Blockchain-based dynamic management method and system for construction cost settlement

Abstract

The application provides a kind of engineering cost settlement dynamic management method and system based on blockchain, related to engineering management technical field, by obtaining the position information, quantity information, specification information of the materials equipment in the stage of engineering cost settlement, and material consumption data;According to the material consumption data, the consumption process of the materials equipment is simulated by using digital twin model, and the simulated consumption list is generated;The results of the above steps are preprocessed locally by the edge computing node driven by blockchain, to obtain the initial data set and encryption, to obtain the encrypted data set;The encrypted data set is verified and matched with the quota, to obtain the quota matching information, and the material inventory data is combined to dynamically update the engineering cost settlement amount, to realize the closed-loop management of materials equipment from the stage of entry verification, construction loss tracking stage to cost settlement stage, and to improve the real-time performance, security and closed-loop control capability of engineering cost settlement.

Description

Technical Field

[0001] This application relates to the field of engineering management technology, and in particular to a blockchain-based method and system for dynamic management of engineering cost settlement. Background Technology

[0002] In the context of the increasing complexity of large-scale infrastructure and construction projects, project cost settlement urgently needs to achieve dynamic management with high precision, real-time performance, and multi-party collaboration. Especially in the stages of material and equipment entry, construction process loss tracking, and final cost accounting, project participants have placed higher demands on the authenticity, timeliness, and tamper-proofness of data. The traditional static quota pricing model is no longer suitable for the reality that material consumption during construction is dynamically affected by multiple factors such as environment, operation, and inventory.

[0003] Currently, existing solutions combine IoT sensors with centralized cloud platforms to collect real-time data on the location, quantity, and status of materials at construction sites. They also use building information models to extrapolate theoretical consumption and compare it with actual usage to dynamically adjust settlement criteria. However, existing solutions rely on centralized processing, making them susceptible to single points of failure and network latency, and unable to support local low-latency decision-making. Data is easily tampered with or selectively reported, lacks multi-party trust mechanisms, and can lead to disputes. Furthermore, the lack of effective preprocessing and encryption of multi-source data at the edge poses risks of information leakage and forgery, making it difficult to achieve trusted closed-loop management throughout the entire process. Summary of the Invention

[0004] The purpose of this application is to provide a blockchain-based method and system for dynamic management of engineering cost settlement, in order to solve the problems of data tampering, response delay and lack of trusted closed-loop management caused by the centralized architecture in the prior art.

[0005] To address the aforementioned technical problems, in a first aspect, this application provides a blockchain-based method for dynamic management of engineering cost settlement, comprising:

[0006] Obtain the location, quantity, and specifications of materials and equipment entering the site during the project cost settlement stage, as well as material consumption data. The material consumption data includes material status data, construction environment data, construction operation related data, material inventory data, and material quality inspection data.

[0007] Based on the material consumption data, a pre-built digital twin model is used to simulate the consumption process of the incoming materials and equipment during the corresponding operation process and the expected construction time, and a simulated consumption list is generated.

[0008] The location, quantity, and specifications of the incoming material equipment, the material consumption data, and the simulated consumption list are preprocessed locally by blockchain-driven edge computing nodes to obtain an initial dataset.

[0009] The initial dataset is encrypted to obtain an encrypted dataset;

[0010] The encrypted dataset is verified and matched with quotas to obtain quota matching information. Combined with material inventory data, the project cost settlement amount is dynamically updated to achieve closed-loop management of materials and equipment from the on-site inspection stage, the construction loss tracking stage to the cost settlement stage.

[0011] Optionally, based on the material consumption data, a pre-built digital twin model is used to simulate the consumption process of the incoming materials and equipment during the corresponding operation procedures and expected construction time, generating a simulated consumption list, including:

[0012] Based on the construction process nodes, standard consumption parameters corresponding to each construction process, batch information of incoming materials and equipment, and process connection records in the material consumption data, determine the sequence of construction process nodes, the estimated construction time, the material usage scenario, and the connection interval time between adjacent construction process nodes.

[0013] The specifications and batch information of the incoming materials and equipment are associated and matched with the adaptation requirements of each material's usage scenario to form a scenario matching table;

[0014] Based on the construction environment data corresponding to each construction process node, an environmental parameter set is constructed using a pre-built digital twin model;

[0015] Based on the material quality testing data of each batch of equipment in the scenario matching table, calculate the consumption rate correction value of different batches of equipment under the corresponding ambient temperature and humidity, and combine the material status data and environmental parameter set to generate a material consumption table of incoming material equipment under the corresponding ambient temperature and humidity.

[0016] Based on the operational qualification level of the construction personnel and the sequence of construction process nodes in the construction operation association data, the standard consumption parameters corresponding to each construction process are adjusted to obtain the target consumption parameters.

[0017] Based on the actual consumption benchmarks of each construction process, the material consumption table, the scenario matching table, and the material inventory data, the digital twin model simulates the consumption process of incoming materials and equipment within the corresponding operation process and the expected construction time. Combined with the scenario matching table, a simulated consumption list is generated.

[0018] Optionally, based on the construction environment data corresponding to each construction process node, an environmental parameter set is constructed using a pre-built digital twin model, including:

[0019] Based on the construction environment data corresponding to each construction process node, the sequence of construction process nodes, and the estimated construction time, environmental parameter groups corresponding to different time intervals for each construction process node are generated.

[0020] The coordinates of the construction area corresponding to each construction process node are extracted from the location information of the incoming materials and equipment. The environmental parameter group corresponding to each construction process node is associated with the coordinates of the construction area to determine the spatial range corresponding to each environmental parameter group.

[0021] Based on the spatial range corresponding to each environmental parameter group and the sequence of construction process nodes, a spatial model corresponding to the coordinates of each construction area is loaded into a pre-built digital twin model, and the environmental parameter group corresponding to the coordinates of each construction area is bound to the spatial model to form an environmental simulation unit corresponding to each construction process node.

[0022] The coordinates of the adapted construction area verified by the digital twin model, the adapted time interval corresponding to each construction process node, and the construction environment parameters within each adapted time interval are extracted from each environmental simulation unit to form an environmental parameter set.

[0023] Optionally, based on the actual consumption benchmark of each construction process, the material consumption table, the scenario matching table, and material inventory data, the digital twin model simulates the consumption process of incoming materials and equipment within the corresponding operation flow and expected construction time. Combined with the scenario matching table, a simulated consumption list is generated, including:

[0024] Based on the target consumption parameters of each construction process node and the consumption rate correction value of the corresponding batch in the material consumption table, calculate the unit material consumption quantity per unit time.

[0025] The construction process is divided into multiple time segments based on the estimated construction time of each construction process node. According to the time sequence of the time segments and the corresponding operation process, the consumption process of incoming materials and equipment is simulated through the digital twin model. Combined with the unit material consumption quantity, the material consumption quantity at the end of each time segment is calculated to obtain simulated consumption data.

[0026] When simulating the connection interval between adjacent construction process nodes, the natural loss amount and remaining inventory amount of each construction process node are calculated based on the natural loss rate of the corresponding batch of equipment in the corresponding environment in the material consumption table.

[0027] During the simulation, the remaining inventory is compared with the inventory threshold of the corresponding construction process node in the scenario matching table. When there is a target inventory node with a remaining inventory less than the inventory threshold, a material adjustment record for the target inventory node is generated.

[0028] The simulated consumption data, natural loss, material adjustment records, and scenario matching table for each construction process node are integrated to generate a simulated consumption list.

