A highway engineering construction remote material anomaly management method

Abstract

The application discloses a kind of highway engineering construction remote material abnormal management method, it is related to material management technical field, including the following steps: acquisition material information parameter is analyzed, obtains analysis result and carries out abnormal identification, exports abnormal identification result, triggers response mechanism to abnormal identification result, carries out automatic early warning, and provides corresponding disposal scheme to carry out abnormal processing, exports processing result and records, by constructing whole-process digital highway engineering construction remote material abnormal management system, solve traditional management multiple problems, integrate multi-source data automatic error correction and generate accurate data, with the help of spatiotemporal dimension cross validation and construction process compliance analysis, combined with preset abnormal feature combination library captures complex risk, adopts hierarchical response and mapping table to realize differentiated early warning, through the use check and block chain storage certificate to ensure responsibility, disposal combines template optimization and double verification to form closed loop, reduce risk, improve management intelligent and fine level.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of material management, and in particular to a remote material abnormality management method for highway engineering construction. BACKGROUND

[0002] In recent years, in the field of highway engineering construction, material management, as a core link of the project, runs through the whole life cycle, including the whole process control of procurement, transportation, storage, use and consumption of various materials. Its efficiency and accuracy directly determine the level of optimized allocation of construction resources. Timely and accurate supply of materials affects the continuity of construction progress. Shortage of inventory or delay of transportation may easily cause work stoppage and material shortage, which increases cost. Material quality and storage environment compliance are related to engineering quality. Unqualified materials may cause structural hidden dangers and increase maintenance costs. Material flow standardization is bound to cost accounting. Abnormal consumption or illegal use may distort accounting and weaken cost control. Therefore, the scientific nature and refinement of material management are key indicators to measure the level of construction management, and are directly related to the overall efficiency of the project and the realization of the construction goal.

[0003] The traditional material management mode mainly relies on manual record, on-site inspection and post-mortem review. There are significant defects. In terms of data collection and verification, the multi-source data of material quantity, state and location are scattered in different systems of manual account, RFID tag and camera monitoring, and there is a lack of unified verification mechanism. For example, the inventory quantity often deviates due to manual input errors and RFID reading errors, and needs to be checked and corrected manually, which lags behind. At the same time, the correlation between material consumption and construction progress and geographical location has not been explored, and it is difficult to locate the cause when the consumption of a certain construction section abnormally fluctuates. In terms of abnormality identification and response, the existing technology mainly sets thresholds for single parameters, lacks correlation analysis of complex scenarios such as inventory shortage accompanied by consumption fluctuation, and is easy to miss high-risk hidden dangers. Moreover, the response mechanism is not graded, the response to key risks lags behind, and low-risk events interfere with resources. In terms of process compliance and responsibility tracing, the matching relationship of material use relies on manual checking, which is easy to appear illegal behavior such as cross-section use and is difficult to locate the responsibility subject. The abnormality disposal record has the risk of tampering and lacks multi-dimensional verification. With the popularization of smart construction, the short boards of traditional mode in real-time and accuracy are highlighted, and new technologies are needed to solve the problems of inaccurate data, difficult abnormality identification, lagging response and unclear responsibility. SUMMARY

[0004] The technical problem solved by the present application is that the traditional material management mode mainly relies on manual account recording, on-site inspection and post-mortem review, and has significant defects. In terms of data collection and verification, multi-source data such as material quantity, state and location are scattered in different systems such as manual account, RFID tag and camera monitoring, and there is a lack of unified verification mechanism. For example, the inventory quantity often deviates due to manual input errors, RFID reading errors and the like, and needs to be checked and corrected manually, which lags behind. At the same time, the correlation between material consumption and construction progress and geographical location has not been explored, and it is difficult to locate the cause in time when the consumption of a certain construction section abnormally fluctuates. In terms of abnormal identification and response, the existing technology sets thresholds for single parameters, lacks correlation analysis of complex scenarios such as inventory shortage accompanied by consumption fluctuation, is prone to miss high-risk hidden dangers, and the response mechanism is not graded, which lags behind in responding to key risks and interferes with resources for low-risk events. In terms of process compliance and responsibility tracing, the matching relationship of material taking depends on manual checking, which is prone to violations such as cross-section taking and it is difficult to locate the responsible subject. The abnormal disposal record is at risk of tampering and lacks multi-dimensional verification. With the popularization of smart construction, the traditional mode has highlighted its shortcomings in real-time and accuracy, and it is urgent to introduce new technologies to solve the problems of inaccurate data, difficult abnormality identification, lagging response and unclear responsibility.

[0005] To solve the above technical problems, the present application provides the following technical scheme: a highway engineering construction remote material abnormality management method, comprising the following steps:

[0006] S1, collecting material information parameters;

[0007] S2, analyzing the material information parameters to obtain an analysis result;

[0008] S3, identifying abnormalities from the analysis result and outputting an abnormality identification result;

[0009] S4, triggering a response mechanism for the abnormality identification result, automatically warning, providing a corresponding disposal scheme for abnormality processing, outputting a processing result and recording.

[0010] As a preferred scheme of the highway engineering construction remote material abnormality management method of the present application, wherein: the material information parameters include quantity data, state data, location data, flow record and reference correlation data;

[0011] The quantity data includes inventory quantity, consumption and incoming quantity;

[0012] The state data includes water content parameter, strength parameter and storage environment parameter;

[0013] The storage environment parameter includes temperature and humidity data of the stacking area and weather warning information, and the weather warning information includes rainfall warning and gale warning;

[0014] The position data includes geographical coordinates of a transportation track, a stacking area, and a construction section;

[0015] The transportation track includes a driving path of a transportation vehicle and corresponding time information;

[0016] The flow record includes an entry time, an exit time, a unit of use, and a transportation vehicle arrival time record;

[0017] The reference correlation data includes an actual demand quantity and a construction progress node, and the construction progress node corresponds to the construction section one by one.

