Intelligent construction site real-time monitoring and intelligent scheduling optimization method and system

Abstract

The application provides a kind of intelligent construction site real-time monitoring and intelligent scheduling optimization method and system, related to construction scheduling optimization technical field, including collecting construction site real-time monitoring data, constructing material consumption heat distribution map, calculating the real-time relative distance between construction personnel and equipment, determining safety risk level, determining material supply sequence and optimal distribution route based on heat gradient and safety risk, generating scheduling instruction, and ensuring instruction reliability through multi-node voting and smart contract verification.The application realizes the precise scheduling of construction resources and the real-time monitoring of safety risks, improves construction efficiency and safety.

Description

TECHNICAL FIELD

[0001] The present application relates to construction scheduling optimization technology, and in particular to a real-time monitoring and intelligent scheduling optimization method and system for intelligent construction sites. BACKGROUND

[0002] With the deepening of the digital transformation of the construction industry, intelligent construction site management systems have been widely used in engineering projects. Traditional construction resource management systems mainly rely on manual experience for scheduling decisions, which is not only inefficient, but also difficult to adapt to complex and changing construction environments, and cannot meet the needs of fine management.

[0003] Although existing construction management systems have introduced data collection and analysis functions, they are mostly limited to single-dimensional data processing. The system fails to effectively integrate construction resource location information and construction environment information, and lacks the ability to deeply mine and analyze multi-source heterogeneous data, resulting in a large deviation between resource scheduling schemes and actual construction needs, affecting construction efficiency and safety management level.

[0004] The credibility and execution effect of construction resource scheduling instructions lack effective verification mechanisms, and problems such as unreasonable resource allocation and material delivery delays are prone to occur. At the same time, the existing system lacks the ability to respond to dynamic changes in the construction site, making it difficult to achieve real-time optimization and dynamic adjustment of resource scheduling schemes. Therefore, there is an urgent need for an intelligent construction site management method that can achieve precise management of construction resources, intelligent scheduling optimization, and credible verification of scheduling instructions. SUMMARY

[0005] The embodiments of the present application provide a real-time monitoring and intelligent scheduling optimization method and system for intelligent construction sites, which can solve the problems in the prior art.

[0006] In a first aspect of the embodiments of the present application, a real-time monitoring and intelligent scheduling optimization method for intelligent construction sites is provided, comprising:

[0007] Collecting real-time monitoring data of the construction site, the real-time monitoring data including construction resource location information and construction environment information;

[0008] According to the construction resource location information, a material consumption heat distribution map is constructed, and the real-time relative distance and distance change trend between the construction personnel and the construction equipment are calculated. The real-time relative distance is compared with the preset multi-level safety threshold, and the safety risk level of the construction area is determined in combination with the distance change trend;

[0009] The heat gradient of each region in the material consumption heat distribution map is calculated, the material supply sequence is determined based on the heat gradient, the region passing cost is divided according to the safety risk level, the equipment quantity control threshold of each region is calculated based on the material supply sequence and the region passing cost, the material distribution route with the minimum passing cost is planned combined with the construction environment information, and the construction resource scheduling instruction is generated;

[0010] The real-time monitoring data, the safety risk level and the construction resource scheduling instruction are generated into data slices in the space-time dimension, a consensus is formed on the data slices through multi-node voting, and the construction resource scheduling instruction that passes the verification is issued to the on-site terminal equipment for execution.

[0011] In an optional embodiment,

[0012] The material consumption heat distribution map is constructed according to the construction resource position information, including:

[0013] The construction resource position information is divided into multiple continuous time windows based on a time span, in each time window, the stay point position coordinates and the corresponding stay duration of the construction resource are extracted, and the movement trajectory of the construction resource is obtained based on the time sequence change of the stay point position coordinates;

[0014] The work state of the construction resource is determined according to the combination characteristics of the stay duration and the movement trajectory, and the material consumption amount of the construction resource is calculated based on the work state;

[0015] The construction site is divided into multiple grid units, the stay point position coordinates are mapped to the corresponding grid units, and the heat value of each grid unit is calculated according to the material consumption amount, the heat value is smoothed by using a kernel function, and the material consumption heat distribution map is constructed;

[0016] When new construction resource position information is obtained, the influence grid units corresponding to the construction resource position information are determined, only the heat values of the influence grid units are recalculated, and the corresponding regions of the material consumption heat distribution map are updated.

[0017] In an optional embodiment,

[0018] The real-time relative distance and distance change trend between the construction personnel and the construction equipment are calculated, the real-time relative distance is compared with the preset multi-level safety threshold, and the safety risk level of the construction region is determined combined with the distance change trend, including:

[0019] The construction resource position information is preprocessed by Kalman filtering, and the coordinate deviation and time interval of adjacent sampling points are calculated to identify abnormal jump points, the abnormal jump points are removed from the preprocessed construction resource position information, and the optimized position information is obtained;

[0020] The optimized location information is divided into multiple continuous time windows according to a preset time span. The location coordinates of the construction personnel and the construction equipment in each time window are extracted. The horizontal and vertical distances between the construction personnel and the construction equipment are calculated to obtain the real-time relative distance.

[0021] Performing least squares curve fitting on the real-time relative distance to obtain distance change direction parameters and distance change rate parameters, determining an equipment type coefficient based on the equipment type of the construction equipment, and setting a multi-level safety threshold based on the equipment type coefficient, wherein the multi-level safety threshold includes a warning distance, a danger distance, and an emergency distance;

[0022] The real-time relative distance is compared with the multi-level safety threshold to determine the basic risk level. The basic risk level is dynamically adjusted according to the distance change direction parameter and the distance change rate parameter. The adjusted risk level is weightedly calculated with the equipment type coefficient to divide the safety risk level of the construction area.

[0023] In an optional embodiment,

[0024] Calculate the thermal gradient of each area in the material consumption thermal distribution map, determine the material supply sequence based on the thermal gradient, and divide the area into different areas according to the safety risk level. The cost includes:

[0025] Obtain the thermal value matrix corresponding to the material consumption thermal distribution diagram, calculate the gradient difference values ​​of the thermal value matrix in the horizontal and vertical directions respectively, obtain the thermal gradient modulus, calculate the thermal diffusion coefficient based on the thermal value difference of adjacent grids in the thermal value matrix, and use the product of the thermal diffusion coefficient and the thermal gradient modulus as the regional material demand change rate;

[0026] A material replenishment priority index is obtained by weightedly combining the regional material demand change rate and the thermal value of the corresponding location. The material demand in the future time window is predicted based on the material replenishment priority index, and the material replenishment order is obtained by comprehensive calculation with the current material replenishment priority index.

[0027] Obtain the personnel distribution density, equipment operation status and construction environment information of the construction site, calculate the personnel aggregation coefficient based on the personnel distribution density, calculate the equipment risk factor based on the equipment operation status value, and calculate the environmental impact value based on the construction environment parameters.

[0028] The regional safety risk level is obtained by taking the weighted sum of the personnel aggregation coefficient, equipment hazard factor and environmental impact value. The shortest path distance from each area to the safe exit is calculated based on the spatial topological structure of the construction site. The regional safety risk level and the shortest path distance are combined according to the adaptive weight to calculate the regional travel cost.

[0029] In an alternative embodiment,

[0030] Based on the material supply sequence and the regional passing cost, the equipment quantity control threshold of each region is calculated, the material distribution route with the minimum passing cost is planned combined with the construction environment information, and the construction resource scheduling instruction is generated, including:

[0031] According to the material supply sequence, the supply priority coefficient of each region is determined, and the ratio of the supply priority coefficient to the regional passing cost is calculated as the regional reference capacity; the ratio of the minimum distance between devices to the preset safe operation distance is obtained as the congestion restriction factor;

[0032] The product of the regional reference capacity and the congestion restriction factor is taken as the initial threshold, and the initial threshold is corrected according to the regional terrain slope coefficient and the construction process interval time to obtain the equipment quantity control threshold of each region;

[0033] According to the construction environment information, the terrain passing difficulty coefficient, the equipment operation restriction degree and the construction interference intensity of each region are calculated and weighted combined as the environmental passing cost, and the weighted sum of the environmental passing cost and the regional passing cost is taken as the comprehensive passing cost; based on the comprehensive passing cost, the path evaluation function is constructed, the minimum cost path to each distribution target point is calculated in turn according to the material supply sequence, and the initial material distribution route is obtained;

[0034] Detection points are set on the initial material distribution route to collect passing state data, the passing cost change value is calculated based on the congestion index, equipment density and personnel flow of the region where the detection point is located; when the passing cost change value exceeds the dynamic threshold, the weight coefficient of the passing cost in the path evaluation function is adjusted, the transfer cost between regions is recalculated based on the updated path evaluation function, the optimal distribution route meeting the material supply sequence constraint is generated, and the optimal distribution route is converted into the construction resource scheduling instruction.

