Intelligent control system of molding sand separator

Abstract

The invention relates to the technical field of equipment control, in particular to an intelligent control system of a molding sand separator, which comprises a separation management and control platform, a facility information unit, a deviation supervision unit, a regulation and control evaluation unit, a performance supervision unit, a reliability evaluation unit and a management response unit. According to the invention, analysis is carried out from two points of the control precision and the sensitivity of the target separation equipment control system, so that the control precision and the control sensitivity of the target separation equipment control system are guaranteed, and control performance supervision, evaluation and analysis are carried out in an information feedback and information fusion mode. Stable operation and efficient separation of the target separation equipment are ensured, and system reliability supervision, evaluation and analysis are performed on operation data of the target separation equipment control system in an in-depth manner, so that the reliability level of the target separation equipment control system is known, adjustment and management are facilitated, and the reliability of the target separation equipment control system is improved. Therefore, stable operation of the target separation equipment control system is ensured.

Description

Technical Field

[0001] The invention relates to the technical field of equipment control, and in particular to an intelligent control system of a molding sand separator. Background Art

[0002] Molding sand can be divided into surface sand, filling sand and single sand. Surface sand is a layer of molding sand close to the casting, with high quality requirements. Filling sand is behind the surface sand layer and does not contact the molten metal, so the quality requirements are not strict. Single sand is molding sand without surface sand and back sand, which is mainly used for machine modeling. When processing molding sand, stone sand separation is required; As an important part of modern separation production, intelligent management of molding sand separator is gradually changing the traditional separation operation mode. Its three typical application scenarios of intelligent monitoring and scheduling, fault diagnosis and early warning, and energy efficiency optimization and management have not only improved the safety and efficiency of separation production, but also brought significant economic benefits to enterprises;

[0003] By using technologies such as the Internet of Things, cloud computing and big data, the molding sand separator is remotely monitored in real time to achieve automatic control of the equipment. However, in the existing technology, it is impossible to supervise the control accuracy, sensitivity and reliability of the molding sand separator control system, which is not conducive to timely adjustment and management of the molding sand separator control system, thereby reducing the control accuracy, control sensitivity and reliability of the molding sand separator control system;

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent control system for a molding sand separator to solve the above-mentioned technical defects. The present invention analyzes the control accuracy and sensitivity of the target separation equipment control system to ensure the control accuracy and control sensitivity of the target separation equipment control system, and performs control performance supervision, evaluation and analysis through information feedback and information fusion to understand the comprehensive control performance of the target separation equipment control system to ensure the stable operation and efficient separation of the target separation equipment, and performs system reliability supervision, evaluation and analysis on the operating data of the target separation equipment control system in an in-depth manner to understand the reliability level of the target separation equipment control system, thereby facilitating the adjustment and management of the target separation equipment control system to ensure the stable operation and safety of the target separation equipment control system.

[0006] The object of the present invention can be achieved by the following technical solutions: an intelligent control system for a molding sand separator, comprising a separation control and management platform, a facility information unit, a deviation supervision unit, a control and assessment unit, a control and assessment unit, a performance supervision unit, a reliability assessment unit, and a management response unit;

[0007] The facility information unit is used to collect the operation performance data and the control response data of the molding sand separator, and send the operation performance data and the control response data to the deviation supervision unit and the control evaluation unit;

[0008] After receiving the operation performance data, the deviation supervision unit immediately performs operation deviation supervision feedback analysis on the operation performance data, compares and analyzes the obtained deviation risk ratio value, and sends the obtained risk signal to the management response unit;

[0009] After receiving the control response data, the control evaluation unit immediately performs control execution supervision evaluation analysis on the control response data, compares and analyzes the obtained response floating index, and sends the obtained alarm signal to the management response unit;

[0010] The performance supervision unit is used to perform control performance supervision evaluation analysis to obtain a precise control risk evaluation coefficient K, and further compare and analyze the precise control risk evaluation coefficient K to obtain a qualified signal and a unqualified signal;

[0011] The reliability evaluation unit responds to the qualified signal, collects the operation data of the control system of the target separation equipment, and performs system reliability supervision evaluation and analysis on the operation data to obtain the reliability level KJ.

