Real-time control system for automatic loading and stacking of bulk materials

Abstract

The invention discloses a bulk material automatic loading material pile real-time control system, which relates to the technical field of real-time control, and comprises a signal receiving module, a preprocessing module, a database, a data prediction module, a safety early warning module, a data evaluation module and a measurement control module, and the data evaluation module evaluates whether the use state of the electric conveying equipment is abnormal or not by combining the comprehensive prediction deviation value and the comprehensive error value, can calculate the prediction deviation and the actual error of the use state parameter data of the electric conveying equipment in real time, and obtains the final use state evaluation value of the electric conveying equipment by combining the prediction deviation and the actual error value. The use of the electric conveying equipment is regulated and controlled in time according to the final estimated value, so that the influence on the use state parameter data of the electric conveying equipment in severe environments such as certain temperature, humidity or electromagnetic interference is reduced, and the stability and reliability of the system are improved.

Description

Technical Field

[0001] The invention relates to the technical field of real-time control, and in particular to a real-time control system for automatic loading of bulk materials into a pile. Background Art

[0002] With the continuous innovation in the fields of Internet of Things technology, big data analysis and artificial intelligence, the real-time control system for automatic loading of bulk materials is accelerating towards an intelligent, networked and remotely controlled future. The core components of this system include advanced electric conveying equipment such as electric belt conveyors and electric screw conveyors. In view of the rapid advancement of technology, the market demand for real-time control systems for automatic loading of bulk materials is also showing a booming growth trend. With its excellent safety operation characteristics, simple operation interface and convenient maintenance process, the intelligent loading system has shown significant advantages in the entire chain of aggregate loading operations.

[0003] However, the real-time control system for automatic loading of bulk materials still faces a series of challenges in practical applications. Although the environmental adaptability factors have been fully considered at the beginning of the system design, under complex conditions such as certain temperature, high humidity environment or strong electromagnetic interference, the working performance and service life of the electric conveying equipment will still be adversely affected by changes in key parameters such as current fluctuations, voltage instability and excessive temperature. These factors not only weaken the overall stability and reliability of the system, but also seriously restrict the efficient application and widespread promotion of the real-time control system for automatic loading of bulk materials. Summary of the invention

[0004] In view of the above situation, the present invention can calculate the predicted deviation and actual error of the usage status parameter data of the electric conveying equipment in real time, and combine the predicted deviation and actual error values ​​to obtain the final usage status valuation of the electric conveying equipment, and make timely adjustments to the use of the electric conveying equipment according to the final valuation.

[0005] The technical solution includes a signal receiving module, a preprocessing module, a database, a data prediction module, a safety warning module, a data evaluation module, and a measurement control module. The working steps of this system are as follows: S1, the signal receiving module receives in real time a data packet of the usage status parameters of the electric conveying equipment in the system sent by the acquisition end; S2, the preprocessing module parses the data packet to extract voltage data A, current data B, temperature data C and health data D, time T, IP address F, and sends the data to the data prediction module and the data evaluation module; S3, the data prediction module extracts the corresponding predicted voltage data A1, current data B1, temperature data C1 and health data D1 at time T in the database according to the IP address F, and then calculates the predicted deviation ratio of the actual voltage data A and the predicted voltage data. Similarly, the predicted deviation ratio of the current data, temperature data and health data is calculated, and finally the comprehensive predicted deviation value of the electric conveying equipment usage status parameter data packet is weighted and calculated; S4, the safety warning module retrieves the corresponding voltage data threshold A2, current data threshold B2, temperature data threshold C2, and health data threshold D2 from the database according to the IP address F, and compares the data beyond the threshold range to be abnormal, otherwise normal, and then calculates the error ratio between the abnormal data exceeding value and the corresponding threshold, and finally weightedly calculates the comprehensive error value of the electric conveying equipment use status parameter data packet; S5, the data evaluation module first evaluates whether the voltage data A, current data B, temperature data C and health data D are abnormal according to the predicted deviation ratio and the error ratio, and then evaluates whether the use status of the electric conveying equipment is abnormal in combination with the comprehensive predicted deviation value and the comprehensive error value, and sends the evaluation result to the measurement control module; S6, the measurement control module performs corresponding response operations according to the evaluation result of the data evaluation module.

