Production monitoring system and method applied to electronic detonator assembly

Abstract

The invention discloses a production monitoring system and method applied to electronic detonator assembly, and relates to the technical field of electronic detonator assembly. The method comprises the steps of resistance value collection, capacitance value measurement, chip data verification and sealing performance and welding firmness detection. And the first ratio is calculated and compared with a preset evaluation value, and the single-point assembly condition is determined. And generating a parameter distribution curve by adopting a moving average method, and counting the number of alarm condition items to determine the assembly abnormality level. And calculating a second difference absolute value according to the acquisition period and the parameter variation, and determining a monitoring point position mark type. And dividing production line areas according to mark types, calculating an actual assembly evaluation value by adopting a weighted average method, comparing the actual assembly evaluation value with a preset standard, and judging the operation mode of the assembly equipment. According to the method, the electronic detonator assembly process can be comprehensively monitored, assembly abnormity can be found in time, and the production efficiency and the product quality are improved. By adjusting the operation mode of the equipment, the assembly process can be optimized, and the product is ensured to meet the standard.

Description

Technical Field

[0001] The present invention belongs to the technical field of electronic detonator assembly, and specifically relates to a production monitoring system and method applied to the assembly of electronic detonators. Background Art

[0002] With the wide application of electronic detonators in blasting engineering, the requirements for their safety and reliability are becoming increasingly strict, which poses higher requirements for quality control in the production process.

[0003] In the traditional production process of electronic detonators, manual inspection and sampling inspection are often relied on. This method is not only inefficient, but also difficult to comprehensively cover all products, and potential quality problems are easily missed. At the same time, due to the precision and complexity of electronic detonators, their various parameters and quality indicators require high-precision measurement and real-time monitoring to ensure the stability and reliability of products.

[0004] In addition, with the development of industrial automation and intelligent technologies, more and more enterprises are seeking to improve production efficiency and product quality through automated equipment and intelligent monitoring systems. However, for products such as electronic detonators with high precision and high safety requirements, existing production monitoring methods and systems often cannot meet their special needs.

[0005] Therefore, this invention patent proposes a production monitoring method applied to the assembly of electronic detonators, aiming to achieve comprehensive monitoring and intelligent control of the production process of electronic detonators through steps such as high-precision data acquisition, determination of single-point assembly situation, generation of parameter distribution curves and determination of abnormal levels, classification and marking of monitoring points, and determination of regional division and operation modes of assembly equipment, so as to improve production efficiency and product quality, and reduce production costs and safety risks. Summary of the Invention

[0006] In order to overcome the above-mentioned disadvantages and deficiencies of the prior art, the first object of the present invention is to provide a production monitoring system applied to the assembly of electronic detonators; the second object of the present invention is to provide a production monitoring method applied to the assembly of electronic detonators.

[0007] The first object of the present invention adopts the following technical solution:

[0008] A production monitoring system applied to the assembly of electronic detonators, comprising

[0009] A data acquisition module: responsible for collecting various parameters and quality indicators of electronic detonators and transmitting the data to subsequent modules;

[0010] A single-point assembly situation determination module: used to receive the data transmitted by the data acquisition module, calculate the ratio and compare it with a preset value to judge the single-point assembly situation;

[0011] Parameter distribution curve generation and abnormal level determination module: used to determine the result of the module according to the single-point assembly situation, generate a parameter distribution curve, and determine the assembly abnormal level;

[0012] Monitoring point classification and marking module: used to classify and mark monitoring points according to the parameter change amount and preset criteria;

[0013] Area division and assembly equipment operation mode determination module: used to divide areas according to the marking type of monitoring points, and calculate the assembly evaluation value to determine the operation mode of the assembly equipment.

[0014] The second object of the present invention adopts the following technical solutions:

[0015] A production monitoring method applied to the assembly of electronic detonators has the following process:

[0016] Step 1, data acquisition: Measure the resistance value and capacitance value of the electronic detonator through a high-precision resistance-capacitance tester, verify the accuracy of the data written into the chip in real time through the CRC check module, perform a sealing detection using a helium mass spectrometer leak detector, and perform a welding strength detection through an ultrasonic welding strength detection device;

[0017] Step 2, determine the single-point assembly situation: Calculate the first ratio R1 of a single actual assembly parameter to the quality index, and compare it with the preset first evaluation value R10. When R1 > R10, it is determined that the monitoring point meets the single alarm condition;

