A casting machine data acquisition and analysis system and method based on the Internet of Things

Abstract

The present invention relates to the technical field of data collection and management of Internet of Things devices, and specifically to a casting machine data collection and analysis system and method based on the Internet of Things. The present invention combines numerical control system communication technology and uses a small embedded industrial computer to develop a corresponding program, which can collect processing parameters of the casting machine during the processing of castings. By analyzing the quantity distribution of samples and defective products generated under each different process parameter setting set in each historical unit cycle, the process parameter settings that can efficiently and high-quality complete the casting of workpieces in each historical unit cycle are collected and sorted, and the operating adjustment range of each operating parameter item is further analyzed. The present invention captures the shortening change law of the operating adjustment range of each operating parameter item at intervals in the historical cycle by comparison, so as to provide a data basis for possible judgment on whether there is a problem with the casting machine or the mold, thereby improving the scientific operation and maintenance management of the casting machine.

Description

Technical Field

[0001] The present invention relates to the technical field of data collection and management of Internet of Things devices, and in particular to a casting machine data collection and analysis system and method based on the Internet of Things. Background Art

[0002] As an important industrial equipment, casting machine is widely used in production. The process parameters of traditional die-casting machines are determined by analyzing the actual quality of die-casting samples and repeatedly adjusting according to the analysis results. In the prior art, in the early stage of die-casting a product, a dedicated person is usually responsible for setting and adjusting the process parameters of the machine as standard injection process parameters.

[0003] The setting and adjustment of machine process parameters in die-casting production directly affects the quality of the product. One parameter may cause multiple defects in the product, and the same defect in the same product may be related to multiple parameters. It is required to carefully analyze the impact of changes in process parameters on casting molding in trial die-casting production.

[0004] Once the working performance of the die-casting machine changes during the production process, if it is not discovered and corrected in time, a large number of defective products will be produced, causing economic losses. Summary of the invention

[0005] The purpose of the present invention is to provide a casting machine data acquisition and analysis system and method based on the Internet of Things to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a casting machine data collection and analysis method based on the Internet of Things, the method comprising:

[0007] Step S1: Building a die-casting processing supervision platform to collect feature data for each die-casting of a workpiece performed by a casting machine; the feature data includes a set of process parameter settings corresponding to each die-casting of a workpiece, and the quality inspection results of the corresponding die-castings;

[0008] Step S2: setting a unit cycle, evaluating the characteristic qualified rate of the casting machine under different process parameter settings in each unit cycle, sorting out the distribution of the process parameter settings presented in each unit cycle, and evaluating the floating range of the process parameter settings corresponding to each unit cycle according to the data range distribution presented by different process parameter settings greater than the characteristic qualified rate threshold;

[0009] Step S3: The process parameter setting floating intervals of each historical unit period are collected in chronological order to obtain a floating interval sequence; every two adjacent process parameter setting floating intervals in the floating interval sequence are set as a floating node, and deviations of corresponding numerical floating intervals are compared between the same process parameter items for each floating node in turn, and characteristic floating parameter items are judged and identified according to the deviation comparison results, and the floating coefficients of each characteristic floating parameter item are calculated, and at the same time, the characteristic floating index of each floating node is evaluated and calculated;

[0010] Step S4: Sort out the distribution of characteristic floating index presented by each floating node in the floating interval sequence, construct a curve function of the characteristic floating index changing with the time stamp T, and decide whether to send an early warning prompt to the management terminal based on the regular trend changes presented by the curve function.

[0011] Preferably, step S1 comprises:

[0012] Step S1-1: realize communication with the casting machine through embedded industrial computer communication technology, realize communication with the quantitative furnace through the serial port server, realize communication with the spot cooling machine and the vacuum machine through the intelligent communication gateway, and collect data on various die-casting process parameters of the casting machine when performing each die-casting of the workpiece;

[0013] Step S1-2: Record the parameter values ​​corresponding to each die-casting process parameter item when performing each die-casting of the workpiece, obtain the process parameter setting set corresponding to each die-casting of the workpiece, and record the results of quality inspection of the die-castings obtained after each die-casting of the workpiece, wherein the results of the quality inspection include samples and defective products; wherein, the die-castings whose performance indicators meet the customer's requirements are samples.

