A digital production method and system for air conditioner compressor liquid accumulator

Abstract

The embodiment of the present application discloses a digital production method and system for an air-conditioning compressor liquid reservoir, which belongs to the field of spinning prediction technology and solves the problem of low production efficiency due to untimely regulation of existing spinning. Receive the first start-up instruction of the spinning machine and generate a product code, and use the spinning machine to perform spinning and stamping shaping on the outlet end of the barrel blank; receive the spinning data collection and processing instruction, and fit and adjust the collected data; obtain the spinning machine operation data, and generate the spinning machine operation fitting curves corresponding to different operating conditions based on the spinning machine operation data; pre-regulate the spinning machine based on the adjusted actual fitting curve and the spinning machine operation fitting curve; receive the spinning grooving instruction and the second start-up instruction of the spinning machine, and produce the remaining part of the liquid reservoir; obtain the production information corresponding to different production links of the liquid reservoir, and map the production information with the product code to trace the production process of the liquid reservoir.

Description

Technical Field

[0001] This application relates to the technical field of spinning prediction, and particularly to a digital production method and system for an accumulator of an air-conditioning compressor. Background Art

[0002] An accumulator is an important component in a refrigeration system. Its main function is to store liquid refrigerant and ensure that the moisture in the refrigerant is effectively removed. In the field of accumulator manufacturing, in order to improve the sealing performance of the accumulator, optimize fluid flow, enhance structural strength, improve production efficiency, and meet specific design requirements, the spinning process at the end of the accumulator plays a crucial role.

[0003] In the prior art, since there are many factors involved in the spinning process, and the interaction between different factors is complex and difficult to accurately quantify. Therefore, even for experienced technicians, it is very difficult to accurately predict the spinning accuracy only based on experience. In addition, the fluctuation of the spinning accuracy will also affect the overall performance of the accumulator. As an important part of the fluid system, the spinning accuracy at the end of the accumulator is directly related to the fluid tightness, stability, and durability. If the spinning accuracy does not meet the standard, it may lead to problems such as fluid leakage and pressure fluctuation, thereby affecting the normal operation of the entire fluid system.

[0004] In the existing accumulator spinning technology, the accuracy is often found not to meet the standard in the inspection link after spinning, and then key parameters such as the spinning angle and pressure are adjusted, resulting in higher production costs and lower production efficiency. Summary of the Invention

[0005] The embodiments of this application provide a digital production method and system for an accumulator of an air-conditioning compressor, which are used to solve the following technical problems: In the existing accumulator spinning technology, the accuracy is often found not to meet the standard in the inspection link after spinning, and then key parameters such as the spinning angle and pressure are adjusted, resulting in higher production costs and lower production efficiency.

[0006] The embodiments of this application adopt the following technical solutions:

[0007] An embodiment of the present application provides a digital production method for an air-conditioning compressor liquid receiver, including obtaining the information that the liquid receiver stock preparation is completed and generating a product code; wherein, the liquid receiver stock preparation includes a cylinder blank, an intake straight pipe, an outlet elbow pipe, a middle partition board, and a filter screen; receiving a first start instruction of a spinning machine, and performing spinning processing and stamping and shaping processing on the outlet end of the cylinder blank through the spinning machine; receiving a spinning data acquisition and processing instruction, dividing the collected radian angle data of the spinning and closing of the liquid receiver end into intervals, and fitting the radian angle data based on the radian angle data differences respectively corresponding to different intervals to obtain an actual fitting curve, and adjusting the actual fitting curve based on the shell data of the liquid receiver; obtaining the operation data of the spinning machine, and generating a spinning machine operation fitting curve respectively corresponding to different operation conditions based on the operation data of the spinning machine; performing advance regulation on the spinning machine based on the adjusted actual fitting curve and the spinning machine operation fitting curve; in the case that the structural data of the outlet end conforms to the first preset structural data, receiving a spinning grooving instruction and a second start instruction of the spinning machine to produce the remaining part of the liquid receiver; obtaining the production information respectively corresponding to different production links of the liquid receiver, and mapping the production information respectively corresponding to different production links with the product code, so as to trace the production process of the liquid receiver through the mapping relationship.

[0008] In the embodiment of the present application, through interval division and by using fitting technology to extract continuous and smooth curves from discrete data points, the changing trend of the radian angle of the spinning and closing is more intuitively reflected. Secondly, the embodiment of the present application introduces shell data, making the adjusted fitting curve closer to the actual production situation, reducing the prediction error caused by shell differences, and improving the accuracy of future spinning prediction. In addition, the embodiment of the present application generates fitting curves under different operation conditions based on the operation data of the spinning machine, which can comprehensively reflect the performance characteristics of the spinning machine. Through prediction, the spinning process is adjusted before production to avoid errors and waste in the production process and improve production efficiency. In the embodiment of the present application, all production information of the liquid receiver production process is stored through the product code, and the production information can be traced, thereby providing more comprehensive production monitoring and management capabilities.

[0009] In an implementation manner of the present application, the production information corresponding to different production links is mapped to the product code, so as to trace the production process of the liquid storage device through the mapping relationship, specifically including: uploading the production information to the blockchain; based on the data type of the production information, dividing the production information into ordinary information and sensitive information, and performing homomorphic encryption on the sensitive information to obtain encrypted sensitive information; performing hash calculation on the encrypted sensitive information and the ordinary information to generate a hash value uniquely corresponding to the production information of the current production link; generating a processing information code corresponding to different production links of the liquid storage device based on the production information and the hash value; based on the production sequence, constructing a connection network between adjacent production links, and associating the processing information codes corresponding to different production links based on the connection network to construct a relationship network corresponding to the processing information code; mapping the relationship network to the product code, so as to trace the liquid storage device product through the mapping relationship.

[0010] In an implementation manner of the present application, the arc angle data of the end spinning and necking of the liquid storage device collected is divided into intervals, and the arc angle data is fitted based on the arc angle data difference corresponding to different intervals to obtain an actual fitting curve, specifically including: obtaining the arc angle data of the spinning and necking through angle sensors arranged at different positions of the end of the liquid storage device; dividing the arc angle data of the spinning and necking into multiple first intervals according to the shape of the spinning and necking at the end of the liquid storage device; obtaining the adjacent change difference of the arc angle data within the first interval, determining an interval connection point based on the adjacent change difference, and dividing the first interval into multiple second intervals based on the interval connection point; determining the interval arc difference corresponding to each of the multiple second intervals, and performing different-order polynomial matching on the multiple second intervals based on the interval arc difference; performing fitting based on the different-order polynomials corresponding to the multiple second intervals and the arc angle data of the spinning and necking corresponding to the multiple second intervals to obtain an actual fitting curve.

[0011] In an implementation manner of the present application, the actual fitting curve is adjusted based on the shell data of the liquid storage device, specifically including: obtaining the shell thickness in the shell data, and determining a first adjustment coefficient based on the difference between the shell thickness and the reference shell thickness; constructing a clamping error detection model based on the relationship between the historical shell clamping vertical angle and the historical fitting curve adjustment coefficient in the historical shell data; obtaining the shell clamping vertical angle in the shell data, inputting the shell clamping vertical angle into the clamping error detection model to obtain a second adjustment coefficient; adjusting the actual fitting curve based on the first adjustment coefficient and the second adjustment coefficient.

