Construction material delivery method, device and equipment based on dynamic geofencing

Abstract

This disclosure presents a method, apparatus, and device for construction material delivery based on dynamic geofencing. One specific implementation of the method includes: acquiring an initial geofencing, a multimodal construction dataset, and device location information corresponding to a target user's smart wearable device; preprocessing the multimodal construction dataset to generate a preprocessed construction dataset; recalculating the geofencing on the preprocessed construction dataset based on a dynamic rule base to generate a recalculated geofencing coordinate set; updating the initial geofencing based on the recalculated geofencing coordinate set to generate an updated geofencing; verifying the target user based on the device location information and the updated geofencing to generate a verification result; and controlling associated transport vehicles to deliver construction materials to the target user based on the verification result. This implementation improves construction progress and avoids the loss of construction materials.

Description

Technical Field

[0001] The embodiments disclosed herein relate to the field of computer technology, and more specifically to a method, apparatus, and equipment for delivering construction materials based on dynamic geofencing. Background Technology

[0002] On construction sites, a large amount of construction materials are usually needed to ensure the smooth progress of construction. How to properly distribute construction materials to workers has become an important research topic. Currently, the common method for distributing construction materials is to determine the construction area and transport all construction materials directly to the workers by transport vehicles for them to choose from.

[0003] However, when using the above method to distribute construction materials, the following technical problems often arise: Different workers often require different construction materials. Transporting the wrong construction materials can render the selected materials unusable, affecting the construction progress. Furthermore, if the worker's identity is not verified, construction materials may be assigned to non-worker locations, leading to the loss of construction materials.

[0004] The information disclosed in this background section is only intended to enhance the understanding of the background of the inventive concept, and therefore may contain information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0006] Some embodiments of this disclosure propose a method, apparatus, electronic device, and computer-readable medium for the delivery of construction materials based on dynamic geofencing to address one or more of the technical problems mentioned in the background section above.

[0007] In a first aspect, some embodiments of this disclosure provide a construction material delivery method based on dynamic geofencing. The method includes: in response to receiving a construction material transportation request from a target terminal, acquiring an initial geofencing, a multimodal construction dataset, and device location information corresponding to the target user's smart wearable device; preprocessing the multimodal construction dataset to generate a preprocessed construction dataset; recalculating the geofencing on the preprocessed construction dataset based on a dynamic rule base to generate a recalculated geofencing coordinate set; updating the initial geofencing based on the recalculated geofencing coordinate set to generate an updated geofencing; verifying the target user based on the device location information and the updated geofencing to generate a verification result; and controlling associated transport vehicles to deliver construction materials corresponding to the target user based on the verification result.

[0008] Secondly, some embodiments of this disclosure provide a construction material delivery device based on a dynamic geofence. The device includes: an acquisition unit configured to acquire an initial geofence, a multimodal construction dataset, and device location information corresponding to a smart wearable device of a target user in response to receiving a construction material transportation request sent by a target terminal; a data preprocessing unit configured to preprocess the multimodal construction dataset to generate a preprocessed construction dataset; a fence recalculation unit configured to recalculate the preprocessed construction dataset based on a dynamic rule base to generate a recalculated fence coordinate set, wherein the dynamic rule base is determined based on the terrain corresponding to the point cloud data included in the multimodal construction dataset; an update unit configured to update the initial geofence based on the recalculated fence coordinate set to generate an updated geofence; a verification unit configured to verify the target user based on the device location information and the updated geofence to generate a verification result; and a control unit configured to control associated transport vehicles to deliver construction materials corresponding to the target user based on the verification result.

[0009] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0010] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0011] The above embodiments of this disclosure have the following beneficial effects: the construction material delivery method based on dynamic geofencing according to some embodiments of this disclosure improves construction progress and avoids the loss of construction materials. Specifically, the reasons for slow or even halted construction progress and loss of construction materials are: different workers often require significantly different construction materials; transporting the wrong construction materials will result in the selected materials being unusable in construction, affecting the construction progress; and if the worker's identity is not verified, construction materials may be allocated to non-worker locations, leading to the loss of construction materials. Based on this, the construction material delivery method based on dynamic geofencing according to some embodiments of this disclosure firstly, in response to receiving a construction material transportation request sent by the target terminal, obtains the initial geofencing, the multimodal construction dataset, and the device location information corresponding to the target user's smart wearable device. This allows the location of the construction personnel to be determined. Secondly, the multimodal construction dataset is preprocessed to generate a preprocessed construction dataset. This allows for the preprocessing of construction data. Then, based on a dynamic rule base, the preprocessed construction dataset is recalculated to generate a recalculated fence coordinate set. This dynamic rule base is determined based on the terrain corresponding to the point cloud data included in the multimodal construction dataset. Based on this recalculated fence coordinate set, the initial geofence is updated to generate an updated geofence. This generates a dynamic construction area corresponding to the construction workers. Next, based on the device location information and the updated geofence, the target user is verified to generate a verification result. This verifies the user's identity information. Finally, based on the verification result, associated transport vehicles are controlled to deliver construction materials to the target user. This allows for the delivery of construction materials to users after identity verification. By setting up dynamic fences, the delivery of construction materials to the wrong location is avoided, and by authenticating user identity information, the delivery of construction materials to the wrong user is prevented, thus preventing the loss of construction materials. Attached Figure Description

