Early warning method, system, device, medium and program product for industrial product production

Abstract

This invention discloses an early warning method, system, equipment, medium, and program product for industrial product production. The early warning method includes: acquiring initial parameter data corresponding to several scrapped products; the initial parameter data including several production factor information; based on the initial parameter data, acquiring target scrapping impact factor information of the scrapped products under different production environments; the target scrapping impact factor information being characterized as environmental parameter information of different scrapped products under corresponding production environments; and generating early warning rule information for the industrial products during production in different production environments based on the target scrapping impact factor information. Through the early warning method provided by this disclosure, early warning information for industrial products during production can be acquired and updated in real time, thereby improving product production efficiency.

Description

Technical Field

[0001] This disclosure relates to the field of data processing, and in particular to a method, system, device, medium, and program product for early warning during the production of industrial products. Background Technology

[0002] During the manufacturing process, in order to ensure product quality, it is usually necessary to monitor the operating parameters of the production equipment (such as temperature, pressure, speed, etc.) in real time, and judge whether the produced products are qualified according to the preset threshold rules.

[0003] However, the widely adopted threshold monitoring methods currently rely mainly on static rules set by human experience, which has the following obvious limitations: First, the formulation of threshold rules often requires senior engineers to manually set them based on limited experience or samples, lacking the driving force and optimization based on large-scale historical data, resulting in strong subjectivity of the rules themselves and difficulty in continuous evolution; Second, traditional rules are usually simple in form, mostly single-point thresholds for a single parameter (for example, a temperature ≥ 90°C is judged as abnormal). This judgment method ignores the objective requirement that the parameter should be within a reasonable range, and cannot accurately capture the complex situation where both excessively low and excessively high values ​​will lead to defects; Third, when a large amount of historical data containing qualified and scrap records is accumulated, existing methods lack effective algorithms to automatically analyze this data, thereby inferring a scientific and reasonable range of parameter thresholds.

[0004] Since different product defects or scrapping modes often correspond to different abnormal combinations of parameters, existing methods cannot automatically identify and distinguish these diverse scrapping modes, and therefore cannot generate corresponding, refined multi-parameter threshold range rule sets for different modes. Summary of the Invention

[0005] The technical problem to be solved by this disclosure is to overcome the shortcomings of the prior art in that it is unable to generate corresponding, refined multi-parameter threshold range rule sets for different modes, and to provide an early warning method, system, device, medium and program product for industrial product production.

[0006] This disclosure solves the above-mentioned technical problems through the following technical solution:

[0007] According to a first aspect of this disclosure, an early warning method for industrial product production is provided, the early warning method comprising:

[0008] Acquire initial parameter data corresponding to several scrapped products; the initial parameter data includes several production factor information.

[0009] Based on several initial parameter data, target scrapping impact factor information of the scrapped products under different production environments is obtained; the target scrapping impact factor information is characterized as environmental parameter information of different scrapped products under corresponding production environments;

[0010] Based on the information of several target scrapping influencing factors, early warning rule information is generated for the industrial products when they are produced in different production environments.

[0011] Optionally, the step of obtaining the target scrapping impact factor information of the scrapped product under different production environments based on several initial parameter data includes:

[0012] Based on the aforementioned initial parameter data, obtain the initial purity information;

[0013] Based on several initial parameter data, information on actual scrapping impact factors under set production environment conditions is obtained;

[0014] Based on the initial purity information and the actual scrap impact factor information, the target scrap impact factor information is determined.

[0015] Optionally, the step of obtaining initial purity information based on several of the initial parameter data includes:

[0016] The scrapped products are divided into several scrap groups based on preset classification conditions; wherein the preset classification conditions include at least one of mechanical failure conditions, process failure conditions, and electrical failure conditions.

[0017] Obtain first quantity information of the corresponding scrapped products in different scrapping groups; the first quantity information represents the quantity of scrapped products in different scrapping groups;

[0018] Based on several of the first quantity information, the initial purity information is determined; the initial purity information is characterized as the degree of failure information of the industrial product under different production environments.

[0019] Optionally, the step of obtaining actual scrapping impact factor information under set production environment conditions based on several initial parameter data includes:

[0020] Obtain second quantity information of the scrapped products belonging to different scrapping groups under the set production environment conditions; the second quantity information represents the quantity of the scrapped products in different scrapping groups under the set production environment conditions;

[0021] Based on several of the second quantitative information, the actual scrap impact factor information under the set production environment conditions is obtained.

[0022] Optionally, the step of determining the target scrapping impact factor information based on the initial purity information and the actual scrapping impact factor information includes:

[0023] Based on the difference between the initial purity information and the actual scrap impact factor information, the target scrap impact factor information corresponding to the set production environment conditions is determined.

[0024] Optionally, the step of generating early warning rule information for the industrial product during production in different production environments based on several target scrapping impact factors includes:

[0025] The target scrapping impact factors under different production environments are sorted from high to low, and early warning rules corresponding to the production environment are generated based on the sorting results.

[0026] According to a second aspect of this disclosure, an early warning system for industrial product manufacturing is provided, the early warning system comprising:

[0027] The data acquisition module is used to acquire initial parameter data corresponding to several scrapped products; the initial parameter data includes several production factor information.

[0028] The scrap impact factor acquisition module is used to acquire target scrap impact factor information of the scrapped product under different production environments based on several initial parameter data; the target scrap impact factor information is characterized as environmental parameter information of different scrapped products under corresponding production environments;

[0029] The early warning classification module is used to generate early warning rule information for the industrial products produced in different production environments based on several target scrapping impact factors.

[0030] Optionally, the scrap impact factor acquisition module includes an initial purity acquisition unit, an actual impact factor acquisition unit, and a target impact factor acquisition unit:

[0031] The initial purity acquisition unit is used to acquire initial purity information based on several of the initial parameter data;

[0032] The actual impact factor acquisition unit is used to acquire actual scrapping impact factor information under set production environment conditions based on several initial parameter data.

[0033] The target impact factor acquisition unit is used to determine the target scrapping impact factor information based on the initial purity information and the actual scrapping impact factor information.

[0034] Optionally, the initial purity acquisition unit is used for:

[0035] The scrapped products are divided into several scrap groups based on preset classification conditions; wherein the preset classification conditions include at least one of mechanical failure conditions, process failure conditions, and electrical failure conditions.

[0036] Obtain first quantity information of the corresponding scrapped products in different scrapping groups; the first quantity information represents the quantity of scrapped products in different scrapping groups;

[0037] Based on several of the first quantity information, the initial purity information is determined; the initial purity information is characterized as the degree of failure information of the industrial product under different production environments.

[0038] Optionally, the actual impact factor acquisition unit is used for:

[0039] Obtain second quantity information of the scrapped products belonging to different scrapping groups under the set production environment conditions; the second quantity information represents the quantity of the scrapped products in different scrapping groups under the set production environment conditions;

[0040] Based on several of the second quantitative information, the actual scrap impact factor information under the set production environment conditions is obtained.

[0041] Optionally, the target impact factor acquisition unit is used for:

[0042] Based on the difference between the initial purity information and the actual scrap impact factor information, the target scrap impact factor information corresponding to the set production environment conditions is determined.

[0043] Optionally, the early warning classification module is used for:

[0044] The target scrapping impact factors under different production environments are sorted from high to low, and early warning rules corresponding to the production environment are generated based on the sorting results.

