Industrial internet data processing method and system based on big data

Abstract

The invention provides an industrial internet data processing method and system based on big data, and relates to the technical field of industrial internet. Identifying part requirements in the order information according to the order information and performing production scheduling, sending part requirement information to suppliers, timely allocating purchased parts to a production line indicated by production scheduling scheme information, recording installation positions and installation parameters of each part, performing corresponding coding on the parts, the installation positions and the suppliers, and performing production scheduling according to the coded parts, the installation positions and the suppliers. According to damaged part information fed back by a user, information corresponding to a damaged part code is periodically called for analysis, so that the production process is improved. According to the method, multiple terminals such as suppliers, part scheduling in a factory, production scheduling, production line assembly production and clients are designed, and data correlation inside and outside the factory is enhanced through unified coding of the multiple terminals, so that deep mining of the data is facilitated, the production process is improved, and the data utilization rate is increased.

Description

Technical Field

[0001] The present invention relates to the field of industrial Internet technology, and in particular to a method and system for industrial Internet data processing based on big data. Background Art

[0002] The Industrial Internet is the product of the deep integration of next-generation information technology and industrial systems. By fully connecting people, equipment, data, and processes, it enables digitalization, networking, and intelligent upgrades throughout the entire production process, reshaping the industrial value chain. It is not only an extension of the Industrial Internet of Things (IIoT), but also a comprehensive ecosystem encompassing cloud computing, big data, artificial intelligence, 5G, and other technologies.

[0003] In the industrial Internet environment, various types of digital information (i.e., industrial Internet data) generated by equipment, systems, business processes, and user interactions are collected, transmitted, and stored in real time through sensors, the Internet of Things (IoT), cloud computing, big data, and other technologies. Industrial Internet data is a core resource that drives intelligent manufacturing, optimizes production processes, and improves decision-making efficiency.

[0004] However, in existing technologies, data sources are diverse and the correlation between data is weak, resulting in low data utilization. Summary of the Invention

[0005] The problem to be solved by the present invention is that in the prior art, data sources are diverse and the correlation between data is weak, resulting in low data utilization.

[0006] To solve the above problems, in a first aspect, the present invention provides an industrial Internet data processing method based on big data, comprising:

[0007] Obtain customer order information and generate parts demand information and production scheduling information based on the order information;

[0008] issuing procurement information to multiple suppliers based on the parts demand information, wherein each supplier has a supplier code;

[0009] According to the production scheduling information, the purchased parts are transported to the corresponding production line. Each part has a part code, which corresponds to the supplier code.

[0010] Record the installation position and installation parameters of each part, and encode the installation position, where the part code, position code and installation parameters are saved in correspondence;

[0011] Obtaining damaged parts information reported by users, wherein the damaged parts information includes the damaged parts code and the time the parts have been used;

[0012] According to the damaged part codes of similar parts, the installation parameters, part usage time, location code and supplier code corresponding to each damaged part code are periodically called for analysis to improve the production process.

[0013] Optionally, the periodically calling, based on the damaged part codes of similar parts, installation parameters, part usage time, location code, and supplier code corresponding to each damaged part code for analysis to improve the production process includes:

[0014] If the part usage time is greater than or equal to the preset service life, the damaged part corresponding to the part usage time is regarded as a normal damaged part, and the installation parameters, damaged part information, location code, and supplier code corresponding to the normal damaged part are removed from the analysis information to obtain information to be analyzed, wherein the analysis information includes the called installation parameters, part usage time, location code, and supplier code corresponding to each damaged part code, as well as the damaged part code;

[0015] The installation parameters corresponding to the damaged part codes are counted, and if the installation parameters meet the preset regular conditions, the installation parameters in the production process are improved.

[0016] Optionally, the statistically counting installation parameters corresponding to the damaged part codes, if the installation parameters meet the preset regularity conditions, improving the installation parameters in the production process includes:

[0017] Counting the installation parameters corresponding to the damaged part codes, and rounding the installation parameters to obtain a damaged installation parameter sequence;

[0018] Count the installation parameters corresponding to all part codes, and round the installation parameters to obtain a complete installation parameter sequence;

[0019] Analyzing the ratio of the number of parameter types in the damaged installation parameter sequence to the number of parameter types in the full installation parameter sequence to obtain a proportion of abnormal parameters;

[0020] If the abnormal parameter ratio is less than the preset parameter ratio, the number of each parameter type in the damaged installation parameter sequence is counted to obtain the number of damaged parameters corresponding to each parameter type;

[0021] According to the number of damaged parameters, the screened installation parameters are marked as bad installation parameters, and the bad installation parameters are eliminated during the production process.

