Building engineering quality safety tracking method and system based on big data

Abstract

The invention discloses a building engineering quality safety tracking method and system based on big data, and relates to the technical field of building engineering quality safety. The method comprises the steps of creating a process evolution credible mark based on building entity information and binding a physical carrier, activating the process evolution credible mark to input operation data when a building entity performs operation, performing evidence storage locking through a block chain technology, constructing an event pulse control sensing cluster in a building engineering operation area, and performing risk hierarchical conduction disposal. The method comprises the following steps: generating risk event tracing data, constructing a construction quality safety tracing chain network based on all process evolution credible marks, carrying out risk causal tracing according to the risk event tracing data, obtaining a root cause guiding tracing map, identifying a related responsible party, carrying out risk rectification, and optimizing an event pulse control sensing cluster and the construction quality safety tracing chain network. According to the invention, the initiative and transparency of quality safety tracking can be improved, and a systematic solution is provided for the whole-cycle quality safety management and control of constructional engineering.

Description

Technical Field

[0001] This invention relates to the field of building engineering quality and safety technology, and in particular to a method and system for tracking building engineering quality and safety based on big data. Background Technology

[0002] Quality and safety tracking in construction projects involves multiple elements such as materials, processes, equipment, and personnel, with a wide range of data coverage and strong dynamism throughout the entire lifecycle. In existing technologies, quality and safety data is mostly scattered across isolated carriers such as construction logs, inspection reports, and supervision records, lacking a unified and reliable correlation mechanism. This results in high risks of data tampering and difficulty in forming a complete entity lifecycle data chain. Risk monitoring often relies on single indicator thresholds to trigger alarms, failing to consider the spatiotemporal evolution of risks, the coupling effects of multiple factors, and the differences in project stages, leading to low early warning accuracy and delayed response. When quality and safety issues occur, due to weak data correlation and ambiguous responsibility nodes, it is difficult to quickly trace the root cause and identify the responsible party. The overall management model is passive and extensive, failing to meet the needs of modern construction projects for refined and intelligent quality and safety management. Therefore, there is an urgent need for an integrated solution that can span the entire lifecycle of a building entity, enabling real-time risk perception, intelligent analysis, and precise responsibility tracing. Summary of the Invention

[0003] This invention provides a method for tracking the quality and safety of construction projects based on big data, including: Step S1: Create a reliable imprint of process evolution for each building entity based on the building entity information and bind it to the physical carrier; Step S2: When each building entity is working, the corresponding building entity's process evolution trusted imprint is activated to record the work data, and the data is stored and locked using blockchain technology. Step S3: Construct event control clusters in the construction project operation area according to the type of quality and safety risk, carry out risk classification, transmission and disposal, and generate risk event tracing data; Step S4: Construct a construction quality and safety tracing chain network based on the credible imprints of all process evolutions, and conduct risk causal tracing based on risk event tracing data to obtain a root cause-oriented tracing map; Step S5: Identify relevant responsible parties based on the root cause-oriented tracing map and carry out risk rectification, and optimize the event pulse control cluster and construction quality and safety tracing chain network.

[0004] The above-described big data-based method for tracking the quality and safety of construction projects includes the following sub-steps: Creating a reliable imprint of the process evolution for each building entity based on its information and binding it to a physical carrier. Step S11: Based on the building entity information, create a reliable imprint of the process evolution for each building entity through a three-level encryption coding mechanism, and perform dynamic exclusive traceability authentication; Step S12: Physically bind the process evolution trust mark to the corresponding building entity through multiple carriers, and simultaneously enter the traceable digital initial file of the corresponding building entity in the process evolution trust mark.

[0005] The above-described big data-based construction project quality and safety tracking method includes the following sub-steps: When work is being carried out on each construction entity, the corresponding construction process evolution trusted imprint is activated and the work data is entered. This data is then stored and locked using blockchain technology. Step S21: When the building entity is performing operations, the corresponding process evolution trust imprint is activated through the physical binding carrier to enter the data input window and add the building entity's operation data. Step S22: After the work is completed, a dual acceptance mechanism is used to conduct quality and safety acceptance, and blockchain technology is used to store and lock the accepted work data.

