CN113434902A - Construction safety monitoring management system and method based on block chain - Google Patents
Construction safety monitoring management system and method based on block chain Download PDFInfo
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Abstract
The invention discloses a construction safety monitoring and management system and method based on a block chain, and belongs to the technical field of construction site accident prevention. The method comprises the following steps: the data acquisition module is used for acquiring target monitoring data and data information of a construction site; the data processing module is used for processing the data and the information through a deep learning algorithm, acquiring and broadcasting event key information, and then performing consistency verification and sequencing on the event key information; the supervision early warning module receives node information broadcasted on the block chain, deploys an intelligent contract according to the accident tree model and the fact key information, and carries out early warning and feedback; and the remote server module records the original information of each device and the operator, and acquires the uploaded and stored node information in real time for inquiry and accident responsibility tracing. The invention is beneficial to ensuring the authenticity of construction data, and simultaneously carries out safety early warning on the construction elevator, finds safety risks in time, is convenient for accident responsibility tracing and improves the safety of construction operation.
Description
Technical Field
The invention belongs to the technical field of construction site accident prevention, and particularly relates to a construction safety monitoring and management system and method based on a block chain.
Background
Current construction activities are highly mechanized. The construction machinery with high-efficiency vertical transportation capability, such as a construction elevator and a tower crane, almost becomes a necessary special operation machinery for construction sites. At the same time, however, various safety problems begin to occur, and over 30% of construction safety accidents are caused by construction equipment failures.
Taking a construction elevator as an example, most of the current equipment operation data of the construction elevator are stored by depending on a black box installed on the construction elevator, but the data recorded by the black box are only limited to a manufacturer to collect working condition test data, lack of authentication of a third party authority, and have the risk of being tampered, so the black box data are often ignored in the process of investigating the construction elevator accident, and other means are selected to investigate the cause of the accident, thereby greatly increasing the cost of accident investigation. This makes real-time monitoring of the construction hoist before an accident occurs and tracing of the accident after the accident becomes a difficult task. This problem has been sustained in the construction industry for decades, and has severely hampered the improvement of the level of construction management. To enhance trust between parties, information acquired and provided by stakeholders such as equipment suppliers, owners, contractors, security supervisors, government quality monitoring stations, etc. must be traceable and transparent, depending not only on the integrity of data collection, but also on the unforgeability and transparency of data transmission and storage. However, in the process of the construction elevator operation, each stakeholder cannot easily capture the equipment information, and it is difficult to safely transmit and store the equipment information, and once the accountability is involved, the accountant has an incentive to tamper with the data to release the accountability. This presents a significant challenge to conventional information collection, transmission and storage techniques.
Therefore, the reliability, traceability and transparency of the safety information of the construction elevator are guaranteed, real-time early warning of equipment risks is realized, and the accident responsibility traceability efficiency is improved.
Disclosure of Invention
Aiming at the defects of the related technology, the invention aims to provide a construction safety monitoring management system and a construction safety monitoring management method based on a block chain, which are used for carrying out safety monitoring, early warning and accident tracing on a construction elevator based on the block chain and the technology of the Internet of things and aim to solve the problems of scattered data storage and difficulty in guaranteeing data authenticity in the traditional construction management.
In order to achieve the above object, an aspect of the present invention provides a construction safety monitoring and management system based on a block chain, including:
the data acquisition module is used for acquiring target monitoring data and data information of a construction site;
the data processing module is used for processing the acquired target monitoring data and the data information through a deep learning algorithm, acquiring event key information, broadcasting the event key information to each node of the block chain, and then performing consistency verification and sequencing on the event key information;
the supervision early warning module is used for receiving node information broadcasted on the block chain, establishing an accident tree model, determining association rules of each fact key information causing accidents to obtain an accident chain, taking a logic relation corresponding to the accident chain as a response rule, taking the fact key information as a trigger condition, deploying an intelligent contract, executing judgment on the node information, automatically executing the intelligent contract when the trigger condition and the response rule are met, and carrying out early warning and feedback;
and the remote server module records the original information of each device and the operator, and acquires the uploaded and stored node information in real time so as to allow each participant to inquire the data and trace the accident responsibility.