[0012] Preferably, the control operation deviation supervision feedback analysis process of the deviation supervision unit is as follows:

[0013] The working time period of the molding sand separator is collected and set as the time threshold. At the same time, the molding sand separator is set as the target separation equipment. The time threshold is divided into i sub-time periods, where i is a natural number greater than zero. The actual separation data of the target separation equipment in each sub-time period is obtained. The actual separation data includes the actual operating power and the actual vibration screening amplitude. Then, the overlap time corresponding to the deviation of the actual separation data from the set separation data is obtained. The ratio between the overlap time corresponding to the deviation of the actual separation data from the set separation data and the time corresponding to the sub-time period is set as the deviation index.

[0014] Compare and analyze the deviation index with the preset deviation index threshold value stored in the internal data entry:

[0015] If the ratio between the deviation index and the preset deviation index threshold is less than 1, the corresponding sub-time period is determined to be a normal period;

[0016] If the ratio between the deviation index and the preset deviation index threshold is greater than or equal to 1, the corresponding sub-time period is determined to be an abnormal period.

[0017] Preferably, the ratio between the number corresponding to the abnormal period and the number corresponding to the normal period is obtained, the ratio between the number corresponding to the abnormal period and the number corresponding to the normal period is set as the deviation risk ratio value, and the deviation risk ratio value is compared and analyzed with the preset deviation risk ratio value threshold value recorded and stored internally:

[0018] If the ratio between the deviation risk ratio value and the preset deviation risk ratio value threshold is less than 1, no signal is generated;

[0019] If the ratio between the deviation risk ratio value and the preset deviation risk ratio value threshold is greater than or equal to 1, a risk signal is generated.

[0020] Preferably, the regulation execution supervision evaluation analysis process of the regulation evaluation unit is as follows:

[0021] Obtain the control response data of the target separation device within the time threshold, the control response data includes the number of controls and the control response index, establish a rectangular coordinate system with the number of controls as the X-axis and the control response index as the Y-axis, draw a control response index curve by plotting points, and then obtain the maximum peak value and the minimum trough value in the control response index curve, and then obtain the difference between the maximum peak value and the minimum trough value, and set the difference between the maximum peak value and the minimum trough value as the response floating index;

[0022] Among them, the control response index represents the time between the target separation device issuing the control command and the start of the control, and the response floating index is compared and analyzed with the preset response floating index threshold value stored in the internal input:

[0023] If the ratio between the response floating index and the preset response floating index threshold is less than 1, no signal is generated;

[0024] If the ratio between the response floating index and the preset response floating index threshold is greater than or equal to 1, an alarm signal is generated.

[0025] Preferably, the control performance supervision evaluation and analysis process of the performance supervision unit is as follows:

[0026] S1: Obtain the residual interference coefficient of the target separation equipment within the time threshold, which represents the product of the ratio of the number of delayed maintenance times in the total number of maintenance times of the target separation equipment and the value of the input time after data normalization processing;

[0027] S2: The input duration represents the duration from the time when the target separation equipment starts to be used to the current time, and the deviation risk ratio and response floating index of the target separation equipment within the time threshold are obtained, and the deviation risk ratio, response floating index and legacy interference coefficient are labeled PZ, XF and YG respectively;

[0028] S3: Substitute the deviation risk ratio PZ, the response floating index XF and the residual interference coefficient YG into the formula K = [(PZ×a1+XF×a2) 2 +YG×a3]×a4 to obtain the precise control risk assessment coefficient, where a1, a2 and a3 are respectively the preset proportional factor coefficients of the deviation risk proportion value PZ, the response floating index XF and the residual interference coefficient YG, a4 is the preset correction factor coefficient, and its value is 1.923. a1, a2 and a3 are all greater than zero. K is the precise control risk assessment coefficient. The precise control risk assessment coefficient K is compared and analyzed with the preset precise control risk assessment coefficient threshold value stored in the internal input:

[0029] If the ratio between the precise control risk assessment coefficient K and the preset precise control risk assessment coefficient threshold is less than 1, a qualified signal is generated;

[0030] If the ratio between the precise control risk assessment coefficient K and the preset precise control risk assessment coefficient threshold is greater than or equal to 1, a failure signal is generated.