[0006] The above-described big data-based method for tracking the quality and safety of construction projects includes the following sub-steps: constructing event control clusters in the construction project area according to the type of quality and safety risk, carrying out risk classification, transmission, and handling, and generating risk event tracing data. Step S31: Deploy edge sensing nodes in the construction project area according to the quality and safety risk type to construct an event pulse control sensing cluster; Step S32: Collect risk perception data in real time through event pulse control clusters, and activate the event pulse control signal when risk data is detected; Step S33: Identify the severity of the risk in the event pulse control signal, classify and handle the risk according to the preset risk classification warning response rules, and generate corresponding risk event tracing data.

[0007] The above-described big data-based construction project quality and safety tracking method, which constructs a construction quality and safety tracing chain network based on the credible imprints of all process evolutions, and performs risk causal tracing based on risk event tracing data to obtain a root cause-oriented tracing map, includes the following sub-steps: Step S41: Based on the credible imprints of all process evolutions, construct a construction quality and safety tracing chain network through predefined engineering quality and safety rules; Step S42: Based on the risk event tracing data, conduct risk event tracing through a preset causal tracing engine in the construction quality and safety tracing chain network to generate a root cause-oriented tracing map.

[0008] The above-described big data-based method for tracing the quality and safety of construction projects includes the following sub-steps: identifying relevant responsible parties and implementing risk rectification based on a root cause-oriented tracing map; and optimizing the event control cluster and the construction quality and safety tracing chain network. Step S51: Identify relevant responsible parties based on the root cause-oriented source tracing map, automatically generate a responsibility determination report, and carry out risk rectification; Step S52: Update the process evolution trusted imprint based on risk rectification data, and optimize the risk classification early warning response rules of the event pulse control cluster, and the construction rules of the construction quality and safety traceability chain network.

[0009] This invention also provides a big data-based construction project quality and safety tracking system, comprising: The process evolution trusted imprint creation and binding module creates process evolution trusted imprints for each building entity based on building entity information and binds them to the physical carrier. The work data entry module activates the corresponding building entity's process evolution trusted imprint record to enter work data when work is carried out on each building entity, and uses blockchain technology for evidence storage and locking. The risk identification and handling module constructs event control clusters in the construction engineering operation area according to the type of quality and safety risk, carries out risk classification and transmission and handling, and generates risk event tracing data. The root cause tracing module constructs a construction quality and safety tracing chain network based on the credible imprints of all process evolutions, and performs risk causal tracking based on risk event tracing data to obtain a root cause-oriented tracing map. The risk rectification and optimization module identifies relevant responsible parties and carries out risk rectification based on the root cause-oriented tracing map, and optimizes the event pulse control cluster and construction quality and safety tracing chain network.

[0010] The above-described big data-based construction project quality and safety tracking system includes a process evolution trusted imprint creation and binding module, which specifically comprises: The process evolution trusted imprint creation submodule creates process evolution trusted imprints for each building entity based on building entity information through a three-level encryption coding mechanism, and performs dynamic exclusive traceability authentication. The process evolution trusted imprint binding submodule physically binds the process evolution trusted imprint to the corresponding building entity through multiple carriers, and simultaneously enters the traceable digital initial file of the corresponding building entity into the process evolution trusted imprint.

[0011] As described above, a construction project quality and safety tracking system based on big data includes, in its work data entry module, the following components: The building entity operation data entry submodule activates the corresponding process evolution trust mark through physical binding carrier to enter the data entry window when the building entity is performing operations, and adds the building entity's operation data. The task data storage and locking submodule performs quality and safety acceptance through a dual acceptance mechanism after the task is completed, and uses blockchain technology to store and lock the accepted task data.

[0012] The above-described big data-based construction project quality and safety tracking system includes a risk identification and handling module, specifically comprising: The event pulse control sensing cluster construction submodule deploys edge sensing nodes in the construction engineering operation area according to the quality and safety risk type to construct the event pulse control sensing cluster; The event pulse control signal activation submodule collects risk perception data in real time through the event pulse control sensor cluster, and activates the event pulse control signal when risk data is detected. The risk event causal data generation submodule identifies the severity and negativity of the event's control signals, performs graded transmission and handling according to preset risk grading and early warning response rules, and generates corresponding risk event causal data.

