Bean hulling equipment state monitoring and early warning method and system

Abstract

The invention discloses a bean hulling equipment state monitoring and early warning method and system, and relates to the technical field of equipment state monitoring. The bean hulling equipment state monitoring and early warning method and system are established, and multi-dimensional dynamic monitoring of a blockage evaluation model and a bearing running state is combined; real-time evaluation and accurate early warning of the whole operation process of the equipment are realized; by means of scientific model construction, a dynamic adjustment mechanism and a data acquisition mode, the accuracy of blockage prediction and fault detection is effectively improved, and the shutdown or damage risk caused by equipment abnormity is greatly reduced, so that the continuity and high efficiency of equipment operation are guaranteed; meanwhile, visual control panel display and early warning information prompt are provided, so that an operator can quickly know the equipment state and take necessary measures, and the maintenance process is effectively simplified; according to the whole method, the operation stability and safety of the shelling equipment are remarkably improved, the service life of the shelling equipment is remarkably prolonged, and meanwhile the production management efficiency is improved through an intelligent means.

Description

Technical Field

[0001] The invention relates to the technical field of equipment status monitoring, and in particular to a method and system for monitoring and warning the status of bean shelling equipment. Background Art

[0002] Beans are an important raw material in the food industry, and their shelling process plays a vital role in the processing process. Existing bean shelling equipment is usually designed as a universal machine that can adapt to the shelling of beans of different types and sizes. However, these devices mainly rely on a single parameter for monitoring during operation, such as using a vibration sensor to monitor equipment vibration or a temperature sensor to monitor local temperature changes. Although these methods can capture abnormal information of the equipment to a certain extent, due to the limited monitoring range, they can only provide limited reference to the operating status of the equipment.

[0003] As for the blockage problem that may occur during the operation of the shelling equipment, the existing technology lacks effective real-time monitoring means and cannot provide timely warnings. Especially when the blockage problem is not identified in the early stage, it is very easy to cause the shelling equipment to suddenly stop during operation, which not only increases the operational risk, but also directly affects the continuity and overall efficiency of the production line. In addition, some technical solutions attempt to optimize the performance of the shelling machine by setting mechanical structure parameters, but these improvements often ignore the mutual influence of multi-dimensional data in the dynamic operating environment, resulting in inaccurate judgment of the blockage problem.

[0004] In existing shelling equipment, it is a common problem that it is difficult to accurately identify blockages during the shelling process. This phenomenon is usually accompanied by complex situations such as bean accumulation inside the sheller and reduced operating speed. However, due to the lack of a dynamic comprehensive evaluation model, it is difficult for equipment managers to discover and deal with problems in a timely manner, which may eventually lead to a stagnation of the production line. In addition, the operation of the sheller relies on bearings as core components, and the vibration, temperature and speed parameters of the bearings have an important impact on the overall performance of the equipment. However, existing technologies rarely link blockage problems with bearing status for overall evaluation, and lack a set of scientific and comprehensive status monitoring and early warning methods, resulting in the safety and stability of equipment operation being difficult to ensure. Therefore, how to construct a technical solution that can integrate blockage conditions and bearing status, monitor the equipment operation status in real time through multi-dimensional dynamic parameters, and effectively warn of potential risks and failures has become a key technical problem that needs to be solved in the field of bean shelling equipment. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a method and system for monitoring and early warning the state of bean shelling equipment, which solves the problems in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for monitoring and early warning the state of bean shelling equipment, comprising:

[0007] Step 1: Establish a blockage evaluation model according to the target bean type;

[0008] Step 2: Determine the blockage fault risk coefficient during the current operation of the hulling machine according to the blockage evaluation model;

[0009] Step 3: During the operation of the hulling machine, determine the bearing vibration coefficient, bearing temperature coefficient, and bearing rotation speed coefficient according to the current bearing vibration amplitude, bearing temperature, and bearing rotation respectively. Then, evaluate the overall operating state of the bearing according to the determined bearing vibration coefficient, bearing temperature coefficient, and bearing rotation speed coefficient, and determine the current overall operating state evaluation coefficient of the bearing;

[0010] Step 4: Monitor and give an early warning of the current state of the hulling machine according to the blockage fault risk coefficient during the current operation of the hulling machine and the current overall operating state evaluation coefficient of the bearing, and judge whether an early warning message is generated.