[0045] According to a third aspect of this disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and for running on the processor, wherein the processor executes the computer program to implement the early warning method for industrial product production as described in the first aspect of this disclosure.

[0046] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the early warning method for industrial product production as described in the first aspect of this disclosure.

[0047] According to a fifth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the early warning method for industrial product production as described in the first aspect of this disclosure.

[0048] Based on common knowledge in the field, the above-mentioned preferred conditions can be combined arbitrarily to obtain various preferred embodiments of this disclosure.

[0049] The positive and progressive effects of this disclosure are as follows:

[0050] The early warning method for industrial product production provided in this disclosure automatically infers the reasonable threshold range of key production factors by directly analyzing the initial parameter data of historical scrapped products. This changes the model of relying on manual experience to set static thresholds and improves the accuracy of product production. Furthermore, by converting historical scrapped data into executable early warning rules and applying them to real-time monitoring and future production, the stability of product production is further improved. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating the early warning method for industrial product production provided in Embodiment 1 of this disclosure;

[0052] Figure 2 This is a flowchart illustrating the process of determining target scrapping impact factor information provided in Embodiment 1 of this disclosure;

[0053] Figure 3 This is a schematic diagram of the process for determining initial purity information provided in Embodiment 1 of this disclosure;

[0054] Figure 4 This is a flowchart illustrating the process for determining actual scrapping impact factors provided in Embodiment 1 of this disclosure;

[0055] Figure 5 This is a schematic diagram of the early warning system for industrial product production provided in Embodiment 2 of this disclosure;

[0056] Figure 6 This is a schematic diagram of the scrap impact factor acquisition module provided in Embodiment 2 of this disclosure;

[0057] Figure 7 This is a schematic diagram of the structure of the electronic device provided in Embodiment 3 of this disclosure. Detailed Implementation

[0058] The present disclosure is further illustrated below by way of embodiments, but the present disclosure is not limited to the scope of the embodiments described herein.

[0059] The prefixes such as "first" and "second" used in this disclosure are merely for distinguishing different descriptive objects and do not limit the position, order, priority, quantity, or content of the described objects. The use of ordinal numbers and other prefixes used to distinguish descriptive objects in this disclosure does not constitute a limitation on the described objects. The description of the described objects is given in the claims or the context of the embodiments, and should not be construed as an unnecessary limitation. Furthermore, in the description of this embodiment, unless otherwise stated, "multiple" means two or more.

[0060] Example 1

[0061] like Figure 1 As shown, this embodiment provides an early warning method for industrial product production, including:

[0062] S11: Obtain initial parameter data corresponding to several scrapped products; the initial parameter data includes several production factor information;

[0063] S12: Based on several initial parameter data, obtain the target scrapping impact factor information of scrapped products under different production environments; the target scrapping impact factor information is represented as the environmental parameter information of different scrapped products under the corresponding production environment;

[0064] S13: Based on information on several target scrapping impact factors, generate early warning rule information for industrial products during production in different production environments.

[0065] In one implementation, the data collected based on IoT (Internet of Things) in this embodiment, combined with the data of the corresponding business dimension, will be saved in the database. During the reporting process, the product will be marked as scrapped; for example, each product has 5 IoT collection parameters (temperature, pressure, speed, vibration, and current).

[0066] The early warning method for industrial product production provided in this disclosure automatically infers the reasonable threshold range of key production factors by directly analyzing the initial parameter data of historical scrapped products. This changes the model of relying on manual experience to set static thresholds and improves the accuracy of product production. Furthermore, by converting historical scrapped data into executable early warning rules and applying them to real-time monitoring and future production, the stability of product production is further improved.

[0067] like Figure 2 As shown, step S12 includes:

[0068] S121: Obtain initial purity information based on several initial parameter data;

[0069] S122: Based on several initial parameter data, obtain information on actual scrapping impact factors under set production environment conditions;

[0070] S123: Based on the initial purity information and the actual scrap impact factor information, determine the target scrap impact factor information.

[0071] like Figure 3 As shown, step S121 includes:

[0072] S1211: Based on preset classification conditions, several scrapped products are divided into several scrap groups; wherein, the preset classification conditions include at least one of mechanical failure conditions, process failure conditions, and electrical failure conditions;

[0073] Based on preset classification conditions related to the root cause (such as mechanical failure, process parameter deviation, electrical component failure, raw material defects, etc.), all scrapped product records in the historical database are automatically classified into multiple scrap groups; the corresponding classification conditions can be defined and matched based on product fault codes, maintenance work order records or expert knowledge bases, thereby ensuring the business relevance of the classification.

[0074] S1212: Obtain the first quantity information of the corresponding scrapped products in different scrapping groups; the first quantity information represents the quantity of scrapped products in different scrapping groups;

[0075] S1213: Based on several first quantitative information, determine the initial purity information; the initial purity information is characterized as the degree of failure information of industrial products under different production environments.

[0076] The formula for calculating purity is: Initial purity = 1 - Σᵢ(Nᵢ / N_total)², where N_total is the total number of scrapped products. This value characterizes the degree of concentration or dispersion of failure cause categories among all scrapped samples.

[0077] By transforming qualitative scrapping phenomena into calculable purity indicators, an objective and quantitative benchmark is provided for subsequent analysis; the lower the purity, the more concentrated the historical scrapping problems are in one or two categories of causes.

[0078] like Figure 4 As shown, step S122 includes:

[0079] S1221: Obtain second quantity information of scrapped products belonging to different scrapping groups under set production environment conditions; the second quantity information represents the quantity of scrapped products in different scrapping groups under set production environment conditions;

[0080] First, a set production environment condition is received (e.g., specific workshop A, using model B equipment, ambient humidity > 70%). Then, under the constraints of this environment condition, the number of scrapped products belonging to each scrapping group is re-screened and counted to obtain the second quantity information.

[0081] S1222: Based on several second quantitative information, obtain information on the actual scrap impact factors under the given production environment conditions.

[0082] Based on the second quantity information, the actual scrap impact factor under this specific environment is calculated; the calculation method is similar to that of the initial purity, but the data range is different: actual scrap impact factor = 1-Σᵢ(Mᵢ / M_total)², where M_total is the total number of scrapped products under this environment. This factor characterizes the degree of concentration of the distribution of scrap causes under this specific environment.

[0083] By calculating independent factors under different environments, the frequency differences of the same failure mode in different environments can be identified, or unique combinations of failure modes in specific environments can be discovered, providing a basis for precise control.

[0084] Step S123 includes:

[0085] Based on the difference between the initial purity information and the actual scrap impact factor information, the target scrap impact factor information under the corresponding set production environment conditions is determined.

[0086] Step S13 in this embodiment includes:

[0087] The impact factors of target scrapping under different production environments are sorted from high to low, and early warning rules for the corresponding production environments are generated based on the sorting results.

[0088] For example: Sorting: Sort all analyzed production environments from low to high (i.e., from high to low risk) according to their corresponding target scrapping impact factor values; the higher the ranking of an environment, the higher its specific risk, and the more priority is needed to formulate strict monitoring rules.

[0089] Rule generation: For each high-risk environment ranked first:

[0090] Identify the dominant failure mode: Backtrack and analyze the one or more scrapped groups that account for the highest proportion of the second quantity information in this environment.

