Intelligent control method for hoisting construction

Abstract

The application provides an intelligent control method for hoisting construction, and belongs to the technical field of intelligent control, which comprises the following steps: step 1, entering a pre-preparation stage, that is, initializing a quality plan and configuring monitoring parameters for hoisting construction; step 2, entering a control-in-process stage, that is, performing real-time monitoring and dynamic disposal for hoisting construction; step 3, after real-time monitoring and dynamic disposal for hoisting construction, performing hierarchical disposal; and step 4, performing a post-tracing stage, that is, recording and analyzing data for hoisting construction. On the basis of existing pre-preparation quality plan inspection, the application adds a real-time monitoring module and dynamic control logic in the process, collects key hoisting parameters through a sensor, judges the construction state in combination with a preset threshold and an algorithm model, automatically triggers an early warning or suspension instruction, and realizes closed-loop disposal by linking a command instrument and a crane control system, and records full-process data to guarantee traceability.

Description

Technical Field

[0001] This invention belongs to the field of intelligent control technology, and specifically relates to an intelligent control method for hoisting construction. Background Technology

[0002] In existing technologies, as mentioned in the technical solution with patent publication number "CN115744639A", the quality control methods for hoisting construction mostly focus on the quality plan inspection in the pre-construction preparation stage. This involves setting five categories of prerequisites: personnel, machinery, raw materials, legal documents, and environment. Hoisting command can only be activated after the inspection and acceptance party signs off and confirms that a score threshold has been reached. However, this model has significant shortcomings in the in-process control phase after the hoisting begins:

[0003] Dynamic change response lag: During the hoisting process, key factors such as weather conditions (such as sudden increase in wind speed and sudden rainfall), mechanical status (such as abnormal noise of winch and decreased sensitivity of limit device), and load distribution (such as loose components of the hoisted equipment) may change in real time. Existing methods rely on manual inspection to detect anomalies, and the response cycle from anomaly identification to suspension of hoisting can be as long as several minutes, which may cause the best time for handling to be missed.

[0004] The current technology relies solely on walkie-talkies to transmit command signals, lacking real-time monitoring and threshold warnings for key hoisting parameters (such as real-time hook load, boom luffing angle, and equipment horizontal offset). It cannot quantitatively determine whether the construction status deviates from the safe range, and it depends on the experience of the command personnel, which is highly subjective and prone to errors.

[0005] Lack of a closed-loop risk management system: When abnormal situations occur, the existing methods do not clearly define the standards for classifying abnormal levels and the corresponding handling procedures. Command personnel need to temporarily coordinate with technical personnel to formulate a plan, resulting in excessively long hoisting pauses and affecting construction efficiency. At the same time, the abnormal handling process is not digitally recorded, making it difficult to trace the root cause of the problem and hindering subsequent construction improvements. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an intelligent control method for hoisting operations. Based on a technical framework of real-time perception, intelligent analysis, dynamic decision-making, and closed-loop handling, it adds a real-time monitoring module and dynamic control logic to the existing pre-operation quality planning and inspection. By collecting key hoisting parameters through sensors and combining preset thresholds and algorithm models to determine the construction status, it automatically triggers early warnings or pause commands and links the command instrument and crane control system to achieve closed-loop handling. At the same time, it records data throughout the entire process to ensure traceability.

[0007] The present invention employs the following technical solution.

[0008] An intelligent control method for hoisting operations includes:

[0009] Step 1: Enter the pre-operation preparation stage, which involves initializing the quality plan and configuring monitoring parameters for the hoisting operation;

[0010] Step 2: Enter the in-process control stage, which involves real-time monitoring and dynamic handling of the hoisting operation;

[0011] Step 3: After real-time monitoring and dynamic handling of the hoisting operation, implement tiered response measures;

[0012] Step 4: Conduct post-event retrospective analysis, which involves recording and analyzing data related to the hoisting operation;

[0013] Step 2 specifically includes:

[0014] Step 2-1: Collect key parameters that need to be monitored during the process in real time and transmit them to the intelligent control terminal;

[0015] Step 2-2: The intelligent control terminal judges parameter anomalies based on the key parameters that need to be monitored during the event, which are uploaded;

[0016] Step 2-1 specifically includes:

[0017] The key parameters that need to be monitored during the event are collected by the equipment at a sampling frequency of 1Hz, and the key parameters that need to be monitored during the event are uploaded to the intelligent control terminal in real time.

[0018] Step 2-2 specifically includes:

[0019] Step 2-2-1: Perform phased dynamic threshold adjustment;

[0020] Step 2-2-2: Calculate the dynamic deviation rate of a single parameter;

[0021] Step 2-2-3: Calculate the multi-parameter coupled risk index;

[0022] Step 2-2-4: Execute the anomaly level determination rules.

