A safe operation control system for mining monorail crane based on the Internet of Things

Abstract

The present invention discloses a mining monorail crane safety operation control system based on the Internet of Things. The present invention relates to the technical field of monorail crane safety operation monitoring, and solves the technical problem of simply collecting working parameters, being unable to conduct in-depth comparative analysis to determine the actual working status of the equipment, and lacking a targeted subsequent control strategy. The present invention innovatively establishes a monorail crane parameter collection unit, which can accurately capture the full-dimensional working parameters of the equipment in real time, and utilizes a parameter comparison and analysis unit to quickly and accurately determine the working status of the equipment by comparing the normal parameter range set by the operator based on rich past records. The present invention integrates geographic information systems and high-precision positioning technologies, intelligently classifies the areas passed by the track route, and dynamically adjusts the operation strategy according to the average passenger flow in the area. The abnormal situation analysis unit breaks through the traditional single alarm mode, deeply explores the correlation factors of abnormal parameters, calculates risk values, and makes flexible decisions based on periodic changes. When the risk value exceeds the threshold, the equipment is shut down immediately.

Description

Technical Field

[0001] The present invention relates to the technical field of monorail crane safe operation monitoring, and in particular to a mining monorail crane safe operation control system based on the Internet of Things. Background Art

[0002] In industrial fields such as mining, monorail cranes, with their efficient material and personnel transport capabilities, have become critical transportation equipment underground or within large factory workshops. However, the working environment of monorail cranes is often complex and changeable, fraught with numerous safety hazards.

[0003] According to publication number CN117566600B, a mining monorail crane safety operation control system based on the Internet of Things is disclosed. The system includes: analyzing the deformation degree of each deformation position on the sliding track structure, identifying whether each deformation position is an acceleration point position, analyzing the travel blocking coefficient of each deformation position of the sliding track structure, and then analyzing the real-time operation health factor of the sliding track structure by identifying whether there are foreign objects on the track operation structure. By real-time detection of the operating temperature measurement indicators of each pulley bracket on the mining monorail crane, the real-time operation safety factor of the pulley structure is analyzed, and then it is confirmed whether to start the parking mode. By identifying the contour shape of each load, evaluating the swaying distance of the load at each acceleration point position, and determining the swaying direction of each load, it is analyzed whether there is a collision risk between the loads.

[0004] However, the operation management of traditional monorail cranes relies heavily on manual inspections and experience-based judgments. Operators need to regularly conduct on-site inspections of the equipment status and track conditions, which is labor-intensive and inefficient. Furthermore, it is difficult for humans to achieve real-time and accurate control of the equipment's operating parameters. Once emergencies such as abnormal operating speed, equipment overheating, and overload occur, they are difficult to detect and deal with in a timely manner, greatly increasing the probability of safety accidents. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a safe operation control system for mining monorail cranes based on the Internet of Things, which solves the problem of simply collecting working parameters, being unable to conduct in-depth comparative analysis to determine the actual working status of the equipment, and lacking targeted subsequent control strategies.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a mining monorail crane safety operation control system based on the Internet of Things, comprising:

[0007] a parameter comparison and analysis unit, configured to analyze the monorail crane operating parameters transmitted by the monorail crane parameter acquisition unit, identify the monorail crane's operating state by comparing the operating parameters, and generate a state identification result, wherein the state identification result includes a normal operating state and an abnormal operating state, and transmit the normal operating state to the normal state analysis unit, and transmit the abnormal operating state to the abnormal state analysis unit;

[0008] The normal situation analysis unit is used to analyze the acquired normal working state, judge the area passed by the monorail crane, and judge the danger of the area passed by the area to generate a danger judgment result. At the same time, the danger judgment result is analyzed in combination with historical data to generate operation control information, and then the operation control information is transmitted to the control information output unit;

[0009] The abnormal situation analysis unit is used to analyze the acquired abnormal working status, analyze the abnormal working parameters, and generate prediction results by combining the corresponding related parameters for risk prediction. At the same time, the prediction results are analyzed based on the periodic changes of the abnormal parameters, and the operation control information is generated and transmitted to the control information output unit.