[0035] In an alternative embodiment,

[0036] Real-time monitoring data, safety risk level and construction resource scheduling instruction are generated as data slices in space-time dimension, a consensus is formed on data slices through multi-node voting, and an intelligent contract is used for verification, and the construction resource scheduling instruction that passes the verification is issued to the on-site terminal equipment for execution, including:

[0037] The time series variation characteristics of real-time monitoring data, safety risk level and construction resource scheduling instruction are calculated, a time window adjustment function is constructed to determine the time slice boundary; the resource density distribution in the construction site is calculated, and a grid unit with dynamic boundary is generated;

[0038] The real-time monitoring data, the safety risk level and the construction resource scheduling instruction are mapped according to the time slicing boundary and the grid unit boundary to generate a space-time data slice;

[0039] A data flow tracking matrix is constructed to record the transmission path and conversion relationship of the real-time monitoring data, the safety risk level and the construction resource scheduling instruction in the space-time data slice; based on the tracking matrix, link breakage, loop and conflict in the space-time data slice are identified in real time, and a trusted score of a verification node is generated by combining the identification result with data transmission intensity; the space-time data slice is subjected to multi-node voting to form a consensus according to the trusted score;

[0040] A hierarchical adaptive verification network is constructed and a plurality of verification channels are set, each verification channel containing verification points of different densities, an optimal verification channel is dynamically selected based on the space-time data slice, and an intelligent contract is constructed using the selected verification channel to verify the construction resource scheduling instruction;

[0041] The construction resource scheduling instruction verified by the intelligent contract is issued to an on-site terminal device for execution, and execution feedback data is collected; the execution feedback data is processed according to the same slicing and verification process, and is used to optimize the density distribution and selection strategy of the verification channel.

[0042] In an optional embodiment,

[0043] A hierarchical adaptive verification network is constructed and a plurality of verification channels are set, each verification channel containing verification points of different densities, an optimal verification channel is dynamically selected based on the space-time data slice, and an intelligent contract is constructed using the selected verification channel to verify the construction resource scheduling instruction, including:

[0044] The verification network is divided into a first verification layer, a second verification layer and a third verification layer, and a plurality of verification channels are set, the resource density gradient, the safety risk change rate and the scheduling instruction complexity in the space-time data slice are calculated to generate a verification demand vector; the coverage, the verification efficiency and the resource consumption of each verification channel are calculated based on the verification demand vector to generate an adaptability score of the verification channel; and the optimal verification channel is selected based on the adaptability score;

[0045] According to the hierarchical features of the optimal verification channel, a contract template library containing different granularities is constructed, the resource constraint information in the space-time data slice is mapped to the corresponding contract template in the contract template library, the constraint transmission rule of the cross-layer verification point is set, and the verification results of different granularities are integrated; a hierarchical execution strategy is adopted to construct an intelligent contract to verify the construction resource scheduling instruction;

[0046] When the security risk change rate in the spatiotemporal data slice rises, the optimal verification channel is switched to a verification channel containing the first verification layer; when the security risk change rate drops, the optimal verification channel is switched to a verification channel containing the second verification layer or the third verification layer; the verification demand vector is updated based on the verification result, the optimal verification channel is reselected, and the smart contract is constructed.

[0047] In a second aspect of the embodiment of the present application, a real-time monitoring and intelligent scheduling optimization system for a construction site is provided, comprising:

[0048] A first unit is configured to collect real-time monitoring data of the construction site, wherein the real-time monitoring data comprises construction resource position information and construction environment information.

[0049] A second unit is configured to construct a material consumption heat distribution map based on the construction resource position information, calculate real-time relative distances between construction personnel and construction equipment and distance change trends, compare the real-time relative distances with preset multi-level safety thresholds, and determine a safety risk level of a construction area in combination with the distance change trends.

[0050] A third unit is configured to calculate heat gradients of regions in the material consumption heat distribution map, determine a material supply sequence based on the heat gradients, divide the regions into areas according to the safety risk level, calculate device quantity control thresholds of the areas based on the material supply sequence and the area passing cost, plan a material distribution route with a minimum passing cost based on the construction environment information, and generate a construction resource scheduling instruction.

[0051] A fourth unit is configured to generate data slices in a spatiotemporal dimension based on the real-time monitoring data, the safety risk level, and the construction resource scheduling instruction, form a consensus on the data slices through multi-node voting, verify the data slices using a smart contract, and issue the construction resource scheduling instruction that passes the verification to a field terminal device for execution.

[0052] In a third aspect of the embodiment of the present application, an electronic device is provided, comprising:

[0053] a processor;

[0054] a memory for storing processor-executable instructions;

[0055] The processor is configured to invoke the instructions stored in the memory to execute the method described above.

[0056] In a fourth aspect of the embodiment of the present application, a computer-readable storage medium is provided, which stores computer program instructions, and the computer program instructions are executed by a processor to implement the method described above.

[0057] In this embodiment, through the real-time monitoring of the construction site and the innovative design of the intelligent scheduling system, efficient allocation of construction resources and precise control of safety risks are realized, effectively improving the construction efficiency and safety level of the construction project. An optimized material supply mechanism based on thermal distribution is constructed, and a safety risk assessment system is formed by combining real-time distance monitoring between personnel and equipment, which maximizes the reduction of safety hazards in the construction process while ensuring construction quality. The construction data is fragmented and consensus is reached by using blockchain technology, and the reliability of the scheduling instructions and the non-tamperability of the data are ensured, providing traceable and verifiable data support for construction decision-making, and realizing the automation and intelligent management of the construction process. BRIEF DESCRIPTION OF DRAWINGS

[0058] Figure 1 FIG. 1 is a flowchart of the intelligent construction site real-time monitoring and intelligent scheduling optimization method according to an embodiment of the present application.

[0059] Figure 2 FIG. 4 is a regional equipment density and passage cost change heat map according to an embodiment of the present application.

[0060] Figure 3 FIG. 6 is a hierarchical execution efficiency analysis column chart of the smart contract according to an embodiment of the present application.

[0061] Figure 4 FIG. 8 is a structural schematic diagram of the intelligent construction site real-time monitoring and intelligent scheduling optimization system according to an embodiment of the present application. DETAILED DESCRIPTION

[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described below in conjunction with the drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0063] The technical solutions of the present application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in some embodiments.

[0064] Figure 1 FIG. 1 is a flowchart of the intelligent construction site real-time monitoring and intelligent scheduling optimization method according to an embodiment of the present application, as shown in the figure, the method comprises: Figure 1

[0065] Collecting real-time monitoring data of the construction site, the real-time monitoring data comprising construction resource position information and construction environment information; ​

[0066] According to the construction resource position information, a material consumption heat distribution map is constructed, and real-time relative distances between construction personnel and construction equipment and distance change trends are calculated. The real-time relative distances are compared with preset multi-level safety thresholds, and the safety risk levels of the construction areas are determined in combination with the distance change trends.

[0067] The heat gradients of the areas in the material consumption heat distribution map are calculated, the material supply sequence is determined based on the heat gradients, the area passing cost is divided according to the safety risk levels, the equipment quantity control threshold of each area is calculated based on the material supply sequence and the area passing cost, the material distribution route of the minimum passing cost is planned based on the construction environment information, and the construction resource scheduling instruction is generated.

[0068] The real-time monitoring data, the safety risk levels and the construction resource scheduling instruction are generated into data shards in the space-time dimension, a consensus is formed on the data shards through multi-node voting, and the construction resource scheduling instruction that passes the verification is issued to the on-site terminal equipment for execution.

[0069] In an optional implementation, according to the construction resource position information, a material consumption heat distribution map is constructed, which includes:

[0070] The construction resource position information is divided into a plurality of continuous time windows based on a time span. In each time window, the stay point position coordinates and the corresponding stay duration of the construction resource are extracted, and the movement trajectory of the construction resource is obtained based on the time sequence change of the stay point position coordinates.

[0071] The work state of the construction resource is determined according to the combination characteristics of the stay duration and the movement trajectory, and the material consumption amount of the construction resource is calculated based on the work state.

[0072] The construction site is divided into a plurality of grid units, the stay point position coordinates are mapped to the corresponding grid units, and the heat value of each grid unit is calculated according to the material consumption amount. The heat value is smoothed by using a kernel function, and a material consumption heat distribution map is constructed.

[0073] When new construction resource position information is obtained, the influence grid units corresponding to the construction resource position information are determined, only the heat values of the influence grid units are recalculated, and the corresponding areas of the material consumption heat distribution map are updated.

[0074] Exemplarily, the construction resource position information is divided into multiple continuous time windows based on time span. In actual application, the size of the time window can be determined according to the specific characteristics of the construction project, for example, each hour, each shift or each day as a time window. Taking concrete pouring construction as an example, the working hours of a day can be divided into two time windows, 9:00-12:00 in the morning and 13:00-17:00 in the afternoon. The system extracts the GPS trajectory data of the construction resource (such as a concrete mixer) in a specific time window from the position information database, including longitude, latitude, time stamp and other information. In each time window, the system extracts the position coordinates of the stay point of the construction resource and the corresponding stay duration. In specific implementation, when it is detected that the construction resource stays at a certain position for more than a preset threshold (such as 2 minutes), it is determined as a stay point. For example, the concrete mixer stays in the unloading area of the construction site for 5 minutes, and the system will record the position coordinates (longitude 116.3912°, latitude 39.9064°) of the stay point and the stay duration of 5 minutes.

[0075] Based on the time sequence change of the stay point position coordinates, the system obtains the moving trajectory of the construction resource. The system connects each position coordinate point in the order of time stamp to form the moving trajectory of the construction resource. For example, the concrete mixer sets off from the mixing station, passes through multiple path points, finally arrives at the unloading area of the construction site, and then returns to the mixing station, which constitutes a complete path as a moving trajectory. The system records the starting point, ending point, passing point and corresponding time stamp of the trajectory for subsequent analysis.

[0076] According to the combination characteristics of the stay duration and the moving trajectory, the system determines the working state of the construction resource. In actual application, different state determination rules can be set. For example, for the concrete mixer, when it moves after staying in the mixing station area for more than 5 minutes, it is determined as the "loading completion" state; when it stays in the unloading area of the construction site for more than 3 minutes, it is determined as the "unloading" state; when it stays in the non-specified area for more than 10 minutes, it is determined as the "abnormal stay" state. Through these rules, the system can accurately identify different working stages of the construction resource.