Further, in the supervision early warning module, the node information is judged, and when the triggering condition and the response rule are met, the intelligent contract is automatically executed, including:
when the node information simultaneously comprises the basic events of the minimal cut set in the accident tree, the intelligent contract is automatically executed.
Further, the step of acquiring the event key information and broadcasting the event key information to each node of the block chain in the data processing module includes:
classifying the previous key node information and accident cause information of the same category by using a deep learning algorithm, and counting the cause-effect logic relationship among all factors causing the same type of accidents by using a tree connection relationship, so as to extract the key information of all the induction factors causing the same type of accidents; extracting the key information from the target monitoring data and the data information by means of key word index of the key information, and determining the key information as the fact key information needing chain linking;
and after the event key information is acquired, digitally encrypting the acquired fact key information through a Hash algorithm, broadcasting the fact key information to each node of the block chain, and sending a data storage request.
Further, in the data processing module, the consistency verification of the event key information includes:
after each node on the block chain receives the data storage request, digital signature is carried out on the fact key information according to the request content, a data storage request receipt is returned, consistency verification is carried out on the receipt returned by the node, and when the block chain system obtains a sufficient amount of receipt and verifies the consistency, the fact key information and the receipt result are combined into a data packet.
Further, in the data processing module, the sorting event key information includes:
and sending the data packet containing the receipt result to a sequencing node, wherein the sequencing node synchronously receives different information, sequences the information according to the time information generated by the original data, packs the information into blocks and broadcasts the blocks to the whole network.
The invention also provides a construction safety monitoring and management method based on the block chain, which comprises the following steps:
collecting target monitoring data and data information of a construction site;
processing the collected target monitoring data and the data information through a deep learning algorithm, acquiring event key information, broadcasting the event key information to each node of a block chain, and then performing consistency verification and sequencing on the event key information;
receiving node information broadcasted on a block chain, establishing an accident tree model, determining association rules of each fact key information causing accidents to obtain an accident chain, taking a logic relation corresponding to the accident chain as a response rule, taking the fact key information as a trigger condition, deploying an intelligent contract, executing judgment on the node information, automatically executing the intelligent contract when the trigger condition and the response rule are met, and performing early warning and feedback;
and recording the original information of each device and each operator, and acquiring the uploaded and stored node information in real time so as to allow each participant to inquire data and trace accident responsibility.
Further, the judging of the node information is executed, and the automatic execution of the intelligent contract when the triggering condition and the response rule are met comprises the following steps:
when the node information simultaneously comprises the basic events of the minimal cut set in the accident tree, the intelligent contract is automatically executed.
Further, the obtaining and broadcasting the event key information to each node of the block chain comprises:
classifying the previous key node information and accident cause information of the same category by using a deep learning algorithm, and counting the cause-effect logic relationship among all factors causing the same type of accidents by using a tree connection relationship, so as to extract the key information of all the induction factors causing the same type of accidents; extracting the key information from the target monitoring data and the data information by means of key word index of the key information, and determining the key information as the fact key information needing chain linking;
and after the event key information is acquired, digitally encrypting the acquired fact key information through a Hash algorithm, broadcasting the fact key information to each node of the block chain, and sending a data storage request.
Further, the consistency verification of the event key information comprises:
after each node on the block chain receives the data storage request, digital signature is carried out on the fact key information according to the request content, a data storage request receipt is returned, consistency verification is carried out on the receipt returned by the node, and after the block chain system obtains a sufficient amount of receipts and verifies the consistency, the fact key information and the receipt result are combined into a data packet.
Further, ranking the event key information includes:
and sending the data packet containing the receipt result to a sequencing node, wherein the sequencing node synchronously receives different information, sequences the information according to the time information generated by the original data, packs the information into blocks and broadcasts the blocks to the whole network.