[0029] Secondly, this application provides a blockchain-based method and system for dynamic management of engineering cost settlement, including:

[0030] The acquisition module is used to acquire the location, quantity, and specifications of materials and equipment entering the site during the project cost settlement stage, as well as material consumption data. The material consumption data includes material status data, construction environment data, construction operation related data, material inventory data, and material quality inspection data.

[0031] The simulation module is used to simulate the consumption process of the incoming materials and equipment during the corresponding operation process and the expected construction time based on the material consumption data using a pre-built digital twin model, and generate a simulated consumption list.

[0032] The processing module is used to perform local preprocessing on the location information, quantity information, specification information, material consumption data, and simulated consumption list of the incoming material equipment through a blockchain-driven edge computing node to obtain an initial dataset;

[0033] An encryption module is used to encrypt the initial dataset to obtain an encrypted dataset;

[0034] The update module is used to verify and match the encrypted dataset to obtain quota matching information. Combined with material inventory data, the project cost settlement amount is dynamically updated to achieve closed-loop management of materials and equipment from the on-site inspection stage, the construction loss tracking stage to the cost settlement stage.

[0035] Thirdly, this application provides an electronic device, comprising:

[0036] Memory, used to store computer programs;

[0037] A processor is configured to execute the computer program to implement the steps of a blockchain-based dynamic management method for engineering cost settlement as described in the first aspect above.

[0038] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the blockchain-based dynamic management method for engineering cost settlement as described in the first aspect above.

[0039] This application presents a blockchain-based dynamic management method for engineering cost settlement. By comprehensively acquiring multi-dimensional information on incoming materials and equipment, along with dynamic consumption data during construction, at the engineering cost settlement stage, a digital twin model is used to perform high-fidelity simulation of material consumption behavior within specific operational processes and construction cycles, generating a realistic simulated consumption list. Subsequently, relying on blockchain-driven edge computing nodes, local preprocessing and encryption of the raw data and simulation results are performed close to the data source, avoiding delays and security risks associated with remote transmission. Finally, by performing trusted verification and quota matching on the encrypted dataset, and dynamically adjusting the settlement amount based on real-time inventory changes, a closed-loop management system is constructed, encompassing material arrival verification, construction loss tracking, and cost settlement. This improves the response speed, data reliability, and multi-party collaboration efficiency of the settlement process.

[0040] Furthermore, by integrating heterogeneous elements from multiple sources, such as construction sequence, standard consumption parameters, batch information, process connections, environmental conditions, personnel qualifications, and quality inspection results, the system accurately characterizes the material compatibility, environmental adaptability, and actual consumption characteristics under different construction scenarios. Based on this, it dynamically generates a simulated consumption list that is highly consistent with the actual site conditions. This enhances the sensitivity and ability of the simulation results to complex construction variables, making quota matching more closely resemble real working conditions. Consequently, it improves the objectivity and interpretability of the settlement basis, strongly supporting the consistent acceptance of settlement data by multiple parties under a decentralized architecture, and alleviating trust discrepancies and management lags caused by information asymmetry or coarse-grained models. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 A flowchart illustrating a blockchain-based dynamic management method for engineering cost settlement, provided as an embodiment of this application;

[0043] Figure 2 A schematic diagram illustrating the construction of an environmental parameter set using a pre-built digital twin model, as provided in an embodiment of this application;

[0044] Figure 3 This is a schematic diagram of the structure of a blockchain-based dynamic management system for engineering cost settlement provided in an embodiment of this application. Detailed Implementation

[0045] To address the issues of data processing delays, susceptibility to tampering, and lack of local trusted collaboration mechanisms caused by existing technologies relying on centralized cloud platforms, this application deeply integrates edge computing and blockchain technology. By performing localized preprocessing and encryption of multi-dimensional dynamic data of materials and equipment at the edge, close to the construction site, and introducing a digital twin model to simulate the material consumption process with high fidelity, the settlement basis not only comes from real-time sensing data but also reflects the influence of complex factors such as the construction environment, operational behavior, and inventory changes. Furthermore, relying on the immutability and consensus verification characteristics of blockchain, it achieves trusted data flow and dynamic closed-loop management throughout the entire process from material entry to cost settlement, breaking through the technical bottlenecks of centralized architecture in terms of response speed, data security, and multi-party mutual trust.

[0046] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0047] The core of this application is to provide a blockchain-based dynamic management method for engineering cost settlement, and a flowchart of one specific implementation method is shown below. Figure 1 As shown, the method includes:

[0048] Step 101: Obtain the location, quantity, and specifications of materials and equipment entering the site during the project cost settlement stage, as well as material consumption data. The material consumption data includes material status data, construction environment data, construction operation related data, material inventory data, and material quality inspection data.

[0049] In this step, material condition data refers to the real-time physical condition information of the incoming materials and equipment, including the degree of integrity, usage progress, and degree of wear and tear.

[0050] Construction environment data refers to environmental conditions within the construction area that affect material consumption. This construction environment data includes ambient temperature, humidity, light intensity, wind speed, etc.

[0051] Construction operation related data refers to the supporting information on construction operations related to the use of materials and equipment. This construction operation related data includes the qualification level of construction personnel, the implementation of operating procedures, and the details of process connection.

[0052] Material inventory data refers to real-time changes in material and equipment inventory. This data includes current inventory levels, inbound quantities, outbound quantities, replenishment batches, and in-transit status.

[0053] Material quality inspection data refers to the quality verification information of materials and equipment during their arrival and use. This material quality inspection data includes the results of quality spot checks, the compliance status of performance parameters, etc.

[0054] Step 102: Based on the material consumption data, use a pre-built digital twin model to simulate the consumption process of the incoming materials and equipment during the corresponding operation process and the expected construction time, and generate a simulated consumption list.

[0055] In this step, the simulated consumption list is a detailed document of material consumption generated by extrapolating from multi-source data through a digital twin model.

[0056] Step 103: The location information, quantity information, specification information of the incoming material equipment, the material consumption data, and the simulated consumption list are preprocessed locally by the blockchain-driven edge computing node to obtain the initial dataset.

[0057] In this step, the initial dataset refers to the structured data set formed by the blockchain-driven edge computing node after it performs local preprocessing on the location, quantity, specifications, material consumption data, and simulated consumption list of the incoming materials and equipment.

[0058] Step 104: Encrypt the initial dataset to obtain an encrypted dataset.

[0059] Step 105: Verify and match the encrypted dataset to obtain quota matching information. Combined with material inventory data, dynamically update the project cost settlement amount to achieve closed-loop management of materials and equipment from the on-site verification stage, the construction loss tracking stage to the cost settlement stage.

[0060] In this step, the quota matching information refers to the amount calculation result formed by combining the engineering quota standard and the actual consumption after the encrypted dataset has been verified.

[0061] In this embodiment of the application, the encrypted dataset is first verified and matched with the quota to obtain quota matching information. Based on the quota matching information, and combined with the current inventory status reflected by the material inventory data and the historical settlement amount data, the current project cost settlement amount is dynamically updated through cumulative accounting. A settlement amount update log is generated simultaneously to clarify the update time, related process nodes and adjustment basis.

[0062] Secondly, a data association chain is established with the settlement amount update log as the link. Then, a two-way traceability channel is opened based on this data association chain. It supports both reverse tracing from the updated project cost settlement amount to the quota matching information, simulated consumption data, and material entry information of the corresponding construction process, and forward tracing from the material entry verification information to the construction loss data, quota matching information, and final settlement amount. Data verification is achieved through two-way traceability, and closed-loop management of materials and equipment from the entry verification stage, the construction loss tracking stage to the cost settlement stage is completed.