[0031] Preferably, the system reliability supervision assessment and analysis process of the reliability assessment unit is as follows:

[0032] T1: Obtain the operation data of the target separation equipment within the time threshold. The operation data includes the fault detection index and the stability assessment coefficient. The stability assessment coefficient represents the proportion of the number of early warnings in the number of historical warnings of the control system of the target separation equipment. The fault detection index and the stability assessment coefficient are labeled GJ and WP respectively. Substitute the fault detection index GJ and the stability assessment coefficient WP into the formula H = (GJ × f1 ÷ WP × f2) × f3 to obtain the reliability assessment coefficient, wherein f1 and f2 are the preset weight factor coefficients of the fault detection index and the stability assessment coefficient respectively, and f3 is the preset fault tolerance factor coefficient. f1, f2 and f3 are all greater than zero. H is the reliability assessment coefficient.

[0033] T2: The fault detection index represents the ratio of the sum of the number of error diagnoses and the number of missed diagnoses in the total number of fault diagnoses of the target separation equipment. The reliability evaluation coefficient H is compared with the preset reliability evaluation coefficient range stored in the internal storage:

[0034] If the reliability evaluation coefficient H is greater than the maximum value in the preset reliability evaluation coefficient range, the target separation equipment control system is determined to be of level three reliability;

[0035] If the reliability evaluation coefficient H falls within the preset reliability evaluation coefficient range, the target separation equipment control system is determined to be of level 2 reliability;

[0036] If the reliability evaluation coefficient H is less than the minimum value in the preset reliability evaluation coefficient range, the target separation equipment control system is determined to be of first-level reliability, and the first-level reliability, second-level reliability and third-level reliability are set as reliability levels KJ, KJ=1, 2, 3.

[0037] The beneficial effects of the present invention are as follows:

[0038] (1) The present invention ensures the control accuracy and control sensitivity of the target separation equipment control system by analyzing the control accuracy and sensitivity of the target separation equipment control system, that is, performing control operation deviation supervision feedback analysis on the operation performance data to understand the deviation between the actual operation and the set operation of the target separation equipment, and then understanding the control error risk and accuracy of the target separation equipment control system, and performing control execution supervision evaluation analysis on the control response data to understand whether the control response of the target separation equipment is timely, so as to upgrade and optimize the target separation equipment to reduce the risk of response delay of the target separation equipment;

[0039] (2) The present invention performs control performance supervision, evaluation and analysis through information feedback and information fusion, thereby understanding the comprehensive control performance of the target separation equipment control system to ensure the stable operation and efficient separation of the target separation equipment, and performs system reliability supervision, evaluation and analysis on the operating data of the target separation equipment control system in an in-depth manner, so as to understand the reliability level of the target separation equipment control system, thereby facilitating the adjustment and management of the target separation equipment control system to ensure the stable operation and safety of the target separation equipment control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The present invention will be further described below in conjunction with the accompanying drawings;

[0041] Figure 1 It is a flowchart of the system of the present invention;

[0042] Figure 2 It is a local analysis reference diagram of the first embodiment of the present invention. DETAILED DESCRIPTION

[0043] 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.

[0044] Embodiment 1:

[0045] See also Figure 1 to Figure 2As shown, the present invention is an intelligent control system for a molding sand separator, comprising a separation control and management platform, a facility information unit, a deviation supervision unit, a control and evaluation unit, a control and evaluation unit, a performance supervision unit, a reliability evaluation unit and a management response unit, wherein the separation control and management platform is connected to the facility information unit in a one-way communication manner, the facility information unit is connected to the deviation supervision unit and the control and evaluation unit in a one-way communication manner, the deviation supervision unit and the control and evaluation unit are connected to the performance supervision unit and the management response unit in a one-way communication manner, the performance supervision unit is connected to the reliability evaluation unit in a one-way communication manner, and the reliability evaluation unit is connected to the management response unit in a one-way communication manner;

[0046] When the separation control platform monitors the operation of any molding sand separator, a supervision instruction is generated and sent to the facility information unit. After receiving the supervision instruction, the facility information unit immediately collects the operation performance data and control response data of the molding sand separator, and sends the operation performance data and control response data to the deviation supervision unit and the control evaluation unit. After receiving the operation performance data, the deviation supervision unit immediately performs control operation deviation supervision feedback analysis on the operation performance data to understand the deviation between the actual operation and the set operation of the target separation equipment, and then understand the control error risk and accuracy of the control system of the target separation equipment. The specific control operation deviation supervision feedback analysis process is as follows:

[0047] The working time period of the molding sand separator is collected and set as the time threshold. At the same time, the molding sand separator is set as the target separation equipment. The time threshold is divided into i sub-time periods, where i is a natural number greater than zero. The actual separation data of the target separation equipment in each sub-time period is obtained. The actual separation data includes the actual operating power, the actual vibration screening amplitude, etc., and then the overlap time corresponding to the deviation of the actual separation data from the set separation data is obtained. The ratio between the overlap time corresponding to the deviation of the actual separation data from the set separation data and the time corresponding to the sub-time period is set as the deviation index. It should be noted that the deviation index is an influencing parameter reflecting the actual separation operation state of the target separation equipment.