[0012] In an implementation manner of the present application, the operating data of the spinning machine is obtained, and the spinning machine operating fitting curves corresponding to different operating conditions are generated based on the operating data of the spinning machine, specifically including: obtaining the operating data of the spinning machine; wherein, the operating data at least includes temperature, pressure, and rotational speed; fitting the operating data through a statistical method to obtain a set of fitting curves corresponding to different operating conditions; normalizing the operating data of the spinning machine and the abscissa of the set of fitting curves; determining a reference curve in the set of fitting curves, comparing the other curves in the set of fitting curves with the reference curve, and determining the relative ordinate value corresponding to the same reference point as the reference curve among the other curves; fitting based on the relative ordinate value and the data after normalization processing to obtain the spinning machine operating fitting curves corresponding to different operating conditions of the spinning machine.

[0013] In an implementation manner of the present application, the spinning machine is pre-regulated based on the adjusted actual fitting curve and the spinning machine operating fitting curve, specifically including: extracting key parameters from the adjusted actual fitting curve; wherein, the extracted features at least include one of the curve slope, curve curvature, and curve extreme points; inputting the extracted key parameters and the spinning machine operating fitting curves corresponding to different operating conditions into a spinning prediction model to output a predicted fitting curve corresponding to a future time period through the spinning prediction model; determining the first contribution value of different operating data features to the predicted fitting curve corresponding to the future time period through the SHAP algorithm, and drawing a feature global interpretation diagram based on the contribution value; constructing a local linear model through the LIME algorithm, determining the second contribution value of each different operating data feature to the predicted fitting curve corresponding to the future time period through the weight of the local linear model, and drawing a feature local interpretation diagram based on the second contribution value; constructing a future spinning prediction fitting curve based on the feature global interpretation diagram and the feature local interpretation diagram, comparing the future spinning prediction fitting curve with the target curve, and pre-regulating the spinning machine based on the comparison result.

[0014] In an implementation manner of the present application, the future spinning prediction fitting curve is compared with the target curve, and the spinning at the end of the liquid storage device is adjusted in advance based on the comparison result. Specifically, it includes: comparing the future spinning prediction fitting curve with the target curve to determine the error points between the two, and dividing the future spinning prediction fitting curve into multiple sections based on the error points; determining the error type based on the error values corresponding to the multiple sections respectively; wherein the error type includes position error, shape error and dimension error; inputting the error type and the error value into a preset error adjustment model, and outputting the corresponding spinning machine adjustment strategy through the preset error adjustment model; obtaining the spinning processing error threshold at the end of the liquid storage device, comparing the data within the section with the spinning processing error threshold to determine the threshold reference point; determining the error adjustment duration based on the distance between the threshold reference point and the initial point of the section, and determining the adjustment time point before the initial point of the section based on the adjustment duration, so as to implement the spinning machine adjustment strategy at the adjustment time point.

[0015] In an implementation manner of the present application, the error type is determined based on the error values corresponding to the multiple sections respectively. Specifically, it includes: generating an error value fluctuation sequence corresponding to each section based on the error value data within the section; determining the regional extreme points in the error value fluctuation sequence, and determining the minimum value in the sequence after the regional extreme points, and constructing an error detection sequence based on the minimum value; detecting the regional extreme points based on the error detection sequence, and constructing a reference sequence based on the regional extreme points that meet the detection conditions; determining the characteristic points of the reference sequence, and determining the sequence floating mode of the reference sequence based on the characteristic points; constructing a sequence floating mode set based on the sequence floating modes corresponding to the multiple sections respectively, and matching the sequence floating mode set with a preset error type library to determine the error type; wherein the preset error type library includes multiple floating mode sets, and also includes the error types corresponding to the multiple floating mode sets respectively.

[0016] In an implementation manner of the present application, when the structural data of the air outlet end conforms to the first preset structural data, a spinning grooving instruction and a second start instruction for the spinning machine are received to produce the remaining part of the liquid storage container. Specifically, it includes: when the structural data of the air outlet end conforms to the first preset structural data, welding the air outlet elbow to the air outlet end; installing the middle partition along the axial direction of the cylinder blank to the first preset position; receiving the first spinning machine grooving instruction, and grooving the outer wall of the cylinder blank corresponding to the first preset position through a grooving machine, so that the outer wall of the cylinder blank bulges towards the inside of the cylinder along the radial direction of the cylinder to axially limit the middle partition; installing the filter screen along the axial direction of the cylinder blank to the second preset position; receiving the second spinning machine grooving instruction, and grooving the outer wall of the cylinder blank corresponding to the second preset position through a grooving machine, so that the outer wall of the cylinder blank bulges towards the inside of the cylinder along the radial direction of the cylinder to axially limit the filter screen; receiving the second start instruction for the spinning machine, and performing spinning processing on the air inlet end of the cylinder blank through the spinning machine. When the structural data of the air inlet end conforms to the second preset structural data, welding the air inlet straight pipe to the air inlet end to realize the digital production of the liquid storage container of the air conditioner compressor.

[0017] The embodiment of the present application provides a digital production system for a liquid storage container of an air conditioner compressor, which is characterized in that the system includes a liquid storage container stock preparation unit, a spinning unit, a spinning data acquisition and processing unit, a remaining part production unit, and a tracing unit; the liquid storage container stock preparation unit is used to obtain the liquid storage container stock preparation completion information and generate a product code; wherein, the liquid storage container stock preparation includes a cylinder blank, an air inlet straight pipe, an air outlet elbow, a middle partition, and a filter screen; the spinning unit is used to receive the first start instruction for the spinning machine to start the spinning machine to perform spinning processing and stamping and shaping processing on the air outlet end of the cylinder blank; the spinning data acquisition and processing unit is used to receive the spinning data acquisition and processing instruction, divide the collected radian angle data of the spinning and closing of the end of the liquid storage container into intervals, and based on the difference in radian angle data corresponding to different intervals, fit the radian angle data to obtain an actual fitting curve, and based on the shell data of the liquid storage container, adjust the actual fitting curve; the spinning data acquisition and processing unit is also used to obtain the operating data of the spinning machine, generate a spinning machine operating fitting curve corresponding to different operating conditions based on the operating data of the spinning machine, and perform advance control on the spinning machine based on the adjusted actual fitting curve and the spinning machine operating fitting curve; the remaining part production unit is used to receive the spinning grooving instruction and the second start instruction for the spinning machine to produce the remaining part of the liquid storage container when the structural data of the air outlet end conforms to the first preset structural data; the tracing unit is used to obtain the production information corresponding to different production links of the liquid storage container, map the production information corresponding to different production links to the product code, so as to trace the production process of the liquid storage container through the mapping relationship.

[0018] The above at least one technical solution adopted in the embodiments of the present application can achieve the following beneficial effects: Through interval division and by using fitting technology to extract continuous and smooth curves from discrete data points, the embodiments of the present application can more intuitively reflect the changing trend of the arc angle during spinning necking. Secondly, by introducing shell data in the embodiments of the present application, the adjusted fitting curve is closer to the actual production situation, reducing the prediction error caused by shell differences and improving the accuracy of future spinning predictions. In addition, based on the operation data of the spinning machine, the embodiments of the present application generate fitting curves under different operating conditions, which can comprehensively reflect the performance characteristics of the spinning machine. Through prediction, the spinning process can be adjusted before production to avoid errors and waste during production and improve production efficiency. By storing all production information of the accumulator production process through product coding, the embodiments of the present application can trace the production information, thereby providing more comprehensive production monitoring and management capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. In the drawings:

[0020] Figure 1 It is a flowchart of a digital production method for an air-conditioning compressor accumulator provided by an embodiment of the present application;

[0021] Figure 2 It is a processing flowchart of an accumulator provided by an embodiment of the present application;

[0022] Figure 3 It is a schematic structural diagram of a digital production system for an air-conditioning compressor accumulator provided by an embodiment of the present application.

