A blockchain sandbox assessment system and method for construction projects

Abstract

The application relates to the technical field of blockchain management, and particularly discloses a building project blockchain sandbox evaluation system and method, which acquires first monitoring data and meteorological environment data in a project construction process in real time by building a blockchain sandbox to deploy monitoring equipment, and acquires standard monitoring data required by a construction project as second monitoring data; the first monitoring data, the meteorological environment data and the second monitoring data are respectively preprocessed, and the first monitoring data, the meteorological environment data, the second monitoring data and a construction log are respectively stored in a blockchain; a data authenticity evaluation model is constructed based on the first monitoring data and the second monitoring data to identify whether abnormal data exist, if yes, data abnormality early warning is performed; if not, the rationality of monitoring equipment arrangement points is inferred by building a dust diffusion analysis strategy; the application helps to make the monitoring data of a building project construction site have high credibility and traceability.

Description

Technical Field

[0001] This invention relates to the field of blockchain management technology, and more specifically, to a blockchain sandbox evaluation system and method for construction projects. Background Technology

[0002] In urban infrastructure construction, building projects are often located near rivers or water bodies. These construction processes, including drilling, pouring concrete, earthwork transportation, and material storage, generate large amounts of dust particles. When construction dust spreads with the airflow, some of it settles into the water under the influence of airflow disturbance and gravity, leading to increased turbidity, decreased dissolved oxygen, and potential secondary water pollution due to the presence of heavy metals or organic pollutants in the dust. Current environmental monitoring methods mainly rely on on-site dust monitoring instruments and water quality monitoring points. However, in actual supervision, problems such as insufficient data accuracy, unreasonable monitoring point locations, discontinuous equipment operation, and manual intervention in data uploading are common, making it difficult for environmental monitoring data to accurately reflect the true pollution level at the construction site.

[0003] Furthermore, traditional environmental supervision largely relies on post-construction sampling or periodic inspections, lacking dynamic recording and accountability mechanisms throughout the entire construction process. When dust deposition causes water quality anomalies, it is difficult to determine the source and timing of pollution, and regulatory authorities struggle to make scientific judgments based on single-point data. To address these issues, a technological system is urgently needed that enables reliable recording, cross-departmental sharing, and traceable supervision of construction environmental data to improve data transparency and regulatory efficiency.

[0004] Therefore, it is necessary to provide a blockchain sandbox evaluation system and method for construction projects to solve the above-mentioned technical problems. In order to solve the above problems, a technical solution is provided. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of existing technologies, this invention provides a blockchain sandbox evaluation system and method for construction projects. This system addresses the problem that existing construction projects are prone to dust settling into water, leading to water pollution. Furthermore, existing monitoring methods suffer from unreliable data, unreasonable site deployment, and a lack of full-process traceability. Therefore, there is an urgent need to establish a reliable and shareable construction environment supervision system.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A blockchain sandbox evaluation system for construction projects includes a sandbox monitoring and deployment module, an on-chain data storage module, a data authenticity evaluation module, a data anomaly early warning module, and a dust diffusion correlation impact optimization module.

[0008] The sandbox monitoring deployment module is used to deploy monitoring equipment by building a blockchain sandbox, to obtain the first monitoring data and meteorological environmental data in real time during the project construction process, and to obtain the standard monitoring data required by the construction project as the second monitoring data.

[0009] The on-chain data storage module is used to preprocess the first monitoring data, meteorological and environmental data, and second monitoring data respectively, and store the first monitoring data, meteorological and environmental data, second monitoring data, and construction logs into the blockchain respectively;

[0010] The data authenticity assessment module is used to construct a data authenticity assessment model based on the first monitoring data and the second monitoring data to identify whether abnormal data exists; if it exists, the data anomaly early warning module is triggered; if it does not exist, the dust diffusion correlation impact optimization module is triggered.

[0011] The data anomaly warning module is used to provide early warnings of data anomalies.

[0012] The dust diffusion correlation impact optimization module is used to build a dust diffusion analysis strategy to predict the migration correlation impact of dust on water quality using the first monitoring data and meteorological environmental data, and to infer the rationality of the monitoring equipment placement based on the migration correlation impact.

[0013] As a further aspect of the present invention, the first monitoring data includes first dust monitoring data and first water quality monitoring data; the second monitoring data includes second dust monitoring data and second water quality monitoring data.

[0014] As a further aspect of the present invention, the data authenticity assessment module is used to construct a data authenticity assessment model based on the first monitoring data and the second monitoring data to identify whether abnormal data exists. Specific steps include:

[0015] The first dust monitoring data within the monitoring period is used as the first monitoring data sequence. ,in, This is the first dust monitoring data at time t. This is the first dust monitoring data at time T. The duration of the monitoring period, and the first water quality monitoring data as the second monitoring data sequence. ,in, This is the first water quality monitoring data at time t. This is the first water quality monitoring data at time T;

[0016] Extract the second dust monitoring data and the second water quality monitoring data from the second monitoring data, combine them with the first monitoring data sequence and the second monitoring data sequence to construct a data authenticity assessment model, obtain data authenticity assessment factors, and determine whether the data authenticity meets the standards;

[0017] Extract the first monitoring data that meets the data authenticity standard, determine the deviation coefficient of the monitoring data in real time, and determine whether there is any abnormality in the data based on the deviation coefficient;

[0018] If an anomaly is detected, the data anomaly warning module will be triggered; if no anomaly is detected, the dust diffusion correlation impact optimization module will be triggered.