[0014] Preferably, step S2 comprises:

[0015] Step S2-1: Traverse the process parameter setting sets appearing in the unit cycle; calculate the characteristic qualification rate η=g / M obtained based on each process parameter setting set in the unit cycle, where M represents the total number of times the casting machine performs die casting according to each process parameter setting set in the unit cycle, and g represents the total number of times the casting machine obtains die castings as samples after performing die casting according to each process parameter setting set in the unit cycle;

[0016] Step S2-2: In each unit period, the process parameter setting sets whose characteristic qualification rate is greater than the characteristic qualification rate threshold are collected to obtain a set Q. In the set Q, the maximum and minimum values ​​displayed on each process parameter item are captured to obtain the numerical floating range corresponding to each process parameter item;

[0017] Step S2-3: In each unit period, the numerical floating ranges corresponding to each process parameter item are collected to obtain the process parameter setting floating range corresponding to each unit period.

[0018] Preferably, step S3 comprises:

[0019] Step S3-1: Set the floating interval W to the i-th process parameter in the floating interval sequence i And the floating interval W of the i+1th process parameter setting i+1 The floating node P{W i ,W i+1}, a process parameter item A is extracted in W i The floating range of the value is F(A) 1 =[f max ,f min ], in W i+1 The floating range of the value is F(A) 2 =[f max ',f min '], where f max 、f min Respectively expressed in W i In the example, a process parameter item A shows the maximum and minimum values ​​of the operation; f max '、f min ' respectively represent in W i+1 In the figure, a process parameter item A shows the maximum and minimum values ​​of the operation;

[0020] Step S3-2: When the or max '-f min ' <f max -f min When a process parameter item A is judged to be a floating node P{W i ,W i+1}, calculate the floating coefficient β of a process parameter item A = (f max -f max ')×(f min -f min ');

[0021] Step S3-3: Extract the characteristic floating parameter items existing in each floating node, and calculate the characteristic floating index α=(y / N)×(β 1 +β 2 +...+βy); where y represents the total number of characteristic floating parameter items existing in each floating node, and N represents the total number of process parameter items contained in any process parameter setting set; where β 1 , β 2, ..., βy represent the floating coefficients corresponding to the 1st, 2nd, ..., yth characteristic floating parameter items respectively.

[0022] Preferably, step S4 comprises:

[0023] Step S4-1: sequentially set the floating nodes whose characteristic floating index in the floating interval sequence is greater than the index threshold as characteristic nodes, collect the characteristic floating index α corresponding to each characteristic node, and the timestamp T corresponding to each characteristic node, wherein the timestamp corresponding to each characteristic node is the timestamp corresponding to the last time the die-casting of the workpiece is completed in the two historical unit cycles contained in each characteristic node, and construct a data group (α, T) corresponding to each characteristic node;

[0024] Step S4-2: curve fitting is performed on all data groups to construct a curve function α=K×T+b that shows the characteristic floating index changes with the time stamp T in the floating interval sequence, where K represents the slope and b is a constant;

[0025] Step S4-3: Setting a characteristic slope. When the slope in the curve function is greater than the characteristic slope, feedback is given to the management terminal, and an early warning prompt is fed back to the management terminal to remind the casting machine and mold components to perform performance maintenance.

[0026] In order to better implement the above method, a casting machine data acquisition and analysis system is also proposed, which includes a processing data supervision module, a processing information floating management module, a characteristic floating index calculation module, and an early warning prompt management module;

[0027] The processing data supervision module is used to build a die-casting processing supervision platform to collect feature data for each workpiece die-casting performed by the casting machine; the feature data includes a set of process parameter settings corresponding to each workpiece die-casting, and the corresponding quality inspection results of the die-casting;

[0028] The processing information floating management module is used to set the unit cycle, evaluate the characteristic qualification rate of the casting machine under different process parameter settings in each unit cycle, sort out the distribution of process parameter settings presented in each unit cycle, and evaluate the floating range of process parameter settings corresponding to each unit cycle according to the data range distribution presented by different process parameter settings greater than the characteristic qualification rate threshold;