[0023] Preferably, in step 1, the method for initializing the quality plan includes:

[0024] Perform quality inspections according to the quality plan.

[0025] Preferably, in step 1, the method for monitoring parameter configuration includes:

[0026] The key parameters to be monitored during the process are preset, including:

[0027] Environmental parameters: Real-time wind speed at the hoisting construction site Instantaneous wind pressure ;

[0028] Mechanical parameters: Real-time load on the hook of the crane used for hoisting operations. Crane boom luffing angle Speed ​​of hoisting machinery winch ;

[0029] Equipment parameters: Horizontal offset of the suspended equipment Vibration frequency of components on the suspended equipment .

[0030] Preferably, in step 1, the device for acquiring the key parameters that need to be monitored during the process is:

[0031] Real-time wind speed at the hoisting construction site The data acquisition device is a wind speed sensor, which is installed at the hoisting construction site.

[0032] Instantaneous wind pressure at the hoisting construction site The data acquisition device is a wind pressure sensor, which is installed at the hoisting construction site;

[0033] Real-time load on the hook of a crane used for lifting operations The data acquisition device is a load sensor, which is installed on the hook of the crane used for hoisting construction.

[0034] boom luffing angle of a crane used for lifting operations The data acquisition device is an angle sensor, which is installed on the boom of the crane used for hoisting construction.

[0035] Speed ​​of hoisting machinery winches used in lifting operations The data acquisition device is an incremental encoder, which is installed at the end of the winch motor shaft of the hoisting machinery winch used for hoisting construction.

[0036] Horizontal offset of the suspended equipment The data acquisition device is a displacement sensor, which is connected to the suspended equipment.

[0037] Vibration frequency of components on the suspended equipment The data acquisition device is a vibration sensor, which is installed on the surface of the components on the suspended equipment.

[0038] Wind speed sensor, wind pressure sensor, load sensor, angle sensor, incremental encoder, displacement sensor and vibration sensor are all connected to the intelligent control terminal.

[0039] Preferably, step 1 further includes:

[0040] For key parameters that need to be monitored during the process, set original baseline safety thresholds, including the real-time wind speed at the hoisting construction site. The original baseline security threshold is Instantaneous wind pressure at the hoisting construction site The original baseline security threshold is Real-time load on the hook of a crane used for hoisting construction The original baseline security threshold is The boom luffing angle of a crane used for hoisting construction. The original baseline security threshold is The speed of the hoisting winch used in hoisting construction The original baseline security threshold is Horizontal offset of the suspended equipment The original baseline security threshold is The vibration frequency of the components on the suspended equipment The original baseline security threshold is .

[0041] Preferably, step 2-2-1 specifically includes:

[0042] Based on the three key stages of hoisting construction, namely the lifting stage of the crane... Translation stage In place stage Assign stage weight coefficients to the key parameters that need to be monitored during each event. ,in , Indicates the lifting stage , Indicates the translation phase , Indicates the in-place phase Then, the dynamic safety thresholds of the key parameters that need to be monitored during the event are dynamically adjusted. , The calculation formula is:

[0043] ;

[0044] in For the stage Dynamic security threshold; For stage weighting coefficients; The original baseline safety threshold for key parameters that need to be monitored during the process.

[0045] Preferably, step 2-2-2 specifically includes:

[0046] By combining the stage dynamic threshold and the parameter change rate, the dynamic deviation rate of a single parameter is calculated. , The calculation formula is:

[0047] ;

[0048] Subscript Representing the Key parameters that need to be monitored during the process; For the first Real-time data collection of key parameters that need to be monitored during the process. For the first Key parameters that need to be monitored during the process of this type of event Dynamic security threshold, For the first The rate of change of key parameters that need to be monitored during the process of this type of event. For the first The key parameters that need to be monitored during the event are set at the intervals. The amount of change within; For the first The maximum permissible rate of change of key parameters that need to be monitored during the process; This represents the trend influence coefficient.

[0049] Preferably, step 2-2-3 specifically includes:

[0050] The risk weights of key parameters that need to be monitored during the event are determined using the analytic hierarchy process (AHP), where the first... The risk weights of the key parameters that need to be monitored during the process are: Next, calculate the comprehensive risk index. , The calculation formula is:

[0051] ;

[0052] in The total number of types of key parameters that need to be monitored during the process; For the first Coupling correction coefficients for key parameters that need to be monitored during the process of class: when the first Key parameters that need to be monitored during the process of this type of event and the first When the key parameters that need to be monitored during the process of this type of event simultaneously approach a set threshold, that is... >5% and When >5% Otherwise, if the first one does not exist... Key parameters that need to be monitored during the process of this type of event and the first When the key parameters that need to be monitored during the process of a class of events are all close to the set threshold, .