[0010] As a further solution of the present invention, it also includes a monorail crane parameter acquisition unit and a control information output unit;

[0011] The monorail crane parameter collection unit is used to collect the working parameters of the monorail crane in the working state and transmit the collected working parameters to the parameter comparison and analysis unit;

[0012] The control information output unit is used to display the acquired operation control information to the corresponding operator.

[0013] As a further solution of the present invention, the parameter comparison and analysis unit analyzes the monorail crane working parameters in the following specific manner:

[0014] Obtain the monorail crane working parameters and the normal working parameters of the monorail crane at the same time, and match the monorail crane working parameters with the normal working parameters. If the two match, it means that the monorail crane is in normal working status, and a normal working status result is generated. Otherwise, if the two do not match, it means that the monorail crane is in abnormal working status, and an abnormal working status result is generated.

[0015] As a further solution of the present invention, the normal situation analysis unit analyzes the normal working state in the following specific manner:

[0016] Obtain the track route of the monorail crane, and at the same time obtain the area passed by the track route and mark it as the passing area i, where i=1, 2, ..., j, where j represents the number of the passing area. Then judge the danger of the passing area i. If the passing area is dangerous, the passing area is correspondingly classified as a dangerous passing area. Otherwise, if the passing area is not dangerous, the passing area is correspondingly classified as a normal passing area.

[0017] Normal monitoring is performed on normal passing areas and normal monitoring information is generated. For dangerous passing areas, secondary analysis signals are generated and processed.

[0018] As a further solution of the present invention, the normal situation analysis unit processes the secondary analysis signal in the following specific manner:

[0019] Obtain the classified dangerous passage areas and the average passenger flow in the dangerous passage areas, and compare the calculated average passenger flow with the preset value. If the average passenger flow is greater than the preset value, it means that the passenger flow in the dangerous passage area is large, and parking control information is generated. Conversely, if the average passenger flow is less than the preset value, it means that the passenger flow in the dangerous passage area is normal, and a deceleration signal is generated, which is then analyzed.

[0020] As a further solution of the present invention, the normal situation analysis unit analyzes the deceleration signal in the following specific manner:

[0021] Obtain historical data, and at the same time obtain historical records similar to the average flow of people in the current dangerous area, and obtain the corresponding running speeds in the historical records, then calculate the average running speed as the standard value, and at the same time obtain the real-time running speed, and compare the real-time running speed with the standard value;

[0022] If the real-time running speed is greater than the standard value, the real-time running speed is adjusted according to the standard value and differential control information is generated. Conversely, if the real-time running speed is less than the standard value, the real-time running speed is maintained and normal driving information is generated.

[0023] As a further solution of the present invention, the abnormal situation analysis unit analyzes the abnormal working state in the following specific manner:

[0024] Obtain the working parameters corresponding to the abnormal working state and record them as abnormal working parameters. At the same time, obtain the associated parameters of the abnormal working parameters based on historical data, and calculate the relationship index between the associated parameters and the abnormal working parameters. Then, identify the risk level of the abnormal working parameters and assign a value to the obtained risk level. Similarly, process the associated parameters and substitute the obtained parameters into the formula Calculate the risk value R of abnormal working parameters, where wn represents the weight of n abnormal working parameters, p n Assign a value to the risk level of abnormal working parameters, H n is the associated parameter weight of abnormal working parameters, is the relationship index;

[0025] The calculated risk value is compared with the threshold. If the risk value is greater than the threshold, a stop message is generated. If the risk value is less than the threshold, a periodic change monitoring signal is generated and analyzed.