[0006] Furthermore, the working steps of the data prediction module are as follows: S3-1, calculate the predicted deviation ratio A11 of the voltage data, A11=|A-A1| / A1; similarly calculate the predicted deviation ratio B11 of the current data, the predicted deviation ratio C11 of the temperature data, and the predicted deviation ratio D11 of the health data; S3-2, calculate the comprehensive prediction deviation value G of the electric conveying equipment usage status parameter data packet, G=A11×0.3+B11×0.3+C11×0.2+D11×0.2.

[0007] Furthermore, the working steps of the safety warning module are as follows: S4-1, retrieve the corresponding voltage data threshold A2 in the database according to the IP address F, determine whether the voltage data A is within the voltage data threshold A2 range, if not, abnormal, calculate the error ratio A21 of the voltage data, A21=|A-A2| / A2, otherwise, normal, the error ratio A21 is recorded as 0; Similarly, the error ratio B21 of the current data, the error ratio C21 of the temperature data, and the error ratio D21 of the health data are calculated respectively; S4-2, calculate the comprehensive error value M of the electric conveying equipment usage status parameter data packet, M=A21×0.3+B21×0.3+C21×0.2+D21×0.2.

[0008] Furthermore, the working steps of the data evaluation module are as follows; S5-1, calculate the voltage data evaluation value A3, A3=A11×0.3+A21×0.7; Current data evaluation value B3, B3=B11×0.3+B21×0.7; Temperature data estimation C3, C3=C11×0.3+C21×0.7; Health data valuation D3, D3=D11×0.4+D21×0.6; S5-2, calculating the comprehensive evaluation value X of the electric conveying equipment usage status parameter data packet, X=G×0.4+M×0.6+ε, where ε represents the error calibration value; S5-3, A3 greater than 0.2 indicates that the voltage of the electric conveying equipment is abnormal in use, and a first warning signal is sent to the measurement and control module; otherwise, it is normal, and a normal voltage signal is sent to the measurement and control module; B3 greater than 0.2 indicates that the current in the use state of the electric conveying equipment is abnormal, and a second warning signal is sent to the measurement and control module; otherwise, it is normal, and a normal current signal is sent to the measurement and control module; C3 greater than 0.2 indicates that the temperature of the electric conveying equipment is abnormal in use, and a third warning signal is sent to the measurement and control module. Otherwise, it is normal, and a normal temperature signal is sent to the measurement and control module; D3 greater than 0.3 indicates that the health data of the electric conveying equipment in use is abnormal, and there is an aging condition of the electric conveying equipment, and the fifth warning signal is sent to the measurement and control module. Otherwise, it is normal, and a normal health data signal is sent to the measurement and control module; If X is greater than 0.1, it indicates that the comprehensive evaluation of the use status of the electric conveying equipment is abnormal, and the sixth warning signal is sent to the measurement and control module. Otherwise, it is normal, and a comprehensive evaluation normal signal is sent to the measurement and control module.

[0009] Further, the measurement control module receives the first warning signal and reminds the user to suspend the use of the electric conveying equipment until the warning signal disappears; The measurement control module receives the second warning signal and reminds the user to suspend the use of the electric conveying equipment until the warning signal disappears; The measurement control module receives the third warning signal and reminds the user to suspend the use of the electric conveying equipment until the warning signal disappears; The measurement control module receives the fourth warning signal and reminds the user to inspect the electric conveying equipment until the warning signal disappears; The measurement control module receives the fifth warning signal and reminds the user to repair or replace the electric conveying equipment until the warning signal disappears; The measurement control module receives the sixth warning signal to remind the user to replace the electric conveying equipment or scrap the electric conveying equipment.

[0010] Furthermore, the predicted voltage data is derived from a voltage data model pre-stored in the database, the voltage data model takes the start time of each use of the electric conveying equipment as the starting point, and the time T is the start time of the electric conveying equipment; Similarly, the predicted current data, predicted temperature data, and predicted health data all come from data models pre-stored in the database, and all start from the time when the electric conveying equipment starts to be used each time.

