequency offset amount, and the transmission error rate.

[0058] It should be noted that the signal strength can be obtained by collecting with a signal strength meter, the signal transmission delay duration can be directly extracted from the system program log, the signal frequency offset amount can be obtained by collecting with a spectrum analyzer, and the transmission error rate can be obtained by collecting with an error meter.

[0059] Based on the environmental impact parameters and the signal transmission status parameters, the SDR signal adjustment requirement indicators for each sub-region are analyzed and processed. The SDR signal adjustment requirement indicators for each sub-region are used to characterize the degree of SDR signal adjustment requirements for each sub-region.

[0060] Extract the reference environmental impact parameters and reference signal transmission status parameters stored in the database, where: the reference environmental impact parameters include the ideal dew point temperature of the region, the reference water vapor pressure, the reference light intensity, and the reference electromagnetic interference intensity, and the reference signal transmission status parameters include the reference signal strength of the region, the reference signal transmission delay duration, the reference signal frequency offset amount, and the reference transmission error rate.

[0061] In a specific embodiment, the SDR signal adjustment requirement indicators for each sub-region are specifically obtained as follows: , Among them, is the SDR signal regulation requirement index of the i-th sub-region, is the dew point temperature of the i-th sub-region, is the water vapor pressure of the i-th sub-region, is the light intensity of the i-th sub-region, is the electromagnetic interference intensity of the i-th sub-region, is the signal intensity of the i-th sub-region, is the signal transmission delay duration of the i-th sub-region, is the signal frequency offset of the i-th sub-region, is the transmission error rate of the i-th sub-region, is the ideal dew point temperature, is the reference water vapor pressure, is the reference light intensity, is the reference electromagnetic interference intensity, is the reference signal intensity, is the reference signal transmission delay duration, is the reference signal frequency offset, is the reference transmission error rate, i is the number of each sub-region, , m is the number of sub-regions, and e is the natural constant.

[0062] It should be noted that the SDR signal adjustment demand index of each sub-area is obtained by analyzing and processing according to the environmental impact parameters and signal transmission state parameters, taking into account the mutual influence between these parameters. For example, when the dew point temperature rises, the air humidity increases, and the water vapor pressure increases accordingly. Water vapor will absorb and scatter the signal, resulting in a weakening of the signal strength. The humidity change may affect the conductive properties of the atmosphere and enhance the electromagnetic interference intensity, thereby increasing the signal frequency offset and the transmission bit error rate. In addition, the signal transmission delay may increase due to the influence of the signal propagation speed. When the water vapor pressure increases, the high water vapor environment will absorb more signal energy and reduce the signal strength. The effect of water vapor molecules on electromagnetic waves may cause electromagnetic interference, affect the signal frequency stability, cause changes in the signal frequency offset, and increase the transmission bit error rate. The signal propagation path in an environment with high water vapor content is more complicated, and the propagation speed changes, resulting in an extension of the signal transmission delay. If the light is too strong, it may cause local temperature rise, affect the state of the atmosphere, and indirectly change the dew point temperature and water vapor pressure. Strong light may also cause electromagnetic interference, interfere with signal transmission, cause signal strength fluctuations, signal frequency deviation, increase the transmission bit error rate, and the signal transmission delay time may also be affected. The increase in electromagnetic interference intensity will directly interfere with signal propagation, reduce signal strength, shift signal frequency, and significantly increase the transmission bit error rate. It will also interfere with the timing of the signal during transmission, resulting in unstable signal transmission delay time. When the signal strength is weakened, it may be affected by the above-mentioned environmental factors during the transmission process. At this time, the receiving end may need more time to process in order to correctly receive the signal, resulting in an increase in the signal transmission delay time. Insufficient signal strength may also make signal decoding difficult and increase the transmission bit error rate. Changes in signal strength may cause the receiving device to automatically adjust the receiving parameters, thereby affecting the signal frequency offset. The increase in signal transmission delay may mean that the signal propagation path is blocked or the signal processing speed is slowed down, which may be related to environmental factors, resulting in further attenuation of signal strength. Transmission delay may also cause the signal to arrive at the receiving end at different times, resulting in phase difference, causing signal frequency offset, and longer delay will increase the probability of data error and increase the transmission bit error rate. When the signal frequency offset changes, it will affect the reception and demodulation of the signal, resulting in unstable signal strength at the receiving end, which may reduce the signal strength. The frequency offset may cause the signal to interfere with other signals during the transmission process, increase the electromagnetic interference intensity, and thus affect the transmission bit error rate. At the same time, when the receiving device adjusts to correct the frequency offset, it may increase the signal processing time, resulting in a change in the signal transmission delay. The increase in the transmission bit error rate indicates that the signal quality has decreased, which may be caused by weak signal strength, strong interference, etc., which will reduce the effective signal received by the receiving end, equivalent to a decrease in signal strength. A high bit error rate may cause signal retransmission, increase the signal transmission delay, and frequent bit errors may also affect the receiving device's accurate judgment of the signal frequency, resulting in changes in the signal frequency offset.