[0029] The characteristic floating index calculation module is used to collect the floating intervals of the process parameter settings of each historical unit period in chronological order to obtain a floating interval sequence; set each two adjacent process parameter setting floating intervals in the floating interval sequence as a floating node, and perform deviation comparison of the corresponding numerical floating intervals between the same process parameter items for each floating node in turn, and judge and identify the characteristic floating parameter items according to the deviation comparison results, calculate the floating coefficient of each characteristic floating parameter item, and evaluate and calculate the characteristic floating index of each floating node;

[0030] The early warning management module is used to sort out the distribution of characteristic floating index presented by each floating node in the floating interval sequence, construct a curve function of the characteristic floating index changing with the time stamp T, and decide whether to send an early warning to the management terminal based on the regular trend changes presented by the curve function.

[0031] Preferably, the processing information floating management module includes a qualified rate evaluation management unit and a floating interval sorting unit;

[0032] The qualification rate evaluation management unit is used to set the unit cycle and evaluate the characteristic qualification rate of the casting machine under different process parameter settings in each unit cycle;

[0033] The floating interval combing unit is used to comb the distribution of process parameter settings presented in each unit period, and evaluate the floating interval of process parameter settings corresponding to each unit period based on the data range distribution presented by different process parameter settings greater than the characteristic qualified rate threshold.

[0034] Preferably, the characteristic floating index calculation module includes a characteristic floating parameter item judgment management unit and an evaluation calculation unit;

[0035] The characteristic floating parameter item judgment management unit is used to collect the process parameter setting floating intervals of each historical unit period in chronological order to obtain a floating interval sequence; set each two adjacent process parameter setting floating intervals in the floating interval sequence as a floating node, and perform deviation comparison of corresponding numerical floating intervals between the same process parameter items for each floating node in turn, and judge and identify the characteristic floating parameter item according to the deviation comparison result;

[0036] The evaluation calculation unit is used to calculate the floating coefficient of each characteristic floating parameter item, and at the same time, to evaluate and calculate the characteristic floating index of each floating node.

[0037] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention combines the communication technology of the numerical control system and develops the corresponding program using a small embedded industrial computer, which can collect the processing parameters running in the process of processing castings, and upload the collected data to the data acquisition platform. By analyzing the quantity distribution of samples and defective products generated under each different set of process parameter settings in each historical unit cycle, the process parameter settings that can efficiently and high-quality complete the casting of workpieces in each historical unit cycle are collected and sorted, and the operating adjustment range of each operating parameter item is further analyzed. The present invention captures the shortening change law of the operating adjustment range of each operating parameter item at intervals of historical cycles by comparison, and provides a data basis for possible judgment on whether there is a problem with the casting machine or the mold, thereby improving the scientific operation and maintenance management of the casting machine. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0039] Figure 1 It is a flow chart of a casting machine data collection and analysis method based on the Internet of Things of the present invention.

[0040] Figure 2 It is a structural schematic diagram of a casting machine data acquisition and analysis system based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] See also Figure 1-Figure 2 The present invention provides a technical solution: a casting machine data collection and analysis method based on the Internet of Things, the method comprising:

[0043] Step S1: Building a die-casting processing supervision platform to collect feature data for each die-casting of a workpiece performed by a casting machine; the feature data includes a set of process parameter settings corresponding to each die-casting of a workpiece, and the quality inspection results of the corresponding die-castings;

[0044] Wherein, step S1 comprises:

[0045] Step S1-1: realize communication with the casting machine through embedded industrial computer communication technology, realize communication with the quantitative furnace through the serial port server, realize communication with the spot cooling machine and the vacuum machine through the intelligent communication gateway, and collect data on various die-casting process parameters of the casting machine when performing each die-casting of the workpiece;

[0046] Step S1-2: recording the parameter values ​​corresponding to each die-casting process parameter item when performing each die-casting of the workpiece, obtaining a process parameter setting set corresponding to each die-casting of the workpiece, and recording the results of quality inspection of the die-casting obtained after each die-casting of the workpiece, wherein the results of the quality inspection include samples and defective products; wherein the die-casting whose various performance indicators meet the customer's requirements is the sample;

