A ring cross belt sorting machine error correction method and system

Abstract

The present invention relates to the field of Internet of Things flow automation technology, and more specifically, to a method and system for troubleshooting a circular cross-belt sorter. The solution includes pre-setting sensors at key positions of the sorter; setting a preprocessing software to perform online estimation and analysis on the data collected by the central control system to form a maximum fluctuation margin; obtaining data when all objects fall, and forming a dynamic warning level coefficient based on the relationship between the total number of transported objects per unit time and the maximum fluctuation margin; adjusting the dynamic warning level coefficient so that the probability of object falling is within a preset range; recording the dynamic warning level coefficient, and minimizing the adjustment of the dynamic warning level coefficient within the same total number of transported objects per unit time through continuous learning and optimization. The solution improves the stability and efficiency of the circular cross-belt sorter and reduces operation and maintenance costs by constructing an efficient and automated method and system for troubleshooting.

Description

Technical Field

[0001] The present invention relates to the field of IoT flow automation technology, and more specifically, to a method and system for troubleshooting a circular cross-belt sorter. Background Art

[0002] In the field of IoT flow automation, the circular cross-belt sorter is a key technical equipment for achieving efficient logistics distribution. In view of the possible faults of the equipment, the use of real-time monitoring and intelligent diagnosis system for troubleshooting not only helps to improve logistics efficiency, but also reduces maintenance costs. According to data released by the International Federation of Automation (IFAC), the logistics center that introduced the intelligent troubleshooting system has increased the mean trouble-free operation time (MTBF) of its equipment by 35%, and shortened the fault response time by 50%. This improvement shows that an effective troubleshooting strategy is crucial to ensure the continuous operation of the sorter. By integrating advanced sensors and data analysis software, potential faults can be predicted and intervened in advance, which has far-reaching significance for maintaining the stability and reliability of the overall logistics system. Therefore, studying the troubleshooting method of the circular cross-belt sorter is not only a need for technological development, but also a key factor in improving the competitiveness of the logistics industry.

[0003] Prior to the technology of the present invention, the existing troubleshooting methods for circular cross-belt sorters mainly relied on manual inspection and regular maintenance, in conjunction with basic fault diagnosis technology, such as manual identification and intervention of abnormal sounds, vibrations, and material blockages in the sorter. The technical difficulty lies in the low efficiency of manual inspection, high requirements on personnel skills, and difficulty in achieving real-time monitoring; while regular maintenance cannot detect and handle sudden failures in a timely manner. The key point is how to improve the automation level of fault detection, reduce dependence on manual labor, and at the same time achieve real-time monitoring and predictive maintenance of the sorter's operating status, in order to detect potential problems in advance and avoid serious failures, thereby improving the stability and efficiency of the overall logistics system. Summary of the invention

[0004] In view of the above problems, the present invention proposes a method and system for troubleshooting a circular cross-belt sorter. By constructing an efficient and automated error troubleshooting method and system, the stability and efficiency of the circular cross-belt sorter are improved, the operation and maintenance costs are reduced, and strong technical support is provided for logistics automation.

[0005] According to a first aspect of an embodiment of the present invention, a method for troubleshooting a circular cross-belt sorter is provided.

[0006] In one or more embodiments, preferably, the method for troubleshooting a circular cross-belt sorter comprises:

[0007] Pre-set sensors at key locations of the sorter to start data collection;

[0008] The data collected by the sensors is transmitted to the central control system using a pre-set high-speed network.

[0009] Setting up a pre-processing software to perform online estimation and analysis on the data collected by the central control system to form the first, second and third maximum fluctuation margins;

[0010] Obtain all the data when objects fall, and form a dynamic warning level coefficient based on the relationship between the total number of transported objects per unit time and the first maximum fluctuation margin, the second maximum fluctuation margin, and the third maximum fluctuation margin;

[0011] Adjust the dynamic warning level coefficient so that the probability of object falling is within the preset range;

[0012] The dynamic warning level coefficient is recorded, and through continuous learning and optimization, the adjustment of the dynamic warning level coefficient within the total number of transported objects per unit time is minimized.

[0013] In one or more embodiments, preferably, the sensors at key positions of the sorting machine are pre-set to start data collection, specifically including:

[0014] Vibration sensors are installed at the passing positions of the package supply system, sorting cart, transmission belt, code reading and weighing system and package drop system to start data collection;

[0015] Photoelectric sensors are installed at the passing positions of the package supply system, sorting cart, transmission belt, code reading and weighing system and package drop system to start data collection;

[0016] The moving speed at different positions is calculated using the total passing time of the photoelectric sensors.

[0017] In one or more embodiments, preferably, the data collected by the sensor is transmitted to the central control system using a pre-set high-speed network to ensure data transmission, specifically including:

[0018] The equipment status data is collected in real time by various sensors deployed at key positions of the sorter, and the data is packaged and processed and stored according to the pre-set network transmission protocol;

[0019] Establish a pre-configured high-speed network that is optimized for data transmission, ensuring high bandwidth and low latency, and use this network to transmit the packaged data to the central control system in real time;

[0020] The central control system receives data from sensors and ensures the integrity and accuracy of the data through data decoding and verification processes.

[0021] In one or more embodiments, preferably, the setting of a preprocessing software to perform online estimation and analysis on the data collected by the central control system to form the first, second and third maximum fluctuation margins specifically includes:

[0022] The vibration frequency and amplitude of each key position are extracted, and the maximum vibration frequency when the transported object is not lost or dropped is used as the first parameter;

[0023] The maximum vibration amplitude when the transported object is not lost or dropped is used as the second parameter;

[0024] The moving speed of each key position is extracted, and the maximum moving speed without loss or drop of the transported object is used as the third parameter;

[0025] Extracting a first maximum fluctuation margin using a first calculation formula;

[0026] extracting a second maximum fluctuation margin using a second calculation formula;

[0027] extracting a third maximum fluctuation margin using a third calculation formula;

[0028] The first calculation formula is:

[0029] M1=0.9×MAX(A)

[0030] Wherein, A is the set of the first parameter, MAX() is the maximum value extraction function, and M1 is the first maximum fluctuation margin;

[0031] The second calculation formula is:

[0032] M2=MAX(B)

[0033] Wherein, B is the set of the second parameter, MAX() is the maximum value extraction function, and M2 is the second maximum fluctuation margin;

[0034] The third calculation formula is:

[0035] M3=0.8×MAX(C)

[0036] Among them, C is the set of third parameters, MAX() is the maximum value extraction function, and M3 is the third maximum fluctuation margin.