[0011] Furthermore, the predicted voltage data A1, the predicted current data B1, and the predicted temperature data C1 are all range thresholds, and the absolute value outside the range is used as the basis for calculating the predicted deviation ratio. Similarly, the voltage data threshold A2, the current data threshold B2, and the temperature data threshold C3 are all range thresholds, and the calculated error ratio is also based on the absolute value outside the range.

[0012] Due to the adoption of the above technical solution, the present invention has the following advantages compared with the prior art: 1. The data prediction module calculates the prediction deviation ratio of the actual voltage data A and the predicted voltage data. Similarly, it calculates the prediction deviation ratio of the current data, temperature data, and health data. Finally, it weightedly calculates the comprehensive prediction deviation value of the electric conveying equipment usage status parameter data packet. At the same time, the safety warning module calculates the error ratio of the abnormal data excess value and the corresponding threshold. Finally, it weightedly calculates the comprehensive error value of the electric conveying equipment usage status parameter data packet. The data evaluation module combines the comprehensive prediction deviation value and the comprehensive error value to evaluate whether the usage status of the electric conveying equipment is abnormal. It can calculate the prediction deviation and actual error of the electric conveying equipment usage status parameter data in real time, and combine the prediction deviation and actual error value to obtain the final electric conveying equipment usage status valuation. According to the final valuation, the use of the electric conveying equipment is timely adjusted to reduce the impact of the electric conveying equipment usage status parameter data under certain temperature, humidity or electromagnetic interference and other harsh environments, thereby improving the stability and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a module diagram of a real-time control system for automatic loading of bulk materials into a pile according to the present invention. DETAILED DESCRIPTION

[0014] The above and other technical contents, features and effects of the present invention are described in detail below with reference to the attached Figure 1 The detailed description of the embodiments will clearly show that the structural contents mentioned in the following embodiments are all based on the drawings in the specification.

[0015] In order to solve the real-time control system of bulk material automatic loading pile, under certain temperature, humidity or electromagnetic interference and other harsh environments, the stability and reliability of the system will also be affected, which greatly affects the use effect of the real-time control system of bulk material automatic loading pile, 1. It includes a signal receiving module, a preprocessing module, and a database, and is characterized in that it also includes a data prediction module, a safety warning module, a data evaluation module, and a measurement control module. The working steps of this system are as follows; S1, the signal receiving module receives in real time a data packet of the usage status parameters of the electric conveying equipment in the system sent by the acquisition end; S2, the preprocessing module parses the data packet to extract voltage data A, current data B, temperature data C and health data D, time T, IP address F, and sends the data to the data prediction module and the data evaluation module; S3, the data prediction module extracts the corresponding predicted voltage data A1, current data B1, temperature data C1 and health data D1 at time T in the database according to the IP address F, and then calculates the predicted deviation ratio of the actual voltage data A and the predicted voltage data. Similarly, the predicted deviation ratio of the current data, temperature data and health data is calculated, and finally the comprehensive predicted deviation value of the electric conveying equipment usage status parameter data packet is weighted and calculated; S4, the safety warning module retrieves the corresponding voltage data threshold A2, current data threshold B2, temperature data threshold C2, and health data threshold D2 from the database according to the IP address F, and compares the data beyond the threshold range to be abnormal, otherwise normal, and then calculates the error ratio between the abnormal data exceeding value and the corresponding threshold, and finally weightedly calculates the comprehensive error value of the electric conveying equipment use status parameter data packet; S5, the data evaluation module first evaluates whether the voltage data A, current data B, temperature data C and health data D are abnormal according to the predicted deviation ratio and the error ratio, and then evaluates whether the use status of the electric conveying equipment is abnormal in combination with the comprehensive predicted deviation value and the comprehensive error value, and sends the evaluation result to the measurement control module; S6, the measurement control module performs corresponding response operations according to the evaluation result of the data evaluation module.

[0016] The working steps of the data prediction module are as follows: S3-1, calculate the predicted deviation ratio A11 of the voltage data, A11=|A-A1| / A1; similarly calculate the predicted deviation ratio B11 of the current data, the predicted deviation ratio C11 of the temperature data, and the predicted deviation ratio D11 of the health data; S3-2, calculate the comprehensive prediction deviation value G of the electric conveying equipment usage status parameter data packet, G=A11×0.3+B11×0.3+C11×0.2+D11×0.2.