[0077] Based on the working state, the system calculates the material consumption of the construction resource. Different types of construction resources have different ways of calculating material consumption. Taking the concrete mixer as an example, a standard mixer can unload about 6 cubic meters of concrete each time. When the system identifies a complete "loading-transportation-unloading" cycle, it records the material consumption, which is the rated loading capacity of the vehicle. If more accurate loading capacity data can be obtained through the vehicle-mounted sensor, the actual loading capacity data is directly used. For other construction resources, such as cement injection machines, the material consumption can be calculated according to the working duration and the rated injection rate (such as 0.5 cubic meters / minute).

[0078] The system divides the construction site into multiple grid cells. According to the size of the construction site and the required accuracy, the grid size is determined, usually 5 meters x 5 meters or 10 meters x 10 meters. For a 100 meters x 100 meters construction site, if a 10 meters x 10 meters grid is used, a total of 100 grid cells are divided, each identified by a unique row and column index. The location coordinates of the stay points are mapped to the corresponding grid cells, for example, the location coordinates (longitude 116.3912°, latitude 39.9064°) are mapped to the cell with grid index (3, 5). Then, the thermal value of each grid cell is calculated according to the material consumption. If a concrete mixer truck unloads 6 cubic meters of concrete in grid (3, 5), the thermal value of this grid increases by 6; if multiple trucks unload in the same grid, the thermal values are added up.

[0079] In order to make the thermal distribution map more visually smooth, the system uses a kernel function to smooth the thermal values. In specific implementation, a Gaussian kernel function is selected to weight and smooth the thermal values of each grid. When smoothing, the current grid is taken as the center, and the grids within a certain range (such as 3x3 or 5x5) around it are considered, and different weights are given according to the distance from the center, with closer distance having larger weight. In this way, the thermal distribution map shows a smooth transition effect, making it more intuitive to show the dense areas of material consumption. After processing all grid cells, the initial material consumption thermal distribution map is generated. The thermal map uses color gradient to represent the size of material consumption, usually red for large consumption, blue for small consumption, and green and yellow for medium consumption.

[0080] When new construction resource location information is obtained, the system first determines the affected grid cells corresponding to this information. Taking the example of a concrete mixer truck adding a new unloading operation, if the unloading location is in grid (4, 6), then this grid and the grids affected by smoothing processing within a certain range (such as 5x5) are marked as affected grid cells. The system only recalculates the thermal values of these affected grid cells, rather than recalculating the entire thermal map, improving the update efficiency.

[0081] After the update calculation is completed, the system locally refreshes the corresponding area of the material consumption thermal distribution map. This incremental update method greatly reduces the calculation amount, enabling the system to respond to the changing construction scene in real time and providing project managers with the latest material consumption distribution situation.

[0082] In the embodiment, by introducing the time window mechanism and the stay point analysis, the actual moving track and the work behavior of the construction resource can be accurately restored, so that the accuracy of the work state recognition is improved. On this basis, combined with the material consumption modeling and the grid heat analysis, the spatial mapping of the material use intensity is realized, so as to facilitate the identification of the high consumption area and the abnormal consumption point. The kernel function smoothing processing is adopted to improve the continuity and readability of the heat map, so as to facilitate the manager to intuitively master the spatio-temporal evolution characteristics of the material consumption of the construction site. Meanwhile, the incremental updating mechanism can realize the rapid recalculation of the local heat value when new data is accessed, so as to reduce the system calculation burden, improve the response efficiency, support the real-time updating and dynamic display of the heat map.

[0083] In an optional embodiment, the real-time relative distance between the construction personnel and the construction equipment and the distance change trend are calculated, the real-time relative distance is compared with a plurality of preset safety threshold values, and the safety risk level of the construction area is determined in combination with the distance change trend, including:

[0084] The position information of the construction resource is preprocessed by Kalman filtering, and the abnormal jump points are identified by calculating the coordinate deviation and the time interval of adjacent sampling points, the abnormal jump points are removed from the preprocessed position information of the construction resource, and the optimized position information is obtained;

[0085] The optimized position information is divided into a plurality of continuous time windows according to a preset time span, the position coordinates of the construction personnel and the position coordinates of the construction equipment in each time window are extracted, the horizontal distance and the vertical distance between the construction personnel and the construction equipment are calculated, and the real-time relative distance is obtained;

[0086] The real-time relative distance is subjected to least square curve fitting to obtain a distance change direction parameter and a distance change rate parameter, a device type coefficient is determined according to the device type of the construction equipment, a plurality of safety threshold values are set based on the device type coefficient, and the plurality of safety threshold values include an alert distance, a dangerous distance and an emergency distance;

[0087] The real-time relative distance is compared with the plurality of safety threshold values to determine a basic risk level, the basic risk level is dynamically adjusted according to the distance change direction parameter and the distance change rate parameter, the adjusted risk level is weighted calculated with the device type coefficient, and the safety risk level of the construction area is divided.

[0088] Exemplarily, the construction resource position information is first subjected to Kalman filtering preprocessing. Raw position data of the construction personnel and equipment is received, including time stamp and coordinate information (x, y, z). For each construction resource, a state vector is established, containing position and velocity components. For example, for a certain construction personnel, the initial state can be set as [100.0, 2.0, 50.0, 0.5, 0.3, 0.0], indicating the initial position (100.0, 2.0, 50.0) meters and the initial velocity (0.5, 0.3, 0.0) meters / second. The system sets the measurement noise parameter according to the accuracy characteristics of the position sensor, and the typical value is 0.5 meters. The process noise parameter is set to 0.1, reflecting the influence of environmental factors on prediction. For each new position data point, a prediction step is performed to calculate the prior estimate, and then an update step is performed to fuse the actual measurement value and the predicted value, outputting the filtered position information.

[0089] An abnormal jump point is identified by calculating the coordinate deviation and time interval of adjacent sampling points. The ratio of the distance change between the two adjacent sampling points to the time interval, i.e. the displacement speed, is calculated. For example, if the time interval of two sampling points is 0.5 seconds and the coordinates change from (105.2, 78.3, 1.2) to (106.0, 98.5, 1.3), the y-direction displacement speed is (98.5-78.3) / 0.5=40.4 meters / second, which is obviously beyond the normal walking speed. The system sets the threshold value to 5 meters / second, and the points exceeding this threshold value are marked as abnormal jump points. For the marked abnormal points, the system will eliminate them from the preprocessed position information and use linear interpolation method to fill in the data gap, ensuring data continuity.

[0090] The optimized position information is divided into multiple continuous time windows according to the preset time span. In the embodiment, the time window is set to 5 seconds, and the adjacent windows overlap by 1 second, enhancing the analysis continuity. In each time window, the system extracts the position coordinates of the construction personnel and equipment. For example, in the time window [10:00:00-10:00:05], the position coordinate sequence of a certain construction personnel is {(120.5, 85.7, 1.2), (121.0, 86.2, 1.2),..., (123.2, 88.1, 1.2)}, and the position coordinate sequence of a certain excavator is {(130.8, 95.3, 0.5), (130.5, 94.8, 0.5),..., (129.6, 93.2, 0.5)}. The horizontal distance and vertical distance between the construction personnel and the construction equipment are calculated. The horizontal distance is calculated by the horizontal coordinates (x, y) of the construction personnel and the equipment, and the vertical distance is the absolute value of the height difference between them. For example, at a certain moment, the position of the construction personnel is (122.1, 87.5, 1.2) and the position of the equipment is (130.1, 94.3, 0.5), then the horizontal distance is 10.85 meters and the vertical distance is 0.7 meters, and the real-time relative distance is 10.87 meters.

[0091] The real-time relative distance is subjected to least square curve fitting to obtain the distance variation trend. In a time window, for example, the collected relative distance sequence is {12.5, 12.2, 11.8, 11.5, 11.0, 10.5} meters, the system uses a linear model for fitting to obtain the distance variation direction parameter as -0.4, indicating that the distance is decreasing; the distance variation rate parameter is 2 meters / minute, indicating that the person and the equipment are approaching at a speed of 2 meters per minute. The equipment type coefficient is determined according to the equipment type of the construction equipment. The equipment is classified, wherein the excavator type coefficient is set as 1.2, the bulldozer as 1.1, the crane as 1.3, and the loader as 1.1. The coefficient value reflects the degree of danger of the equipment, which is used for subsequent risk assessment. Based on the equipment type coefficient, a multi-level safety threshold is set: for the excavator (type coefficient 1.2), the warning distance is set as 8 meters, the dangerous distance as 5 meters, and the emergency distance as 3 meters.

[0092] The real-time relative distance is compared with the multi-level safety threshold to determine the basic risk level. If the real-time distance is greater than the warning distance, it is determined as a safe state, and the risk level is 0; if it is between the warning distance and the dangerous distance, it is determined as a warning state, and the risk level is 1; if it is between the dangerous distance and the emergency distance, it is determined as a dangerous state, and the risk level is 2; if it is less than the emergency distance, it is determined as an emergency state, and the risk level is 3. The basic risk level is dynamically adjusted according to the distance variation direction parameter and the rate parameter. If the distance decreases (the direction parameter is negative) and the change rate is greater than 1 meter / minute, the risk level is increased by 1 level; if the distance increases (the direction parameter is positive) and the change rate is greater than 1 meter / minute, the risk level is decreased by 1 level; if the distance change is not obvious (the rate is less than 0.2 meters / minute), the original risk level is maintained. Finally, the adjusted risk level is weighted with the equipment type coefficient to divide the safety risk level of the construction area. For example, the adjusted risk level is 2, and the equipment is an excavator (type coefficient 1.2), then the weighted risk score is 2x1.2=2.4. The system divides the final risk level according to the weighted score: the score less than 1 is low risk (green alert); the score between 1 and 2 is medium risk (yellow alert); the score between 2 and 3 is high risk (orange alert); the score greater than 3 is extremely high risk (red alert). The system displays the risk level in real time on the monitoring interface, and triggers the corresponding alarm mechanism according to the risk level.