[0063] This application's embodiments improve prediction accuracy by relying on digital twin models to simulate the consumption process; furthermore, by using local encryption processing at edge nodes, it balances efficiency and security; and by combining quota matching and inventory dynamics, it achieves real-time linkage updates of settlement amounts, thereby improving the transparency, timeliness, and closed-loop control capabilities of engineering cost management.

[0064] This application provides a specific embodiment. Step 102 involves simulating the consumption process of the incoming materials and equipment within the corresponding operation flow and expected construction time using a pre-built digital twin model based on the material consumption data, and generating a simulated consumption list. This specifically includes the following steps:

[0065] Step 201: Based on the construction process nodes, standard consumption parameters corresponding to each construction process, batch information of incoming materials and equipment, and process connection records in the material consumption data, determine the sequence of construction process nodes, the estimated construction time, the material usage scenario, and the connection interval between adjacent construction process nodes.

[0066] In this step, the standard consumption parameter refers to the benchmark of the amount of materials required to complete a unit of work under ideal conditions for each construction process, which is preset by the industry or project. The standard consumption parameter includes the unit material usage, the upper limit of the loss rate, etc.

[0067] In this embodiment, the following steps are first taken: extracting each construction process node, the corresponding standard consumption parameters, batch information of incoming materials and equipment, and process connection records from the material consumption data; analyzing the logical relationships in the process connection records, such as the requirement that rebar tying must follow formwork support, to determine the sequence of each construction process node; combining historical construction data of similar projects, process workload, and personnel configuration, calculating the estimated construction time of each construction process node; clarifying the material usage scenarios corresponding to each construction process node based on the functional positioning and material usage requirements of each construction process; and extracting the connection gap duration by analyzing the connection records of adjacent construction processes to determine the connection interval time between adjacent construction process nodes.

[0068] Step 202: Associate and match the specifications and batch information of the incoming materials and equipment with the adaptation requirements of each material's usage scenario to form a scenario matching table.

[0069] In this step, the scenario matching table refers to a structured table formed by associating the specifications and batch information of the incoming materials and equipment with the adaptation requirements of each material's usage scenario.

[0070] In this embodiment, the adaptation requirements for each material usage scenario are first clarified. For example, high-strength steel bars are required for structural construction scenarios, and anti-corrosion coatings are required for decorative construction scenarios. Then, the specification information and batch information of the incoming materials and equipment are extracted and compared with the adaptation requirements of each material usage scenario to screen out the suitable target material batches. The batch priority of each target material batch is set according to factors such as material quality inspection data and warehousing time, and then integrated with the inventory threshold under each material usage scenario to form a scenario matching table.

[0071] Step 203: Based on the construction environment data corresponding to each construction process node, construct an environmental parameter set using a pre-built digital twin model.

[0072] In this step, the environmental parameter set refers to a structured dataset that integrates all the coordinates of the adapted construction area, the adapted time interval, and the corresponding construction environmental parameters.

[0073] Step 204: Based on the material quality inspection data of each batch of equipment in the scenario matching table, calculate the consumption rate correction value of different batches of equipment under the corresponding ambient temperature and humidity, and combine the material status data and environmental parameter set to generate a material consumption table of incoming material equipment under the corresponding ambient temperature and humidity.

[0074] In this step, the consumption rate correction value refers to the correction factor that adjusts the theoretical consumption rate of each batch of equipment under corresponding environmental temperature and humidity conditions, based on the material quality test data of each batch of equipment. The material consumption table is a structured table that integrates the consumption rate correction values, material condition data, and environmental parameter sets of each batch of equipment under different environmental temperatures and humidity conditions.

[0075] In this embodiment, material quality inspection data for each batch of equipment, such as pass rate and performance parameter compliance, are extracted from the scene matching table. Based on this material quality inspection data, correction rules are set, and the consumption rate correction value for each batch of equipment is determined according to these rules. For example, the consumption rate correction value for batches of equipment with a pass rate of 98% or higher is 1.0, the consumption rate correction value for batches of equipment with a pass rate between 90% and 98% is 1.05, and the consumption rate correction value for batches of equipment with a pass rate below 90% is 1.1. This embodiment does not limit the range of the pass rate value; it can be set according to the actual situation.

[0076] Different combinations of ambient temperature and humidity are extracted from the environmental parameter set. Combined with material status data, the basic consumption rate of each batch of equipment is multiplied by the consumption rate correction value to obtain the actual consumption rate under different ambient temperature and humidity. The basic consumption rate is determined based on standard consumption parameters. The batch information of incoming materials and equipment, ambient temperature and humidity combinations, actual consumption rates, and material status data are integrated to generate a material consumption table.

[0077] Step 205: Based on the operational qualification level of the construction personnel and the sequence of construction process nodes in the construction operation association data, adjust the standard consumption parameters corresponding to each construction process to obtain the target consumption parameters.

[0078] In this step, the target consumption parameter refers to the consumption benchmark that conforms to the actual construction conditions after adjusting the standard consumption parameter.

[0079] In this embodiment, the operational qualification level of the construction personnel corresponding to each construction process node is extracted from the construction operation association data. A qualification adjustment coefficient is set according to the operational qualification level. For example, the qualification adjustment coefficient for Level 1 qualification is 0.95, the qualification adjustment coefficient for Level 2 qualification is 1.0, and the qualification adjustment coefficient for ordinary qualification is 1.05. This embodiment does not limit the size of the qualification adjustment coefficient, and it can be set according to the actual situation. Combined with the sequence of construction process nodes, the connection effect of adjacent processes is analyzed. For example, it is analyzed whether the construction quality of the preceding process meets the standards and whether there is a risk of rework. The process connection adjustment coefficient is set according to the analysis results.

[0080] For example, when the quality of the preceding process meets the standard and there is no risk of rework, the process connection adjustment coefficient is set to 1.0. When the preceding process has fewer than the preset number of quality problems that need to be made up by the subsequent process, the process connection adjustment coefficient is set to 1.02. This application embodiment does not limit the size of the process connection adjustment coefficient, and it can be set according to the actual situation. The standard consumption parameters corresponding to each construction process are adjusted twice to finally obtain the target consumption parameters corresponding to each construction process. The target consumption parameters corresponding to each construction process node are obtained by calculating the product of the standard consumption parameters, the qualification adjustment coefficient and the process connection adjustment coefficient.

[0081] Step 206: Based on the actual consumption benchmark of each construction process, the material consumption table, the scenario matching table, and the material inventory data, the digital twin model is used to simulate the consumption process of incoming materials and equipment within the corresponding operation process and the expected construction time. Combined with the scenario matching table, a simulated consumption list is generated.

[0082] Optionally, such as Figure 2As shown, step 203 involves constructing an environmental parameter set using a pre-built digital twin model based on the construction environment data corresponding to each construction process node. This specifically includes the following steps:

[0083] Step 211: Based on the construction environment data corresponding to each construction process node, the sequence of construction process nodes, and the estimated construction time, generate environmental parameter groups for different time intervals corresponding to each construction process node.

[0084] In this step, the environmental parameter group refers to the integrated set of environmental data divided into time intervals according to each construction process node. This set of environmental data includes key environmental parameters such as temperature, humidity, wind force, and light intensity for the corresponding time interval.

[0085] Step 212: Extract the coordinates of the construction area corresponding to each construction process node from the location information of the incoming materials and equipment, and associate the environmental parameter group corresponding to each construction process node with the coordinates of the construction area to determine the spatial range corresponding to each environmental parameter group.