[0018] Step 3, generate a parameter distribution curve and determine the assembly abnormal level: Use the moving average method to generate a parameter distribution curve, count the number N of items that meet the single alarm condition, divide the assembly abnormal level into level 1 or level 2 according to the N value, and trigger corresponding maintenance or alarm signals;

[0019] Step 4, monitoring point classification and marking: Calculate the absolute value of the second difference based on the parameter change amount within the acquisition period, and mark it as a normal type, under-assembly type, or over-assembly type after comparing it with the preset second evaluation value;

[0020] Step 5, area division and determination of the assembly equipment operation mode: Divide the production line area according to the marking type, calculate the actual assembly evaluation value V through the weighted average method, and compare it with the preset standard evaluation value V0 to adjust the operation mode of the assembly equipment.

[0021] Preferably, in step 1, the CRC check module checks the written data M(X) through the generating polynomial G(X), and the calculation formula is:

[0022] CRC = X n M(X) mod G(X);

[0023] Among them, M(X) is the data polynomial to be sent; G(X) is the generating polynomial; n is the highest power of G(X); if the comparison result is consistent, it indicates that the data written into the chip is accurate; if not, relevant information is recorded and an error prompt is issued; and the generated check code is compared with the check code in the chip;

[0024] The helium mass spectrometer leak detector calculates the leak rate by detecting the helium ion current, and the calculation formula is: Among them, Q is the leak rate; K is the instrument constant; ΔP is the pressure change before and after detection; Δt is the detection time;

[0025] The working frequency of the ultrasonic welding strength detection equipment is 20 kHz - 100 kHz, and the welding firmness is judged by the amplitude and phase change of the reflected wave.

[0026] Preferably, the calculation formula for the first ratio R1 in step two is:

[0027]

[0028] P is a single actual assembly parameter, including resistance value and capacitance value; Q is the actual quality index monitored by the quality detection sensor, including the leak rate corresponding to the sealing performance and the ultrasonic reflection index corresponding to the welding firmness;

[0029] The preset first evaluation value is R10. When R1 ≤ R10, it is determined that the single-point assembly meets the standard; when R1 > R10, a single alarm condition is triggered and the station number and parameters are recorded.

[0030] Preferably, the calculation formula for the moving average method in step three is: Among them, k is the time window size of the moving average, which is set according to the actual production situation; R j is the time series resistance value, which is used to generate the parameter distribution curve;

[0031] The determination standard for the assembly abnormality level is: by traversing each monitoring point, the number N of items that meet the single alarm condition is counted; when N = 0, it is determined that the actual overall assembly situation meets the preset standard, indicating that the parameters and quality indicators of each assembly station in the assembly process of the entire production line match well and the production process is stable;

[0032] When N ≠ 0 and N = 1, it is determined that the actual overall assembly situation does not meet the preset standard, and the assembly abnormality level is grade one. At this time, the system sends a first maintenance signal, and the signal is transmitted to the controller of the relevant assembly equipment through the communication network on the production line. After receiving the signal, the controller records the equipment number and the abnormality information, and prompts the maintenance personnel to repair the equipment;

[0033] When N≠0 and N>1, it is determined that the actual overall assembly situation does not meet the preset standard, the assembly abnormality level is secondary, the system emits a first alarm signal, and on the one hand, this signal sounds an alarm at the production site through an audible and visual alarm device.

[0034] Preferably, the calculation formula for the parameter change amount ΔP in step four is: ΔP = P i -P i-1 ; the calculation formula for the absolute value of the second difference |ΔD| is: |ΔD| = |ΔP - ΔP0|;

[0035] The preset second evaluation value is D0. When |ΔD| ≤ D0, mark this monitoring point as the normal type, indicating that this assembly station is within the normal fluctuation range in terms of parameter changes;

[0036] When |ΔD| > D0 and ΔP < ΔP0, mark this monitoring point as the under-assembly type, indicating that there may be a situation of insufficient assembly during the assembly process, including insufficient welding strength resulting in parameter changes less than the standard value;

[0037] When |ΔD| > D0 and ΔP > ΔP0, mark this monitoring point as the over-assembly type, indicating that there may be a problem of over-assembly, including the capacitance value exceeding the normal change range due to assembly process problems.