[0093] In this embodiment, the accuracy and stability of the position information are effectively improved through Kalman filtering and jump point elimination processing, providing a reliable data basis for subsequent calculations. Combined with the relative position extraction and distance change trend analysis within the time window, the approach behavior between construction personnel and equipment can be perceived in real time. The introduction of multi-level safety thresholds and equipment type coefficients makes risk assessment targeted and discriminatory, adapting to the actual requirements of different equipment for safety distances. Furthermore, the direction and rate of distance change are extracted through least squares fitting, and the basic risk level is dynamically adjusted to achieve a leap from static judgment to trend perception. Ultimately, the safety risk level assessment results formed based on the weighted mechanism have higher real-time, accuracy and adaptability, and can provide intelligent and forward-looking early warning support for on-site safety management, effectively reducing the risk of contact between personnel and equipment.

[0094] In an optional embodiment, calculating the thermal gradient of each area in the material consumption thermal distribution diagram, determining the material replenishment order based on the thermal gradient, and dividing the regional access costs according to the safety risk level include:

[0095] Obtain the thermal value matrix corresponding to the material consumption thermal distribution diagram, calculate the gradient difference values ​​of the thermal value matrix in the horizontal and vertical directions respectively, obtain the thermal gradient modulus, calculate the thermal diffusion coefficient based on the thermal value difference of adjacent grids in the thermal value matrix, and use the product of the thermal diffusion coefficient and the thermal gradient modulus as the regional material demand change rate;

[0096] A material replenishment priority index is obtained by weightedly combining the regional material demand change rate and the thermal value of the corresponding location. The material demand in the future time window is predicted based on the material replenishment priority index, and the material replenishment order is obtained by comprehensive calculation with the current material replenishment priority index.

[0097] Obtain the personnel distribution density, equipment operation status and construction environment information of the construction site, calculate the personnel aggregation coefficient based on the personnel distribution density, calculate the equipment risk factor based on the equipment operation status value, and calculate the environmental impact value based on the construction environment parameters.

[0098] The regional safety risk level is obtained by taking the weighted sum of the personnel aggregation coefficient, equipment hazard factor and environmental impact value. The shortest path distance from each area to the safe exit is calculated based on the spatial topological structure of the construction site. The regional safety risk level and the shortest path distance are combined according to the adaptive weight to calculate the regional travel cost.

[0099] For example, a thermal distribution map of material consumption at a construction site is obtained. This map is stored in a matrix format, such as a 10×10 thermal value matrix, where each element represents the thermal value of material consumption in the corresponding grid area. The thermal value range is typically set from 0 to 100, with larger values ​​indicating more frequent material consumption in that area.

[0100] To calculate the thermal gradient, the horizontal and vertical difference of the thermal value matrix is calculated. Specifically, for the thermal value H(i, j) at position (i, j) in the matrix, the horizontal gradient difference value Gx(i, j) is obtained by calculating H(i, j+1)-H(i, j-1), and the vertical gradient difference value Gy(i, j) is obtained by calculating H(i+1, j)-H(i-1, j). For the boundary grid, a one-sided difference method is used. Then, the thermal gradient modulus value G(i, j) is calculated by the square root of the sum of the squares of the horizontal and vertical gradient difference values. For example, if the horizontal gradient difference value of a position (3, 4) is 15 and the vertical gradient difference value is -20, then the thermal gradient modulus value of the position is 25.

[0101] To calculate the thermal diffusion coefficient, the thermal diffusion coefficient D(i, j) is calculated by analyzing the thermal value difference of adjacent grids in the thermal value matrix. Specifically, for position (i, j), the average of the absolute values of the thermal value differences with the eight surrounding grid points is calculated, and then converted to a diffusion coefficient by a preset mapping function, with a value range of 0.1-1.0. For example, if the average of the absolute values of the thermal value differences of the eight surrounding grid points of a position (5, 5) is 12, then the thermal diffusion coefficient obtained by the mapping function is 0.6.

[0102] The regional material demand change rate R(i, j) is calculated by the product of the thermal diffusion coefficient D(i, j) and the thermal gradient modulus value G(i, j). Continuing the above example, the material demand change rate of position (5, 5) is 0.6x25=15. To determine the material supply priority index, the regional material demand change rate R(i, j) is combined with the thermal value H(i, j) at the corresponding position by weighting. In the calculation formula of the material supply priority index P(i, j), the thermal value weight is set to 0.7, and the material demand change rate weight is set to 0.3. For example, if the thermal value of a position is 80 and the material demand change rate is 15, then the material supply priority index is 0.7x80+0.3x15=60.5.

[0103] Based on the material supply priority index, the material demand in the future time window (usually set to 2 hours) is predicted. The prediction method uses time series analysis, considering the periodicity and trend of historical material consumption data, and combines the current material supply priority index for comprehensive prediction. The prediction result is combined with the current material supply priority index in a ratio of 7:3 to obtain the final material supply sequence. For example, the construction site is divided into 25 areas, and the calculated material supply sequence is: Area A (priority 93.2), Area C (priority 87.5), Area F (priority 82.1), etc. Next, the personnel distribution density, equipment operating status, and construction environment information of the construction site are obtained. The personnel distribution density is obtained in real time through the intelligent monitoring system of the construction site, expressed as the number of people per 100 square meters; the equipment operating status is obtained through equipment sensor data, including equipment temperature, vibration frequency, load rate, etc.; the construction environment information includes temperature, humidity, noise, dust concentration, etc.

[0104] The personnel gathering coefficient is calculated according to the personnel distribution density. When the personnel density of a certain area exceeds the safety threshold (such as 8 people per 100 square meters), the personnel gathering coefficient increases exponentially. For example, the personnel density of a certain area is 12 people per 100 square meters, and the calculated personnel gathering coefficient is 1.8. The equipment danger factor is calculated according to the equipment operating status value. The equipment danger factor considers factors such as the degree of deviation of equipment temperature from the normal value, the degree of abnormality of vibration frequency, and load rate. For example, the temperature of a tower crane exceeds the normal range by 20%, the abnormality degree of vibration frequency is 15%, and the load rate is 85%, and the calculated equipment danger factor is 1.65.

[0105] The environmental impact value is calculated according to the construction environment parameters. The environmental impact value considers the deviation of factors such as temperature, humidity, noise, and dust concentration from safety standards. For example, the temperature in a certain area is 38°C (exceeding the standard by 30%), the noise is 95 decibels (exceeding the standard by 20%), and the dust concentration is 8.5 mg / m³ (exceeding the standard by 70%), and the calculated environmental impact value is 1.9. The personnel gathering coefficient, equipment danger factor, and environmental impact value are weighted and summed to obtain the regional safety risk level. The weight distribution is: personnel gathering coefficient 0.4, equipment danger factor 0.35, environmental impact value 0.25. The risk level ranges from 1 to 5, and the larger the value, the higher the risk. For example, the safety risk level calculation result of a certain area is 0.4 x 1.8 + 0.35 x 1.65 + 0.25 x 1.9 = 1.77.

[0106] The shortest path distance from each region to the safety exit is calculated based on the spatial topology structure of the construction site. An improved Dijkstra algorithm is used, which introduces an obstacle shielding mechanism and a channel width weighting mechanism when constructing the path graph. The algorithm dynamically adjusts the reachability and passing cost of the edges, and combines the actual situation of the construction site to realize accurate calculation of the shortest path to the safety exit. For example, the shortest path distance from region A to the nearest safety exit is 45 meters. Finally, the region safety risk level and the shortest path distance are combined to calculate the region passing cost according to the adaptive weight. The adaptive weight is dynamically adjusted according to the risk level, and the higher the risk level, the greater the weight. The region passing cost ranges from 1 to 100, and the larger the value, the more difficult it is to pass. For example, the safety risk level of a certain region is 1.77, and the shortest path distance is 45 meters. The calculated region passing cost is 62.

[0107] Through the above method, accurate analysis of the material consumption heat distribution of the construction site can be realized, the material supply sequence is optimized, and the region passing cost is reasonably divided according to the safety risk, thereby improving the construction efficiency and safety. Through gradient difference and diffusion coefficient analysis of the heat value matrix, the material demand change trend of each region can be dynamically reflected, and then based on the supply priority index, the material demand in the future time window can be accurately predicted and scheduled, thereby improving the timeliness and matching degree of material supply. At the same time, the region safety risk level is constructed by combining personnel distribution, equipment state and environmental parameters, and the passing path cost is calculated based on the site topology structure, so that in the process of material distribution or emergency escape, the safety and passing efficiency can be considered comprehensively, and the path selection and operation area management can be optimized. The adaptive weight mechanism ensures that the evaluation results have flexible response ability in different construction stages, and the overall material supply efficiency, safety guarantee level and dynamic management ability of the construction site are improved.