[0086] In this step, construction area coordinates refer to standardized coordinate data selected from the location information of incoming materials and equipment, which identifies the specific spatial location of each construction process node. The spatial range is the specific spatial area to which the environmental parameter set is applicable, defined by the construction area coordinates.

[0087] Step 213: Based on the spatial range corresponding to each environmental parameter group and the sequence of construction process nodes, load the spatial model corresponding to the coordinates of each construction area into the pre-constructed digital twin model, and bind the environmental parameter group corresponding to the coordinates of each construction area to the spatial model to form an environmental simulation unit corresponding to each construction process node.

[0088] In this step, the spatial model is a pre-constructed 1:1 scale three-dimensional digital model that matches the actual spatial structure of the construction area. The environmental simulation unit refers to a functional unit formed by binding the environmental parameter set corresponding to the coordinates of the construction area to the spatial model. This unit has the ability to dynamically switch environmental parameters according to the construction sequence and simulate actual environmental changes.

[0089] Step 214: Extract the adapted construction area coordinates verified by the digital twin model, the adapted time interval corresponding to each construction process node, and the construction environment parameters within each adapted time interval from each environmental simulation unit to form an environmental parameter set.

[0090] In this step, the adapted construction area coordinates refer to the coordinate data that perfectly matches the actual construction area after verification using a digital twin model. The adapted time interval refers to the time interval precisely corresponding to the environmental parameter set, adjusted according to the actual construction rhythm of the construction process. The construction environmental parameters refer to the specific environmental data within the adapted time interval.

[0091] In this embodiment of the application, step 203 is implemented through steps 211-214. The specific process is as follows: First, step 211 is executed: extract the construction environment data corresponding to each construction process node from the material consumption data. The construction environment data includes real-time monitored temperature and humidity data and historical environmental statistics data for the same period. Combined with the sequence of construction process nodes and the expected construction duration, the time interval is divided according to the principle of synchronizing construction progress with environmental changes. The construction environment data is then classified according to the divided time intervals. Finally, environmental parameter groups corresponding to different time intervals for each construction process node are generated to ensure that each time interval corresponds to a complete and unique set of environmental parameters.

[0092] Next, proceed to step 212: Filter out the material deployment location data specific to each construction process node from the location information of incoming materials and equipment, and convert it into construction area coordinates in a unified format; bind and associate the environmental parameter groups corresponding to different time intervals of each construction process node with the construction area coordinates of that construction process to obtain the associated environmental parameter groups, and correct the association deviation in the associated environmental parameter groups through coordinate range verification, and finally determine the spatial range corresponding to each environmental parameter group.

[0093] Next, step 213 is executed: In the pre-built digital twin model, the corresponding spatial model is loaded according to the construction area coordinates of each construction process node. For example, for the bridge pier rebar binding process, the three-dimensional structural model of the middle of the bridge pier is loaded, and for the formwork support process, the three-dimensional structural model of the outer formwork of the bridge pier is loaded, etc. In the order of time interval, each environmental parameter group is bound to the corresponding spatial model, and finally the environmental simulation unit corresponding to each construction process node is formed. For example, a spatial model is bound to the environmental parameter group with a temperature of 25℃ on the first day and the environmental parameter group with a temperature of 26℃ on the second day.

[0094] Finally, step 214 is executed: digital twin model verification is performed on each environmental simulation unit. The specific process is as follows: verify the consistency of the spatial model with the actual construction area in terms of size, and the adaptability of the environmental parameter set with the construction period. Based on the verification results, the deviation is corrected to obtain the coordinates of the adapted construction area and the adapted time interval. The construction environmental parameters in each adapted time interval are extracted from each environmental simulation unit. Finally, the coordinates of the adapted construction area, the adapted time interval, and the construction environmental parameters are classified and integrated according to the construction process node number to form an environmental parameter set, so as to ensure that each set of data can accurately correspond to a certain time period and space of a certain process.

[0095] Optionally, step 206 involves simulating the consumption process of incoming materials and equipment within the corresponding operation flow and expected construction duration using the digital twin model, based on the actual consumption benchmark of each construction process, the material consumption table, the scenario matching table, and the material inventory data. Combined with the scenario matching table, a simulated consumption list is generated, specifically including the following steps:

[0096] Step 221: Calculate the unit material consumption quantity per unit time based on the target consumption parameters of each construction process node and the consumption rate correction value of the corresponding batch in the material consumption table.

[0097] In this step, the unit material consumption quantity refers to the quantity of materials consumed per unit time corresponding to a certain construction process node in a certain batch.

[0098] Step 222: Divide the construction process into multiple time segments according to the estimated construction time of each construction process node. According to the time sequence of the time segments and the corresponding operation process, simulate the consumption process of incoming materials and equipment through the digital twin model. Combined with the unit material consumption quantity, calculate the material consumption quantity at the end of each time segment to obtain simulated consumption data.

[0099] In this step, a time segment refers to a continuous period of time divided according to the principle of evenly dividing the expected construction time of each construction process node or dividing it according to the construction stage. Simulated consumption data refers to the total consumption data obtained by accumulating the material consumption of each time segment.

[0100] Step 223: When simulating the connection interval time to adjacent construction process nodes, calculate the natural loss amount and remaining inventory amount of each construction process node according to the natural loss rate of the corresponding batch of equipment in the corresponding environment in the material consumption table.

[0101] In this step, the natural wastage rate refers to the amount of material naturally lost per unit time under specific conditions, as recorded in the material consumption table for the corresponding batch. Natural wastage amount refers to the total amount of material lost due to environmental factors during the interval between connections. Inventory remaining quantity refers to the amount of material remaining after the completion of each construction process and natural wastage.

[0102] Step 224: During the simulation, the remaining inventory is compared with the inventory threshold of the corresponding construction process node in the scenario matching table. When there is a target inventory node with a remaining inventory less than the inventory threshold, a material adjustment record for the target inventory node is generated.

[0103] In this step, the inventory threshold is the minimum material inventory quantity preset in the scenario matching table to ensure continuous construction. The target inventory node refers to the construction process node where the remaining inventory is less than the inventory threshold. The material adjustment record is a structured document generated for the target inventory node, including adjustment measures, adjustment time, and adjustment basis.

[0104] Step 225: Integrate the simulated consumption data, natural loss amount, material adjustment records, and scenario matching table for each construction process node to generate a simulated consumption list.

[0105] In this embodiment of the application, step 206 is implemented through steps 221-225. The specific process is as follows: First, step 221 is executed: based on the target consumption parameters of each construction process node and the consumption rate correction value of the corresponding batch in the material consumption table, the target consumption parameters and consumption rate correction values ​​per unit time are multiplied to obtain the unit material consumption quantity of the corresponding batch of materials for each construction process node.

[0106] Next, proceed to step 222: Based on the estimated construction time of each construction process node, divide the time into multiple time segments according to the principle of synchronizing construction progress with operation process. For example, the concrete pouring process, which is expected to take 5 days, is divided into 3 time segments according to the three stages of pouring preparation, layered pouring, and surface finishing. The duration of each time segment is 1 day, 3 days, and 1 day, respectively. According to the order of the time segments, load the corresponding process environment simulation unit in the digital twin model to simulate the construction operation process of the time segment. Combined with the unit material consumption quantity, calculate the material consumption quantity at the end of each time segment.

[0107] For example, if a certain time segment lasts for 3 days, multiplying it by the unit material consumption quantity of 80 kg / m² gives the material consumption quantity at the end of that time segment as 240 kg / m². By summing up the material consumption quantities of all time segments at the same construction process node, the simulated consumption data for each construction process can be obtained.