[0011] As a further solution of the present invention: In the above Step 1, the specific method for establishing the blockage evaluation model according to the target bean type is as follows:

[0012] AS1: For target beans of the same type, place the target beans for each hulling in the hulling funnel, obtain the weight of the target beans for each hulling, and at the same time, obtain the weight of the hulling funnel in real time during the operation of the hulling machine, and record it as ;

[0013] AS2: Then obtain the hulling rate during each hulling process of the hulling machine, and record it as R(t);

[0014] AS3: During the operation of the hulling machine, obtain the weight of the target hulled beans in real time, and record it as ;

[0015] AS4: According to the weight of the hulling funnel obtained in real time each time , the hulling rate R(t) during each hulling process of the hulling machine, and the weight of the target hulled beans in real time each time establish a blockage evaluation model, and the blockage evaluation model is reflected by the following formula:

[0016]

[0017] Among them, is the result of the blockage evaluation model, represents the functional relationship of time, target bean weight, hulling rate, and blockage evaluation model.

[0018] As a further solution of the present invention: In the above Step 2, the specific method for determining the blockage fault risk coefficient during the operation of the hulling machine according to the blockage evaluation model is as follows:

[0019] BS1: Obtain the weight of the target beans being shelled currently and the shelling rate during the shelling process of the current sheller. At the same time, determine the duration of the current sheller from startup to the current operation. By taking these three parameters as inputs and predicting through the blockage evaluation model, evaluate the weight of the current target shelled beans, and denote it as ;

[0020] BS2: At the same moment, obtain the weight of the actual target shelled beans, and denote it as ;

[0021] BS3: Then determine the blockage fault risk coefficient during the operation of the sheller through the following formula:

[0022]

[0023] Wherein, represents the blockage fault risk coefficient, is the dynamic adjustment factor, reflecting the dynamic change trend of the sheller, and , represents the change factor; represents the allowable error range threshold.

[0024] As a further solution of the present invention: In the third step, determine the bearing vibration coefficient, bearing temperature coefficient, and bearing rotation speed coefficient according to the current bearing vibration amplitude, bearing temperature, and bearing rotation respectively. Then, according to the determined bearing vibration coefficient, bearing temperature coefficient, and bearing rotation speed coefficient, evaluate the overall operating state of the bearing. The specific method for determining the overall operating state evaluation coefficient of the bearing is as follows:

[0025] CS1: Obtain the bearing vibration amplitude during the operation of the sheller, and denote it as , and perform real-time bearing vibration evaluation through the following evaluation function to determine the bearing vibration coefficient:

[0026]

[0027] Wherein, and represent the lower limit value and the upper limit value of the normal vibration value range during bearing vibration operation;

[0028] CS2: Obtain the bearing temperature during the operation of the sheller, and denote it as , and perform real-time bearing temperature evaluation through the following evaluation function to determine the bearing temperature coefficient:

[0029]

[0030] Wherein, and represent the minimum and maximum values of the normal temperature range during bearing temperature operation;

[0031] CS3: Then obtain the set parameters of the bearing rotation during the operation of the shell machine and record them as , and at the same time, obtain the actual parameters of the bearing rotation during the operation in real time and record them as ; Combine and to determine the deviation value of the bearing rotation during the real-time operation, and ; Then, the bearing speed is evaluated in real time through the following evaluation function to determine the bearing speed coefficient:

[0032]

[0033] where represents the lower limit value of the allowable bearing rotation deviation, represents the upper limit value of the allowable bearing rotation deviation.