[0023] REFERENCE SIGNS:

[0024] 1: Cylinder body, 11: Gas outlet end, 111: Gas outlet elbow mounting hole, 112: Flattened part, 113: Arc part, 12: Gas inlet end, 121: Gas inlet straight pipe mounting hole, 21: Gas outlet elbow, 22: Gas inlet straight pipe, 23: Welding flange, 231: First limiting step, 232: Second limiting step; 3: Filter screen; 4: Middle partition board;

[0025] 200: Digital production system for an air-conditioning compressor accumulator, 201: Accumulator stock preparation unit, 202: Spinning unit, 203: Spinning data acquisition and processing unit, 204: Remaining part production unit, 205: Traceability unit. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] An embodiment of the present application provides a digital production method and system for an air-conditioning compressor liquid receiver.

[0027] In order to enable those skilled in the art to better understand the technical solutions in the present application, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0028] The technical solutions proposed in the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0029] Figure 1 It is a flowchart of a digital production method for an air-conditioning compressor liquid receiver provided by an embodiment of the present application. As Figure 1 shown, the end spinning prediction and control method for the liquid receiver includes the following steps:

[0030] Step 101: Obtain the information that the liquid receiver stock preparation is completed and generate a product code.

[0031] In an embodiment of the present application, the liquid receiver stock preparation includes a cylinder blank, an intake straight pipe, an outlet elbow pipe, a middle partition plate, and a filter screen. It will also include other components to be installed. The cylinder blank is cut to a fixed length from a pipe according to the specifications of the liquid receiver cylinder. The system determines that all the materials for producing the liquid receiver have been prepared by receiving the information that the liquid receiver stock preparation is completed.

[0032] In an embodiment of the present application, a two-dimensional code label is attached to the liquid receiver product to be produced currently, and identity data information is given to the liquid receiver. During the production process, data such as product information corresponding to each process, production photo and video, important process management information records, and visual inspection information will be connected to this two-dimensional code label, so as to realize the traceability of the entire production process of the liquid receiver through this two-dimensional code label.

[0033] Step 102: Receive the first start instruction of the spinning machine, and perform spinning treatment and stamping and shaping treatment on the outlet end of the cylinder blank through the spinning machine.

[0034] In an embodiment of the present application, a cut cylindrical blank is installed on the pressure main shaft of a spinning machine. After receiving the first start instruction of the spinning machine, the pressure main shaft of the spinning machine drives the cylindrical blank to rotate and cooperate with a spinning wheel to spin one end of the cylindrical blank into a spherical shape, making it the initial gas outlet end. In the embodiment of the present application, the pressure main shaft drives the cylindrical blank to move and contact with the bottom die, driving the upper die to act, cooperating with the bottom die to shape the initial gas outlet end to make the bottom of the initial gas outlet end flat, and punching an air outlet elbow installation hole for installing an air outlet elbow at the center of the initial gas outlet end, completing the processing of the gas outlet end.

[0035] Step 103: Receive a spinning data acquisition and processing instruction, divide the acquired radian angle data of the spinning and closing of the end of the liquid storage device into intervals, and based on the difference in radian angle data corresponding to different intervals, fit the radian angle data to obtain an actual fitting curve, and adjust the actual fitting curve based on the shell data of the liquid storage device.

[0036] In an embodiment of the present application, radian angle data of the spinning and closing is obtained through angle sensors arranged at different positions of the end of the liquid storage device. According to the spinning and closing shape of the end of the liquid storage device, the radian angle data of the spinning and closing is divided into multiple first intervals. The adjacent change difference of the radian angle data within the first interval is obtained, and based on the adjacent change difference, interval connection points are determined. Based on the interval connection points, the first interval is divided into multiple second intervals. The interval radian differences corresponding to the multiple second intervals are determined, and different-order polynomial matching is performed on the multiple second intervals based on the interval radian differences. Based on the different-order polynomials corresponding to the multiple second intervals and the radian angle data of the spinning and closing corresponding to the multiple second intervals, fitting is performed to obtain an actual fitting curve.

[0037] Specifically, during the process of the spinning machine processing the gas outlet end, angle sensors are arranged at different positions of the end of the liquid storage device, and the radian angle data at the spinning and closing position is collected through the sensors. For example, angle sensors are arranged at the upper, middle, and lower positions of the end of the liquid storage device respectively. According to the spinning and closing shape of the end of the liquid storage device, the collected radian angle data is divided into multiple first intervals.

[0038] Specifically, based on the acquired radian angle data of the spinning and closing, the shape characteristics of the spinning and closing are determined. Based on the shape characteristics of the spinning and closing, the preset number of first intervals is determined. Based on the radian angle data of the spinning and closing, a data density distribution diagram is drawn. The data density distribution diagram is detected to determine the density values between different data. The density values are compared with the preset region division density threshold, and reference points corresponding to the preset region division density threshold are determined on the density distribution diagram. The distances between the reference points are determined, and based on the distances, the widths of different first intervals are determined.

[0039] Specifically, a set of radian angle data of spin-forming necking is obtained, and statistical quantities are calculated according to the distribution trend of the data. For example, if the data shows a trend of increasing first and then decreasing, and there is an obvious inflection point. At this time, three preset first intervals can be initially set: the increasing interval before the inflection point, the turning interval at the inflection point, and the decreasing interval after the inflection point. The above spin-forming necking data is transformed into a density curve, which will have an obvious peak at the inflection point and is relatively smooth in the increasing and decreasing intervals.

[0040] Furthermore, in the embodiment of the present application, the peak points in the data density distribution diagram are determined through a density peak detection algorithm, and these peak points are compared with the density thresholds of the preset area division to determine the reference points corresponding to the thresholds. For example, in the density distribution diagram, the peak at the inflection point is determined as the peak point, the density threshold is set based on a preset ratio of the peak height, and the reference point corresponding to the threshold is found. Further, the differences between adjacent reference points are calculated, and through these differences, the widths of different first intervals can be determined more accurately, so as to achieve a fine division of the data distribution.

[0041] Furthermore, within each first interval, the adjacent change differences of the radian angle data are obtained, and the interval connection points are determined according to these differences. Then, based on these connection points, the first interval is further divided into multiple second intervals, so as to more finely analyze the data changes within each interval. For example, in the second first interval [30°, 45°], if the change differences between adjacent data points are large, for example, due to uneven spin-forming, there are adjacent data with large differences in the change from 30° to 45°, and the change rate from 30° to 35° is different from the change rate from 35° to 45°. According to these differences, the interval connection point 35° is determined, and thus this interval is divided into two second intervals [30°, 35°] and [35°, 45°].