[0048] In the implementation of the present invention, actual separation data include, but are not limited to: actual operating power, amplitude of vibration screening used;

[0049] Compare and analyze the deviation index with the preset deviation index threshold stored in the internal storage:

[0050] If the ratio between the deviation index and the preset deviation index threshold is less than 1, the corresponding sub-time period is determined to be a normal period;

[0051] If the ratio between the deviation index and the preset deviation index threshold is greater than or equal to 1, the corresponding sub-time period is determined to be an abnormal time period, and the ratio between the number corresponding to the abnormal time period and the number corresponding to the normal time period is obtained. The ratio between the number corresponding to the abnormal time period and the number corresponding to the normal time period is set as the deviation risk ratio value, and the deviation risk ratio value is compared and analyzed with the preset deviation risk ratio value threshold value stored in the internal entry:

[0052] If the ratio between the deviation risk ratio value and the preset deviation risk ratio value threshold is less than 1, no signal is generated;

[0053] If the ratio between the deviation risk ratio value and the preset deviation risk ratio value threshold is greater than or equal to 1, a risk signal is generated and sent to the management response unit. After receiving the risk signal, the management response unit immediately performs a preset early warning operation corresponding to the risk signal, and then rationally manages and adjusts the control system of the target separation equipment to ensure the operation stability and efficiency of the target separation equipment;

[0054] After receiving the control response data, the control evaluation unit immediately performs control execution supervision evaluation analysis on the control response data to understand whether the control response of the target separation equipment is timely, so as to upgrade and optimize the target separation equipment to reduce the risk of delayed response of the target separation equipment. The specific control execution supervision evaluation analysis process is as follows:

[0055] The control response data of the target separation device within the time threshold is obtained, and the control response data includes the number of controls and the control response index. A rectangular coordinate system is established with the number of controls as the X-axis and the control response index as the Y-axis. The control response index curve is drawn by plotting points, and the maximum peak value and the minimum trough value in the control response index curve are obtained, and the difference between the maximum peak value and the minimum trough value is obtained. The difference between the maximum peak value and the minimum trough value is set as the response floating index. It should be noted that the larger the value of the response floating index, the greater the control risk of the target separation device;

[0056] Compare and analyze the response floating index with the preset response floating index threshold stored in the internal storage:

[0057] If the ratio between the response floating index and the preset response floating index threshold is less than 1, no signal is generated;

[0058] If the ratio between the response floating index and the preset response floating index threshold is greater than or equal to 1, an alarm signal is generated and sent to the management response unit. After receiving the alarm signal, the management response unit immediately displays the preset warning text corresponding to the alarm signal, and then controls and upgrades the target separation equipment to improve the control response performance of the target separation equipment, so as to ensure the timeliness of the control of the target separation equipment;

[0059] In the embodiment of the present invention, the control response index represents the time between when the target separation device issues a control instruction and when the control starts. It should be noted that the analysis is performed from the perspective of the control performance of the target separation device to understand the control stability of the target separation device.

[0060] Embodiment 2:

[0061] The performance monitoring unit is used to conduct control performance monitoring, evaluation and analysis on the target separation equipment, and then understand whether the control sensitivity and accuracy of the target separation equipment are qualified as a whole, so as to ensure the stable control and efficient separation of the target separation equipment. The specific control performance monitoring, evaluation and analysis process is as follows:

[0062] The legacy interference coefficient of the target separation equipment within the time threshold is obtained. The legacy interference coefficient represents the product of the ratio of the number of delayed maintenance times to the total number of maintenance times of the target separation equipment and the value of the input time after data normalization. It should be noted that the larger the value of the legacy interference coefficient, the greater the risk of abnormal control accuracy of the target separation equipment;

[0063] In the embodiment of the present invention, the input duration refers to the duration between the time when the target separation device starts to be used and the current time;

[0064] Obtain the deviation risk ratio value and response floating index of the target separation equipment within the time threshold, and label the deviation risk ratio value, response floating index and residual interference coefficient as PZ, XF and YG respectively;