[0012] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0013] Figure 1 This is a flowchart of some embodiments of the construction material delivery method based on dynamic geofencing according to the present disclosure; Figure 2 These are schematic diagrams of some embodiments of a construction material delivery device based on a dynamic geofence, according to the present disclosure. Figure 3This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0014] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0015] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0016] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0017] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0018] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0019] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] Figure 1 A flow 100 of some embodiments of a construction material delivery method based on dynamic geofencing according to this disclosure is shown. This construction material delivery method based on dynamic geofencing includes the following steps: Step 101: In response to receiving a construction material transportation request sent by the target terminal, obtain the initial geofence, multimodal construction dataset, and device location information corresponding to the target user's smart wearable device.

[0021] In some embodiments, the execution entity (e.g., a server) of the construction material delivery method based on dynamic geofencing can, in response to receiving a construction material transportation request sent by a target terminal, obtain an initial geofencing, a multimodal construction dataset, and device location information corresponding to the target user's smart wearable device. The target terminal can be a terminal connected to the execution entity via a wired or wireless connection, used to send the construction material transportation request at a predefined time point. For example, the predefined time point could be 8:00 AM every day. Here, the target terminal can also, in response to receiving a construction material demand list sent by the target user's smart wearable device, generate a construction material transportation request corresponding to the construction material demand list and send it to the execution entity. The construction material demand list can be a list of at least one construction material required by the target user. The smart wearable device can be a device with integrated positioning chip and information transmission capabilities. For example, the smart wearable device can be a wristband or a safety helmet. The positioning chip can be a GPS positioning chip or a BeiDou positioning chip. The target user can be a worker.

[0022] Step 102: Perform data preprocessing on the multimodal construction dataset to generate a preprocessed construction dataset.

[0023] In some embodiments, the aforementioned execution entity may perform data preprocessing on the aforementioned multimodal construction dataset to generate a preprocessed construction dataset.

[0024] In practice, the aforementioned implementing entities can perform data preprocessing on the multimodal construction dataset using the following steps to generate a preprocessed construction dataset: The first step is to perform time alignment processing on the various multimodal construction data in the aforementioned multimodal construction dataset to generate an aligned construction dataset. This time alignment processing can involve unifying the timestamps of the various multimodal construction data to the same time zone and synchronizing them. This time synchronization can be achieved by setting multiple alignment time points at a fixed frequency (e.g., 10Hz) and aligning the various multimodal construction data at these multiple alignment time points.

[0025] The second step involves denoising and correcting at least one alignment data point representing the video from the aforementioned alignment data set to generate at least one denoised alignment data set. In practice, firstly, the intrinsic parameters and distortion coefficients of the shooting device can be determined using the Zhang Zhengyou calibration method. Secondly, a distortion correction mapping function is used to map the curved pixels in each video frame of the aforementioned at least one alignment data set representing the video to their corresponding positions for correction. As an example, the distortion correction mapping function could be the InitUndistortMap function from the OpenCV library. Thirdly, a Gaussian filtering algorithm can be used to denoise each corrected video frame.

[0026] The third step involves transforming the aligned construction data in the spatial representation of the aforementioned aligned construction dataset to generate at least one transformed construction dataset. This coordinate transformation can involve converting the aligned construction data in the spatial representation to the same coordinate system. For example, the aligned construction data in the spatial representation can be converted to a pre-defined construction area coordinate system. This construction area coordinate system can be generated using a point within the construction area as its origin.

[0027] The fourth step is to combine the remaining alignment construction data, at least one denoised alignment construction data, and at least one transformed construction data in the above alignment construction dataset into a preprocessed construction dataset.

[0028] Optionally, after step 102, the following steps are also included: The first step is to classify each preprocessed construction data in the above preprocessed construction dataset to generate a classified construction data set.

[0029] In some embodiments, the executing entity may classify the various preprocessed construction data in the preprocessed construction dataset to generate a categorized construction data set. This classification may be based on the data type of the preprocessed construction dataset. For example, preprocessed construction data with data types corresponding to video data, image data, and BIM model data may be classified.

[0030] The second step is to perform feature extraction processing on each category of construction data in the above-mentioned category construction data set to generate a category construction feature set.

[0031] In some embodiments, the execution entity may perform feature extraction processing on each category of construction data in each category of the aforementioned categorized construction data set to generate a categorized construction feature set. Here, different feature extraction algorithms can be used for different categorized construction data sets. For example, in response to image data corresponding to a categorized construction data set, feature extraction can be performed using a target detection algorithm. The aforementioned target extraction algorithm can be a target extraction algorithm based on the YOLO model.