[0108] In an optional implementation, the device quantity control threshold of each region is calculated based on the material supply sequence and the region passing cost, and the material distribution route with the minimum passing cost is planned combined with the construction environment information, and the construction resource scheduling instruction is generated, including:

[0109] The supply priority coefficient of each region is determined according to the material supply sequence, and the ratio of the supply priority coefficient to the region passing cost is calculated to obtain the region reference capacity; the ratio of the minimum distance value between devices to the preset safe operation distance is obtained to obtain the congestion restriction factor;

[0110] The product of the region reference capacity and the congestion restriction factor is taken as the initial threshold, and the initial threshold is corrected according to the region terrain slope coefficient and the construction process interval time length to obtain the device quantity control threshold of each region;

[0111] The terrain passing difficulty coefficient, equipment operation restriction degree and construction interference intensity of each region are calculated according to the construction environment information, and are combined by weighting, as an environment passing cost, and a weighted sum of the environment passing cost and the region passing cost is taken as a comprehensive passing cost; a path evaluation function is constructed based on the comprehensive passing cost, and the minimum cost paths to each distribution target point are calculated in turn according to the material supply sequence, so as to obtain an initial material distribution route;

[0112] A detection point is arranged on the initial material distribution route to collect passing state data, and a passing cost change value is calculated based on the congestion index, equipment density and personnel flow of the region where the detection point is located; when the passing cost change value exceeds a dynamic threshold value, a weight coefficient of the passing cost in the path evaluation function is adjusted, the transfer cost between regions is recalculated based on the updated path evaluation function, and an optimal distribution route meeting the material supply sequence constraint is generated, and the optimal distribution route is converted into a construction resource scheduling instruction.

[0113] Exemplarily, the basic data of the construction site is acquired, including region division information, material supply sequence, equipment size parameter, region passing basic cost, etc. It is assumed that a construction site is divided into four regions A, B, C and D, the material supply sequence is A→C→B→D, and the passing basic costs of the regions are 12, 8, 15 and 10 (the cost unit is standard time consumption minute) respectively. The supply priority coefficients of the regions are determined according to the material supply sequence. For the region A which needs to be supplied first, the priority coefficient is set to 1.0; for the regions which are supplied subsequently, the priority coefficient is reduced by 0.1 for each subsequent sequence. Therefore, the supply priority coefficients of the regions A, C, B and D are 1.0, 0.9, 0.8 and 0.7 respectively. Then, the reference capacities of the regions are calculated. The reference capacity of a region is equal to the ratio of the supply priority coefficient to the region passing cost, multiplied by 100 as a reference conversion. Taking the region A as an example, the reference capacity thereof is 1.0 / 12×100=8.33 equipment; similarly, the reference capacities of the regions C, B and D are 11.25, 5.33 and 7.00 equipment respectively.

[0114] The minimum safe distance value between equipment and the preset safe operation distance are acquired. It is assumed that the minimum safe distance of a certain type of engineering equipment is 3 meters, and the preset safe operation distance is 5 meters, so the crowding degree limiting factor is 3 / 5=0.6.

[0115] The initial threshold of each area is obtained by multiplying the area reference capacity with the congestion limit factor. The initial thresholds of areas A, C, B, and D are 8.33 x 0.6 = 5.00, 11.25 x 0.6 = 6.75, 5.33 x 0.6 = 3.20, and 7.00 x 0.6 = 4.20 devices, respectively. Considering the influence of terrain slope and process interval length, the system corrects the initial threshold. Assuming that the terrain slope of area A is 8%, the corresponding slope coefficient is 0.85; the process interval length is 1.2 times the standard length, and the corresponding length correction coefficient is 0.9. The final device quantity control threshold of area A is 5.00 x 0.85 x 0.9 = 3.83, which is rounded to 3 devices. Similarly, the control thresholds of other areas are calculated, which are 5 devices for area C, 2 devices for area B, and 4 devices for area D.

[0116] The system calculates the comprehensive passing cost based on the construction environment information. First, the terrain passing difficulty coefficient of each area is obtained (for example, 1.2 for area A, indicating high terrain complexity), the device running restriction degree (for example, 0.8 for area A, indicating moderate device activity range), and the construction interference intensity (for example, 1.3 for area A, indicating strong interference). Assuming that the weights of the three factors are 0.5, 0.3, and 0.2, respectively, the environmental passing cost of area A is 1.2 x 0.5 + 0.8 x 0.3 + 1.3 x 0.2 = 1.12. Assuming that the weights of the environmental passing cost and the basic passing cost are 0.4 and 0.6, respectively, the comprehensive passing cost of area A is 1.12 x 0.4 + 12 x 0.6 = 7.65.

[0117] Similarly, the comprehensive passing costs of other areas are calculated, which are 9.2 for area C, 10.8 for area B, and 7.2 for area D. Based on these comprehensive passing values, the system constructs a path evaluation function. For example, the path evaluation value from the starting point to area A is distance x 7.65. The system calculates the minimum cost path from the material warehouse to each area through Dijkstra algorithm to form the initial material distribution route: warehouse → A → C → B → D.

[0118] The system sets several detection points on the distribution route, such as the A zone entrance, the connection section between A zone and C zone, etc. The system collects the passing state data of each detection point in real time through sensors, including device density, personnel flow, and congestion index. Assuming that the congestion index of the A zone entrance detection point rises from 0.2 to 0.8 (full value 1.0), the device density increases from 2 devices / 100 square meters to 4 devices / 100 square meters, and the personnel flow increases from 15 people / minute to 25 people / minute.

[0119] The system calculates the change value of the passage cost: (0.8-0.2) x 0.5 + (4-2) / 2 x 0.3 + (25-15) / 15 x 0.3 = 0.6. Assuming that the dynamic threshold is 0.5, since the change value of the passage cost 0.6 exceeds the dynamic threshold 0.5, the system will increase the weight coefficient of the passage cost in the A area in the path evaluation function from the original 1.0 to 1.5.

[0120] Based on the updated evaluation function, the system recalculates the transition cost between each area and may generate a new distribution route: warehouse -> C -> A -> B -> D, while ensuring that the constraint of the material supply sequence (the A area is prioritized over the B area) is met.

[0121] Finally, the system converts the optimal distribution route into specific construction resource scheduling instructions, for example:

[0122] 1. The first batch of material distribution: 9:00 from the warehouse, via the southwest channel to the C area unloading point;

[0123] 2. The second batch of material distribution: 10:30 from the warehouse, via the east channel to the A area unloading point;

[0124] 3. The third batch of material distribution: 13:00 from the warehouse, via the north channel to the B area unloading point;

[0125] 4. The fourth batch of material distribution: 15:30 from the warehouse, via the northeast channel to the D area unloading point.

[0126] These instructions are sent to the relevant execution personnel through the construction site management system to achieve precise scheduling of material distribution.

[0127] Figure 2 The region device density and passage cost change heat map for the embodiment of the present application is as follows: Figure 2As shown in the figure, the figure shows the influence of different regional device density and personnel flow combination conditions on the change of travel cost in the form of a heat map. The horizontal axis represents the regional device density (0.5-3.0 units / 100m²), the vertical axis represents the personnel flow (10-60 people / hour), and the color scale represents the size of the travel cost change value (dark color represents large change value). The data shows that as the device density and personnel flow increase, the travel cost change value increases nonlinearly. When the device density is 0.5 units / 100m² and the personnel flow is 10 people / hour, the travel cost change value is only 0.05, which is within the safe range; when the device density increases to 2.5 units / 100m² and the personnel flow reaches 60 people / hour, the travel cost change value increases to 1.72, entering the dynamic threshold range, at which point the system needs to trigger path re-planning. It is particularly noted that at the critical point of device density of 1.5 units / 100m², the average increase of travel cost change value is 0.17 for every 10 people / hour increase in personnel flow; when the device density reaches 2.5 units / 100m², the average increase of travel cost change value caused by the same personnel flow growth is 0.26, an increase of 52.9%. This technical solution can predict potential traffic difficulties in advance by precisely quantifying these change relationships, and when the travel cost change value exceeds the set dynamic threshold (1.0 in this example), it automatically adjusts the weight coefficient of the travel cost in the path evaluation function, recalculates the delivery route, and effectively avoids the delivery delay and resource waste that may be caused by traditional fixed evaluation methods in high-density areas.

[0128] In the prior art, construction site material delivery and equipment scheduling mostly use static path planning or empirical threshold setting, which fails to fully consider the dynamic changes of the construction environment and regional differences, easily leading to traffic congestion, resource waste, and safety risks. The present application introduces a mechanism for calculating equipment quantity control thresholds based on material supply sequence and regional travel cost, achieving dynamic matching of scheduling resources and regional carrying capacity, and avoiding excessive aggregation of equipment. By incorporating factors such as terrain slope and process duration to correct the equipment threshold, the environmental adaptability of the scheduling strategy is improved. At the same time, a comprehensive travel cost evaluation function is constructed, introducing parameters such as equipment operation restriction degree and construction interference intensity, to achieve fine-grained judgment of material delivery routes. Unlike existing methods that are based only on fixed map path calculation, the present application sets detection points on the delivery path, collects travel state information in real time, and dynamically adjusts the path weight when the travel cost changes significantly, ensuring that material delivery is always performed along the optimal route. The above improvements aim to improve the flexibility, accuracy, and safety of resource scheduling, significantly enhancing the real-time response capability of resource flow and the overall operation efficiency of the system at the construction site.

[0129] In an alternative embodiment, real-time monitoring data, safety risk level and construction resource scheduling instructions are generated in data slices in space-time dimensions, a consensus is formed on data slices through multi-node voting, and a smart contract is used for verification, and the construction resource scheduling instructions that pass the verification are issued to the on-site terminal equipment for execution, including:

[0130] The time series variation characteristics of real-time monitoring data, safety risk level and construction resource scheduling instructions are calculated, a time window adjustment function is constructed to determine the time slice boundary; the resource density distribution in the construction site is calculated to generate a grid cell with dynamic boundary;

[0131] Real-time monitoring data, safety risk level and construction resource scheduling instructions are mapped according to the time slice boundary and grid cell boundary to generate space-time data slices;

[0132] A data flow tracking matrix is constructed to record the transmission path and conversion relationship of real-time monitoring data, safety risk level and construction resource scheduling instructions in the space-time data slices; based on the tracking matrix, link breakage, loops and conflicts in the space-time data slices are identified in real time, and the identification results are combined with data transmission intensity to generate a trusted score of the verification node; according to the trusted score, multi-node voting is performed on the space-time data slices to form a consensus;

[0133] A hierarchical adaptive verification network is constructed and multiple verification channels are set, each verification channel contains verification points of different densities, the optimal verification channel is dynamically selected based on the space-time data slices, the selected verification channel is used to construct a smart contract, and the construction resource scheduling instructions are verified;

[0134] The construction resource scheduling instructions verified by the smart contract are issued to the on-site terminal equipment for execution, and execution feedback data is collected; the execution feedback data is processed according to the same slicing and verification process, and is used to optimize the density distribution and selection strategy of the verification channel.