[0053] Preferably, step 2-2-4 specifically includes:

[0054] Based on comprehensive risk index Classification of abnormality levels:

[0055] like It is judged as a level one anomaly; if It is classified as a level 2 abnormality; if It was determined to be a level three abnormality.

[0056] Preferably, step 3 specifically includes:

[0057] Level 1 anomaly handling: The intelligent control terminal issues a voice warning through the standard voice transmission and intercom module of the digital quality control type crane command instrument, and at the same time displays the key parameters and deviation rate under the level 1 anomaly condition in the display score function module;

[0058] Level 2 anomaly handling: The intelligent control terminal automatically triggers the crane operation command function module of the digital quality control crane command instrument to suspend the current lifting action and sends the anomaly information under Level 2 anomaly conditions to the construction management platform, notifying technical personnel to remotely analyze the cause; after the technical personnel formulate a handling plan, it is issued to the command instrument through the platform, and the command personnel execute the plan after confirmation. After the handling is completed, key parameters are re-collected. The hoisting can be restored;

[0059] Three-level abnormality handling: The terminal immediately triggers an emergency stop command through the digital quality control crane command instrument to cut off the crane's power source, activates the audible and visual alarm, and simultaneously notifies all personnel on site to evacuate to a safe area; after the risk is eliminated, the pre-existing quality inspection according to the quality plan must be carried out again, and the hoisting can only be restarted after the score meets the standard.

[0060] Preferably, step 4 specifically includes:

[0061] The intelligent control terminal records the following data through the storage module of the digital quality control crane operator:

[0062] Real-time change curves of key parameters that need to be monitored during the process;

[0063] Log of abnormal events;

[0064] Command instructions record;

[0065] After hoisting is completed, the recorded data can be exported via memory card or transmitted to the construction management platform to generate hoisting construction quality control reports and analyze the causes of abnormalities.

[0066] The beneficial effects of the present invention are as follows, compared with the prior art:

[0067] Improve dynamic response speed during events: Through 1Hz high-frequency sampling and automatic anomaly judgment, the anomaly identification time is shortened from 5-10 minutes of manual inspection in the existing technology to real-time terminal response of less than 1 second, avoiding accidents caused by response delays.

[0068] Achieve precise risk classification and control: Based on the classification of parameter deviation rate, avoid a one-size-fits-all suspension of hoisting (such as only issuing a warning for a level 1 anomaly without suspending the operation), reduce unnecessary construction interruptions while ensuring safety, and improve hoisting efficiency (calculated to reduce 20%-30% of ineffective pause time).

[0069] Improve closed-loop quality control management: Full-process data recording (parameter curves, anomaly handling, instruction records) enables problem traceability, cause analysis, and solution optimization. Subsequently, safety thresholds can be optimized through historical data (such as adjusting wind speed thresholds for specific areas) to continuously improve the level of quality control.

[0070] Strengthen inherent safety capabilities: By implementing an unmanned intervention logic of real-time monitoring, automatic early warning, and tiered response, the reliance on the experience of command personnel is reduced, and the risk of human error is lowered (such as avoiding misjudging load anomalies due to insufficient experience), thereby fundamentally improving the safety redundancy of hoisting operations. Attached Figure Description

[0071] Figure 1 This is a flowchart of the intelligent control method used for hoisting construction in this invention. Detailed Implementation

[0072] To address the problems existing in the prior art, this invention proposes an intelligent control method for hoisting construction. By real-time monitoring, dynamic early warning, hierarchical handling, and full-process traceability, it strengthens the control capability during hoisting and realizes intelligent quality control throughout the entire process of prevention, management, and traceability.

[0073] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, other embodiments obtained by those skilled in the art without creative effort are all within the protection scope of this invention.

[0074] like Figure 1 As shown, an intelligent control method for hoisting construction includes:

[0075] Step 1: Enter the pre-operation preparation stage, which involves initializing the quality plan and configuring monitoring parameters for the hoisting operation;

[0076] In a preferred but non-limiting embodiment of the present invention, the method for initializing the quality plan in step 1 includes:

[0077] Perform the quality inspection according to the quality plan mentioned in the existing technology, such as the technical solution with patent publication number "CN115744639A" (the quality inspection includes determining the prerequisites, recording the actual situation, the score of the accepting party, and judging the score threshold), to ensure that personnel, machinery, raw materials, laws, and environment all meet the requirements before hoisting.