[0013] As described above, a big data-based construction project quality and safety tracking system includes a root cause tracing acquisition module, which specifically comprises a construction quality and safety tracing chain network construction submodule. This submodule constructs a construction quality and safety tracing chain network based on the credible imprints of all process evolutions and through predefined engineering quality and safety rules. The root cause-oriented tracing map generation submodule, based on risk event tracing data, uses a preset causal tracing engine to trace the causal origins of risk events within the construction quality and safety tracing chain network, generating a root cause-oriented tracing map.

[0014] The above-described big data-based construction project quality and safety tracking system includes a risk rectification and optimization module, specifically comprising: The responsibility identification and risk rectification submodule identifies relevant responsible parties based on the root cause-oriented source tracing map, automatically generates a responsibility identification report, and carries out risk rectification. The cluster and network optimization submodule updates the trusted imprint of process evolution based on risk rectification data, and optimizes the risk classification, early warning and response rules of the event pulse control cluster, as well as the construction rules of the construction quality and safety traceability chain network.

[0015] The beneficial effects achieved by this invention are as follows: This invention enables full-process, tamper-proof, and traceable management of quality and safety data, ensuring the authenticity and reliability of data at each stage from material entry and construction to acceptance and delivery, providing a solid technical guarantee for the lifelong responsibility system for quality. Simultaneously, it enables an intelligent upgrade of risk management from real-time perception and tiered early warning to precise root cause tracing, significantly improving the accuracy and efficiency of risk handling and fundamentally optimizing the level of quality and safety tracking in construction projects. Furthermore, this invention can further enhance the initiative and transparency of quality and safety tracking through the reliable correlation of quality and safety data and the clear definition of responsibility, ultimately providing a systematic solution for the full-cycle quality and safety management of construction projects. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention 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 recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0017] Figure 1 This is a flowchart of a big data-based method for tracking the quality and safety of construction projects, provided in Embodiment 1 of this application. Figure 2 This is a schematic diagram of a construction project quality and safety tracking system based on big data, provided in Embodiment 2 of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1 like Figure 1 As shown in Embodiment 1 of this application, a method for tracking the quality and safety of construction projects based on big data is provided. The method includes the following steps: Step S1: Create a reliable imprint of process evolution for each building entity based on the building entity information and bind it to the physical carrier; Furthermore, creating a trusted imprint of process evolution for each building entity based on building entity information and binding it to a physical carrier includes the following sub-steps: Step S11: Based on the building entity information, create a reliable imprint of the process evolution for each building entity through a three-level encryption coding mechanism, and perform dynamic exclusive traceability authentication; Specifically, the three-level encryption encoding mechanism includes three levels of encoding. The first level is the project identification layer encoding, which is encoded using the regional code and the construction project filing number. The second level is the entity type layer encoding, which is assigned values ​​according to the entity attributes in the industry. The third level is the unique identification layer encoding, which is encoded based on the generation sequence and production time of the building entity. The three levels of encoding are concatenated to create a unique process evolution trusted imprint for the corresponding entity. At the same time, the hash value is calculated based on the concatenated three levels of encoding and stored in the preset construction project consortium chain. A dynamic evolution log pointer is added inside the process evolution trusted imprint, pointing to an appendable file address for subsequent data appending. When creating the process evolution trusted imprint, an asymmetric encryption key pair is generated for the imprint through a certificate authentication service for dynamic exclusive traceability authentication. The public key is used to verify the authenticity of the imprint, and the private key is used for the electronic signature of subsequent data appending.

[0020] Step S12: Physically bind the process evolution trust mark to the corresponding building entity through multiple carriers, and simultaneously enter the traceable digital initial file of the corresponding building entity in the process evolution trust mark; Specifically, the process evolution trusted imprint is written into physical carriers such as RFID chips, encrypted QR codes, and NFC tags. The binding combination of physical carriers is determined according to the entity type for physical binding. Beidou positioning tags are bound to the physical carriers of important equipment or components that require real-time location tracking for locating the entity. For example, when the bound entity is a prefabricated building material, an RFID chip is embedded inside the material during manufacturing and an encrypted QR code is attached to the outside.

[0021] While binding the physical carrier, based on the entity's basic information, a traceable digital initial file is recorded in the process evolution trusted imprint. At the same time, a unique hash value is generated for the traceable digital initial file, and the hash value is stored in the construction project consortium blockchain for evidence storage. The traceable digital initial file is stored in association with the hash value. The traceable digital initial file includes the entity's basic attributes, specification certificates, warehousing information, and other initial data of the construction entity.