[0034] As a further solution of the present invention: After the step CS3, it further includes:

[0035] CS4: Evaluate the overall operating state of the bearing according to the bearing vibration coefficient , the bearing temperature coefficient and the bearing speed coefficient to determine the current overall operating state evaluation coefficient of the bearing:

[0036]

[0037] where represents the overall operating state evaluation coefficient of the bearing, is the weight coefficient, and .

[0038] As a further solution of the present invention: In the fourth step, the specific method for monitoring and warning the current state of the shelling machine according to the current risk coefficient of the blockage fault and the current overall operating state evaluation coefficient of the bearing is as follows:

[0039] DS1: Obtain the current risk coefficient of the blockage fault and the current overall operating state evaluation coefficient of the bearing during the operation of the current shelling machine, and determine the current operating index of the shelling machine state through the following stability evaluation model. The stability evaluation model is reflected by the following formula:

[0040]

[0041] where represents the current operating index of the shelling machine state, , and are expressed as stability prediction coefficients;

[0042] DS2: Monitor and give early warning of the current state of the hulling machine according to the current operating indicators of the hulling machine , and give early warning of the current state of the hulling machine:

[0043] If , it indicates that the current state of the hulling machine is within the normal operating range;

[0044] If , it indicates that the current state of the hulling machine is out of the normal operating range, and warning information is generated;

[0045] Wherein, is a preset value, which is specifically set by professional staff;

[0046] DS3: Judge whether warning information is generated. If warning information is judged, an alarm is issued for notification, otherwise no processing is done.

[0047] As a further solution of the present invention: After the step four, it further includes:

[0048] Step five: When it is judged that warning information is generated, display the risk coefficient of the blockage failure during the current operation of the hulling machine and the evaluation coefficient of the overall operation state of the current bearing on the control panel, and prompt the operator to check the state of the hulling machine or adjust the process parameters to ensure the smooth operation of the hulling machine.

[0049] The state monitoring and early warning system for bean hulling equipment includes:

[0050] The blockage evaluation model determination module is used to establish a blockage evaluation model according to the target bean variety;

[0051] The blockage failure risk determination module is used to determine the risk coefficient of the blockage failure during the current operation of the hulling machine according to the blockage evaluation model;

[0052] The bearing evaluation determination module is used to respectively determine the bearing vibration coefficient, bearing temperature coefficient and bearing rotation speed coefficient according to the current bearing vibration amplitude, bearing temperature and bearing rotation during the operation of the hulling machine, and then evaluate the overall operation state of the bearing according to the determined bearing vibration coefficient, bearing temperature coefficient and bearing rotation speed coefficient, and determine the current overall operation state evaluation coefficient of the bearing;

[0053] The monitoring and early warning module is used to monitor and give early warning of the current state of the hulling machine according to the risk coefficient of the blockage failure during the current operation of the hulling machine and the evaluation coefficient of the overall operation state of the current bearing, and judge whether warning information is generated;

[0054] A monitoring and display terminal is used to display the risk coefficient of blockage failure during the current operation of the huller and the evaluation coefficient of the overall operating state of the current bearing on the control panel when it is determined that a warning message is generated, prompting the operator to check the state of the huller or adjust the process parameters to ensure the smooth operation of the huller.

[0055] The present invention provides a method and system for monitoring and warning the state of bean hulling equipment. Compared with the prior art, it has the following beneficial effects:

[0056] By establishing a method and system for monitoring and warning the state of bean hulling equipment, combining a blockage evaluation model and multi-dimensional dynamic monitoring of the bearing operating state, the present invention realizes real-time evaluation and accurate warning of the whole process of equipment operation. Its scientific model construction, dynamic adjustment mechanism and data acquisition method effectively improve the accuracy of blockage prediction and fault detection, greatly reduce the risk of downtime or damage caused by equipment abnormalities, and thus ensure the continuity and efficiency of equipment operation.

