Mining monorail crane safe operation control system based on Internet of Things

Abstract

The invention discloses a mining monorail crane safe operation control system based on the Internet of Things, relates to the technical field of monorail crane safe operation monitoring, and solves the technical problems that the real working state of equipment cannot be deeply compared, analyzed and judged by purely collecting working parameters, but a targeted subsequent control strategy is lacked. The monorail crane parameter acquisition unit is innovatively arranged, full-dimension working parameters of equipment can be accurately grabbed in real time, the working state of the equipment can be rapidly and accurately judged by using the parameter comparison and analysis unit in comparison with normal parameter intervals set by operators on the basis of past abundant records, a geographic information system and a high-precision positioning technology are fused, and the working efficiency of the equipment is improved. The track route passing areas are intelligently classified, an operation strategy is dynamically adjusted according to the average pedestrian flow of the areas, an abnormal condition analysis unit breaks through a traditional single alarm mode, abnormal parameter correlation factors are deeply excavated, a risk value is calculated, a decision is flexibly made in combination with periodic changes, and when the risk value exceeds a threshold value, the machine is stopped immediately.

Description

Technical Field

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

[0002] In industrial fields such as mining, monorail cranes have become key transportation equipment underground or in large factory workshops due to their efficient material transportation and personnel carrying capabilities. However, the working environment of monorail cranes is often complex and changeable, and is full of many safety hazards.

[0003] According to the publication number CN117566600B, a safe operation control system for a mining monorail crane based on the Internet of Things is disclosed. The system includes: by 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 blockage 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 object, the swaying distance of the load object at each acceleration point position is evaluated, and the swaying direction of each load object is determined, and based on this, it is analyzed whether there is a collision risk between each load object.

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

[0005] In view of the shortcomings of the prior art, the present invention provides a safe operation control system for a mining monorail crane based on the Internet of Things, which solves the problem of simply collecting working parameters, being unable to conduct in-depth comparative analysis and 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 is used to analyze the monorail crane working parameters transmitted by the monorail crane parameter acquisition unit, identify the working state of the monorail crane by comparing the working parameters, and generate a state identification result, wherein the state identification result includes a normal working state and an abnormal working state, and transmit the normal working state to the normal situation analysis unit, and transmit the abnormal working state to the abnormal situation analysis unit;

[0008] The normal situation analysis unit is used to analyze the acquired normal working state, judge the passing area of ​​the monorail crane, and judge the danger of the passing area to generate a danger judgment result, 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;

[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 specific method in which the parameter comparison and analysis unit analyzes the working parameters of the monorail crane is:

[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 state and generates a normal working state result. Otherwise, if the two do not match, it means that the monorail crane is in abnormal working state and generates an abnormal working state result.

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

[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 areas, and 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 specific manner in which the normal situation analysis unit processes the secondary analysis signal is as follows:

[0019] The classified dangerous passage areas are obtained, and the average flow of people in the dangerous passage areas is obtained, and the calculated average flow of people is compared with the preset value. 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.

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

[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 speed in the historical records, then calculate the average value of the 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 specific manner in which the abnormal situation analysis unit analyzes the abnormal working state is:

[0024] The working parameters corresponding to the abnormal working state are obtained and recorded as abnormal working parameters. At the same time, the associated parameters of the abnormal working parameters are obtained based on historical data, and the relationship index between the associated parameters and the abnormal working parameters is calculated. Then, the risk level of the abnormal working parameters is identified and the obtained risk level is assigned. Similarly, the associated parameters are processed and the obtained parameters are substituted into the formula The risk value R of abnormal working parameters is calculated, 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 in which the abnormal situation analysis unit analyzes 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. Otherwise, if the periodic change risk value is less than the threshold, a periodic monitoring information 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 prior art, it has the following beneficial effects:

[0029] 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 uses a parameter comparison and analysis unit to quickly and accurately judge the working status of the equipment by comparing the normal parameter range set by the operator based on rich past records. It integrates the geographic information system and high-precision positioning technology to intelligently classify the areas passed by the rail route, accurately distinguish between dangerous and normal areas, and dynamically adjust the operation strategy according to 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 the risk value, 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 operating 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 be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0033] For example, see 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 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, wherein 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 working parameters of the monorail crane, 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 transmit the normal working status to the normal situation analysis unit, and transmit the abnormal working status to the abnormal situation analysis unit.

[0036] Obtain the working parameters of the monorail crane, and at the same time obtain the normal working parameters of the monorail crane. 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 range of load weight to 1-3 tons, and the normal range of brake pressure 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 in normal working condition and generates a normal working condition result. Otherwise, if the two do not match, it means that the monorail crane is in abnormal working condition and generates an abnormal working condition 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 working parameter ranges, then it can be determined that the monorail crane is working normally. Conversely, 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 working parameters are not within the normal range, then it means that the monorail crane is working abnormally.