[0108] Next, execute step 223: When the simulated progress of the digital twin model reaches the connection interval time between adjacent construction procedures, extract the natural loss rate of the corresponding batch of materials in the current environment from the material consumption table, and calculate the natural loss amount of each construction procedure node. The natural loss amount of each construction procedure node is obtained by multiplying the natural loss rate by the connection interval time. Extract the initial input material quantity of the construction procedure node from the material inventory data, and calculate the remaining inventory quantity of each construction procedure node. The remaining inventory quantity of each construction procedure node is obtained by subtracting the simulated consumption data from the initial input quantity, and then subtracting the natural loss amount.

[0109] Then, proceed to step 224: During the entire process of simulating material consumption in the digital twin model, firstly, retrieve the remaining inventory of each construction process node in real time, and at the same time, extract the corresponding inventory threshold from the scene matching table; compare the remaining inventory with the inventory threshold in real time to determine whether there is a situation where the remaining inventory is less than the threshold; if there is a construction process node with a remaining inventory less than the inventory threshold, then mark the construction process node as the target inventory node.

[0110] Next, query the batch inventory of the secondary priority materials for that construction process in the scenario matching table: if there is available inventory for the secondary priority batch, record the adjustment measures for batch switching, including the switching time, the remaining quantity of the original batch, and the input quantity of the secondary priority batch; if there is no inventory for the secondary priority batch, record the adjustment measures for supplementary procurement, including the procurement quantity, the expected delivery time, and the procurement batch number; finally, integrate the node identifier, adjustment measures, adjustment time, and adjustment basis of the target inventory node to generate the material adjustment record for the target inventory node.

[0111] Finally, step 225 is executed: extract the basic material information and inventory thresholds corresponding to each construction process from the scenario matching table. The basic material information includes batch number, specification parameters, and production time. The simulated consumption data, natural loss, material adjustment records, basic material information, and inventory thresholds of each construction process node are classified and associated according to the construction process node number to generate a simulated consumption list, so as to ensure that each set of data can correspond to the entire consumption process of a certain construction process.

[0112] The embodiments of this application can accurately adapt to dynamic construction scenarios, improve the fit and reliability of material consumption accounting, provide reliable data support for subsequent preprocessing, encryption and settlement, and enhance the scientific nature and full-process controllability of dynamic management of project cost settlement.

[0113] This application provides a specific embodiment. Step 103 involves using a blockchain-driven edge computing node to perform local preprocessing on the location information, quantity information, specification information of the incoming material equipment, the material consumption data, and the simulated consumption list to obtain an initial dataset. This specifically includes the following steps:

[0114] Step 301: Extract the unique identifier of the equipment from the specification information of the incoming material equipment through the blockchain-driven edge computing node, generate a first hash value corresponding to the unique identifier of the equipment based on the blockchain hash algorithm, and integrate the specification information, location information and quantity information of the incoming material equipment with the first hash value as the association basis to form an association table.

[0115] In this step, the first hash value refers to the unique value obtained by calculating the device's unique identifier using a blockchain-based hash algorithm.

[0116] The associated benchmark refers to the core reference used to integrate various information about materials and equipment. In this step, it is the first hash value, which is used to bind and associate different dimensions of basic information about the same material and equipment.

[0117] The association table refers to a structured table that integrates the specifications, location, and quantity information of incoming materials and equipment based on the first hash value.

[0118] In this embodiment, a unique identifier for each incoming material and equipment is extracted from its specification information. A blockchain hash algorithm is used to calculate the first hash value corresponding to this unique identifier. First, intermediate parameters are calculated by multiplying the unique identifier by a feature weight coefficient and adding a timestamp offset. These intermediate parameters are then converted to a string format using the hash algorithm, and the string is hashed to generate the final first hash value. The intermediate parameters refer to transitional values ​​calculated based on the unique identifier, feature weight coefficient, and timestamp offset. The feature weight coefficient is set based on the equipment type code in the unique identifier and is used to distinguish the identifier weight of different types of material and equipment, improving the uniqueness of the hash value. The timestamp offset is the last 6 digits of the system timestamp when the unique identifier was extracted, used to avoid hash collisions caused by similar unique identifiers of different material and equipment.

[0119] Using the first hash value as the association benchmark for integrated data, the specification information, location information, and quantity information of incoming materials and equipment are associated and integrated according to the association benchmark, ensuring that all kinds of basic information of the same material and equipment are bound to the same first hash value, and finally forming an association table including the first hash value, specification information, location information, and quantity information.

[0120] Step 302: Based on the data association rules between nodes pre-stored in the blockchain, match the material status data, construction environment data, construction operation association data, and material inventory data corresponding to each construction process node with the association table to form the first association dataset.

[0121] In this step, the data association rules between nodes refer to the rules pre-stored in the blockchain that define the matching logic between each construction process node and various types of consumption-related data.

[0122] The first associated dataset refers to a dataset that integrates the associated tables with the material status data, construction environment data, construction operation associated data, and material inventory data corresponding to each construction process node.

[0123] In this embodiment, the pre-stored data association rules between nodes are first retrieved from the node data rule storage module of the blockchain. Then, the material status data, construction environment data, construction operation association data, and material inventory data corresponding to each construction process node are preprocessed. The preprocessing process includes adding a node identifier corresponding to the construction process node to each type of data and extracting the material type code from each type of data. Then, according to the rule matching logic, the combination of node identifier and material type code is used as the retrieval condition to find the corresponding first hash value in the association table and embed it into each type of data of the corresponding construction process node to realize the binding of consumption data with the first hash value.

[0124] After binding is completed, a second verification is performed according to the rules and standards. For example, it checks whether the current inventory in the material inventory data is consistent with the initial quantity bound to the first hash value in the association table minus the simulated consumption data. If they are inconsistent, they are re-matched until all data meet the requirements. Finally, the basic material information bound to the same first hash value is integrated with various types of data to form the first association dataset.

[0125] Step 303: According to the blockchain transaction format, bind the simulated consumption data, natural loss amount and material adjustment records corresponding to each construction process node with the first associated dataset to generate the second associated dataset.

[0126] In this step, the blockchain transaction format refers to the standardized format for data storage and transmission in the blockchain.

[0127] The second associated dataset refers to the first associated dataset, which is bound to simulated consumption data, natural loss, and material adjustment records in a blockchain transaction format.

[0128] In this embodiment of the application, in accordance with the specification requirements of blockchain transaction format, the simulated consumption data, natural loss amount and material adjustment records corresponding to each construction process node are bound to the first hash value in the first associated dataset, so as to ensure that the consumption result data, loss data and adjustment records can be accurately associated with the corresponding materials, equipment and construction process, so as to achieve complete connection between basic information, consumption-related data and result data, and finally generate the second associated dataset.

[0129] Step 304: Based on the time sequence rules of the construction process nodes pre-stored in the blockchain, classify and integrate the various types of data in the second associated dataset to form a process group dataset.

[0130] In this step, the timing rules of the construction process nodes refer to the rules pre-stored in the blockchain that clearly define the order of each construction process and the data classification standards.

[0131] Various types of data refer to all data included in the second associated dataset, such as basic material information, consumption-related data, simulated consumption data, natural loss, and material adjustment records.

[0132] The process group dataset refers to the dataset that is categorized and integrated according to the construction process nodes. Each group of data corresponds to the complete related data of a construction process node.

[0133] In this embodiment of the application, the timing rules of the construction process nodes pre-stored in the blockchain are retrieved. According to the rules, the various types of data in the second associated dataset are classified according to the construction process nodes. The material basic information, consumption-related data, result data and adjustment records corresponding to the same construction process node are integrated into a group, and finally a process group dataset divided according to the construction process nodes is formed.