[0032] The third step is to perform data fusion processing on the various construction features included in the above-mentioned classification construction feature set to generate fused data, which serves as the preprocessed construction dataset.

[0033] In some embodiments, the execution entity may perform data fusion processing on the various classification construction features included in the aforementioned classification construction feature set to generate fused data as a preprocessed construction dataset. Specifically, the data fusion processing may combine the various classification construction features into a single dataset as the preprocessed construction dataset.

[0034] Step 103: Based on the dynamic rule base, recalculate the fence on the preprocessed construction dataset to generate a recalculated fence coordinate set.

[0035] In some embodiments, the execution entity can recalculate the fence of the preprocessed construction dataset based on a dynamic rule base to generate a recalculated fence coordinate set. The dynamic rule base can be a pre-defined database composed of "IF-THEN" rules, used to map the preprocessed construction data to recalculated fence coordinates. For example, a dynamic rule in the rule base could be "IF wind speed level > 5 THEN remove all areas near high-altitude work points." This would remove the pre-defined high-altitude work area from the area enclosed by the initial geofence, use the coordinates of at least one edge point connecting the high-altitude work area to the area enclosed by the initial geofence as the first recalculated fence coordinate set, delete the coordinates of at least one edge point not connected to the area enclosed by the initial geofence from the fence coordinate set of the initial geofence to generate a second recalculated fence coordinate set, and combine the first and second recalculated fence coordinate sets into a recalculated fence coordinate set. The dynamic rule base is determined based on the terrain corresponding to the point cloud data included in the multimodal construction dataset. In practice, firstly, a digital terrain model can be established by extracting ground points from preprocessed construction data representing mixed point clouds using the window seed growth method. Secondly, the slope and curvature of the digital terrain model are extracted, and the corresponding terrain is determined using a pre-defined mapping table between slope and curvature and terrain. These terrains can include, but are not limited to, river valleys, mountains, canyons, and flat sand dunes. Different rule bases can be set for different terrains; after determining the terrain, the rule base corresponding to the terrain is defined as a dynamic rule base.

[0036] In addressing the technical problems mentioned in the background section, and considering the application scenario of multiple excavating devices working collaboratively within a mine, involving both open-pit and underground mines, the following technical challenges arise: The steps and slopes of open-pit mines, and the roadways, stopes, and chambers of underground mines together form a complex three-dimensional network. Two-dimensional fencing cannot display spatial constraints and fails to consider the periodic movement of the equipment, leading to collision risks among multiple excavating devices during construction. This collision could cause mine collapses, resulting in low safety in the construction area. To meet the following requirements for this application scenario: considering the periodic movement of construction equipment, avoiding discrepancies between rules in the rule base and the actual construction scenario, and accurately determining the areas occupied by the construction equipment, we have decided to adopt the following solution: In practice, the following steps can be used to recalculate the fence on the preprocessed construction dataset based on a dynamic rule base to generate a recalculated fence coordinate set: The first step is to initialize the initial 3D construction model to generate a 3D construction model. This initial 3D construction model can be a pre-set Building Information Modeling (BIM) model. The initialization process involves importing the geometric structure, semantic information (such as component type and materials), and spatial relationships of the area enclosed by the initial geofence into the initial 3D construction model to generate the 3D construction model.

[0037] The second step involves importing the aforementioned multimodal construction dataset and equipment positioning information into the aforementioned 3D construction model to generate a real-time construction model. Here, the collected multimodal construction dataset and equipment positioning information can be continuously accessed, and the coordinates included in the equipment positioning information can be mapped onto the aforementioned 3D construction model. Through video recognition, the vacant areas on the ground and the areas where construction materials are placed are marked on the aforementioned 3D construction model using marker points or heat maps to generate a real-time construction model.

[0038] Third, for each construction device in the initial geofence described above, perform the following processing steps: The first processing sub-step involves acquiring the historical equipment operation information set of the aforementioned construction equipment. This historical equipment operation information set can be the operation information of the construction equipment within a historical time period. This historical equipment operation information may include, but is not limited to, at least one of the following: equipment ID, acquisition time, three-dimensional spatial coordinates, equipment attitude information (e.g., rotation angle, boom elevation angle), working status (e.g., no load, excavation, rotation, pouring), and operating speed. As an example, assuming the aforementioned construction equipment is a tower crane, the historical equipment operation information could be: "Equipment ID: A08, Acquisition Time: XX Day XX Hour, Three-dimensional Coordinates: [x, y, z], Equipment Attitude Information: Boom Elevation Angle 50°, Working Status: Rotation".

[0039] The second processing sub-step involves performing operation prediction processing on the construction equipment based on the historical equipment operation information set, to generate a predicted operation information sequence. This operation prediction processing can involve predicting the equipment operation information of the construction equipment within a future time period using the historical equipment operation information set, thereby generating the predicted operation information sequence. For example, given that the construction equipment is a tower crane, to predict the tower crane's operation information at 10:00 AM, the historical equipment operation information of the tower crane at 10:00 AM each day for the past ten days can be selected as the target operation information set. Furthermore, the target operation information with the highest frequency of occurrence of the working state and whose collection time is closest to the current time is selected from the target operation information set as the predicted operation information for the tower crane at 10:00 AM.