[0063] It should be understood that the softsign function is a built-in function in Python, .

[0064] Extract the signal adjustment parameters corresponding to each adjustment requirement index interval stored in the database, and map and extract the signal adjustment parameters corresponding to the interval where the SDR signal adjustment requirement index of each sub-region is located, which are denoted as the SDR signal adjustment parameters of each sub-region.

[0065] The SDR signal adjustment parameters include the signal transmission power adjustment value of the SDR signal source and the signal synchronization clock frequency adjustment value.

[0066] It should be noted that the larger the SDR signal adjustment requirement index of a sub-region, the greater the environmental interference and signal transmission problems in that sub-region, the more urgent the need for signal adjustment, and the larger the corresponding signal adjustment parameters. By extracting the SDR signal adjustment parameters of each sub-region according to the SDR signal adjustment requirement index of the sub-region, the SDR signal source can be adaptively adjusted according to the actual situation of different sub-regions, so that parameters such as the signal transmission power and the signal synchronization clock frequency can be reasonably optimized, thereby effectively improving the signal quality of each sub-region, ensuring the stable and reliable transmission of signals in the complex and changeable construction site environment, providing accurate data support for the project cost management system, and ensuring the efficient operation of the entire monitoring and management work.

[0067] Adaptive adjustment is performed on the SDR signal source signals of each sub-region according to the SDR signal adjustment parameters.

[0068] In a specific embodiment, assume that in the project cost monitoring area of a large-scale line construction site, multiple sub-regions are determined according to previous analysis, and the SDR signal adjustment requirement indexes and corresponding signal adjustment parameters of each sub-region are obtained. The signal adjustment parameters corresponding to the SDR signal adjustment requirement index of one of the sub-regions are that the signal transmission power needs to be increased by 5 dB and the signal synchronization clock frequency needs to be accelerated by 10 kHz. When performing adaptive adjustment, for the SDR signal source of this sub-region, the system will automatically increase its signal transmission power by 5 dB on the current basis to enhance the signal transmission ability and enable it to better penetrate interference and reach the UAV receiving end; at the same time, accelerate the signal synchronization clock frequency by 10 kHz to better match the signal transmission rhythm with the receiving end and reduce the transmission delay and error rate.

[0069] It should also be noted that during the adjustment process, the signal status after adjustment is monitored in real time, and the adjustment effect is evaluated by collecting the signal impact data again. If it is found that the SDR signal adjustment requirement index is significantly reduced, it indicates that this adjustment is effective; on the contrary, if the ideal state is still not reached, the system will recalculate the adjustment requirement index according to the new signal impact data, and extract appropriate adjustment parameters from the database again for further optimization adjustment until the signal quality in this sub-region meets the requirements of stable and reliable transmission, so as to ensure that under the complex construction site environment, the unmanned aerial vehicle can accurately collect the data related to the project cost in each sub-region and stably transmit it back to the control center, providing accurate data support for subsequent intelligent project cost management.

[0070] S3. Obtain the UAV flight monitoring images corresponding to the layout points of each SDR signal source, collect the monitoring image quality data of each SDR signal source layout point, and analyze the abnormal feature evaluation indexes of the monitoring images of each SDR signal source layout point.

[0071] In this embodiment, the analysis of the abnormal feature evaluation indexes of the monitoring images of each SDR signal source layout point is as follows: Collect the monitoring image quality data of each SDR signal source layout point, including the resolution, contrast, noise standard deviation, and the number of missing pixels of the monitoring images of each SDR signal source layout point.

[0072] It should be noted that the image quality data can all be obtained through image analysis software. In a specific embodiment, the image analysis software can be ImageJ (Image Processing and Analysis in Java).

[0073] Extract the reference image quality data stored in the database. The reference image quality data includes the reference resolution, reference contrast, reference noise standard deviation, and reference number of missing pixels.

[0074] According to the monitoring image quality data of each SDR signal source layout point, analyze and process to obtain the abnormal feature evaluation indexes of the monitoring images of each SDR signal source layout point. The abnormal feature evaluation indexes of the monitoring images of each SDR signal source layout point are used to characterize the image quality abnormality degree of the monitoring images of each SDR signal source layout point.