[0037] In one or more embodiments, preferably, the acquisition of all data when objects are dropped forms a dynamic warning level coefficient according to the relationship between the total number of transported objects per unit time and the first maximum fluctuation margin, the second maximum fluctuation margin and the third maximum fluctuation margin, specifically including:

[0038] Read all the data when objects are dropped, and extract the total number of transported objects per unit time;

[0039] Calculate the state sequence number using the fourth calculation formula according to the total number of transported objects per unit time;

[0040] Calculate the first frequency dynamic warning coefficient using the fifth calculation formula according to the state sequence number;

[0041] Calculate the second frequency dynamic warning coefficient using the sixth calculation formula according to the state sequence number;

[0042] Calculate the first vibration dynamic warning coefficient using the seventh calculation formula according to the state sequence number;

[0043] Calculate the second vibration dynamic warning coefficient using the eighth calculation formula according to the state sequence number;

[0044] Calculate the first speed dynamic warning coefficient using the ninth calculation formula according to the state sequence number;

[0045] Calculate the second speed dynamic warning coefficient using the tenth calculation formula according to the state sequence number;

[0046] The fourth calculation formula is:

[0047] Y=1,Sd<S1

[0048] Y=2,S1≤Sd≤S2

[0049] Y=3,S2<Sd

[0050] Wherein, Y is the state number, Y=1 indicates low risk, Y=2 indicates medium risk, Y=3 indicates high risk, Sd is the total number of transported objects per unit time, S1 is the first comparison margin, and S2 is the second comparison margin;

[0051] The fifth calculation formula is:

[0052] K1=P1(Y)÷M1

[0053] Wherein, P1(Y) is the average vibration frequency when the drop rate is 1% under the state number Y, and K1 is the first frequency dynamic warning coefficient;

[0054] The sixth calculation formula is:

[0055] K2=P5(Y)÷M1

[0056] Among them, P5(Y) is the average vibration frequency when the drop rate is 5% under the state number Y, and K2 is the second frequency dynamic warning coefficient;

[0057] The seventh calculation formula is:

[0058] K3=Q1(Y)÷M2

[0059] Among them, Q1(Y) is the average vibration amplitude when the drop rate is 1% under the state number Y, and K3 is the first vibration dynamic warning coefficient;

[0060] The eighth calculation formula is:

[0061] K4=Q5(Y)÷M2

[0062] Among them, Q5(Y) is the average vibration amplitude when the drop rate is 5% under the state number Y, and K4 is the second vibration dynamic warning coefficient;

[0063] The ninth calculation formula is:

[0064] K5=V1(Y)÷M3

[0065] Among them, V1(Y) is the average vibration speed when the drop rate is 1% under the state number Y, and K5 is the first speed dynamic warning coefficient;

[0066] The tenth calculation formula is:

[0067] K6=V5(Y)÷M3

[0068] Among them, V5(Y) is the average vibration speed when the drop rate is 5% under the state number Y, and K6 is the second speed dynamic warning coefficient.

[0069] In one or more embodiments, preferably, adjusting the dynamic warning level coefficient so that the probability of the object falling is within a preset range specifically includes:

[0070] Adjust the dynamic warning level coefficient in real time according to the current status sequence number Y;

[0071] When the vibration frequency of an object is greater than K1×M1, it is determined that there is a 1% probability that the object will fall;

[0072] When the vibration frequency of an object is greater than K2×M1, it is determined that there is a 5% probability that the object will fall;

[0073] When the vibration amplitude of an object is greater than K3×M2, it is determined that there is a 1% probability that the object will fall;

[0074] When the vibration amplitude of an object is greater than K4×M2, it is determined that there is a 5% probability that the object will fall;

[0075] When the moving speed of an object is greater than K5×M3, it is determined that there is a 1% probability that the object will fall;

[0076] When the moving speed of an object is greater than K6×M3, it is determined that there is a 5% probability that the object will fall;

[0077] And it issues early warnings of possible falling objects through laser marking.

[0078] In one or more embodiments, preferably, the recording of the dynamic warning level coefficient minimizes the adjustment of the dynamic warning level coefficient within the total number of transported objects per unit time through continuous learning and optimization, specifically including:

[0079] Continuously record each dynamic warning level coefficient;

[0080] If K1, K2, K3, K4, K5 and K6 do not exceed the preset size under the same state sequence number Y, the dynamic warning level coefficients K1, K2, K3, K4, K5 and K6 will not be adjusted.

[0081] According to a second aspect of an embodiment of the present invention, there is provided an error-correcting system for an endless cross-belt sorting machine.

[0082] In one or more embodiments, preferably, the circular cross-belt sorting machine error sorting system comprises:

[0083] Deploy sensor modules to pre-set sensors at key locations on the sorter and start data collection;

[0084] Real-time transmission module, used to transmit the data collected by sensors to the central control system using a pre-set high-speed network;

[0085] A data processing module is used to set a pre-processing software to perform online estimation and analysis on the data collected by the central control system to form the first, second and third maximum fluctuation margins;

[0086] The early warning identification module is used to obtain data when all objects fall, and form a dynamic early warning level coefficient according to the relationship between the total number of transported objects per unit time and the first maximum fluctuation margin, the second maximum fluctuation margin and the third maximum fluctuation margin;

[0087] Intelligent response module, used to adjust the dynamic warning level coefficient so that the probability of an object falling is within a preset range;

[0088] The optimization and upgrade module is used to record the dynamic warning level coefficient and minimize the adjustment of the dynamic warning level coefficient within the total number of the same transported objects per unit time through continuous learning and optimization.

[0089] According to a third aspect of an embodiment of the present invention, there is provided a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method as described in any one of the first aspect of the embodiment of the present invention is implemented.

[0090] According to a fourth aspect of an embodiment of the present invention, there is provided an electronic device, comprising a memory and a processor, wherein the memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement any one of the methods described in the first aspect of the embodiment of the present invention.