[0038] Preferably, in step five, the region division adopts a connected region division algorithm based on graph theory, and adjacent assembly stations with the same marked type are divided into the same region; the calculation formula for the actual assembly evaluation value V is: where n1, n2, and n3 respectively represent the number of assembly stations of the normal type, under-assembly type, and over-assembly type; ω1, ω2, and ω3 are the corresponding weights and satisfy ω1 + ω2 + ω3 = 1.

[0039] Preferably, the weights are set as w1 = 0.8, w2 = 0.1, w3 = 0.1; when V ≥ V0, it is determined that the equipment operation mode is normal, and when V < V0, it is adjusted to the adjusted operation mode, including increasing the welding current or reducing the assembly speed.

[0040] Preferably, the shutdown alarm signal triggered by the secondary abnormality in step three is executed in the following manner:

[0041] (1) Control the audible and visual alarm device to sound an alarm;

[0042] (2) Send a notice to the production management personnel through the network;

[0043] (3) Force the relevant assembly equipment to stop and enter the debugging process until manual reset.

[0044] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present invention are:

[0045] 1. The present invention ensures the accurate measurement of key parameters of electronic detonators (such as resistance value, capacitance value, sealing performance, welding firmness, etc.) by adopting advanced detection technologies such as high-precision resistance-capacitance testers, helium mass spectrometry leak detectors, and ultrasonic welding strength detection equipment. In particular, the use of CRC algorithm for detecting the accuracy of data written into the chip and helium mass spectrometry leak detector for realizing sealing detection greatly improves the quality control level of electronic detonators, thus ensuring the reliability and safety of the final products.

[0046] 2. The present invention determines whether the single-point assembly condition meets the standard by calculating the first ratio R1 and comparing it with the preset evaluation value; at the same time, the moving average method is used to generate a parameter distribution curve to determine the assembly abnormality level, and corresponding measures (such as sending a maintenance signal or alarm and stopping the machine) are taken according to different abnormality levels. In addition, the monitoring points are classified and marked by setting the acquisition period, calculating the parameter change amount, etc., and based on this, the area is divided, and the weighted average method is used to calculate the actual assembly evaluation value V to determine the operation mode of the assembly equipment. These measures work together to achieve real-time monitoring and dynamic adjustment of the production line, effectively preventing the occurrence of potential problems and improving the stability and efficiency of production.

[0047] 3. The present invention divides the production line into different areas by logically judging the marking types of each monitoring point and associating the spatial positions, and uses the connected region division algorithm based on graph theory to adjust the operation mode of the assembly equipment. This method not only helps to quickly locate and solve problems, but also can reasonably allocate resources according to the specific situation of under-assembly or over-assembly, such as increasing the welding current or slowing down the assembly speed, etc., further optimizing the production process. This refined management method can improve production efficiency, reduce unnecessary losses, and at the same time provide a scientific basis for managers, which is conducive to making more accurate decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0049] Figure 1 Shows a module diagram of a production monitoring system for electronic detonator assembly according to the present invention;

[0050] Figure 2 Shows a flowchart of a production monitoring method for electronic detonator assembly according to the present invention;

[0051] Figure 3The flowchart for the present invention to determine the single-point assembly situation is shown. Detailed implementation manners

[0052] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part rather than all of the embodiments of the present invention. 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.

[0053] In addition, the described features, structures or characteristics may be combined in any suitable manner in one or more exemplary embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the exemplary embodiments of the present disclosure. However, those skilled in the art will realize that the technical solutions of the present disclosure may be practiced omitting one or more of the specific details, or using other methods, components, steps, etc. In other cases, well-known structures, methods, implementations or operations are not shown or described in detail to avoid obscuring aspects of the present disclosure.

[0054] Embodiment 1:

[0055] Referring to Figure 1 as shown, the production monitoring system applied to the assembly of electronic detonators in this embodiment includes

[0056] A data acquisition module: responsible for collecting various parameters and quality indicators of electronic detonators and transmitting the data to subsequent modules.

[0057] A single-point assembly situation determination module: used to receive the data transmitted by the data acquisition module, calculate the ratio and compare it with a preset value to judge the single-point assembly situation.

[0058] A parameter distribution curve generation and abnormal level determination module: used to generate a parameter distribution curve and determine the assembly abnormal level according to the result of the single-point assembly situation determination module.

[0059] A monitoring point classification and marking module: used to classify and mark monitoring points according to the parameter change amount and preset criteria.