[0042] Furthermore, for each second interval, the corresponding interval radian difference is determined, that is, the difference between the maximum radian angle and the minimum radian angle within the interval. Then, based on these interval radian differences, polynomial matching of different orders is performed for each second interval. For the second second interval [30°, 35°], if its interval radian difference is 5°, a first-order or second-order polynomial can be used for matching. If the data changes relatively smoothly, a first-order polynomial is matched; if the data changes relatively complexly, a higher-order polynomial is matched. The embodiment of the present application is provided with a polynomial matching data table, which includes different interval radian differences and also includes the polynomials corresponding to different radian differences. Based on the polynomials of different orders corresponding to each second interval and the radian angle data within these intervals, fitting is performed to obtain the actual fitting curve. The fitted curve can reflect the change trend of the radian angle of the spin-forming necking at the end of the liquid storage device.

[0043] In one embodiment of the present application, the shell thickness in the shell data is obtained, and based on the difference between the shell thickness and the reference shell thickness, a first adjustment coefficient is determined. Based on the relationship between the historical shell clamping vertical angle and the historical fitting curve adjustment coefficient in the historical shell data, a clamping error detection model is constructed. The shell clamping vertical angle in the shell data is obtained, and the shell clamping vertical angle is input into the clamping error detection model to obtain a second adjustment coefficient. Based on the first adjustment coefficient and the second adjustment coefficient, the actual fitting curve is adjusted.

[0044] Specifically, the shell thickness is extracted from the shell data, the shell thickness is compared with the reference shell thickness, the difference is calculated, and the first adjustment coefficient is determined according to the difference. This coefficient reflects the influence of the shell thickness on the fitting curve. For example, the thickness of the shell extracted from the database is 2.6 mm, the difference between the shell thickness and the reference thickness is calculated, that is, 2.5 mm - 2.0 mm = 0.6 mm. According to the preset difference adjustment rule, for every 0.1 mm increase in thickness, the adjustment coefficient increases by 0.05. Therefore, for a difference of 0.5 mm, the first adjustment coefficient is 0.6 / 0.1 × 0.05 = 0.3.

[0045] Using the relationship between the historical shell clamping vertical angle and the historical fitting curve adjustment coefficient in the historical shell data, a clamping error detection model is constructed. This clamping error detection model can predict the clamping error according to the clamping vertical angle. The training process of this model is to use the historical shell clamping vertical angle as the input sample, and the historical fitting curve adjustment coefficient corresponding to this input sample as the output sample to train a preset neural network model to obtain this clamping error detection model. The shell clamping vertical angle is extracted from the current shell data, and this angle is input into the clamping error detection model to obtain a predicted second adjustment coefficient. This coefficient reflects the influence of the clamping error on the fitting curve. For example, the clamping vertical angle extracted from the current shell data is 87°, and the clamping vertical angle is input into the clamping error detection model to obtain a clamping error of 0.03. According to the predicted clamping error setting rule, for example, for every 0.01 increase in clamping error, the adjustment coefficient decreases by 0.02. Therefore, for a clamping error of 0.03, the second adjustment coefficient is 0.03 / 0.01 × (-0.02) = -0.06.

[0046] Furthermore, combining the first adjustment coefficient and the second adjustment coefficient, the actual fitting curve is adjusted. The purpose of the adjustment is to make the fitting curve more conform to the actual shape and clamping situation of the shell. In the embodiment of the present application, the weighted summation method is used to combine the adjustment coefficients, and the weights are equal (i.e., each accounts for 50%), then the combined adjustment coefficient is (0.3 - 0.06) / 2 = 0.12.

[0047] Step 104: Obtain the operating data of the spinning machine and generate the spinning machine operating fitting curves corresponding to different operating conditions based on the operating data of the spinning machine.

[0048] In an embodiment of the present application, the operating data of the spinning machine is obtained; wherein, the operating data includes at least temperature, pressure, and rotational speed. The operating data is fitted by a statistical method to obtain a set of fitting curves corresponding to different operating conditions respectively. The operating data of the spinning machine and the abscissas of the set of fitting curves are normalized. A reference curve is determined from the set of fitting curves, and other curves in the set of fitting curves are compared with the reference curve, and the relative values of the ordinates corresponding to the same reference point as the reference curve are determined among the other curves. Based on the relative values of the ordinates and the data after normalization processing, a spinning machine operating fitting curve corresponding to different operating conditions of the spinning machine is obtained.

[0049] Specifically, the key parameters during the operation of the spinning machine are obtained, including temperature, pressure, and rotational speed. These data can be directly measured by sensors and stored in the data recording system. Statistical methods are used to fit these data to obtain a set of fitting curves corresponding to different operating conditions (such as different combinations of temperature, pressure, and rotational speed). Each curve represents the data trend under a specific combination of operating conditions. For example, for each temperature, a curve regarding pressure and rotational speed is fitted, for each pressure, a curve regarding temperature and rotational speed is fitted, and so on. According to the obtained series of fitting curves, a set of fitting curves is formed.

[0050] Furthermore, the operating data of the spinning machine and the abscissas of the set of fitting curves (such as pressure, temperature, or rotational speed) are normalized. A reference curve is determined from the set of fitting curves, and each curve in the set of fitting curves is compared with the reference curve to find the relative values of the ordinates (such as temperature or rotational speed) at the same reference point (such as the same normalized pressure value). For example, for the point with a normalized pressure value of 0.5, compare the temperature values of the reference curve and other curves at this point to obtain the relative values. These relative values reflect the relative changes in the performance of the spinning machine under different operating conditions. The calculated relative values and the data after normalization processing are used as new input data, and statistical methods are used to fit the new data to obtain the spinning machine operating fitting curve describing the performance changes of the spinning machine under different operating conditions.

[0051] Step 105: Based on the adjusted actual fitting curve and the spinning machine operating fitting curve, the spinning machine is pre-regulated.

[0052] In an embodiment of the present application, key parameters are extracted from the adjusted actual fitting curve; among them, the extracted features include at least one of the curve slope, curve curvature, and curve extreme points. The extracted key parameters and the corresponding fitting curves of the spinning machine under different operating conditions are input into the spinning prediction model to output the predicted fitting curve corresponding to the future time period through the spinning prediction model. The first contribution value of different operating data features to the predicted fitting curve corresponding to the future time period is determined by the SHAP algorithm, and a global feature interpretation graph is drawn based on the contribution value. A local linear model is constructed by the LIME algorithm, and the second contribution value of each different operating data feature to the predicted fitting curve corresponding to the future time period is determined by the weights of the local linear model, and a local feature interpretation graph is drawn based on the second contribution value. Based on the global feature interpretation graph and the local feature interpretation graph, a future spinning prediction fitting curve is constructed, and the future spinning prediction fitting curve is compared with the target curve, and the spinning machine is adjusted in advance based on the comparison result.

[0053] Specifically, key parameters are extracted from the adjusted actual fitting curve, and the extracted key parameters and the corresponding fitting curves of the spinning machine under different operating conditions are used as input features and input into the spinning prediction model, and the predicted fitting curve corresponding to the future time period is output through the spinning prediction model. Among them, the training process of the spinning prediction model is as follows: the key parameters of the historical fitting curve and the fitting curves corresponding to different historical operating conditions are used as input samples, and the historical fitting curve corresponding to the input sample is used as the output sample, and a pre-set neural network model is trained to obtain the spinning prediction model.