[0091] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0092] In the solution of the present invention, machine learning algorithm analysis is used to accurately predict and diagnose potential faults based on data analysis, which significantly improves the troubleshooting efficiency and accuracy.

[0093] In the solution of the present invention, the working parameters are automatically adjusted or switched to the backup system according to the diagnosis results, which effectively reduces the impact of the fault on the logistics operation and improves the operating stability and reliability of the sorting machine.

[0094] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0095] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0097] Figure 1 The present invention is a flowchart of a method for troubleshooting a circular cross-belt sorter according to an embodiment of the present invention.

[0098] Figure 2 The present invention is a flowchart of presetting sensors at key positions of a sorting machine and starting data collection in a method for troubleshooting a circular cross-belt sorting machine according to an embodiment of the present invention.

[0099] Figure 3The present invention is a flowchart of a method for troubleshooting a circular cross-belt sorter in which data collected by sensors are transmitted to a central control system by using a pre-set high-speed network.

[0100] Figure 4 A method for troubleshooting a circular cross-belt sorter according to an embodiment of the present invention includes setting a preprocessing software to perform online estimation and analysis on data collected by a central control system to form a flow chart of the first, second and third maximum fluctuation margins.

[0101] Figure 5 It is a flow chart of obtaining data when all objects fall in a method for troubleshooting a circular cross-belt sorter in one embodiment of the present invention, and forming a dynamic warning level coefficient based on the relationship between the total number of transported objects per unit time and the first maximum fluctuation margin, the second maximum fluctuation margin and the third maximum fluctuation margin.

[0102] Figure 6 The present invention is a flowchart of adjusting a dynamic warning level coefficient in a method for troubleshooting a circular cross-belt sorter in accordance with an embodiment of the present invention so that the probability of an object falling is within a preset range.

[0103] Figure 7 It is a flowchart of recording dynamic warning level coefficients in a method for troubleshooting a circular cross-belt sorter according to an embodiment of the present invention, and minimizing the adjustment of the dynamic warning level coefficients within the total number of the same transported objects per unit time through continuous learning and optimization.

[0104] Figure 8 The present invention is a structural diagram of an error-correcting system for a circular cross-belt sorter according to an embodiment of the present invention.

[0105] Fig. 9 It is a structural diagram of an electronic device in one embodiment of the present invention. DETAILED DESCRIPTION

[0106] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0107] 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0108] In the field of IoT flow automation, the circular cross-belt sorter is a key technical equipment for achieving efficient logistics distribution. In view of the possible faults of the equipment, the use of real-time monitoring and intelligent diagnosis system for troubleshooting not only helps to improve logistics efficiency, but also reduces maintenance costs. According to data released by the International Federation of Automation (IFAC), the logistics center that introduced the intelligent troubleshooting system has increased the mean trouble-free operation time (MTBF) of its equipment by 35%, and shortened the fault response time by 50%. This improvement shows that an effective troubleshooting strategy is crucial to ensure the continuous operation of the sorter. By integrating advanced sensors and data analysis software, potential faults can be predicted and intervened in advance, which has far-reaching significance for maintaining the stability and reliability of the overall logistics system. Therefore, studying the troubleshooting method of the circular cross-belt sorter is not only a need for technological development, but also a key factor in improving the competitiveness of the logistics industry.

[0109] Prior to the technology of the present invention, the existing troubleshooting methods for circular cross-belt sorters mainly relied on manual inspection and regular maintenance, in conjunction with basic fault diagnosis technology, such as manual identification and intervention of abnormal sounds, vibrations, and material blockages in the sorter. The technical difficulty lies in the low efficiency of manual inspection, high requirements on personnel skills, and difficulty in achieving real-time monitoring; while regular maintenance cannot detect and handle sudden failures in a timely manner. The key point is how to improve the automation level of fault detection, reduce dependence on manual labor, and at the same time achieve real-time monitoring and predictive maintenance of the sorter's operating status, in order to detect potential problems in advance and avoid serious failures, thereby improving the stability and efficiency of the overall logistics system.

[0110] In the embodiment of the present invention, a method and system for troubleshooting a circular cross-belt sorter are provided. This solution improves the stability and efficiency of the circular cross-belt sorter by constructing an efficient and automated method and system for troubleshooting errors, reduces operation and maintenance costs, and provides strong technical support for logistics automation.

[0111] According to a first aspect of an embodiment of the present invention, a method for troubleshooting a circular cross-belt sorter is provided.

[0112] Figure 1The present invention is a flowchart of a method for troubleshooting a circular cross-belt sorter according to an embodiment of the present invention.

[0113] In one or more embodiments, preferably, the method for troubleshooting a circular cross-belt sorter comprises:

[0114] S101, pre-set sensors at key positions of the sorting machine and start data collection;

[0115] S102, transmitting the data collected by the sensor to the central control system using a pre-set high-speed network guarantee data;

[0116] S103, setting a preprocessing software to perform online estimation and analysis on the data collected by the central control system to form the first, second and third maximum fluctuation margins;

[0117] S104, obtaining data when all objects are dropped, and forming a dynamic warning level coefficient according to the relationship between the total number of transported objects per unit time and the first maximum fluctuation margin, the second maximum fluctuation margin, and the third maximum fluctuation margin;

[0118] S105, adjusting the dynamic warning level coefficient so that the probability of the object falling is within a preset range;

[0119] S106. Record the dynamic warning level coefficient, and minimize the adjustment of the dynamic warning level coefficient within the total number of the same transported objects per unit time through continuous learning and optimization.

[0120] In the embodiment of the present invention, a closed-loop intelligent troubleshooting system is constructed through an integrated sensor network, real-time data transmission, advanced data analysis and processing, fault prediction diagnosis, automatic adjustment response and maintenance feedback mechanism, aiming to improve the operating reliability and logistics efficiency of the circular cross-belt sorting machine, reduce manual intervention, reduce operation and maintenance costs, and significantly improve the level of logistics automation.

[0121] Figure 2 The present invention is a flowchart of presetting sensors at key positions of a sorting machine and starting data collection in a method for troubleshooting a circular cross-belt sorting machine according to an embodiment of the present invention.