[0019] As a further aspect of the present invention, the second dust monitoring data and the second water quality monitoring data are extracted from the second monitoring data, and a data authenticity assessment model is constructed by combining the first monitoring data sequence and the second monitoring data sequence. Data authenticity assessment factors are obtained, and data authenticity is determined. The specific steps for constructing the data authenticity assessment model are as follows:

[0020] The first difference value was obtained by extracting the first monitoring data sequence and the second dust data respectively. , The first difference value at time t. For the second dust data, the second monitoring data sequence, and the second water quality monitoring data, obtain the second difference value. , The second difference value at time t. This is the second set of water quality data;

[0021] The first deviation mean and the second deviation mean are calculated by taking the mean of the first difference value and the second difference value within the statistical monitoring period, respectively.

[0022] The first ratio is calculated as the proportion of the first deviation mean to the second dust data, and the second ratio is calculated as the proportion of the second deviation mean to the second water quality data. The data authenticity assessment factor is obtained by combining the first ratio and the second ratio.

[0023] The data authenticity assessment factor is compared with the preset assessment threshold. If the data authenticity assessment factor is less than the preset assessment threshold, it indicates that the data authenticity meets the standard; if the data authenticity assessment factor is greater than the preset assessment threshold, it indicates that the data authenticity does not meet the standard.

[0024] As a further aspect of the present invention, first monitoring data that meets the data authenticity standard is extracted, the deviation coefficient of the monitoring data is determined in real time, and the presence of anomalies in the data is determined based on the deviation coefficient. The specific steps are as follows:

[0025] When the data authenticity meets the standard, the first monitoring data and the second monitoring data are imported into the data deviation monitoring model. The first deviation coefficient is obtained in real time based on the first dust monitoring data and the second dust monitoring data, and the second deviation coefficient is obtained in real time based on the first water quality monitoring data and the second water quality monitoring data.

[0026] The first deviation coefficient and the second deviation coefficient are compared with the preset deviation threshold range to determine whether there is any abnormality in the data.

[0027] As a further aspect of the present invention, the first deviation coefficient and the second deviation coefficient are compared with a preset deviation threshold range to determine whether there is any abnormality in the data. The specific steps are as follows:

[0028] The first deviation coefficient is compared with the preset deviation threshold. If the first deviation coefficient is greater than or equal to the preset deviation threshold, the first dust monitoring data at the corresponding time time is abnormal; if the first deviation coefficient is less than the preset deviation threshold, the first dust monitoring data at the corresponding time time is not abnormal.

[0029] The second deviation coefficient is compared with the preset deviation threshold range. If the second deviation coefficient is greater than or equal to the preset deviation threshold, the first water quality monitoring data at the corresponding time time is abnormal; if the second deviation coefficient is less than the preset deviation threshold, the first water quality monitoring data at the corresponding time time is not abnormal.

[0030] As a further aspect of the present invention, the specific steps of the dust diffusion analysis strategy are as follows:

[0031] By uniformly dividing the monitoring area into several monitoring units, the first monitoring data in each monitoring unit is obtained, and the first monitoring data after removing abnormal monitoring data is used as the monitoring data to be analyzed.

[0032] Based on the monitoring data to be analyzed and meteorological environmental data, predict the migration correlation coefficient of dust on water quality;

[0033] The rationality of monitoring equipment placement can be inferred based on migration correlation coefficients.

[0034] As a further aspect of the present invention, the candidate dust impact area is determined based on the monitoring data to be analyzed and the meteorological environment data, the monitoring data to be analyzed for each monitoring unit is obtained, the mean value of the monitoring data to be analyzed within the monitoring area is calculated, the ratio of the mean value to the second monitoring data is used as the correlation factor, the meteorological impact factor is obtained by weighted calculation based on the meteorological environment data, and the migration correlation impact coefficient is obtained by combining the correlation factor and the meteorological impact factor.

[0035] As a further aspect of the present invention, the rationality of the monitoring equipment placement locations is inferred based on the migration correlation coefficient, and the specific steps are as follows:

[0036] Obtain the migration-related impact coefficient and compare it with the preset impact threshold. If the migration-related impact coefficient is greater than or equal to the preset impact threshold, the location of the monitoring equipment is reasonable; if the migration-related impact coefficient is less than the preset impact threshold, the location of the monitoring equipment is unreasonable.

[0037] A blockchain sandbox evaluation method for construction projects includes the following steps:

[0038] By building a blockchain sandbox to deploy monitoring equipment, the first monitoring data and meteorological environmental data during the project construction process can be obtained in real time, and the standard monitoring data required by the construction project can be obtained as the second monitoring data.

[0039] The first monitoring data, meteorological and environmental data, and second monitoring data are preprocessed respectively, and the first monitoring data, meteorological and environmental data, second monitoring data, and construction logs are stored in the blockchain respectively;

[0040] Based on the first and second monitoring data, a data authenticity assessment model is constructed to identify whether abnormal data exists. If it exists, an early warning of data anomalies is issued; if it does not exist, the rationality of the monitoring equipment placement points is inferred by building a dust diffusion analysis strategy.

[0041] The technical effects and advantages of this invention, a blockchain sandbox assessment system and method for construction projects, are as follows: This invention deploys monitoring equipment within a blockchain sandbox to acquire first monitoring data and meteorological environmental data in real time during the project construction process. Standard monitoring data required by the construction project is then acquired as second monitoring data. The first, meteorological, and second monitoring data are preprocessed and stored in the blockchain along with construction logs. The immutability and traceability of the blockchain sandbox ensure the authenticity and integrity of the data, providing a reliable foundation for subsequent analysis. A data authenticity assessment model is constructed based on the first and second monitoring data to identify the presence of abnormal data. If abnormal data is found, a data anomaly warning is issued. If not, a dust diffusion analysis strategy is used to infer the rationality of the monitoring equipment placement, ensuring the effectiveness of monitoring in key areas. This helps provide construction site managers with a basis for rapid response, thereby reducing the potential impact of the construction process on surrounding water bodies and the air environment.