[0018] As a preferred scheme of the highway engineering construction remote material abnormality management method, the material information parameters are analyzed by establishing a multi-source data consistency checking rule library, a time-space dimension cross verification mechanism, and a construction process compliance correlation analysis, and an analysis result is obtained;

[0019] The analysis result includes a first-stage analysis result, a second-stage analysis result, and a third-stage analysis result;

[0020] The material information parameters are checked by the multi-source data consistency checking rule library, and the first-stage analysis result is obtained;

[0021] The multi-source data consistency checking rule library specifically includes:

[0022] An independent data source is set for the material of the same management unit for checking, and the checking specifically includes:

[0023] The inventory quantity is read by an RFID tag;

[0024] The stacking volume of the material is visually recognized by a camera on site;

[0025] An entry ledger is manually entered, and the entry ledger includes an entry quantity and the entry time;

[0026] When the real-time data difference of any two types of data sources exceeds a first threshold value, a data error correction process is triggered;

[0027] The data difference and the information of the corresponding data source form a data deviation point;

[0028] The data error correction process specifically includes:

[0029] The inventory quantity and the stacking volume automatically collected by the Internet of Things equipment are preferentially adopted, manual input data abnormalities are synchronously marked, and the manual input data abnormalities are pushed to a site administrator for review;

[0030] After the review is confirmed, the data sources that meet the consistency condition are identified to form an effective data source identification.

[0031] Generate standardized data based on two or more data sources meeting consistency conditions, the standardized data being unified data after error correction;

[0032] The first stage analysis result includes data deviation points, valid data source identifiers, and unified data after error correction.

[0033] As a preferred scheme of the highway engineering construction remote material anomaly management method, based on the first stage analysis result, the material information parameters are analyzed through a time-space dimension cross-validation mechanism to obtain a second stage analysis result.

[0034] The time-space dimension cross-validation mechanism specifically includes:

[0035] The quantity data and the state data are time-space mapped according to construction progress nodes and corresponding construction paragraph geographic coordinates to generate material consumption-location-time three-dimensional data grids.

[0036] When the time information, construction progress nodes, and construction paragraph geographic coordinates in the mapping process do not match, time-space mapping contradiction points are formed.

[0037] The consumption amounts of the same construction paragraph for a continuous a time period are analyzed in a year-on-year manner.

[0038] When the consumption amount deviates from the historical same period mean value by more than a second threshold value, a material consumption-location-time three-dimensional correlation anomaly is identified, and the temperature and humidity data of the corresponding stacking area and the arrival time records of the transport vehicles within a continuous b time period are synchronously retrieved.

[0039] Through data correlation analysis, a preliminary judgment is made on the causes of the abnormal deviation to form a preliminary deviation reason judgment.

[0040] The second stage analysis result includes time-space mapping contradiction points, material consumption-location-time three-dimensional correlation anomalies, and preliminary deviation reason judgments.

[0041] As a preferred scheme of the highway engineering construction remote material anomaly management method, based on the second stage analysis result, the material information parameters are analyzed through a construction process compliance correlation analysis to obtain a third stage analysis result.

[0042] The construction process compliance correlation analysis specifically includes:

[0043] A taking unit-construction paragraph-material type correspondence table is extracted from the reference correlation data.

[0044] The responsibility attribution relationship of the taking unit and the construction paragraph and the matching rule of the construction paragraph and the required material type are associated to form a check benchmark for the compliance association analysis of the construction process;

[0045] The material type includes structural material, functional material, turnover material, auxiliary facility material and special material;

[0046] When the corresponding relationship table does not match, a process violation point is generated;

[0047] The process violation point is directly determined as a material flow abnormality, and an artificial review warning is triggered synchronously;

[0048] Based on the existence and severity of the process violation point, a compliance or non-compliance compliance determination conclusion is made;

[0049] According to the information of the taking unit, the corresponding association responsibility subject is determined;

[0050] The non-matching corresponding relationship table includes non-corresponding construction paragraph taking material or taking bulk material in advance in the planned period;

[0051] The third stage analysis result includes process violation point, compliance determination conclusion and associated responsibility subject.

[0052] As a preferred scheme of the highway engineering construction remote material abnormality management method, the analysis result is subjected to abnormality recognition by establishing a preset abnormality feature combination library, and an abnormality recognition result is output;

[0053] The preset abnormality feature combination library is established, and specifically includes:

[0054] The abnormality feature combination includes three types of composite scenes;

[0055] The three types of composite scenes include transportation overtime plus GPS signal interruption, inventory quantity below the safety threshold plus fluctuation amplitude abnormality of near-c-day consumption, and water content parameter exceeding the standard plus drainage system fault alarm of the stacking area;

[0056] When two or more single abnormality features in the combination library appear in the analysis result at the same time, an abnormality recognition result of the corresponding level is output;

[0057] The abnormality recognition result includes high risk, medium risk and low risk.

[0058] As a preferred scheme of the highway engineering construction remote material abnormality management method, the response mechanism adopts a hierarchical triggering mode, and specifically includes:

[0059] When the abnormal identification result is high risk, a first level response is triggered, and a sound and light alarm is sent to the project general manager, the material management department manager and the on-site supervisor synchronously;

[0060] The early warning information is pushed to the mobile terminal through an encryption channel and a pop-up window is forced to appear, and the disposal progress is fed back within a preset time;

[0061] The early warning information includes abnormal characteristic parameters, associated data snapshots and geographic coordinate positioning links;

[0062] When the abnormal identification result is medium risk, a second level response is triggered, a message and a system message reminder are sent to the material administrator and the construction paragraph manager, and abnormal checking is completed within a preset time;

[0063] When the abnormal identification result is low risk, a third level response is triggered, and an abnormal log is generated in the management system, which is summarized and processed by the on-site administrator every day.

[0064] As a preferred scheme of the highway engineering construction remote material abnormal management method, the automatic early warning is performed through an abnormal type-responsibility subject mapping relationship table to push early warning information;

[0065] The abnormal types include a transportation path abnormal type, a material state exceeding standard type and a stock and circulation abnormal type;

[0066] The early warning of the transportation path abnormal type is pushed to the logistics scheduling group and is synchronously copied to the supervision party for record;

[0067] The early warning of the material state exceeding standard type is pushed to the quality detection group;

[0068] The early warning of the stock and circulation abnormal type is pushed to the warehouse management group;

[0069] The mapping relationship table presets the supervision party as a record receiving party of all abnormal types, and when the abnormal type involves a key process material, the supervision party electronic signature confirmation process is synchronously triggered.