[0026] As a further solution of the present invention, the specific method for the abnormal situation analysis unit to analyze the periodic change monitoring signal is:

[0027] Obtain the change value of the abnormal working parameter within the time period T, and at the same time obtain the change value corresponding to the associated parameter, and substitute the obtained change value into the risk value calculation formula to calculate the periodic change risk value, and then compare the periodic change risk value with the threshold. If the periodic change risk value is greater than the threshold, a stop message is generated. Conversely, if the periodic change risk value is less than the threshold, a periodic monitoring message is generated.

[0028] The present invention provides a safe operation control system for a mining monorail crane based on the Internet of Things. Compared with the existing technology, it has the following advantages:

[0029] The present invention innovatively establishes a monorail crane parameter collection unit, which can accurately capture the full-dimensional operating parameters of the equipment in real time. Utilizing a parameter comparison and analysis unit, the system quickly and accurately determines the equipment's operating status by comparing it with the normal parameter range set by the operator based on extensive past records. By integrating geographic information systems with high-precision positioning technology, the system intelligently classifies areas passed by the track route, accurately distinguishes between dangerous and normal areas, and dynamically adjusts the operating strategy based on the average passenger flow in the area.

[0030] The abnormal situation analysis unit breaks through the traditional single alarm mode, deeply explores the correlation factors of abnormal parameters, calculates risk values, and makes flexible decisions based on periodic changes. When the risk value exceeds the threshold, the machine is shut down immediately; if it does not exceed the threshold, it will continue to monitor and make real-time adjustments based on the working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a block diagram of the system principle of the present invention. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0033] For example 1, please refer to Figure 1 The present application provides a safe operation control system for a mining monorail crane based on the Internet of Things, including a monorail crane parameter acquisition unit, a parameter comparison and analysis unit, an abnormal situation analysis unit, a normal situation analysis unit and a control information output unit. It can be seen from the accompanying drawings that the above-mentioned functional units are unidirectionally electrically connected.

[0034] The monorail crane parameter acquisition unit is used to collect the working parameters of the monorail crane in the working state, and transmit the collected working parameters to the parameter comparison and analysis unit. The working parameters include operating speed, position, load, equipment temperature and other parameters.

[0035] A parameter comparison and analysis unit is used to analyze the obtained monorail crane working parameters, identify the working status of the monorail crane by comparing the working parameters, and generate a status identification result, wherein the status identification result includes a normal working status and an abnormal working status, and transmits the normal working status to the normal situation analysis unit, and transmits the abnormal working status to the abnormal situation analysis unit.

[0036] Obtain the working parameters of the monorail crane and the normal working parameters of the monorail crane at the same time. The normal working parameters here are set by the operator according to past work records and are interval values. For example, set the normal operating speed range of the monorail crane to 5-8 m / s, the normal operating temperature range of the motor to 50-70°C, the normal load weight range to 1-3 tons, and the normal braking pressure range to 2-4 MPa. Match the monorail crane working parameters with the normal working parameters. If the two match, it means that the monorail crane is working normally and generates a normal working status result. Otherwise, if the two do not match, it means that the monorail crane is working abnormally and generates an abnormal working status result. The match here means that all parameters are within the normal working parameter range. If any set of parameters is not within the normal working parameter range, it indicates a mismatch.

[0037] Assuming that the current operating speed of the monorail crane is 6.5 m / s, the motor temperature is 60°C, the load weight is 2 tons, and the braking pressure is 3 MPa, and these parameters are all within their corresponding normal operating parameter ranges, then it can be determined that the monorail crane is working normally. On the contrary, if the obtained operating speed of the monorail crane is 9 m / s, which exceeds the normal operating speed range, or the motor temperature reaches 80°C, which is higher than the normal temperature range, and any one or more operating parameters are not within the normal range, then it means that the monorail crane is working abnormally.