Through the technical scheme, compared with the prior art, the invention has the following beneficial effects:
(1) the invention realizes safety early warning by using the intelligent contract, analyzes accident events by using an accident tree analysis method, clears all risk factors causing the accident and causal logical relations among the risk factors, establishes an accident chain, analyzes possible paths causing the accident to obtain the minimum cut set and the shortest path of the accident causing the accident, and further deploys the factors and association rules among the factors as the triggering conditions and the response rules of the intelligent contract in the intelligent contract.
And then, identifying the information stored in the block chain through an intelligent contract deployed in the block chain system, timely early warning the discovered risk factors, timely feeding back the risk factors to the interest relevant parties, and archiving the accident early warning result for later use. The reliability and the authenticity of the early warning information are guaranteed through the block chain technology, the possible path of the accident and the key nodes causing the accident are calculated by means of the accident tree analysis method, and the logical relationship and the importance relationship among all causes of the accident are cleared, so that the intelligent contract can be better deployed, and the accident early warning process is optimized. And the early warning result is filed in time, so that the responsibility of each party can be determined, and certain effects on responsibility confirmation and work effect evaluation of each party are achieved.
(2) The invention screens mass data by means of a deep learning algorithm, extracts key information closely related to equipment safety, optimizes data acquisition and data processing flows, and provides reference for correctly selecting trigger factors in an accident early warning system.
(3) By means of the Internet of things technology, channels for acquiring data are widened, reliability of data sources is guaranteed, then the characteristics of transparency, openness, traceability and non-falsification of a block chain are fully utilized, authenticity and reliability of acquired data are guaranteed, risks of data falsification are reduced, the data can be used as a reliable basis for accident investigation more than before, and efficiency of accident traceability and accident liability pursuit is accelerated.
In general, the method and the system enable all interest-related parties to share and acquire accurate equipment and operator information in time, and realize real-time monitoring and early warning on the information, so that the purposes of finding risk factors in time, correcting deviation in time, tracing responsibility safety in the later period and improving the safety management level of construction equipment are achieved.
Drawings
Fig. 1 is a block chain-based construction safety monitoring management system framework diagram according to an embodiment of the present invention.
Fig. 2 is a schematic view of a block chain-based construction safety monitoring management process according to an embodiment of the present invention.
Fig. 3 is an overall schematic view of a block chain-based construction safety monitoring management system according to an embodiment of the present invention.
FIG. 4 is a causal graph of the factors of a drop accident of a construction elevator cage according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The following describes the content of the present invention with the safety precaution and accident tracing of the construction hoist as a preferred embodiment.
With reference to fig. 1, a block chain-based safety monitoring and management system for a construction elevator comprises a data acquisition module, a data transmission and processing module, a supervision and early warning module and a remote server module;
the data acquisition module comprises a two-dimensional code, an RFID, a face recognition device, a video monitoring device, an inclination angle sensor, a wind speed sensor, a corrosion degree sensor, an obstacle sensor, an operation signal acquisition device, a safety system state information acquisition device and other necessary information acquisition devices, and is used for acquiring data of equipment state information, worker operation information, worker basic information and the like;
the data processing module processes the acquired information such as equipment state information, worker operation information and the like by means of a deep learning algorithm to acquire key information, and uploads the key information to the block chain to complete sequencing and consistency verification and realize distributed storage;
the supervision early warning module receives node information broadcasted in a network, executes judgment, finds out risk factors, carries out early warning in time, and judges whether data is tampered;
the remote server module records the basic information of each participant and the equipment, simultaneously acquires the uploaded and stored node information in real time, and each participant can inquire data and trace accident responsibility and also can inquire the accident handling result information uploaded to the block chain, thereby reducing responsibility disputes and simultaneously providing experience training for subsequent work.
The equipment state information comprises equipment inclination angle information, field wind speed information, equipment verticality information, equipment corrosion degree information, equipment torque information, equipment operation signal information, safety control system state information, video monitoring information and the like.
The parties involved include stakeholders of the project, such as equipment suppliers, general contractors, subcontractors, owners, proctorists, and governmental agencies.
The information uploaded to the blockchain includes, in addition to the daily collected device and operation information, accident occurrence process information and accident handling process information.