[0070] As a preferred scheme of the highway engineering construction remote material abnormal management method, the disposal scheme specifically includes:

[0071] A preset basic disposal template is matched based on the abnormal identification result, and the template includes an emergency processing step, a required emergency disposal equipment list and safety precautions;

[0072] The scheme parameters are automatically optimized according to quantity data, state data, position data, circulation records and reference associated data, and specifically include:

[0073] When the water content parameter exceeds the standard and is accompanied by rainfall warning, the rain cover coverage range is automatically added in the basic template, and the drainage pump is automatically dispatched, the available amount of the drainage pump is judged according to the inventory quantity of the unified data after the error correction, and the dispatching instruction is generated;

[0074] The coverage range is determined according to the stacking area in the position data;

[0075] When the inventory quantity is lower than the safety threshold and the consumption amount in the last d days deviates from the normal threshold, an emergency material allocation process is automatically triggered, the circulation record is called, the material usage amount is determined according to the material usage of the construction paragraph, and the inventory redundancy paragraph with the inventory quantity greater than the actual demand quantity is screened out according to the inventory quantity and the actual demand quantity;

[0076] The material is allocated from the inventory redundancy paragraph, and an urgent replenishment order is sent to the supplier at the same time;

[0077] When the material is taken by a non-corresponding construction paragraph, the material flow tracing is added in the disposal scheme, the actual use scene of the material is checked through the arrival time record of the transport vehicle and the monitoring video of the stacking area, a responsibility subject identification report is generated, the disposal of the abnormal taking of the material is realized, and the processing result is obtained;

[0078] The processing result is recorded in the form of blockchain notarization, and the content of the record includes the identity information of the disposer, the operation timestamp and the comparison of the material parameters before and after the disposal;

[0079] The material parameter comparison includes the inventory quantity, the water content parameter and the associated equipment operation log.

[0080] As a preferred scheme of the highway engineering construction remote material abnormal management method, the processing result includes record content and notarization mechanism;

[0081] The record content includes abnormal disposal whole process data, and the abnormal disposal whole process data includes the identity information of the disposer, the operation timestamp, the comparison of the material parameters before and after the disposal and the associated equipment operation log;

[0082] The material parameter comparison includes the inventory quantity and the water content parameter;

[0083] The notarization mechanism refers to that a unique hash value is generated for each record content, a chain association is formed with the last record content, only new record content is allowed and the record content cannot be modified, and the record content is regularly synchronized to the project supervision platform for backup;

[0084] The disposal scheme includes double verification;

[0085] The double verification specifically includes:

[0086] When the post-treatment image data uploaded by the on-site operator meets the real-time data feedback of the sensor and meets the preset conditions, the double verification is successful.

[0087] If the double verification fails, a secondary treatment process is automatically triggered, and the warning level is upgraded.

[0088] The beneficial effects of the present application: by constructing a full-process digital highway engineering construction remote material abnormal management system, the problems of large data deviation, extensive abnormal identification, low response efficiency and difficult responsibility tracing in traditional management are effectively solved, the multi-source data consistency verification rule library integrates RFID, multi-dimensional data of visual identification, automatically corrects standardization data in real time, ensures the accuracy and reliability of inventory quantity and pile volume information; with the help of space-time dimension cross verification and construction process compliance analysis, combined with the preset abnormal feature combination library, the space-time mapping contradiction, consumption anomaly and process violation and other complex risk scenarios can be accurately captured, avoiding the limitations of single parameter judgment; using a hierarchical response mechanism and an abnormal type-responsibility subject mapping table, differentiated early warning and directional push of high, medium and low risks are realized, and the efficiency of emergency resource allocation is improved; through the use of the use relationship verification and the blockchain storage technology, the illegal use is found in real time, and the unalterable disposal process record is formed, and the responsibility subject is clear; the disposal scheme is combined with the basic template dynamic optimization and the double verification mechanism, which ensures the abnormal treatment effect and forms a management closed loop, significantly reduces the risk of shutdown, quality hidden danger and cost overrun, and comprehensively improves the intelligent and fine level of construction material management. BRIEF DESCRIPTION OF DRAWINGS

[0089] Figure 1 A step flowchart of a highway engineering construction remote material abnormal management method provided by an embodiment of the present application. DETAILED DESCRIPTION

[0090] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present application, not all embodiments.

[0091] Embodiment, refer to Figure 1 For an embodiment of the present application, a highway engineering construction remote material abnormal management method is provided, which includes the following steps:

[0092] S1, collecting material information parameters;

[0093] S2, analyzing the material information parameters to obtain analysis results;

[0094] S3, identifying the analysis results for abnormalities, and outputting abnormal identification results;

[0095] S4, triggering a response mechanism for abnormal identification results, automatic early warning, and providing corresponding treatment schemes for abnormal treatment, outputting the processing results and recording.

[0096] In one embodiment, first, the material information parameters including quantity data, state data, location data, flow record and reference association data are collected by Internet of Things devices (such as RFID tags and cameras) and manual input, the full life cycle data archives are constructed, the material information parameters are analyzed in multiple dimensions, the deviation data is removed to generate standardized data through multi-source data consistency verification rule library (such as RFID inventory, camera stack volume and manual account cross verification), and then the material consumption, location and time three-dimensional mapping is used to identify consumption abnormalities and space-time contradictions through space-time dimension cross verification mechanism, the matching relationship of the use unit, construction paragraph and material type is checked by using construction process compliance association analysis, the analysis results including data deviation, space-time anomaly and process violation are formed, the analysis results are matched relying on the preset abnormal feature combination library, and the abnormal identification results of high risk, medium risk and low risk are output; finally, the hierarchical response mechanism is triggered according to the risk level, the audible and visual alarm and forced pop-up window warning are sent to the project manager, the supervisor and the like for high risk, the short message is sent to the responsible administrator for medium risk, the system log is generated for low risk, the warning information is synchronously pushed according to the abnormal type (such as transportation abnormality to push logistics group), the preset treatment template is matched and the real-time data optimization parameters (such as water content parameter exceeding standard to schedule drainage pump) are combined, the treatment is verified through on-site image and sensor data, the treatment full process data (including treatment person, time stamp and material parameter comparison) is block chain notarized to generate unique hash value chain association, and the supervision platform backup is regularly synchronized.