[0060] A region division and assembly equipment operation mode determination module: used to divide regions according to the marking types of monitoring points and calculate the assembly evaluation value to determine the operation mode of the assembly equipment.

[0061] The beneficial effects of this embodiment are as follows: comprehensively and accurately monitor the electronic detonator assembly process, judge the single-point assembly quality in real time, discover abnormalities in a timely manner through the parameter distribution curve, and classify and process them. At the same time, intelligently classify and monitor the points, optimize the area management, scientifically adjust the equipment operation, and improve the production efficiency and product qualification rate.

[0062] Embodiment 2:

[0063] Refer to Figure 2 As shown, the production monitoring method applied to the electronic detonator assembly in this embodiment has the following process:

[0064] Step 1: Data acquisition.

[0065] Selection of acquisition equipment: Select a high-precision resistance-capacitance tester to measure the resistance value and capacitance value of the electronic detonator. For example, a specific model of tester can be used, and its measurement accuracy can reach ±0.01%, which can meet the requirements for measuring the parameters of high-precision components such as electronic detonators.

[0066] Detection of the accuracy of data written into the chip: Through the verification module connected to the chip writing device, the data written into the chip is verified in real time. This verification module uses the CRC (Cyclic Redundancy Check) algorithm to generate a verification code while writing data into the chip and compares it with the verification code written into the chip.

[0067] The specific calculation formula is: CRC = X n M(X) mod G(X); where M(X) is the data polynomial to be sent; G(X) is the generating polynomial; n is the highest power of G(X). If the comparison result is consistent, it indicates that the data written into the chip is accurate; if not, record the relevant information and issue an error prompt.

[0068] Monitoring of quality indicators:

[0069] Sealing detection: Use a helium mass spectrometer leak detector to detect the sealing of the electronic detonator. Its principle is to use helium as a tracer gas, fill a certain pressure of helium into the electronic detonator, and then put the electronic detonator into the detection chamber of the helium mass spectrometer leak detector. If there is a leak in the electronic detonator, the helium will escape from the leak and be detected by the leak detector. The leak detector determines the leak rate by detecting the magnitude of the helium ion current. The specific calculation formula is: Where Q is the leak rate; K is the instrument constant; ΔP is the pressure change before and after detection; Δt is the detection time.

[0070] Welding firmness detection: Use ultrasonic welding strength detection equipment to emit ultrasonic waves to the welding part, and judge the welding firmness according to the reflection and transmission of ultrasonic waves at the welding part. The equipment uses an ultrasonic transducer to convert electrical energy into ultrasonic waves, and its frequency is usually between 20kHz and 100kHz. According to the amplitude and phase changes of the reflected wave, combined with the preset welding firmness standard curve, judge whether the welding is firm.

[0071] Step 2: Determine the single-point assembly situation.

[0072] S21: Calculate the first ratio.

[0073] Let the single actual assembly parameter monitored by the acquisition module be P (such as resistance value, capacitance value, etc.), and the actual quality index monitored by the quality detection sensor be Q (such as the leakage rate corresponding to the sealing performance, the ultrasonic reflection index corresponding to the welding firmness, etc.). Then the calculation formula for the first ratio R1 is:

[0074]

[0075] S22: Compare with the preset evaluation value.

[0076] The preset first evaluation value is R10.

[0077] When R1 ≤ R10, it indicates that the actual single-point assembly situation at this monitoring point meets the preset standard. This means that at this assembly station, the assembly parameters and quality indicators of the electronic detonator are within a reasonable matching range. For example, if the first ratio of the resistance value and the welding firmness is within the preset range, it means that the resistance assembly and the welding process cooperate well.

[0078] When R1 > R10, it is determined that the actual single-point assembly situation at this monitoring point does not meet the preset standard, and this monitoring point meets the single alarm condition. At this time, the system will record the number of this assembly station and the specific parameter values and quality index values for subsequent analysis.

[0079] Step 3: Generate a parameter distribution curve and determine the assembly abnormality level.

[0080] S31: Generate a parameter distribution curve.

[0081] Use the moving average method to process each single actual assembly parameter to generate a parameter distribution curve. For example, for the resistance value parameter, let the time series be t1, t2, t3,..., tn; the corresponding resistance values be R1, R2, R3,..., Rn. The calculation formula for the moving average value Ravg(i) at time ti is: Where k is the time window size of the moving average, which can be set according to the actual production situation. For example, k = 10 (indicating that the average value of the resistance values at the nearest 10 time points is taken). By calculating the moving average values at different time points, a resistance value distribution curve is plotted. Similarly, distribution curves of other parameters such as capacitance values can be generated.