The method has the technical effects of improving the transparency of construction equipment data, judging the induction reason of the accident, carrying out safety early warning on the possible accident and simultaneously facilitating the tracing of the accident at the later stage.
With reference to fig. 2, fig. 3, and fig. 4, a construction elevator safety monitoring management system and method based on a block chain according to an embodiment of the present invention are specifically described as an example of a construction elevator use and maintenance phase:
the method comprises the steps that the Internet of things equipment is installed on each necessary monitoring position, if a wall-attached frame horizontal inclination angle sensor is installed at the joint of each wall-attached frame and a machine body, a verticality sensor is arranged at the top and the bottom of a guide rail frame, a torque sensor is installed at the peripheral bolts of each standard section, a corrosion sensor is arranged on a standard section rod piece, a top wind speed monitoring sensor is installed at the top of the guide rail frame of the construction elevator, and a video monitoring device is installed inside a car of the construction elevator.
The qualification needs to be verified before the construction elevator operator operates, meanwhile, the face recognition needs to be carried out, the operator is authorized to enter the equipment area for operation only when the face information and the qualification information are consistent with the pre-input information, and if the identification information is inconsistent, the operator is not allowed to enter the equipment area for operation.
On the premise that an operator is authorized to operate the equipment, a data acquisition module of the safety early warning system of the construction elevator based on the block chain acquires running state information of the equipment and behavior monitoring information of the operator, wherein the running state information of the equipment comprises information transmitted by an inclination angle sensor, a verticality sensor, a torque sensor, a corrosion degree sensor, a top wind speed sensor, a load sensor, various operation switch buttons, a video monitoring device and the like, and the behavior monitoring information of the operator comprises uplink and downlink operation information, control system operation information, safety system operation information and other behavior information.
While collecting information of equipment and operators in real time, the inspection results of safety inspection units, the reexamination results of general packet units, the final inspection results of supervision units and the construction elevator use state inspection acceptance table signed by each party need to be uploaded regularly. During the maintenance of the construction elevator, the lubrication state information of each part of the construction elevator, and information such as fault repair and part replacement need to be uploaded at intervals such as daily, weekly, monthly, quarterly, and yearly. All the collected information is stored in a database after being summarized and is archived for later use.
The data processing module classifies and classifies accident cause information and important node information of the same type of construction elevators according to historical data and existing accident cases by means of a deep learning algorithm, divides the falling accidents of the construction elevator cages into two types of cage falling caused by equipment driving and safety device problems and cage falling caused by collapse and overturning as shown in figure 4, divides the former into two types of driving system faults and safety device faults, divides the latter into two types of unsafe complete machines and mechanical faults of guide rail frames, cages, wall-attached frames and the like, and further subdivides the two types of the cage falling accidents into specific cause factors such as gear faults, wheel-dependent faults, insufficient driving force, prevention of loss of safers, unstable foundations, bolt loosening, guide rail frame rusting and the like on the basis. And then, on the basis of classification and classification of the causative factors, performing tree statistics on causal logical relations among the factors causing the same type of accidents, thereby extracting key information in all the factors causing the type of accidents and preparing for extracting which information from a database in the next step. And then, by means of key word index of the key information, data extraction is carried out on the information of the current stage stored in the database in the steps, and key information related to accidents is screened out, wherein the key information comprises equipment running state information, personnel operation information, key data and the like. For example, in terms of the type of the construction elevator cage falling, starting from the aspects of machinery, personnel, environment and management, the method finds out a plurality of factors causing accidents from a plurality of similar accidents, and finding out the causal logical relationship among the factors, such as the conditions of aging of components such as a gear and a rack, a depended wheel and the like, the failure of a braking device and the failure of an anti-falling safety device due to the influence of environmental factors, supervision and management, incomplete examination, incomplete construction scheme, incomplete maintenance and the like, leading the cage of the construction hoist to fall, obtaining key factors leading to the accidents through analysis, namely gear and rack aging, brake device failure, safety device failure, inadequate supervision, inadequate maintenance and the like, determining the key factor information as factual key information needing to be extracted from a database and linked;