[0065] Substitute the deviation risk ratio PZ, the response floating index XF and the residual interference coefficient YG into the formula K = [(PZ×a1+XF×a2) 2 +YG×a3]×a4 to obtain the precise control risk assessment coefficient, where a1, a2 and a3 are the preset proportional factor coefficients of the deviation risk proportion value PZ, the response floating index XF and the residual interference coefficient YG respectively. The proportional factor coefficient is used to correct the deviation of various parameters in the formula calculation process, so as to make the calculation result more accurate. a4 is the preset correction factor coefficient, which is 1.923. a1, a2 and a3 are all greater than zero. K is the precise control risk assessment coefficient. The precise control risk assessment coefficient K is compared with the preset precise control risk assessment coefficient threshold value stored in the internal input for analysis:

[0066] If the ratio between the precise control risk assessment coefficient K and the preset precise control risk assessment coefficient threshold is less than 1, a qualified signal is generated;

[0067] If the ratio between the precision control risk assessment coefficient K and the preset precision control risk assessment coefficient threshold is greater than or equal to 1, an unqualified signal is generated, and the qualified signal and the unqualified signal are sent to the management response unit via the reliability assessment unit. After receiving the qualified signal and the unqualified signal, the management response unit immediately displays the preset warning texts corresponding to the qualified signal and the unqualified signal, thereby intuitively understanding the comprehensive control performance of the target separation equipment, so as to reasonably manage and adjust the target separation equipment to ensure the control accuracy and control sensitivity of the target separation equipment;

[0068] When a qualified signal is generated, the reliability evaluation unit responds to the qualified signal, collects the operation data of the target separation equipment control system, and performs system reliability supervision evaluation and analysis on the operation data, so as to understand the reliability level of the target separation equipment control system, thereby facilitating the adjustment and management of the target separation equipment control system. The specific system reliability supervision evaluation and analysis process is as follows:

[0069] The operation data of the target separation equipment within the time threshold is obtained. The operation data includes a fault detection index and a stability assessment coefficient. The stability assessment coefficient indicates the proportion of the number of early warnings in the number of historical warnings of the control system of the target separation equipment. The fault detection index and the stability assessment coefficient are labeled GJ and WP, ​​respectively. The fault detection index GJ and the stability assessment coefficient WP are substituted into the formula H = (GJ × f1 ÷ WP × f2) × f3 to obtain the reliability assessment coefficient, wherein f1 and f2 are respectively the preset weight factor coefficients of the fault detection index and the stability assessment coefficient, f3 is the preset fault tolerance factor coefficient, f1, f2 and f3 are all greater than zero, H is the reliability assessment coefficient, and the reliability assessment coefficient H is compared and analyzed with the preset reliability assessment coefficient range stored in the internal input:

[0070] If the reliability evaluation coefficient H is greater than the maximum value in the preset reliability evaluation coefficient range, the target separation equipment control system is determined to be of level three reliability;

[0071] If the reliability evaluation coefficient H falls within the preset reliability evaluation coefficient range, the target separation equipment control system is determined to be of level 2 reliability;

[0072] If the reliability evaluation coefficient H is less than the minimum value in the preset reliability evaluation coefficient range, the target separation equipment control system is determined to be of the first-level reliability, wherein the reliabilities corresponding to the first-level reliability, the second-level reliability and the third-level reliability are successively reduced, and the first-level reliability, the second-level reliability and the third-level reliability are set as reliability levels KJ, KJ=1, 2, 3, that is, when the reliability level KJ=1, it indicates the first-level reliability, when the reliability level KJ=2, it indicates the second-level reliability, and when the reliability level KJ=3, it indicates the third-level reliability, and the reliability level KJ is sent to the management response unit. After receiving the reliability level KJ, the management response unit immediately displays the preset warning text corresponding to the reliability level KJ, and then intuitively understands the control reliability of the target separation equipment control system, so as to reasonably manage and adjust the target separation equipment to ensure the stable operation and safety of the target separation equipment control system;

[0073] In the embodiment of the present invention, the fault detection index represents the ratio of the sum of the number of error diagnosis times and the number of missed diagnosis times in the total number of fault diagnoses of the target separation device. It should be noted that the fault detection value is an influencing parameter of the reliability of a feedback control system. The reliability of the control system is not only reflected in its ability to work normally for a long time, but also in its ability to ensure timely response and repair in the face of faults through means such as fault diagnosis, backup system and fault-tolerant technology, so as to maintain the stable operation and safety of the system;