[0082] S32. Determine the assembly anomaly level.

[0083] By traversing each monitoring point, count the number N of items that meet the single alarm condition. When N = 0, it is determined that the actual overall assembly situation meets the preset standard, indicating that the parameters and quality indicators of each assembly station in the entire production line match well during the assembly process, and the production process is stable. When N ≠ 0 and N = 1, it is determined that the actual overall assembly situation does not meet the preset standard, and the assembly anomaly level is the first level. At this time, the system sends out a first maintenance signal, and the signal is transmitted to the controller of the relevant assembly equipment through the communication network on the production line. After receiving the signal, the controller records the equipment number and the anomaly information, and prompts the maintenance personnel to repair the equipment.

[0084] When N ≠ 0 and N > 1, it is determined that the actual overall assembly situation does not meet the preset standard, and the assembly anomaly level is the second level. The system sends out a first alarm signal. On the one hand, this signal sounds an alarm at the production site through the sound and light alarm device, and on the other hand, it notifies the production management personnel through the network. At the same time, after receiving the signal, the controller of the relevant assembly equipment immediately controls the equipment to stop and enters the re - debugging process.

[0085] Step Four. Classify and mark the monitoring points.

[0086] S41. Set the acquisition period and obtain the parameter change amount.

[0087] The acquisition period T can be set according to the production rhythm and data analysis requirements. For example, it is set to T = 5 minutes. In each acquisition period, let the single actual assembly parameter in the previous period be P i-1 , and the single actual assembly parameter in this period be P i , then the calculation formula for the change amount ΔP of the single actual assembly parameter is: ΔP = P i - P i-1 .

[0088] S42. Calculate the absolute value of the second difference and determine the marking type.

[0089] Preset the change amount ΔP0 of the single standard assembly parameter. When the actual overall assembly situation in the target assembly area meets the preset standard or the assembly anomaly level is the first level, calculate the absolute value of the second difference |ΔD|, and its calculation formula is: |ΔD| = |ΔP - ΔP0|.

[0090] The preset second evaluation value is D0. When |ΔD| ≤ D0, mark this monitoring point as the normal type, indicating that this assembly station is within the normal fluctuation range in terms of parameter changes.

[0091] When |ΔD| > D0 and ΔP < ΔP0, mark this monitoring point as the under-assembly type, which means that there may be a situation of insufficient assembly during the assembly process, such as insufficient welding strength resulting in parameter changes less than the standard value.

[0092] When |ΔD| > D0 and ΔP > ΔP0, mark this monitoring point as the over-assembly type, and there may be a problem of over-assembly, such as the capacitance value exceeding the normal change range due to assembly process problems.

[0093] Step Five: Region Division and Determination of the Operating Mode of Assembly Equipment.

[0094] S51: Region Division.

[0095] The division module divides the production line into different regions according to the marked types of each monitoring point. For example, all assembly stations marked as the normal type are divided into the normal region, those marked as the under-assembly type are divided into the under-assembly region, and those marked as the over-assembly type are divided into the over-assembly region. The region division is achieved through logical judgment and spatial position association of the marked information of each assembly station. The specific algorithm can adopt the connected region division algorithm based on graph theory. Let the assembly stations be the nodes of the graph. If the marked types of two adjacent stations are the same, there is an edge between them. By traversing the graph, the connected nodes are divided into the same region, and the number of assembly stations involved in each region, that is, the actual area, is calculated.

[0096] S52: Integrate according to the marked types of each monitoring point, and use the weighted average method to calculate the actual assembly evaluation value V. Let the number of assembly stations of the normal type, under-assembly type, and over-assembly type be n1, n2, and n3 respectively, and the corresponding weights be ω1, ω2, ω3 (ω1 + ω2 + ω3 = 1, and the weights can be set according to the importance of different types in actual production, such as w1 = 0.8, w2 = 0.1, w3 = 0.1).

[0097] Then the calculation formula for the actual assembly evaluation value V is:

[0098]

[0099] The preset standard assembly evaluation value is V0. When V ≥ V0, determine that the actual operating mode of each assembly equipment is the normal operating mode, and the equipment continues to operate according to the established production parameters and rhythm.