[0078] In a preferred but non-limiting embodiment of the present invention, the method for monitoring parameter configuration in step 1 includes:

[0079] Steps for configuring new monitoring parameters: Based on the hoisting plan (such as equipment weight, hoisting machinery model, and operating radius), preset the key parameters that need to be monitored during the process, specifically including:

[0080] Environmental parameters: Real-time wind speed at the hoisting construction site Instantaneous wind pressure ;

[0081] Mechanical parameters: Real-time load on the hook of the crane used for hoisting operations. Crane boom luffing angle Speed ​​of hoisting machinery winch ;

[0082] Equipment parameters: Horizontal offset of the suspended equipment Vibration frequency of components on the suspended equipment ;

[0083] The key parameter that needs to be monitored during the process is the real-time wind speed at the hoisting construction site. Instantaneous wind pressure Real-time load on the hook of a crane used for hoisting operations Crane boom luffing angle Speed ​​of hoisting machinery winch Horizontal offset of the suspended equipment Or the vibration frequency of components on the suspended equipment .

[0084] In a preferred but non-limiting embodiment of the present invention, in step 1, the device for acquiring the key parameters that need to be monitored during the process is:

[0085] Real-time wind speed at the hoisting construction site The data acquisition device is a wind speed sensor, which is installed at the hoisting construction site.

[0086] Instantaneous wind pressure at the hoisting construction site The data acquisition device is a wind pressure sensor, which is installed at the hoisting construction site;

[0087] Real-time load on the hook of a crane used for lifting operations The data acquisition device is a load sensor, which is installed on the hook of the crane used for hoisting construction.

[0088] boom luffing angle of a crane used for lifting operations The data acquisition device is an angle sensor, which is installed on the boom of the crane used for hoisting construction.

[0089] Speed ​​of hoisting machinery winches used in lifting operations The data acquisition device is an incremental encoder, which is installed at the end of the winch motor shaft of the hoisting machinery winch used for hoisting construction.

[0090] Horizontal offset of the suspended equipment The data acquisition device is a displacement sensor, which is connected to the suspended equipment.

[0091] Vibration frequency of components on the suspended equipment The data acquisition device is a vibration sensor, which is installed on the surface of the components on the suspended equipment.

[0092] Wind speed sensors, wind pressure sensors, load sensors, angle sensors, incremental encoders, displacement sensors, and vibration sensors are all connected to the intelligent control terminal, which is also connected to the digital quality control type crane operator. Key parameters that need to be monitored during operation are collected and transmitted to the intelligent control terminal. The intelligent control terminal can be a PLC or an industrial computer.

[0093] In a preferred but non-limiting embodiment of the present invention, step 1 further includes:

[0094] For key parameters that need to be monitored during the process, set original baseline safety thresholds, including the real-time wind speed at the hoisting construction site. The original baseline security threshold is , The specific requirements are set according to the type of equipment, such as wind power equipment hoisting. The instantaneous wind pressure at the hoisting construction site can be 10 m / s. The original baseline security threshold is , The real-time load of the crane hook used for hoisting operations is set according to specific requirements. The original baseline security threshold is , Can be , The boom luffing angle of the crane used for lifting operations is the rated weight of the equipment being lifted. The original baseline security threshold is , The speed of the hoisting winch used in hoisting operations is set according to specific requirements. The original baseline security threshold is , The horizontal offset of the suspended equipment is set according to specific requirements. The original baseline security threshold is , It can be 0.5m, the vibration frequency of the components on the suspended equipment. The original baseline security threshold is , It can be 5Hz;

[0095] The original baseline safety threshold for the key parameters that need to be monitored during the process is: , , , , , or .

[0096] Step 2: Enter the in-process control stage, which involves real-time monitoring and dynamic handling of the hoisting operation;

[0097] In a preferred but non-limiting embodiment of the present invention, step 2 specifically includes:

[0098] Step 2-1: Collect key parameters that need to be monitored during the process in real time and transmit them to the intelligent control terminal;

[0099] In a preferred but non-limiting embodiment of the present invention, step 2-1 specifically includes:

[0100] The key parameters that need to be monitored during the event are collected by the acquisition device at a sampling frequency of 1Hz, and the key parameters that need to be monitored during the event are uploaded to the intelligent control terminal in real time.

[0101] Step 2-2: The intelligent control terminal judges parameter anomalies based on the key parameters that need to be monitored during the event.

[0102] In a preferred but non-limiting embodiment of the present invention, step 2-2 specifically includes:

[0103] In existing technologies, the method used by intelligent control terminals to determine parameter anomalies based on uploaded key parameters that need to be monitored during the event is mainly based on the parameter deviation rate. Calculation formula The following core defects exist:

[0104] Static threshold limitations: The original baseline safety threshold for key parameters that need to be monitored during the process. These are fixed values ​​and do not take into account the differences in parameter sensitivity during the hoisting stages (such as lifting, translation, and positioning) (for example, the load fluctuation tolerance during the hoisting stage should be lower than that during the translation stage).

[0105] Single parameter isolated judgment: Anomalies are judged solely by the deviation rate of a single parameter from the original baseline safety threshold of the key parameters to be monitored during the event, ignoring the risks of multi-parameter coordination (such as the risk of high wind speed and large horizontal offset being far greater than the risk of a single parameter exceeding the limit).