[0097] The material information parameters include quantity data, state data, location data, flow record and reference association data;

[0098] The quantity data includes inventory quantity, consumption quantity and incoming quantity;

[0099] The state data includes water content parameter, strength parameter and storage environment parameter;

[0100] The storage environment parameter includes temperature and humidity data of the stacking area and weather warning information, and the weather warning information includes rainfall warning and gale warning;

[0101] The location data includes geographical coordinates of the transportation track, stacking area and construction paragraph;

[0102] The transportation track includes driving path of the transportation vehicle and corresponding time information;

[0103] The flow record includes incoming time, outgoing time, use unit and transportation vehicle arrival time record;

[0104] The benchmark correlation data includes actual demand quantity and construction progress nodes, and the construction progress nodes correspond to construction paragraphs one by one.

[0105] In one of the embodiments, the collected material information parameters include five types of key data, the quantity data includes inventory quantity, consumption quantity and incoming quantity, the state data includes water content parameter, strength parameter and storage environment parameter (stacking area temperature and humidity data, meteorological information including rainfall warning and gale warning), the position data relates to transportation track including driving path and corresponding time, stacking area geographic coordinates and construction paragraph geographic coordinates, the flow record includes incoming time, outgoing time, taking unit and transportation vehicle arrival time record, the benchmark correlation data includes actual demand quantity and construction progress nodes corresponding to construction paragraphs one by one, and the material full life cycle data basis is constructed through multi-source data collection.

[0106] The material information parameters are analyzed through establishment of a multi-source data consistency checking rule library, a time-space dimension cross verification mechanism and a construction process compliance correlation analysis, and analysis results are obtained.

[0107] The analysis results include first stage analysis results, second stage analysis results and third stage analysis results.

[0108] The material information parameters are checked through the multi-source data consistency checking rule library, and the first stage analysis results are obtained.

[0109] The multi-source data consistency checking rule library specifically includes:

[0110] Independent data sources are set for materials of the same management unit for checking, and the checking specifically includes:

[0111] The inventory quantity is read through an RFID tag;

[0112] The stack volume of the material is visually recognized through an on-site camera;

[0113] The incoming account is manually entered, and the incoming account includes incoming quantity and incoming time;

[0114] When the real-time data difference of any two types of data sources exceeds the first threshold value of 5%, a data error correction process is triggered;

[0115] The data difference and the information of the corresponding data source form a data deviation point;

[0116] The data error correction process specifically includes:

[0117] The inventory quantity and the stack volume are preferentially collected by the Internet of Things equipment, the manually entered data is synchronously marked as abnormal and is pushed to the on-site administrator for review;

[0118] After review and confirmation, the data sources that meet the consistency condition are identified to form valid data source identification;

[0119] Based on two or more data sources that meet the consistency condition, standardized data is generated, which is the unified data after error correction;

[0120] The first stage analysis results include data deviation points, valid data source identification and unified data after error correction.

[0121] In one embodiment, the first stage analysis of material information parameters is performed by establishing a multi-source data consistency verification rule library: for the same management unit material, set RFID tag reading inventory quantity, camera visual recognition pile volume and manual entry including incoming quantity and incoming time incoming ledger as independent data sources, when the real-time data difference of any two types of data sources exceeds 5% (the first threshold is set to 5%, based on the data verification accuracy requirement), trigger the data error correction process; preferentially collect inventory quantity and pile volume data automatically collected by Internet of Things devices, mark manual entry data as abnormal and push to the on-site administrator for review, after review and confirmation, identify the data sources that meet the consistency condition to form valid data source identification, generate unified standardized data after error correction based on two or more consistent data sources, and the first stage analysis results include data deviation points, valid data source identification and standardized data.

[0122] Based on the first stage analysis results, the material information parameters are analyzed through a time-space dimension cross verification mechanism to obtain second stage analysis results;

[0123] The time-space dimension cross verification mechanism specifically includes:

[0124] Map the quantity data and state data according to the construction progress node and the geographical coordinates of the corresponding construction section to generate a material consumption-location-time three-dimensional data grid;

[0125] When the time information, construction progress node and construction section geographical coordinates in the mapping process do not match, a time-space mapping contradiction point is formed;

[0126] Conduct a year-on-year analysis of the consumption amount of the same construction section for a consecutive a period of time;

[0127] When the consumption amount and historical same period average deviation exceeds the second threshold of 20%, identify the material consumption-location-time three-dimensional correlation anomaly, and simultaneously retrieve the temperature and humidity data of the corresponding stacking area and the arrival time record of the transport vehicle within the consecutive a period of time;

[0128] Through data correlation analysis, make a preliminary judgment on the cause of the abnormal deviation to form a preliminary judgment of the deviation reason;

[0129] The second stage analysis result includes the time-space mapping contradiction point, the material consumption-location-time three-dimensional correlation anomaly and the preliminary deviation cause determination.

[0130] In one of the embodiments, based on the first stage analysis result, the second stage analysis is carried out through the time-space dimension cross verification mechanism: the quantity data and the state data are mapped in time and space according to the construction progress node and the corresponding construction paragraph geographical coordinates to generate a material consumption-location-time three-dimensional data grid. When the time information, the construction progress node and the construction paragraph geographical coordinates in the mapping do not match, a time-space mapping contradiction point is formed. The consumption amount of the same construction paragraph in three consecutive time periods (a is set to 3, which is set based on the reasonableness of data statistical period) is analyzed in a ring. When the consumption amount deviates from the historical same period average value by more than 20% (the second threshold is set to 20%, which is set in combination with the conventional threshold of industry data fluctuation), it is identified as a material consumption-location-time three-dimensional correlation anomaly. The temperature and humidity data of the corresponding stacking area and the arrival time record of the transport vehicle in the continuous three (b is set to 3) time periods are synchronously called. Through data correlation analysis, a preliminary deviation cause determination is formed. The second stage analysis result includes the time-space mapping contradiction point, the three-dimensional correlation anomaly and the preliminary deviation cause determination.

[0131] According to the second stage analysis result, the material information parameters are analyzed through construction process compliance correlation analysis to obtain the third stage analysis result;

[0132] The construction process compliance correlation analysis specifically includes:

[0133] The corresponding relationship table of the taking unit-construction paragraph-material type is extracted from the benchmark correlation data;

[0134] The responsibility attribution relationship of the taking unit and the construction paragraph, and the matching rule formed by the construction paragraph and the required material type are mapped through correlation to serve as the verification benchmark of the construction process compliance correlation analysis;

[0135] The material type includes structural material, functional material, turnover material, auxiliary facility material and special material;

[0136] When the corresponding relationship table is not met, a process violation point is generated;

[0137] The process violation point is directly determined as a material flow transfer anomaly, and an artificial review warning is triggered synchronously;

[0138] Based on the existence and severity of the process violation point, a compliance or non-compliance compliance determination conclusion is made;

[0139] According to the information of the taking unit, the corresponding associated responsible subject is determined;

[0140] The cases not meeting the corresponding relationship table include non-corresponding construction paragraph material taking or large bulk material taking in advance beyond the planned period;

[0141] The third stage analysis result includes process violation points, compliance determination conclusions and associated responsible subjects.