[0054] Furthermore, the first contribution value of different operating data features to the predicted fitting curve corresponding to the future time period is determined by the SHAP algorithm, that is, the importance degree of each feature to the prediction result. Then, a global feature interpretation graph is drawn based on these contribution values to intuitively show the overall impact of each feature on the prediction result. For example, the SHAP algorithm is used to interpret the prediction result of the model, and it is determined that the pressure feature has the largest contribution value to the prediction result, followed by the temperature feature. These contribution values are visualized as a global interpretation graph, where the length of each feature represents the size of its contribution value, and the color represents the positive or negative of the contribution value.

[0055] Furthermore, the LIME algorithm is used to construct a local linear model and determine the second contribution value of each different operating data feature to the predicted fitting curve corresponding to the future time period. Based on these contribution values, a feature local interpretation graph is plotted to visually display the local impact of each feature on the prediction result near a specific data point. For example, by interpreting the prediction result of the model under specific operating conditions using the LIME algorithm, it is found that the local contribution value of the temperature feature to the prediction result is the largest under this condition. These local contribution values are visualized as a local interpretation graph, where the length of each feature represents the magnitude of its contribution value near a specific data point, and the color represents the positive or negative of the contribution value.

[0056] Furthermore, based on the feature global interpretation graph and the feature local interpretation graph, the prediction numerical interpretation corresponding to the future spinning prediction fitting curve is determined. The global interpretation graph provides the overall impact of each feature on the prediction result, while the local interpretation graph provides the local impact of each feature near a specific data point. For example, in the global interpretation graph, the contribution value of the pressure feature to the prediction result is the largest, while in the local interpretation graph, the contribution value of the temperature feature to the prediction result is the largest under specific operating conditions. This means that generally, pressure is the key factor affecting the performance of the spinning machine, but under specific conditions, the impact of temperature is more significant. Therefore, it can be proposed that when optimizing the performance of the spinning machine, the pressure parameter should be adjusted first, and the temperature parameter should be particularly concerned under specific conditions.

[0057] Furthermore, if the global interpretation graph shows that a certain feature has a great impact on the prediction result, the weight of this feature can be increased in the spinning prediction model; if the local interpretation graph shows that a certain feature has a significant impact under certain specific conditions, a conditional judgment can be introduced in the spinning prediction model to dynamically adjust the weight of this feature. The optimized spinning prediction model will be able to more accurately reflect the performance change trend of the spinning machine in the future time period. The operating data is input into the optimized model to generate the future spinning prediction fitting curve.

[0058] In an embodiment of the present application, the future spinning prediction fitting curve is compared with the target curve to determine the error points between the two. Based on the error points, the future spinning prediction fitting curve is divided into multiple sections. Based on the error values corresponding to the multiple sections, the error type is determined; among them, the error type includes position error, shape error, and dimension error. The error type and the error value are input into a preset error regulation model, and the corresponding spinning machine regulation strategy is output through the preset error regulation model. The spinning processing error threshold at the end of the liquid storage device is obtained, and the data within the section is compared with the spinning processing error threshold to determine the threshold reference point. Based on the distance between the threshold reference point and the initial point of the section, the error adjustment duration is determined, and based on the adjustment duration, the regulation time point is determined before the initial point of the section to implement the spinning machine regulation strategy at the regulation time point.

[0059] Specifically, compare the future spinning prediction fitting curve with the target curve to determine the difference between them, that is, the error points. Based on these error points, divide the prediction fitting curve into multiple sections. Each section represents different error characteristics. Then, according to the error values of each section, determine the types of errors, such as position error, where the position of the curve shifts on the X-axis; shape error, where the shape of the curve does not match the target; and dimension error, where the dimension of the curve deviates on the Y-axis. Input the determined error types and error values into a preset error regulation model. The preset error regulation model outputs corresponding spinning machine regulation strategies according to the input error information, such as adjusting the spinning speed, pressure, or path. For example, if the model detects a position error, it will output a suggestion to increase or decrease the movement amount of the spinning head to adjust the position. The training process of this preset error regulation model is to use the historical error types and historical error values as input samples, and the regulation strategies corresponding to the input samples as output samples to train the preset neural network to obtain this preset error regulation model.

[0060] Furthermore, in the embodiment of the present application, a spinning processing error threshold is set. Compare the data in each section with this threshold to find the points that exceed the threshold. These points are called threshold reference points. Based on the distance between the threshold reference point and the initial point of the section, calculate how much time is required for error adjustment, that is, the error adjustment duration. Based on this duration, determine a regulation time point before the initial point of the section to ensure that the regulation strategy can be implemented before the error occurs. For example, a error adjustment duration can be determined forward from the initial point of the section to obtain the regulation time point. For example, if the distance between the threshold reference point and the initial point of the section is small, then advance the regulation at a time point with a larger distance before the initial point of the section to prevent the situation where the regulation is not completed in time when reaching the threshold reference point.

[0061] In an embodiment of the present application, based on the error value data within the section, generate an error value fluctuation sequence corresponding to each section. Determine the regional extreme points in the error value fluctuation sequence, and determine the minimum value in the sequence after the regional extreme points. Construct an error detection sequence based on the minimum value. Detect the regional extreme points based on the error detection sequence, and construct a reference sequence based on the regional extreme points that meet the detection conditions. Determine the characteristic points of the reference sequence, and determine the sequence floating mode of the reference sequence based on the characteristic points. Based on the sequence floating modes corresponding to multiple sections respectively, construct a sequence floating mode set, and match the sequence floating mode set with a preset error type library to determine the error type; where the preset error type library includes multiple floating mode sets, and also includes the error types corresponding to multiple floating mode sets respectively.

[0062] Specifically, the specific process of determining the error type based on the error values corresponding to multiple sections in the embodiments of the present application is as follows: For each section, an error value fluctuation sequence is generated based on the error value data within the section. This sequence describes the change of the error value over time. Assuming the error value data composed of multiple sections is [0.1, 0.2, 0.15, 0.3, 0.25, 0.2, 0.18], then the corresponding error value fluctuation sequence is these values themselves. In the error value fluctuation sequence, all local maximum points and local minimum points are determined. Then, in the sequence after each regional extreme point, the minimum value is found. For example, in the above error value fluctuation sequence, assuming the local maximum point is 0.3, then in the sequence [0.25, 0.2, 0.18] after it, the minimum value is 0.18.

[0063] Further, based on the minimum value after each regional extreme point, an error detection sequence is constructed. This sequence is used for subsequent detection of regional extreme points. The error detection sequence is used to detect regional extreme points, and the regional extreme points that meet specific conditions (such as fluctuation amplitude, duration, etc.) are screened out. These points will form a reference sequence. In the reference sequence, characteristic points are found. These points represent the main change characteristics of the sequence. Then, based on these characteristic points, the sequence floating mode of the reference sequence is determined. For example, the characteristic points are inflection points, extreme points, etc. in the sequence. The sequence floating mode describes the relative positions and change trends between these characteristic points. The determined sequence floating mode is matched with a preset error type library to determine the error type. Among them, the preset error type library in the embodiments of the present application includes multiple error types, and also includes the sequence floating modes corresponding to multiple error types respectively.

[0064] Step 106: When the structural data at the air outlet end meets the first preset structural data, receive the spinning grooving instruction and the second start instruction of the spinning machine, and perform production on the remaining part of the liquid storage device.