[0135] The embodiment provides an implementation method for generating data slices in time and space dimensions according to real-time monitoring data, safety risk levels and construction resource scheduling instructions. The method forms a consensus through multi-node voting and verifies by using a smart contract, and finally issues the construction resource scheduling instruction that passes the verification to the on-site terminal equipment for execution. Specifically, first, time-space data slicing generation is performed, and real-time monitoring data including temperature, humidity, vibration, noise and the like, and information such as personnel density and equipment operating state are received. By calculating the time series variation characteristics of these data, the system constructs a time window adjustment function. Analyzing the change rate of the monitoring data in the past 24 hours, when the change rate exceeds the preset threshold of 10%, the time window is reduced to 5 minutes; when the change rate is less than 5%, the time window is expanded to 30 minutes. For example, when the temperature in a certain area rises from 25°C to 35°C within 1 hour, the system adjusts the time window of the area to 5 minutes to capture rapidly changing environmental data.

[0136] At the same time, the resource density distribution in the construction site is calculated, and the construction site is divided into grid units with dynamic boundaries. In actual application, taking a construction site of 100m x 100m as an example, the initial grid size is set to 10m x 10m. When the personnel density in a certain grid exceeds 0.5 people per square meter, the system will subdivide the grid into 4 subgrids of 5m x 5m; when the personnel density difference between adjacent grids is less than 0.1 person per square meter, the system will merge these grids. For example, the personnel density in area A is 0.6 person per square meter, and the system will subdivide it into 4 subgrids; while the personnel density in areas B and C is 0.2 and 0.25 person per square meter respectively, the system will merge these two areas into one grid.

[0137] The real-time monitoring data, safety risk levels and construction resource scheduling instructions are mapped according to the determined time slicing boundaries and grid unit boundaries to generate time-space data slices. For example, in the 14:00-14:30 time period, in the grid unit numbered G15, the system records the temperature 33°C, humidity 65%, wind speed 2.5m / s, personnel density 0.3 person per square meter, and the corresponding safety risk level is medium (level 3), and the resource scheduling instruction is "assign 2 safety officers for inspection". Then a data flow tracking matrix is constructed to record the transfer path and conversion relationship of each item of data in the time-space data slice. Each element in the matrix represents the conversion weight from one data type to another. For example, the system records that the weight of temperature data affecting the safety risk level is 0.6, and the weight of the safety risk level affecting the resource scheduling instruction is 0.8. By analyzing the historical data flow, the system identifies broken links, loops and conflicts in real time. For example, when it is found that the scheduling instruction from "temperature anomaly" to "assign fire personnel" lacks the intermediate "fire risk assessment" link, the system identifies this as a data broken link.

[0138] According to the identification result and the data transmission intensity, the trust score of the verification node is calculated. Specifically, for each data flow direction, a basic score of 10 points is given, and when the data transmission intensity is higher than 0.7, 5 points are added, and when there is data link breakage or conflict, 8 points are deducted. For example, the data flow direction continuity score of node A is 10 points, the data transmission intensity is 0.8, 5 points are added, and the total score is 15 points; and node B has data link breakage, 8 points are deducted, and the total score is 2 points. According to the trust score, the spatiotemporal data slices are voted by multiple nodes to form a consensus. In the voting mechanism, nodes with a trust score of more than 12 points have a weight of 2 votes, nodes with a trust score between 8 and 12 points have a weight of 1 vote, and nodes with a trust score of less than 8 points have no voting right. When a data slice obtains more than 65% of the total votes, a consensus is formed.

[0139] A hierarchical adaptive verification network is constructed and multiple verification channels are set, each of which contains verification points of different densities. For example, 20 verification points are set in the high-density channel for high-risk areas, 10 verification points are set in the medium-density channel for medium-risk areas, and 5 verification points are set in the low-density channel for low-risk areas. The system dynamically selects a verification channel based on the characteristics of the spatiotemporal data slices, such as selecting a high-density verification channel for a region with a risk level of 4, and selecting a low-density verification channel for a region with a risk level of 2.

[0140] The system uses the selected verification channel to construct a smart contract to verify the construction resource scheduling instructions. The smart contract contains conditional judgment logic, such as "if the temperature exceeds 35°C and lasts for more than 30 minutes, the risk level is raised to level 4", "if the risk level is level 4 and the personnel density in the region exceeds 0.4 person / m2, the 'evacuate non-essential personnel' instruction is issued". The system verifies the rationality of the construction resource scheduling instructions against these conditions.

[0141] The construction resource scheduling instructions verified by the smart contract are issued to the on-site terminal devices for execution, such as pushing the "evacuate immediately" instruction to the tablet computers and safety helmet displays in a specific area. At the same time, the system collects execution feedback data, including instruction reception confirmation time, execution completion time, execution effect evaluation, etc. For example, it is recorded that after the G15 region received the instruction to increase the number of safety officers, 2 safety officers arrived at the site in 4 minutes, the region personnel evacuation was completed in 6 minutes, and the risk level was reduced to level 2 in 15 minutes.

[0142] The system processes the execution feedback data according to the same slicing and verification process, and uses it to optimize the density distribution and selection strategy of the verification channel. For example, when it is found that the instruction execution efficiency of a certain region is lower than expected, the system will increase the density of the verification points in that region from 10 to 15; when the execution result of a certain type of instruction does not meet the expectation multiple times, the system will adjust the verification logic of the related smart contract and increase the strictness of the corresponding conditional judgment.

[0143] By mapping the real-time monitoring data, safety risk level and resource scheduling instructions in the construction process in space and time and data sharding management, the traceability and credibility of the scheduling instructions are effectively improved. In the prior art, scheduling instructions are mostly processed and issued by a single scheduling node, lacking a multi-node collaboration mechanism and dynamic verification process, which is prone to instruction errors, data conflicts or uncontrollable scheduling behaviors. The application realizes the fine division of the space-time information of the construction site by constructing a time window adjustment function and a dynamic grid division mechanism, so that the scheduling instructions can be accurately mapped to the site state. Further, through data flow tracking and multi-node voting consensus mechanism, the reliable transmission and processing capacity of the space-time data shards are improved, effectively avoiding data link breakage, duplication or contradiction problems. At the same time, combined with the trusted score and the hierarchical adaptive verification network, the verification channel is dynamically selected and the instruction verification is completed through the smart contract, which not only enhances the transparency and controllability of the scheduling process, but also improves the correctness and response efficiency of the instruction execution. Finally, the feedback data can be used for self-optimization of the verification channel strategy, realizing the continuous adaptive optimization of the scheduling system, and overall improving the collaborative scheduling efficiency and safety guarantee level of the construction site.

[0144] In an optional implementation, a hierarchical adaptive verification network is constructed and multiple verification channels are set, each verification channel containing different densities of verification points, the optimal verification channel is dynamically selected based on the space-time data shards, and the smart contract is constructed using the selected verification channel to verify the construction resource scheduling instructions, including:

[0145] The verification network is divided into a first verification layer, a second verification layer and a third verification layer, and multiple verification channels are set, the resource density gradient, safety risk change rate and scheduling instruction complexity in the space-time data shards are calculated, and a verification demand vector is generated; the coverage, verification efficiency and resource consumption of each verification channel are calculated based on the verification demand vector, and a verification channel fitness score is generated; the optimal verification channel is selected based on the fitness score;

[0146] According to the hierarchical features of the optimal verification channel, a contract template library containing different granularities is constructed, the resource constraint information in the space-time data shards is mapped to the corresponding contract template in the contract template library, the constraint transmission rules of the cross-layer verification points are set, and the verification results of different granularities are integrated; a hierarchical execution strategy is used to construct a smart contract to verify the construction resource scheduling instructions;

[0147] When the safety risk change rate in the space-time data shards rises, the optimal verification channel is switched to the verification channel containing the first verification layer; when the safety risk change rate decreases, the optimal verification channel is switched to the verification channel containing the second verification layer or the third verification layer; the verification demand vector is updated based on the verification result, the optimal verification channel is reselected and the smart contract is constructed.

[0148] Exemplarily, the verification network is divided into three levels: the first verification layer is responsible for high-granularity security constraint verification, the second verification layer is responsible for medium-granularity resource conflict verification, and the third verification layer is responsible for low-granularity execution efficiency verification. Multiple verification channels are set in each verification layer, for example, the first verification layer contains 5 verification channels, and the verification point density of each channel is 10, 8, 6, 4 and 2 per square meter respectively; the second verification layer contains 4 verification channels, and the verification point density is 5, 4, 3 and 2 per square meter respectively; the third verification layer contains 3 verification channels, and the verification point density is 3, 2 and 1 per square meter respectively.