[0142] In one of the embodiments, according to the second stage analysis result, the third stage analysis is carried out through construction process compliance association analysis, the taking unit-construction paragraph-material type corresponding relationship table (material types include structural materials, functional materials, turnover materials, auxiliary facility materials and special materials) is extracted and established from the benchmark association data, which is used as a verification benchmark; when the cases not meeting the corresponding relationship table such as non-corresponding construction paragraph material taking or large bulk material taking in advance beyond the planned period occur, process violation points are generated, directly determined as material flow transfer abnormality and triggered artificial review warning; the compliance determination conclusion is made based on the existence and severity of the violation points, and the associated responsible subject is determined according to the taking unit information, and the third stage analysis result includes process violation points, compliance determination conclusions and associated responsible subjects.

[0143] The analysis result is subjected to abnormality identification through the establishment of a preset abnormal feature combination library, and the abnormality identification result is output;

[0144] The preset abnormal feature combination library is established, specifically including:

[0145] The abnormal feature combination includes three types of composite scenes;

[0146] The three types of composite scenes include transportation overtime plus GPS signal interruption, inventory quantity below the safety threshold plus fluctuation amplitude abnormality of near c-day consumption, and moisture content parameter exceeding the standard plus drainage system failure alarm of the stacking area;

[0147] When two or more single abnormal features in the combination library appear in the analysis result at the same time, the corresponding level of abnormality identification result is output;

[0148] The abnormality identification result includes high risk, medium risk and low risk.

[0149] In one of the embodiments, the analysis result is identified for abnormality by establishing a preset abnormal feature combination library, the combination library includes three types of composite scenarios, respectively, transportation overtime (more than 20% of the expected transportation time, according to the conventional overtime judgment threshold of logistics management), GPS signal interruption (continuous interruption greater than or equal to 30 minutes, according to the conventional time threshold of equipment failure monitoring), inventory quantity below the safety threshold (set to 20% of the total inventory, according to the safety inventory standard of inventory management), and near 3-day (c is set to 3, based on the conventional cycle of material consumption fluctuation monitoring) consumption fluctuation abnormality (defined as the daily average consumption deviating from the historical average by more than 30%, according to the reasonable fluctuation range of material consumption), and water content parameter exceeding the standard (the safety water content parameter threshold of sand and stone materials is less than or equal to 6%, according to the "Highway Engineering Aggregate Test Regulations (JTG 3432-2024)"), and pile area drainage system failure alarm; when two or more single abnormal features in the combination library appear in the analysis result at the same time, the abnormal identification result of the corresponding level is output according to the preset rule, including high risk, medium risk and low risk, wherein the preset rule is: including transportation overtime and GPS signal interruption combination, it is determined as high risk (double failure of transportation path safety and supervision); single triggering inventory quantity below the safety threshold and near 3-day consumption fluctuation abnormality or water content parameter exceeding the standard and drainage system failure alarm composite scenario, it is determined as medium risk (single link material supply or quality risk), multi-feature combination across composite scenarios (such as inventory abnormality and water content parameter abnormality appearing at the same time), according to the influence degree, it is determined as high risk, non-key material combination is determined as medium risk, only single single abnormal feature in the combination library appears, or slight abnormality does not meet the composite scenario triggering condition, it is determined as low risk, by specifying the numerical threshold of each scene and the industry setting basis, the standardized classification identification of material abnormal risk is realized.

[0150] The response mechanism adopts a hierarchical triggering mode, specifically including:

[0151] When the abnormal identification result is high risk, the first level response is triggered, and the sound and light alarm is sent to the project general manager, the material management department manager and the site supervisor at the same time;

[0152] And the warning information is pushed to the mobile terminal through an encrypted channel and forced to pop up, and the disposal progress is fed back within a preset time;

[0153] The warning information includes abnormal feature parameters, associated data snapshots and geographic coordinate positioning links;

[0154] When the abnormal identification result is medium risk, the second level response is triggered, a message and a system message reminder are sent to the material administrator and the construction paragraph responsible person, and the abnormality check is completed within a preset time;

[0155] When the abnormality identification result is low risk, a three-level response is triggered, and an abnormality log is generated in the management system, which is summarized and processed by the on-site administrator every day.

[0156] In one of the embodiments, the response mechanism adopts a hierarchical triggering mode. When the abnormality identification result is high risk, a first-level response is triggered, and a sound and light alarm is sent to the project general manager, the manager of the material management department, and the on-site supervisor. The pre-warning information including the abnormal characteristic parameters, the associated data snapshot, and the geographic coordinate positioning link is pushed to the mobile terminal through an encrypted channel and a pop-up window is forced to appear, requiring feedback on the disposal progress within 15 minutes (the preset time is 15 minutes, which is set according to the response requirements of major risk hidden dangers in the “Highway Engineering Construction Safety Technical Specification” and the instant messaging efficiency). When the abnormality identification result is medium risk, a second-level response is triggered, and a message and a system message reminder are sent to the material administrator and the construction paragraph manager, requiring the completion of abnormality check within 2 hours (the preset time is set to 2 hours, which is set according to the time consumption of the comprehensive on-site verification process and the time characteristics of the construction shift). When the abnormality identification result is low risk, a three-level response is triggered, and an abnormality log is generated in the management system, which is summarized and processed by the on-site administrator every day. The differential pre-warning and disposal are realized through the corresponding setting of risk level and response measure.

[0157] The automatic pre-warning is realized by establishing an abnormal type-responsibility subject mapping table for pre-warning information pushing;

[0158] The abnormal types include the transportation path abnormality type, the material state exceeding standard type, and the inventory and circulation abnormality type;

[0159] The pre-warning of the transportation path abnormality type is pushed to the logistics scheduling group, and the supervision party is simultaneously copied for record;

[0160] The pre-warning of the material state exceeding standard type is pushed to the quality detection group;

[0161] The pre-warning of the inventory and circulation abnormality type is pushed to the warehouse management group;

[0162] The mapping table presets the supervision party as the record receiving party of all abnormal types. When the abnormal type involves key process materials, the supervision party electronic signature confirmation process is simultaneously triggered.