[0022] Optionally, the statistically counting installation parameters corresponding to the damaged part codes, if the installation parameters meet the preset regularity conditions, improving the installation parameters in the production process includes:

[0023] Count the installation parameters corresponding to the damaged part codes and sort them in ascending order to obtain a damaged installation parameter sequence;

[0024] Analyze the distance between two adjacent installation parameters in the damaged installation parameter sequence to obtain the parameter distance;

[0025] If the parameter distance is greater than or equal to the preset parameter distance, disconnecting from the two installation parameters corresponding to the parameter distance, and dividing the damaged installation parameter sequence into multiple damaged installation parameter subsequences;

[0026] Count the number of elements of the installation parameters in the damaged installation parameter subsequence;

[0027] If the number of elements is less than the first preset number of elements, discard the damaged installation parameter subsequence;

[0028] If the number of elements is greater than or equal to the first preset number of elements, retain the damaged installation parameter subsequence;

[0029] Pick up the top three retained damaged installation parameter subsequences with the largest number of elements, divide the number of elements in each picked damaged installation parameter subsequence by the total number of elements in all retained damaged installation parameter subsequences, and obtain the damaged parameter ratio corresponding to each picked damaged installation parameter subsequence;

[0030] If the damaged parameter ratio corresponding to the picked damaged installation parameter subsequence is greater than or equal to the preset damaged parameter ratio and the number of elements corresponding thereto is greater than or equal to the second preset number of elements, then the damaged installation parameter subsequence is marked as a bad installation parameter sequence;

[0031] The first and last installation parameters of the bad installation parameter sequence are picked up to obtain a bad installation parameter range, and the bad installation parameter range is eliminated during the production process.

[0032] Optionally, the statistically calculating installation parameters corresponding to the damaged part codes, if the installation parameters meet the preset regularity conditions, and improving the installation parameters in the production process, the industrial Internet data processing method based on big data includes:

[0033] If the installation parameters do not meet the preset regular conditions, counting the position codes corresponding to all damaged part codes to obtain the number of codes for each position code;

[0034] Divide the number of codes for each position code by the total number of codes for all position codes to obtain the percentage of damaged parts corresponding to each position code;

[0035] If the damaged parts ratio is greater than the preset damaged parts ratio, the part at the position code corresponding to the damaged parts ratio is marked as a wrongly selected part, and a reminder message for selecting a replacement part is generated.

[0036] Optionally, the industrial Internet data processing method based on big data further includes:

[0037] Determining the quantity of each supplier code according to the supplier code in the information to be analyzed;

[0038] Based on the quantity of each supplier code and the total quantity of all supplier codes, the proportion of damaged parts supplied corresponding to each supplier code is obtained;

[0039] If the proportion of damaged parts supplied is greater than the preset proportion of damaged parts supplied, the supplier corresponding to the supplier code will be listed as a supplier to be inspected;

[0040] If a supplier is listed as a supplier to be reviewed for multiple consecutive periods, the procurement information sent to this supplier will be reduced or cancelled.

[0041] Optionally, the acquiring of customer order information and generating parts demand information and production scheduling information according to the order information includes:

[0042] Determine the product type, product quantity, and delivery time for each product type based on the order information;

[0043] Obtain the product types that each production line can produce and the unit production time of each product type on each production line, where each production line can produce two product types, each product type can be produced on two production lines, and the number of product types is equal to the number of production lines;

[0044] Determine the corresponding delivery production time for each product type based on the current time and delivery time;

[0045] Based on the product quantity of all product types and the unit time required for each production line to produce each product type, and with the actual production time being less than or equal to the delivery production time as the constraint condition, a production scheduling model is constructed to obtain production scheduling plan information.

[0046] Alternatively, assuming that the first production line can produce products of category 1 and category 2, the second production line can produce products of category 2 and category 3, the third production line can produce products of category 3 and category 4, and so on, the n-1th production line can produce products of category n-1 and category n, and the nth production line can produce products of category n and category 1; the first production line first produces products of category 1, the second production line first produces products of category 2, the third production line first produces products of category 3, and so on, the nth production line first produces products of category n;

[0047] The production scheduling model is:

[0048]

[0049] Among them, max() means taking the maximum value, k nn k represents the unit production time of the nth production line producing the nth type of product, (n-1)n A represents the unit production time of the n-1 production line producing the n-th type of product, nn A represents the number of products of type n produced by the n-th production line. (n-1)n represents the number of products of type n produced by the n-1 production line, t n represents the actual production time of the nth type of product, T n Indicates the delivery production time of the nth type of product.

[0050] Optionally, the production scheduling model is constructed based on the product quantity of all product types and the unit time required for each production line to produce each product type, with the actual production time being less than or equal to the delivered production time as a constraint, and the obtained production scheduling plan information includes:

[0051] If the minimum actual production time analyzed according to the production scheduling model is greater than the delivery production time, the minimum actual production time will be used as the delivery production time, and delivery time exception information will be generated, wherein the delivery time exception information includes a delivery time exception reminder and the minimum actual production time.

[0052] In a second aspect, the present invention further provides an industrial Internet data processing system based on big data, comprising:

[0053] An order information parsing module is used to obtain customer order information and generate parts demand information and production scheduling information based on the order information;

[0054] A purchasing information issuing module is used to issue purchasing information to multiple suppliers based on the parts demand information, wherein each supplier has a supplier code;

[0055] The parts scheduling module is used to transfer purchased parts to the corresponding production line according to the production scheduling plan information. Each part has a part code, which corresponds to the supplier code.