[0022] Step S2: When each building entity is working, the corresponding building entity's process evolution trusted imprint is activated to record the work data, and the data is stored and locked using blockchain technology. Furthermore, during the operation of each building entity, the corresponding building entity's process evolution trusted imprint is activated and the operation data is entered. This data is then stored and locked using blockchain technology, including the following sub-steps: Step S21: When the building entity is performing operations, the corresponding process evolution trust imprint is activated through the physical binding carrier to enter the data input window and add the building entity's operation data. Specifically, when the building entity is performing operations, the process evolution trusted imprint is activated by scanning the physical carrier through the operation event trigger. A data entry request with a timestamp and operator identity is initiated to the process evolution trusted imprint. After the request is verified, a data entry window is opened, and a temporary write permission is opened by the operation start event and closed by the dual acceptance completion event. Operation data is added to the process evolution trusted imprint through authorized devices. The operation data includes data information such as execution entity data, process parameter data, and environmental data.

[0023] Step S22: After the work is completed, a dual acceptance mechanism is used to conduct quality and safety acceptance, and blockchain technology is used to store and lock the accepted work data. Specifically, after the work is completed, a dual acceptance mechanism is used for quality and safety acceptance. This mechanism involves independent acceptance by both the work operator's quality inspector and the supervising engineer, generating electronic signatures including acceptance conclusions, rectification opinions, and acceptance images. When the acceptance is successful, the accepted work data, dual electronic signature information, and other work and acceptance data are packaged into a data block. The blockchain notarization service is called to calculate the hash value of the data block, and this hash value is broadcast to the construction project consortium blockchain for notarization. A globally unique blockchain transaction hash is returned, and the blockchain transaction hash and the data block are written to the appendable file address pointed to by the process evolution trusted imprint dynamic evolution log pointer, so that the newly added data is notarized and locked in the process evolution trusted imprint. When the acceptance fails, rectification is carried out, and re-acceptance is conducted based on the rectified data through the dual acceptance mechanism.

[0024] Step S3: Construct event control clusters in the construction project operation area according to the type of quality and safety risk, carry out risk classification, transmission and disposal, and generate risk event tracing data; Furthermore, within the construction project work area, event control clusters are constructed according to the type of quality and safety risk. Risk classification, transmission, and handling are then carried out to generate risk event tracing data, including the following sub-steps: Step S31: Deploy edge sensing nodes in the construction project area according to the quality and safety risk type to construct an event pulse control sensing cluster; Specifically, sensor nodes are matched according to the quality and safety risk type in the construction project operation area. Each sensor node is connected to the nearest edge computing gateway with local data processing and logical judgment capabilities through a low-power wide area network. Sensor nodes in the same operation area are grouped into a pulse control cluster to build an event pulse control cluster. Data from nodes within the cluster is shared to the edge gateway in real time, and cluster-based data linkage analysis is performed for different risk types.

[0025] Step S32: Collect risk perception data in real time through event pulse control clusters, and activate the event pulse control signal when risk data is detected; Specifically, risk perception data is collected in real time through sensor nodes, and the collected data is denoised and anomaly filtered through an edge computing gateway. Based on construction safety inspection standards, signal trigger thresholds for different risk types are set. When the detected risk perception data exceeds the trigger threshold, an event pulse control signal identification formula is used. Generate event pulse control signals, where, For time Event pulse control signal, The number of sensor nodes, The range of values ​​is , For the first Risk monitoring coefficient of each sensor node For the first Each sensing node in time Risk perception readings on For the first The baseline value of each sensor node, For the first Standard deviation of readings at each sensor node This is an adjustment coefficient for sudden risks. The number of fault detection nodes. The range of values ​​is , This is a function for indicating sensor node faults. For the first The fault detection threshold of each fault monitoring node, when hour, , indicating the first The fault detection node detects a fault in the sensor node, when hour, , indicating the first The sensor nodes detected by the fault detection node are normal.