[0091] Extracting key parameter thresholds: For these dominant failure modes, statistically analyze the initial parameter data (such as temperature, pressure, etc.) from all relevant scrapped product data in this environment, and automatically fit the dangerous range of parameters that cause the failure (e.g., temperature <85°C or >115°C).

[0092] Combined rule generation: Ultimately generates one or more early warning rules. For example: IF Production Environment = Workshop A & Equipment Model = B & Ambient Humidity > 70% THEN Early Warning Trigger Condition: Temperature Sensor X Reading < 85°C OR > 115°C.

[0093] The implementation principle of the early warning method for industrial product production in this embodiment is explained below with specific implementation methods:

[0094] First, prepare the data: The combined data from IoT (Internet of Things) collection and business dimension data will be stored in the database. During the reporting process, products will be marked as scrapped. The data we need is the collection + business dimension data corresponding to the scrapped products.

[0095] Assume there are 150 scrapped products, each with 5 IoT data collection parameters. The specific product attributes are as follows:

[0096] Product 1: Temperature 85°C, Pressure 4.8MPa, Speed ​​1200rpm, Vibration 12mm / s, Current 40A.

[0097] Product 2: Temperature 95°C, Pressure 5.2MPa, Speed ​​1100rpm, Vibration 8mm / s, Current 45A.

[0098] Product 3: Temperature 84°C, Pressure 4.7MPa, Speed ​​1210rpm, Vibration 13mm / s, Current 39A.

[0099] (Total of 150 products)

[0100] Secondly, cluster centers are selected based on different fault problems. In this embodiment, mechanical fault problems, process fault problems, and electrical fault problems are selected as the initial cluster centers:

[0101] Mechanical failure problem as center 1: temperature 90°C, pressure 5.0MPa, speed 1150rpm, vibration 10mm / s, current 42A.

[0102] Process failure issue as center 2: temperature 80°C, pressure 4.5MPa, speed 1250rpm, vibration 15mm / s, current 38A.

[0103] Electrical fault issues as center 3: temperature 100°C, pressure 5.5MPa, speed 1050rpm, vibration 5mm / s, current 48A.

[0104] Then calculate the distances: for each product, calculate the distance to the three centers.

[0105] Distance formula:

[0106] Distance = √[(Temperature difference)² + (Pressure difference)² + (Velocity difference)² + (Vibration difference)² + (Current difference)²]

[0107] Example: Distance from product 1 to center 1~3.

[0108] Distance 1 (from product 1 to center 1):

[0109] Distance 1 = √[(85-90)² + (4.8-5.0)² + (1200-1150)² + (12-10)² + (40-42)²] = √[(-5)² + (-0.2)² + (50)² + (2)² + (-2)²] = √[25 + 0.04 + 2500 + 4 + 4] = √2533.04 = 50.33

[0110] Conclusion: Product 1 is closest to center 1 (50.33), and should be assigned to group 1.

[0111] Finally, the different products are grouped according to the nearest center to each product:

[0112] For example: Product 1 is closest to center 1, so it is assigned to group 1.

[0113] Product 2 is closest to center 3, so it is assigned to group 3.

[0114] Product 3 is closest to center 1 and is assigned to group 1.

[0115] (All products are grouped).

[0116] After being grouped:

[0117] Mechanical failure issues - Group 1: 60 products.

[0118] Process failure issues - Group 2: 30 products.

[0119] Electrical fault issues - Group 3: 60 products.

[0120] After grouping, the reasons for the corresponding product scrapping are analyzed using a decision tree analysis algorithm:

[0121] Mechanical Failure Problem Group (60 products):

[0122] Product 1: Temperature 85°C, Pressure 4.8MPa, Speed ​​1200rpm, Vibration 12mm / s, Current 40A, Temperature fluctuation 2.5°C, Pressure fluctuation 0.3MPa.

[0123] Product 2: Temperature 86°C, Pressure 4.9MPa, Speed ​​1180rpm, Vibration 11mm / s, Current 41A, Temperature fluctuation 2.8°C, Pressure fluctuation 0.2MPa.

[0124] Product 3: Temperature 84°C, Pressure 4.7MPa, Speed ​​1220rpm, Vibration 13mm / s, Current 39A, Temperature fluctuation 2.7°C, Pressure fluctuation 0.2MPa.

[0125] (60 products in total)

[0126] Process failure issue group (30 products):

[0127] Product 1: Temperature 95°C, Pressure 5.2MPa, Speed ​​1100rpm, Vibration 8mm / s, Current 45A, Temperature fluctuation 3.5°C, Pressure fluctuation 0.4MPa.

[0128] Product 2: Temperature 96°C, Pressure 5.3MPa, Speed ​​1080rpm, Vibration 7mm / s, Current 46A, Temperature fluctuation 3.8°C, Pressure fluctuation 0.4MPa.

[0129] Product 3: Temperature 94°C, Pressure 5.1MPa, Speed ​​1120rpm, Vibration 9mm / s, Current 44A, Temperature fluctuation 3.2°C, Pressure fluctuation 0.3MPa.

[0130] (Total of 30 products)

[0131] Electrical Fault Problem Group (60 products):

[0132] Product 1: Temperature 100°C, Pressure 5.5MPa, Speed ​​1050rpm, Vibration 5mm / s, Current 48A, Temperature fluctuation 4.0°C, Pressure fluctuation 0.5MPa.

[0133] Product 2: Temperature 101°C, Pressure 5.6MPa, Speed ​​1030rpm, Vibration 4mm / s, Current 49A, Temperature fluctuation 4.2°C, Pressure fluctuation 0.5MPa.

[0134] Product 3: Temperature 99°C, Pressure 5.4MPa, Speed ​​1070rpm, Vibration 6mm / s, Current 47A, Temperature fluctuation 3.8°C, Pressure fluctuation 0.4MPa.

[0135] (60 products in total)

[0136] Calculate the initial purity based on different fault problems:

[0137] Specifically: Initial purity = 1 - (number of mechanical failures / total number of products)² - (number of process problems / total number of products)² - (number of electrical problems / total number of products)²

[0138] =1-(60 / 150)²-(30 / 150)²-(60 / 150)²

[0139] =1-(0.4)²-(0.2)²-(0.4)²=1-0.16-0.04-0.16=0.64

[0140] Then, the optimal split point is determined for different production environments:

[0141] Try the temperature cutoff point:

[0142] Dividing point 1: Temperature = 90°C

[0143] Temperature > 90°C: 90 products, of which 0 had mechanical failures, 30 had process problems, and 60 had electrical problems.

[0144] Temperature ≤ 90°C: 60 products, of which 60 had mechanical failures, 0 had process problems, and 0 had electrical problems.

[0145] Purity at temperatures >90°C:

[0146] The purity at temperatures > 90°C = 1 - (0 / 90)² - (30 / 90)² - (60 / 90)² = 1 - 0 - (1 / 3)² - (2 / 3)² = 1 - 0 - 0.111 - 0.444 = 0.445

[0147] Purity at temperatures ≤90°C:

[0148] Purity at temperatures ≤ 90°C = 1 - (60 / 60)² - (0 / 60)² - (0 / 60)² = 1 - 1.0 - 0 - 0 = 0

[0149] Information gain:

[0150] Information gain = Initial purity - Weighted average purity = 0.64 - (90 / 150 × 0.445 + 60 / 150 × 0) = 0.64 - (0.6 × 0.445 + 0.4 × 0) = 0.64 - (0.267 + 0) = 0.64 - 0.267 = 0.373

[0151] Division point 2: Temperature = 95°C

[0152] Temperature > 95°C: 60 products, of which 0 had mechanical failures, 30 had process problems, and 30 had electrical problems.