[0042] This invention helps to ensure the high reliability and traceability of monitoring data at construction sites, while enabling intelligent optimization of monitoring points and dynamic analysis of the potential impact of dust on water quality, providing strong technical support for environmental supervision, pollution prevention and control, and safety management in construction projects. Attached Figure Description

[0043] Figure 1 A schematic diagram of the structure of a blockchain sandbox evaluation system for construction projects provided in an embodiment of the present invention;

[0044] Figure 2 A flowchart of a blockchain sandbox evaluation method for construction projects provided in an embodiment of the present invention. Detailed Implementation

[0045] The technical solutions of this invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described technical solutions are only a part of this invention, and not all of it. All other technical solutions obtained by those skilled in the art based on the technical solutions of this invention without inventive effort are within the scope of protection of this invention.

[0046] like Figure 1 The diagram shown is a structural schematic of a blockchain sandbox evaluation system for construction projects provided by an embodiment of the present invention. The system includes a sandbox monitoring and deployment module, an on-chain data storage module, a data authenticity evaluation module, a data anomaly early warning module, and a dust diffusion correlation impact optimization module. The sandbox monitoring and deployment module is connected to the on-chain data storage module, the on-chain data storage module is connected to the data authenticity evaluation module, and the data authenticity evaluation module is connected to the data anomaly early warning module and the dust diffusion correlation impact optimization module, respectively.

[0047] The sandbox monitoring deployment module is used to deploy monitoring equipment by building a blockchain sandbox, to obtain the first monitoring data and meteorological environmental data in real time during the project construction process, and to obtain the standard monitoring data required by the construction project as the second monitoring data.

[0048] The on-chain data storage module is used to preprocess the first monitoring data, meteorological and environmental data, and second monitoring data respectively, and store the first monitoring data, meteorological and environmental data, second monitoring data, and construction logs into the blockchain respectively;

[0049] The data authenticity assessment module is used to construct a data authenticity assessment model based on the first monitoring data and the second monitoring data to identify whether abnormal data exists; if it exists, the data anomaly early warning module is triggered; if it does not exist, the dust diffusion correlation impact optimization module is triggered.

[0050] The data anomaly warning module is used to provide early warnings of data anomalies.

[0051] The dust diffusion correlation impact optimization module is used to build a dust diffusion analysis strategy to predict the migration correlation impact of dust on water quality using the first monitoring data and meteorological environmental data, and to infer the rationality of the monitoring equipment placement based on the migration correlation impact.

[0052] Preferably, the first monitoring data includes first dust monitoring data and first water quality monitoring data;

[0053] Meteorological and environmental data include wind speed and ambient temperature;

[0054] The second monitoring data includes the second dust monitoring data and the second water quality monitoring data.

[0055] It should be noted that dust monitoring data includes dust concentration, particle size distribution, settling rate, etc.

[0056] Water quality monitoring data includes water turbidity, water heavy metal content, etc.

[0057] It should be noted that the monitoring equipment includes dust monitoring sensors, meteorological monitoring devices, and water quality monitoring equipment;

[0058] The dust monitoring sensor is used to acquire first dust monitoring data and second dust monitoring data; the meteorological monitoring device is used to acquire meteorological environmental data; and the water quality monitoring equipment is used to acquire first water quality monitoring data and second water quality monitoring data.

[0059] In one embodiment of the present invention, the construction area is adjacent to a river. The concrete dust and fine particles of drilling slag generated during the construction process are easily dispersed by the wind and enter the water body with dustfall or rainwater runoff, causing potential pollution to the surrounding water quality. Therefore, the construction project blockchain sandbox assessment system provided by the present invention is used to conduct dynamic monitoring and assessment of dust emissions and water quality changes at the construction site throughout the entire process.

[0060] Dust monitoring sensors were deployed at locations 50m and 100m upwind and downwind of the main construction area and the river channel to collect real-time dust monitoring data, including dust concentration, particle size distribution, and settling rate calculated from particle size and gravity. Meteorological monitoring devices were installed on a monitoring tower 10m above the site to collect real-time meteorological environmental data, including wind speed and ambient temperature, providing input parameters for dust diffusion analysis. Water quality monitoring equipment was deployed at two upstream and two downstream sections of the river channel near the construction area to continuously collect first-level water quality monitoring data, including water turbidity, suspended particulate matter concentration, and heavy metal ion content.

[0061] At the same time, second monitoring data, including the permissible dust emission standards for the construction project (i.e., second dust monitoring data) and water quality evaluation benchmarks (i.e., second water quality monitoring data), are retrieved from the project design documents and environmental impact assessment standard database for subsequent data authenticity verification and environmental impact analysis.

[0062] After all collected monitoring data enters the system, it first undergoes preprocessing operations by the on-chain data storage module, including data time synchronization, noise reduction, outlier smoothing, and unit standardization. Subsequently, the system writes the initial dust monitoring data, initial water quality monitoring data, meteorological environmental data, and construction logs into the blockchain network after hash encryption, achieving distributed storage and tamper-proof protection. The blockchain nodes are jointly maintained by the construction unit, supervision unit, and environmental regulatory department to ensure data traceability throughout its entire lifecycle.

[0063] Through the deployment and operation of this invention, full-cycle, full-chain, and reliable monitoring of construction dust and water pollution is achieved. Compared with traditional manual sampling and offline analysis methods, this invention significantly improves data authenticity, response speed, and regulatory credibility, providing a traceable, verifiable, and optimizable environmental monitoring and governance decision support platform for construction projects near water bodies.