[0038] Normal situation analysis unit, this unit is used to analyze the normal working status obtained, generate a danger judgment result by judging the area where the monorail crane passes and the danger of the area where it passes, and analyze the danger judgment result in combination with historical data to generate operation control information, and then transmit the operation control information to the control information output unit.

[0039] Obtain the track route of the monorail crane, and at the same time obtain the areas that the track route passes through and mark them as passing areas i, where i=1, 2, ..., j, where j represents the number of passing areas. With the help of high-precision positioning and mapping technology, accurately obtain the track route information of the monorail crane. While obtaining the track route, use the geographic information system (GIS) or special area identification software to obtain and identify each area that the track route passes through in detail, and then judge the danger of passing area i. If there is danger in the passing area, the passing area is correspondingly classified as a dangerous passing area. Conversely, if there is no danger in the passing area, the passing area is correspondingly classified as a normal passing area. Normal monitoring is performed on the normal passing area, and normal monitoring information is generated. For the dangerous passing area, a secondary analysis signal is generated and processed.

[0040] For example, for transit area 1, if the area is located inside a workshop with frequent personnel activities and a relatively narrow space, past accident records show that a collision accident has occurred, and real-time monitoring detects the presence of some temporarily placed obstacles in the area, then based on these factors, it can be determined that the transit area is dangerous and classified as a dangerous transit area. For transit area 3, this area is an open and well-sighted dedicated transportation channel with no obvious obstacles and no other interference factors in the surrounding area, and no safety accidents have occurred in the past, then it is determined that the transit area is not dangerous and is classified as a normal transit area.

[0041] The classified dangerous passage areas are obtained, and the average flow of people in the dangerous passage areas is obtained at the same time. The average flow of people is obtained by calculating the flow of people in different time periods during working hours. For example, if the time period is divided into 1 hour, then a day can be divided into 24 periods. At the same time, the corresponding working time periods are obtained, and the corresponding flow of people is calculated. Finally, the calculated flow of people in all periods is summed up to calculate the average value, and the calculated average flow of people is compared with a preset value. The specific value of the preset value is set by the operator, specifically, it is calculated based on the flow of people corresponding to the number of accidents in the past historical data. If the average flow of people is greater than the preset value, it means that the flow of people in the dangerous passage area is large, and parking control information is generated. On the contrary, if the average flow of people is less than the preset value, it means that the flow of people in the dangerous passage area is normal, and a deceleration signal is generated, and then the generated deceleration signal is analyzed;

[0042] Obtain historical data, and at the same time obtain historical records in the historical data that are similar to the average flow of people in the current dangerous area, and obtain the corresponding running speed in the historical records, then calculate the average value of the running speed and record it as the standard value, and at the same time obtain the real-time running speed, and compare the real-time running speed with the standard value. If the real-time running speed is greater than the standard value, adjust the real-time running speed according to the standard value and generate differential control information. Conversely, if the real-time running speed is less than the standard value, maintain the real-time running speed and generate normal driving information.

[0043] For example, a monorail crane transport system within a factory is about to enter a dangerous area—the maintenance workshop aisle. Workers frequently move back and forth in this area, carrying equipment and parts, and the average flow of people fluctuates dynamically. An infrared traffic monitor at the workshop entrance measures an average flow of 60 people per minute over the past 30 minutes. The system quickly searches the historical database for matches and selects five historical records with similar flow rates. The corresponding monorail crane speeds are 3.5 m / s, 3.7 m / s, 3.6 m / s, 3.4 m / s, and 3.8 m / s, respectively. The calculated standard value is approximately 3.6 m / s. At this point, the speed sensor on the monorail crane reports a real-time speed of 4.0 m / s, exceeding the standard value. The intelligent speed control system immediately activates. Based on the speed deviation (4.0 - 3.6 = 0.4 m / s), the PID controller quickly calculates a motor deceleration command, gradually reducing the motor's power and steadily decreasing the monorail crane's speed. After a few seconds of adjustment, the speed reaches 3.6 m / s.