[0047] For example, the die-casting process parameter items include die-casting process parameter item 1, die-casting process parameter item 2, die-casting process parameter item 3, and die-casting process parameter item 4; wherein, in one die-casting of a workpiece, the casting machine displays a parameter value of x1 on die-casting process parameter item 1, a parameter value of x2 on die-casting process parameter item 2, a parameter value of x3 on die-casting process parameter item 3, and a parameter value of x4 on die-casting process parameter item 4;

[0048] In summary, the process parameter setting set corresponding to the die casting of the workpiece is {die casting process parameter item 1: x1, die casting process parameter item 2: x2, die casting process parameter item 3: x3, die casting process parameter item 4: x4};

[0049] Step S2: setting a unit cycle, evaluating the characteristic qualified rate of the casting machine under different process parameter settings in each unit cycle, sorting out the distribution of the process parameter settings presented in each unit cycle, and evaluating the floating range of the process parameter settings corresponding to each unit cycle according to the data range distribution presented by different process parameter settings greater than the characteristic qualified rate threshold;

[0050] Wherein, step S2 comprises:

[0051] Step S2-1: Traverse the process parameter setting sets appearing in the unit cycle; calculate the characteristic qualification rate η=g / M obtained based on each process parameter setting set in the unit cycle, where M represents the total number of times the casting machine performs die casting according to each process parameter setting set in the unit cycle, and g represents the total number of times the casting machine obtains die castings as samples after performing die casting according to each process parameter setting set in the unit cycle;

[0052] Step S2-2: In each unit period, the process parameter setting sets whose characteristic qualification rate is greater than the characteristic qualification rate threshold are collected to obtain a set Q. In the set Q, the maximum and minimum values ​​displayed on each process parameter item are captured to obtain the numerical floating range corresponding to each process parameter item;

[0053] For example, the first process parameter setting set is {die casting process parameter item 1: x1, die casting process parameter item 2: x2, die casting process parameter item 3: x3, die casting process parameter item 4: x4};

[0054] The second process parameter setting set is {die casting process parameter item 1: y1, die casting process parameter item 2: y2, die casting process parameter item 3: y3, die casting process parameter item 4: y4};

[0055] The third process parameter setting set is {die casting process parameter item 1: z1, die casting process parameter item 2: z2, die casting process parameter item 3: z3, die casting process parameter item 4: z4};

[0056] The fourth process parameter setting set is {die casting process parameter item 1: f1, die casting process parameter item 2: f2, die casting process parameter item 3: f3, die casting process parameter item 4: f4};

[0057] Among them, x1 <y1<f1<z1,y2<x2<f2<z2,y3<z3<f3<x3,y4<z4<f4<x4;

[0058] Among them, the feature qualification rates corresponding to the first and third process parameter setting sets are both greater than the feature qualification rate threshold. In summary,

[0059] The numerical floating range corresponding to die-casting process parameter item 1 is [x1, z1], the numerical floating range corresponding to die-casting process parameter item 2 is: [x2, z2], the numerical floating range corresponding to die-casting process parameter item 3 is: [z3, x3], and the numerical floating range corresponding to die-casting process parameter item 4 is: [z4, x4];

[0060] Step S2-3: In each unit period, the numerical floating ranges corresponding to each process parameter item are collected to obtain the process parameter setting floating range corresponding to each unit period;

[0061] Step S3: The process parameter setting floating intervals of each historical unit period are collected in chronological order to obtain a floating interval sequence; every two adjacent process parameter setting floating intervals in the floating interval sequence are set as a floating node, and deviations of corresponding numerical floating intervals are compared between the same process parameter items for each floating node in turn, and characteristic floating parameter items are judged and identified according to the deviation comparison results, and the floating coefficients of each characteristic floating parameter item are calculated, and at the same time, the characteristic floating index of each floating node is evaluated and calculated;

[0062] Wherein, step S3 comprises:

[0063] Step S3-1: Set the floating interval W to the i-th process parameter in the floating interval sequence i And the floating interval W of the i+1th process parameter setting i+1 The floating node P{W i ,W i+1}, a process parameter item A is extracted in W i The floating range of the value is F(A) 1 =[f max ,f min ], in W i+1 The floating range of the value is F(A) 2 =[f max ',f min '], where f max 、f min Respectively expressed in W i In the example, a process parameter item A shows the maximum and minimum values ​​of the operation; f max '、f min ' respectively represent in W i+1 In the figure, a process parameter item A shows the maximum and minimum values ​​of the operation;

[0064] Step S3-2: When the or max '-f min ' <f max -f min When a process parameter item A is judged to be a floating node P{W i ,W i+1}, calculate the floating coefficient β of a process parameter item A = (f max -f max ')×(f min -f min ');

[0065] Step S3-3: Extract the characteristic floating parameter items existing in each floating node, and calculate the characteristic floating index α=(y / N)×(β 1+β 2 +...+βy); where y represents the total number of characteristic floating parameter items existing in each floating node, and N represents the total number of process parameter items contained in any process parameter setting set; where β 1 , β 2 , ..., βy represent the floating coefficients corresponding to the 1st, 2nd, ..., yth characteristic floating parameter items respectively;

[0066] Step S4: sort out the characteristic floating index distribution of each floating node in the floating interval sequence, construct a curve function of the characteristic floating index changing with the time stamp T, and decide whether to send an early warning prompt to the management terminal based on the regular trend change presented by the curve function;

[0067] Wherein, step S4 comprises:

[0068] Step S4-1: sequentially set the floating nodes whose characteristic floating index in the floating interval sequence is greater than the index threshold as characteristic nodes, collect the characteristic floating index α corresponding to each characteristic node, and the timestamp T corresponding to each characteristic node, wherein the timestamp corresponding to each characteristic node is the timestamp corresponding to the last time the die-casting of the workpiece is completed in the two historical unit cycles contained in each characteristic node, and construct a data group (α, T) corresponding to each characteristic node;

[0069] Step S4-2: curve fitting is performed on all data groups to construct a curve function α=K×T+b that shows the characteristic floating index changes with the time stamp T in the floating interval sequence, where K represents the slope and b is a constant;

[0070] Step S4-3: Setting a characteristic slope. When the slope in the curve function is greater than the characteristic slope, feedback is given to the management terminal, and an early warning prompt is fed back to the management terminal to remind the casting machine and mold components to perform performance maintenance.

[0071] In order to better implement the above method, a casting machine data acquisition and analysis system is also proposed, which includes a processing data supervision module, a processing information floating management module, a characteristic floating index calculation module, and an early warning prompt management module;

[0072] The processing data supervision module is used to build a die-casting processing supervision platform to collect feature data for each workpiece die-casting performed by the casting machine; the feature data includes a set of process parameter settings corresponding to each workpiece die-casting, and the corresponding quality inspection results of the die-casting;

[0073] The processing information floating management module is used to set the unit cycle, evaluate the characteristic qualification rate of the casting machine under different process parameter settings in each unit cycle, sort out the distribution of process parameter settings presented in each unit cycle, and evaluate the floating range of process parameter settings corresponding to each unit cycle according to the data range distribution presented by different process parameter settings greater than the characteristic qualification rate threshold;

[0074] Among them, the processing information floating management module includes a qualified rate assessment management unit and a floating interval sorting unit;

[0075] The qualification rate evaluation management unit is used to set the unit cycle and evaluate the characteristic qualification rate of the casting machine under different process parameter settings in each unit cycle;

[0076] The floating interval combing unit is used to comb the distribution of process parameter settings presented in each unit cycle, and evaluate the floating interval of process parameter settings corresponding to each unit cycle according to the data range distribution presented by different process parameter settings greater than the characteristic qualified rate threshold;

[0077] The characteristic floating index calculation module is used to collect the floating intervals of the process parameter settings of each historical unit period in chronological order to obtain a floating interval sequence; set each two adjacent process parameter setting floating intervals in the floating interval sequence as a floating node, and perform deviation comparison of the corresponding numerical floating intervals between the same process parameter items for each floating node in turn, and judge and identify the characteristic floating parameter items according to the deviation comparison results, calculate the floating coefficient of each characteristic floating parameter item, and evaluate and calculate the characteristic floating index of each floating node;