[0153] Temperature ≤ 95°C: 90 products, of which 60 had mechanical failures, 0 had process problems, and 30 had electrical problems.

[0154] Purity at temperatures >95°C:

[0155] Purity at temperatures > 95°C = 1 - (0 / 60)² - (30 / 60)² - (30 / 60)² = 1 - 0 - 0.25 - 0.25 = 0.50

[0156] Purity at temperatures ≤95°C:

[0157] The purity at temperatures ≤ 95°C = 1 - (60 / 90)² - (0 / 90)² - (30 / 90)² = 1 - (2 / 3)² - 0 - (1 / 3)² = 1 - 0.444 - 0 - 0.111 = 0.445

[0158] Information gain:

[0159] Information gain = 0.64 - (60 / 150 × 0.50 + 90 / 150 × 0.445) = 0.64 - (0.4 × 0.50 + 0.6 × 0.445) = 0.64 - (0.20 + 0.267) = 0.64 - 0.467 = 0.173

[0160] Division point 3: Temperature = 93°C

[0161] Temperature > 93°C: 60 products, of which 0 had mechanical failures, 0 had process problems, and 60 had electrical problems.

[0162] Temperature ≤ 93°C: 90 products, of which 60 had mechanical failures, 30 had process problems, and 0 had electrical problems.

[0163] Purity at temperatures >93°C:

[0164] Purity at temperatures > 93°C = 1 - (0 / 60)² - (0 / 60)² - (60 / 60)² = 1 - 0 - 0 - 1.0 = 0

[0165] Purity at temperatures ≤93°C:

[0166] The purity at temperatures ≤ 93°C = 1 - (60 / 90)² - (30 / 90)² - (0 / 90)² = 1 - (2 / 3)² - (1 / 3)² - 0 = 1 - 0.444 - 0.111 - 0 = 0.445

[0167] Information gain:

[0168] Information gain = 0.64 - (60 / 150 × 0 + 90 / 150 × 0.445) = 0.64 - (0.4 × 0 + 0.6 × 0.445) = 0.64 - (0 + 0.267) = 0.64 - 0.267 = 0.373

[0169] The optimal information gain (maximum) for temperature characteristics is 0.373 (temperature = 90°C or 93°C).

[0170] Try other feature segmentation:

[0171] Try using a vibration of 6.5 mm / s for segmentation:

[0172] Vibration > 6.5 mm / s: 90 products, of which 60 were mechanical failures, 30 were process problems, and 0 were electrical problems.

[0173] Vibration ≤ 6.5 mm / s: 60 products, of which 0 had mechanical failures, 0 had process problems, and 60 had electrical problems.

[0174] Purity with vibrations > 6.5 mm / s:

[0175] The purity of vibrations > 6.5 mm / s = 1 - (60 / 90)² - (30 / 90)² - (0 / 90)² = 1 - (2 / 3)² - (1 / 3)² - 0 = 1 - 0.444 - 0.111 - 0 = 0.445

[0176] Purity with vibration ≤ 6.5 mm / s:

[0177] The purity of vibration ≤ 6.5 mm / s = 1 - (0 / 60)² - (0 / 60)² - (60 / 60)² = 1 - 0 - 0 - 1.0 = 0

[0178] Information gain:

[0179] Information gain = 0.64 - (90 / 150 × 0.445 + 60 / 150 × 0) = 0.64 - (0.6 × 0.445 + 0.4 × 0) = 0.64 - (0.267 + 0) = 0.373

[0180] The optimal information gain for vibration characteristics is 0.373.

[0181] Compare the information gain of all features:

[0182] Temperature: 0.373

[0183] Vibration: 0.372

[0184] Pressure: 0.285

[0185] Temperature fluctuation: 0.198

[0186] Speed: 0.156

[0187] Current: 0.134

[0188] Pressure fluctuation: 0.089

[0189] Temperature has the highest information gain (0.373), so temperature is chosen as the first split point.

[0190] Constructing the first layer of the decision tree:

[0191] First layer segmentation: Temperature > 90°C

[0192] Left branch (temperature ≤90°C): 60 products, of which 60 had mechanical failures, 0 had process problems, and 0 had electrical problems.

[0193] Right branch (temperature > 90°C): 90 products, of which 0 were mechanical failures, 30 were process problems, and 60 were electrical problems.

[0194] Second layer (taking the right branch of the first layer as an example):

[0195] First-level right branch data: 90 products, including 30 process issues and 60 electrical issues.

[0196] Try different feature segmentation:

[0197] Experiment with vibration breakpoint: Breakpoint: Vibration = 6.5 mm / s

[0198] Vibration > 6.5 mm / s: 30 products, 30 of which had process issues, and 0 had electrical issues.

[0199] Vibration ≤ 6.5 mm / s: 60 products, of which 0 had process issues and 60 had electrical issues.

[0200] Calculate purity (using vibration as an example):

[0201] Purity with vibrations > 6.5 mm / s:

[0202] The purity of vibrations > 6.5 mm / s = 1 - (30 / 30)² - (0 / 30)² = 1 - 1.0 - 0 = 0

[0203] Purity with vibration ≤ 6.5 mm / s:

[0204] The purity of vibration ≤ 6.5 mm / s = 1 - (0 / 60)² - (60 / 60)² = 1 - 0 - 1.0 = 0

[0205] Information gain:

[0206] Information gain = 0.445 - (30 / 90 × 0 + 60 / 90 × 0) = 0.445 - (0 + 0) = 0.445

[0207] Other segments

[0208] Finally, it was calculated that the information gain of vibration was the largest (0.445), and vibration was chosen as the split point of the right branch.

[0209] Continue recursively splitting until the stopping condition is met.

[0210] Stop condition:

[0211] All branches have a purity of 0 (completely pure).

[0212] Reach the maximum depth (e.g., 3 layers).

[0213] Current status check:

[0214] Left branch (temperature ≤ 90°C): 60 products, all with mechanical failures, purity = 0.

[0215] Right branch (temperature > 90°C): 90 products, need to be further divided.

[0216] Sub-branches of the right branch:

[0217] Vibration > 6.5 mm / s: 30 products, all with process issues, purity = 0.

[0218] Vibration ≤ 6.5 mm / s: 60 products, all with electrical problems, purity = 0.

[0219] The purity of all branches is 0, which satisfies the stopping condition, so the splitting stops.

[0220] The importance of each feature is equal to the sum of the information gains of that feature across all segmentations.

[0221] Temperature: 0.373 (first division).

[0222] Vibration: 0.445 (right branch split).

[0223] Pressure: 0.285 (right branch split).

[0224] Temperature fluctuation: 0.198 (right branch split).

[0225] Speed: 0.156 (right branch split).

[0226] Current: 0.134 (right branch split).

[0227] Pressure fluctuation: 0.089 (right branch split).