[0044] The obtained differential speed control information and normal driving information are combined to obtain operation control information, and the operation control information is transmitted to the control information output unit.

[0045] A control information output unit is used to display the acquired operation control information to the corresponding operator.

[0046] Example 2, as Example 2 of the present invention, is implemented on the basis of Example 1, and differs from Example 1 in the following aspects:

[0047] The abnormal situation analysis unit is used to analyze the acquired abnormal working status, analyze the abnormal working parameters, and generate prediction results by combining the corresponding associated parameters for risk prediction. At the same time, the prediction results are analyzed based on the periodic changes of the abnormal parameters to generate operation control information.

[0048] Obtain the working parameters corresponding to the abnormal working state and record them as abnormal working parameters. At the same time, obtain the associated parameters of the abnormal working parameters based on historical data. The associated parameters here represent data that changes as the abnormal working parameters change. Calculate the relationship index between the associated parameters and the abnormal working parameters. Then identify the risk level of the abnormal working parameters and assign a value to the obtained risk level. Similarly, process the associated parameters and substitute the obtained parameters into the formula Calculate the risk value R of abnormal working parameters, where w n represents the weight of n abnormal working parameters, p n Assign a value to the risk level of abnormal working parameters, H n is the associated parameter weight of abnormal working parameters, is the relationship index;

[0049] The calculated risk value is compared with a threshold value, and the specific value of the threshold value is set by the operator. If the risk value is greater than the threshold value, it means that the abnormal operating parameters pose a risk to the overall operation of the monorail crane, and a stop message is generated. If the risk value is less than the threshold value, it means that the abnormal operating parameters have not yet posed a risk to the overall operation of the monorail crane, and a periodic change monitoring signal is generated at the same time.

[0050] Obtain the change value of the abnormal working parameter within the time period T, and at the same time obtain the change value corresponding to the associated parameter, and substitute the obtained change value into the risk value calculation formula to calculate the periodic change risk value. The risk value calculation formula here is the same as the above formula. Then compare the periodic change risk value with the threshold. If the periodic change risk value is greater than the threshold, a stop message is generated. Conversely, if the periodic change risk value is less than the threshold, a periodic monitoring message is generated.

[0051] The obtained stop information and period monitoring information are combined to obtain operation control information, and the operation control information is transmitted to the control information output unit.

[0052] A control information output unit is used to display the acquired operation control information to the corresponding operator.

[0053] Embodiment 3: As the embodiment 3 of the present invention, the focus is on combining the implementation processes of embodiment 1 and embodiment 2.

[0054] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0055] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A mining monorail crane safety operation control system based on the Internet of Things, characterized in that: include: a parameter comparison and analysis unit, configured to analyze the monorail crane operating parameters transmitted by the monorail crane parameter acquisition unit, identify the monorail crane's operating state by comparing the operating parameters, and generate a state identification result, wherein the state identification result includes a normal operating state and an abnormal operating state, and transmit the normal operating state to the normal state analysis unit, and transmit the abnormal operating state to the abnormal state analysis unit; The normal situation analysis unit is used to analyze the acquired normal working status, judge the area passed by the monorail crane, and judge the danger of the area passed by to generate a danger judgment result. At the same time, the danger judgment result is analyzed in combination with historical data to generate operation control information, and then the operation control information is transmitted to the control information output unit. The specific processing method is: Obtain the track route of the monorail crane, and at the same time obtain the area passed by the track route and mark it as the passing area i, where i=1, 2, ..., j, where j represents the number of the passing area. Then judge the danger of the passing area i. If the passing area is dangerous, the passing area is correspondingly classified as a dangerous passing area. Otherwise, if the passing area is not dangerous, the passing area is correspondingly classified as a normal passing area. For normal passing areas, normal monitoring is carried out and normal monitoring information is generated. For dangerous passing areas, secondary analysis signals are generated and processed. Obtaining the classified dangerous passage area and the average passenger flow in the dangerous passage area, and comparing the calculated average passenger flow with a preset value. If the average passenger flow is greater than the preset value, it indicates that the passenger flow in the dangerous passage area is large, and parking control information is generated. Conversely, if the average passenger flow is less than the preset value, it indicates that the passenger flow in the dangerous passage area is normal, and a deceleration signal is generated. The generated deceleration signal is then analyzed; The abnormal situation analysis unit is used to analyze the acquired abnormal working status, analyze the abnormal working parameters, and generate prediction results by combining the corresponding related parameters for risk prediction. At the same time, the prediction results are analyzed based on the periodic changes of the abnormal parameters, and the operation control information is generated and transmitted to the control information output unit.