[0040] The third processing sub-step involves determining the equipment construction area information sequence based on the aforementioned predicted operational information sequence. In practice, different equipment construction area information sequences can be generated for different construction equipment using their corresponding predicted operational information sequences. The equipment construction area information in the aforementioned equipment construction area information sequence can be the area occupied by the construction equipment during construction. For example, the aforementioned equipment construction area information can be the area where the tower crane boom periodically moves.

[0041] As an example, in response to the construction equipment being a tower crane, for the predicted operation information in the above predicted operation information sequence, the slewing angle and boom length can be selected from the predicted operation information, the trajectory point at the end of the tower crane boom can be calculated, and a circular area representing the swing safety radius of the hoisting construction materials can be generated with the trajectory point as the center. The coordinates of each edge point of the above circular area can be used as the equipment construction area information.

[0042] In practice, the equipment construction area information sequence can also be determined based on the above predicted operation information sequence through the following sub-steps: The first sub-step involves discretizing the three-dimensional space represented by the real-time construction model to generate a voxel model. Here, a uniform mesh generation algorithm can be used to discretize the three-dimensional space represented by the real-time construction model to generate the voxel model.

[0043] The second sub-step involves expanding the voxel model along the time axis to generate a four-dimensional occupancy mesh. In practice, for each voxel in the voxel model, a predetermined number of voxels can be copied along the time axis to form a mesh voxel group; the generated mesh voxels are then combined into a four-dimensional occupancy mesh.

[0044] The third sub-step involves mapping each predicted operation information in the predicted operation information sequence to the aforementioned four-dimensional occupancy grid to generate the four-dimensional occupancy grid. Each voxel in the four-dimensional occupancy grid corresponds to a device occupancy probability. Here, for each predicted operation information, at least one voxel can be selected from the four-dimensional occupancy grid that intersects with the position of the construction equipment represented by the predicted operation information at the current time, and the device occupancy probability is assigned to the corresponding voxel. Since a single voxel may correspond to multiple device occupancy probabilities, the maximum value can be taken to determine the device occupancy probability corresponding to the voxel. The aforementioned device occupancy probability can be the ratio of the number of times the current voxel is occupied by construction equipment in the historical device operation information set to the number of historical device operation information entries included in the historical device operation information set.

[0045] The fourth sub-step involves generating a probability field corresponding to the construction equipment based on the aforementioned four-dimensional occupancy grid, serving as the equipment construction area information. In practice, trilinear interpolation and time-indexed linear interpolation can be used to calculate the probability field corresponding to the construction equipment on the aforementioned four-dimensional occupancy grid, thus serving as the equipment construction area information.

[0046] The fifth sub-step involves sorting and processing the generated equipment construction area information to obtain a sequence of equipment construction area information. In practice, firstly, the probability field of each construction device can be sliced ​​along the time dimension to generate a three-dimensional probability field corresponding to each time point. This field is used to characterize the equipment occupancy probability of each voxel at future time points, and the generated three-dimensional probability fields are then used as the sequence of equipment construction area information.

[0047] The fourth step involves determining the total construction area information sequence within a preset time period in the aforementioned real-time construction model based on the generated construction area sequences for each piece of equipment. This total construction area information sequence can be the total area occupied by all construction equipment at a specific point in time. In practice, firstly, at least one equipment construction area with the same acquisition time can be aggregated and sorted according to its corresponding acquisition time order to generate an aggregated construction area group sequence. Secondly, for each aggregated construction area group in the aforementioned aggregated construction area group sequence, each aggregated construction area in the aggregated construction area group can be mapped to the aforementioned real-time construction model, and the coordinates of each edge point of each aggregated construction area displayed in the aforementioned real-time construction model are determined as the total construction area information.

[0048] Fifth, based on the aforementioned construction area information sequence, update the dynamic rule base to generate an updated rule base. In practice, the area represented by the construction area information in the aforementioned construction area information sequence can be identified as a prohibited area, and update rules for the prohibited area can be generated. These updated rules are then added to the dynamic rule base, and any pre-defined rules in the dynamic rule base that contradict the updated rules are deleted, resulting in the updated rule base. For example, the updated rule could be "IF 10:00-12:00 THEN prohibit the target user from entering the prohibited area". Step 6: Based on the updated rule base described above, recalculate the fence on the preprocessed construction dataset to generate a recalculated fence coordinate set. Here, the recalculated fence coordinate set can be generated according to the fence recalculation steps of the embodiment of step 103 described above.