[0064] Preferably, the data authenticity assessment module is used to construct a data authenticity assessment model based on the first monitoring data and the second monitoring data to identify whether abnormal data exists. Specific steps include:

[0065] The first dust monitoring data within the monitoring period is used as the first monitoring data sequence. ,in, This is the first dust monitoring data at time t. This is the first dust monitoring data at time T. The duration of the monitoring period, and the first water quality monitoring data as the second monitoring data sequence. ,in, This is the first water quality monitoring data at time t. This is the first water quality monitoring data at time T;

[0066] Extract the second dust monitoring data and the second water quality monitoring data from the second monitoring data, combine them with the first monitoring data sequence and the second monitoring data sequence to construct a data authenticity assessment model, obtain data authenticity assessment factors, and determine whether the data authenticity meets the standards;

[0067] Extract the first monitoring data that meets the data authenticity standard, determine the deviation coefficient of the monitoring data in real time, and determine whether there is any abnormality in the data based on the deviation coefficient;

[0068] If an anomaly is detected, the data anomaly warning module will be triggered; if no anomaly is detected, the dust diffusion correlation impact optimization module will be triggered.

[0069] In one embodiment of the present invention, during the blasting demolition of a steel bridge support beam, the construction area is located near a river, and dust monitoring sensors, meteorological monitoring devices, and water quality monitoring equipment are deployed around the site to continuously collect real-time data on air and water. During blasting and construction operations, the system records monitoring data every 5 minutes within the monitoring period, constructs a data authenticity assessment model to identify abnormal data, and ensures the reliability of monitoring information.

[0070] During the data acquisition phase, dust monitoring data for the monitoring period is first obtained through dust monitoring sensors, forming the first dust monitoring data sequence. Simultaneously, water quality monitoring equipment collects real-time water quality data from upstream and downstream sections of the river, constituting the first water quality monitoring data sequence.

[0071] In the blockchain sandbox environment, the construction environmental impact assessment database and historical monitoring baseline data are called, and the corresponding secondary monitoring data, including secondary dust monitoring data and secondary water quality monitoring data, are extracted and input into the data authenticity assessment model. The data authenticity assessment factor is calculated using time series similarity analysis and multivariate bias test methods.

[0072] Once the system identifies a monitoring section with high credibility, it automatically extracts dust and water quality data from it and calculates the deviation coefficient at each time point. Through the above data authenticity assessment process, this embodiment of the invention achieves dynamic self-checking and credibility verification of monitoring data. Combined with a blockchain notarization mechanism, the immutability and traceability of monitoring data throughout the entire process of transmission, analysis, and storage can be ensured, thereby significantly improving the reliability of environmental monitoring results and the credibility of regulatory oversight.

[0073] Preferably, the second dust monitoring data and the second water quality monitoring data are extracted from the second monitoring data. A data authenticity assessment model is constructed by combining the first monitoring data sequence and the second monitoring data sequence. Data authenticity assessment factors are obtained, and data authenticity is determined. The specific steps for constructing the data authenticity assessment model are as follows:

[0074] The first difference value was obtained by extracting the first monitoring data sequence and the second dust data respectively. , The first difference value at time t. For the second dust data, the second monitoring data sequence, and the second water quality monitoring data, obtain the second difference value. , The second difference value at time t. This is the second set of water quality data;

[0075] The mean of the first deviation is obtained by averaging the first and second differences within the statistical monitoring period. and the second deviation mean ;

[0076] The first ratio is calculated as the proportion of the first deviation mean to the second dust data, and the second ratio is calculated as the proportion of the second deviation mean to the second water quality data. The data authenticity assessment factor is obtained by combining the first ratio and the second ratio.

[0077] The data authenticity assessment factor is compared with the preset assessment threshold. If the data authenticity assessment factor is less than the preset assessment threshold, it indicates that the data authenticity meets the standard; if the data authenticity assessment factor is greater than the preset assessment threshold, it indicates that the data authenticity does not meet the standard.

[0078] In one embodiment of the present invention, the construction site is located near a river, and dust during construction is easily dispersed by airflow and settles into the water, posing a risk of water pollution. To ensure the accuracy and reliability of the monitoring data, the project management team deployed dust monitoring sensors and water quality monitoring equipment in the construction area and downstream river section, and built a data authenticity assessment model in a blockchain sandbox environment to identify and verify the authenticity of the monitoring data.

[0079] During the monitoring phase, dust monitoring sensors continuously collect data on dust concentration, particle size distribution, and settling rate in the air at the construction site, forming the first dust monitoring data sequence. Simultaneously, water quality monitoring equipment collects information on turbidity and heavy metal content in the river water, forming the first water quality monitoring data sequence. The system retrieves standard monitoring data for the corresponding operating conditions from the blockchain database, serving as the second dust and second water quality data sets, providing a reference benchmark for subsequent authenticity assessments.

[0080] During the model calculation phase, the first and second monitoring data sequences were extracted separately, and the difference between them at each time point was calculated to reflect the degree of deviation between the on-site monitoring data and the standard data. Subsequently, the difference values ​​of dust and water quality were statistically averaged over the entire monitoring period to obtain the mean dust deviation and the mean water quality deviation, which are used to describe the overall stability of the monitoring data and its degree of conformity with the standard data.

[0081] Next, the ratio between the average dust deviation and the standard dust data is used as the first ratio, and the ratio between the average water quality deviation and the standard water quality data is used as the second ratio. By combining these two ratios, the system generates a data authenticity assessment factor to quantify the consistency level between the on-site monitoring data and the standard data. During the assessment phase, the obtained data authenticity assessment factor is compared with a pre-set assessment threshold. If the assessment factor is below the threshold, it indicates that the difference between the monitoring data and the standard value is small, and the data authenticity is high; the result is stored on the blockchain as a valid input for subsequent dust diffusion correlation impact analysis. If the assessment factor exceeds the threshold, it indicates that the monitoring data may have abnormal fluctuations, equipment drift, or data forgery, automatically triggering the data anomaly warning module, and storing the anomaly information in the blockchain in the form of a timestamp for regulatory authorities to trace and verify.