[0065] In an embodiment of the present application, when the stamping and shaping at the air outlet end are completed and the structural data after processing the air outlet end meets the requirements, a welding instruction will be received. At this time, the air outlet elbow is installed in the air outlet elbow installation hole, and at the same time, it is assisted by the welding flange on the air outlet elbow pipe body for positioning to ensure that the straight pipe part of the air outlet elbow in cooperation with the cylinder body is coaxial with the air outlet elbow installation hole and the cylinder body, and the air outlet elbow is welded and fixed to the air outlet elbow installation hole through the resistance welding process to complete the installation of the air outlet elbow.

[0066] In one embodiment of the present application, the middle partition is axially installed on the cylindrical blank to a first preset position. Receive the first grooving instruction of the spinning machine, and use a grooving machine to groove the outer wall of the cylindrical blank corresponding to the first preset position, so that the outer wall of the cylindrical blank bulges inward along the radial direction of the cylinder to axially limit the middle partition. Install the filter screen axially on the cylindrical blank to a second preset position. Receive the second grooving instruction of the spinning machine, and use a grooving machine to groove the outer wall of the cylindrical blank corresponding to the second preset position, so that the outer wall of the cylindrical blank bulges inward along the radial direction of the cylinder to axially limit the filter screen.

[0067] Specifically, first install the middle partition to the first preset position inside the cylinder. Receive the first grooving instruction of the spinning machine, and use a grooving machine to groove the outer wall of the cylinder to make the outer wall of the cylinder bulge inward along the radial direction of the cylinder to axially limit the middle partition. Then install the filter screen to the second preset position inside the cylinder. Receive the second grooving instruction of the spinning machine, and also use a grooving machine to groove the outer wall of the cylinder to make the outer wall of the cylinder bulge inward along the radial direction of the cylinder to axially limit the filter screen.

[0068] In one embodiment of the present application, after the installation of the middle partition and the filter screen is completed, receive the second start instruction of the spinning machine, install the cylinder reversely on the pressure main shaft of the spinning machine, and the pressure main shaft drives the cylinder to rotate and cooperate with the spinning wheel to perform spinning processing on the other end of the cylinder to form an air inlet end. When the structural data of the air inlet end conforms to the second preset structural data, weld the air inlet straight pipe to the air inlet end to realize the digital production of the air conditioner compressor liquid receiver.

[0069] In one embodiment of the present application, when the structural data of the air outlet end conforms to the first preset structural data, start the first instruction for cleaning the inside of the cylindrical blank. After welding the air outlet elbow to the air outlet end, start the second instruction for cleaning the inside of the cylindrical blank. After performing spinning grooving at the preset position for axial limiting, start the third instruction for cleaning the inside of the cylindrical blank. When the structural data of the air inlet end conforms to the second preset structural data, start the fourth instruction for cleaning the inside of the cylindrical blank.

[0070] Specifically, if the structural data of the air outlet end conforms to the first preset structural data, the first instruction for cleaning the inside of the cylinder blank is activated to ensure that the inside of the cylinder blank reaches a certain cleanliness standard before welding or other processing steps. After welding is completed, the second instruction for cleaning the inside of the cylinder blank is activated to remove possible impurities or contaminants generated during the welding process and ensure the smooth progress of subsequent processing steps. After the spinning and grooving is completed, the third instruction for cleaning the inside of the cylinder blank is activated to remove metal chips or other fine particles generated during the spinning and grooving process and ensure the cleanliness of the inside of the cylinder blank. If the structural data of the air inlet end conforms to the second preset structural data, the fourth instruction for cleaning the inside of the cylinder blank is activated to ensure that the cylinder blank reaches the required cleanliness standard before final assembly or delivery.

[0071] As a feasible implementation manner, Figure 2 A processing flow chart of a liquid storage device provided by an embodiment of the present application is as follows. As shown in the first and second figures in Figure 2 The blank of the cylinder 1 is driven by the pressure main shaft to move and contact with the bottom die, the upper die is driven to act, and the bottom die is cooperated to shape the initial air outlet end to make the bottom of the initial air outlet end flat, and an air outlet elbow mounting hole 111 for mounting the air outlet elbow 21 is punched in the center of the initial air outlet end to complete the processing of the air outlet end 11. The air outlet end 11 includes a flat portion 112 and an arc portion 113. The air outlet elbow 21 is installed into the air outlet elbow mounting hole 111, and at the same time, the welding flange 23 on the pipe body of the air outlet elbow 21 is used for auxiliary positioning to ensure that the straight pipe portion of the air outlet elbow 21 cooperating with the cylinder 1 is coaxial with the air outlet elbow mounting hole 111 and the cylinder 1, and the air outlet elbow 21 is welded and fixed to the air outlet elbow mounting hole 111 by resistance welding process to complete the installation of the air outlet elbow 21.

[0072] As shown in Figure 2 the third figure in, the middle partition plate 4 is axially installed inside the cylinder 1, and a groove is cut on the outer wall of the cylinder 1 by a grooving machine to make the outer wall of the cylinder 1 bulge radially inward of the cylinder 1 to axially limit the middle partition plate 4. Then, the filter screen 3 is axially installed inside the cylinder 1, and similarly, a groove is cut on the outer wall of the cylinder 1 by a grooving machine to make the outer wall of the cylinder 1 bulge radially inward of the cylinder 1 to axially limit the filter screen 3.

[0073] As shown in Figure 2As shown in the fourth and fifth figures, the pressure main shaft drives the cylinder body 1 to rotate and cooperates with the spinning wheel to perform spinning processing on the other end of the cylinder body 1 to form the air inlet end 12. The air inlet straight pipe 22 is installed into the air inlet straight pipe installation hole 121, and at the same time, the welding flange 23 on the pipe body of the air inlet straight pipe 22 is used for auxiliary positioning to ensure that the air inlet straight pipe 22, the air inlet straight pipe installation hole 121 and the cylinder body 1 are coaxially arranged, and the air inlet straight pipe 22 is welded and fixed to the air inlet straight pipe installation hole 121 through the resistance welding process to complete the installation of the air inlet straight pipe 22. Furthermore, the entire digital production and processing of the liquid storage device is completed. A first limiting step 231 adapted to the air inlet straight pipe installation hole 121 is arranged on one side of the welding flange 23 of the air inlet straight pipe 22 facing the air inlet straight pipe installation hole 121, and a second limiting step 232 adapted to the air outlet elbow installation hole 111 is arranged on the side of the welding flange 23 of the air outlet elbow 21 facing the air outlet elbow installation hole 111. The setting of the first limiting step 231 and the second limiting step 232 facilitates the positioning and installation of the air inlet straight pipe 22 and the air outlet elbow 21, and also facilitates the fixation during the welding process, ensures the welding effect, and improves the processing and production efficiency.

[0074] Step 107: Obtain the production information corresponding to different production links of the liquid storage device, map the production information corresponding to different production links to the product code, so as to trace the production process of the liquid storage device through the mapping relationship.

[0075] In an embodiment of the present application, the production information is uploaded to the blockchain. Based on the data type of the production information, the production information is divided into ordinary information and sensitive information, and the sensitive information is homomorphically encrypted to obtain the encrypted sensitive information. Hash calculation is performed on the encrypted sensitive information and the ordinary information to generate a hash value uniquely corresponding to the production information of the current production link. Based on the production information and the hash value, the processing information codes corresponding to different production links of the liquid storage device are generated. Based on the production sequence, a connection network is constructed between adjacent production links, and the processing information codes corresponding to different production links are associated based on the connection network to construct a relationship network corresponding to the processing information codes. The relationship network is mapped to the product code, so as to trace the liquid storage device product through the mapping relationship.