[0149] For spatio-temporal data slicing processing, the system divides the construction site into multiple spatio-temporal data slices according to a 20m x 20m grid. For each spatio-temporal data slice, the system calculates three key indicators: resource density gradient, safety risk change rate, and scheduling instruction complexity. The resource density gradient is calculated by the spatial distribution difference of the number of construction equipment, materials and personnel in the slice, for example, there are 0.5 resource units per square meter in a slice, and the highest density area reaches 2.3 resource units per square meter; the safety risk change rate is calculated by continuously monitoring the safety sensor data, for example, the risk index of a slice increases from 0.2 to 0.7 at two consecutive time points, with a change rate of 0.5; the scheduling instruction complexity is calculated by the number of resource types involved, the moving distance and the number of operation steps in the scheduling instruction, for example, a scheduling instruction involves 3 types of resources, with an average moving distance of 15 meters and 6 operation steps, with a complexity of 0.75.

[0150] Based on the above three indicators, the system generates a verification demand vector [0.8, 0.5, 0.75], where the three components correspond to the normalized values of resource density gradient, safety risk change rate and scheduling instruction complexity respectively. Then, the system calculates three performance indicators of each verification channel: coverage, verification efficiency and resource consumption. Coverage represents the proportion of the area monitored by the verification point to the total area, verification efficiency represents the number of verification tasks that can be completed per unit time, and resource consumption represents the calculation and storage resources required for execution verification. For example, the coverage of a verification channel (density of 8 per square meter) in the first verification layer is 0.95, the verification efficiency is 4 instructions per second, and the resource consumption is 0.7 of the total system resources.

[0151] Based on these performance indicators, the fitness score of each verification channel is calculated. The fitness score calculation takes into account three factors: the match between the verification requirement vector and the channel performance, the current resource state of the system, and the historical verification effect. For example, for the verification requirement vector [0.8, 0.5, 0.75] mentioned above, the fitness score of the verification channel (density 8 / m2) in the first verification layer is 0.86, the fitness score of the verification channel (density 4 / m2) in the second verification layer is 0.72, and the fitness score of the verification channel (density 2 / m2) in the third verification layer is 0.58. The system selects the verification channel with the highest fitness score as the optimal verification channel, which in this example is the verification channel (density 8 / m2) in the first verification layer.

[0152] According to the selected optimal verification channel, a contract template library is constructed. The contract template library contains templates of three granularities: high-granularity safety constraint templates (such as "the minimum safety distance between device A and device B is not less than 3 meters"), medium-granularity resource conflict templates (such as "resource R cannot be used for tasks T1 and T2 at the same time point"), and low-granularity execution efficiency templates (such as "the resource movement speed of path P should not be lower than 5 meters per minute"). The system maps the resource constraint information in the spatio-temporal data slice to these templates, for example, mapping the constraint "no personnel gathering within the radius of the tower crane working" in the slice to the high-granularity safety constraint template to generate specific contract clauses. The constraint transmission rules of cross-layer verification points are set to ensure the consistency of information between different verification layers. For example, the verification result of the safety distance of the device in the first verification layer is transmitted to the second verification layer for resource conflict detection. The system uses a hierarchical execution strategy to construct the smart contract, first performs high-granularity safety constraint verification, then performs medium-granularity resource conflict verification, and finally performs low-granularity execution efficiency verification, and integrates the verification results of the three layers.

[0153] In terms of dynamic adjustment mechanism, when the system detects that the safety risk change rate in a spatio-temporal data slice rises from 0.3 to 0.7, the system automatically switches the optimal verification channel to a high-density verification channel containing the first verification layer (such as a channel with a density of 10 / m2); when the safety risk change rate drops from 0.7 to 0.2, the system switches the optimal verification channel to a low-density verification channel containing the second verification layer or the third verification layer (such as a channel with a density of 4 / m2 or 2 / m2) to save system resources.

[0154] According to the verification result of the smart contract (pass / fail and specific failure reason), the verification demand vector is updated. For example, if the verification finds a large number of resource conflict problems, the system will increase the weight of the resource density gradient in the verification demand vector; if the security problem is found to be reduced, the weight of the security risk change rate is reduced. Then, the system recalculates the fitness score of each verification channel based on the updated verification demand vector, selects a new optimal verification channel, and constructs a new smart contract for the next round of verification.

[0155] Through the implementation of the above hierarchical adaptive verification network, the system can select the most suitable verification strategy according to the dynamic changes of the construction site, ensuring construction safety and resource optimization, and avoiding unnecessary waste of computing resources, improving the accuracy and efficiency of construction resource scheduling verification.

[0156] Figure 3 For the efficiency analysis column chart of the smart contract hierarchical execution of the embodiment of the present application, as shown in Figure 3 The execution efficiency of the three contract verification methods under different verification scenarios is shown in the figure. The present technical solution (white column) has a significant performance advantage in various verification scenarios: in the security verification scenario, the present solution achieves 23.5 transactions per second, while the single-level contract (diagonal filled column) and the traditional Ethereum contract (grid filled column) only achieve 11.0 and 9.0 transactions per second, respectively; in the resource conflict verification scenario, the present solution achieves 27.5 transactions per second, nearly double that of the single-level contract (14.5 transactions per second) and the traditional Ethereum contract (12.5 transactions per second); in the space-time constraint verification scenario, the present solution achieves the highest efficiency of 30.0 transactions per second, far exceeding the other two methods; in the scheduling optimization verification scenario, the present solution maintains a high efficiency of 26.5 transactions per second; finally, in the comprehensive verification scenario, the present solution still maintains superior performance with an efficiency of 29.0 transactions per second. This significant efficiency improvement is mainly due to the three-layer verification network architecture and the adaptive contract construction mechanism of the present technical solution, i.e., constructing a contract template library of different granularities according to the verification demand vector and setting constraint transfer rules for cross-layer verification points. Compared with the traditional Ethereum contract, the present solution improves the execution efficiency by an average of 123% in each verification scenario; compared with the single-level contract, the efficiency is improved by about 85%. In particular, in the complex comprehensive verification scenario, the hierarchical execution strategy of the present solution can efficiently allocate verification tasks to different verification layers, avoiding the waste of computing resources and execution bottlenecks in traditional methods, so that the system can process a large number of resource scheduling instruction verification requests at near real-time speed.

[0157] The core innovation of the scheme is to generate a verification demand vector by calculating the resource density gradient, security risk change rate and scheduling instruction complexity of the space-time data slice in real time, evaluate the adaptability of each verification channel, and then dynamically select the optimal verification channel. Through the hierarchical execution strategy, the verification level is matched with the actual changes of the construction environment, effectively dealing with different security risk levels and construction task complexity. When the risk increases, it will automatically switch to a higher level verification channel to ensure a more rigorous verification process; when the risk is low, a low-level verification channel can be selected to improve efficiency and save resources. The adaptability and real-time response capability of the construction site management are improved, ensuring that the construction resource scheduling instructions can be verified in a timely and accurate manner in a changing environment, and reducing the redundancy and unnecessary resource consumption in the verification process. Compared with the traditional technology, the improvement of the scheme effectively optimizes the efficiency and accuracy of the verification process, ensuring efficient and safe execution of construction resource scheduling.

[0158] Figure 4 The structure diagram of the intelligent construction site real-time monitoring and intelligent scheduling optimization system of the embodiment of the application is shown in Figure 4 The system comprises:

[0159] A first unit is configured to collect real-time monitoring data of the construction site, wherein the real-time monitoring data comprises construction resource position information and construction environment information.

[0160] A second unit is configured to construct a material consumption heat distribution map according to the construction resource position information, calculate real-time relative distances between construction personnel and construction equipment and distance change trends, compare the real-time relative distances with preset multi-level safety thresholds, and determine the safety risk level of the construction area in combination with the distance change trends.

[0161] A third unit is configured to calculate heat gradients of regions in the material consumption heat distribution map, determine material supply sequences based on the heat gradients, divide region passing costs according to the safety risk level, calculate device quantity control thresholds of the regions based on the material supply sequences and the region passing costs, plan material distribution routes with minimum passing costs based on the construction environment information, and generate construction resource scheduling instructions.

[0162] A fourth unit is configured to generate data slices in the time and space dimensions according to the real-time monitoring data, the safety risk level and the construction resource scheduling instructions, form a consensus on the data slices through multi-node voting, verify the data slices by using an intelligent contract, and issue the construction resource scheduling instructions that pass the verification to on-site terminal devices for execution.

[0163] In a third aspect, the embodiment of the application provides an electronic device, comprising:

[0164] a processor;

[0165] a memory for storing processor-executable instructions;

[0166] wherein the processor is configured to invoke the instructions stored by the memory to perform the method as described above.

[0167] In a fourth aspect, the present application provides a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method as described above.

[0168] The present application can be a method, an apparatus, a system, and / or a computer program product. The computer program product can include a computer readable storage medium having computer readable program instructions stored therein, which, when executed by a processor, perform various aspects of the present application.