[0228] Normalization process:

[0229] The total sum is 0.373 + 0.445 + 0.285 + 0.198 + 0.156 + 0.134 + 0.089 = 1.680

[0230] The importance of normalization:

[0231] Temperature: 0.373 / 1.680 = 0.222 (22.2%)

[0232] Vibration: 0.445 / 1.680 = 0.265 (26.5%)

[0233] Pressure: 0.285 / 1.680 = 0.170 (17.0%)

[0234] Temperature fluctuation: 0.198 / 1.680 = 0.118 (11.8%)

[0235] Speed: 0.156 / 1.680 = 0.093 (9.3%)

[0236] Current: 0.134 / 1.680 = 0.080 (8.0%)

[0237] Pressure fluctuation: 0.089 / 1.680 = 0.053 (5.3%)

[0238] Practical significance:

[0239] By combining decision tree analysis with K-means clustering results, we found that:

[0240] Vibration is the most important factor (26.5%), and it can effectively distinguish between process problems and electrical problems.

[0241] Temperature was the second most important factor (22.2%), and it was able to effectively distinguish mechanical failures from other problems.

[0242] Stress is the third most important factor (17.0%), and it also has a significant impact on problem classification.

[0243] Decision-making rules:

[0244] If the temperature is ≤90°C, it is determined to be a mechanical failure.

[0245] If the temperature is >90°C and the vibration is >6.5 mm / s, it is determined to be a process problem.

[0246] If the temperature is >90°C and the vibration is ≤6.5mm / s, it is considered an electrical problem.

[0247] This analysis result is completely consistent with the findings of K-means clustering, forming a complete analytical chain: first, three scrapping patterns are discovered through clustering, and then the key features and rules that distinguish these three patterns are identified through decision tree analysis.

[0248] New rules are generated based on the above data:

[0249] Group 1 Mechanical Failure Problems (Low Temperature and High Vibration Mode): 60 products, temperature 85°C, pressure 4.8MPa, speed 1200rpm, vibration 12mm / s, current 40A.

[0250] Group 2 Process Issues (Medium Temperature and Medium Vibration Mode): 30 products, temperature 95°C, pressure 5.2MPa, speed 1100rpm, vibration 8mm / s, current 45A.

[0251] Group 3 Electrical Issues (High Temperature and Low Vibration Mode): 60 products, temperature 100°C, pressure 5.5MPa, speed 1050rpm, vibration 5mm / s, current 48A.

[0252] Results of the decision tree:

[0253] Decision tree structure: Temperature > 90°C → Vibration > 6.5 mm / s → Process problem / Electrical problem.

[0254] Importance of characteristics: temperature 22.2%, vibration 26.5%, pressure 17.0%, temperature fluctuation 11.8%, velocity 9.3%, current 8.0%, pressure fluctuation 5.3%.

[0255] Furthermore, rules are generated based on clustering results.

[0256] Cluster 1 Mechanical Failure Problems (Low Temperature High Vibration Mode):

[0257] Sample size: 60 products

[0258] Average temperature: 85°C

[0259] Average pressure: 4.8 MPa

[0260] Vibration mean: 12 mm / s

[0261] Average speed: 1200 rpm

[0262] Average current: 40A

[0263] Rule parameter calculation:

[0264] Temperature threshold calculation:

[0265] Mean = (83 + 84 + 85 + 86 + 87 + ... + 86) / 60 = 5100 / 60 = 85°C

[0266] Variance = Σ(each value - mean)² / sample size = [(83-85)² + (84-85)² + (85-85)² + (86-85)² + (87-85)² + ...] / 60 = 240 / 60 = 4 Standard deviation = √variance = √4 = 2°C

[0267] The lower limit of the temperature threshold = 85°C - 2°C = 83°C (the average value minus 2°C is used as the warning threshold).

[0268] Upper limit of temperature threshold = 85°C + 2°C = 87°C

[0269] Pressure threshold calculation:

[0270] The lower limit of the pressure threshold = 4.8MPa - 0.2MPa = 4.6MPa (the average value minus 0.2MPa is used as the warning threshold).

[0271] Upper limit of pressure threshold = 4.8 MPa + 0.2 MPa = 5.0 MPa

[0272] Vibration threshold calculation:

[0273] Vibration threshold lower limit = 12mm / s - 1mm / s = 11mm / s (mean value minus 1mm / s is used as the warning threshold)

[0274] Vibration threshold upper limit = 12mm / s + 1mm / s = 13mm / s

[0275] Rule 1: Mechanical Fault Early Warning

[0276] Rule 1: Mechanical fault warning IF temperature ∈ [83°C, 87°C] AND pressure ∈ [4.6MPa, 5.0MPa] AND vibration ∈ [11mm / s, 13mm / s] THEN trigger alarm (Confidence: High, Sample size: 60).

[0277] Cluster 2 (Medium-temperature, medium-vibration mode):

[0278] Sample size: 30 products

[0279] Average temperature: 95°C

[0280] Average pressure: 5.2 MPa

[0281] Vibration mean: 8 mm / s

[0282] Average speed: 1100 rpm

[0283] Average current: 45A

[0284] Rule parameter calculation:

[0285] Lower limit of temperature threshold = 95°C - 2°C = 93°C

[0286] Upper limit of temperature threshold = 95°C + 2°C = 97°C

[0287] Lower limit of pressure threshold = 5.2 MPa - 0.2 MPa = 5.0 MPa

[0288] Upper limit of pressure threshold = 5.2MPa + 0.2MPa = 5.4MPa Lower limit of vibration threshold = 8mm / s - 1mm / s = 7mm / s

[0289] Vibration threshold upper limit = 8mm / s + 1mm / s = 9mm / s

[0290] Generation Rule 2: Early Warning of Process Issues

[0291] Rule 2: Process Problem Warning IF Temperature ∈ [93°C, 97°C] AND Pressure ∈ [5.0MPa, 5.4MPa] AND Vibration ∈ [7mm / s, 9mm / s] THEN Trigger Alarm (Confidence: Medium, Sample Size: 30)

[0292] Cluster 3 (High Temperature Low Vibration Mode):

[0293] Sample size: 60 products

[0294] Average temperature: 100°C

[0295] Average pressure: 5.5 MPa

[0296] Vibration mean: 5 mm / s

[0297] Average speed: 1050 rpm

[0298] Average current: 48A

[0299] Rule parameter calculation:

[0300] Lower limit of temperature threshold = 100°C - 2°C = 98°C

[0301] Upper limit of temperature threshold = 100°C + 2°C = 102°C

[0302] Lower limit of pressure threshold = 5.5MPa - 0.2MPa = 5.3MPa

[0303] Upper limit of pressure threshold = 5.5MPa + 0.2MPa = 5.7MPa

[0304] Vibration threshold lower limit = 5mm / s - 1mm / s = 4mm / s

[0305] Vibration threshold upper limit = 5mm / s + 1mm / s = 6mm / s

[0306] Rule 3: Electrical Problem Warning

[0307] Rule 3: Electrical problem warning IF temperature ∈ [98°C, 102°C] AND pressure ∈ [5.3MPa, 5.7MPa] AND vibration ∈ [4mm / s, 6mm / s] THEN trigger alarm (Confidence: High, Sample size: 60).

[0308] Rule generation is based on feature importance.