[0122] like Figure 2 As shown, in one or more embodiments, preferably, the sensors at key positions of the sorting machine are pre-set to start data collection, specifically including:

[0123] S201. Vibration sensors are installed at the passing positions of the package supply system, sorting trolley, transmission belt, code reading and weighing system and package drop system, and data collection is started;

[0124] S202, photoelectric sensors are installed at the passing positions of the package supply system, sorting cart, transmission belt, code reading and weighing system and package drop system to start data collection;

[0125] S203, calculating the moving speed at different positions using the passing time of all the photoelectric sensors obtained.

[0126] In the embodiment of the present invention, first, sensors are pre-installed at key positions of the sorting machine. Specifically, vibration sensors and photoelectric sensors are installed at the passing positions of the package supply system, sorting cart, transmission belt, code reading and weighing system, and package drop system. The arrangement of these sensors covers the entire process from the entry of items into the sorting system to their exit.

[0127] At the beginning of the operation, all sensors are activated at the same time to collect data. The vibration sensor can detect whether the items are transferred smoothly at each key node and whether there is abnormal vibration, which is crucial to ensure the safety of the items during the transmission process. At the same time, the photoelectric sensor is used to accurately measure the time it takes for the items to pass through each node.

[0128] By analyzing the data collected by the photoelectric sensor, we can calculate the moving speed of the items at different locations. This step is critical to optimizing the sorting process because it helps us understand the actual flow of items in the sorting system, thereby adjusting the working parameters of the sorting machine to ensure that the entire system runs more efficiently and accurately.

[0129] For example, if the moving speed at the conveyor belt is significantly lower than that at other locations, it may mean that there is a potential blockage problem or mechanical failure at that location, which needs to be checked and resolved in a timely manner. On the contrary, if the speed of a certain link is too fast, it may lead to insufficient handling of items and affect the sorting quality. Therefore, by accurately measuring and adjusting the speed of each part, the performance of the entire sorting system can be greatly improved.

[0130] In summary, by setting vibration and photoelectric sensors at key positions of the sorting machine and using the photoelectric sensor data to calculate the moving speed at different positions, this embodiment provides an effective way to ensure high efficiency and high accuracy of the sorting process, thereby optimizing the performance of the entire logistics sorting system.

[0131] Package supply system: The package supply system consists of multiple package supply belts, dynamic weight scales and photoelectric position detection switches, and is responsible for accurately delivering packages to the sorting cart.

[0132] Conveyor belt: Conveyor belt is used for long-term, large-volume package transportation and sorting operations, and has a direct impact on sorting efficiency and accuracy.

[0133] Sorting trolley: The sorting trolley runs on a circular track, and the positioning accuracy and reliability of the trolley are improved through barcode scanner positioning control technology.

[0134] Barcode reading and weighing system: The barcode reading and weighing system is used to automatically identify the characteristics of items and quickly sort them according to preset rules to improve accuracy and efficiency.

[0135] Package drop system: The package drop system is responsible for accurately transporting the parcel from the sorting trolley to the designated location, performing the dropping action, and completing the entire sorting process.

[0136] Figure 3 The present invention is a flowchart of a method for troubleshooting a circular cross-belt sorter in which data collected by sensors are transmitted to a central control system by using a pre-set high-speed network.

[0137] like Figure 3 As shown, in one or more embodiments, preferably, the data collected by the sensor is transmitted to the central control system using a pre-set high-speed network to ensure data transmission, specifically including:

[0138] S301, collecting equipment status data in real time according to various sensors deployed at key positions of the sorting machine, encapsulating and processing the data, and storing them according to a preset network transmission protocol;

[0139] S302, establishing a pre-set high-speed network, which is optimized for data transmission and ensures high bandwidth and low latency, and using this network to transmit the packaged data to the central control system in real time;

[0140] S303. The central control system receives data from the sensor and ensures the integrity and accuracy of the data through data decoding and verification processes.

[0141] In the embodiment of the present invention, an efficient data transmission method is described for transmitting the data collected by sensors in the sorting machine system to the central control system in real time. First, we deploy a variety of sensors (such as vibration sensors and photoelectric sensors in the above embodiments) at key locations of the sorting machine, which are responsible for real-time monitoring and collecting the status data of the equipment. The collected data is then packaged and processed to comply with the pre-set network transmission protocol to ensure the standardization and compatibility of the data.

[0142] In order to ensure the efficiency and stability of data transmission, we have established a high-speed network optimized for data transmission. This network has the characteristics of high bandwidth and low latency, which can support the rapid transmission of large amounts of data. Using this high-speed network, the packaged data is transmitted to the central control system in real time.

[0143] At the central control system end, the received data undergoes a decoding and verification process. This step is crucial because it ensures the integrity and accuracy of the data and avoids possible errors or damage during data transmission. In this way, the central control system can obtain reliable real-time data to effectively monitor and manage the operating status of the sorting machine.

[0144] For example, when a photoelectric sensor detects that an item is moving at an abnormal speed at a specific location, the relevant data will be immediately sent to the central control system through the network. After receiving the data, the central control system will decode and verify the data to confirm that it is correct. Based on this information, the operating parameters of the sorting machine can be adjusted, or maintenance alarms can be issued.

[0145] In summary, this embodiment collects data by deploying sensors at key locations, uses a preset high-speed network to achieve rapid data transmission, and decodes and verifies data in a central control system, thereby ensuring the efficiency and accuracy of data transmission. This not only improves the intelligence level of the sorting system, but also provides strong support for the stable operation and timely maintenance of the system.

[0146] Figure 4 A method for troubleshooting a circular cross-belt sorter according to an embodiment of the present invention includes setting a preprocessing software to perform online estimation and analysis on data collected by a central control system to form a flow chart of the first, second and third maximum fluctuation margins.