[0026] Step S33: Identify the severity of the risk in the event pulse control signal, classify and handle the risk according to the preset risk classification warning response rules, and generate corresponding risk event tracing data; Specifically, based on event pulse control signals, a formula for identifying severe negative risks is used. The risk level is considered severely negative, among which... The risk level is extremely negative. This represents the instantaneous severity coefficient of the risk. In the time window Peak event pulse control signal within , This refers to the risk persistence coefficient. For the duration of the risk, This represents the spatial diffusion impact coefficient. The number of affected sensor nodes. This represents the total number of sensor nodes. For personnel exposure coefficient, The number of people in the high-risk area. The safe number of people allowed in this risk area.

[0027] The risk classification and early warning response rules are based on the risk's negative mode, setting corresponding negative mode response rules and early warning modes. For example, the response rule and early warning mode for minor risks is to trigger the on-site audible and visual alarm in the corresponding area, and simultaneously push the risk negative mode to the safety officer belonging to the event pulse control cluster for handling. After the risk event is handled, the complete event pulse control signal, handling process record, and related entity information are packaged into risk event tracing data.

[0028] Step S4: Construct a construction quality and safety tracing chain network based on the credible imprints of all process evolutions, and conduct risk causal tracing based on risk event tracing data to obtain a root cause-oriented tracing map; Furthermore, a construction quality and safety tracing chain network is constructed based on the credible imprints of all process evolutions. Risk causal tracking is performed based on risk event tracing data to obtain a root cause-oriented tracing map, including the following sub-steps: Step S41: Based on the credible imprints of all process evolutions, construct a construction quality and safety tracing chain network through predefined engineering quality and safety rules; Specifically, the engineering quality and safety rules are defined based on national construction standards, expert experience databases, and historical accident case databases, along with their weights. Based on the credible imprints of all process evolutions, quality and safety inspection nodes such as entity nodes, execution nodes, operation nodes, environmental nodes, and risk nodes are defined. Potential causal relationships between nodes are mined using association rule mining algorithms. Based on the engineering quality and safety rules, rules are matched to these potential causal relationships and weights are assigned, generating a construction quality and safety tracing chain network.

[0029] Step S42: Based on the risk event tracing data, conduct risk event tracing through a preset causal tracing engine in the construction quality and safety tracing chain network to generate a root cause-oriented tracing map. Specifically, the causal tracing engine receives risk event tracing data, locates risk nodes in the construction quality and safety tracing chain network, and then, starting from that risk node, traverses backward through all preceding nodes in the construction quality and safety tracing chain network, using the root cause dynamic tracing formula. Calculate the root cause tracing value of each preceding node that led to the risk event, where, For the preceding node Risk event nodes The root cause of the dynamic value, The range of values ​​is , Risk event nodes The total number of predecessor nodes, For the preceding node To the risk event node The set of all paths For a path in the set, For path The edge, For the edge Risk weights, Risk event nodes in historical data Corresponding events and preceding nodes The number of times the corresponding events occur simultaneously. Risk event nodes in historical data The total number of times the corresponding event occurred. It is the minimum value. This is the path attenuation coefficient. For path The length.

[0030] Based on the root cause tracing values ​​of each preceding node leading to the risk event, the most probable causal paths are selected. A root cause-oriented tracing graph is generated using a hierarchical tree structure, with the top layer representing the risk event node, the next layer representing the direct root cause nodes, and the next layer representing the indirect root cause nodes. The nodes are connected by weighted lines. This graph visually indicates the direct and indirect causes leading to the risk event and their probability percentages. For example, the root cause-oriented tracing graph for a crack risk event can indicate that the main cause of the crack is inadequate maintenance (probability 85%), which in turn is attributed to a malfunction in the automatic sprinkler system (probability 78%) and low nighttime temperatures (probability 86%).