[0134] Step 305: Based on the data collection time corresponding to each type of data, arrange the data of each group in the process group dataset to obtain the arranged information, and assign the arranged information an association identifier based on the blockchain chain structure to form the initial dataset.

[0135] In this step, the sorted information refers to the ordered information formed by arranging the data of each group in the process group dataset according to the order of data collection time.

[0136] The association identifier based on the blockchain chain structure refers to a unique identifier generated according to the blockchain chain structure to connect the entire process data of the same material and equipment.

[0137] The initial dataset refers to a structured dataset that includes sorted information and associated identifiers.

[0138] In this embodiment of the application, the data collection time corresponding to each group of data in the process group dataset is extracted, and each group of data is arranged in chronological order of collection time to obtain the arranged information. The arranged information is then assigned a related identifier based on a blockchain chain structure, and finally an initial dataset including ordered data and related identifiers is formed.

[0139] This application's embodiments achieve accurate association and orderly organization of multi-source data; rely on local preprocessing at edge nodes to avoid the delay and failure risks of centralized processing; and ensure data traceability and immutability through a blockchain chain structure, providing structured and reliable data support for subsequent encrypted transmission and settlement processing, thereby strengthening the dynamic management capabilities of engineering cost settlement.

[0140] This application provides a specific embodiment. Step 104 involves encrypting the initial dataset to obtain an encrypted dataset, specifically including the following steps:

[0141] Step 401: Extract the unique equipment identifier corresponding to each incoming material equipment from the initial dataset, and generate an attribute identifier set based on the unique equipment identifier. The attribute identifier set includes equipment type code, production batch code, and specification parameter code.

[0142] In this step, the attribute identifier set refers to the set of core attribute codes of materials and equipment generated based on the unique identifier of the equipment. The attribute identifier set includes equipment type code, production batch code, and specification parameter code. Among them, the equipment type code refers to the code extracted based on the function and purpose of the materials and equipment, the production batch code refers to the code extracted based on the production batch information of the materials and equipment, and the specification parameter code refers to the code extracted based on the model and performance parameters of the materials and equipment.

[0143] In this embodiment of the application, the unique identifiers of each incoming material equipment are selected from the initial dataset. Each unique identifier is then decomposed in a structured manner. The specific decomposition process includes: extracting the equipment type code based on the function and purpose of the material equipment; extracting the production batch code based on the production batch information of the material equipment; extracting the specification parameter code based on the model, parameters, and other information of the material equipment; integrating the equipment type code, production batch code, and specification parameter code into a complete set of attribute identifiers; and finally integrating the attribute identifiers of all incoming material equipment into an attribute identifier set.

[0144] Step 402: Determine the encryption attribute association rule corresponding to the attribute identifier set according to the authorized access rules of the engineering cost management platform.

[0145] In this step, the authorized access rules refer to the rules preset by the engineering cost management platform that clearly define the scope of data that different access subjects can access. The encryption attribute association rules refer to the rules determined based on the authorized access rules and the sensitivity level of the attribute identifier set, which clarify the encryption strategy corresponding to different attribute codes.

[0146] In this embodiment, the authorized access rules preset by the engineering cost management platform are retrieved, and the sensitivity level of each code in the attribute identifier set is analyzed based on the rules, thereby determining the encryption attribute association rules corresponding to the attribute identifier set.

[0147] Step 403: Based on the encryption attribute association rules and the attribute dimensions of the device's unique identifier, generate encryption subkeys corresponding to each attribute dimension, and integrate the encryption subkeys into a global encryption key through the attribute association algorithm.

[0148] In this step, the attribute dimension refers to the core attribute category after the device's unique identifier is disassembled. This attribute dimension includes the device type dimension, production batch dimension, and specification parameter dimension. Each attribute dimension corresponds to an encryption subkey.

[0149] A cryptographic subkey refers to a unique key generated based on cryptographic attribute association rules and a single attribute dimension. This cryptographic subkey has the ability to encrypt data of the corresponding attribute dimension.

[0150] The global encryption key refers to the key generated by integrating various encryption subkeys through an attribute association algorithm. This global encryption key has the ability to encrypt all fields of the initial dataset.

[0151] In this embodiment, the unique identifier of the device is first defined, including the device type dimension, production batch dimension, and specification parameter dimension. Based on the encryption attribute association rule, a corresponding encryption algorithm parameter is assigned to each attribute dimension, and a unique encryption subkey for each attribute dimension is generated based on the encryption algorithm parameter. Then, through the attribute association algorithm, each encryption subkey is multiplied by the device type subkey by the production batch subkey, and then added to the specification parameter subkey to obtain a global encryption key with global encryption capability, so as to eliminate redundancy and interference between subkeys.

[0152] Step 334: Encrypt the initial dataset according to the global encryption key to form an initial encrypted dataset.

[0153] In this embodiment of the application, the initial dataset is encrypted field by field using a symmetric encryption method based on the global encryption key. Specifically, the core related fields such as the first hash value and the association identifier in the initial dataset are first encrypted, and then the business fields such as the material basic information and consumption data are encrypted. During the encryption process, it is ensured that the encryption result of each field corresponds to the original field to avoid data corruption. After encryption is completed, all encrypted fields are integrated to form the initial encrypted dataset.

[0154] Step 405: Bind the node identity of the blockchain-driven edge computing node to the initial encrypted dataset to generate an encrypted credential. Combine the initial encrypted dataset to form an encrypted dataset. The encrypted credential includes the node identity, attribute encryption policy index, and global encryption key verification information.

[0155] In this step, node identity refers to the unique identifier of a blockchain-driven edge computing node.

[0156] The attribute encryption strategy index refers to the index information of the rules associated with the encryption attributes.

[0157] Global encryption key verification information refers to information used to verify the legitimacy of the global encryption key.

[0158] In this embodiment, the node identity of the blockchain-driven edge computing node is bound to the initial encrypted dataset to generate an encrypted credential. The encrypted credential must include the node identity, attribute encryption policy index, and global encryption key verification information. Finally, the initial encrypted dataset and the encrypted credential are structurally integrated to form an encrypted dataset including the data ontology and verification information.

[0159] This application embodiment implements targeted encryption of edge-side data to ensure data security, while the credential information provides a basis for subsequent verification, thus building a solid security defense for the transmission of engineering cost settlement data and improving the reliability of data throughout the entire process.

[0160] This application provides a specific embodiment. Step 105 involves verifying and matching the encrypted dataset to obtain quota matching information, specifically including the following steps:

[0161] Step 501: Based on the attribute encryption policy index and encrypted attribute association rules in the encrypted dataset, combined with the platform authorization access rules and the attribute dimension decoding permissions of the device's unique identifier, the initial encrypted dataset is decrypted to obtain the decrypted dataset.

[0162] In this step, the platform's authorized access rules refer to the rules set by the engineering cost management platform to regulate the data access permissions of different access subjects. These rules include the data dimensions that can be accessed, the decryption permission level, etc.

[0163] Attribute dimension decoding permission refers to the permission granted to the access subject based on the platform's authorization access rules to decrypt the attribute dimension data of the device's unique identifier. Different access subjects have different decoding permissions.

[0164] In this embodiment, an attribute encryption strategy index is extracted from the encrypted dataset, and the encrypted attribute association rules are retrieved through this index. At the same time, the platform authorization access rules of the engineering cost management platform are retrieved to confirm the attribute dimension decoding permissions corresponding to the current access subject. Based on the encryption strategy clearly defined by the encrypted attribute association rules, combined with the attribute dimension decoding permissions, the initial encrypted dataset is decrypted in layers. Specifically, highly sensitive specification parameter related fields are decrypted first, then moderately sensitive production batch fields are decrypted, and finally basic sensitive equipment type fields are decrypted, ensuring that the decryption result of each field is consistent with the original field structure, and finally obtaining a decrypted dataset including complete original data.