[0163] In one of the embodiments, the automatic early warning is realized by establishing an abnormal type-responsible subject mapping table to realize directional push. The abnormal types are divided into transportation path abnormal class, material state exceeding standard class, and inventory and circulation abnormal class. The transportation path abnormal class early warning is directionally pushed to the logistics scheduling group and simultaneously copied to the supervision party for record. The material state exceeding standard class early warning is directionally pushed to the quality detection group. The inventory and circulation abnormal class early warning is directionally pushed to the warehouse management group. The mapping table presets the supervision party as the record receiver of all abnormal types. When the abnormality involves key process materials, the supervision party electronic signature confirmation process is triggered synchronously. Through the accurate mapping of the responsible subject and the abnormal type, the efficient distribution and processing of the early warning information are realized.

[0164] The treatment scheme specifically includes:

[0165] Based on the abnormal identification result, a preset basic treatment template is matched. The template includes emergency treatment steps, a list of required emergency treatment equipment, and safety precautions.

[0166] According to the quantity data, the state data, the position data, the circulation record and the benchmark correlation data, the scheme parameters are automatically optimized, specifically including:

[0167] When the water content parameter exceeds the standard and is accompanied by a rainfall warning, the rain cover range is automatically added in the basic template, and the drainage pump is automatically dispatched. The available amount of the drainage pump is determined according to the inventory quantity of the unified data after correction, and the dispatching instruction is generated.

[0168] The coverage range is determined according to the stacking area in the position data.

[0169] When the inventory quantity is lower than the safety threshold and the consumption amount in the last d days deviates from the normal threshold, the material emergency allocation process is automatically triggered, and the circulation record is called. According to the material usage of the construction paragraph, the material usage amount is determined. According to the inventory quantity and the actual demand quantity, the inventory redundancy paragraph with the inventory quantity greater than the actual demand quantity is screened out.

[0170] The material is allocated from the inventory redundancy paragraph, and an urgent replenishment order is sent to the supplier synchronously.

[0171] When the material is taken in a non-corresponding construction paragraph, the material flow tracing is added in the treatment scheme. The actual use scene of the material is checked through the arrival time record of the transport vehicle and the monitoring video of the stacking area. A responsible subject identification report is generated to realize the disposal of the abnormal taking of the material and obtain the processing result.

[0172] The processing result is recorded in the form of blockchain evidence. The recorded content includes the identity information of the handler, the operation timestamp and the comparison of the material parameters before and after the disposal.

[0173] The material parameter comparison includes the inventory quantity, the water content parameter, and the associated equipment operation log.

[0174] In one of the embodiments, the treatment scheme is implemented through a template matching and parameter optimization mechanism. First, a pre-set basic treatment template is matched based on the abnormality identification result. The template includes emergency handling steps, a list of required emergency treatment equipment, and specific safety precautions (including wearing insulation protective equipment for electrical equipment operation, checking the grounding reliability before using the drainage pump; obeying the speed limit regulations on the construction site during material allocation and transportation, and prohibiting overloading or rough loading and unloading during the transportation process; wearing a safety helmet and confirming the environmental safety in advance when entering the stacking area, and at least two people should go together for on-site verification). Then, the scheme is automatically optimized in combination with real-time material information parameters. When the water content rate parameter exceeds the standard and triggers the rainfall warning, the raincoat coverage range is determined according to the stacking area location data, the available amount of the drainage pump is judged by synchronously calling the corrected inventory quantity, and the electrical equipment safety operation specification is strictly implemented during the period. The covering measures are automatically added, and the drainage pump dispatching instruction is generated. When the inventory quantity is lower than the safety threshold and the consumption amount deviates from the normal threshold in the last 3 days (d is set to 3, based on the material consumption data monitoring period), the material emergency allocation process is triggered. The actual demand is calculated through the flow record and the material usage in the construction section. The material is allocated from the inventory redundant section where the inventory quantity is greater than the actual demand, and an urgent replenishment order is sent to the supplier. The transportation equipment safety state is checked during the allocation process, and the material flow tracing is added in the treatment scheme when the material is taken from a non-corresponding construction section. The actual use scenario is checked through the transportation vehicle arrival time record and the stacking area monitoring video. The on-site verification needs to be accompanied by a guardian and the entry and exit time is recorded in advance. The responsibility subject identification report is generated. After the treatment is completed, the treatment result is recorded by using the blockchain storage method. The content covers the treatment person's identity information, operation timestamp, and material parameter comparison before and after the treatment (including inventory quantity, water content rate parameter, and associated equipment operation log). The treatment process is traceable.

[0175] The treatment result includes the recording content and the storage mechanism;

[0176] The recording content includes the abnormality treatment whole process data, which includes the treatment person's identity information, operation timestamp, material parameter comparison before and after the treatment, and associated equipment operation log;

[0177] The material parameter comparison includes the inventory quantity and the water content rate parameter;

[0178] The storage mechanism refers to that a unique hash value is generated for each recording content, which is chain-associated with the previous recording content. Only new recording content is allowed, and the recording content cannot be modified. It is regularly synchronized to the project supervision platform for backup;

[0179] The treatment scheme includes double verification;

[0180] The double verification specifically includes:

[0181] When the post-treatment image data uploaded by the on-site operator meets the standard feedback of the sensor real-time data and meets the preset conditions, the double verification is successful.

[0182] If the double verification fails, the secondary treatment process is automatically triggered, and the warning level is upgraded.