[0147] like Figure 4 As shown, in one or more embodiments, preferably, a preprocessing software is set to perform online estimation and analysis on the data collected by the central control system to form the first, second and third maximum fluctuation margins, specifically including:

[0148] S401, extracting the vibration frequency and amplitude of each key position, and taking the maximum vibration frequency when the transported object is not lost or dropped as the first parameter;

[0149] S402, taking the maximum vibration amplitude when the transported object is not lost or dropped as the second parameter;

[0150] S403, extracting the moving speed of each key location, and taking the maximum moving speed when no transported object is lost or dropped as the third parameter;

[0151] S404, extracting a first maximum fluctuation margin using a first calculation formula;

[0152] S405, extracting a second maximum fluctuation margin using a second calculation formula;

[0153] S406, extracting a third maximum fluctuation margin using a third calculation formula;

[0154] The first calculation formula is:

[0155] M1=0.9×MAX(A)

[0156] Wherein, A is the set of the first parameter, MAX() is the maximum value extraction function, and M1 is the first maximum fluctuation margin;

[0157] The second calculation formula is:

[0158] M2=MAX(B)

[0159] Wherein, B is the set of the second parameter, MAX() is the maximum value extraction function, and M2 is the second maximum fluctuation margin;

[0160] The third calculation formula is:

[0161] M3=0.8×MAX(C)

[0162] Among them, C is the set of third parameters, MAX() is the maximum value extraction function, and M3 is the third maximum fluctuation margin.

[0163] In the embodiment of the present invention, a preprocessing software is described for online estimation and analysis of the data collected by the central control system and forming the first, second and third maximum fluctuation margins. First, we extract the vibration frequency and amplitude data of each key position and the moving speed data from the central control system. These data are obtained without the transported object being lost or dropped.

[0164] For the vibration frequency, we extract the maximum vibration frequency as the first parameter; for the vibration amplitude, we extract the maximum vibration amplitude as the second parameter; for the moving speed, we extract the maximum moving speed as the third parameter.

[0165] Next, use the following calculation formula to extract the corresponding maximum fluctuation margin:

[0166] 1. The calculation formula of the first maximum fluctuation margin is: M1=0.9×MAX(A), where A is the set of first parameters, MAX() is the maximum value extraction function, and M1 is the first maximum fluctuation margin.

[0167] 2. The calculation formula for the second maximum fluctuation margin is: M2=MAX(B), where B is the set of second parameters, MAX() is the maximum value extraction function, and M2 is the second maximum fluctuation margin.

[0168] 3. The calculation formula of the third maximum fluctuation margin is: M3=0.8×MAX(C), where C is the set of the third parameters, MAX() is the maximum value extraction function, and M3 is the third maximum fluctuation margin.

[0169] Through such pre-processing software, we can analyze and estimate the key parameters of the sorting machine in real time, so as to discover potential problems or abnormal situations in advance, and take corresponding measures for adjustment and maintenance to ensure the stable operation and efficient performance of the sorting system.

[0170] Figure 5 It is a flow chart of obtaining data when all objects fall in a method for troubleshooting a circular cross-belt sorter in one embodiment of the present invention, and forming a dynamic warning level coefficient based on the relationship between the total number of transported objects per unit time and the first maximum fluctuation margin, the second maximum fluctuation margin and the third maximum fluctuation margin.

[0171] like Figure 5 As shown, in one or more embodiments, preferably, the acquisition of data when all objects are dropped forms a dynamic warning level coefficient according to the relationship between the total number of transported objects per unit time and the first maximum fluctuation margin, the second maximum fluctuation margin and the third maximum fluctuation margin, specifically including:

[0172] S501, reading all the data when objects are dropped, and extracting the corresponding total number of transported objects per unit time;

[0173] S502, calculating the state sequence number using the fourth calculation formula according to the total number of transported objects per unit time;

[0174] S503, calculating the first frequency dynamic warning coefficient using the fifth calculation formula according to the state sequence number;

[0175] S504, calculating the second frequency dynamic warning coefficient using the sixth calculation formula according to the state sequence number;

[0176] S505, calculating the first vibration dynamic warning coefficient using the seventh calculation formula according to the state sequence number;

[0177] S506, calculating the second vibration dynamic warning coefficient using the eighth calculation formula according to the state sequence number;

[0178] S507, calculating the first speed dynamic warning coefficient using the ninth calculation formula according to the state sequence number;

[0179] S508, calculating the second speed dynamic warning coefficient using the tenth calculation formula according to the state sequence number;

[0180] The fourth calculation formula is:

[0181] Y=1,Sd<S1

[0182] Y=2,S1≤Sd≤S2

[0183] Y=3,S2<Sd

[0184] Wherein, Y is the state number, Y=1 indicates low risk, Y=2 indicates medium risk, Y=3 indicates high risk, Sd is the total number of transported objects per unit time, S1 is the first comparison margin, and S2 is the second comparison margin;

[0185] The fifth calculation formula is:

[0186] K1=P1(Y)÷M1

[0187] Wherein, P1(Y) is the average vibration frequency when the drop rate is 1% under the state number Y, and K1 is the first frequency dynamic warning coefficient;

[0188] The sixth calculation formula is:

[0189] K2=P5(Y)÷M1

[0190] Among them, P5(Y) is the average vibration frequency when the drop rate is 5% under the state number Y, and K2 is the second frequency dynamic warning coefficient;

[0191] The seventh calculation formula is:

[0192] K3=Q1(Y)÷M2

[0193] Among them, Q1(Y) is the average vibration amplitude when the drop rate is 1% under the state number Y, and K3 is the first vibration dynamic warning coefficient;

[0194] The eighth calculation formula is:

[0195] K4=Q5(Y)÷M2

[0196] Among them, Q5(Y) is the average vibration amplitude when the drop rate is 5% under the state number Y, and K4 is the second vibration dynamic warning coefficient;

[0197] The ninth calculation formula is:

[0198] K5=V1(Y)÷M3

[0199] Among them, V1(Y) is the average vibration speed when the drop rate is 1% under the state number Y, and K5 is the first speed dynamic warning coefficient;

[0200] The tenth calculation formula is:

[0201] K6=V5(Y)÷M3

[0202] Among them, V5(Y) is the average vibration speed when the drop rate is 5% under the state number Y, and K6 is the second speed dynamic warning coefficient.

[0203] In an embodiment of the present invention, a method for establishing a dynamic early warning system is described, which is used to monitor and warn of potential object falling risks in a logistics sorting machine system in real time. First, the system collects data when all object falling events occur, including the total number of transported objects per unit time and related vibration frequency, vibration amplitude and moving speed data.