[0049] The steps one through six described above, as an inventive point of this disclosure, combined with step "106" below, solve the technical problem: "The steps and slopes of open-pit mines, and the roadways, mining areas, and chambers of underground mines together constitute a complex three-dimensional network. Using two-dimensional fences cannot display spatial constraint information and does not consider the periodic activity of equipment, leading to a risk of collision between multiple excavating devices during construction, which may cause mine collapse." The reasons for the low safety of the construction area are as follows: The steps and slopes of open-pit mines, and the roadways, mining areas, and chambers of underground mines together constitute a complex three-dimensional network. Using two-dimensional fences cannot display spatial constraint information and does not consider the periodic activity of equipment, leading to a risk of collision between multiple excavating devices during construction, which may cause mine collapse. Solving these factors can improve the safety of the construction area. To achieve this effect, this disclosure first initializes the initial three-dimensional construction model to generate a three-dimensional construction model. Thus, the initial three-dimensional model of the construction area can be determined. Secondly, the aforementioned multimodal construction dataset and equipment location information are imported into the aforementioned 3D construction model to generate a real-time construction model. This allows for the generation of a real-time 3D twin model of the current construction area based on the collected data. Then, for each construction device in the initial geofence, the following processing steps are performed: First, the historical equipment operation information set of the construction device is obtained. This allows the determination of the construction device's operation data at the current historical point in time. Second, based on the historical equipment operation information set, the operation prediction processing of the construction device is performed to generate a predicted operation information sequence. This allows for the prediction of the construction device's operation data for a future period. Third, based on the predicted operation information sequence, the equipment construction area information sequence is determined. This allows the determination of the area occupied by the construction device during construction. Afterwards, based on the generated construction area sequences of each device, the total construction area information sequence within a preset time period is determined in the aforementioned real-time construction model; based on the total construction area information sequence, the aforementioned dynamic rule base is updated to generate an updated rule base. This allows for the updating of the dynamic rule base to prevent transport vehicles and target users from being located within the construction area of ​​the construction device. Next, based on the updated rule base, the preprocessed construction dataset is recalculated to generate a recalculated fence coordinate set. This allows for the regeneration of fence coordinates. Combined with step 106 below, based on the verification results, the associated transport vehicles are controlled to deliver construction materials to the target users. This avoids collisions between transport vehicles and construction equipment during material transport, improving the safety of the construction area.

[0050] Step 104: Based on the recalculated fence coordinate set, update the initial geofence to generate an updated geofence.

[0051] In some embodiments, the execution entity may update the initial geofence based on the recalculated geofence coordinate set to generate an updated geofence.

[0052] In practice, the aforementioned executing entity can perform geometric Boolean operations on the initial geofence and the recalculated geofence coordinate set to update the initial geofence and obtain the updated geofence.

[0053] Step 105: Based on the device location information and updated geofence, perform verification processing on the target user to generate verification results.

[0054] In some embodiments, the aforementioned execution entity may perform verification processing on the aforementioned target user based on the aforementioned device location information and the aforementioned updated geofence, so as to generate a verification result.

[0055] In practice, the aforementioned implementing entity can perform verification processing on the aforementioned target user based on the aforementioned device location information and the aforementioned updated geofence through the following steps to generate verification results: The first step is to determine whether the location represented by the above device location information is within the updated geofence.

[0056] The second step involves controlling the associated authentication device to collect identity data from the target user, based on the location information represented by the aforementioned device, within the updated geofence. This collects the user identity data, which includes user identification, facial image, and voiceprint waveform.

[0057] The third step is to perform a hash operation on the aforementioned user identity dataset to generate a data hash value. This hash operation can be performed using a hash algorithm to calculate the data hash value of the user identity dataset. For example, the hash operation can be performed using SHA-256 to calculate the data hash value.

[0058] The fourth step is to sign the hash value of the data based on the preset private key to generate a digital signature. The preset private key can be a pre-stored, randomly generated private key.

[0059] The fifth step involves combining the aforementioned data hash value, the corresponding timestamp, and the aforementioned digital signature to generate data authentication information.

[0060] The sixth step involves performing blockchain verification on the aforementioned data authentication information to generate a verification result. In practice, it can be determined whether the identity hash value pre-stored in the blockchain is the same as the data hash value included in the aforementioned data authentication information. If they are the same, a verification result indicating successful verification is generated; if they are different, a verification result indicating verification failure is generated.

[0061] In addressing the technical problems mentioned in the background section, the following technical challenges arise in the application scenario: tunnel engineering or mining engineering. During tunnel excavation and mining, high-risk construction materials (e.g., detonators) are often used. Failure to verify and record the issuance, transportation, and handover of these materials can lead to their loss and irretrievability. Furthermore, signal strength in tunnel and mining engineering is typically poor, hindering the simultaneous transmission of large amounts of data. To meet the following requirements for this application scenario: identifying personnel receiving high-risk construction materials, ensuring the integrity of high-risk material transfer information, preventing the disclosure of personnel with authorization to receive high-risk materials, and minimizing the impact of weak signals on data transmission, we have decided to adopt the following solution: In some optional implementations of certain embodiments, the execution entity may perform verification processing on the target user based on the device location information and the updated geofence through the following steps to generate a verification result: The first step is to add encrypted attribute information corresponding to the target user to the aforementioned smart wearable device. In practice, the non-sensitive user information of the target user can be encrypted using an attribute-based encryption (ABE) algorithm to generate encrypted attribute information, which is then added to the storage device of the smart wearable device. The non-sensitive user information can be user information that does not contain direct identity information. Here, the non-sensitive user information can include, but is not limited to, at least one of the following: job type, work group, and responsible project. As an example, the encrypted attribute information could be {project code, high-risk material operation permission, blaster, work group Y, validity period}.