[0076] Specifically, after uploading the production information of the liquid storage device to the blockchain, the production information is divided into ordinary information and sensitive information. Among them, the ordinary information can be information that does not involve core secrets, such as production date, production batch, production line number, etc. The sensitive information can be information that needs to be kept confidential, such as production process parameters, raw material supplier information, etc. Homomorphic encryption is performed on the sensitive information to protect the security of the sensitive information during transmission and storage, while allowing necessary calculations and analyses to be performed in the encrypted state. Hash calculations are performed on the encrypted sensitive information and the ordinary information. Through the hash calculations, a hash value uniquely corresponding to the production information of the current production link can be generated. Based on the production information and the hash value, processing information codes corresponding to different production links of the liquid storage device are generated. This code is unique and can accurately identify the production information of the liquid storage device in different production links.

[0077] Furthermore, based on the production sequence, a connection network is constructed between adjacent production links. This network can clearly show the whole process of the liquid storage device from raw materials to finished products, as well as the correlation between each production link. Based on the connection network, the processing information codes corresponding to different production links are associated, thereby constructing a complete relational network corresponding to the processing information codes. This network contains all the key information of the liquid storage device during the production process. The relational network is mapped with the product code. The product code is the unique identifier of the liquid storage device, and through it, the corresponding production information can be quickly found in the relational network. When it is necessary to trace the production information of a certain liquid storage device, only the product code is needed, and all the processing information codes and the corresponding hash values of the liquid storage device in different production links can be queried on the blockchain through the mapping relationship, thereby realizing product traceability.

[0078] Figure 3 This is a schematic structural diagram of a digital production system for an air-conditioning compressor liquid storage device provided by an embodiment of the present application. As Figure 3As shown in the figure, the digital production system 200 of an air-conditioning compressor liquid receiver includes: a liquid receiver stock preparation unit 201, a spinning unit 202, a spinning data acquisition and processing unit 203, a remaining part production unit 204, and a traceability unit 205; the liquid receiver stock preparation unit 201 is used to obtain the information that the liquid receiver stock preparation is completed and generate a product code; among them, the liquid receiver stock preparation includes a cylinder blank, an intake straight pipe, an outlet elbow pipe, a middle partition plate, and a filter screen; the spinning unit 202 is used to receive the first start instruction of the spinning machine to start the spinning machine to perform spinning processing and stamping and shaping processing on the outlet end of the cylinder blank; the spinning data acquisition and processing unit 203 is used to receive the spinning data acquisition and processing instruction, divide the collected radian angle data of the spinning and closing of the liquid receiver end into intervals, and based on the radian angle data difference corresponding to different intervals, fit the radian angle data to obtain an actual fitting curve, and based on the shell data of the liquid receiver, adjust the actual fitting curve; the spinning data acquisition and processing unit 203 is also used to obtain the operating data of the spinning machine, generate a spinning machine operating fitting curve corresponding to different operating conditions based on the spinning machine operating data, and perform early regulation on the spinning machine based on the adjusted actual fitting curve and the spinning machine operating fitting curve; the remaining part production unit 204 is used to receive the spinning grooving instruction and the second start instruction of the spinning machine to produce the remaining part of the liquid receiver when the structural data of the outlet end meets the first preset structural data; the traceability unit 205 is used to obtain the production information corresponding to different production links of the liquid receiver, map the production information corresponding to different production links to the product code, so as to trace the production process of the liquid receiver through the mapping relationship.

[0079] Each embodiment in this application is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of devices, equipment, and non-volatile computer storage media, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0080] The above are only the embodiments of this application and are not used to limit this application. For those skilled in the art, the embodiments of this application can have various changes and modifications. These modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A digital production method for an air-conditioning compressor accumulator, characterized in that: The method comprises: Obtaining the completion information of the liquid reservoir material preparation and generating a product code; wherein the liquid reservoir material preparation includes a barrel blank, an air inlet straight pipe, an air outlet elbow pipe, a middle partition plate and a filter screen; receiving a first start-up instruction of a spinning machine, and performing a spinning process and a stamping process on the outlet end of the barrel blank through the spinning machine; Receive a spinning data collection and processing instruction, divide the collected radian angle data of the spinning closing of the end of the liquid reservoir into intervals, fit the radian angle data based on the radian angle data differences corresponding to different intervals, obtain an actual fitting curve, and adjust the actual fitting curve based on the tube shell data of the liquid reservoir; Acquire the spinning machine operation data, and generate the spinning machine operation fitting curves corresponding to different operation conditions respectively based on the spinning machine operation data; Pre-regulating the spinning machine based on the adjusted actual fitting curve and the spinning machine operation fitting curve; When the structural data of the gas outlet end meets the first preset structural data, a spinning grooving instruction and a second starting instruction of the spinning machine are received to produce the remaining part of the liquid reservoir; Acquire production information corresponding to different production links of the liquid reservoir, and map the production information corresponding to the different production links with the product code, so as to trace the production process of the liquid reservoir through the mapping relationship; The production information corresponding to the different production links is mapped with the product code to trace the production process of the liquid reservoir through the mapping relationship, specifically including: Uploading the production information to the blockchain; Based on the data type of the production information, the production information is divided into common information and sensitive information, and the sensitive information is homomorphically encrypted to obtain encrypted sensitive information; Performing hash calculation on the encrypted sensitive information and the general information to generate a hash value uniquely corresponding to the production information of the current production link; Based on the production information and the hash value, generate processing information codes corresponding to different production links of the liquid reservoir; Based on the production sequence, a connection network is constructed between adjacent production links, and based on the connection network, the processing information codes corresponding to different production links are associated to construct a relationship association network corresponding to the processing information codes; The relationship association network is mapped with the product code to trace the source of the liquid reservoir product through the mapping relationship.

2. The digital production method of an air-conditioning compressor accumulator according to claim 1, characterized in that: The collected radian angle data of the spinning and closing of the end of the liquid reservoir are divided into intervals, and the radian angle data are fitted based on the radian angle data differences corresponding to different intervals to obtain an actual fitting curve, specifically including: The radian angle data of the spinning closing is obtained by using angle sensors arranged at different positions of the end of the liquid reservoir; According to the shape of the spinning closing end of the liquid reservoir, the arc angle data of the spinning closing end is divided into a plurality of first intervals; Acquire adjacent change differences of the radian angle data in the first interval, determine interval connection points based on the adjacent change differences, and divide the first interval into a plurality of second intervals based on the interval connection points; Determine interval radian differences corresponding to a plurality of the second intervals respectively, and perform polynomial matching of different orders on the plurality of the second intervals based on the interval radian differences; Based on polynomials of different orders corresponding to a plurality of the second intervals, and the radian angle data of the spinning closure corresponding to a plurality of the second intervals, fitting is performed to obtain the actual fitting curve.