[0183] In one of the embodiments, the processing result includes record content and storage mechanism: the record content is the abnormal treatment full-process data, including the treatment person's identity information, operation timestamp, comparison of material parameters before and after treatment (including inventory quantity and water content parameters) and associated equipment operation log; the storage mechanism generates a unique hash value for each record content, forms a chain association with the previous record, only allows new records and cannot be modified, is regularly synchronized to the project supervision platform backup, the treatment scheme sets a double verification mechanism: the preset conditions include that the post-treatment image data clearly shows that the material covering, allocation or traceability treatment measures are fully implemented (such as the rain cover is not damaged, the material stacking area is consistent with the record); the sensor real-time data feedback meets the standard, that is, the water content parameter is less than or equal to the safe water content threshold of the material (the threshold is preset according to the material type according to the industry standard or project specification, such as the safe water content of sand and stone materials is less than or equal to 6%), the inventory quantity is greater than or equal to the safe threshold, when the post-treatment image data uploaded by the on-site operator meets the standard feedback of the sensor real-time data and meets the above conditions, the double verification is successful; if the verification fails, the secondary treatment process is automatically triggered, and the warning level is upgraded (follow the three-level system of high risk, medium risk and low risk, when the verification fails, the current level is upgraded by one level, the high risk level is maintained and the treatment measures are strengthened), the treatment process is realized through the full-process data record, the block chain storage and the double verification, and the risk closed-loop management.

[0184] The present application effectively solves the problems of large data deviation, extensive abnormal identification, low response efficiency and difficult responsibility tracing in traditional management by constructing a full-process digital highway engineering construction remote material abnormal management system. Through multi-source data consistency verification rule library integration of RFID and multi-dimensional data of visual identification, real-time automatic error correction and standardized data generation are realized to ensure the accuracy and reliability of inventory quantity and pile volume information. With the help of spatio-temporal dimension cross verification and construction process compliance analysis, combined with the preset abnormal feature combination library, complex risk scenarios such as spatio-temporal mapping contradiction, consumption anomaly and process violation can be accurately captured to avoid the limitations of single parameter judgment. A hierarchical response mechanism and abnormal type-responsibility subject mapping table are adopted to realize differentiated early warning and directional push of high-risk, medium-risk and low-risk, and to improve the efficiency of emergency resource allocation. Through the use of taking relationship verification and blockchain storage technology, irregular taking can be found in real time and an unalterable disposal full-process record is formed to clarify the responsibility subject. The disposal scheme is combined with the basic template dynamic optimization and double verification mechanism to ensure the abnormal treatment effect and form a management closed loop, which significantly reduces the risks of shutdown, quality hidden danger and cost overrun, and comprehensively improves the intelligent and fine level of construction material management.

[0185] Those skilled in the art will appreciate that embodiments of the application can be provided as methods, systems or computer program products. Accordingly, the application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the application can take the form of a computer program product on one or more computer-usable storage media (including volatile and non-volatile storage media, and removable and non-removable storage media) embodying computer-usable program code. The storage media can be any available media that can be read by a computer. By way of example, such computer-usable program code can be implemented in Java, C++, or any other programming language. Such program code includes, but is not limited to, device dependent code, device independent code, object-oriented software architecture, and / or any other programming code. Figure 1 one or more processes and / or blocks Figure 1the function specified in the one or more blocks.

[0186] It should be noted that the above-mentioned embodiments are only used to illustrate the technical solutions of the present application, not to limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalent replaced without departing from the spirit and scope of the technical solutions of the present application, which should be covered in the scope of the claims of the present application.

Claims

1. A method for remote material anomaly management in highway engineering construction, characterized in that, Includes the following steps: S1, collect material information parameters; S2, the material information parameters are analyzed by establishing a multi-source data consistency verification rule base, a spatiotemporal cross-validation mechanism, and a construction process compliance correlation analysis to obtain the analysis results; S3, perform anomaly identification on the analysis results and output the anomaly identification results; S4, trigger the response mechanism for the abnormal identification result, provide an automatic warning, provide a corresponding handling plan for abnormal handling, output the handling result and record it; The material information parameters include quantity data, status data, location data, flow records, and baseline correlation data; The baseline correlation data includes the actual required quantity and construction progress nodes, and the construction progress nodes correspond one-to-one with the construction segments. The analysis results include the results of the first stage analysis, the results of the second stage analysis, and the results of the third stage analysis. The material information parameters are verified using a multi-source data consistency verification rule base to obtain the first-stage analysis results; The multi-source data consistency verification rule base specifically includes: For materials within the same management unit, an independent data source is set up for verification. The verification specifically includes: Inventory quantities are read using RFID tags; The volume of the material pile is visually identified using on-site cameras; The entry log is manually entered, and the entry log includes the quantity and time of entry. When the real-time data difference between any two data sources exceeds the first threshold, the data correction process is triggered. The data differences and the corresponding information from the data source form data deviation points; Based on the results of the first phase analysis, the material information parameters are analyzed through a spatiotemporal cross-validation mechanism to obtain the results of the second phase analysis. The spatiotemporal cross-validation mechanism specifically includes: The quantity data and status data are spatiotemporally mapped according to the construction progress nodes and the geographical coordinates of the corresponding construction sections to generate a three-dimensional data grid of material consumption-location-time. When the time information and construction progress nodes in the time-space mapping process do not match the geographical coordinates of the construction section, a time-space mapping contradiction is formed. A comparative analysis of the consumption over a consecutive time period for the same construction section was conducted. When the consumption amount deviates from the historical average value by more than the second threshold, it is identified as an abnormal three-dimensional correlation between material consumption, location and time, and the temperature and humidity data of the corresponding stacking area and the arrival time record of the transport vehicle are retrieved simultaneously within b consecutive time periods. By analyzing data correlation, a preliminary judgment is made on the causes of abnormal deviations, thus forming a preliminary determination of the causes of deviations; The results of the second phase of analysis include spatiotemporal mapping contradictions, anomalies in the three-dimensional correlation between material consumption, location, and time, and preliminary determination of the causes of deviations; Based on the results of the second phase analysis, the material information parameters were analyzed through the construction process compliance correlation analysis to obtain the results of the third phase analysis. The compliance correlation analysis of the construction process specifically includes: Extract the correspondence table of requisitioning unit-construction section-material type from the aforementioned benchmark correlation data; The system is formed by associating the responsibility attribution between the requisitioning unit and the construction section, and the matching rules between the construction section and the required material types, and serves as the verification benchmark for the compliance association analysis of the construction process. The material types include structural materials, functional materials, turnover materials, auxiliary facility materials, and special materials; When a situation occurs that does not conform to the corresponding relationship table, a process violation point is generated. The aforementioned process violations will be directly identified as material flow abnormalities, and a manual review and warning will be triggered simultaneously. Based on the existence and severity of the violations in the process, a compliance determination of whether the process is compliant or non-compliant will be made. Based on the information of the receiving unit, the corresponding related responsible parties are identified; The situations that do not conform to the corresponding relationship table include the requisition of materials for non-corresponding construction sections or the requisition of bulk materials ahead of the planned time. The results of the third-stage analysis include process violations, compliance determinations, and related responsible parties.