[0075] In a specific embodiment, the specific acquisition method of the abnormal feature evaluation indexes of the monitoring images of each SDR signal source layout point is as follows: , wherein, is the abnormal feature evaluation index of the monitoring image of the j-th SDR signal source layout point, is the resolution of the monitoring image of the j-th SDR signal source layout point, is the contrast of the monitoring image at the j-th SDR signal source deployment point, is the noise standard deviation of the monitoring image at the j-th SDR signal source deployment point, is the number of missing pixels in the monitoring image at the j-th SDR signal source deployment point, is the reference resolution, is the reference contrast, is the reference noise standard deviation, is the reference number of missing pixels. j is the number of each SDR signal source deployment point, , n is the number of SDR signal source deployment points, and e is the natural constant.

[0076] It should be noted that by analyzing and processing the monitoring image quality data of each SDR signal source deployment point, the abnormal feature evaluation index of the monitoring image of each SDR signal source deployment point is obtained. Considering the mutual influence between these parameters. For example, when the resolution is increased, the image can present more details, which may make the contrast more visually obvious because more details help to distinguish different brightness regions. However, a higher resolution may amplify the noise in the image, resulting in an increase in the noise standard deviation. If there are problems in the image acquisition or transmission process, the increase in resolution may make the problem of missing pixels that was not obvious more prominent, and the number of missing pixels may increase relatively. When the contrast increases, the edges and details of the objects in the image become clearer, which improves the visual effect of the resolution to a certain extent and makes the image look sharper. High contrast may mask some noise, making the noise standard deviation relatively lower visually. When the noise standard deviation increases, it will interfere with the image details, reduce the effective resolution of the image, make the image blurred, and it is difficult to distinguish the original clear edges and details, thus reducing the contrast. When the noise is severe, it may damage the pixel information, resulting in an increase in the number of missing pixels. For example, noise spikes may cause some pixel values to exceed the normal range and cannot be displayed correctly. When the number of missing pixels increases, it will directly damage the integrity of the image, resulting in the loss of image information and a reduction in resolution. The change of pixel values around the missing pixel area may affect the contrast calculation, making the contrast inaccurate. At the same time, the missing pixels may be regarded as outliers in the statistical analysis, increasing the overall noise standard deviation and affecting the image quality assessment.

[0077] S4. Based on the abnormal feature evaluation index of the monitoring image of each SDR signal source deployment point, determine the permission upload label of the monitoring image, identify and extract the abnormal SDR signal source deployment points, denoted as each abnormal deployment point, and synchronously control the unmanned aerial vehicle to complete the monitoring along the monitoring planning path of the unmanned aerial vehicle.

[0078] In this embodiment, based on the abnormal feature evaluation index of the monitoring image of each SDR signal source deployment point, determine the permission upload label of the monitoring image, identify and extract the abnormal SDR signal source deployment points, denoted as each abnormal deployment point. The specific analysis process is as follows: The monitored image permission upload tags include allowed upload images and restricted upload images.

[0079] Extract the preset monitored image anomaly verification threshold in the database.

[0080] If the monitored image anomaly feature evaluation index of a certain SDR signal source layout point is less than the monitored image anomaly verification threshold, then mark the monitored image of this SDR signal source layout point as an allowed upload image.

[0081] It should be noted that the monitored image anomaly feature evaluation index of the SDR signal source layout point being less than the monitored image anomaly verification threshold indicates that the quality of the monitored image at this layout point is relatively good, and its image can more accurately and clearly reflect the actual situation of the project cost monitoring area. Therefore, mark the corresponding monitored image as an allowed upload image to provide a data basis for subsequent analysis and processing.

[0082] If the monitored image anomaly feature evaluation index of a certain SDR signal source layout point is greater than or equal to the monitored image anomaly verification threshold, then mark the monitored image of this SDR signal source layout point as a restricted upload image.

[0083] Synchronously extract the SDR signal source layout points where the monitored image anomaly feature evaluation index is greater than or equal to the monitored image anomaly verification threshold, and mark them as abnormal layout points.

[0084] The monitored image anomaly feature evaluation index of the SDR signal source layout point being greater than or equal to the monitored image anomaly verification threshold indicates that there are obvious quality problems with the monitored image at this layout point, which may affect the accurate judgment of the project cost monitoring area and the reliability of data collection. Therefore, mark the corresponding monitored image of this layout point as a restricted upload image, and mark this layout point as an abnormal layout point for subsequent secondary monitoring to ensure the accuracy and effectiveness of the monitoring data.

[0085] Extract and count each abnormal layout point and upload the location information of each abnormal layout point to the control center.

[0086] It should be noted that in another specific embodiment, the image can be uploaded to Autodesk Revit (Autodesk's Building Information Modeling software Revit). Revit has powerful 3D modeling capabilities and data processing capabilities. It can accurately create corresponding 3D models based on the information in the image, such as building structures, equipment forms, terrain, etc. During the model construction process, it can effectively integrate data such as dimensions and positions in the image to generate a BIM model containing rich attribute information, such as the material and engineering quantity of components. In this way, it provides an intuitive and accurate 3D visualization model for project cost management, improves the efficiency and accuracy of project cost management, and realizes the effective transformation from monitoring images to the output of valuable BIM models.