[0057] In addition, the system provides an intuitive display on the control panel and warning message prompts, enabling the operator to quickly understand the equipment state and take necessary measures, effectively simplifying the maintenance process. The overall method not only significantly improves the operation stability, safety and service life of the hulling equipment, but also enhances the production management efficiency through intelligent means, providing technical support and practical reference for the bean processing industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The present invention will be further described below with reference to the accompanying drawings.

[0059] Figure 1 is a flowchart of the steps of the method for monitoring and warning the state of bean hulling equipment of the present invention;

[0060] Figure 2 is a structural framework diagram of the system for monitoring and warning the state of bean hulling equipment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0061] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0062] Embodiment 1

[0063] Please refer to Figure 1 , the present invention provides a method for monitoring and warning the state of bean hulling equipment, including:

[0064] Step 1: Establish a blockage evaluation model according to the target bean variety;

[0065] It should be noted that the target beans refer to the beans that need to be shelled by the shelling machine each time. Generally, the shelling machine can shell different types of beans. In this embodiment, different types of beans need to establish different blockage evaluation models;

[0066] The specific method for establishing the blockage evaluation model according to the type of target beans is as follows:

[0067] AS1: For the target beans of the same type, place the target beans to be shelled each time in the shelling funnel, obtain the weight of the target beans shelled each time, and at the same time, obtain the weight of the shelling funnel in real time during the operation of the shelling machine, and record it as ;

[0068] Specifically, a weight sensor is installed on the shelling funnel of the shelling machine. Place the target beans to be shelled each time in the shelling funnel to obtain the weight of the target beans shelled each time;

[0069] AS2: Then obtain the shelling rate during the shelling process of the shelling machine each time, and record it as R(t);

[0070] AS3: During the operation of the shelling machine, obtain the weight of the target shelled beans in real time, and record it as ;

[0071] It should be noted that: the target shelled beans refer to the beans that have been completely shelled by the shelling machine for the target beans, and are collected through a storage box equipped with a weight sensor, and at the same time, the weight of the target shelled beans is obtained in real time;

[0072] AS4: According to the weight of the shelling funnel obtained in real time each time the shelling rate R(t) during the shelling process of the shelling machine each time, and the weight of the target shelled beans in real time each time establish a blockage evaluation model, and the blockage evaluation model is reflected by the following formula:

[0073]

[0074] Among them, is the result of the blockage evaluation model, represents the functional relationship between time, target bean weight, shelling rate, and the blockage evaluation model;

[0075] It should be noted that in step AS4, by obtaining a large number of recorded data in AS1 - AS3 and conducting experiments, the relationship between the shelling rate of the shelling machine and the target bean weight changing with time can be obtained; through multiple recordings, a blockage evaluation model can be established, and this model can be obtained through existing technical methods such as regression analysis and machine learning, which will not be elaborated here;

[0076] By establishing a blockage assessment model for different types of beans, taking into account the weight of the target beans, the shelling rate, and the changes in real-time acquired data, the blockage situation during the shelling process can be accurately evaluated, which can dynamically reflect the operating status of the sheller; in addition, by using a weight sensor and other real-time data recording devices, the model construction process becomes more scientific and rigorous; through the accumulation and analysis of experimental data multiple times, advanced technologies such as machine learning are used to optimize the model, and finally a high-precision prediction of the blockage situation is achieved, providing a stability guarantee for the shelling process;

[0077] Step 2: Determine the blockage fault risk coefficient during the current operation of the sheller according to the blockage assessment model;

[0078] The specific method for determining the blockage fault risk coefficient during the operation of the sheller according to the blockage assessment model is as follows:

[0079] BS1: Obtain the weight of the target beans for the current shelling and the shelling rate during the shelling process of the current sheller, and at the same time determine the duration of the current sheller from startup to the current operation. By using these three parameters as inputs to predict through the blockage assessment model, the weight of the current target shelled beans is evaluated and denoted as ;

[0080] BS2: At the same moment, obtain the weight of the actual target shelled beans and denote it as ;

[0081] BS3: Then determine the blockage fault risk coefficient during the operation of the sheller through the following formula:

[0082]

[0083] Among them, represents the blockage fault risk coefficient, is a dynamic adjustment factor, reflecting the dynamic change trend of the sheller, and , represents the change factor, which is specifically set by professional staff. The longer the running time , the closer the dynamic adjustment factor of the blockage is to 1; represents the allowable error range threshold;

[0084] In this step, the clogging evaluation model combines key parameters such as the real-time target bean weight and hulling rate to dynamically calculate the clogging fault risk coefficient. A dynamic adjustment factor and an error threshold are incorporated into the calculation of the clogging fault risk coefficient, making the evaluation process more adaptable and flexible, and capable of continuous optimization as the hulling machine runs for a longer time. This mechanism realizes early warning of clogging risks, helps operators take intervention measures before clogging occurs, thus effectively reducing the impact of clogging on equipment operation and extending the service life of the equipment.

[0085] Step 3: During the operation of the hulling machine, determine the bearing vibration coefficient, bearing temperature coefficient, and bearing rotation speed coefficient respectively according to the current bearing vibration amplitude, bearing temperature, and bearing rotation. Then, evaluate the overall operating state of the bearing based on the determined bearing vibration coefficient, bearing temperature coefficient, and bearing rotation speed coefficient, and determine the current overall operating state evaluation coefficient of the bearing.

[0086] The specific method of determining the bearing vibration coefficient, bearing temperature coefficient, and bearing rotation speed coefficient respectively according to the current bearing vibration amplitude, bearing temperature, and bearing rotation, and then evaluating the overall operating state of the bearing based on the determined bearing vibration coefficient, bearing temperature coefficient, and bearing rotation speed coefficient to determine the overall operating state evaluation coefficient of the bearing is as follows:

[0087] CS1: Obtain the bearing vibration amplitude during the operation of the hulling machine and record it as , and perform real-time bearing vibration evaluation through the following evaluation function to determine the bearing vibration coefficient:

[0088]

[0089] Among them, and represent the lower limit value and upper limit value of the normal vibration value range during bearing vibration operation;

[0090] CS2: Obtain the bearing temperature during the operation of the hulling machine and record it as , and perform real-time bearing temperature evaluation through the following evaluation function to determine the bearing temperature coefficient:

[0091]

[0092] Among them, and represent the minimum value and maximum value of the normal temperature value range during bearing temperature operation;

[0093] CS3: Then obtain the set parameters of the bearing rotation during the operation of the hulling machine and record it as , and at the same time, obtain the actual parameters of the bearing rotation during the operation in real time and record it as ; Combine and , determine the deviation value of the bearing rotation during the real-time operation , and ; then, evaluate the bearing rotation speed in real time through the following evaluation function to determine the bearing rotation speed coefficient:

[0094]

[0095] where represents the lower limit value of the allowable bearing rotation deviation, represents the upper limit value of the allowable bearing rotation deviation;

[0096] CS4: According to the bearing vibration coefficient , the bearing temperature coefficient and the bearing rotation speed coefficient evaluate the overall operating state of the bearing to determine the current overall operating state evaluation coefficient of the bearing:

[0097]

[0098] where represents the overall operating state evaluation coefficient of the bearing, is the weight coefficient, and ;

[0099] It should be noted that: the above-mentioned bearing vibration amplitude is obtained through a vibration sensor fixed on the bearing housing or the support structure near the core area of the bearing. When obtaining the actual parameters of the bearing temperature and bearing rotation during the operation of the hulling machine, they are obtained by installing a temperature sensor and a rotation speed sensor;