[0309] Importance of temperature: 22.2%

[0310] Rule 4: Temperature Anomaly Warning IF Temperature < 87°C OR Temperature > 93°C THEN Trigger Alarm (Confidence: High, Importance: 22.2%)

[0311] Vibration importance: 26.5%

[0312] Rule 5: Vibration Anomaly Warning IF Vibration <6mm / s OR Vibration >10mm / s THEN Trigger Alarm (Confidence: High, Importance: 26.5%)

[0313] Importance of stress: 17.0%

[0314] Rule 6: Pressure Anomaly Warning IF Pressure < 4.9 MPa OR Pressure > 5.3 MPa THEN Trigger Alarm (Confidence: Medium, Importance: 17.0%)

[0315] Path generation rules based on decision tree

[0316] Path 1: Temperature ≤ 90°C → Mechanical failure.

[0317] Rule 7: Mechanical Fault Warning IF Temperature ≤ 90°C THEN Trigger Alarm (Confidence: High, Problem Type: Mechanical Fault).

[0318] Path 2: Temperature > 90°C, vibration > 6.5 mm / s → process problem.

[0319] Rule 8: Process Problem Warning IF Temperature > 90°C AND Vibration > 6.5 mm / s THEN Trigger Alarm (Confidence: High, Problem Type: Process Problem).

[0320] Path 3: Temperature > 90°C, vibration ≤ 6.5 mm / s → electrical problem.

[0321] Rule 9: Electrical Problem Warning IF Temperature > 90°C AND Vibration ≤ 6.5 mm / s THEN Trigger Alarm (Confidence: High, Problem Type: Electrical Problem).

[0322] Sorting based on rule priority

[0323] High-priority rules (based on clustering results):

[0324] Rule 1: Early warning of mechanical failure (sample size: 60, confidence level: high).

[0325] Rule 3: Electrical problem warning (sample size: 60, confidence level: high).

[0326] Rule 2: Early warning of process problems (sample size: 30, confidence level: medium).

[0327] Medium priority rules (based on feature importance):

[0328] Rule 5: Vibration anomaly warning (Importance: 26.5%).

[0329] Rule 4: Temperature Anomaly Warning (Importance: 22.2%).

[0330] Rule 6: Warning of abnormal stress (Importance: 17.0%).

[0331] Low-priority rules (based on decision tree paths):

[0332] Rule 8: Early warning of process problems (decision tree path).

[0333] Rule 9: Electrical Problem Warning (Decision Tree Path).

[0334] Rule 7: Mechanical Failure Early Warning (Decision Tree Path).

[0335] The above rules are deduplicated and merged.

[0336] Deduplication analysis:

[0337] Both Rule 1 and Rule 7 apply to mechanical failures, but the conditions differ.

[0338] Both Rule 2 and Rule 8 address process issues, but the conditions differ.

[0339] Both Rule 3 and Rule 9 address electrical issues, but the conditions differ.

[0340] Merge strategy:

[0341] Retain rules based on clustering results (more precise).

[0342] The decision tree path rule is used as an auxiliary rule.

[0343] Final rule set:

[0344] High-priority rules:

[0345] Rule 1: Mechanical fault warning IF temperature ∈ [83°C, 87°C] AND pressure ∈ [4.6MPa, 5.0MPa] AND vibration ∈ [11mm / s, 13mm / s] THEN trigger alarm (Confidence: High, Sample size: 60).

[0346] Rule 2: Electrical problem warning IF temperature ∈ [98°C, 102°C] AND pressure ∈ [5.3MPa, 5.7MPa] AND vibration ∈ [4mm / s, 6mm / s] THEN trigger alarm (Confidence: High, Sample size: 60).

[0347] Rule 3: Process problem warning IF temperature ∈ [93°C, 97°C] AND pressure ∈ [5.0MPa, 5.4MPa] AND vibration ∈ [7mm / s, 9mm / s] THEN trigger alarm (confidence level: medium, sample size: 30).

[0348] Medium priority rules:

[0349] Rule 4: Vibration Anomaly Warning IF Vibration <6mm / s OR Vibration >10mm / s THEN Trigger Alarm (Confidence: High, Importance: 26.5%).

[0350] Rule 5: Temperature Anomaly Warning IF Temperature < 87°C OR Temperature > 93°C THEN Trigger Alarm (Confidence: High, Importance: 22.2%).

[0351] Rule 6: Pressure Anomaly Warning IF Pressure < 4.9 MPa OR Pressure > 5.3 MPa THEN Trigger Alarm (Confidence: Medium, Importance: 17.0%).

[0352] Low priority rules:

[0353] Rule 7: Comprehensive early warning IF Temperature > 90°C AND Vibration > 6.5mm / s THEN Trigger Alarm (Confidence: Medium, Problem Type: Process Problem).

[0354] Rule 8: Comprehensive warning IF Temperature > 90°C AND Vibration ≤ 6.5mm / s THEN Trigger Alarm (Confidence: Medium, Problem Type: Electrical Problem).

[0355] Rule 9: Comprehensive early warning IF temperature ≤ 90°C THEN trigger alarm (Confidence level: Medium, Problem type: Mechanical failure).

[0356] Optimize rule parameters based on historical data to improve the accuracy and effectiveness of the rules.

[0357] Detailed calculation process:

[0358] Step 1: Calculate rule coverage

[0359] Coverage rate = Number of scrapped products triggered by the rule / Total number of scrapped products

[0360] Step 2: Calculate the rule accuracy

[0361] Accuracy = Number of scrapped products triggered by the rule / Total number of products triggered by the rule

[0362] Step 3: Calculate rule validity

[0363] Effectiveness = Coverage × Accuracy

[0364] Step 4: Optimize rule parameters

[0365] Adjust rule parameters based on validity:

[0366] Rule 1 optimization:

[0367] Original: Temperature ∈ [83°C, 87°C] AND Pressure ∈ [4.6MPa, 5.0MPa] AND Vibration ∈ [11mm / s, 13mm / s].

[0368] After optimization: Temperature ∈ [84°C, 86°C] AND Pressure ∈ [4.7MPa, 4.9MPa] AND Vibration ∈ [11.5mm / s, 12.5mm / s].

[0369] Reason: To improve accuracy and reduce false alarms.

[0370] Rule 2 optimization:

[0371] Original: Temperature ∈ [93°C, 97°C] AND Pressure ∈ [5.0MPa, 5.4MPa] AND Vibration ∈ [7mm / s, 9mm / s].

[0372] After optimization: Temperature ∈ [94°C, 96°C] AND Pressure ∈ [5.1MPa, 5.3MPa] AND Vibration ∈ [7.5mm / s, 8.5mm / s].

[0373] Reason: To improve accuracy and reduce false alarms.

[0374] Rule 3 optimization:

[0375] Original: Temperature ∈ [98°C, 102°C] AND Pressure ∈ [5.3MPa, 5.7MPa] AND Vibration ∈ [4mm / s, 6mm / s].

[0376] After optimization: Temperature ∈ [99°C, 101°C] AND Pressure ∈ [5.4MPa, 5.6MPa] AND Vibration ∈ [4.5mm / s, 5.5mm / s].

[0377] Reason: To improve accuracy and reduce false alarms.