[0087] S5. Based on each abnormal layout point, control the replacement of the drone to conduct secondary monitoring on each abnormal layout point, analyze the secondary monitoring verification indicators of each abnormal layout point, and determine the permission upload label of the secondary monitoring image. Synchronously determine each verified abnormal layout point and generate and release early warning information for management reminder.

[0088] In this embodiment, each verified abnormal layout point is determined and early warning information is generated and released for management reminder. The specific analysis process is as follows: Based on the abnormal feature evaluation indicators of the monitoring images of each SDR signal source layout point, extract the monitoring image abnormal indicators of each abnormal layout point, denoted as the monitoring image abnormal indicators of each abnormal layout point.

[0089] Obtain the quality data of the secondary monitoring images of each abnormal layout point, analyze and process to obtain the abnormal indicators of the secondary monitoring images of each abnormal layout point. The abnormal indicators of the secondary monitoring images of each abnormal layout point are used to characterize the degree of image quality abnormality of the secondary monitoring images of each abnormal monitoring point.

[0090] The quality data of the secondary monitoring images of each abnormal layout point includes the resolution, contrast, noise standard deviation, and number of missing pixels of the secondary monitoring images of each abnormal layout point.

[0091] Obtain the reference image quality data stored in the database. The reference image quality data includes reference resolution, reference contrast, reference noise standard deviation, and reference number of missing pixels.

[0092] In a specific embodiment, the abnormal indicators of the secondary monitoring images of each abnormal layout point are obtained in the following specific manner: , where is the abnormal indicator of the secondary monitoring image of the kth abnormal layout point, is the resolution of the secondary monitoring image of the kth abnormal layout point, is the contrast of the secondary monitoring image of the kth abnormal layout point, is the noise standard deviation of the secondary monitoring image of the k-th abnormal layout point, is the number of missing pixels in the secondary monitoring image of the k-th abnormal layout point, is the reference resolution, is the reference contrast, is the reference noise standard deviation, is the reference number of missing pixels, k is the number of each abnormal layout point, , r is the number of abnormal layout points, and e is the natural constant.

[0093] Based on the abnormal indicators of the monitoring images of each abnormal layout point and the abnormal indicators of the secondary monitoring images of each abnormal layout point, the secondary monitoring verification indicators of each abnormal layout point are obtained through analysis and processing. The secondary monitoring verification indicators of each abnormal layout point are used to characterize the abnormal degree of each abnormal layout point.

[0094] In a specific embodiment, the method for specifically obtaining the secondary monitoring verification indicators of each abnormal layout point is as follows: , , where, is the secondary monitoring verification indicator of the k-th abnormal layout point, is the abnormal indicator characterization factor of the secondary monitoring image of the k-th abnormal layout point, is the abnormal indicator of the monitoring image of the k-th abnormal layout point, is the abnormal indicator of the secondary monitoring image of the k-th abnormal layout point, k is the number of each abnormal layout point, , r is the number of abnormal layout points, and e is the natural constant.

[0095] It should be understood that the swith function is a built-in function in Python, .

[0096] In a specific embodiment, by analyzing and processing the abnormal indicators of the monitoring images of each abnormal layout point and the abnormal indicators of the secondary monitoring images of each abnormal layout point, the secondary monitoring verification indicators of each abnormal layout point are obtained. By performing secondary monitoring and calculating the secondary monitoring verification indicators in combination with the abnormal indicators of the images of the two monitorings, the errors and uncertainties of single monitoring can be effectively excluded, and it can be more accurately determined whether there is an abnormal situation that seriously affects the accuracy of the monitoring data at this layout point, avoiding misjudgment. After accurately identifying the real abnormal layout points, corresponding measures can be taken, which helps to improve the reliability of the project cost management data.

[0097] Extract the preset secondary monitoring verification threshold in the database.

[0098] The secondary monitoring image permission upload tags include allowing the upload of secondary monitoring images and restricting the upload of secondary monitoring images.

[0099] If the secondary monitoring verification index of a certain abnormally arranged point is less than the secondary monitoring verification threshold, the secondary monitoring image of this abnormally arranged point is recorded as an uploadable secondary monitoring image.

[0100] If the secondary monitoring verification index of a certain abnormally arranged point is greater than or equal to the secondary monitoring verification threshold, the secondary monitoring image of this abnormally arranged point is recorded as a restricted upload secondary monitoring image, and this abnormally arranged point is simultaneously recorded as a verified abnormally arranged point.

[0101] Traverse each abnormally arranged point in sequence, count each verified abnormally arranged point, and upload the position information of each verified abnormally arranged point to the control center, and simultaneously generate corresponding warning information for warning processing.