[0169] It should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application, rather than limiting the present application; although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that the technical solutions recorded in the above-mentioned embodiments can be modified or equivalent replacements can be made to some or all of the technical features; and the modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. Intelligent construction site real-time monitoring and intelligent scheduling optimization method, characterized by: include: Collecting real-time monitoring data of the construction site, including construction resource location information and construction environment information; A material consumption heat distribution map is constructed based on construction resource location information. The real-time relative distance between construction personnel and construction equipment, as well as the distance change trend, is calculated. This real-time relative distance is compared with pre-set multi-level safety thresholds, and the safety risk level of the construction area is determined based on the distance change trend. Calculate the thermal gradient of each area in the material consumption thermal distribution map, determine the material supply sequence based on the thermal gradient, and divide the regional access costs according to the safety risk level. Calculate the equipment quantity control threshold for each area based on the material supply sequence and regional access costs. Combined with construction environment information, plan the material distribution route with the minimum access cost and generate construction resource scheduling instructions. Real-time monitoring data, safety risk levels, and construction resource scheduling instructions are generated into data shards based on time and space dimensions. Consensus on the data shards is reached through multi-node voting and verified using smart contracts. Verified construction resource scheduling instructions are then sent to on-site terminal devices for execution. Based on the material replenishment sequence and regional access costs, the equipment quantity control threshold for each area is calculated. The material distribution route with the minimum access cost is planned in combination with the construction environment information. Construction resource scheduling instructions are generated, including: The replenishment priority coefficient of each area is determined based on the material replenishment sequence, and the regional baseline capacity is calculated by the ratio of the replenishment priority coefficient to the regional access cost; the congestion limit factor is obtained by the ratio of the minimum spacing between equipment to the preset safe operating distance; The product of the regional baseline capacity and the congestion limit factor is used as the initial threshold, and the initial threshold is modified according to the regional terrain slope coefficient and the construction process interval to obtain the equipment quantity control threshold of each area; Based on the construction environment information, the terrain access difficulty coefficient, the degree of equipment operation restriction, and the intensity of construction interference in each area are calculated and weighted together to form the environmental access cost. The weighted sum of the environmental access cost and the regional access cost is used as the comprehensive access cost. Based on the comprehensive access cost, a path evaluation function is constructed. The minimum cost path to each distribution target point is calculated in sequence according to the material supply order to obtain the initial material distribution route. Checkpoints are set up on the initial material distribution route to collect traffic status data, and the change in traffic cost is calculated based on the congestion index, equipment density and personnel flow of the area where the checkpoints are located. When the change in traffic cost exceeds the dynamic threshold, the weight coefficient of the traffic cost in the path evaluation function is adjusted, and the transfer costs between each area are recalculated based on the updated path evaluation function to generate an optimal distribution route that meets the material replenishment sequence constraints, and the optimal distribution route is converted into a construction resource scheduling instruction.

2. The method according to claim 1, characterized in that Constructing a material consumption heat distribution map based on construction resource location information includes: The construction resource location information is divided into multiple continuous time windows based on the time span. Within each time window, the location coordinates of the construction resource's stop points and the corresponding stop duration are extracted, and the movement trajectory of the construction resource is obtained based on the temporal changes of the stop point location coordinates. Determining the operating status of the construction resource according to the combined characteristics of the stay duration and the movement trajectory, and calculating the material consumption of the construction resource based on the operating status; Divide the construction site into multiple grid cells, map the location coordinates of the stop points to the corresponding grid cells, calculate the thermal value of each grid cell based on the material consumption, smooth the thermal value using a kernel function, and construct a material consumption thermal distribution map; When new construction resource location information is obtained, the affected grid unit corresponding to the construction resource location information is determined, only the thermal value of the affected grid unit is recalculated, and the corresponding area of ​​the material consumption thermal distribution map is updated.

3. The method according to claim 1, characterized in that Calculate the real-time relative distance between construction personnel and construction equipment, as well as the distance change trend. Compare the real-time relative distance with the preset multi-level safety thresholds. Combined with the distance change trend, determine the safety risk level of the construction area, including: Performing Kalman filter preprocessing on the construction resource location information, and identifying abnormal jump points by calculating the coordinate deviation and time interval of adjacent sampling points, and removing the abnormal jump points from the preprocessed construction resource location information to obtain optimized location information; The optimized location information is divided into multiple continuous time windows according to a preset time span. The location coordinates of the construction personnel and the construction equipment in each time window are extracted. The horizontal and vertical distances between the construction personnel and the construction equipment are calculated to obtain the real-time relative distance. Performing least squares curve fitting on the real-time relative distance to obtain distance change direction parameters and distance change rate parameters, determining an equipment type coefficient based on the equipment type of the construction equipment, and setting a multi-level safety threshold based on the equipment type coefficient, wherein the multi-level safety threshold includes a warning distance, a danger distance, and an emergency distance; The real-time relative distance is compared with the multi-level safety threshold to determine the basic risk level. The basic risk level is dynamically adjusted according to the distance change direction parameter and the distance change rate parameter. The adjusted risk level is weightedly calculated with the equipment type coefficient to divide the safety risk level of the construction area.

4. The method according to claim 1, wherein Calculate the thermal gradient of each area in the material consumption thermal distribution map, determine the material supply sequence based on the thermal gradient, and divide the area into different areas according to the safety risk level. The cost includes: Obtain the thermal value matrix corresponding to the material consumption thermal distribution diagram, calculate the gradient difference values ​​of the thermal value matrix in the horizontal and vertical directions respectively, obtain the thermal gradient modulus, calculate the thermal diffusion coefficient based on the thermal value difference of adjacent grids in the thermal value matrix, and use the product of the thermal diffusion coefficient and the thermal gradient modulus as the regional material demand change rate; A material replenishment priority index is obtained by weightedly combining the regional material demand change rate and the thermal value of the corresponding location. The material demand in the future time window is predicted based on the material replenishment priority index, and the material replenishment order is obtained by comprehensive calculation with the current material replenishment priority index. Obtain the personnel distribution density, equipment operation status and construction environment information of the construction site, calculate the personnel aggregation coefficient based on the personnel distribution density, calculate the equipment risk factor based on the equipment operation status value, and calculate the environmental impact value based on the construction environment parameters. The regional safety risk level is obtained by taking the weighted sum of the personnel aggregation coefficient, equipment hazard factor and environmental impact value. The shortest path distance from each area to the safe exit is calculated based on the spatial topological structure of the construction site. The regional safety risk level and the shortest path distance are combined according to the adaptive weight to calculate the regional travel cost.

5. The method according to claim 1, wherein Real-time monitoring data, safety risk levels, and construction resource scheduling instructions are generated into data shards based on time and space dimensions. Consensus is reached on the data shards through multi-node voting and verified using smart contracts. Verified construction resource scheduling instructions are then sent to on-site terminal devices for execution, including: Calculate the time series variation characteristics of real-time monitoring data, safety risk levels, and construction resource scheduling instructions, and construct a time window adjustment function to determine the time slice boundaries; calculate the resource density distribution in the construction site and generate grid cells with dynamic boundaries; Mapping the real-time monitoring data, safety risk levels, and construction resource scheduling instructions according to the time slice boundaries and grid unit boundaries to generate spatiotemporal data slices; Construct a data flow tracking matrix to record the transmission paths and conversion relationships of real-time monitoring data, safety risk levels, and construction resource scheduling instructions in the spatiotemporal data shards. Based on the tracking matrix, identify broken links, loops, and conflicts in the spatiotemporal data shards in real time, and combine the identification results with the data transmission strength to generate a trust score for the verification node. Based on the trust score, multi-node voting is performed on the spatiotemporal data shards to reach a consensus. Construct a hierarchical adaptive verification network and set up multiple verification channels. Each verification channel contains verification points of different densities. The optimal verification channel is dynamically selected based on spatiotemporal data sharding. The selected verification channel is used to build a smart contract to verify construction resource scheduling instructions. The construction resource scheduling instructions verified by the smart contract are sent to the on-site terminal equipment for execution, and the execution feedback data is collected; the execution feedback data is processed according to the same sharding and verification process, and used to optimize the density distribution and selection strategy of the verification channel.

6. The method according to claim 5, characterized in that Construct a hierarchical adaptive verification network and set up multiple verification channels. Each verification channel contains verification points of different densities. Dynamically select the optimal verification channel based on spatiotemporal data sharding. Use the selected verification channel to build a smart contract to verify construction resource scheduling instructions, including: The verification network is divided into a first verification layer, a second verification layer, and a third verification layer, and multiple verification channels are set up. The resource density gradient, security risk change rate, and scheduling instruction complexity in the spatiotemporal data slices are calculated to generate a verification requirement vector. Based on the verification requirement vector, the coverage, verification efficiency, and resource consumption of each verification channel are calculated to generate a verification channel fitness score. The optimal verification channel is selected based on the fitness score. Based on the hierarchical characteristics of the optimal verification channel, a contract template library with different granularities is constructed. The resource constraint information in the spatiotemporal data shards is mapped to the corresponding contract templates in the contract template library. The constraint transfer rules for cross-layer verification points are set, and the verification results of different granularities are integrated. A hierarchical execution strategy is used to build smart contracts to verify construction resource scheduling instructions. When the security risk change rate in the spatiotemporal data shard increases, the optimal verification channel is switched to a verification channel including the first verification layer; when the security risk change rate decreases, the optimal verification channel is switched to a verification channel including the second verification layer or the third verification layer; based on the verification result, the verification requirement vector is updated, the optimal verification channel is reselected, and the smart contract is constructed.

7. An intelligent construction site real-time monitoring and intelligent scheduling optimization system, used to implement the method according to any one of claims 1 to 6, characterized in that: include: The first unit is used to collect real-time monitoring data of the construction site, wherein the real-time monitoring data includes construction resource location information and construction environment information; The second unit is used to construct a material consumption heat distribution map based on the location information of construction resources. It also calculates the real-time relative distance between construction personnel and construction equipment and the distance change trend. The real-time relative distance is compared with the preset multi-level safety thresholds, and the safety risk level of the construction area is determined based on the distance change trend. The third unit is used to calculate the thermal gradient of each area in the material consumption thermal distribution map, determine the material supply sequence based on the thermal gradient, and divide the regional access costs according to the safety risk level. Based on the material supply sequence and regional access costs, the equipment quantity control threshold of each area is calculated. In combination with the construction environment information, the material distribution route with the minimum access cost is planned and the construction resource scheduling instructions are generated. The fourth unit is used to generate data shards based on the time and space dimensions for real-time monitoring data, safety risk levels, and construction resource scheduling instructions. It reaches a consensus on the data shards through multi-node voting, verifies them using smart contracts, and sends the verified construction resource scheduling instructions to on-site terminal equipment for execution.

8. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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