digitally encrypting the acquired factual key information through a Hash algorithm, broadcasting the factual key information to each node of the block chain network, and sending a data storage request;
after receiving a data storage request, each node on the block chain digitally signs monitoring data according to the request content and returns a data storage request receipt, each node returns a receipt which has uncertainty and needs to be verified for consistency, and when the block chain system obtains enough receipts and verifies consistency, the monitoring data and the receipt results are combined into a data packet;
sending a data packet containing a receipt result to a sequencing node, wherein the sequencing node synchronously receives different information, sequences the information according to time information generated by original data, packs the information into blocks, broadcasts the blocks to the whole network, and all nodes on a block chain can acquire the stored information;
each node acquiring the information can verify the packaged information content to ensure that each information is judged and verified by the related node and the receipt results are consistent, and if the information is not consistent, the information is examined and retained;
after the verification is successful, each node also needs to ensure that the state of the ledger of the current recorded information is consistent with the state of the ledger when the information is generated, so that the updated ledger is ensured to be consistent with the ledger updated by other nodes in the network, the whole transaction process can be called as consensus, and all nodes in the process reach consistency in sequencing and information storage content;
the supervision early warning module receives node information broadcasted on a block chain, and deploys an intelligent contract through event key information extracted by means of a deep learning algorithm in the past, preset trigger conditions and response rules, so that node information is judged, the intelligent contract can be automatically executed after risk factors are found, timely early warning is realized, and the risk factors are timely fed back to all responsible persons, such as project responsible persons, technical persons, safety management persons, safety supervision and the like, so that the construction site is remedied.
An intelligent contract is a computer program which is realized by computer technology and automatically executed when certain conditions are met, and the triggering, processing and data saving of transactions are carried out on a chain. The setting of the partial response rule mainly depends on the causal logical relationship among accident causative factors of the construction elevator, and the purpose can be realized by combining an accident tree analysis method.
The accident tree method is a systematic analysis method for deducing consequences from reasons, and mainly comprises the following steps: determining accident type, understanding and analyzing the principle, factors and method of the system where the accident is located, extensively investigating and analyzing the accident which has already occurred, reasonably predicting the accident which may occur in the future, determining the association rule and probability among all the influencing factors, and establishing an accident tree model.
After qualitative and quantitative analysis is carried out on the target event through the accident tree, the influence and the causal logical relationship among all factors of accident occurrence or accident prevention can be known, so that which factors are more critical factors can be known, meanwhile, an accident chain is established according to the relationship among the factors, a possible path leading to each accident occurrence is obtained, and then the path is used as an association rule, an intelligent contract is deployed, and early warning is carried out in time when risk factors are found. The accident tree analysis method can be used for knowing main factors and paths causing accidents, so that the possible paths causing the accidents and key nodes causing the accidents can be calculated, logical relations and importance relations among all causes of the accidents are cleared, and the intelligent contract can be better deployed. According to the accident analysis method, the top event is an unexpected event, namely a construction elevator accident or a type of accident that a cage falls; basic events, namely various events or factors which can cause accidents, can be used as trigger conditions in the intelligent contract; the AND gate and the OR gate in the accident tree analysis represent that the event happens when all events happen simultaneously or any event happens, reflect the logical relation and the correlation among all the events or all the factors, and can provide a valuable reference basis for deploying the association rule in the intelligent contract; in the accident tree analysis method, the minimal cut set refers to the combination of the least basic events causing the top events, if any event does not occur in the minimal cut set, the accident will not occur, therefore, in order to prevent the occurrence of the accident, the basic events in the minimal cut set of each accident must be avoided from occurring simultaneously, the minimal cut set can be understood as the basic elements causing the occurrence of the accident, which are combined together according to the sequence and the logical relationship, so as to form the shortest path causing the accident, that is, no matter how many factors the path causing the accident includes and how long the path includes, as long as all the basic events in the minimal cut set or the shortest path causing the accident, the accident will be likely to occur, and the logical relationship among the basic factors in the minimal cut set and the shortest path are the association rules needed in the deployment of the intelligent contract, namely, when the factors simultaneously appear, accidents can happen, and therefore accident early warning is carried out.