[0102] It should be noted that if the secondary monitoring verification index of an abnormally arranged point is less than the secondary monitoring verification threshold, it indicates that the image quality collected by the secondary monitoring is good and can be used as an analysis image for project cost management. Therefore, the corresponding secondary monitoring image is determined to be an allowable upload secondary monitoring image, providing a data basis for subsequent analysis.

[0103] If the secondary monitoring verification index of an abnormally arranged point is greater than or equal to the secondary monitoring verification threshold, it indicates that the image quality of the secondary monitoring is still relatively abnormal, and there is a greater risk at this abnormally arranged point. It is necessary to promptly issue warning information to prompt the operator.

[0104] It should also be noted that in a specific embodiment, the warning information can be: "The secondary monitoring verification index of the abnormally arranged point located at [specific coordinate position, such as (X = 1500, Y = 1000)] seriously exceeds the standard, the image quality is poor, affecting the accuracy of data collection, please pay attention."

[0105] S6. Determine the uploadable monitoring images according to the monitoring image permission upload tags and the secondary monitoring image permission upload tags, and upload them to the BIM analysis library, thereby performing project cost management.

[0106] In this embodiment, the process of determining the uploadable monitoring images according to the monitoring image permission upload tags and the secondary monitoring image permission upload tags, and uploading them to the BIM analysis library for project cost management is as follows: Extract and count the allowable upload images and the allowable upload secondary monitoring images, and record them as uploadable images; Upload the uploadable image to the BIM analysis library. Use a convolutional neural network to accurately identify and classify building components, materials, construction techniques, etc. in the uploaded image. Synchronously extract the cost information of various components and materials pre-stored in the BIM information library, automatically match and calculate the corresponding costs, and thus complete the project cost management.

[0107] It should be noted that when the uploadable image is fed into the convolutional neural network on which the BIM analysis library relies, in the initial stage, the convolutional layer uses a set of pre-set convolutional kernels to traverse the image pixel matrix in the form of a sliding window, and efficiently extracts the low-level features of the image through convolutional operations, such as information on edges, lines, textures, etc. These preliminary feature maps build the basic framework for subsequent in-depth recognition. As the data flows, the pooling layer is activated. The common max-pooling operation takes regions as units and selects the maximum value in the feature map as the representative of the region, greatly reducing the data volume, which not only reduces the computational complexity but also retains the key features, making the subsequent processing more efficient. For example, when facing an image of a building exterior wall, the pooling layer can accurately retain key local features such as the corners and window frame outlines, and eliminate the large-area repeated wall texture details, ensuring the representativeness and effectiveness of the features. After multiple rounds of alternating processing of convolution and pooling, the image features are gradually refined and strengthened and reach the fully connected layer. At this time, based on the feature vectors extracted layer by layer before, the fully connected layer combines the weight parameters obtained from large-scale training to build a complex classification model to accurately discriminate building components and classify them into specific categories such as columns, beams, and trusses; for materials, it can accurately distinguish different types such as steel bars, cement, and thermal insulation materials; for construction techniques, it can finely distinguish various forms such as cast-in-place, precast, tying, and spraying. The entire process relies on the powerful learning and generalization ability of the deep neural network, strictly follows the logic in the training model, accurately identifies and classifies the key elements in the uploaded image, and provides the core data basis for the accurate calculation and control of project costs.

[0108] It should also be noted that the convolutional neural network is a deep learning model architecture specifically designed to process data with grid structures (especially image data) and plays a key role in the field of project cost management. By constructing multiple layers of neuron combinations to gradually extract the complex features of the image, its core components include the convolutional layer, the pooling layer, and the fully connected layer. The convolutional layer uses learnable convolutional kernels to slide on the image to capture basic visual features such as lines, edges, and textures, which are the cornerstones of subsequent recognition and classification. The pooling layer reduces the dimensionality of the data through strategies such as max-pooling or average-pooling while retaining the key feature information, greatly improving the computational efficiency and ensuring that the model can quickly process a large number of building images. The fully connected layer integrates the refined feature information extracted from the previous layers and, based on the weight parameters learned from a large number of training samples, accurately classifies and determines target objects such as building components, materials, and construction techniques.

[0109] In a specific embodiment, assume that in a certain construction stage, 50 steel beams of a specific specification are newly installed in a region through a convolutional neural network. The comprehensive cost of each steel beam matched from the BIM information database is 5,000 yuan. Then the cost of this part of the steel beams is 250,000 yuan. For another example, if it is identified that 100 cubic meters of concrete of a specific grade is used in a certain region, and combined with the cost of 800 yuan per cubic meter of this grade of concrete in the BIM information database, the cost of this part of the concrete can be calculated as 80,000 yuan.