[0078] Among them, the characteristic floating index calculation module includes a characteristic floating parameter item judgment management unit and an evaluation calculation unit;

[0079] The characteristic floating parameter item judgment management unit is used to collect the process parameter setting floating intervals of each historical unit period in chronological order to obtain a floating interval sequence; set each two adjacent process parameter setting floating intervals in the floating interval sequence as a floating node, and perform deviation comparison of corresponding numerical floating intervals between the same process parameter items for each floating node in turn, and judge and identify the characteristic floating parameter item according to the deviation comparison result;

[0080] An evaluation and calculation unit is used to calculate the floating coefficient of each characteristic floating parameter item, and to evaluate and calculate the characteristic floating index of each floating node;

[0081] The early warning management module is used to sort out the distribution of characteristic floating index presented by each floating node in the floating interval sequence, construct a curve function of the characteristic floating index changing with the time stamp T, and decide whether to send an early warning to the management terminal based on the regular trend changes presented by the curve function.

[0082] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0083] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A casting machine data collection and analysis method based on the Internet of Things, characterized in that: The method comprises: Step S1: Building a die-casting processing supervision platform to collect feature data for each die-casting of a workpiece performed by a casting machine; the feature data includes a set of process parameter settings corresponding to each die-casting of the workpiece, and a corresponding quality inspection result of the die-casting; Step S2: setting a unit cycle, evaluating the characteristic qualified rate of the casting machine under different process parameter settings in each unit cycle, sorting out the distribution of the process parameter settings presented in each unit cycle, and evaluating the floating range of the process parameter settings corresponding to each unit cycle according to the data range distribution presented by different process parameter settings greater than the characteristic qualified rate threshold; Step S3: The process parameter setting floating intervals of each historical unit period are collected in chronological order to obtain a floating interval sequence; every two adjacent process parameter setting floating intervals in the floating interval sequence are set as a floating node, and deviations of corresponding numerical floating intervals are compared between the same process parameter items for each floating node in turn, and characteristic floating parameter items are judged and identified according to the deviation comparison results, and the floating coefficients of each characteristic floating parameter item are calculated, and at the same time, the characteristic floating index of each floating node is evaluated and calculated; Step S4: sort out the characteristic floating index distribution of each floating node in the floating interval sequence, construct a curve function of the characteristic floating index changing with the time stamp T, and decide whether to send an early warning prompt to the management terminal based on the regular trend change presented by the curve function; The step S2 comprises: Step S2-1: Traverse the process parameter setting sets appearing in the unit cycle; calculate the characteristic qualification rate η=g / M obtained based on each process parameter setting set in the unit cycle, where M represents the total number of times the casting machine performs die casting according to each process parameter setting set in the unit cycle, and g represents the total number of times the casting machine obtains die castings as samples after performing die casting according to each process parameter setting set in the unit cycle; Step S2-2: In each unit period, the process parameter setting sets whose characteristic qualification rate is greater than the characteristic qualification rate threshold are collected to obtain a set Q, in which the maximum and minimum values ​​displayed on each process parameter item are captured to obtain the numerical floating range corresponding to each process parameter item; Step S2-3: In each unit period, the numerical floating ranges corresponding to each process parameter item are collected to obtain the process parameter setting floating range corresponding to each unit period; The step S3 comprises: Step S3-1: Set the floating interval W to the i-th process parameter in the floating interval sequence i and the floating interval W of the i+1th process parameter setting i+1 The floating node P{W i ,W i+1 }, a process parameter item A is extracted in W i The floating range of the value is F(A) 1 =[f max ,f min ], in W i+1 The floating range of the value is F(A) 2 =[f max ',f min '], where f max 、f min Respectively expressed in W i In the example, a process parameter item A shows the maximum and minimum values ​​of the operation; f max '、f min ' respectively represent in W i+1 In the figure, a process parameter item A shows the maximum and minimum values ​​of the operation; Step S3-2: When F(A) is satisfied 2 ⊆F(A) 1 or max '-f min ' <f max -f min When the process parameter item A is determined to be the floating node P{W i ,W i+1 }, calculate the floating coefficient β of the process parameter item A = (f max -f max ')×(f min -f min '); Step S3-3: Extract the characteristic floating parameter items existing in each floating node, and calculate the characteristic floating index α=(y / N)×(β1+β2+...+βy) for each floating node; wherein y represents the total number of characteristic floating parameter items existing in the said floating nodes, and N represents the total number of process parameter items included in any process parameter setting set; wherein β1, β2, ..., βy represent the floating coefficients corresponding to the 1st, 2nd, ..., yth characteristic floating parameter items, respectively.