[0106] Parameter change trends are not considered: only the real-time values ​​of key parameters that need to be monitored during the process are considered. The static difference between the original baseline safety threshold of the key parameters that need to be monitored during the event does not include the parameter change rate (e.g., the risk of wind speed suddenly increasing from 5m / s to 10m / s within 10 seconds is much higher than that of slowly increasing to 10m / s).

[0107] The aforementioned defects result in insufficient accuracy in anomaly detection, which can easily lead to false alarms (such as a brief exceedance of the threshold during the translation phase being misjudged as a level 2 anomaly) or missed alarms (such as an accident caused by the superposition of multiple slight exceedances of the limit).

[0108] With improvements, the method for the intelligent control terminal of this invention to determine parameter anomalies based on the uploaded key parameters that need to be monitored during the process is as follows:

[0109] This method is based on a three-dimensional framework of dynamic threshold adjustment, multi-parameter coupled analysis, and trend prediction, and achieves breakthroughs through the following innovations:

[0110] Introduce dynamic weights for each stage and adjust the safety thresholds of each parameter according to the hoisting stage (lifting / translation / positioning);

[0111] Construct a multi-parameter coupled risk model to quantify the comprehensive risk of the synergistic effect between parameters;

[0112] By adding a trend factor, potential risks can be predicted based on the rate of parameter change, achieving early warning. The specific method is as follows:

[0113] Step 2-2-1: Perform phased dynamic threshold adjustment;

[0114] In a preferred but non-limiting embodiment of the present invention, step 2-2-1 specifically includes:

[0115] Based on the three key stages of hoisting construction, namely the lifting stage of the crane... Translation stage In place stage Assign stage weight coefficients to the key parameters that need to be monitored during each event. ,in , Indicates the lifting stage , Indicates the translation phase , Indicates the in-place phase Then, the dynamic safety thresholds of the key parameters that need to be monitored during the event are dynamically adjusted. , The calculation formula is:

[0116] ;

[0117] in For the stage Dynamic security threshold; For stage weight coefficients ( It can be set according to specific requirements, and its value range is 0.8-0.9. The lifting stage is risk-sensitive, so the threshold should be strictly controlled. It can be set according to specific requirements, and its value range is 1.0-1.1, with appropriate relaxation during the translation stage; It can be set according to specific requirements, and its value range is 0.7-0.8 (the highest accuracy requirement and the strictest threshold are required in the placement stage). The original baseline safety threshold for key parameters that need to be monitored during the process.

[0118] Step 2-2-2: Calculate the dynamic deviation rate of a single parameter;

[0119] In a preferred but non-limiting embodiment of the present invention, step 2-2-2 specifically includes:

[0120] Combined with stage dynamic threshold (stage dynamic threshold is...) ) and the rate of change of the parameter, calculate the dynamic deviation rate of the single parameter. , The calculation formula is:

[0121] ;

[0122] Subscript Representing the Key parameters that need to be monitored during the process (key parameters include real-time wind speed at the hoisting construction site) Instantaneous wind pressure Real-time load on the hook of a crane used for hoisting operations Crane boom luffing angle Speed ​​of hoisting machinery winch Horizontal offset of the suspended equipment Vibration frequency of components on the suspended equipment These categories, This refers to the category number of the key parameters set, such as wind speed. =1, load =2 (etc.) For the first Real-time data collection of key parameters that need to be monitored during the process. For the first Key parameters that need to be monitored during the process of this type of event Dynamic security threshold, For the first The rate of change of key parameters that need to be monitored during this type of event (such as the rate of change of wind speed in m / s) 2 ), For the first The key parameters that need to be monitored during the event are set at the intervals. The change within (this change is the current time) The Data of key parameters that need to be monitored during the process of this type of event minus the time. The (The difference obtained from the data of key parameters that need to be monitored during the process of this type of event). For the first The maximum permissible rate of change of key parameters that need to be monitored during the event (this maximum permissible rate of change is preset according to specific requirements, such as a maximum wind speed change rate of 2 m / s). 2 ); Trend influence coefficient ( It is set according to specific requirements, and its value range can be 0.3-0.5. (Used to adjust the contribution weight of the rate of change to the deviation rate).