[0110] Referring Figure 2 as shown, the second aspect of the present invention provides a system for an intelligent project cost management method based on BIM, including: An SDR signal source layout module, configured to collect the status information of the project cost monitoring area, analyze the status characteristic indicators of the project cost monitoring area, and thereby perform the layout of the SDR signal source in the project cost monitoring area.

[0111] A drone monitoring path planning module, configured to obtain the drone flight monitoring path based on the layout result of the SDR signal source in the project cost monitoring area, denoted as the drone monitoring planning path.

[0112] An SDR signal source adaptive adjustment module, configured to start the BIM cost management signal by the control center, thereby perform drone flight monitoring based on the drone monitoring planning path, collect the signal impact data of the corresponding areas of each SDR signal source layout point, analyze and obtain the sub-region SDR signal adjustment parameters, and perform SDR signal source adaptive adjustment based on the sub-region SDR signal adjustment parameters.

[0113] A monitoring image anomaly analysis module, configured to obtain the drone flight monitoring images corresponding to each SDR signal source layout point, collect the monitoring image quality data of each SDR signal source layout point, and analyze the anomaly characteristic evaluation indicators of the monitoring images of each SDR signal source layout point.

[0114] An abnormal layout point extraction module, configured to determine the monitoring image permission upload label based on the anomaly characteristic evaluation indicators of the monitoring images of each SDR signal source layout point, identify and extract the abnormal SDR signal source layout points, denoted as each abnormal layout point, and synchronously control the drone to complete the monitoring along the drone monitoring planning path.

[0115] An abnormal layout point secondary monitoring module, configured to perform secondary monitoring on each abnormal layout point based on each abnormal layout point, control and replace the drone to perform secondary monitoring on each abnormal layout point, analyze the secondary monitoring verification indicators of each abnormal layout point, and determine the secondary monitoring image permission upload label, and synchronously determine each verified abnormal layout point and generate and release a warning message for management reminder.

[0116] The project cost management module is used to determine the monitorable images that can be uploaded according to the monitorable image permission upload tags and the secondary monitorable image permission upload tags, and upload them to the BIM analysis library, thereby performing project cost management.

[0117] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods for substitution. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should fall within the protection scope of the present invention.

Claims

1. A BIM-based intelligent engineering cost management method, characterized in that: include: S1, collect the state information of the engineering cost monitoring area, analyze the state characteristic indicators of the engineering cost monitoring area, and then arrange the SDR signal source in the engineering cost monitoring area, so as to obtain the UAV flight monitoring path, which is recorded as the UAV monitoring planning path; S2, the control center starts the BIM cost management signal, thereby performing drone flight monitoring based on the drone monitoring planning path, collecting signal impact data of the area corresponding to each SDR signal source deployment point, and analyzing and obtaining the sub-area SDR signal adjustment parameters, and performing SDR signal source adaptive adjustment based on the sub-area SDR signal adjustment parameters; S3, obtaining the UAV flight monitoring images corresponding to each SDR signal source deployment point, collecting the monitoring image quality data of each SDR signal source deployment point, and analyzing the abnormal feature evaluation index of the monitoring image of each SDR signal source deployment point; S4, based on the abnormal feature evaluation index of the monitoring image of each SDR signal source deployment point, determine the monitoring image permission upload label, and identify and extract the abnormal SDR signal source deployment points, record them as each abnormal deployment point, and synchronously control the drone to complete the monitoring according to the drone monitoring planning path; S5, based on each abnormal deployment point, control the replacement of the drone to conduct secondary monitoring of each abnormal deployment point, analyze the secondary monitoring verification indicators of each abnormal deployment point, and determine the secondary monitoring image permission upload label, simultaneously determine each verification abnormal deployment point and generate and release warning information for management reminder; S6, determining that the monitoring image can be uploaded according to the monitoring image permission upload tag and the secondary monitoring image permission upload tag, and uploading it to the BIM analysis library, thereby performing engineering cost management.

2. According to claim 1, a BIM-based intelligent engineering cost management method is characterized by: The analysis of the state characteristic indicators of the engineering cost monitoring area is used to arrange the SDR signal source in the engineering cost monitoring area, thereby obtaining the UAV flight monitoring path. The specific analysis process is as follows: During the preset monitoring period, the status information of the construction cost monitoring area is collected, including the area of ​​the construction cost monitoring area, the standard deviation of the altitude, the obstacle density and the average signal attenuation coefficient; Based on the analysis and processing of the state information of the engineering cost monitoring area, a state characteristic index of the engineering cost monitoring area is obtained, and the state characteristic index of the engineering cost monitoring area is used to characterize the difficulty of UAV flight monitoring in the engineering cost monitoring area; Extract the number of SDR signal source deployments corresponding to each state characteristic index interval stored in the database, and map and extract the number of SDR signal source deployments corresponding to the interval where the state characteristic index of the engineering cost monitoring area is located, and record it as the number of SDR signal source deployments in the engineering cost monitoring area; Deploy SDR signal sources in the engineering cost monitoring area based on the number of SDR signal sources deployed in the engineering cost monitoring area, and simultaneously upload the SDR signal source deployment point information to the control center; The control center obtains the UAV flight monitoring path based on the analysis and processing of the SDR signal source layout points.