The following specifically introduces the deployment and execution logic of the intelligent contract of the system by taking an accident of a certain construction elevator as an example:
the factors causing this accident are: the limiting device fails; the anti-falling safety device fails; loss of the safety hook; damage to the overload alarm; the gear rack is seriously aged; overdue service; overload operation; the standard joint connecting piece of the guide rail frame is not stressed enough; insufficient qualification of operators; insufficient maintenance, etc.
1) Deployment logic:
and (3) according to past historical data and future expectation, calculating the shortest path between the minimum cut of the accident and the possible accident by combining key factors and key events extracted by deep learning and an accident tree analysis method, and substituting the shortest path into the known risk factors or risk events for analysis so as to obtain a plurality of possible paths for the accident. Possible factors and paths that may occur, for example, in the event of a fall in the cage are: due to insufficient maintenance, inadequate data audit and inadequate field supervision, the situations of overproof and overdue scrapping of a field construction elevator occur, so that the whole machine is unsafe, the guide rail frame collapses and topples, and finally the cage incidentally falls; due to the fact that on-site supervision is not in place, a construction scheme is not complete, and data verification is not in place, the foundation base of the construction elevator is not stable, the guide rail frame collapses and topples, and finally the cage falls; the influence of environmental factors is added with the condition that the system is imperfect, the field supervision and management is not in place, the data audit is not in place, the connecting pieces of the guide rail frame, the suspension cage, the wall-attached frame and other members are not in accordance with the requirements, and the operation of related personnel is violated, so that the elevator collapses and topples, the suspension cage falls and the like. Then, based on the importance degree of each risk factor obtained from the accident tree, a minimal cut set or a shortest path which may cause an accident is found out, that is, when which risk factors occur simultaneously or sequentially, an accident may occur, for example, the minimal cut set which causes a certain cage falling accident has: a. switch failure, unqualified quality, failure of the speed limiter, failure of the upper and lower limit limiters and failure of the upper and lower limit limiters; b. the switch fails, the speed limiter fails, the upper limit limiter and the lower limit limiter fail, and the operator operates in violation; c. the ground bearing capacity is insufficient, overload and vibration are excessive, the mounting design is unreasonable, overload and vibration are excessive, e overload occurs, the overload safety alarm device fails, the anti-falling safety device is damaged, and the like. Obtaining the shortest path and the importance degree of each factor which are possible to happen to the accident according to the accident minimum cut sets and the historical data information, obtaining the association rule which can lead to the accident when some factors appear, thereby clearing the accident caused by each factor, and then deploying the series of logic relations as response rules in the intelligent contract; aiming at indexes with threshold requirements, according to past historical data, future expectation and existing industrial standards, threshold values of all indexes are found out and are deployed in intelligent contracts, for example, when the corrosion degree of a guide rail frame is set to be a threshold value, the corrosion thickness does not exceed 25% of the factory thickness, the inclination angle of the guide rail frame is set to be a threshold value which is less than or equal to +/-8 degrees, the set threshold value of the top wind speed use stage of a construction elevator is generally less than or equal to 20m/s, and if the index data stored in the block chain link points exceed the threshold value, the intelligent contracts in the block chain system can be identified as risk factors which can cause accidents. When the information stored in the block chain node contains the factors of the accident tree minimum cut set, the intelligent contract is automatically executed, an early warning signal is sent out, and meanwhile, the early warning result is stored in a database which is used for storing the information collected by the data collection module in advance.