[0074] In summary, the present invention ensures the control accuracy and control sensitivity of the target separation equipment control system by analyzing the control accuracy and sensitivity of the target separation equipment control system, that is, performing control operation deviation supervision feedback analysis on the operation performance data to understand the deviation between the actual operation and the set operation of the target separation equipment, and then understanding the control error risk and accuracy of the target separation equipment control system, and performing control execution supervision evaluation analysis on the control response data to understand whether the control response of the target separation equipment is timely, so as to upgrade and optimize the target separation equipment to reduce the risk of response delay of the target separation equipment, and performing control performance supervision evaluation analysis through information feedback and information fusion to understand the comprehensive control performance of the target separation equipment control system to ensure the stable operation and efficient separation of the target separation equipment, and performing system reliability supervision evaluation analysis on the operation data of the target separation equipment control system in an in-depth manner to understand the reliability level of the target separation equipment control system, and then facilitating the adjustment and management of the target separation equipment control system to ensure the stable operation and safety of the target separation equipment control system.

[0075] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0076] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula that is close to the actual value. The coefficients in the formula are set by technical personnel in this field according to actual conditions. The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited to this. Any technical personnel familiar with the technical field within the technical scope disclosed by the present invention, according to the technical solution and the inventive concept of the present invention, make equivalent replacement or change, which should be covered within the protection scope of the present invention.

Claims

1. An intelligent control system for a molding sand separator. It is characterized in that It includes a separation control platform, a facility information unit, a deviation supervision unit, a control and assessment unit, a control and assessment unit, a performance supervision unit, a reliability assessment unit, and a management response unit; The facility information unit is used to collect the operation performance data and the control response data of the molding sand separator, and send the operation performance data and the control response data to the deviation supervision unit and the control evaluation unit; After receiving the operation performance data, the deviation supervision unit immediately performs operation deviation supervision feedback analysis on the operation performance data, compares and analyzes the obtained deviation risk ratio value, and sends the obtained risk signal to the management response unit; After receiving the control response data, the control evaluation unit immediately performs control execution supervision evaluation analysis on the control response data, compares and analyzes the obtained response floating index, and sends the obtained alarm signal to the management response unit; The performance supervision unit is used to perform control performance supervision evaluation analysis to obtain a precise control risk evaluation coefficient K, and further compare and analyze the precise control risk evaluation coefficient K to obtain a qualified signal and a unqualified signal; The reliability evaluation unit responds to the qualified signal, collects the operation data of the control system of the target separation equipment, and performs system reliability supervision evaluation and analysis on the operation data to obtain the reliability level KJ.

2. According to claim 1, an intelligent control system for a molding sand separator, It is characterized in that The deviation supervision feedback analysis process of the control operation deviation supervision unit is as follows: The working time period of the molding sand separator is collected and set as the time threshold. At the same time, the molding sand separator is set as the target separation equipment. The time threshold is divided into i sub-time periods, where i is a natural number greater than zero. The actual separation data of the target separation equipment in each sub-time period is obtained. The actual separation data includes the actual operating power and the actual vibration screening amplitude. Then, the overlap time corresponding to the deviation of the actual separation data from the set separation data is obtained. The ratio between the overlap time corresponding to the deviation of the actual separation data from the set separation data and the time corresponding to the sub-time period is set as the deviation index. Compare and analyze the deviation index with the preset deviation index threshold value stored in the internal data entry: If the ratio between the deviation index and the preset deviation index threshold is less than 1, the corresponding sub-time period is determined to be a normal period; If the ratio between the deviation index and the preset deviation index threshold is greater than or equal to 1, the corresponding sub-time period is determined to be an abnormal period.

3. An intelligent control system for a molding sand separator according to claim 2, It is characterized in that The ratio between the number corresponding to the abnormal period and the number corresponding to the normal period is obtained, and the ratio between the number corresponding to the abnormal period and the number corresponding to the normal period is set as the deviation risk ratio value, and the deviation risk ratio value is compared and analyzed with the preset deviation risk ratio value threshold value entered and stored internally: If the ratio between the deviation risk ratio value and the preset deviation risk ratio value threshold is less than 1, no signal is generated; If the ratio between the deviation risk ratio value and the preset deviation risk ratio value threshold is greater than or equal to 1, a risk signal is generated.