3. According to the BIM-based intelligent engineering cost management method of claim 1, it is characterized by: The SDR signal source is adaptively adjusted based on the sub-region SDR signal adjustment parameters, and the specific analysis process is as follows: Collect signal impact data of the area corresponding to each SDR signal source layout point, and record the area corresponding to each SDR signal source layout point as each sub-area, thereby counting the signal impact data of each sub-area; Based on the analysis and processing of the signal impact data of each sub-region, the SDR signal adjustment demand index of each sub-region is obtained; Extract the signal adjustment parameters corresponding to each adjustment demand index interval stored in the database, and map and extract the signal adjustment parameters corresponding to the interval where the SDR signal adjustment demand index of each sub-region is located, and record them as the SDR signal adjustment parameters of each sub-region; The SDR signal adjustment parameters include a signal transmission power adjustment value and a signal synchronization clock frequency adjustment value of the SDR signal source; The SDR signal source signal of each sub-area is adaptively adjusted according to the SDR signal adjustment parameters.

4. According to claim 3, a BIM-based intelligent engineering cost management method is characterized by: The specific analysis process of the SDR signal adjustment demand index of each sub-region is as follows: The signal impact data of each sub-area includes environmental impact parameters and signal transmission state parameters; The environmental impact parameters include the dew point temperature, water vapor pressure, light intensity and electromagnetic interference intensity of each sub-area; The signal transmission status parameters include the signal strength of each sub-area, the signal transmission delay time, the signal frequency offset and the transmission bit error rate; Based on the environmental impact parameters and the signal transmission state parameters, the SDR signal adjustment requirement index of each sub-region is obtained through analysis and processing, and the SDR signal adjustment requirement index of each sub-region is used to characterize the degree of SDR signal adjustment requirement of each sub-region.

5. The BIM-based intelligent engineering cost management method according to claim 1 is characterized by: The analysis of the abnormal feature evaluation index of the monitoring image at each SDR signal source deployment point is specifically performed as follows: Collect monitoring image quality data at each SDR signal source deployment point, including the resolution, contrast, noise standard deviation and number of missing pixels of the monitoring image at each SDR signal source deployment point; According to the image quality data monitored at each SDR signal source layout point, analysis and processing are performed to obtain abnormal feature assessment indicators for the images monitored at each SDR signal source layout point. The abnormal feature assessment indicators for the images monitored at each SDR signal source layout point are used to characterize the degree of abnormality in image quality of the images monitored at each SDR signal source layout point.

6. The BIM-based intelligent engineering cost management method according to claim 5 is characterized by: The monitoring image abnormal feature evaluation index based on each SDR signal source layout point determines the monitoring image permission upload label, and identifies and extracts the abnormal SDR signal source layout points, which are recorded as each abnormal layout point. The specific analysis process is as follows: The monitoring image permission upload tag includes allowing image upload and restricting image upload; Extracting the abnormal verification threshold of the monitoring image preset in the database; If the abnormal feature assessment index of the monitoring image at a certain SDR signal source deployment point is less than the monitoring image abnormality verification threshold, the monitoring image of the SDR signal source deployment point is recorded as an image that is allowed to be uploaded; If the abnormal feature assessment index of the monitoring image at a certain SDR signal source deployment point is less than the monitoring image abnormality verification threshold, the monitoring image of the SDR signal source deployment point is recorded as a restricted upload image; Synchronously extract the SDR signal source deployment points whose monitoring image abnormal feature evaluation index is greater than or equal to the monitoring image abnormality verification threshold and record them as abnormal deployment points; Extract and count each abnormal layout point and upload the location information of each abnormal layout point to the control center.