[0031] Step S5: Identify relevant responsible parties based on the root cause-oriented tracing map and carry out risk rectification, and optimize the event pulse control cluster and construction quality and safety tracing chain network; Furthermore, based on the root cause-oriented tracing map, relevant responsible parties are identified and risk rectification is carried out. Optimizing the event pulse cluster and construction quality and safety tracing chain network includes the following sub-steps: Step S51: Identify relevant responsible parties based on the root cause-oriented source tracing map, automatically generate a responsibility determination report, and carry out risk rectification; Specifically, based on the mapping relationship between each root cause node and entities, operations, and execution nodes in the root cause-oriented tracing map, the responsible party is automatically matched. For example, if the root cause node is sprinkler equipment malfunction, corresponding to the operation node of inadequate maintenance, and the entity performing this operation is the maintenance team, then the responsible party is the maintenance team. Based on the matched responsible party and the root cause-oriented tracing map, the responsibility determination data corresponding to the risk event is obtained, and a responsibility determination report is automatically generated. The responsibility determination report includes data information such as a risk event overview, root cause-oriented tracing map, a list of responsible parties, the basis for responsibility, and rectification requirements. Based on the responsibility determination report, rectification tasks are assigned to the responsible parties for risk rectification, and the rectification progress is monitored in real time. After rectification is completed, acceptance is conducted. Upon successful acceptance, risk rectification data including before-and-after comparison images and a rectification inspection report is automatically generated.

[0032] Step S52: Update the process evolution trust imprint based on risk rectification data, and optimize the risk classification early warning response rules of the event pulse control cluster, and the construction rules of the construction quality and safety traceability chain network; Specifically, risk rectification data is added as a new data block to the trusted imprint of the relevant entity's process evolution, and a hash value is generated and added to the construction project consortium blockchain. Based on the risk rectification data analysis, the matching degree between the original risk classification and early warning response rules and the actual risks is analyzed. When the matching degree is lower than a preset matching threshold, the risk classification and early warning response rules for the corresponding event pulse sensing cluster are automatically adjusted based on historical risk rectification data. Based on the risk rectification data analysis, the accuracy, coverage, and rationality of the original construction rules of the construction quality and safety tracing chain network are evaluated. When the rule evaluation data indicators of the original construction rules are lower than the preset qualified indicators, the construction rules of the construction quality and safety tracing chain network are automatically optimized based on historical risk rectification data.

[0033] Example 2 like Figure 2 As shown, Embodiment 2 of this application provides a construction project quality and safety tracking system based on big data, including: The process evolution trusted imprint creation and binding module 21 creates process evolution trusted imprints for each building entity based on building entity information and binds them to the physical carrier. Furthermore, the process evolution trusted imprint creation and binding module 21 includes the following sub-modules: The process evolution trusted imprint creation submodule creates process evolution trusted imprints for each building entity based on building entity information through a three-level encryption coding mechanism, and performs dynamic exclusive traceability authentication. The process evolution trusted imprint binding submodule physically binds the process evolution trusted imprint to the corresponding building entity through multiple carriers, and simultaneously enters the traceable digital initial file of the corresponding building entity into the process evolution trusted imprint. The work data entry module 22 activates the corresponding building entity's process evolution trusted imprint record to enter work data when work is carried out on each building entity, and uses blockchain technology for evidence storage and locking. Furthermore, the task data entry module 22 includes the following sub-modules: The building entity operation data entry submodule activates the corresponding process evolution trust mark through physical binding carrier to enter the data entry window when the building entity is performing operations, and adds the building entity's operation data. The task data storage and locking submodule performs quality and safety acceptance through a dual acceptance mechanism after the task is completed, and uses blockchain technology to store and lock the accepted task data. Risk identification and handling module 23 constructs event pulse sensing clusters in the construction engineering operation area according to the type of quality and safety risks, carries out risk classification and transmission handling, and generates risk event tracing data; Furthermore, the risk identification and handling module 23 includes the following sub-modules: The event pulse control sensing cluster construction submodule deploys edge sensing nodes in the construction engineering operation area according to the quality and safety risk type to construct the event pulse control sensing cluster; The event pulse control signal activation submodule collects risk perception data in real time through the event pulse control sensor cluster, and activates the event pulse control signal when risk data is detected. The risk event causal data generation submodule identifies the severity and negativity of the event's control signals, performs graded transmission and handling according to preset risk grading and early warning response rules, and generates corresponding risk event causal data. The root cause tracing module 24 constructs a construction quality and safety tracing chain network based on the credible imprints of all process evolutions, performs risk causal tracking based on risk event tracing data, and obtains a root cause-oriented tracing map. Furthermore, the root cause tracing acquisition module 24 includes the following sub-modules: The sub-module for constructing the construction quality and safety cause-of-fact chain network is based on the credible imprints of all process evolutions and constructs the construction quality and safety cause-of-fact chain network through predefined engineering quality and safety rules. The root cause-oriented tracing map generation submodule, based on risk event tracing data, performs risk event causal tracing through a preset causal tracing engine in the construction quality and safety tracing chain network, and generates a root cause-oriented tracing map. Risk rectification and optimization module 25 identifies relevant responsible parties and carries out risk rectification based on the root cause-oriented tracing map, and optimizes the event pulse control cluster and construction quality and safety tracing chain network. Furthermore, the risk rectification and optimization module 25 includes the following sub-modules: The responsibility identification and risk rectification submodule identifies relevant responsible parties based on the root cause-oriented source tracing map, automatically generates a responsibility identification report, and carries out risk rectification. The cluster and network optimization submodule updates the process evolution trusted imprint based on risk rectification data, and optimizes the risk classification early warning response rules of the event pulse control cluster and the construction rules of the construction quality and safety traceability chain network. Corresponding to the above embodiments, the present invention provides a computer storage medium, including: at least one memory and at least one processor; The memory is used to store one or more program instructions; A processor is used to run one or more program instructions to execute a big data-based method for tracking the quality and safety of construction projects.