[0100] When V < V0, it is determined that the actual operation mode of each assembly device is the adjustment operation mode. For example, if the proportion of the under-assembly area is relatively large, the assembly force parameter of the assembly device can be adjusted through the controller, such as increasing the welding current or pressure, etc., to improve the assembly quality; if the proportion of the over-assembly area is relatively large, the assembly speed can be appropriately slowed down to make the assembly process more precise. The adjustment process is implemented through the control software of the device. The control software sends instructions to the actuator of the device according to the calculated adjustment parameters to realize the adjustment of the device operation mode.

[0101] The beneficial effects of this embodiment are as follows: This production monitoring method collects data through high-precision devices and can accurately control the assembly parameters and quality indicators. The logic of each step is tight, and it can timely detect assembly abnormalities, divide the problem areas, and adjust the device operation according to the evaluation value. It not only ensures the assembly quality of electronic detonators, but also improves production efficiency, reduces the defective rate, and realizes intelligent production management.

[0102] The weights of the present invention are used to measure the influence degree of different factors or variables on a certain result or decision. The definition of weight refers to the numerical value assigned to each factor when comparing and evaluating multiple factors to reflect its importance or priority. These weights can be determined according to specific situations and requirements, and are usually formulated and confirmed jointly by professionals or relevant stakeholders. By reasonably setting the weights, it can help the program or system make more accurate decisions or predictions.

[0103] As mentioned above, only the preferred specific embodiments of the present invention are given, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent replacements or changes, and should be covered within the protection scope of the present invention.

[0104] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to only the specific embodiments. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments to better explain the principle and practical application of the present invention, so that those skilled in the art can better understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A production monitoring system applied to the assembly of electronic detonators, characterized in that, The system includes a data acquisition module: responsible for collecting various parameters and quality indicators of electronic detonators and transmitting the data to subsequent modules; a single-point assembly condition determination module: used to receive the data transmitted by the data acquisition module, calculate the ratio and compare it with a preset value to determine the single-point assembly condition; a parameter distribution curve generation and abnormal level determination module: used to generate a parameter distribution curve and determine the assembly abnormal level according to the result of the single-point assembly condition determination module; a monitoring point classification and marking module: used to classify and mark monitoring points according to the parameter change amount and preset standards; a region division and assembly equipment operation mode determination module: used to divide regions according to the marking types of monitoring points and calculate the assembly evaluation value to determine the operation mode of the assembly equipment.

2. A production monitoring method applied to the assembly of electronic detonators, for implementing the production monitoring system applied to the assembly of electronic detonators as described in claim 1, characterized in that, The method flow is as follows: Step 1, data acquisition: Measure the resistance value and capacitance value of the electronic detonator through a high-precision resistance-capacitance tester, verify the accuracy of the data written into the chip in real time through a CRC check module, perform a sealing detection using a helium mass spectrometer leak detector, and perform a welding firmness detection through an ultrasonic welding strength detection device; Step 2, determine the single-point assembly condition: Calculate the first ratio R1 of a single actual assembly parameter to the quality indicator, and compare it with the preset first evaluation value R10. When R1 > R10, it is determined that the monitoring point meets the single alarm condition; Step 3, generate a parameter distribution curve and determine the assembly abnormal level: Use the moving average method to generate a parameter distribution curve, count the number N of items that meet the single alarm condition, divide the assembly abnormal level into level 1 or level 2 according to the N value, and trigger corresponding maintenance or alarm signals; Step 4, monitoring point classification and marking: Calculate the absolute value of the second difference based on the parameter change amount within the acquisition period, and mark it as a normal type, under-assembly type, or over-assembly type after comparing it with the preset second evaluation value; Step 5, region division and determination of the assembly equipment operation mode: Divide the production line region according to the marking type, calculate the actual assembly evaluation value V through the weighted average method, and compare it with the preset standard evaluation value V0 to adjust the operation mode of the assembly equipment.

3. The production monitoring method applied to the assembly of electronic detonators according to claim 2, characterized in that, In step 1, the CRC check module checks the data M(X) written in through the generating polynomial G(X), and the calculation formula is: CRC = X n M(X) mod G(X); where M(X) is the data polynomial to be transmitted; G(X) is the generating polynomial; n is the highest power of G(X); if the comparison result is consistent, it indicates that the data written into the chip is accurate; if not, record the relevant information and issue an error prompt; and compare the generated check code with the check code in the chip; The helium mass spectrometer leak detector calculates the leak rate by detecting the helium ion current, and the calculation formula is as follows: where Q is the leak rate; K is the instrument constant; ΔP is the pressure change before and after detection; Δt is the detection time; The working frequency of the ultrasonic welding strength detection device is 20 kHz - 100 kHz, and the welding firmness is judged by the amplitude and phase changes of the reflected wave.