[0017] The working steps of the safety warning module are as follows: S4-1, retrieve the corresponding voltage data threshold A2 in the database according to the IP address F, determine whether the voltage data A is within the voltage data threshold A2 range, if not, abnormal, calculate the error ratio A21 of the voltage data, A21=|A-A2| / A2, otherwise, normal, the error ratio A21 is recorded as 0; Similarly, the error ratio B21 of the current data, the error ratio C21 of the temperature data, and the error ratio D21 of the health data are calculated respectively; S4-2, calculate the comprehensive error value M of the electric conveying equipment usage status parameter data packet, M=A21×0.3+B21×0.3+C21×0.2+D21×0.2.

[0018] The working steps of the data evaluation module are as follows: S5-1, calculate the voltage data evaluation value A3, A3=A11×0.3+A21×0.7; Current data evaluation value B3, B3=B11×0.3+B21×0.7; Temperature data estimation C3, C3=C11×0.3+C21×0.7; Health data valuation D3, D3=D11×0.4+D21×0.6; S5-2, calculating the comprehensive evaluation value X of the electric conveying equipment usage status parameter data packet, X=G×0.4+M×0.6+ε, where ε represents the error calibration value; S5-3, A3 greater than 0.2 indicates that the voltage of the electric conveying equipment is abnormal in use, and a first warning signal is sent to the measurement and control module; otherwise, it is normal, and a normal voltage signal is sent to the measurement and control module; B3 greater than 0.2 indicates that the current in the use state of the electric conveying equipment is abnormal, and a second warning signal is sent to the measurement and control module; otherwise, it is normal, and a normal current signal is sent to the measurement and control module; C3 greater than 0.2 indicates that the temperature of the electric conveying equipment is abnormal in use, and a third warning signal is sent to the measurement and control module. Otherwise, it is normal, and a normal temperature signal is sent to the measurement and control module; D3 greater than 0.3 indicates that the health data of the electric conveying equipment in use is abnormal, and there is an aging condition of the electric conveying equipment, and the fifth warning signal is sent to the measurement and control module. Otherwise, it is normal, and a normal health data signal is sent to the measurement and control module; If X is greater than 0.1, it indicates that the comprehensive evaluation of the use status of the electric conveying equipment is abnormal, and the sixth warning signal is sent to the measurement and control module. Otherwise, it is normal, and a comprehensive evaluation normal signal is sent to the measurement and control module.

[0019] 5. The measurement control module receives the first warning signal and reminds the user to suspend the use of the electric conveying equipment until the warning signal disappears; The measurement control module receives the second warning signal and reminds the user to suspend the use of the electric conveying equipment until the warning signal disappears; The measurement control module receives the third warning signal and reminds the user to suspend the use of the electric conveying equipment until the warning signal disappears; The measurement control module receives the fourth warning signal and reminds the user to inspect the electric conveying equipment until the warning signal disappears; The measurement control module receives the fifth warning signal and reminds the user to repair or replace the electric conveying equipment until the warning signal disappears; The measurement control module receives the sixth warning signal to remind the user to replace the electric conveying equipment or scrap the electric conveying equipment.

[0020] The predicted voltage data comes from the voltage data model pre-stored in the database. The voltage data model takes the start time of each use of the electric conveying equipment as the starting point, and the time T is the start time of the electric conveying equipment. Similarly, the predicted current data, predicted temperature data, and predicted health data all come from data models pre-stored in the database, and all start from the time when the electric conveying equipment starts to be used each time.

[0021] The predicted voltage data A1, predicted current data B1, and predicted temperature data C1 are all range thresholds. When calculating the predicted deviation ratio, the absolute value outside the range is used as the benchmark. Similarly, the voltage data threshold A2, current data threshold B2, and temperature data threshold C3 are all range thresholds, and the calculated error ratio is also based on the absolute value outside the range.