2. The method for remote material anomaly management in highway engineering construction as described in claim 1, characterized in that: The quantity data includes inventory quantity, consumption quantity, and quantity received; The status data includes moisture content parameters, strength parameters, and storage environment parameters; The storage environment parameters include temperature and humidity data of the stacking area and meteorological early warning information, including rainfall warnings and strong wind warnings; The location data includes the transportation trajectory, stacking area, and geographic coordinates of the construction section; The transportation trajectory includes the driving path of the transport vehicle and the corresponding time information; The circulation records include entry time, exit time, receiving unit, and arrival time of transport vehicles.

3. The method for remote material anomaly management in highway engineering construction as described in claim 2, characterized in that: The data correction process specifically includes: Prioritize the use of IoT devices to automatically collect inventory quantities and stack volumes, and simultaneously mark abnormal manually entered data and push it to the site administrator for review; After the verification and confirmation, data sources that meet the consistency conditions are identified to form valid data source identifiers; Based on two or more types of data sources that meet the consistency conditions, standardized data is generated, which is the unified data after error correction; The results of the first stage of analysis include data deviation points, valid data source identification, and unified data after error correction.

4. The method for remote material anomaly management in highway engineering construction as described in claim 3, characterized in that: The analysis results are used to identify anomalies by establishing a pre-defined library of anomaly feature combinations, and the anomaly identification results are output. The establishment of the preset abnormal feature combination library specifically includes: The combination of abnormal features includes three types of composite scenarios; The three types of composite scenarios include transportation timeout plus GPS signal interruption, inventory quantity below the safety threshold plus abnormal fluctuation of consumption in the past c days, and moisture content parameter exceeding the standard plus drainage system failure alarm in the stacking area. When two or more individual abnormal features from the combined library appear simultaneously in the analysis results, the corresponding level of abnormality identification result is output. The anomaly identification results include high risk, medium risk, and low risk.

5. The method for remote material anomaly management in highway engineering construction as described in claim 4, characterized in that: The response mechanism adopts a tiered triggering mode, specifically including: When the anomaly identification result is high risk, a level 1 response is triggered, and an audible and visual alarm is sent simultaneously to the project manager, the materials management department manager and the site supervisor. The system pushes warning information to mobile devices via encrypted channels and forces pop-up windows, and provides feedback on the handling progress within a preset time. The early warning information includes abnormal feature parameters, associated data snapshots, and geographic coordinate location links; When the anomaly identification result is medium risk, a level 2 response is triggered, sending SMS and system message reminders to the material administrator and the person in charge of the construction section, and completing the anomaly verification within a preset time. When the anomaly identification result is low risk, a level 3 response is triggered, and an anomaly log is generated in the management system, which is then summarized and processed daily by the on-site administrator.

6. The method for remote material anomaly management in highway engineering construction as described in claim 5, characterized in that: The automatic early warning system pushes early warning information by establishing an anomaly type-responsible entity mapping table. The anomaly types include transportation route anomalies, material status exceeding standards, and inventory and circulation anomalies; Warnings regarding abnormal transportation routes are pushed to the logistics dispatch team and simultaneously copied to the supervisor for record-keeping. Warnings regarding material status exceeding standards are pushed to the quality inspection team. The alerts for inventory and turnover anomalies are pushed to the warehouse management group. The mapping table pre-sets the supervisor as the recipient of all anomaly filings. When an anomaly involves materials in a critical process, the supervisor's electronic signature confirmation process is triggered simultaneously.

7. The method for remote material anomaly management in highway engineering construction as described in claim 6, characterized in that: The specific handling plan includes: Based on the anomaly identification results, a preset basic handling template is matched. The template includes emergency handling steps, a list of required emergency handling equipment, and safety precautions. The scheme parameters are automatically optimized based on quantity data, status data, location data, flow records, and baseline correlation data, specifically including: When the moisture content parameter exceeds the standard and is accompanied by a rainfall warning, the rainproof tarpaulin coverage area is automatically added to the basic template, and the drainage pump is automatically scheduled. The available quantity of drainage pumps is determined based on the inventory quantity of the unified data after error correction, and a scheduling instruction is generated. The coverage area is determined based on the stacking area in the location data; When the inventory quantity is lower than the safety threshold and the consumption in the past d days deviates from the normal threshold, the emergency material allocation process is automatically triggered, and the circulation records are retrieved. Based on the material usage of the construction section, the material usage is determined, and based on the inventory quantity and the actual demand quantity, the inventory redundancy section with the inventory quantity greater than the actual demand quantity is selected. Materials are transferred from the aforementioned inventory surplus section, and expedited replenishment orders are simultaneously sent to suppliers; When materials are requisitioned for a work section other than the corresponding construction section, the material flow traceability is added to the disposal plan. The actual use of materials is verified by recording the arrival time of transport vehicles and monitoring videos of the stacking area. A report on the identification of the responsible party is generated to handle abnormal material requisition and obtain the processing results. The processing results are recorded using blockchain notarization, and the recorded content includes the identity information of the person handling the disposal, the operation timestamp, and a comparison of material parameters before and after the disposal. The comparison of material parameters includes inventory quantity, moisture content parameters, and related equipment operation logs.

8. The method for remote material anomaly management in highway engineering construction as described in claim 7, characterized in that: The processing results include recorded content and evidence preservation mechanisms; The recorded content includes data on the entire process of handling anomalies, which includes the identity information of the person handling the anomaly, the operation timestamp, a comparison of material parameters before and after the handling, and the operation logs of related equipment. The comparison of material parameters includes inventory quantity and moisture content parameters; The evidence storage mechanism refers to generating a unique hash value for each record, forming a chain association with the previous record, allowing only the addition of new record content and prohibiting the modification of record content, and periodically synchronizing to the project monitoring platform for backup; The proposed solution includes dual verification; The dual verification specifically includes: When the post-processing video data uploaded by the on-site operator and the real-time data from the sensor simultaneously meet the preset conditions, the double verification is successful. If the dual verification fails, a secondary processing procedure will be automatically triggered, and the warning level will be upgraded.

Images