[0100] This step comprehensively evaluates the operating state of the bearing. Through the dynamic monitoring of key indicators such as vibration amplitude, temperature, and rotation speed, accurately calculates the bearing vibration coefficient, temperature coefficient, and rotation speed coefficient, and comprehensively obtains the overall operating state evaluation coefficient in combination with the weight model; by collecting data through real-time sensors and comparing them with the standard value range, it can quickly locate the anomalies in the bearing operation and avoid equipment shutdown or damage problems caused by bearing failures;

[0101] Step Four: According to the current blockage fault risk coefficient during the operation of the current hulling machine and the current overall operating state evaluation coefficient of the bearing, monitor and warn the current state of the hulling machine to determine whether a warning message is generated;

[0102] The specific method of monitoring and warning the current state of the hulling machine according to the current blockage fault risk coefficient during the operation of the current hulling machine and the current overall operating state evaluation coefficient of the bearing is:

[0103] DS1: Obtain the risk coefficient of blockage failure during the current operation of the hulling machine and the evaluation coefficient of the overall operation state of the current bearing. Determine the current operation index of the hulling machine through the following stability evaluation model, and the stability evaluation model is reflected by the following formula:

[0104]

[0105] Among them, represents the current operation index of the hulling machine, 、 and represent the stability prediction coefficients;

[0106] DS2: Monitor and give early warning of the current state of the hulling machine according to the current operation index of the hulling machine:

[0107] If , it means that the current state of the hulling machine is within the normal operation range;

[0108] If , it means that the current state of the hulling machine deviates from the normal operation range, and a warning message is generated;

[0109] Among them, is a preset value, which is specifically set by professional staff;

[0110] DS3: Judge whether a warning message is generated. If a warning message is judged, an alarm is issued for notification, otherwise no processing is done;

[0111] In this step, by combining the risk coefficient of blockage failure and the evaluation coefficient of the overall operation state of the bearing, the stability evaluation model is used to dynamically calculate the operation state index of the hulling machine, so as to comprehensively monitor the overall state of the equipment; by clearly defining the index range to define the normal and abnormal operation states, warning messages are generated in real time and alarms are triggered, effectively reducing the risk of sudden failures during the operation of the equipment; this step makes the warning mechanism more intelligent and real-time, ensuring that the hulling machine can respond in time when abnormal situations occur, and guaranteeing the continuity and stability of the production process;

[0112] Step Five: When it is judged that a warning message is generated, display the risk coefficient of blockage failure during the current operation of the hulling machine and the evaluation coefficient of the overall operation state of the current bearing on the control panel, and prompt the operator to check the state of the hulling machine or adjust the process parameters to ensure the smooth operation of the hulling machine.

[0113] Embodiment Two

[0114] Please refer to Figure 2, in the specific implementation process of this embodiment, based on Embodiment 1 and different from Embodiment 1, this embodiment further provides a state monitoring and early warning system for bean shelling equipment, including:

[0115] A clogging evaluation model determination module, configured to establish a clogging evaluation model according to the target bean variety;

[0116] A clogging fault risk determination module, configured to determine the clogging fault risk coefficient during the current operation of the sheller according to the clogging evaluation model;

[0117] A bearing evaluation determination module, during the operation of the sheller, respectively determine the bearing vibration coefficient, bearing temperature coefficient and bearing rotation speed coefficient according to the current bearing vibration amplitude, bearing temperature and bearing rotation, and then evaluate the overall operation state of the bearing according to the determined bearing vibration coefficient, bearing temperature coefficient and bearing rotation speed coefficient to determine the current overall operation state evaluation coefficient of the bearing;

[0118] A monitoring and early warning module, configured to monitor and early warn the current state of the sheller according to the clogging fault risk coefficient during the current operation of the sheller and the current overall operation state evaluation coefficient of the bearing, and determine whether an early warning message is generated;

[0119] A monitoring and display terminal, when it is determined that an early warning message is generated, display the clogging fault risk coefficient during the current operation of the sheller and the current overall operation state evaluation coefficient of the bearing on the control panel, prompting the operator to check the state of the sheller or adjust the process parameters to ensure the smooth operation of the sheller.