[0204] Based on these data, the system uses the fourth calculation formula to calculate the state number Y, which reflects the current risk level. The calculation of the state number Y depends on the relationship between the total number of transported objects per unit time (Sd) and the preset first comparison margin (S1) and the second comparison margin (S2). State number Y = 1 indicates low risk, Y = 2 indicates medium risk, and Y = 3 indicates high risk.

[0205] Next, the system calculates each dynamic warning coefficient using the fifth to tenth calculation formulas according to the state number Y. These formulas take into account the average vibration frequency, vibration amplitude and movement speed at the drop rate of 1% and 5% at different risk levels, and compare them with the first, second and third maximum fluctuation margins.

[0206] Specifically, the fifth and sixth calculation formulas respectively calculate the first frequency dynamic warning coefficient (K1) and the second frequency dynamic warning coefficient (K2), which evaluate the impact of vibration frequency on the risk of falling. The seventh and eighth calculation formulas respectively calculate the first vibration dynamic warning coefficient (K3) and the second vibration dynamic warning coefficient (K4), which evaluate the impact of vibration amplitude on the risk of falling. The ninth and tenth calculation formulas respectively calculate the first speed dynamic warning coefficient (K5) and the second speed dynamic warning coefficient (K6), which evaluate the impact of moving speed on the risk of falling.

[0207] Through these dynamic warning coefficients, the system can comprehensively consider the contribution of different factors to the risk of falling, thus forming a comprehensive dynamic warning level coefficient. This coefficient can reflect the operating status of the sorting machine system in real time, and issue a warning in time when potential risks are detected, so that the operator or the automation system can take measures, such as adjusting machine parameters or suspending operations for inspection, to avoid objects from falling and ensure the efficient and safe operation of the sorting system.

[0208] Figure 6 The present invention is a flowchart of adjusting a dynamic warning level coefficient in a method for troubleshooting a circular cross-belt sorter in accordance with an embodiment of the present invention so that the probability of an object falling is within a preset range.

[0209] like Figure 6As shown, in one or more embodiments, preferably, the adjusting the dynamic warning level coefficient so that the probability of the object falling is within a preset range specifically includes:

[0210] S601, adjusting the dynamic warning level coefficient in real time according to the current state sequence number Y;

[0211] S602: When it is determined that the vibration frequency of the object is greater than K1×M1, it is determined that there is a 1% probability that the object will fall;

[0212] S603, when it is determined that the vibration frequency of the object is greater than K2×M1, it is determined that there is a 5% probability that the object will fall;

[0213] S604, when it is determined that the vibration amplitude of the object is greater than K3×M2, it is determined that there is a 1% probability that the object will fall;

[0214] S605, when it is determined that the vibration amplitude of the object is greater than K4×M2, it is determined that there is a 5% probability that the object will fall;

[0215] S606: When it is determined that the moving speed of the object is greater than K5×M3, it is determined that there is a 1% probability that the object will fall;

[0216] S607, when it is determined that the moving speed of the object is greater than K6×M3, it is determined that there is a 5% probability that the object will fall;

[0217] S608, and issue a warning for possible falling objects through laser marking.

[0218] In the embodiment of the present invention, the probability of an object falling is ensured to be maintained within a preset range by adjusting the dynamic warning level coefficient in real time. First, the dynamic warning level coefficient is adjusted according to the current state sequence number Y. Next, the vibration frequency, vibration amplitude and moving speed of the object are monitored respectively.

[0219] When the vibration frequency of the object is greater than K1×M1, a warning signal is issued that there is a 1% probability of the object falling. Similarly, when the vibration frequency of the object further increases and exceeds K2×M1, a warning signal is issued that there is a 5% probability of the object falling.

[0220] We also use similar judgment logic for the vibration amplitude of the object. When the vibration amplitude of the object is greater than K3×M2, a warning signal is issued that there is a 1% probability that the object will fall. When the vibration amplitude of the object increases further and exceeds K4×M2, a warning signal is issued that there is a 5% probability that the object will fall.

[0221] In addition, the moving speed of the object is also monitored. When the moving speed of the object is greater than K5×M3, a signal is issued that there is a 1% probability of the object falling. When the moving speed of the object further increases and exceeds K6×M3, a warning signal is issued that there is a 5% probability of the object falling.

[0222] Finally, for objects that may fall, a warning signal is issued through laser marking. This warning method can effectively remind relevant personnel to pay attention to potential safety risks, so that they can take corresponding preventive measures to prevent objects from falling.

[0223] Figure 7 It is a flowchart of recording dynamic warning level coefficients in a method for troubleshooting a circular cross-belt sorter according to an embodiment of the present invention, and minimizing the adjustment of the dynamic warning level coefficients within the total number of the same transported objects per unit time through continuous learning and optimization.

[0224] like Figure 7 As shown, in one or more embodiments, preferably, the recording of the dynamic warning level coefficient minimizes the adjustment of the dynamic warning level coefficient within the total number of transported objects per unit time through continuous learning and optimization, specifically including:

[0225] S701, continuously record each dynamic warning level coefficient;

[0226] S702: If K1, K2, K3, K4, K5 and K6 do not exceed the preset size under the same state sequence number Y, the dynamic warning level coefficients K1, K2, K3, K4, K5 and K6 are not adjusted.

[0227] In an embodiment of the present invention, a system is described that optimizes the performance of a sorting machine system and minimizes the adjustment of the dynamic warning level coefficient within the total number of the same transported objects per unit time by continuously recording and learning the dynamic warning level coefficient.

[0228] The system continuously records the dynamic warning level coefficients (K1, K2, K3, K4, K5 and K6) calculated each time. These coefficients are calculated according to the method described above and reflect the operating status of the sorter under different status numbers Y (representing different risk levels).

[0229] Over time, the system has collected a large amount of dynamic warning level coefficient data, which is used to deeply analyze the performance of the sorter under different total number of transport objects per unit time (Sd) and the corresponding risk level (state number Y).

[0230] The system sets a preset threshold value to determine whether the dynamic warning level coefficient needs to be adjusted. Under the same state sequence number Y, if the calculated values ​​of K1, K2, K3, K4, K5 and K6 do not exceed this preset value, it means that the current dynamic warning level coefficient has accurately reflected the risk status of the sorter, so there is no need to adjust these coefficients.