[0123] Step 2-2-3: Calculate the multi-parameter coupled risk index;

[0124] In a preferred but non-limiting embodiment of the present invention, step 2-2-3 specifically includes:

[0125] The risk weights of key parameters that need to be monitored during the event are determined using the Analytic Hierarchy Process (AHP), where the first... The risk weights of the key parameters that need to be monitored during the process are: (e.g., risk weight of load) =0.3, risk weight of wind speed =0.25, risk weight for horizontal offset =0.2, risk weight of boom angle =0.15, risk weight of vibration frequency =0.1), then calculate the comprehensive risk index. , The calculation formula is:

[0126] ;

[0127] in The total number of types of key parameters that need to be monitored during the process; For the first Coupling correction coefficients for key parameters that need to be monitored during the process of class: when the first Key parameters that need to be monitored during the process of this type of event and the first When the key parameters that need to be monitored during a given event (i.e., two key parameters of different types that need to be monitored during a given event) simultaneously approach a set threshold, that is... >5% and When >5% (Strengthen collaborative risk); otherwise, if the first Key parameters that need to be monitored during the process of this type of event and the first When the key parameters that need to be monitored during the process of a class of events are all close to the set threshold, .

[0128] Step 2-2-4: Execute the anomaly level determination rules.

[0129] In a preferred but non-limiting embodiment of the present invention, step 2-2-4 specifically includes:

[0130] Based on comprehensive risk index Classification of abnormality levels:

[0131] like It is judged as a level one anomaly; if It is classified as a level 2 abnormality; if It was determined to be a level three abnormality.

[0132] The technical effects of the method for judging parameter anomalies based on the key parameters to be monitored during the process, as described in this invention, are as follows:

[0133] Improve the accuracy of anomaly detection: Reduce the false alarm rate by using dynamic thresholds and trend analysis (40%-50% less than existing methods). For example, if the wind speed briefly exceeds the original threshold but not the dynamic threshold during the translation phase, no warning will be triggered. Achieve early warning of risks: Introduce a rate of change factor to provide a warning 2-3 seconds in advance for sudden changes in parameters (such as a sudden increase in wind speed of 5 m / s within 10 seconds), leaving buffer time for handling.

[0134] Covering multi-parameter collaborative risks: through coupling correction coefficients It can identify the combined risks of slightly higher wind speed and slightly larger horizontal offset (such risks are easily missed in existing methods), thus improving the identification rate of major risks by more than 30%.

[0135] Adaptable to the entire construction cycle: Stage dynamic thresholds ensure strict control of high-risk stages such as lifting and positioning, while avoiding excessive intervention during the translation stage, thus improving construction efficiency (reducing unnecessary downtime by 15%-20%).

[0136] This method overcomes the limitations of existing technologies that are static, isolated, and lagging. Through multi-dimensional dynamic coupling analysis, it significantly improves the intelligence and precision of in-process control during hoisting operations.

[0137] Step 3: After real-time monitoring and dynamic handling of the hoisting operation, implement tiered response measures;

[0138] In a preferred but non-limiting embodiment of the present invention, step 3 specifically includes:

[0139] Level 1 anomaly handling: The intelligent control terminal issues a voice warning (such as "Wind speed has reached 8m / s, approaching the safety threshold, please pay attention") through the standard voice transmission and intercom module of the digital quality control type crane command instrument. At the same time, the key parameters and deviation rate under the level 1 anomaly condition are displayed in the display score function module. Commanders can adjust the hoisting speed according to the prompts (such as reducing the winch speed) without pausing the hoisting.

[0140] Level 2 anomaly handling: The intelligent control terminal automatically triggers the crane operation command function module of the digital quality control crane command instrument to pause the current lifting action (such as stopping hook lifting and boom luffing), and sends the anomaly information under Level 2 anomaly conditions (types, real-time values, and deviation rates of various key parameters under Level 2 anomaly conditions) to the construction management platform (a remote computer connected to the intelligent control terminal for construction management), notifying technical personnel to remotely analyze the cause; after the technical personnel formulate a handling plan, it is issued to the command instrument through the platform, and the command personnel execute the plan after confirmation (such as adjusting the sling angle to correct horizontal deviation). After the handling is completed, the key parameters are re-collected. The hoisting can be restored;

[0141] Three-level abnormality handling: The terminal immediately triggers an emergency stop command through the digital quality control crane controller to cut off the crane's power source. At the same time, it activates the audible and visual alarm (controlled by the digital quality control crane controller, which controls a flashing red light and a high-frequency buzzer connected to the digital quality control crane controller), and simultaneously notifies all personnel on site to evacuate to a safe area. After the risk is eliminated (such as when the wind speed drops below its dynamic safety threshold or the faulty machinery is repaired), the pre-existing quality inspection according to the quality plan must be carried out again. The hoisting can only be restarted after the score meets the standard.

[0142] Step 4: Conduct the post-event retrospective phase, which involves recording and analyzing data related to the hoisting operation.

[0143] In a preferred but non-limiting embodiment of the present invention, step 4 specifically includes:

[0144] The intelligent control terminal records the following data with timestamps (accurate to milliseconds) through the storage function module of the digital quality control type crane operator:

[0145] Real-time change curves of key parameters that need to be monitored during the event (the key parameters that need to be monitored during the event and their acquisition time are fitted using the least squares method to form real-time change curves, such as wind speed-time curves and load-time curves).