[0378] Rule Validation Algorithm - Validating Rule Effects

[0379] Verify the effectiveness of the generated rules in practical applications to ensure their reliability.

[0380] Detailed calculation process:

[0381] Step 1: Cross-validation

[0382] The effect of using K-fold cross-validation to verify the rules:

[0383] 50% cross-validation:

[0384] The 150 scrapped products were divided into 5 groups of 30 each.

[0385] Use 4 sets of training rules and 1 set of validation rules.

[0386] Repeat 5 times and calculate the average effect.

[0387] Step 2: Calculate the validation index

[0388] Accuracy = Number of correctly predicted scrapped products / Total number of scrapped products Recall = Number of correctly predicted scrapped products / Actual number of scrapped products F1 score = 2 × (accuracy × recall) / (accuracy + recall).

[0389] Step 3: Rule Effectiveness Evaluation

[0390] Rule 1: Precision = 95%, Recall = 90%, F1 score = 92%.

[0391] Rule 2: Precision = 88%, Recall = 85%, F1 score = 86%.

[0392] Rule 3: Precision = 92%, Recall = 88%, F1 score = 90%.

[0393] Step 4: Sorting by Rules

[0394] Sort the rules according to their F1 scores:

[0395] Rule 1: F1 score = 92%.

[0396] Rule 3: F1 score = 90%.

[0397] Rule 2: F1 score = 86%.

[0398] The final generated rule set

[0399] High-priority rules:

[0400] Rule 1: Mechanical Failure Warning IF Temperature ∈ [84°C, 86°C] AND Pressure ∈ [4.7MPa, 4.9MPa] AND Vibration ∈ [11.5mm / s, 12.5mm / s] THEN Trigger Alarm (Confidence: High, F1 Score: 92%).

[0401] Rule 2: Electrical problem warning IF temperature ∈ [99°C, 101°C] AND pressure ∈ [5.4MPa, 5.6MPa] AND vibration ∈ [4.5mm / s, 5.5mm / s] THEN trigger alarm (Confidence: High, F1 score: 90%).

[0402] Rule 3: Process Problem Warning IF Temperature ∈ [94°C, 96°C] AND Pressure ∈ [5.1MPa, 5.3MPa] AND Vibration ∈ [7.5mm / s, 8.5mm / s] THEN Trigger Alarm (Confidence: Medium, F1 Score: 86%).

[0403] Medium priority rules:

[0404] Rule 4: Temperature Anomaly Warning IF Temperature < 87°C OR Temperature > 93°C THEN Trigger Alarm (Confidence: High, Importance: 22.2%).

[0405] Rule 5: Vibration Anomaly Warning IF Vibration <6mm / s OR Vibration >10mm / s THEN Trigger Alarm (Confidence: High, Importance: 26.5%).

[0406] Rule 6: Pressure Anomaly Warning IF Pressure < 4.9 MPa OR Pressure > 5.3 MPa THEN Trigger Alarm (Confidence: Medium, Importance: 17.0%).

[0407] Low priority rules:

[0408] Rule 7: Comprehensive early warning IF Temperature > 90°C AND Vibration > 6.5mm / s THEN Trigger Alarm (Confidence: Medium, Problem Type: Process Problem).

[0409] Rule 8: Comprehensive warning IF Temperature > 90°C AND Vibration ≤ 6.5mm / s THEN Trigger Alarm (Confidence: Medium, Problem Type: Electrical Problem).

[0410] Rule 9: Comprehensive early warning IF temperature ≤ 90°C THEN trigger alarm (Confidence level: Medium, Problem type: Mechanical failure).

[0411] Practical application effect

[0412] Before optimization:

[0413] Accuracy rate of early warning for scrapped products: 75%.

[0414] False alarm rate: 15%.

[0415] Early warning time: 5 minutes.

[0416] After optimization:

[0417] Accuracy rate of early warning for obsolete products: 92%.

[0418] False alarm rate: 8%.

[0419] Early warning time: 15 minutes.

[0420] The early warning method for industrial product production provided in this disclosure automatically infers the reasonable threshold range of key production factors by directly analyzing the initial parameter data of historical scrapped products. This changes the model of relying on manual experience to set static thresholds and improves the accuracy of product production. Furthermore, by converting historical scrapped data into executable early warning rules and applying them to real-time monitoring and future production, the stability of product production is further improved.

[0421] Example 2

[0422] like Figure 5 As shown, this embodiment provides an early warning system for industrial product production. The early warning system includes:

[0423] The data acquisition module 100 is used to acquire initial parameter data corresponding to several scrapped products; the initial parameter data includes several production factor information.

[0424] The scrap impact factor acquisition module 200 is used to acquire target scrap impact factor information of scrapped products under different production environments based on several initial parameter data; the target scrap impact factor information is represented as environmental parameter information of different scrapped products under corresponding production environments;

[0425] The early warning classification module 300 is used to generate early warning rule information for industrial products in different production environments based on information on several target scrapping impact factors.

[0426] like Figure 6 As shown, the scrap impact factor acquisition module 200 in this embodiment includes an initial purity acquisition unit 201, an actual impact factor acquisition unit 202, and a target impact factor acquisition unit 203.

[0427] The initial purity acquisition unit 201 is used to acquire initial purity information based on several initial parameter data;

[0428] The actual impact factor acquisition unit 202 is used to acquire actual scrapping impact factor information under set production environment conditions based on several initial parameter data;

[0429] The target impact factor acquisition unit 203 is used to determine the target scrap impact factor information based on the initial purity information and the actual scrap impact factor information.

[0430] The initial purity acquisition unit 201 in this embodiment is used for:

[0431] Based on preset classification conditions, several scrapped products are divided into several scrap groups; wherein the preset classification conditions include at least one of mechanical failure conditions, process failure conditions, and electrical failure conditions;

[0432] Obtain the first quantity information of the corresponding scrapped products in different scrapping groups; the first quantity information represents the quantity of scrapped products in different scrapping groups;

[0433] Based on several primary quantitative information, the initial purity information is determined; the initial purity information is characterized as the degree of failure information of industrial products under different production environments.

[0434] In this embodiment, the actual impact factor acquisition unit 202 is used for:

[0435] Obtain second quantity information of scrapped products belonging to different scrapping groups under set production environment conditions; the second quantity information represents the quantity of scrapped products in different scrapping groups under set production environment conditions;

[0436] Based on several second-order quantitative information, information on actual scrapping impact factors under given production environment conditions is obtained.

[0437] In this embodiment, the target influence factor acquisition unit 203 is used for:

[0438] Based on the difference between the initial purity information and the actual scrap impact factor information, the target scrap impact factor information under the corresponding set production environment conditions is determined.

[0439] In this embodiment, the early warning classification module 300 is used for:

[0440] The impact factors of target scrapping under different production environments are sorted from high to low, and early warning rules for the corresponding production environments are generated based on the sorting results.

[0441] For the system embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The system embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs.

[0442] The early warning system for industrial product production provided in this disclosure automatically infers reasonable threshold ranges for key production factors by directly analyzing the initial parameter data of historical scrapped products. This changes the model of relying on manual experience to set static thresholds and improves the accuracy of product production. Furthermore, by converting historical scrapped data into executable early warning rules and applying them to real-time monitoring and future production, the stability of product production is further improved.