[0082] Through the operation of this model, this embodiment of the invention can achieve real-time self-verification and reliable assessment of environmental monitoring data at construction sites, ensuring the authenticity and reliability of data entering the analysis and decision-making stages. Combining the immutability and traceability characteristics of a blockchain sandbox, this method effectively prevents the risk of data being artificially modified or falsified during collection, transmission, and analysis, providing scientific and transparent technical support for the environmental compliance supervision of construction projects.

[0083] Preferably, the first monitoring data that meets the data authenticity standard is extracted, the deviation coefficient of the monitoring data is judged in real time, and the abnormality of the data is judged based on the deviation coefficient. The specific steps are as follows:

[0084] When the data authenticity meets the standard, the first and second monitoring data are acquired and imported into the data deviation monitoring model. Based on the first and second dust monitoring data, the first deviation coefficient is obtained in real time. The second deviation coefficient is obtained in real time based on the first and second water quality monitoring data. ;

[0085] The first deviation coefficient and the second deviation coefficient are compared with the preset deviation threshold range to determine whether there is any abnormality in the data.

[0086] In one embodiment of the present invention, at a construction site near a river, the system uses a blockchain sandbox to conduct a preliminary assessment of the authenticity of the monitoring data. After confirming that the monitoring data meets the authenticity standards, it enters the data deviation monitoring stage. The purpose of this stage is to further identify abnormal fluctuations caused by factors such as short-term construction disturbances, weather changes, or equipment accuracy errors during the actual monitoring process, ensuring that the input data for subsequent dust diffusion analysis and water quality migration assessment are stable and reliable.

[0087] First, the first monitoring data that has passed authenticity verification and the second monitoring data that meet the standard are imported into the data deviation monitoring model. By calculating the relative deviation of the two types of data at each time point, the deviation coefficients for dust and water quality are obtained. The dust deviation coefficient describes the proportion of fluctuation in on-site dust concentration relative to the standard concentration, while the water quality deviation coefficient reflects the intensity of change in indicators such as turbidity and heavy metal content of water bodies in the construction area relative to the standard values.

[0088] During actual monitoring, the deviation coefficients of dust and water quality are calculated in real time based on the sampling data at each moment, and compared with the preset deviation threshold range. If the deviation coefficients of dust or water quality are both within the threshold range, the data at that moment is considered to be within the normal fluctuation range, the environmental monitoring results are considered reliable, and the data is automatically stored in the blockchain as a valid input for dust diffusion trend analysis. If either deviation coefficient exceeds the threshold range, it indicates that the monitoring data has abnormal fluctuations or unreasonable sudden changes.

[0089] At this point, the data anomaly warning module is automatically triggered, and the abnormal data, along with its corresponding time, location, sensor number, and other information, are synchronously recorded to the blockchain node to ensure the traceability and subsequent verification of the data anomaly. Managers can query the complete record of the anomaly event through the blockchain sandbox interface to analyze possible causes of the deviation, such as sudden changes in local wind speed, dust clogging of monitoring probes, or a sudden increase in turbidity caused by water disturbance. This allows for timely adjustments to the status of on-site equipment or optimization of monitoring point deployment.

[0090] This deviation coefficient monitoring mechanism enables real-time dynamic verification of monitoring data and multi-dimensional anomaly identification. The trusted data closed loop formed by combining blockchain sandbox technology not only improves the stability and accuracy of the monitoring system but also provides a reliable data foundation for subsequent environmental response analysis and regulatory assessment.

[0091] Preferably, the first deviation coefficient and the second deviation coefficient are compared with a preset deviation threshold range to determine whether there is any anomaly in the data. The specific steps are as follows:

[0092] The first deviation coefficient is compared with the preset deviation threshold. If the first deviation coefficient is greater than or equal to the preset deviation threshold, the first dust monitoring data at the corresponding time time is abnormal; if the first deviation coefficient is less than the preset deviation threshold, the first dust monitoring data at the corresponding time time is not abnormal.

[0093] The second deviation coefficient is compared with the preset deviation threshold range. If the second deviation coefficient is greater than or equal to the preset deviation threshold, the first water quality monitoring data at the corresponding time time is abnormal; if the second deviation coefficient is less than the preset deviation threshold, the first water quality monitoring data at the corresponding time time is not abnormal.

[0094] In this embodiment of the invention, multiple sets of dust and water quality monitoring devices are installed around the construction area to monitor the impact of the construction process on air and water bodies in real time. After receiving real-time data uploaded by the monitoring devices, the monitoring data that meets the requirements of sensor accuracy and data timing is first screened through an authenticity verification module. Then, these verified data are imported into the deviation monitoring model to calculate the corresponding dust and water quality deviation coefficients.

[0095] During model operation, the first deviation coefficient calculated in real time is automatically compared with a preset deviation threshold range. When the dust deviation coefficient at a certain moment is greater than or equal to the threshold, it is determined that the dust monitoring data at that moment has abnormal fluctuations, possibly caused by dust generated during the blast or disturbance to the monitoring equipment. The abnormal data is immediately marked as "abnormal state," and the corresponding time, monitoring point number, and environmental condition parameters are recorded, proceeding to the subsequent anomaly confirmation and correction process. If the dust deviation coefficient is less than the threshold range, the dust monitoring data at that moment is considered to be within the normal range and is automatically included in the reliable monitoring sequence for subsequent diffusion trend analysis and source strength inversion calculation.

[0096] Similarly, a real-time threshold comparison is performed on the second deviation coefficient. When the water quality deviation coefficient exceeds the preset threshold, the system automatically determines that the water quality monitoring data at that moment is abnormal. For example, a sharp increase in river turbidity or suspended solids concentration shortly after a blast can cause the monitoring data to deviate from the normal background value. An anomaly report is then generated, triggering the water quality emergency monitoring mechanism to initiate intensive sampling and short-term data compensation analysis at nearby monitoring points to avoid misjudging the overall trend due to a single-point anomaly. If the water quality deviation coefficient is below the threshold, the water quality change at that moment is considered to be within an acceptable range, and the data is directly incorporated into the water quality migration analysis model.