4. The intelligent control system of a molding sand separator according to claim 1, It is characterized in that The control execution supervision evaluation analysis process of the control evaluation unit is as follows: Obtain the control response data of the target separation device within the time threshold, the control response data includes the number of controls and the control response index, establish a rectangular coordinate system with the number of controls as the X-axis and the control response index as the Y-axis, draw a control response index curve by plotting points, and then obtain the maximum peak value and the minimum trough value in the control response index curve, and then obtain the difference between the maximum peak value and the minimum trough value, and set the difference between the maximum peak value and the minimum trough value as the response floating index; Among them, the control response index represents the time between the target separation device issuing the control command and the start of the control, and the response floating index is compared and analyzed with the preset response floating index threshold value stored in the internal input: If the ratio between the response floating index and the preset response floating index threshold is less than 1, no signal is generated; If the ratio between the response floating index and the preset response floating index threshold is greater than or equal to 1, an alarm signal is generated.

5. The intelligent control system of a molding sand separator according to claim 1, It is characterized in that The control performance supervision evaluation and analysis process of the performance supervision unit is as follows: S1: Obtain the residual interference coefficient of the target separation equipment within the time threshold, which represents the product of the ratio of the number of delayed maintenance times in the total number of maintenance times of the target separation equipment and the value of the input time after data normalization processing; S2: The input duration represents the duration from the time when the target separation equipment starts to be used to the current time, and the deviation risk ratio and response floating index of the target separation equipment within the time threshold are obtained, and the deviation risk ratio, response floating index and legacy interference coefficient are labeled PZ, XF and YG respectively; S3: Substitute the deviation risk ratio PZ, the response floating index XF and the residual interference coefficient YG into the formula K = [(PZ×a1+XF×a2) 2 +YG×a3]×a4 to obtain the precise control risk assessment coefficient, where a1, a2 and a3 are respectively the preset proportional factor coefficients of the deviation risk proportion value PZ, the response floating index XF and the residual interference coefficient YG, a4 is the preset correction factor coefficient, and its value is 1.

923. a1, a2 and a3 are all greater than zero. K is the precise control risk assessment coefficient. The precise control risk assessment coefficient K is compared and analyzed with the preset precise control risk assessment coefficient threshold value stored in the internal input: If the ratio between the precise control risk assessment coefficient K and the preset precise control risk assessment coefficient threshold is less than 1, a qualified signal is generated; If the ratio between the precise control risk assessment coefficient K and the preset precise control risk assessment coefficient threshold is greater than or equal to 1, a failure signal is generated.

6. The intelligent control system of a molding sand separator according to claim 1, It is characterized in that The system reliability supervision assessment and analysis process of the reliability assessment unit is as follows: T1: Obtain the operation data of the target separation equipment within the time threshold. The operation data includes the fault detection index and the stability assessment coefficient. The stability assessment coefficient represents the proportion of the number of early warnings in the number of historical warnings of the control system of the target separation equipment. The fault detection index and the stability assessment coefficient are labeled GJ and WP respectively. Substitute the fault detection index GJ and the stability assessment coefficient WP into the formula H = (GJ × f1 ÷ WP × f2) × f3 to obtain the reliability assessment coefficient, wherein f1 and f2 are the preset weight factor coefficients of the fault detection index and the stability assessment coefficient respectively, and f3 is the preset fault tolerance factor coefficient. f1, f2 and f3 are all greater than zero. H is the reliability assessment coefficient. T2: The fault detection index represents the ratio of the sum of the number of error diagnoses and the number of missed diagnoses in the total number of fault diagnoses of the target separation equipment. The reliability evaluation coefficient H is compared with the preset reliability evaluation coefficient range stored in the internal storage: If the reliability evaluation coefficient H is greater than the maximum value in the preset reliability evaluation coefficient range, the target separation equipment control system is determined to be of level three reliability; If the reliability evaluation coefficient H falls within the preset reliability evaluation coefficient range, the target separation equipment control system is determined to be of level 2 reliability; If the reliability evaluation coefficient H is less than the minimum value in the preset reliability evaluation coefficient range, the target separation equipment control system is determined to be of first-level reliability, and the first-level reliability, second-level reliability and third-level reliability are set as reliability levels KJ, KJ=1, 2, 3.

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