[0443] Example 3

[0444] like Figure 7 As shown, Figure 7 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of this disclosure. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the methods described in the above embodiments. Figure 7 The electronic device 30 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0445] like Figure 7 As shown, the electronic device 30 can be represented in the form of a general computing device, such as a server device. The components of the electronic device 30 may include, but are not limited to: at least one processor 31, at least one memory 32, and a bus 33 connecting different system components (including memory 32 and processor 31).

[0446] Bus 33 includes a data bus, an address bus, and a control bus.

[0447] The memory 32 may include volatile memory, such as random access memory (RAM) 321 and / or cache memory 322, and may further include read-only memory (ROM) 323.

[0448] The memory 32 may also include a program tool 325 having a set (at least one) of program modules 324, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0449] The processor 31 performs various functional applications and data processing, such as the methods described in the above embodiments of this disclosure, by running computer programs stored in the memory 32.

[0450] Electronic device 30 can also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). This communication can be performed via input / output (I / O) interface 35. Furthermore, the model-generated electronic device 30 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 36. Figure 7 As shown, network adapter 36 communicates with other modules of the model-generated electronic device 30 via bus 33. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the model-generated electronic device 30, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems.

[0451] It should be noted that although several units / modules or sub-units / modules of the electronic device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0452] Example 4

[0453] This disclosure also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the early warning method for industrial product production provided in any of the above embodiments.

[0454] The readable storage medium may be more specifically adopted, including but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0455] Example 5

[0456] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the early warning method for the production of industrial products as described above.

[0457] The program code for executing the computer program product of this disclosure can be written in any combination of one or more programming languages, and the program code can be executed entirely on a user device, partially on a user device, as a stand-alone software package, partially on a user device and partially on a remote device, or entirely on a remote device.

[0458] While specific embodiments of this disclosure have been described above, those skilled in the art should understand that these are merely illustrative examples, and the scope of protection of this disclosure is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principles and essence of this disclosure, but all such changes and modifications fall within the scope of protection of this disclosure.

Claims

1. A method of early warning in the production of an industrial product, characterized in that, The early warning method includes: Acquire initial parameter data corresponding to several scrapped products; the initial parameter data includes several production factor information. Based on several initial parameter data, target scrapping impact factor information of the scrapped products under different production environments is obtained; the target scrapping impact factor information is characterized as environmental parameter information of different scrapped products under corresponding production environments; Based on several target scrapping impact factors, early warning rule information is generated for the industrial products during production in different production environments. The step of obtaining the target scrapping impact factor information of the scrapped product under different production environments based on several initial parameter data includes: Based on the aforementioned initial parameter data, obtain the initial purity information; Based on several initial parameter data, information on actual scrapping impact factors under set production environment conditions is obtained; Based on the initial purity information and the actual scrap impact factor information, the target scrap impact factor information is determined; The step of obtaining initial purity information based on several of the initial parameter data includes: The scrapped products are divided into several scrap groups based on preset classification conditions; wherein the preset classification conditions include at least one of mechanical failure conditions, process failure conditions, and electrical failure conditions. Obtain first quantity information of the corresponding scrapped products in different scrapping groups; the first quantity information represents the quantity of scrapped products in different scrapping groups; Based on several of the first quantity information, the initial purity information is determined; the initial purity information is characterized as the degree of failure information of the industrial product under different production environments. The step of obtaining actual scrapping impact factor information under set production environment conditions based on several initial parameter data includes: Obtain second quantity information of the scrapped products belonging to different scrapping groups under the set production environment conditions; the second quantity information represents the quantity of the scrapped products in different scrapping groups under the set production environment conditions; Based on several of the second quantitative information, the actual scrap impact factor information under the set production environment conditions is obtained; The step of determining the target scrapping impact factor information based on the initial purity information and the actual scrapping impact factor information includes: Based on the difference between the initial purity information and the actual scrap impact factor information, the target scrap impact factor information corresponding to the set production environment conditions is determined. The step of generating early warning rule information for the industrial product during production in different production environments based on several target scrapping impact factors includes: The target scrapping impact factor information under different production environments is sorted from high to low, and a warning rule corresponding to the production environment is generated based on the sorting result. The steps for generating early warning rules in the production environment include: For high-risk production environments identified based on the target scrap impact factor information, one or more scrap groups with the highest proportion in the production environment are identified based on the second quantity information to determine the dominant failure mode; For the dominant failure mode, statistical analysis of initial parameter data is performed from relevant scrapped product data in the production environment to fit the dangerous range of parameters that lead to the dominant failure mode; and rules are generated by combining them.

2. A pre-alarm system in the production of industrial products, characterized in that, The early warning system includes: The data acquisition module is used to acquire initial parameter data corresponding to several scrapped products; the initial parameter data includes several production factor information. The scrap impact factor acquisition module is used to acquire target scrap impact factor information of the scrapped product under different production environments based on several initial parameter data; the target scrap impact factor information is characterized as environmental parameter information of different scrapped products under corresponding production environments; The early warning classification module is used to generate early warning rule information for the industrial products when they are produced in different production environments, based on several target scrapping impact factors. The scrap impact factor acquisition module includes an initial purity acquisition unit, an actual impact factor acquisition unit, and a target impact factor acquisition unit: The initial purity acquisition unit is used to acquire initial purity information based on several of the initial parameter data; The actual impact factor acquisition unit is used to acquire actual scrapping impact factor information under set production environment conditions based on several initial parameter data. The target impact factor acquisition unit is used to determine the target scrapping impact factor information based on the initial purity information and the actual scrapping impact factor information; The initial purity acquisition unit is used for: The scrapped products are divided into several scrap groups based on preset classification conditions; wherein the preset classification conditions include at least one of mechanical failure conditions, process failure conditions, and electrical failure conditions. Obtain first quantity information of the corresponding scrapped products in different scrapping groups; the first quantity information represents the quantity of scrapped products in different scrapping groups; Based on several of the first quantity information, the initial purity information is determined; the initial purity information is characterized as the degree of failure information of the industrial product under different production environments. The actual impact factor acquisition unit is used for: Obtain second quantity information of the scrapped products belonging to different scrapping groups under the set production environment conditions; the second quantity information represents the quantity of the scrapped products in different scrapping groups under the set production environment conditions; Based on several of the second quantitative information, the actual scrap impact factor information under the set production environment conditions is obtained; The target impact factor acquisition unit is used for: Based on the difference between the initial purity information and the actual scrap impact factor information, the target scrap impact factor information corresponding to the set production environment conditions is determined. The early warning classification module is used for: The target scrapping impact factor information under different production environments is sorted from high to low, and a warning rule corresponding to the production environment is generated based on the sorting result. The early warning classification module is also used for: For high-risk production environments identified based on the target scrap impact factor information, one or more scrap groups with the highest proportion in the production environment are identified based on the second quantity information to determine the dominant failure mode; For the dominant failure mode, statistical analysis of initial parameter data is performed from relevant scrapped product data in the production environment to fit the dangerous range of parameters that lead to the dominant failure mode; and rules are generated by combining them.

3. An electronic device comprising a memory, a processor, and a computer program stored on the memory for running on the processor, characterized in that, When the processor executes the computer program, it implements the early warning method for industrial product production as described in claim 1.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the early warning method for industrial product production as described in claim 1.

5. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the early warning method for industrial product production as described in claim 1.

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