[0097] This dynamic judgment mechanism, based on the comparison of deviation coefficients and thresholds, can automatically identify abnormal states in environmental monitoring data without relying on human intervention, ensuring the stability and reliability of input data. Furthermore, by combining it with a blockchain sandbox recording mechanism, the time, location, and equipment status of any abnormal event can be traced, achieving reliable management of monitoring data throughout the entire process and providing a reliable basis for environmental impact assessment and pollution response optimization for blasting operations.

[0098] Preferably, the dust diffusion correlation impact optimization module is used to build a dust diffusion analysis strategy to predict the migration correlation impact of dust on water quality using the first monitoring data and meteorological environmental data, and to infer the rationality of the monitoring equipment placement based on the migration correlation impact. The specific steps of the dust diffusion analysis strategy are as follows:

[0099] By uniformly dividing the monitoring area into several monitoring units, the first monitoring data in each monitoring unit is obtained, and the first monitoring data after removing abnormal monitoring data is used as the monitoring data to be analyzed.

[0100] Based on the monitoring data to be analyzed and meteorological environmental data, predict the migration correlation coefficient of dust on water quality;

[0101] Obtain the migration-related impact coefficient and compare it with the preset impact threshold. If the migration-related impact coefficient is greater than or equal to the preset impact threshold, the location of the monitoring equipment is reasonable; if the migration-related impact coefficient is less than the preset impact threshold, the location of the monitoring equipment is unreasonable.

[0102] It should be noted that by uniformly dividing the monitoring area into several monitoring units, the first monitoring data in each monitoring unit is obtained, and the first monitoring data after removing abnormal monitoring data is used as the monitoring data to be analyzed. If there are no abnormalities in the data, the first monitoring data is directly used as the monitoring data to be analyzed.

[0103] In this embodiment of the invention, the construction area is adjacent to a river, and the dust generated by blasting and construction may eventually settle into the river water through air diffusion, potentially affecting water quality. To scientifically assess the migration-related impact of construction activities on water bodies and optimize the placement of monitoring equipment, dust and water quality monitoring sensors were deployed in the construction area and downstream waters, and a dust diffusion-related impact optimization module was run in a blockchain sandbox environment.

[0104] First, the monitoring area is evenly divided into several monitoring units according to the distribution characteristics of the construction site and downstream water bodies. Within each monitoring unit, the first monitoring data, after authenticity assessment and anomaly detection, is extracted to form the monitoring dataset to be analyzed. By removing outlier data, it can be ensured that subsequent analysis will not be affected by sudden anomalies or equipment failure data, thereby improving the accuracy and reliability of predictions.

[0105] After data processing, the system combines real-time meteorological and environmental data and applies a dust diffusion analysis strategy to model the correlation between dust concentration and water quality index changes in each monitoring unit, predicting the migration and related impacts of dust diffusion in the air and sedimentation into water. An algorithm is used to calculate the migration correlation impact coefficient for each monitoring unit to quantify the contribution of dust from that unit to potential water pollution.

[0106] During the impact assessment phase, the migration correlation coefficient of each monitoring unit is compared with the preset impact threshold. If the impact coefficient of a monitoring unit is greater than or equal to the threshold, it indicates that the current monitoring equipment layout can effectively capture dust migration and water quality changes in the area, and the layout is reasonable. If the impact coefficient is lower than the threshold, it indicates that the monitoring points of the unit may not be adequately covered or are in an unreasonable location, and cannot accurately reflect the impact of dust migration on water bodies. This will prompt adjustments to the monitoring layout or an increase in the number of sensors.

[0107] Through the operation of this module, construction units can not only understand the potential impact trends of dust on water bodies in real time, but also dynamically optimize the layout of monitoring equipment to ensure that dust and water quality changes in key areas are fully monitored. Combined with blockchain sandbox technology, monitoring data, analysis results, and site optimization decisions can be recorded throughout the entire process, achieving reliable evidence storage and traceable management of construction environmental supervision data, providing reliable technical support for construction safety and environmental protection.

[0108] Preferably, the dust candidate impact area is determined based on the monitoring data to be analyzed and the meteorological environment data, the monitoring data to be analyzed for each monitoring unit is obtained, the mean value of the monitoring data to be analyzed within the monitoring area is calculated, the ratio of the mean value to the second monitoring data is used as the correlation factor, the meteorological impact factor is obtained by weighted calculation based on the meteorological environment data, and the migration correlation impact coefficient is obtained by combining the correlation factor and the meteorological impact factor.

[0109] It should be noted that the formula for the correlation factor is:

[0110]

[0111] In the formula: As a correlation factor, This represents the first dust monitoring data of the i-th monitoring unit at time t. This represents the first water quality monitoring data for the i-th monitoring unit at time t. This is the second dust monitoring data. The total number of monitoring units. This is the second set of water quality monitoring data.

[0112] The formula for meteorological influencing factors is:

[0113]

[0114] In the formula: Meteorological influencing factors, This is the weighting coefficient for wind speed. Let t be the wind speed. The weighting coefficient for ambient temperature. Let t be the ambient temperature at time t.

[0115] This invention addresses the potential risks of dust dispersion and water pollution in the construction area and downstream river channels. To quantify the migration-related impact of dust on water quality and optimize the layout of monitoring equipment, the system first determines candidate dust impact areas in the construction and downstream regions based on the dust and water quality monitoring data to be analyzed and real-time meteorological data. This area covers the construction site and water bodies along the windward direction that may be affected by dust deposition. The system divides this area into several monitoring units, each corresponding to a set of dust and water quality monitoring data.