[0231] If the preset size is exceeded, the dynamic warning level coefficient needs to be fine-tuned. This adjustment is based on a continuous learning and optimization process, aiming to improve the accuracy of the warning system, reduce false alarms and missed alarms, thereby ensuring the stable operation of the sorting machine and reducing the risk of objects falling.

[0232] In this way, the system is able to optimize itself and adapt to the actual operation of the sorter. In the long term, this will lead to a gradual reduction in the adjustment of the dynamic warning level coefficient within the total number of transported objects per unit time, thereby improving the overall efficiency and safety of the sorter system.

[0233] According to a second aspect of an embodiment of the present invention, there is provided an error-correcting system for an endless cross-belt sorting machine.

[0234] Figure 8 The present invention is a structural diagram of an error-correcting system for a circular cross-belt sorter according to an embodiment of the present invention.

[0235] In one or more embodiments, preferably, the circular cross-belt sorting machine error sorting system comprises:

[0236] Deploy the sensor module 801 to pre-set sensors at key positions of the sorter and start data collection;

[0237] Real-time transmission module 802, used to transmit the data collected by the sensor to the central control system using a pre-set high-speed network guarantee data;

[0238] The data processing module 803 is used to set a pre-processing software to perform online estimation and analysis on the data collected by the central control system to form the first, second and third maximum fluctuation margins;

[0239] The early warning identification module 804 is used to obtain data when all objects fall, and form a dynamic early warning level coefficient according to the relationship between the total number of transported objects per unit time and the first maximum fluctuation margin, the second maximum fluctuation margin and the third maximum fluctuation margin;

[0240] Intelligent response module 805, used to adjust the dynamic warning level coefficient so that the probability of the object falling is within a preset range;

[0241] The optimization and upgrading module 806 is used to record the dynamic warning level coefficient and minimize the adjustment of the dynamic warning level coefficient within the total number of the same transported objects per unit time through continuous learning and optimization.

[0242] In the embodiment of the present invention, a system suitable for different structures is realized through a series of modular designs. The system can achieve closed-loop, reliable and efficient execution through collection, analysis and control.

[0243] According to a third aspect of an embodiment of the present invention, there is provided a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method as described in any one of the first aspect of the embodiment of the present invention is implemented.

[0244] According to a fourth aspect of an embodiment of the present invention, an electronic device is provided. Fig. 9 It is a structural diagram of an electronic device in one embodiment of the present invention. Fig. 9 The electronic device shown is a universal circular cross-belt sorter error-correcting device. Fig. 9 As shown, the electronic device 900 includes a central processing unit (CPU) 901, which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) 902 or computer program instructions loaded from a storage unit 908 into a random access memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the electronic device 900 can also be stored. The CPU 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0245] Multiple components in the electronic device 900 are connected to the I / O interface 905, including: an input unit 906, an output unit 907, a storage unit 908, and the processing unit 901 performs the various methods and processes described above, such as the method described in the first aspect of the embodiment of the present invention. For example, in some embodiments, the method described in the first aspect of the embodiment of the present invention may be implemented as a computer software program, which is stored in a machine-readable medium, such as the storage unit 908. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 900 via the ROM 902 and / or the communication unit 909. When the computer program is loaded into the RAM 903 and executed by the CPU 901, one or more operations of the method described in the first aspect of the embodiment of the present invention may be performed. Alternatively, in other embodiments, the CPU 901 may be configured as one or more actions of the method described in the first aspect of the embodiment of the present invention by any other appropriate means (e.g., by means of firmware).

[0246] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0247] In the solution of the present invention, machine learning algorithm analysis is used to accurately predict and diagnose potential faults based on data analysis, which significantly improves the troubleshooting efficiency and accuracy.

[0248] In the solution of the present invention, the working parameters are automatically adjusted or switched to the backup system according to the diagnosis results, which effectively reduces the impact of the fault on the logistics operation and improves the operating stability and reliability of the sorting machine.

[0249] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0250] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0251] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0252] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1The steps for the functions specified in one or more boxes.