2. The safe operation control system for a mining monorail crane based on the Internet of Things according to claim 1 is characterized in that: It also includes a monorail crane parameter acquisition unit and a control information output unit; The monorail crane parameter collection unit is used to collect the working parameters of the monorail crane in the working state and transmit the collected working parameters to the parameter comparison and analysis unit; The control information output unit is used to display the acquired operation control information to the corresponding operator.

3. The safe operation control system for mining monorail crane based on Internet of Things according to claim 1 is characterized in that: The specific method by which the parameter comparison and analysis unit analyzes the working parameters of the monorail crane is as follows: Obtain the monorail crane working parameters and the normal working parameters of the monorail crane at the same time, and match the monorail crane working parameters with the normal working parameters. If the two match, it means that the monorail crane is in normal working status, and a normal working status result is generated. Otherwise, if the two do not match, it means that the monorail crane is in abnormal working status, and an abnormal working status result is generated.

4. The safe operation control system for a mining monorail crane based on the Internet of Things according to claim 1 is characterized in that: The specific method for the normal situation analysis unit to analyze the deceleration signal is as follows: Obtain historical data, and at the same time obtain historical records similar to the average flow of people in the current dangerous area, and obtain the corresponding running speeds in the historical records, then calculate the average running speed as the standard value, and at the same time obtain the real-time running speed, and compare the real-time running speed with the standard value; If the real-time running speed is greater than the standard value, the real-time running speed is adjusted according to the standard value and differential control information is generated. Conversely, if the real-time running speed is less than the standard value, the real-time running speed is maintained and normal driving information is generated.

5. The safe operation control system for mining monorail crane based on Internet of Things according to claim 1 is characterized in that: The specific method for the abnormal situation analysis unit to analyze the abnormal working state is as follows: Obtain the working parameters corresponding to the abnormal working state and record them as abnormal working parameters. At the same time, obtain the associated parameters of the abnormal working parameters based on historical data, and calculate the relationship index between the associated parameters and the abnormal working parameters. Then, identify the risk level of the abnormal working parameters and assign a value to the obtained risk level. Similarly, process the associated parameters and substitute the obtained parameters into the formula Calculate the risk value R of abnormal working parameters, where w n represents the weight of n abnormal working parameters, p n Assign a value to the risk level of abnormal working parameters, H n is the associated parameter weight of abnormal working parameters, is the relationship index; The calculated risk value is compared with the threshold. If the risk value is greater than the threshold, a stop message is generated. If the risk value is less than the threshold, a periodic change detection signal is generated and analyzed.

6. The safe operation control system for a mining monorail crane based on the Internet of Things according to claim 5 is characterized in that: The specific method for the abnormal situation analysis unit to analyze the periodic change monitoring signal is as follows: Obtain the change value of the abnormal working parameter within the time period T, and at the same time obtain the change value corresponding to the associated parameter, and substitute the obtained change value into the risk value calculation formula to calculate the periodic change risk value, and then compare the periodic change risk value with the threshold. If the periodic change risk value is greater than the threshold, a stop message is generated. Conversely, if the periodic change risk value is less than the threshold, a periodic monitoring message is generated.

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