2) Execution logic:
the key information collected during the monitoring process of the accident is as follows: the system comprises a block chain, a limiting device, an anti-falling safety device, a safety hook, load information, guide rail frame corrosion degree information, gear state information, wheel leaning state information, service state information, operating personnel operation information, operating personnel qualification information, maintenance information and the like, wherein the information is stored on the block chain according to the aforementioned data storage and identification processes. According to the standard and threshold value preset in the intelligent contract, the risk factors of failure of the existing limit device, failure of an anti-falling safety device, loss of a safety hook, damage of an overload alarm, serious aging of a gear and a rack, overdue service, overload operation, insufficient stress of a guide rail frame standard joint, insufficient qualification of operators, insufficient maintenance and the like are identified, then according to the response conditions and the response rules preset in the intelligent contract, the accident can be caused by the fact that only the overload operation and the overload alarm device are simultaneously carried out or the risk factors of serious aging of the super gear rack and the anti-falling safety device, failure and the like are simultaneously carried out in the event, therefore, when the risk factors exist on the block chain, the method can be automatically executed, trigger the early warning mechanism, realize the safety early warning, and the early warning result is fed back to each interest relevant party in time and is stored in the database at the same time.
The system realizes distributed storage of the information of the construction elevator equipment and the operators, sequences the node information, can effectively prevent falsification, can inquire and trace the node information after a safety accident occurs, defines key nodes of the accident, and facilitates quality responsibility determination of all parties.
On the remote server, all stakeholders and external management personnel, such as equipment suppliers, quality monitoring stations of governments and the like can inquire and supervise the safety information of the construction elevator equipment on a plurality of construction sites in the same city. For example, for an equipment supplier, the system management of the multi-site construction equipment can be realized through the remote server, the problems existing in the equipment can be found, the equipment can be optimized and managed in time, the safety responsibility can be defined, whether the accident cause is related to the equipment when a safety accident happens is judged, and the quality responsibility dispute between the equipment supplier and other participants is avoided; for external monitoring mechanisms, such as quality monitoring stations and the like, the state information of the equipment in multiple places can be monitored and managed through the remote server, problems can be found in time and remedied in time, and the responsibility of each party can be traced in time when an accident occurs;
after the accident equipment safety accident occurs, while all parties handle the accident information and the accident handling result in time, the accident information and the accident handling result are uploaded to the construction equipment safety early warning and accident traceability system based on the block chain, so that all parties can conveniently identify and trace the accident handling result, authenticity and validity of accident historical data can be guaranteed, and the accident experience teaching has certain reference significance for later construction equipment management.
Compared with the prior art, the invention has the following advantages:
the invention provides a construction safety monitoring and management system and method based on a block chain, which aim to improve the reliability, traceability and transparency of engineering equipment information and personnel operation information. The invention realizes the distributed storage of the construction elevator equipment and the operator information, can effectively prevent falsification, can inquire and trace the node information after a safety accident occurs, defines key nodes of the accident occurrence, facilitates the quality responsibility confirmation of all parties, enables all interest-related parties to share and obtain accurate equipment and operator information in time, and realizes the real-time monitoring of the construction elevator, thereby finding risk factors in time, facilitating the timely deviation correction and the later accident tracing, improving the efficiency of safety monitoring and accident responsibility confirmation, and improving the safety management level of construction equipment. Meanwhile, external management personnel can monitor and early warn safety accidents systematically on the construction elevator equipment in the same city or the same area.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. The utility model provides a construction safety monitoring management system based on block chain which characterized in that includes:
the data acquisition module is used for acquiring target monitoring data and data information of a construction site;
the data processing module is used for processing the target monitoring data and the data information through a deep learning algorithm, acquiring event key information, broadcasting the event key information to each node of the block chain, and then performing consistency verification and sequencing on the event key information;
the supervision early warning module is used for receiving node information broadcasted on the block chain, establishing an accident tree model, determining association rules of each fact key information causing accidents to obtain an accident chain, taking a logic relation corresponding to the accident chain as a response rule, taking the fact key information as a trigger condition, deploying an intelligent contract, executing judgment on the node information, automatically executing the intelligent contract when the trigger condition and the response rule are met, and carrying out early warning and feedback;
and the remote server module records the original information of each device and the operator, and acquires the uploaded and stored node information in real time so as to allow each participant to inquire the data and trace the accident responsibility.
2. The construction safety monitoring and management system according to claim 1, wherein in the supervision and early warning module, the judgment of the node information is performed, and the automatic execution of the intelligent contract when the triggering condition and the response rule are satisfied comprises:
when the node information simultaneously comprises the basic events of the minimal cut set in the accident tree, the intelligent contract is automatically executed.