7. The BIM-based intelligent engineering cost management method according to claim 1 is characterized by: The secondary monitoring verification indicators of each abnormal deployment point are analyzed, and the secondary monitoring image permission upload label is determined, and each verification abnormal deployment point is simultaneously determined and the early warning information is generated and issued for management reminder. The specific analysis process is as follows: Based on the abnormal feature evaluation index of the monitoring image at each SDR signal source deployment point, the abnormal index of the monitoring image at each abnormal deployment point is extracted and recorded as the abnormal index of the monitoring image at each abnormal deployment point; Acquire the secondary monitoring image quality data of each abnormal deployment point, analyze and process to obtain the abnormal index of the secondary monitoring image of each abnormal deployment point, wherein the abnormal index of the secondary monitoring image of each abnormal deployment point is used to characterize the abnormal degree of image quality of the secondary monitoring image of each abnormal monitoring point; Based on the abnormal index of the monitoring image of each abnormal layout point and the abnormal index of the secondary monitoring image of each abnormal layout point, the secondary monitoring verification index of each abnormal layout point is analyzed and processed to obtain the secondary monitoring verification index of each abnormal layout point, and the secondary monitoring verification index of each abnormal layout point is used to characterize the abnormal degree of each abnormal layout point; Extracting the preset secondary monitoring verification threshold in the database; The secondary monitoring image permission upload tag includes allowing uploading of secondary monitoring images and restricting uploading of secondary monitoring images; If the secondary monitoring verification index of a certain abnormal deployment point is less than the secondary monitoring verification threshold, the secondary monitoring image of the abnormal deployment point is recorded as the uploaded secondary monitoring image; If the secondary monitoring verification index of a certain abnormal deployment point is greater than or equal to the secondary monitoring verification threshold, the secondary monitoring image of the abnormal deployment point is recorded as a restricted secondary monitoring image to be uploaded, and the abnormal deployment point is simultaneously recorded as a verified abnormal deployment point; Traverse each abnormal layout point in turn, count each verification abnormal layout point, and upload the point information of each verification abnormal layout point to the control center, and simultaneously generate corresponding warning information for warning processing.

8. The BIM-based intelligent engineering cost management method according to claim 1 is characterized by: The monitoring image that can be uploaded is determined according to the monitoring image permission upload tag and the secondary monitoring image permission upload tag, and uploaded to the BIM analysis library, thereby performing engineering cost management. The specific analysis process is as follows: Extract and count images that are allowed to be uploaded and secondary monitoring images that are allowed to be uploaded, and record them as uploadable images; Upload uploadable images to the BIM analysis library, and use convolutional neural networks to accurately identify and classify building components, materials, construction processes, etc. in the uploaded images. Simultaneously extract the cost information of various components and materials pre-stored in the BIM information library, automatically match and calculate the corresponding costs, thereby completing project cost management.

9. The BIM-based intelligent engineering cost management method according to claim 8 is characterized by: The specific acquisition method of the secondary monitoring verification index of each abnormal deployment point is as follows: ， ， in, is the secondary monitoring verification index of the kth abnormal deployment point, is the abnormal index characterization factor of the secondary monitoring image of the kth abnormal layout point, The kth abnormal point is used to monitor the abnormal index of the image. is the abnormal index of the secondary monitoring image of the kth abnormal layout point, k is the number of each abnormal layout point, , r is the number of abnormal layout points, and e is a natural constant.

10. A system using a BIM-based intelligent engineering cost management method as claimed in any one of claims 1 to 9, characterized in that: include: The SDR signal source deployment module is used to collect the status information of the engineering cost monitoring area, analyze the status characteristic indicators of the engineering cost monitoring area, and thus deploy the SDR signal source in the engineering cost monitoring area; The UAV monitoring path planning module is used to obtain the UAV flight monitoring path based on the SDR signal source layout results in the engineering cost monitoring area, which is recorded as the UAV monitoring planning path; The SDR signal source adaptive adjustment module is used to control the center to start the BIM cost management signal, thereby performing drone flight monitoring based on the drone monitoring planning path, collecting signal impact data of the area corresponding to each SDR signal source deployment point, thereby analyzing and obtaining the sub-area SDR signal adjustment parameters, and performing SDR signal source adaptive adjustment based on the sub-area SDR signal adjustment parameters; The monitoring image anomaly analysis module is used to obtain the UAV flight monitoring images corresponding to each SDR signal source deployment point, collect the monitoring image quality data of each SDR signal source deployment point, and analyze the monitoring image abnormality feature evaluation index of each SDR signal source deployment point; The abnormal deployment point extraction module is used to determine the monitoring image permission upload label based on the abnormal feature evaluation index of each SDR signal source deployment point monitoring image, and identify and extract the abnormal SDR signal source deployment points, record them as each abnormal deployment point, and synchronously control the drone to complete the monitoring with the drone monitoring planning path; The secondary monitoring module of abnormal deployment points is used to control the replacement of drones to conduct secondary monitoring of each abnormal deployment point based on each abnormal deployment point, analyze the secondary monitoring verification indicators of each abnormal deployment point, and determine the secondary monitoring image permission upload label, synchronously determine each verification abnormal deployment point and generate and release early warning information for management reminders; The project cost management module is used to determine the uploadable monitoring images according to the monitoring image permission upload tag and the secondary monitoring image permission upload tag, and upload them to the BIM analysis library, thereby performing project cost management.

Images