[0253] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for troubleshooting a circular cross-belt sorting machine, characterized in that: The method includes: Pre-set sensors at key locations of the sorter to start data collection; The data collected by the sensors is transmitted to the central control system using a pre-set high-speed network. Setting up a pre-processing software to perform online estimation and analysis on the data collected by the central control system to form the first, second and third maximum fluctuation margins; Obtain all the data when objects fall, and form a dynamic warning level coefficient based on the relationship between the total number of transported objects per unit time and the first maximum fluctuation margin, the second maximum fluctuation margin, and the third maximum fluctuation margin; Adjust the dynamic warning level coefficient so that the probability of object falling is within the preset range; Record the dynamic warning level coefficient, and minimize the adjustment of the dynamic warning level coefficient within the total number of transported objects per unit time through continuous learning and optimization; The method of setting up a preprocessing software to perform online estimation and analysis on the data collected by the central control system to form the first, second and third maximum fluctuation margins specifically includes: The vibration frequency and amplitude of each key position are extracted, and the maximum vibration frequency when the transported object is not lost or dropped is used as the first parameter; The maximum vibration amplitude when the transported object is not lost or dropped is used as the second parameter; The moving speed of each key position is extracted, and the maximum moving speed without loss or drop of the transported object is used as the third parameter; Extracting a first maximum fluctuation margin using a first calculation formula; extracting a second maximum fluctuation margin using a second calculation formula; extracting a third maximum fluctuation margin using a third calculation formula; The first calculation formula is: M1=0.9×MAX(A) Wherein, A is the set of the first parameter, MAX() is the maximum value extraction function, and M1 is the first maximum fluctuation margin; The second calculation formula is: M2=MAX(B) Wherein, B is the set of the second parameter, MAX() is the maximum value extraction function, and M2 is the second maximum fluctuation margin; The third calculation formula is: M3=0.8×MAX(C) Wherein, C is the set of the third parameter, MAX() is the maximum value extraction function, and M3 is the third maximum fluctuation margin; The acquisition of data when all objects are dropped forms a dynamic warning level coefficient according to the relationship between the total number of transported objects per unit time and the first maximum fluctuation margin, the second maximum fluctuation margin and the third maximum fluctuation margin, specifically including: Read all the data when objects are dropped, and extract the total number of transported objects per unit time; Calculate the state sequence number using the fourth calculation formula according to the total number of transported objects per unit time; Calculate the first frequency dynamic warning coefficient using the fifth calculation formula according to the state sequence number; Calculate the second frequency dynamic warning coefficient using the sixth calculation formula according to the state sequence number; Calculate the first vibration dynamic warning coefficient using the seventh calculation formula according to the state sequence number; Calculate the second vibration dynamic warning coefficient using the eighth calculation formula according to the state sequence number; Calculate the first speed dynamic warning coefficient using the ninth calculation formula according to the state sequence number; Calculate the second speed dynamic warning coefficient using the tenth calculation formula according to the state sequence number; The fourth calculation formula is: Y=1,Sd<S1 Y=2,S1≤Sd≤S2 Y=3,S2<Sd Wherein, Y is the state number, Y=1 indicates low risk, Y=2 indicates medium risk, Y=3 indicates high risk, Sd is the total number of transported objects per unit time, S1 is the first comparison margin, and S2 is the second comparison margin; The fifth calculation formula is: K1=P1(Y)÷M1 Among them, P1(Y) is the average vibration frequency when the drop rate is 1% under the state number Y, and K1 is the first frequency dynamic warning coefficient; The sixth calculation formula is: K2=P5(Y)÷M1 Among them, P5(Y) is the average vibration frequency when the drop rate is 5% under the state number Y, and K2 is the second frequency dynamic warning coefficient; The seventh calculation formula is: K3=Q1(Y)÷M2 Among them, Q1(Y) is the average vibration amplitude when the drop rate is 1% under the state number Y, and K3 is the first vibration dynamic warning coefficient; The eighth calculation formula is: K4=Q5(Y)÷M2 Among them, Q5(Y) is the average vibration amplitude when the drop rate is 5% under the state number Y, and K4 is the second vibration dynamic warning coefficient; The ninth calculation formula is: K5=V1(Y)÷M3 Among them, V1(Y) is the average vibration speed when the drop rate is 1% under the state number Y, and K5 is the first speed dynamic warning coefficient; The tenth calculation formula is: K6=V5(Y)÷M3 Among them, V5(Y) is the average vibration speed when the drop rate is 5% under state number Y, and K6 is the second speed dynamic warning coefficient.

2. A method for troubleshooting a circular cross belt sorter as claimed in claim 1, characterized in that: The sensors at key positions of the sorting machine are pre-set to start data collection, specifically including: Vibration sensors are installed at the passing positions of the package supply system, sorting cart, transmission belt, code reading and weighing system and package drop system to start data collection; Photoelectric sensors are installed at the passing positions of the package supply system, sorting cart, transmission belt, code reading and weighing system and package drop system to start data collection; The moving speed at different positions is calculated using the total passing time of the photoelectric sensors.

3. The method for troubleshooting a circular cross belt sorter according to claim 1, characterized in that: The data collected by the sensor is transmitted to the central control system using a pre-set high-speed network to ensure data transmission, specifically including: The equipment status data is collected in real time by various sensors deployed at key positions of the sorting machine, and the data is packaged and processed and stored according to the pre-set network transmission protocol; Establish a pre-configured high-speed network that is optimized for data transmission, ensuring high bandwidth and low latency, and use this network to transmit the packaged data to the central control system in real time; The central control system receives data from sensors and ensures the integrity and accuracy of the data through data decoding and verification processes.

4. The method for troubleshooting a circular cross belt sorter according to claim 1, characterized in that: The step of adjusting the dynamic warning level coefficient so that the probability of the object falling is within a preset range specifically includes: Adjust the dynamic warning level coefficient in real time according to the current status sequence number Y; When the vibration frequency of an object is greater than K1×M1, it is determined that there is a 1% probability that the object will fall; When the vibration frequency of an object is greater than K2×M1, it is determined that there is a 5% probability that the object will fall; When the vibration amplitude of an object is greater than K3×M2, it is determined that there is a 1% probability that the object will fall; When the vibration amplitude of an object is greater than K4×M2, it is determined that there is a 5% probability that the object will fall; When the moving speed of an object is greater than K5×M3, it is determined that there is a 1% probability that the object will fall; When the moving speed of an object is greater than K6×M3, it is determined that there is a 5% probability that the object will fall; And it issues early warnings of possible falling objects through laser marking.

5. The method for troubleshooting a circular cross belt sorter according to claim 1, characterized in that: The recording of the dynamic warning level coefficient minimizes the adjustment of the dynamic warning level coefficient within the total number of transported objects per unit time through continuous learning and optimization, specifically including: Continuously record the dynamic warning level coefficient of each time; If K1, K2, K3, K4, K5 and K6 do not exceed the preset size under the same state sequence number Y, the dynamic warning level coefficients K1, K2, K3, K4, K5 and K6 will not be adjusted.

6. A ring-shaped cross-belt sorting machine error-correcting system, characterized in that: The system is used to implement the method according to any one of claims 1 to 5, and the system comprises: Deploy sensor modules to pre-set sensors at key locations on the sorter and start data collection; Real-time transmission module, used to transmit the data collected by sensors to the central control system using a pre-set high-speed network; A data processing module is used to set a pre-processing software to perform online estimation and analysis on the data collected by the central control system to form the first, second and third maximum fluctuation margins; The early warning identification module is used to obtain data when all objects fall, and form a dynamic early warning level coefficient according to the relationship between the total number of transported objects per unit time and the first maximum fluctuation margin, the second maximum fluctuation margin and the third maximum fluctuation margin; Intelligent response module, used to adjust the dynamic warning level coefficient so that the probability of an object falling is within a preset range; The optimization and upgrade module is used to record the dynamic warning level coefficient and minimize the adjustment of the dynamic warning level coefficient within the total number of the same transported objects per unit time through continuous learning and optimization.

7. A computer-readable storage medium storing computer program instructions, characterized in that: The computer program instructions implement the method according to any one of claims 1 to 5 when executed by a processor.

8. An electronic device, comprising a memory and a processor, characterized in that: The memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method according to any one of claims 1-5.

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