[0146] Abnormal event records (occurrence time, deviation rate, abnormality level, handling plan, handling time, and recovery time for each level of abnormality);

[0147] Command and control instructions (lifting action instructions, early warning instructions, pause / stop instructions);

[0148] After hoisting is completed, the recorded data can be exported or transmitted to the construction management platform via a storage card to generate hoisting construction quality control reports, analyze the causes of anomalies (such as whether frequent first-level anomalies are related to weather forecast deviations), and optimize the parameter threshold settings of subsequent hoisting plans.

[0149] The beneficial effects of the present invention are as follows, compared with the prior art:

[0150] Improve dynamic response speed during events: Through 1Hz high-frequency sampling and automatic anomaly judgment, the anomaly identification time is shortened from 5-10 minutes of manual inspection in the existing technology to real-time terminal response of less than 1 second, avoiding accidents caused by response delays.

[0151] Achieve precise risk classification and control: Based on the classification of parameter deviation rate, avoid a one-size-fits-all suspension of hoisting (such as only issuing a warning for a level 1 anomaly without suspending the operation), reduce unnecessary construction interruptions while ensuring safety, and improve hoisting efficiency (calculated to reduce 20%-30% of ineffective pause time).

[0152] Improve closed-loop quality control management: Full-process data recording (parameter curves, anomaly handling, instruction records) enables problem traceability, cause analysis, and solution optimization. Subsequently, safety thresholds can be optimized through historical data (such as adjusting wind speed thresholds for specific areas) to continuously improve the level of quality control.

[0153] Strengthen inherent safety capabilities: By implementing an unmanned intervention logic of real-time monitoring, automatic early warning, and tiered response, the reliance on the experience of command personnel is reduced, and the risk of human error is lowered (such as avoiding misjudging load anomalies due to insufficient experience), thereby fundamentally improving the safety redundancy of hoisting operations.

[0154] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention without departing from the spirit and scope of the present invention. Any modifications or equivalent substitutions should be covered within the scope of protection of the claims of the present invention.

Claims

1. An intelligent control method for hoisting construction, characterized in that, include: Step 1: Enter the pre-operation preparation stage, which involves initializing the quality plan and configuring monitoring parameters for the hoisting operation; Step 2: Enter the in-process control stage, which involves real-time monitoring and dynamic handling of the hoisting operation; Step 3: After real-time monitoring and dynamic handling of the hoisting operation, implement tiered response measures; Step 4: Conduct post-event retrospective analysis, which involves recording and analyzing data related to the hoisting operation; Step 2 specifically includes: Step 2-1: Collect key parameters that need to be monitored during the process in real time and transmit them to the intelligent control terminal; Step 2-2: The intelligent control terminal judges parameter anomalies based on the key parameters that need to be monitored during the event, which are uploaded; Step 2-1 specifically includes: The key parameters that need to be monitored during the event are collected by the equipment at a sampling frequency of 1Hz, and the key parameters that need to be monitored during the event are uploaded to the intelligent control terminal in real time. Step 2-2 specifically includes: Step 2-2-1: Perform dynamic threshold adjustment for each stage; Step 2-2-2: Calculate the dynamic deviation rate of a single parameter; Step 2-2-3: Calculate the multi-parameter coupled risk index; Step 2-2-4: Execute the anomaly level determination rules; Step 1 also includes: Set original baseline safety thresholds for key parameters that need to be monitored during the process; Step 2-2-1 specifically includes: Based on the three key stages of hoisting construction, namely the lifting stage of the crane... Translation stage In place stage Assign stage weight coefficients to the key parameters that need to be monitored during each event. ,in , Indicates the lifting stage , Indicates the translation phase , Indicates the in-place phase Then, the dynamic safety thresholds of the key parameters that need to be monitored during the event are dynamically adjusted. , The calculation formula is: ； in For the stage Dynamic security threshold; For stage weighting coefficients; The original baseline safety threshold for key parameters that need to be monitored during the process; Step 2-2-2 specifically includes: By combining the stage dynamic threshold and the parameter change rate, the dynamic deviation rate of a single parameter is calculated. , The calculation formula is: ； Subscript Representing the Key parameters that need to be monitored during the process; For the first Real-time data collection of key parameters that need to be monitored during the process. For the first Key parameters that need to be monitored during the process of this type of event Dynamic security threshold, For the first The rate of change of key parameters that need to be monitored during the process of this type of event. For the first The key parameters that need to be monitored during the event are set at the intervals. The amount of change within; For the first The maximum permissible rate of change of key parameters that need to be monitored during the process; This represents the trend influence coefficient. Step 2-2-3 specifically includes: The risk weights of key parameters that need to be monitored during the event are determined using the analytic hierarchy process (AHP), where the first... The risk weights of the key parameters that need to be monitored during the process are: Next, calculate the comprehensive risk index. , The calculation formula is: ； in The total number of types of key parameters that need to be monitored during the process; For the first Coupling correction coefficients for key parameters that need to be monitored during the process of class: when the first Key parameters that need to be monitored during the process of this type of event and the first When the key parameters that need to be monitored during the process of this type of event simultaneously approach a set threshold, that is... >5% and When >5% Otherwise, if the first one does not exist... Key parameters that need to be monitored during the process of this type of event and the first When the key parameters that need to be monitored during the process of a class of events are all close to the set threshold, .