[0034] Corresponding to the above embodiments, this embodiment of the invention provides a computer-readable storage medium containing one or more program instructions, which are used by a processor to implement a big data-based method for tracking the quality and safety of construction projects.

[0035] The embodiments disclosed in this invention provide a computer-readable storage medium storing computer program instructions. When the computer program instructions are executed on a computer, the computer performs the above-described method for tracking the quality and safety of construction projects based on big data.

[0036] In this embodiment of the invention, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0037] The various methods, steps, and logic diagrams disclosed in the embodiments of this invention can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The processor reads information from the storage medium and, in conjunction with its hardware, completes the steps of the above methods.

[0038] The storage medium can be memory, such as volatile memory or non-volatile memory, or may include both volatile and non-volatile memory.

[0039] Among them, non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory.

[0040] Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (Synchlink DRAM, SLDRAM), and direct memory bus RAM (DRRAM).

[0041] The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0042] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in this invention can be implemented using a combination of hardware and software. When applied as software, the corresponding functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of computer programs from one place to another. Storage media can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0043] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solution of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for tracking the quality and safety of construction projects based on big data, characterized in that, include: Step S1: Create a reliable imprint of process evolution for each building entity based on the building entity information and bind it to the physical carrier; Step S2: When each building entity is working, the corresponding building entity's process evolution trusted imprint is activated to record the work data, and the data is stored and locked using blockchain technology. Step S3: Construct event control clusters in the construction project operation area according to the type of quality and safety risk, carry out risk classification, transmission and disposal, and generate risk event tracing data; Step S4: Construct a construction quality and safety tracing chain network based on the credible imprints of all process evolutions, and conduct risk causal tracing based on risk event tracing data to obtain a root cause-oriented tracing map; Step S5: Identify relevant responsible parties based on the root cause-oriented tracing map and carry out risk rectification, and optimize the event pulse control cluster and construction quality and safety tracing chain network.

2. The method for tracking the quality and safety of construction projects based on big data as described in claim 1, characterized in that, Creating a trusted imprint of process evolution for each building entity based on building entity information and binding it to a physical carrier includes the following sub-steps: Step S11: Based on the building entity information, create a reliable imprint of the process evolution for each building entity through a three-level encryption coding mechanism, and perform dynamic exclusive traceability authentication; Step S12: Physically bind the process evolution trust mark to the corresponding building entity through multiple carriers, and simultaneously enter the traceable digital initial file of the corresponding building entity in the process evolution trust mark.

3. The method for tracking the quality and safety of construction projects based on big data as described in claim 1, characterized in that, When work is being carried out on each building entity, the corresponding building entity's process evolution trusted imprint is activated and the work data is entered. The data is then stored and locked using blockchain technology, including the following sub-steps: Step S21: When the building entity is performing operations, the corresponding process evolution trust imprint is activated through the physical binding carrier to enter the data input window and add the building entity's operation data. Step S22: After the work is completed, a dual acceptance mechanism is used to conduct quality and safety acceptance, and blockchain technology is used to store and lock the accepted work data.