[0116] Within each monitoring unit, the mean of the monitoring data to be analyzed is calculated after authenticity verification and anomaly removal. For the monitoring data at each time point, the ratio of the mean dust value to the standard dust value, and the ratio of the mean water quality value to the standard water quality value, are added together to obtain the correlation factor for that time point. The correlation factor is used to quantitatively describe the overall deviation of dust and water quality within the monitoring area, as well as the potential impact of dust on water quality. Through the correlation factor, it is possible to quickly identify which monitoring units may contribute a large or small pollution load under the current operating conditions, providing a reference for subsequent analysis.

[0117] Simultaneously, real-time meteorological and environmental data are used to weight and correct the associated factors. Meteorological parameters such as wind speed and ambient temperature are included in the calculation by setting weight coefficients to form meteorological influence factors. These factors reflect the moderating effect of meteorological conditions on the direction, speed, and settling characteristics of dust diffusion. For example, increased wind speed may accelerate dust diffusion along the prevailing wind direction, while temperature changes may affect air density and the settling rate of suspended particles.

[0118] Finally, the correlation factors and meteorological influence factors are combined to obtain the migration correlation influence coefficient for each monitoring unit at each time point. This coefficient quantifies the potential impact of dust from the construction area on downstream water bodies and can be used to determine the coverage effectiveness of monitoring points. If the migration correlation influence coefficient of a unit is high, it indicates that the area has a significant impact on water quality and the monitoring equipment is reasonably arranged; if the coefficient is low, it suggests that there may be monitoring blind spots in the area, requiring the addition of sensors or adjustment of the monitoring point layout.

[0119] This method enables construction companies to monitor the dynamic relationship between dust diffusion and water quality changes in real time, and to store and trace the analysis results in the blockchain, ensuring the credibility of monitoring data and assessment conclusions, while providing a scientific basis for environmental management and construction process optimization.

[0120] A blockchain sandbox evaluation method for construction projects includes the following steps:

[0121] By building a blockchain sandbox to deploy monitoring equipment, the first monitoring data and meteorological environmental data during the project construction process can be obtained in real time, and the standard monitoring data required by the construction project can be obtained as the second monitoring data.

[0122] The first monitoring data, meteorological and environmental data, and second monitoring data are preprocessed respectively, and the first monitoring data, meteorological and environmental data, second monitoring data, and construction logs are stored in the blockchain respectively;

[0123] Based on the first and second monitoring data, a data authenticity assessment model is constructed to identify whether abnormal data exists. If it exists, an early warning of data anomalies is issued; if it does not exist, the rationality of the monitoring equipment placement points is inferred by building a dust diffusion analysis strategy.

[0124] like Figure 2 The diagram shown is a flowchart of a blockchain sandbox evaluation method for construction projects according to an embodiment of the present invention, which can be used to execute... Figure 1 The system embodiment shown is similar in principle and technical effect, and will not be described again here.

[0125] Through the above embodiments, this invention achieves full-process digitalization and intelligentization of construction site environmental monitoring, data management, and risk early warning through the collaborative operation of multiple modules. Firstly, the sandbox monitoring deployment module can flexibly deploy dust, meteorological, and water quality monitoring equipment at the construction site, collecting initial monitoring data in real time and combining it with meteorological and environmental data to provide the system with accurate environmental monitoring input, ensuring comprehensive monitoring coverage and timely data collection. The on-chain data storage module preprocesses and stores the collected data and construction logs, utilizing the immutability and traceability characteristics of the blockchain sandbox to ensure the authenticity and integrity of the data, providing a reliable foundation for subsequent analysis.

[0126] The data authenticity assessment module plays a crucial role in the system. By constructing a data assessment model, it compares and analyzes the primary monitoring data with the standard secondary monitoring data, effectively identifying abnormal or falsified data and ensuring the reliability of environmental monitoring results. Combined with the data anomaly early warning module, the system can issue timely warnings when abnormal data or sudden environmental events occur, providing construction site managers with a basis for rapid response and thus reducing the potential impact of the construction process on the surrounding water and air environment.

[0127] The dust diffusion correlation impact optimization module combines monitoring data with meteorological and environmental data to establish a dust diffusion analysis strategy, quantitatively predict the migration impact of dust on water quality, and conduct a rationality assessment of the monitoring equipment layout based on the migration correlation impact coefficient. This not only ensures the monitoring effectiveness in key areas but also provides a scientific basis for environmental management and emergency decision-making during construction.

[0128] This invention helps to ensure the high reliability and traceability of monitoring data at construction sites, while enabling intelligent optimization of monitoring points and dynamic analysis of the potential impact of dust on water quality, providing strong technical support for environmental supervision, pollution prevention and control, and safety management in construction projects.