[0165] Step 502: Based on the decrypted dataset, calculate the second hash value, compare the second hash value with the first hash value in the decrypted dataset, and at the same time compare the node identity identifier in the decrypted dataset with the node identifier of the target edge node pre-stored by the engineering cost management platform to obtain the verification dataset;

[0166] In this step, the target edge node refers to a blockchain-driven edge computing node that has been pre-approved and registered by the engineering cost management platform and has the qualifications for data collection and preprocessing.

[0167] The second hash value refers to the hash value calculated based on the original data of the decrypted dataset using the same hash algorithm as the first hash value.

[0168] A verified dataset refers to a set of data that has been marked as valid after passing hash value integrity verification and node identity legitimacy verification, thus excluding tampered data and data from illegal sources.

[0169] In this embodiment, based on the decrypted dataset, a second hash value is calculated using the same hash algorithm as that used to generate the first hash value. The second hash value is then compared bit by bit with the first hash value stored in the decrypted dataset to verify whether the data has been tampered with during transmission. Simultaneously, node identity identifiers are extracted from the decrypted dataset, and these identifiers are matched with the node identifiers of the target edge nodes pre-stored by the engineering cost management platform to verify the legitimacy of the data source. Data portions with matching hash values ​​and matching node identity identifiers are marked as valid data. Finally, all valid data are integrated into a verification dataset.

[0170] Step 503: Match the simulated consumption data and natural loss in the verification dataset with the corresponding engineering quota standards to calculate the quota matching amount for each construction process node;

[0171] In this step, the engineering quota standard refers to the material consumption and cost accounting benchmark set by the industry or project according to the construction process. The engineering quota standard includes the unit quota price of materials for each process, the allowable loss range, and the cost calculation rules.

[0172] The quota matching amount refers to the amount calculated based on the total consumption data in the verification dataset and the unit price in the engineering quota standard.

[0173] In this embodiment, the engineering cost management platform retrieves the preset engineering quota standards, extracts the simulated consumption data and natural loss of each construction process node from the verification dataset, and adds the two to obtain the total consumption data of each construction process. According to the type of construction process node, the corresponding unit quota price is found in the engineering quota standards. The quota matching amount of each construction process node is obtained by calculating the product of the total consumption data of each construction process and the corresponding unit quota price.

[0174] Step 504: Based on the remaining inventory and material adjustment records of each construction process node, calculate the procurement cost and transportation cost of supplementary materials, and generate quota matching information by combining the quota matching amount of each construction process node.

[0175] In this embodiment, the remaining inventory and material adjustment records for each construction process node are extracted from the verification dataset. The supplementary procurement information and transportation information in the material adjustment records are analyzed. The supplementary procurement information includes the quantity and unit price of the supplementary materials, and the transportation information includes the transportation mileage and unit transportation cost. The procurement cost of the supplementary materials is obtained by multiplying the quantity and unit price of the supplementary materials, and the transportation cost is obtained by multiplying the transportation mileage and unit transportation cost. The quota matching amount for each construction process node is added to the corresponding procurement cost and transportation cost to obtain the total cost of each process. Finally, the total cost, quota matching amount, procurement cost, and transportation cost of all construction process nodes are integrated into quota matching information.

[0176] This application's embodiments ensure data security and trustworthiness through layered decryption and dual verification; it accurately calculates matching amounts based on quota standards and combines supplementary cost generation results to avoid the one-sidedness of static pricing.

[0177] Figure 3 This is a schematic diagram illustrating a specific implementation of a blockchain-based dynamic management system for engineering cost settlement, as provided in this application. (Refer to...) Figure 3 The system may include:

[0178] The acquisition module 21 is used to acquire the location information, quantity information, specification information, and material consumption data of the materials and equipment entering the site during the project cost settlement stage. The material consumption data includes material status data, construction environment data, construction operation related data, material inventory data, and material quality inspection data.

[0179] Simulation module 22 is used to simulate the consumption process of the incoming materials and equipment in the corresponding operation process and expected construction time based on the material consumption data using a pre-built digital twin model, and generate a simulated consumption list;

[0180] Processing module 23 is used to perform local preprocessing on the location information, quantity information, specification information, material consumption data, and simulated consumption list of the incoming material equipment through a blockchain-driven edge computing node to obtain an initial dataset;

[0181] Encryption module 24 is used to encrypt the initial dataset to obtain an encrypted dataset;

[0182] The update module 25 is used to verify and match the encrypted dataset to obtain quota matching information. Combined with material inventory data, it dynamically updates the project cost settlement amount to achieve closed-loop management of materials and equipment from the on-site verification stage, the construction loss tracking stage to the cost settlement stage.

[0183] This application provides an embodiment of a blockchain-based dynamic management system for engineering cost settlement, which is used to implement the aforementioned blockchain-based dynamic management method for engineering cost settlement. Therefore, the specific implementation of the blockchain-based dynamic management system for engineering cost settlement can be found in the embodiment section of the blockchain-based dynamic management method for engineering cost settlement described above. The specific implementation can be referred to the description of the corresponding embodiments, and will not be repeated here.

[0184] This application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of any of the above-described blockchain-based dynamic management methods for engineering cost settlement.

[0185] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described blockchain-based dynamic management methods for engineering cost settlement.

[0186] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0187] The embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the blockchain-based dynamic management method for engineering cost settlement.

[0188] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0189] The foregoing has provided a detailed description of a blockchain-based dynamic management method and system for engineering cost settlement. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A blockchain-based dynamic management method for engineering cost settlement, characterized in that, include: Obtain the location, quantity, and specifications of materials and equipment entering the site during the project cost settlement stage, as well as material consumption data. The material consumption data includes material status data, construction environment data, construction operation related data, material inventory data, and material quality inspection data. Based on the material consumption data, a pre-built digital twin model is used to simulate the consumption process of the incoming materials and equipment during the corresponding operation process and the expected construction time, and a simulated consumption list is generated. The location, quantity, and specifications of the incoming material equipment, the material consumption data, and the simulated consumption list are preprocessed locally by blockchain-driven edge computing nodes to obtain an initial dataset. The initial dataset is encrypted to obtain an encrypted dataset; The encrypted dataset is verified and matched with the quota to obtain quota matching information. Combined with material inventory data, the project cost settlement amount is dynamically updated to achieve closed-loop management of materials and equipment from the on-site inspection stage, the construction loss tracking stage to the cost settlement stage. The initial dataset is encrypted to obtain an encrypted dataset, including: Extract the unique equipment identifier corresponding to each incoming material equipment from the initial dataset, and generate an attribute identifier set based on the unique equipment identifier. The attribute identifier set includes equipment type code, production batch code, and specification parameter code. Based on the authorized access rules of the engineering cost management platform, determine the encryption attribute association rules corresponding to the attribute identifier set; Based on the encryption attribute association rules and the attribute dimensions of the device's unique identifier, encryption subkeys corresponding to each attribute dimension are generated, and the encryption subkeys are integrated into a global encryption key through the attribute association algorithm. The initial dataset is encrypted using the global encryption key to form an initial encrypted dataset. The node identity of the blockchain-driven edge computing node is bound to the initial encrypted dataset to generate an encrypted credential. Combined with the initial encrypted dataset, an encrypted dataset is formed. The encrypted credential includes the node identity, attribute encryption policy index, and global encryption key verification information.