[0062] The second step involves generating location verification information for the target user based on the device location information and the updated geofence. This location verification information indicates whether the location represented by the device location information is within the range of the updated geofence. In practice, firstly, this can be determined by mapping the location represented by the device location information and the updated geofence to a unified coordinate system. Secondly, in response to the assumption that the location represented by the device location information is within the range of the updated geofence, location information indicating that the location represented by the device location information is within the range of the updated geofence is generated. Thirdly, a proof of the location information is generated using a zero-knowledge proof algorithm, serving as the location verification information.

[0063] The third step is to generate identity verification information based on the aforementioned encrypted attribute information. In practice, this can be achieved by running an attribute-based encryption algorithm on the private key stored in the smart wearable device. This identity verification information can then demonstrate that the target user is authorized to use the construction materials.

[0064] The fourth step is to concatenate the location verification information and the identity verification information to generate concatenated information.

[0065] The fifth step is to send the concatenated information to the target terminal for verification, thereby generating a verification result. In practice, a zero-knowledge attribute proof algorithm can be used to verify the concatenated information and generate a verification result.

[0066] Step six: In response to the verification result indicating successful verification, a hash value corresponding to the verification result is generated as the verification hash value. In practice, a hash algorithm can be used to generate the hash value corresponding to the verification result. The hash algorithm can be SHA-256.

[0067] Step 7: In response to receiving at least one verification hash value, generate a verification hash value binary tree based on the aforementioned verification hash value. In practice, a binary tree construction algorithm can be used to generate the verification hash value binary tree by using the aforementioned verification hash value as the leaf nodes of the binary tree.

[0068] Step 8: Determine the Merkle root of the aforementioned binary tree for verifying hash values.

[0069] The ninth step involves performing multidimensional aggregation on the generated verification results to generate verification commitments and verification proofs. This multidimensional aggregation can be achieved by applying KZG commitments to the generated verification results.

[0070] Step 10: Upload the aforementioned verification commitment and verification proof to the data availability layer. This data availability layer can be a DA (Data Availability Layer).

[0071] Step 11: Verify the above verification proof at the data availability layer to generate a verification result. Here, the above verification proof can be verified at the data availability layer using open proofs based on KZG commitments to generate a verification result.

[0072] Step 12: In response to the above proof verification result indicating that the verification is passed, upload the above Merkel root, the above verification commitment, and the above verification proof to the blockchain.

[0073] Steps one through eight above, as an inventive point of this disclosure, combined with step 106 below, solve the technical problem: "In tunnel engineering or mining engineering scenarios, the following technical problem often arises: High-risk construction materials (e.g., detonators) are frequently used during tunnel excavation and mining operations. Failure to verify and record the requisition, transportation, and handover of these materials may lead to their loss and irretrievability." The reasons for the loss and irretrievability of high-risk construction materials are as follows: High-risk construction materials (e.g., detonators) are frequently used during tunnel excavation and mining operations. Failure to verify and record the requisition, transportation, and handover of these materials may lead to their loss and irretrievability. Solving these factors can prevent the loss and irretrievability of high-risk construction materials. To achieve this effect, this disclosure, firstly, adds encrypted attribute information corresponding to the target user to the aforementioned smart wearable device. Therefore, only non-sensitive user information of the target user can be added for identity verification, avoiding information leakage of the target user. Second, based on the aforementioned device location information and the updated geofence, location verification information corresponding to the target user is generated. This confirms that the target user is within the area where high-risk construction materials can be received. Third, based on the aforementioned encrypted attribute information, identity verification information is generated; the location verification information and the identity verification information are concatenated to generate concatenated information. This generates information for verifying the target user's identity and location. Fourth, the concatenated information is sent to the target terminal for verification to generate a verification result. This determines whether the target user is qualified to receive high-risk construction materials. Fifth, in response to the verification result indicating successful verification, a hash value corresponding to the verification result is generated as the verification hash value; in response to receiving at least one verification hash value, a verification hash value binary tree is generated based on the at least one verification hash value. This generates a binary tree structure storing multiple verification results. Sixth, the Merkle root of the verification hash value binary tree is determined. Therefore, when receiving verification results from multiple target users, the Merkel root can transmit verification data to the blockchain only once, avoiding situations where multiple verification data cannot be transmitted due to weak signals. Furthermore, information on the requisition of high-risk construction materials can be stored in the blockchain to prevent alteration. Seventh, the generated verification results undergo multi-dimensional aggregation processing to generate verification commitments and verification proofs; these commitments and proofs are uploaded to the data availability layer; the proofs are verified in the data availability layer to generate a proof verification result; in response to the proof verification result indicating successful verification, the Merkel root, verification commitment, and proof are uploaded to the blockchain. Thus, it can be proven that all verifications corresponding to the Merkel root are valid.In conjunction with step 106 below, based on the above verification results, the associated transport vehicles are controlled to deliver the construction materials corresponding to the target users. This allows for the delivery of high-risk construction materials, preventing their loss and loss due to lack of traceability.