[0022] When the present invention is used specifically, based on the prior art, the signal receiving module receives in real time a data packet of the use status parameters of the electric conveying equipment in the system sent by the acquisition end; The preprocessing module parses the data packet to extract voltage data A, current data B, temperature data C and health data D, time T, IP address F, and sends the data to the data prediction module and the data evaluation module; The data prediction module extracts the corresponding predicted voltage data A1, current data B1, temperature data C1 and health data D1 at time T in the database according to the IP address F, and then calculates the predicted deviation ratio of the actual voltage data A and the predicted voltage data. Similarly, the predicted deviation ratio of the current data, temperature data and health data is calculated, and finally the comprehensive predicted deviation value of the electric conveying equipment usage status parameter data packet is weightedly calculated; The safety warning module retrieves the corresponding voltage data threshold A2, current data threshold B2, temperature data threshold C2, and health data threshold D2 from the database according to the IP address F, and compares the data beyond the threshold range to be abnormal, otherwise it is normal, and then calculates the error ratio between the abnormal data exceeding value and the corresponding threshold, and finally weightedly calculates the comprehensive error value of the electric conveying equipment use status parameter data packet; The data evaluation module first evaluates whether the voltage data A, current data B, temperature data C and health data D are abnormal according to the predicted deviation ratio and the error ratio, and then evaluates whether the use status of the electric conveying equipment is abnormal in combination with the comprehensive predicted deviation value and the comprehensive error value, and sends the evaluation result to the measurement control module; The measurement control module makes corresponding response operations according to the evaluation results of the data evaluation module, and can calculate the predicted deviation and actual error of the usage status parameter data of the electric conveying equipment of this system in real time, and combine the predicted deviation and actual error values ​​to obtain the final electric conveying equipment usage status valuation, and make timely adjustments to the use of the electric conveying equipment according to the final valuation.

[0023] The above is a further detailed description of the present invention in combination with a specific implementation method, and it cannot be determined that the specific implementation of the present invention is limited to this; for technical personnel in the technical field to which the present invention belongs and related technical fields, based on the technical solution of the present invention, the expansion and replacement of operating methods and data should all fall within the protection scope of the present invention.

Claims

1. A real-time control system for automatic loading of bulk materials, comprising a signal receiving module, a preprocessing module, and a database, characterized in that: It also includes data prediction module, safety warning module, data evaluation module, and measurement control module. The working steps of this system are as follows; S1, the signal receiving module receives in real time a data packet of the usage status parameters of the electric conveying equipment in the system sent by the acquisition end; S2, the preprocessing module parses the data packet to extract voltage data A, current data B, temperature data C and health data D, time T, IP address F, and sends the data to the data prediction module and the data evaluation module; S3, the data prediction module extracts the corresponding predicted voltage data A1, current data B1, temperature data C1 and health data D1 at time T in the database according to the IP address F, and then calculates the predicted deviation ratio of the actual voltage data A and the predicted voltage data. Similarly, the predicted deviation ratio of the current data, temperature data and health data is calculated, and finally the comprehensive predicted deviation value of the electric conveying equipment usage status parameter data packet is weighted and calculated; S4, the safety warning module retrieves the corresponding voltage data threshold A2, current data threshold B2, temperature data threshold C2, and health data threshold D2 from the database according to the IP address F, and compares the data beyond the threshold range to be abnormal, otherwise normal, and then calculates the error ratio between the abnormal data exceeding value and the corresponding threshold, and finally weightedly calculates the comprehensive error value of the electric conveying equipment use status parameter data packet; S5, the data evaluation module first evaluates whether the voltage data A, current data B, temperature data C and health data D are abnormal according to the predicted deviation ratio and the error ratio, and then evaluates whether the use status of the electric conveying equipment is abnormal in combination with the comprehensive predicted deviation value and the comprehensive error value, and sends the evaluation result to the measurement control module; S6, the measurement control module performs corresponding response operations according to the evaluation result of the data evaluation module.

2. A real-time control system for automatic loading of bulk materials as claimed in claim 1, characterized in that: The working steps of the data prediction module are as follows: S3-1, calculate the predicted deviation ratio A11 of the voltage data, A11=|A-A1| / A1; similarly calculate the predicted deviation ratio B11 of the current data, the predicted deviation ratio C11 of the temperature data, and the predicted deviation ratio D11 of the health data; S3-2, calculate the comprehensive prediction deviation value G of the electric conveying equipment usage status parameter data packet, G=A11×0.3+B11×0.3+C11×0.2+D11×0.

2.