3. The construction safety monitoring and management system according to claim 1, wherein the data processing module, acquiring and broadcasting the event key information to each node of the block chain, comprises:
classifying the previous key node information and accident cause information of the same category by using a deep learning algorithm, and counting the cause-effect logic relationship among all factors causing the same type of accidents by using a tree connection relationship, so as to extract the key information of all the induction factors causing the same type of accidents; extracting the key information from the target monitoring data and the data information by means of key word index of the key information, and determining the key information as the fact key information needing chain linking;
and after the event key information is acquired, digitally encrypting the acquired fact key information through a Hash algorithm, broadcasting the fact key information to each node of the block chain, and sending a data storage request.
4. The construction safety monitoring and management system according to claim 3, wherein the data processing module, performing consistency verification on the event key information, comprises:
after each node on the block chain receives the data storage request, digital signature is carried out on the fact key information according to the request content, a data storage request receipt is returned, consistency verification is carried out on the receipt returned by the node, and when the block chain system obtains a sufficient amount of receipt and verifies the consistency, the fact key information and the receipt result are combined into a data packet.
5. The construction safety monitoring and management system according to claim 4, wherein the data processing module, the sequencing of the event key information comprises:
and sending the data packet containing the receipt result to a sequencing node, wherein the sequencing node synchronously receives different information, sequences the information according to the time information generated by the original data, packs the information into blocks and broadcasts the blocks to the whole network.
6. A construction safety monitoring and management method based on a block chain is characterized by comprising the following steps:
collecting target monitoring data and data information of a construction site;
processing the collected target monitoring data and the data information through a deep learning algorithm, acquiring event key information, broadcasting the event key information to each node of a block chain, and then performing consistency verification and sequencing on the event key information;
receiving node information broadcasted on a block chain, establishing an accident tree model, determining association rules of each fact key information causing accidents to obtain an accident chain, taking a logic relation corresponding to the accident chain as a response rule, taking the fact key information as a trigger condition, deploying an intelligent contract, executing judgment on the node information, automatically executing the intelligent contract when the trigger condition and the response rule are met, and performing early warning and feedback;
and recording the original information of each device and each operator, and acquiring the uploaded and stored node information in real time so as to allow each participant to inquire data and trace accident responsibility.
7. The construction safety monitoring and management method according to claim 6, wherein the execution of the judgment on the node information and the automatic execution of the intelligent contract when the trigger condition and the response rule are satisfied comprises:
when the node information simultaneously comprises the basic events of the minimal cut set in the accident tree, the intelligent contract is automatically executed.
8. The construction safety monitoring and management method according to claim 6, wherein the step of obtaining and broadcasting the event key information to each node of the blockchain comprises:
classifying the previous key node information and accident cause information of the same category by using a deep learning algorithm, and counting the cause-effect logic relationship among all factors causing the same type of accidents by using a tree connection relationship, so as to extract the key information of all the induction factors causing the same type of accidents; extracting the key information from the target monitoring data and the data information by means of key word index of the key information, and determining the key information as the fact key information needing chain linking;
and after the event key information is acquired, digitally encrypting the acquired fact key information through a Hash algorithm, broadcasting the fact key information to each node of the block chain, and sending a data storage request.
9. The construction safety monitoring and management method according to claim 8, wherein the consistency verification of the event key information comprises:
after each node on the block chain receives the data storage request, digital signature is carried out on the fact key information according to the request content, a data storage request receipt is returned, consistency verification is carried out on the receipt returned by the node, and after the block chain system obtains a sufficient amount of receipts and verifies the consistency, the fact key information and the receipt result are combined into a data packet.
10. The construction safety monitoring and management method according to claim 9, wherein the sequencing of the event key information comprises:
and sending the data packet containing the receipt result to a sequencing node, wherein the sequencing node synchronously receives different information, sequences the information according to the time information generated by the original data, packs the information into blocks and broadcasts the blocks to the whole network.
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