4. The method for tracking the quality and safety of construction projects based on big data as described in claim 1, characterized in that, In the construction engineering work area, an event pulse control cluster is constructed according to the type of quality and safety risk. Risk classification, transmission and handling are carried out, and risk event tracing data is generated. This includes the following sub-steps: Step S31: Deploy edge sensing nodes in the construction project area according to the quality and safety risk type to construct an event pulse control sensing cluster; Step S32: Collect risk perception data in real time through event pulse control clusters, and activate the event pulse control signal when risk data is detected; Step S33: Identify the severity of the risk in the event pulse control signal, classify and handle the risk according to the preset risk classification warning response rules, and generate corresponding risk event tracing data.

5. The method for tracking the quality and safety of construction projects based on big data as described in claim 1, characterized in that, Based on the credible imprints of all process evolutions, a construction quality and safety tracing chain network is constructed. Risk causal tracking is performed based on risk event tracing data to obtain a root cause-oriented tracing map, including the following sub-steps: Step S41: Based on the credible imprints of all process evolutions, construct a construction quality and safety tracing chain network through predefined engineering quality and safety rules; Step S42: Based on the risk event tracing data, conduct risk event tracing through a preset causal tracing engine in the construction quality and safety tracing chain network to generate a root cause-oriented tracing map.

6. A construction project quality and safety tracking system based on big data, characterized in that, include: The process evolution trusted imprint creation and binding module creates process evolution trusted imprints for each building entity based on building entity information and binds them to the physical carrier. The work data entry module activates the corresponding building entity's process evolution trusted imprint record to enter work data when work is carried out on each building entity, and uses blockchain technology for evidence storage and locking. The risk identification and handling module constructs event control clusters in the construction engineering operation area according to the type of quality and safety risk, carries out risk classification and transmission and handling, and generates risk event tracing data. The root cause tracing module constructs a construction quality and safety tracing chain network based on the credible imprints of all process evolutions, and performs risk causal tracking based on risk event tracing data to obtain a root cause-oriented tracing map. The risk rectification and optimization module identifies relevant responsible parties and carries out risk rectification based on the root cause-oriented tracing map, and optimizes the event pulse control cluster and construction quality and safety tracing chain network.

7. A construction project quality and safety tracking system based on big data as described in claim 6, characterized in that, The process evolution trusted imprint creation and binding module specifically includes: The process evolution trusted imprint creation submodule creates process evolution trusted imprints for each building entity based on building entity information through a three-level encryption coding mechanism, and performs dynamic exclusive traceability authentication. The process evolution trusted imprint binding submodule physically binds the process evolution trusted imprint to the corresponding building entity through multiple carriers, and simultaneously enters the traceable digital initial file of the corresponding building entity into the process evolution trusted imprint.

8. A construction project quality and safety tracking system based on big data as described in claim 6, characterized in that, The task data entry module specifically includes: The building entity operation data entry submodule activates the corresponding process evolution trust mark through physical binding carrier to enter the data entry window when the building entity is performing operations, and adds the building entity's operation data. The task data storage and locking submodule performs quality and safety acceptance through a dual acceptance mechanism after the task is completed, and uses blockchain technology to store and lock the accepted task data.

9. A construction project quality and safety tracking system based on big data as described in claim 6, characterized in that, The risk identification and handling module specifically includes: The event pulse control sensing cluster construction submodule deploys edge sensing nodes in the construction engineering operation area according to the quality and safety risk type to construct the event pulse control sensing cluster; The event pulse control signal activation submodule collects risk perception data in real time through the event pulse control sensor cluster, and activates the event pulse control signal when risk data is detected. The risk event causal data generation submodule identifies the severity and negativity of the event's control signals, performs graded transmission and handling according to preset risk grading and early warning response rules, and generates corresponding risk event causal data.

10. A construction project quality and safety tracking system based on big data as described in claim 6, characterized in that, The root cause tracing module includes: The sub-module for constructing the construction quality and safety cause-of-fact chain network is based on the credible imprints of all process evolutions and constructs the construction quality and safety cause-of-fact chain network through predefined engineering quality and safety rules. The root cause-oriented tracing map generation submodule, based on risk event tracing data, uses a preset causal tracing engine to trace the causal origins of risk events within the construction quality and safety tracing chain network, generating a root cause-oriented tracing map.

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