[0120] Embodiment 3

[0121] In the specific implementation process of this embodiment, it includes all the implementation processes of the above two groups of embodiments.

[0122] Some of the data in the above formulas are numerically calculated after removing their dimensions, and the content not described in detail in this specification belongs to the prior art well known to those skilled in the art.

[0123] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for monitoring and early warning the state of bean shelling equipment, characterized in that: include: Step 1: Establish a blockage assessment model based on the target bean type; Step 2: Determine the risk factor of blockage failure during the current operation of the sheller according to the blockage assessment model; Step 3: During the operation of the sheller, the bearing vibration coefficient, bearing temperature coefficient and bearing speed coefficient are determined according to the current bearing vibration amplitude, bearing temperature and bearing rotation, and then the overall operation state of the bearing is evaluated according to the determined bearing vibration coefficient, bearing temperature coefficient and bearing speed coefficient to determine the current bearing overall operation state evaluation coefficient; Step 4: Based on the blocking failure risk coefficient during the operation of the current shelling machine and the current bearing overall operation status assessment coefficient, the current shelling machine status is monitored and warned to determine whether to generate warning information.

2. The bean shelling equipment status monitoring and early warning method according to claim 1 is characterized in that: In the step 1, the specific method of establishing the blockage assessment model according to the target bean type is: AS1: For target beans of the same type, place the target beans to be shelled each time in the shelling hopper, obtain the weight of the target beans to be shelled each time, and obtain the weight of the shelling hopper in real time during the operation of the shelling machine, and record it as ; AS2: Then obtain the shelling rate of each shelling process of the shelling machine and record it as R(t); AS3: During the operation of the shelling machine, the weight of the target shelled beans is obtained in real time and recorded as ; AS4: Get the weight of the shelling hopper in real time each time , the shelling rate R(t) during each shelling process of the shelling machine and the weight of the shelled beans in real time A congestion assessment model is established, which is embodied by the following formula: in, For the blocking assessment model results, Expressed as a function of time, target bean weight, shelling rate, and clogging assessment model.

3. The bean shelling equipment status monitoring and early warning method according to claim 2 is characterized in that: In the step 2, the specific method of determining the blockage failure risk coefficient during the operation of the sheller according to the blockage assessment model is: BS1: Get the current target bean weight for shelling and the shelling rate of the shelling machine during the shelling process, and determine the duration of the shelling machine from the start to the current operation. The three parameters are used as input to predict the current target bean weight through the congestion assessment model, and the weight of the current target shelled beans is estimated and recorded as ; BS2: At the same time, the actual target shelled bean weight is obtained and recorded as ; BS3: Then determine the risk factor of blockage failure during the operation of the sheller using the following formula: in, Expressed as the blocking failure risk coefficient, is a dynamic adjustment factor, reflecting the dynamic change trend of the sheller, and , Expressed as a factor of variation; Indicates that it is allowed The error range threshold.