3. The digital production method of an air-conditioning compressor accumulator according to claim 1, characterized in that: The adjusting of the actual fitting curve based on the tube and shell data of the liquid reservoir specifically includes: Acquire a tube shell thickness in the tube shell data, and determine a first adjustment coefficient based on a difference between the tube shell thickness and a reference tube shell thickness; Based on the relationship between the historical shell and tube clamping vertical angles in the historical shell and tube data and the adjustment coefficients of the historical fitting curve, a clamping error detection model is constructed; Acquire a tube shell clamping vertical angle in the tube shell data, input the tube shell clamping vertical angle into the clamping error detection model, and obtain a second adjustment coefficient; The actual fitting curve is adjusted based on the first adjustment coefficient and the second adjustment coefficient.

4. The digital production method of an air-conditioning compressor accumulator according to claim 1, characterized in that: The step of acquiring the spinning machine operation data and generating spinning machine operation fitting curves corresponding to different operation conditions based on the spinning machine operation data specifically includes: Acquire operating data of the spinning machine; wherein the operating data at least includes temperature, pressure and rotation speed; Fitting the operating data by statistical methods to obtain fitting curve groups corresponding to different operating conditions; Normalizing the operating data of the spinning machine and the abscissa of the fitting curve group; Determine a reference curve in the fitting curve group, compare other curves in the fitting curve group with the reference curve, and determine, in the other curves, relative values ​​of ordinates corresponding to the same reference points as the reference curve; Based on the relative value of the ordinate and the normalized data, a fitting curve of the spinning machine operation corresponding to the spinning machine under different operating conditions is obtained.

5. The digital production method of an air-conditioning compressor accumulator according to claim 1, characterized in that: The step of controlling the spinning machine in advance based on the adjusted actual fitting curve and the spinning machine operation fitting curve specifically includes: Extracting key parameters of the adjusted actual fitting curve; wherein the extracted features include at least one of the slope of the curve, the curvature of the curve, and the extreme point of the curve; Inputting the extracted key parameters and the corresponding spinning machine operation fitting curves under different operating conditions into a spinning prediction model, so as to output a prediction fitting curve corresponding to a future time period through the spinning prediction model; Determine the first contribution value of different operation data features to the prediction fitting curve corresponding to the future time period by the SHAP algorithm, and draw a feature global explanation map based on the contribution value; A local linear model is constructed by using the LIME algorithm, second contribution values ​​of different operation data features to the prediction fitting curve corresponding to the future time period are determined by using the weights of the local linear model, and a feature local explanation map is drawn based on the second contribution values; Based on the feature global interpretation map and the feature local interpretation map, a future spinning prediction fitting curve is constructed, and the future spinning prediction fitting curve is compared with a target curve, and the spinning machine is adjusted in advance based on the comparison result.

6. The digital production method of the air conditioner compressor accumulator according to claim 5, characterized in that: The comparing the future spinning prediction fitting curve with the target curve and adjusting the spinning machine in advance based on the comparison result specifically includes: Comparing the future spinning prediction fitting curve with the target curve to determine an error point between the two, and dividing the future spinning prediction fitting curve into a plurality of sections based on the error point; Based on the error values ​​respectively corresponding to the plurality of sections, the error type is determined; wherein the error type includes position error, shape error and size error; Inputting the error type and the error value into a preset error control model, and outputting a corresponding spinning machine control strategy through the preset error control model; Obtaining a spinning error threshold value for the end of the reservoir, comparing the data in the section with the spinning error threshold value, and determining a threshold reference point; Based on the distance between the threshold reference point and the initial point of the section, the error adjustment duration is determined, and based on the adjustment duration, a control time point is determined before the initial point of the section, so as to implement the spinning machine control strategy at the control time point.

7. The digital production method of the air conditioner compressor accumulator according to claim 6, characterized in that: The determining of the error type based on the error values ​​respectively corresponding to the plurality of segments specifically includes: Based on the error value data in the segment, the error value fluctuation sequence corresponding to each segment is generated; Determine a regional extreme point in the error value fluctuation sequence, determine a minimum value in the sequence after the regional extreme point, and construct an error detection sequence based on the minimum value; Detecting the regional extreme value points based on the error detection sequence, and constructing a reference sequence based on the regional extreme value points that meet the detection conditions; Determining characteristic points of the reference sequence, and determining a sequence floating mode of the reference sequence based on the characteristic points; Based on the sequence floating patterns corresponding to the multiple segments respectively, a sequence floating pattern set is constructed, and the sequence floating pattern set is matched with a preset error type library to determine the error type; wherein the preset error type library includes multiple floating pattern sets, and also includes multiple error types corresponding to the floating pattern sets respectively.

8. The digital production method of an air-conditioning compressor accumulator according to claim 1, characterized in that: When the structural data of the gas outlet end meets the first preset structural data, a spinning groove engraving instruction and a second starting instruction of the spinning machine are received to produce the remaining part of the liquid reservoir, specifically including: When the structural data of the gas outlet end meets the first preset structural data, welding the gas outlet elbow to the gas outlet end; Installing the middle partition plate to a first preset position along the axial direction of the barrel blank; Receiving a notching instruction from a first spinning machine, notching the outer wall of the barrel blank corresponding to the first preset position by the notching machine, so that the outer wall of the barrel blank protrudes radially toward the inside of the barrel, and axially limits the middle partition; Installing the filter screen to a second preset position along the axial direction of the barrel blank; Receiving a notching instruction from a second spinning machine, notching the outer wall of the barrel blank corresponding to the second preset position by the notching machine, so that the outer wall of the barrel blank protrudes radially toward the inside of the barrel, and axially limiting the filter screen; Receive the second start-up instruction of the spinning machine, perform spinning processing on the air inlet end of the cylinder blank through the spinning machine, and when the structural data of the air inlet end meets the second preset structural data, weld the air inlet straight pipe to the air inlet end to realize the digital production of the air-conditioning compressor liquid reservoir.

9. A digital production system for an air-conditioning compressor accumulator, characterized in that: Applicable to a digital production method for an air-conditioning compressor accumulator as claimed in any one of claims 1 to 8, the system comprising a accumulator material preparation unit, a spinning unit, a spinning data acquisition and processing unit, a remaining part production unit and a tracing unit; The liquid reservoir material preparation unit is used to obtain the liquid reservoir material preparation completion information and generate a product code; wherein the liquid reservoir material preparation includes a barrel blank, an air inlet straight pipe, an air outlet elbow, a middle partition plate and a filter screen; The spinning unit is used to receive a first start instruction of the spinning machine to start the spinning machine to perform spinning and stamping shaping on the outlet end of the barrel blank; The spinning data acquisition and processing unit is used to receive a spinning data acquisition and processing instruction, divide the collected radian angle data of the spinning closing of the end of the liquid reservoir into intervals, fit the radian angle data based on the radian angle data differences corresponding to different intervals, obtain an actual fitting curve, and adjust the actual fitting curve based on the tube shell data of the liquid reservoir; The spinning data acquisition and processing unit is further used to obtain the spinning machine operation data, generate the spinning machine operation fitting curves corresponding to different operation conditions based on the spinning machine operation data, and perform early control on the spinning machine based on the adjusted actual fitting curve and the spinning machine operation fitting curve; The remaining part production unit is used to receive the spinning grooving instruction and the second starting instruction of the spinning machine when the structural data of the gas outlet end meets the first preset structural data, and produce the remaining part of the liquid reservoir; The tracing unit is used to obtain the production information corresponding to different production links of the liquid reservoir, and map the production information corresponding to the different production links with the product code, so as to trace the production process of the liquid reservoir through the mapping relationship.

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