2. The intelligent control method for hoisting construction according to claim 1, characterized in that, In step 1, the method for initializing the quality plan includes: Perform quality inspections according to the quality plan; In step 1, the method for monitoring parameter configuration includes: The key parameters to be monitored during the process are preset, including: Environmental parameters: Real-time wind speed at the hoisting construction site Instantaneous wind pressure ; Mechanical parameters: Real-time load on the hook of the crane used for hoisting operations. Crane boom luffing angle Speed ​​of hoisting machinery winch ; Equipment parameters: Horizontal offset of the suspended equipment Vibration frequency of components on the suspended equipment ; In step 1, the key parameters that need to be monitored during the process are acquired by the following devices: Real-time wind speed at the hoisting construction site The data acquisition device is a wind speed sensor, which is installed at the hoisting construction site. Instantaneous wind pressure at the hoisting construction site The data acquisition device is a wind pressure sensor, which is installed at the hoisting construction site; Real-time load on the hook of a crane used for lifting operations The data acquisition device is a load sensor, which is installed on the hook of the crane used for hoisting construction. boom luffing angle of a crane used for lifting operations The data acquisition device is an angle sensor, which is installed on the boom of the crane used for hoisting construction. Speed ​​of hoisting machinery winches used in lifting operations The data acquisition device is an incremental encoder, which is installed at the end of the winch motor shaft of the hoisting machinery winch used for hoisting construction. Horizontal offset of the suspended equipment The data acquisition device is a displacement sensor, which is connected to the suspended equipment. Vibration frequency of components on the suspended equipment The data acquisition device is a vibration sensor, which is installed on the surface of the components on the suspended equipment. Wind speed sensor, wind pressure sensor, load sensor, angle sensor, incremental encoder, displacement sensor and vibration sensor are all connected to the intelligent control terminal.

3. The intelligent control method for hoisting construction according to claim 2, characterized in that, The real-time wind speed at the hoisting construction site The original baseline security threshold is Instantaneous wind pressure at the hoisting construction site The original baseline security threshold is Real-time load on the hook of a crane used for hoisting construction The original baseline security threshold is The boom luffing angle of a crane used for hoisting construction. The original baseline security threshold is The speed of the hoisting winch used in hoisting construction The original baseline security threshold is Horizontal offset of the suspended equipment The original baseline security threshold is The vibration frequency of the components on the suspended equipment The original baseline security threshold is .

4. The intelligent control method for hoisting construction according to claim 3, characterized in that, Step 2-2-4 specifically includes: Based on comprehensive risk index Classification of abnormality levels: like It is judged as a level one anomaly; if It is classified as a level 2 abnormality; if It was determined to be a level three abnormality.

5. The intelligent control method for hoisting construction according to claim 4, characterized in that, Step 3 specifically includes: Level 1 anomaly handling: The intelligent control terminal issues a voice warning through the standard voice transmission and intercom module of the digital quality control type crane command instrument, and at the same time displays the key parameters and deviation rate under the level 1 anomaly condition in the display score function module; Level 2 anomaly handling: The intelligent control terminal automatically triggers the crane operation command function module of the digital quality control crane command instrument to suspend the current lifting action and sends the anomaly information under Level 2 anomaly conditions to the construction management platform, notifying technical personnel to remotely analyze the cause; after the technical personnel formulate a handling plan, it is issued to the command instrument through the platform, and the command personnel execute the plan after confirmation. After the handling is completed, key parameters are re-collected. The hoisting can be restored; Three-level abnormality handling: The terminal immediately triggers an emergency stop command through the digital quality control crane command instrument to cut off the crane's power source, and at the same time activates the audible and visual alarms and notifies all personnel on site to evacuate to a safe area; after the risk is eliminated, the quality inspection according to the quality plan must be carried out again, and the hoisting can only be restarted after the score meets the standard; Step 4 specifically includes: The intelligent control terminal records the following data through the storage module of the digital quality control crane operator: Real-time change curves of key parameters that need to be monitored during the process; Log of abnormal events; Command instructions record; After hoisting is completed, the recorded data can be exported via memory card or transmitted to the construction management platform to generate hoisting construction quality control reports and analyze the causes of abnormalities.

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