2. The method according to claim 1, characterized in that, Based on the material consumption data, a pre-built digital twin model is used to simulate the consumption process of the incoming materials and equipment during the corresponding operation procedures and expected construction time, generating a simulated consumption list, including: Based on the construction process nodes, standard consumption parameters corresponding to each construction process, batch information of incoming materials and equipment, and process connection records in the material consumption data, determine the sequence of construction process nodes, the estimated construction time, the material usage scenario, and the connection interval time between adjacent construction process nodes. The specifications and batch information of the incoming materials and equipment are matched with the adaptation requirements of each material's usage scenario to form a scenario matching table; Based on the construction environment data corresponding to each construction process node, an environmental parameter set is constructed using a pre-built digital twin model; Based on the material quality testing data of each batch of equipment in the scenario matching table, calculate the consumption rate correction value of different batches of equipment under the corresponding ambient temperature and humidity, and combine the material status data and environmental parameter set to generate a material consumption table of incoming material equipment under the corresponding ambient temperature and humidity. Based on the operational qualification level of the construction personnel and the sequence of construction process nodes in the construction operation association data, the standard consumption parameters corresponding to each construction process are adjusted to obtain the target consumption parameters. Based on the actual consumption benchmarks of each construction process, the material consumption table, the scenario matching table, and the material inventory data, the digital twin model simulates the consumption process of incoming materials and equipment within the corresponding operation process and the expected construction time. Combined with the scenario matching table, a simulated consumption list is generated.

3. The method according to claim 2, characterized in that, Based on the construction environment data corresponding to each construction process node, an environmental parameter set is constructed using a pre-built digital twin model, including: Based on the construction environment data corresponding to each construction process node, the sequence of construction process nodes, and the estimated construction time, environmental parameter groups corresponding to different time intervals for each construction process node are generated. The coordinates of the construction area corresponding to each construction process node are extracted from the location information of the incoming materials and equipment. The environmental parameter group corresponding to each construction process node is associated with the coordinates of the construction area to determine the spatial range corresponding to each environmental parameter group. Based on the spatial range corresponding to each environmental parameter group and the sequence of construction process nodes, a spatial model corresponding to the coordinates of each construction area is loaded into a pre-built digital twin model, and the environmental parameter group corresponding to the coordinates of each construction area is bound to the spatial model to form an environmental simulation unit corresponding to each construction process node. The coordinates of the adapted construction area verified by the digital twin model, the adapted time interval corresponding to each construction process node, and the construction environment parameters within each adapted time interval are extracted from each environmental simulation unit to form an environmental parameter set.

4. The method according to claim 2, characterized in that, Based on the actual consumption benchmarks for each construction process, the material consumption table, the scenario matching table, and material inventory data, the digital twin model simulates the consumption process of incoming materials and equipment within the corresponding operation flow and expected construction duration. Combined with the scenario matching table, a simulated consumption list is generated, including: Based on the target consumption parameters of each construction process node and the consumption rate correction value of the corresponding batch in the material consumption table, calculate the unit material consumption quantity per unit time. The construction process is divided into multiple time segments based on the estimated construction time of each construction process node. According to the time sequence of the time segments and the corresponding operation process, the consumption process of incoming materials and equipment is simulated through the digital twin model. Combined with the unit material consumption quantity, the material consumption quantity at the end of each time segment is calculated to obtain simulated consumption data. When simulating the connection interval between adjacent construction process nodes, the natural loss amount and remaining inventory amount of each construction process node are calculated based on the natural loss rate of the corresponding batch of equipment in the corresponding environment in the material consumption table. During the simulation, the remaining inventory is compared with the inventory threshold of the corresponding construction process node in the scenario matching table. When there is a target inventory node with a remaining inventory less than the inventory threshold, a material adjustment record for the target inventory node is generated. The simulated consumption data, natural loss, material adjustment records, and scenario matching table for each construction process node are integrated to generate a simulated consumption list.

5. The method according to claim 1, characterized in that, The location, quantity, and specifications of the incoming material equipment, the material consumption data, and the simulated consumption list are preprocessed locally by blockchain-driven edge computing nodes to obtain an initial dataset, including: The edge computing node driven by blockchain extracts the unique identifier of the equipment from the specification information of the incoming material equipment. Based on the hash algorithm of blockchain, a first hash value corresponding to the unique identifier of the equipment is generated. Using the first hash value as the association benchmark, the specification information, location information and quantity information of the incoming material equipment are integrated to form an association table. Based on the data association rules between nodes pre-stored in the blockchain, the material status data, construction environment data, construction operation association data, and material inventory data corresponding to each construction process node are matched with the association table to form the first association dataset; According to the blockchain transaction format, the simulated consumption data, natural loss amount and material adjustment records corresponding to each construction process node are bound with the first associated dataset to generate the second associated dataset; Based on the time sequence rules of the construction process nodes pre-stored in the blockchain, the various types of data in the second associated dataset are classified and integrated to form a process group dataset; Based on the data collection time corresponding to each type of data, the data in each group of the process group dataset is arranged to obtain the arranged information, and the arranged information is assigned an association identifier based on a blockchain chain structure to form an initial dataset.

6. The method according to claim 1, characterized in that, The encrypted dataset is verified and subjected to quota matching processing to obtain quota matching information, including: Based on the attribute encryption policy index and encryption attribute association rules in the encrypted dataset, combined with the platform authorization access rules and the attribute dimension decoding permissions of the device's unique identifier, the initial encrypted dataset is decrypted to obtain the decrypted dataset. Based on the decrypted dataset, a second hash value is calculated, and the second hash value is compared with the first hash value in the decrypted dataset. At the same time, the node identity identifier in the decrypted dataset is compared with the node identifier of the target edge node pre-stored by the engineering cost management platform to obtain the verification dataset. The simulated consumption data and natural loss in the verification dataset are matched with the corresponding engineering quota standards to calculate the quota matching amount for each construction process node. Based on the remaining inventory and material adjustment records of each construction process node, calculate the procurement cost and transportation cost of supplementary materials, and generate quota matching information by combining the quota matching amount of each construction process node.

7. A blockchain-based dynamic management system for engineering cost settlement, used in the blockchain-based dynamic management method for engineering cost settlement as described in any one of claims 1 to 6, characterized in that, include: The acquisition module is used to acquire the location, quantity, and specifications of materials and equipment entering the site during the project cost settlement stage, as well as material consumption data. The material consumption data includes material status data, construction environment data, construction operation related data, material inventory data, and material quality inspection data. The simulation module is used to simulate the consumption process of the incoming materials and equipment during the corresponding operation process and the expected construction time based on the material consumption data using a pre-built digital twin model, and generate a simulated consumption list. The processing module is used to perform local preprocessing on the location information, quantity information, specification information, material consumption data, and simulated consumption list of the incoming material equipment through a blockchain-driven edge computing node to obtain an initial dataset; An encryption module is used to encrypt the initial dataset to obtain an encrypted dataset; The update module is used to verify and match the encrypted dataset to obtain quota matching information. Combined with material inventory data, the project cost settlement amount is dynamically updated to achieve closed-loop management of materials and equipment from the on-site inspection stage, the construction loss tracking stage to the cost settlement stage.

8. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a blockchain-based dynamic management method for engineering cost settlement as described in any one of claims 1 to 6.

9. A computer storage medium, characterized in that, The system contains a computer program that, when executed by a computer, implements a blockchain-based dynamic management method for engineering cost settlement as described in any one of claims 1 to 6.

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