[0038] The normal situation analysis unit is used to analyze the normal working status obtained, generate a danger judgment result by judging the passing area of ​​the monorail crane and the danger of the passing area, 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 area through which the track route passes and mark it as the passing area i, where i=1, 2, ..., j, where j represents the number of passing areas. With the help of high-precision positioning and mapping technology, the track route information of the monorail crane can be accurately obtained. While obtaining the track route, use the geographic information system (GIS) or special area identification software to obtain and identify each area passed by the track route in detail, and then judge the danger of the passing area i. If there is danger in the passing area, the passing area is correspondingly classified as a dangerous passing area. On the contrary, if there is no danger in the passing area, the passing area is correspondingly classified as a normal passing area. For the normal passing area, normal monitoring is carried out and normal monitoring information is generated. For the dangerous passing area, a secondary analysis signal is generated and the secondary analysis signal is processed;

[0040] For example, for passing 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 shows that there are some temporarily placed obstacles in the area, then based on these factors, it can be determined that the passing area is dangerous and it is classified as a dangerous passing area. For passing area 3, this area is an open and dedicated transportation channel with good visibility, 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 passing area is not dangerous and it is classified as a normal passing area.

[0041] The classified dangerous passage area is obtained, and the average flow of people in the dangerous passage area 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, a day can be divided into 24 periods. At the same time, the corresponding working time period is 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, and is specifically 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, the real-time running speed is adjusted according to the standard value and differential control information is generated. On the contrary, 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.

[0043] Take the monorail crane transportation system in a factory as an example. The monorail crane is about to enter a dangerous area - the aisle of the maintenance workshop. Workers often shuttle back and forth in this area to carry equipment and parts. The average flow of people changes dynamically. The infrared flow monitor at the entrance of the workshop measures that the average flow of people in the past 30 minutes is 60 people / minute. The system quickly searches for matches in the historical database and selects historical records of 5 similar flow periods. The corresponding operating speeds of the monorail crane are 3.5m / s, 3.7m / s, 3.6m / s, 3.4m / s, and 3.8m / s, respectively. The standard value is calculated to be about 3.6m / s. At this time, the speed sensor on the monorail crane feedbacks that the real-time operating speed is 4.0m / s, which is greater than the standard value. The intelligent speed regulation system starts immediately. The PID controller quickly calculates the motor deceleration command based on the speed deviation (4.0-3.6=0.4m / s), drives the motor to gradually reduce the power, and makes the speed of the monorail crane drop steadily. After a few seconds of adjustment, the speed reaches 3.6m / 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] Embodiment 2, as Embodiment 2 of the present invention, is implemented on the basis of Embodiment 1, and differs from Embodiment 1 in that:

[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 related 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] The working parameters corresponding to the abnormal working state are obtained and recorded as abnormal working parameters. At the same time, the associated parameters of the abnormal working parameters are obtained based on historical data. The associated parameters here represent data that changes with the abnormal working parameters. The relationship index between the associated parameters and the abnormal working parameters is calculated. Then, the risk level of the abnormal working parameters is identified and the obtained risk level is assigned. Similarly, the associated parameters are processed and the obtained parameters are substituted into the formula. The risk value R of abnormal working parameters is calculated, 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 the 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 working 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 working parameters have not yet caused 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, and the risk value calculation formula here is the same as the above formula, and then compare the periodic change risk value with the threshold value. If the periodic change risk value is greater than the threshold value, a stop message is generated. On the contrary, if the periodic change risk value is less than the threshold value, 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: Embodiment 3 of the present invention focuses 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 rather than to limit it. 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 safe operation control system for a mining monorail crane based on the Internet of Things, characterized in that: include: A parameter comparison and analysis unit is used to analyze the monorail crane working parameters transmitted by the monorail crane parameter acquisition unit, identify the working state of the monorail crane by comparing the working parameters, and generate a state identification result, wherein the state identification result includes a normal working state and an abnormal working state, and transmit the normal working state to the normal situation analysis unit, and transmit the abnormal working state to the abnormal situation analysis unit; The normal situation analysis unit is used to analyze the acquired normal working state, judge the passing area of ​​the monorail crane, and judge the danger of the passing area to generate a danger judgment result, 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; 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. According to the Internet of Things-based mining monorail crane safety operation control system of claim 1, it 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. According to the Internet of Things-based mining monorail crane safety operation control system of claim 1, it 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 state and generates a normal working state result. Otherwise, if the two do not match, it means that the monorail crane is in abnormal working state and generates an abnormal working state result.

4. According to the Internet of Things-based mining monorail crane safety operation control system of claim 1, it is characterized in that: The specific method for the normal situation analysis unit to analyze the normal working state is as follows: 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 areas, and 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; 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.

5. The safe operation control system for mining monorail crane based on Internet of Things according to claim 4 is characterized in that: The specific way in which the normal situation analysis unit processes the secondary analysis signal is as follows: The classified dangerous passage areas are obtained, and the average flow of people in the dangerous passage areas is obtained, and the calculated average flow of people is compared with the preset value. 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.

6. The safe operation control system for mining monorail crane based on Internet of Things according to claim 5 is characterized in that: The specific method in which the normal situation analysis unit analyzes 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 speed in the historical records, then calculate the average value of the 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.

7. 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: The working parameters corresponding to the abnormal working state are obtained and recorded as abnormal working parameters. At the same time, the associated parameters of the abnormal working parameters are obtained based on historical data, and the relationship index between the associated parameters and the abnormal working parameters is calculated. Then, the risk level of the abnormal working parameters is identified and the obtained risk level is assigned. Similarly, the associated parameters are processed and the obtained parameters are substituted into the formula The risk value R of abnormal working parameters is calculated, 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.

8. The safe operation control system for mining monorail crane based on Internet of Things according to claim 7 is characterized in that: The specific method in which the abnormal situation analysis unit analyzes 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. Otherwise, if the periodic change risk value is less than the threshold, a periodic monitoring information is generated.

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