[0074] Step 106: Based on the verification results, control the associated transport vehicles to deliver the construction materials corresponding to the target user.

[0075] In some embodiments, the executing entity may, based on the verification results, control associated transport vehicles to deliver construction materials to the target user. The associated transport vehicles may be vehicles used for transporting construction materials that are connected to the executing entity via wired or wireless connections. For example, the transport vehicle may be an unmanned truck.

[0076] The above embodiments of this disclosure have the following beneficial effects: the construction material delivery method based on dynamic geofencing according to some embodiments of this disclosure improves construction progress and avoids the loss of construction materials. Specifically, the reasons for slow or even halted construction progress and loss of construction materials are: different workers often require significantly different construction materials; transporting the wrong construction materials will result in the selected materials being unusable in construction, affecting the construction progress; and if the worker's identity is not verified, construction materials may be allocated to non-worker locations, leading to the loss of construction materials. Based on this, the construction material delivery method based on dynamic geofencing according to some embodiments of this disclosure firstly, in response to receiving a construction material transportation request sent by the target terminal, obtains the initial geofencing, the multimodal construction dataset, and the device location information corresponding to the target user's smart wearable device. This allows the location of the construction personnel to be determined. Secondly, the multimodal construction dataset is preprocessed to generate a preprocessed construction dataset. This allows for the preprocessing of construction data. Then, based on a dynamic rule base, the preprocessed construction dataset is recalculated to generate a recalculated geofencing coordinate set; based on the recalculated geofencing coordinate set, the initial geofencing is updated to generate an updated geofencing. This allows for the generation of dynamic construction zones corresponding to construction workers. Then, based on the aforementioned device location information and the updated geofence, the target users are verified to generate verification results. This verifies the user's identity information. Finally, based on the verification results, associated transport vehicles are controlled to deliver construction materials to the target users. This allows for the transportation of construction materials to users after identity verification. By setting up dynamic geofences, the delivery of construction materials to the wrong locations is avoided. Furthermore, because user identity information is authenticated, the delivery of construction materials to the wrong users is prevented, thus preventing the loss of construction materials.

[0077] Further reference Figure 2 As an implementation of the methods shown in the above figures, this disclosure provides some embodiments of a construction material delivery device based on dynamic geofencing. These device embodiments are similar to... Figure 1 Corresponding to the method embodiments shown, this construction material delivery device based on dynamic geofencing can be specifically applied to various electronic devices.

[0078] like Figure 2 As shown, a construction material delivery device 200 based on dynamic geofencing in some embodiments includes: an acquisition unit 201, a data preprocessing unit 202, a fence recalculation unit 203, an update unit 204, a verification unit 205, and a control unit 206. The acquisition unit 201 is configured to, in response to receiving a construction material transportation request sent by the target terminal, acquire an initial geofence, a multimodal construction dataset, and device location information corresponding to the target user's smart wearable device; the data preprocessing unit 202 is configured to preprocess the multimodal construction dataset to generate a preprocessed construction dataset; the fence recalculation unit 203 is configured to recalculate the preprocessed construction dataset based on a dynamic rule base to generate a recalculated fence coordinate set; the update unit 204 is configured to update the initial geofence based on the recalculated fence coordinate set to generate an updated geofence; the verification unit 205 is configured to verify the target user based on the device location information and the updated geofence to generate a verification result; and the control unit 206 is configured to control the associated transport vehicle to deliver the construction materials corresponding to the target user based on the verification result.

[0079] It is understandable that the units described in the construction material delivery device 200 based on dynamic geofencing are related to the reference. Figure 1 The steps described in the method correspond to each other. Therefore, the operations, features, and beneficial effects described above for the method are also applicable to the construction material delivery device 200 based on dynamic geofencing and the units contained therein, and will not be repeated here.

[0080] The following is for reference. Figure 3 This document illustrates a structural schematic of an electronic device 300 suitable for implementing some embodiments of the present disclosure. The electronic devices in some embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 3The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0081] like Figure 3 As shown, the electronic device 300 may include a processing unit 301 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the electronic device 300. The processing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0082] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 An electronic device 300 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 3 Each box shown can represent a device or multiple devices as needed.

[0083] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 309, or installed from storage device 308, or installed from ROM 302. When the computer program is executed by processing device 301, it performs the functions defined in the methods of some embodiments of this disclosure.