[0129] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

[0130] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A construction project blockchain sandbox assessment system, characterized in that, The sandbox monitoring deployment module, the on-chain data storage module, the data authenticity evaluation module, the data anomaly early warning module, and the dust diffusion correlation influence optimization module are included. The sandbox monitoring deployment module is configured to deploy monitoring equipment through a blockchain sandbox, to obtain first monitoring data and meteorological environment data in a real-time manner during a project construction process, and to obtain standard monitoring data required by the construction project as second monitoring data. The first monitoring data includes first dust monitoring data and first water quality monitoring data. The second monitoring data includes second dust monitoring data and second water quality monitoring data. The on-chain data storage module is configured to preprocess the first monitoring data, the meteorological environment data, and the second monitoring data, and to store the first monitoring data, the meteorological environment data, the second monitoring data, and a construction log in a blockchain. The data authenticity evaluation module is configured to construct a data authenticity evaluation model based on the first monitoring data and the second monitoring data to identify whether abnormal data exists. The specific steps include: The first dust monitoring data in a monitoring period is obtained as a first monitoring data sequence. wherein, is the first dust monitoring data at time t, is the first dust monitoring data at time T, is the monitoring period length, and the first water quality monitoring data as the second monitoring data sequence wherein, is the first water quality monitoring data at time t, is the first water quality monitoring data at time T; The second dust monitoring data and the second water quality monitoring data in the second monitoring data are extracted, and a data authenticity evaluation model is constructed based on the first monitoring data sequence and the second monitoring data sequence. A data authenticity evaluation factor is obtained to determine whether the data authenticity meets a standard. The specific steps of constructing the data authenticity evaluation model are as follows: extracting a first monitoring data sequence and a second dust data to obtain a first difference value , is the first difference value at time t, extracting a second water quality monitoring data, the second dust data, the second monitoring data sequence and the second water quality monitoring data to obtain a second difference value , is the second difference value at time t, is the second water quality data; The first difference value and the second difference value in the monitoring period are counted to obtain a first deviation mean and a second deviation mean. A first ratio is calculated as a proportion of the first deviation mean to the second dust data, and a second ratio is calculated as a proportion of the second deviation mean to the second water quality data. The data authenticity evaluation factor is obtained by comprehensively considering the first ratio and the second ratio. The data authenticity evaluation factor is compared with a preset evaluation threshold. If the data authenticity evaluation factor is less than the preset evaluation threshold, it indicates that the data authenticity meets the standard. If the data authenticity evaluation factor is greater than the preset evaluation threshold, it indicates that the data authenticity does not meet the standard. The first monitoring data that meets the data authenticity standard is extracted, and a deviation coefficient of the monitoring data is determined in real time. Whether the data is abnormal is determined based on the deviation coefficient. If there is an abnormality, the data anomaly early warning module is triggered. If there is no abnormality, the dust diffusion correlation influence optimization module is triggered. The data anomaly early warning module is configured to perform data anomaly early warning. The dust diffusion correlation influence optimization module is configured to build a dust diffusion analysis strategy based on the first monitoring data and the meteorological environment data to predict the migration correlation influence of dust on water quality, and to speculate the rationality of the monitoring equipment arrangement point based on the migration correlation influence. The specific steps of the dust diffusion analysis strategy are as follows: The monitoring area is evenly divided into a plurality of monitoring units, and the first monitoring data in each monitoring unit is obtained. The first monitoring data after removing abnormal monitoring data is used as analysis monitoring data. According to the to-be-analyzed monitoring data and meteorological environment data, a migration correlation influence coefficient of dust on water quality is predicted, specifically as follows: a dust candidate influence area is determined according to the to-be-analyzed monitoring data and meteorological environment data, the to-be-analyzed monitoring data of each monitoring unit is obtained, the mean value of the to-be-analyzed monitoring data in the monitoring area is calculated, the ratio of the mean value to the second monitoring data is taken as a correlation factor, a meteorological influence factor is obtained by weighted calculation according to the meteorological environment data, and the migration correlation influence coefficient is obtained by comprehensively combining the correlation factor and the meteorological influence factor; The rationality of the monitoring equipment arrangement point is inferred based on the migration correlation coefficient.

2. The building project blockchain sandbox evaluation system of claim 1, wherein, The first monitoring data meeting the data authenticity standard is extracted, the deviation coefficient of the monitoring data is judged in real time, and whether the data is abnormal is judged according to the deviation coefficient, specifically as follows: When the data authenticity meets the standard, the first monitoring data and the second monitoring data are imported into the data deviation monitoring model, the first deviation coefficient is obtained based on the first dust monitoring data and the second dust monitoring data, and the second deviation coefficient is obtained based on the first water quality monitoring data and the second water quality monitoring data. The first deviation coefficient and the second deviation coefficient are compared with the preset deviation threshold range respectively, and whether the data is abnormal is judged.

3. The building project blockchain sandbox evaluation system of claim 2, wherein, The first deviation coefficient and the second deviation coefficient are compared with the preset deviation threshold range, and whether the data is abnormal is judged, specifically as follows: The first deviation coefficient is compared with the preset deviation threshold, and if the first deviation coefficient is greater than or equal to the preset deviation threshold, the first dust monitoring data at the corresponding time exists abnormality. If the first deviation coefficient is less than the preset deviation threshold, the first dust monitoring data at the corresponding time does not exist abnormality. The second deviation coefficient is compared with the preset deviation threshold range, and if the second deviation coefficient is greater than or equal to the preset deviation threshold, the first water quality monitoring data at the corresponding time exists abnormality. If the second deviation coefficient is less than the preset deviation threshold, the first water quality monitoring data at the corresponding time does not exist abnormality.

4. The building project blockchain sandbox evaluation system of claim 1, wherein, The rationality of the monitoring equipment arrangement point is inferred based on the migration correlation coefficient, specifically as follows: The migration correlation influence coefficient is obtained, the migration correlation influence coefficient is compared with the preset influence threshold, if the migration correlation influence coefficient is greater than or equal to the preset influence threshold, the monitoring equipment arrangement point is reasonable, and if the migration correlation influence coefficient is less than the preset influence threshold, the monitoring equipment arrangement point is unreasonable. 5.A method for evaluating a building project blockchain sandbox, applied to the building project blockchain sandbox evaluation system according to any one of claims 1-4, characterized in that, Including the following steps: The monitoring equipment is deployed by building a blockchain sandbox, the first monitoring data and meteorological environment data in the project construction process are obtained in real time, and the standard monitoring data required by the construction project is taken as the second monitoring data; The first monitoring data, meteorological environment data and second monitoring data are preprocessed respectively, and the first monitoring data, meteorological environment data, second monitoring data and construction log are stored in the blockchain respectively; A data authenticity evaluation model is constructed based on the first monitoring data and the second monitoring data to identify whether abnormal data exists, and if so, data anomaly warning is performed; If not, the rationality of the monitoring equipment arrangement point is inferred by building a dust diffusion analysis strategy.

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