3. A real-time control system for automatic loading of bulk materials as claimed in claim 2, characterized in that: The working steps of the safety warning module are as follows: S4-1, retrieve the corresponding voltage data threshold A2 in the database according to the IP address F, determine whether the voltage data A is within the voltage data threshold A2 range, if not, abnormal, calculate the error ratio A21 of the voltage data, A21=|A-A2| / A2, otherwise, normal, the error ratio A21 is recorded as 0; Similarly, the error ratio B21 of the current data, the error ratio C21 of the temperature data, and the error ratio D21 of the health data are calculated respectively; S4-2, calculate the comprehensive error value M of the electric conveying equipment usage status parameter data packet, M=A21×0.3+B21×0.3+C21×0.2+D21×0.

2.

4. A real-time control system for automatic loading of bulk materials as claimed in claim 3, characterized in that: The working steps of the data evaluation module are as follows: S5-1, calculate the voltage data evaluation value A3, A3=A11×0.3+A21×0.7; Current data evaluation value B3, B3=B11×0.3+B21×0.7; Temperature data estimation C3, C3=C11×0.3+C21×0.7; Health data valuation D3, D3=D11×0.4+D21×0.6; S5-2, calculating the comprehensive evaluation value X of the electric conveying equipment usage status parameter data packet, X=G×0.4+M×0.6+ε, where ε represents the error calibration value; S5-3, A3 greater than 0.2 indicates that the voltage of the electric conveying equipment is abnormal in use, and a first warning signal is sent to the measurement and control module. Otherwise, it is normal, and a normal voltage signal is sent to the measurement and control module; B3 greater than 0.2 indicates that the current in the use state of the electric conveying equipment is abnormal, and a second warning signal is sent to the measurement and control module; otherwise, it is normal, and a normal current signal is sent to the measurement and control module; C3 greater than 0.2 indicates that the temperature of the electric conveying equipment is abnormal in use, and a third warning signal is sent to the measurement and control module. Otherwise, it is normal, and a normal temperature signal is sent to the measurement and control module; D3 greater than 0.3 indicates that the health data of the electric conveying equipment in use is abnormal, and there is an aging condition of the electric conveying equipment, and the fifth warning signal is sent to the measurement and control module. Otherwise, it is normal, and a normal health data signal is sent to the measurement and control module; If X is greater than 0.1, it indicates that the comprehensive evaluation of the use status of the electric conveying equipment is abnormal, and the sixth warning signal is sent to the measurement and control module. Otherwise, it is normal, and a comprehensive evaluation normal signal is sent to the measurement and control module.

5. A real-time control system for automatic loading of bulk materials as claimed in claim 4, characterized in that: The measurement control module receives the first warning signal and reminds the user to suspend the use of the electric conveying equipment until the warning signal disappears; The measurement control module receives the second warning signal and reminds the user to suspend the use of the electric conveying equipment until the warning signal disappears; The measurement control module receives the third warning signal and reminds the user to suspend the use of the electric conveying equipment until the warning signal disappears; The measurement control module receives the fourth warning signal and reminds the user to inspect the electric conveying equipment until the warning signal disappears; The measurement control module receives the fifth warning signal and reminds the user to repair or replace the electric conveying equipment until the warning signal disappears; The measurement control module receives the sixth warning signal to remind the user to replace the electric conveying equipment or scrap the electric conveying equipment.

6. A real-time control system for automatic loading of bulk materials according to any one of claims 1 to 5, characterized in that: The predicted voltage data comes from the voltage data model pre-stored in the database. The voltage data model takes the start time of each use of the electric conveying equipment as the starting point, and the time T is the start time of the electric conveying equipment. Similarly, the predicted current data, predicted temperature data, and predicted health data all come from data models pre-stored in the database, and all start from the time when the electric conveying equipment starts to be used each time.

7. A real-time control system for automatic loading of bulk materials as claimed in claim 6, characterized in that: The predicted voltage data A1, predicted current data B1, and predicted temperature data C1 are all range thresholds. When calculating the predicted deviation ratio, the absolute value outside the range is used as the benchmark. Similarly, the voltage data threshold A2, current data threshold B2, and temperature data threshold C3 are all range thresholds, and the calculated error ratio is also based on the absolute value outside the range.

Images