2. The method for data collection and analysis of a casting machine based on the Internet of Things according to claim 1 is characterized in that: The step S1 comprises: Step S1-1: realize communication with the casting machine through embedded industrial computer communication technology, realize communication with the quantitative furnace through the serial port server, realize communication with the spot cooling machine and the vacuum machine through the intelligent communication gateway, and collect data on various die-casting process parameters of the casting machine when performing each die-casting of the workpiece; Step S1-2: Record the parameter values ​​corresponding to each die-casting process parameter item when performing each die-casting of the workpiece, obtain the process parameter setting set corresponding to each die-casting of the workpiece, and record the results of quality inspection of the die-casting parts obtained after each die-casting of the workpiece, wherein the results of the quality inspection include samples and defective products.

3. The method for data collection and analysis of a casting machine based on the Internet of Things according to claim 1 is characterized in that: Described step S4 comprises: Step S4-1: sequentially set the floating nodes whose characteristic floating index in the floating interval sequence is greater than the index threshold as characteristic nodes, collect the characteristic floating index α corresponding to each characteristic node, and the timestamp T corresponding to each characteristic node, wherein the timestamp corresponding to each characteristic node is the timestamp corresponding to the last time the die-casting of the workpiece is completed in the two historical unit cycles contained in each characteristic node, and construct a data group (α, T) corresponding to each characteristic node; Step S4-2: curve fitting is performed on all data groups to construct a curve function α=K×T+b that shows the characteristic floating index changes with the time stamp T in the floating interval sequence, where K represents the slope and b is a constant; Step S4-3: Setting a characteristic slope. When the slope in the curve function is greater than the characteristic slope, feedback is given to the management terminal, and an early warning prompt is fed back to the management terminal to remind the casting machine and mold components to perform performance maintenance.

4. A casting machine data collection and analysis system, used to execute a casting machine data collection and analysis method based on the Internet of Things as described in any one of claims 1 to 3, characterized in that: The system includes a processing data supervision module, a processing information floating management module, a characteristic floating index calculation module, and an early warning prompt management module; The processing data supervision module is used to build a die-casting processing supervision platform to collect feature data for each workpiece die-casting performed by the casting machine; the feature data includes a set of process parameter settings corresponding to each workpiece die-casting, and the corresponding quality inspection results of the die-casting; The processing information floating management module is used to set the unit cycle, evaluate the characteristic qualification rate presented by the casting machine under different process parameter settings in each unit cycle, sort out the distribution of process parameter settings presented in each unit cycle, and evaluate the floating range of process parameter settings corresponding to each unit cycle according to the data range distribution presented by different process parameter settings greater than the characteristic qualification rate threshold; The characteristic floating index calculation module is used to collect the floating intervals of the process parameter settings of each historical unit period in chronological order to obtain a floating interval sequence; set each two adjacent process parameter setting floating intervals in the floating interval sequence as a floating node, and perform deviation comparison of the corresponding numerical floating intervals between the same process parameter items for each floating node in turn, and judge and identify the characteristic floating parameter items according to the deviation comparison results, calculate the floating coefficient for each characteristic floating parameter item, and at the same time perform evaluation calculation of the characteristic floating index for each floating node; The early warning management module is used to sort out the distribution of characteristic floating indexes presented by each floating node in the floating interval sequence, construct a curve function of the characteristic floating index changing with the time stamp T, and decide whether to send an early warning to the management terminal based on the regular trend changes presented by the curve function.

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