[0084] It should be noted that, in some embodiments of this disclosure, the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0085] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0086] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to: in response to receiving a construction material transportation request sent by a target terminal, acquire an initial geofence, a multimodal construction dataset, and device location information corresponding to the target user's smart wearable device; preprocess the aforementioned multimodal construction dataset to generate a preprocessed construction dataset; recalculate the geofence based on a dynamic rule base to generate a recalculated geofence coordinate set; update the aforementioned initial geofence based on the recalculated geofence coordinate set to generate an updated geofence; verify the aforementioned target user based on the device location information and the updated geofence to generate a verification result; and control the associated transport vehicle to deliver the construction materials corresponding to the aforementioned target user based on the verification result.

[0087] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0088] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0089] The units described in some embodiments of this disclosure can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor may be described as including an acquisition unit, a data preprocessing unit, a fence recalculation unit, an update unit, a verification unit, and a control unit. The names of these units do not necessarily limit the specific unit itself; for example, the acquisition unit may also be described as "a unit that, in response to receiving a construction material transportation request from a target terminal, acquires an initial geofence, a multimodal construction dataset, and device location information corresponding to the target user's smart wearable device."

[0090] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0091] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A construction material delivery method based on dynamic geofencing, applied to a smart wearable device integrated with a positioning chip, characterized in that... The method includes: In response to receiving a construction material transportation request from the target terminal, the system acquires the initial geofence, multimodal construction dataset, and device location information corresponding to the target user's smart wearable device. The multimodal construction dataset is preprocessed to generate a preprocessed construction dataset. Based on a dynamic rule base, the preprocessed construction dataset is recalculated to generate a recalculated fence coordinate set. The dynamic rule base is determined based on the terrain corresponding to the point cloud data included in the multimodal construction dataset. Based on the recalculated fence coordinate set, the initial geofence is updated to generate an updated geofence; Based on the device location information and the updated geofence, the target user is verified to generate a verification result. Based on the verification results, the associated transport vehicles are controlled to deliver the construction materials corresponding to the target user.

2. The method according to claim 1, characterized in that, The step of preprocessing the multimodal construction dataset to generate a preprocessed construction dataset includes: The multimodal construction data in the multimodal construction dataset are time-aligned to generate an aligned construction dataset. At least one alignment construction data representing the video in the alignment construction dataset is subjected to denoising correction processing to generate at least one denoised alignment construction data. The alignment construction data in the central spatial representation of the alignment construction dataset is subjected to coordinate system transformation to generate at least one transformed construction data. The remaining aligned construction data, the at least one denoised aligned construction data, and the at least one transformed construction data in the aligned construction dataset are combined into a preprocessed construction dataset.

3. The method according to claim 2, characterized in that, The method further includes: The preprocessed construction data in the preprocessed construction dataset are classified to generate a classified construction data set. For each category of construction data group in the category construction data group set, feature extraction processing is performed on each category of construction data in the category construction data group to generate a category construction feature group set. The various construction features included in the classification construction feature set are subjected to data fusion processing to generate fused data, which serves as a preprocessed construction dataset.

4. The method according to claim 1, characterized in that, The step of updating the initial geofence based on the recalculated geofence coordinate set to generate an updated geofence includes: Geometric Boolean operations are performed on the initial geofence and the recalculated geofence coordinate set to update the initial geofence and obtain the updated geofence.

5. The method according to claim 1, characterized in that, The step of verifying the target user based on the device location information and the updated geofence to generate a verification result includes: Determine whether the location represented by the device location information is within the updated geofence; In response to the location represented by the device location information being within the updated geofence, the associated authentication device is controlled to collect identity data of the target user to generate a user identity dataset, wherein the user identity data in the user identity dataset includes: user code, timestamp, face image and voiceprint waveform. Perform a hash operation on the user identity dataset to generate a data hash value; Based on a preset private key, the data hash value is signed to generate a digital signature; The data hash value, the corresponding timestamp, and the digital signature are combined to generate data authentication information; The data authentication information is subjected to blockchain verification processing to generate a verification result.

6. A construction material delivery device based on dynamic geofencing, characterized in that, include: The acquisition unit is configured to acquire, in response to receiving a construction material transportation request sent by the target terminal, an initial geofence, a multimodal construction dataset, and device location information corresponding to the target user's smart wearable device; The data preprocessing unit is configured to preprocess the multimodal construction dataset to generate a preprocessed construction dataset. The fence recalculation unit is configured to recalculate the fences on the preprocessed construction dataset based on a dynamic rule base to generate a recalculated fence coordinate set, wherein the dynamic rule base is determined based on the terrain corresponding to the point cloud data included in the multimodal construction dataset. The update unit is configured to update the initial geofence based on the recalculated geofence coordinate set to generate an updated geofence. The verification unit is configured to perform verification processing on the target user based on the device location information and the updated geofence, so as to generate a verification result; The control unit is configured to control the associated transport vehicle to deliver construction materials to the target user based on the verification results.

7. An electronic device, characterized in that, include: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 5.

8. A computer-readable medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 5.

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