4. The production monitoring method applied to the assembly of electronic detonators according to claim 2, wherein, The calculation formula of the first ratio R1 in step 2 is: P is a single actual assembly parameter, including the resistance value and capacitance value; Q is the actual quality indicator monitored by the quality detection sensor, including the leakage rate corresponding to the sealing performance and the ultrasonic reflection index corresponding to the welding firmness; The preset first evaluation value is R10. When R1 ≤ R10, it is determined that the single-point assembly meets the standard; when R1 > R10, a single alarm condition is triggered and the station number and parameters are recorded.

5. The production monitoring method applied to the assembly of electronic detonators according to claim 2, characterized in that, The calculation formula of the moving average method in the third step is as follows: where k is the time window size of the moving average, which is set according to the actual production situation; R j is the time series resistance value, which is used to generate the parameter distribution curve; The determination standard for the assembly abnormality level is as follows: By traversing each monitoring point, the number N of items that meet the single alarm condition is counted; when N = 0, it is determined that the actual overall assembly situation meets the preset standard, indicating that the parameters of each assembly station in the assembly process of the entire production line match well with the quality indicators and the production process is stable; When N ≠ 0 and N = 1, it is determined that the actual overall assembly situation does not meet the preset standard, and the assembly abnormality level is grade one. At this time, the system sends out a first maintenance signal, and the signal is transmitted to the controller of the relevant assembly equipment through the communication network on the production line. After receiving the signal, the controller records the equipment number and the abnormality information, and prompts the maintenance personnel to repair the equipment; When N ≠ 0 and N > 1, it is determined that the actual overall assembly situation does not meet the preset standard, and the assembly abnormality level is grade two. The system sends out a first alarm signal, and on the one hand, this signal sounds an alarm at the production site through an audible and visual alarm device.

6. The production monitoring method applied to the assembly of electronic detonators according to claim 2, characterized in that, The calculation formula for the parameter change amount ΔP in the fourth step is: ΔP = P i - P i-1 ; the calculation formula for the absolute value of the second difference |ΔD| is: |ΔD| = |ΔP - ΔP0|; The preset second evaluation value is D0. When |ΔD| ≤ D0, this monitoring point is marked as the normal type, indicating that the assembly station is within the normal fluctuation range in terms of parameter changes; When |ΔD| > D0 and ΔP < ΔP0, this monitoring point is marked as the under-assembly type, indicating that there may be a situation of insufficient assembly in the assembly process, including insufficient welding strength resulting in parameter changes less than the standard value; When |ΔD| > D0 and ΔP > ΔP0, this monitoring point is marked as the over-assembly type, indicating that there may be a problem of over-assembly, including the capacitance value exceeding the normal change range due to assembly process problems.

7. The production monitoring method applied to the assembly of electronic detonators according to claim 2, wherein In step five, the area division adopts a connected area division algorithm based on graph theory, and the assembly workstations that are adjacent and have the same marking type are divided into the same area; the calculation formula for the actual assembly evaluation value V is as follows: where n1, n2, and n3 respectively represent the number of assembly workstations of normal type, under-assembly type, and over-assembly type; ω1, ω2, and ω3 are the corresponding weights and satisfy ω1 + ω2 + ω3 = 1.

8. The production monitoring method applied to the assembly of electronic detonators according to claim 7, characterized in that, The weights are set as w1 = 0.8, w2 = 0.1, w3 = 0.1; when V ≥ V0, it is determined that the equipment operation mode is normal, and when V < V0, it is adjusted to the adjustment operation mode, including increasing the welding current or reducing the assembly speed.

9. The production monitoring method applied to the assembly of electronic detonators according to claim 2, wherein, The shutdown alarm signal triggered by the secondary abnormality in step three is executed in the following manner: (1) Control the audible and visual alarm device to sound an alarm; (2) Send a notice to the production management personnel through the network; (3) Force the relevant assembly equipment to stop and enter the debugging process until manual reset.

Images