4. The bean shelling equipment status monitoring and early warning method according to claim 3 is characterized in that: In step three, the bearing vibration coefficient, bearing temperature coefficient and bearing speed coefficient are determined respectively according to the current bearing vibration amplitude, bearing temperature and bearing rotation, and then the overall operation state of the bearing is evaluated according to the determined bearing vibration coefficient, bearing temperature coefficient and bearing speed coefficient. The specific method for determining the evaluation coefficient of the overall operation state of the bearing is: CS1: Obtain the bearing vibration amplitude during the sheller operation and record it as , the bearing vibration coefficient is determined by performing bearing vibration evaluation in real time through the following evaluation function: in, and It is expressed as the lower and upper limits of the normal vibration value range when the bearing is vibrating during operation; CS2: Obtain the bearing temperature during the sheller operation and record it as , the bearing temperature is evaluated in real time through the following evaluation function to determine the bearing temperature coefficient: in, and It is expressed as the minimum and maximum values ​​of the normal temperature range of the bearing temperature during operation; CS3: Then obtain the setting parameters of the bearing rotation during the operation of the shell machine and record them as At the same time, the actual parameters of the bearing rotation during operation are obtained in real time and recorded as ; Combine and , determine the deviation value of the bearing rotation during real-time operation ,and ; Then the bearing speed is evaluated in real time through the following evaluation function to determine the bearing speed coefficient: in, It is expressed as the lower limit of the allowable bearing rotation deviation. Indicates the upper limit of the allowable bearing rotation deviation.

5. The bean shelling equipment status monitoring and early warning method according to claim 4 is characterized in that: The step CS3 further includes: CS4: Based on bearing vibration coefficient , bearing temperature coefficient and bearing speed factor Evaluate the overall operating status of the bearing and determine the current overall operating status evaluation coefficient of the bearing: in, Expressed as the bearing overall operating condition assessment coefficient, is the weight coefficient, and .

6. The bean shelling equipment status monitoring and early warning method according to claim 5 is characterized in that: In the step 4, the specific method of monitoring and warning the current state of the shelling machine according to the blockage failure risk coefficient during the operation of the shelling machine and the current overall operation state evaluation coefficient of the bearing is: DS1: Obtain the blocking fault risk coefficient and the overall operating status evaluation coefficient of the current bearing during the operation of the current sheller. The current sheller operating index is determined by the following stability evaluation model. The stability evaluation model is reflected by the following formula: in, Indicates the current sheller status operating indicator. , and Expressed as stability prediction coefficient; DS2: Run indicators based on current sheller status , monitor and warn the current shelling machine status: like , indicating that the current sheller status is within the normal operating range; like , indicating that the current shelling machine status is out of the normal operating range, and a warning message is generated; in, It is a preset value, which is set by professional staff; DS3: Determine whether a warning message is generated. If it is determined that there is a warning message, an alarm will be issued to notify, otherwise no action will be taken.

7. The bean shelling equipment status monitoring and early warning method according to claim 6 is characterized in that: The step 4 further includes: Step 5: When it is determined that a warning message is generated, the blocking failure risk coefficient during the current operation of the sheller and the current overall bearing operation status assessment coefficient are displayed on the control panel, prompting the operator to check the sheller status or adjust the process parameters to ensure the smooth operation of the sheller.

8. A bean shelling equipment status monitoring and early warning system, applied to the bean shelling equipment status monitoring and early warning method according to any one of claims 1 to 7, characterized in that: include: A blockage assessment model determination module is used to establish a blockage assessment model according to the target bean type; A blocking fault risk determination module is used to determine the blocking fault risk coefficient during the current shelling machine operation according to the blocking assessment model; The bearing evaluation and determination module is used to determine the bearing vibration coefficient, bearing temperature coefficient and bearing speed coefficient according to the current bearing vibration amplitude, bearing temperature and bearing rotation during the operation of the sheller, and then evaluate the overall operation state of the bearing according to the determined bearing vibration coefficient, bearing temperature coefficient and bearing speed coefficient to determine the current bearing overall operation state evaluation coefficient; The monitoring and early warning module is used to monitor and warn the current state of the sheller according to the blockage failure risk coefficient during the operation of the sheller and the current overall operation state evaluation coefficient of the bearing, and determine whether to generate early warning information; The monitoring and display terminal is used to display the blockage failure risk factor during the current operation of the sheller and the current bearing overall operation status assessment factor on the control panel when an early warning message is generated, prompting the operator to check the sheller status or adjust the process parameters to ensure the smooth operation of the sheller.

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