[0056] An installation information recording module is used to record the installation position and installation parameters of each part and encode the installation position, wherein the part code is saved in correspondence with the position code and the installation parameters;

[0057] A feedback information collection module is used to obtain damaged parts information reported by users, wherein the damaged parts information includes the damaged parts code and the time the parts have been used;

[0058] The feedback information analysis module periodically calls the installation parameters, part usage time, location code and supplier code corresponding to each damaged part code for analysis based on the damaged part code of the same type of parts to improve the production process.

[0059] The present invention provides a method and system for processing industrial Internet data based on big data. Compared with the existing technology, it has the following advantages:

[0060] Based on order information, parts requirements are identified and production scheduling is performed, allowing for rational production and procurement. Parts requirement information is sent to suppliers, and purchased parts are promptly dispatched to the production line indicated by the production schedule. During the production and assembly process, the installation location and parameters of each part are recorded, and corresponding codes are assigned to the parts, installation locations, and suppliers. Data from different data sources are then correlated. Based on user feedback on damaged parts, information corresponding to the damaged part codes is periodically retrieved and analyzed to improve the production process. This method addresses multiple endpoints, including suppliers, in-factory parts scheduling, production scheduling, production line assembly, and customers. By uniformly coding these multiple endpoints, the interconnectedness of data within and outside the factory is enhanced, facilitating in-depth data mining, thereby improving the production process and increasing data utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0062] Figure 1 A flowchart of an industrial Internet data processing method based on big data provided by an embodiment of the present invention;

[0063] Figure 2 A schematic diagram of a damaged parts coding analysis process provided by an embodiment of the present invention;

[0064] Figure 3 A schematic diagram of another damaged parts coding analysis process provided by an embodiment of the present invention;

[0065] Figure 4 A schematic diagram of the structure of an industrial Internet data processing system based on big data provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0066] To make the objectives, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application are clearly and completely described. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0067] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0068] like Figure 1 As shown, an industrial Internet data processing method based on big data provided by an embodiment of the present application includes:

[0069] S1: Obtain customer order information, and generate parts demand information and production scheduling information based on the order information.

[0070] S2: Based on the parts demand information, purchasing information is issued to multiple suppliers, wherein each supplier has a supplier code.

[0071] S3: According to the production scheduling information, the purchased parts are transported to the corresponding production line. Each part has a part code, which corresponds to the supplier code.

[0072] S4: Record the installation position and installation parameters of each part, and encode the installation position, wherein the part code, position code and installation parameters are saved in correspondence.

[0073] S5: Obtaining damaged parts information reported by the user, wherein the damaged parts information includes a damaged parts code and a usage time of the parts.

[0074] S6: Based on the damaged part codes of similar parts, periodically call the installation parameters, part usage time, location code and supplier code corresponding to each damaged part code for analysis to improve the production process.

[0075] In this optional embodiment, the parts demand is identified based on the order information and production scheduling is performed, reasonable production and procurement are carried out, the parts demand information is sent to the supplier, and the purchased parts are promptly transferred to the production line indicated by the production schedule information. During the production and assembly process, the installation location and installation parameters of each part are recorded, and the parts, installation locations, and suppliers are correspondingly coded. The data from different data sources are associated, and then based on the damaged parts information fed back by the user, the information corresponding to the damaged parts code is periodically called for analysis, thereby improving the production process. This method designs multiple terminals such as suppliers, parts scheduling within the factory, production scheduling, production line assembly production, and customers. By uniformly coding multiple terminals, the correlation between data inside and outside the factory is enhanced, thereby facilitating in-depth data mining, thereby improving the production process and increasing data utilization.

[0076] Each step is described in detail below.

[0077] S1: Obtain customer order information and generate parts demand information and production scheduling information based on the order information. This specifically includes the following sub-steps.

[0078] S110: Determine the product type, product quantity, and delivery time corresponding to each product type based on the order information.

[0079] Specifically, the product type, product quantity and customer's expected delivery time can be identified in the order information placed by the customer. It should be noted that the customer's expected delivery time may not necessarily be met and needs to be negotiated later based on the actual production schedule.

[0080] S120: Obtain the product types that each production line can produce and the unit production time of each production line for producing each product type, wherein each production line can produce two product types, each product type can be produced on two production lines, and the number of product types is equal to the number of production lines.

[0081] Assume that production line 1 can produce products in categories 1 and 2, production line 2 can produce products in categories 2 and 3, production line 3 can produce products in categories 3 and 4, and so on. Production line n-1 can produce products in categories n-1 and n, and production line n can produce products in categories n and 1. Production line 1 produces products in category 1 first, production line 2 produces products in category 2 first, production line 3 produces products in category 3 first, and so on, production line n produces products in category n first. On production line 1, the unit production time for producing one product in category 1 is k11, and the unit production time for producing one product in category 2 is k12. Similarly, on production line n, the unit production time for producing one product in category n is knn, and the unit production time for producing one product in category 1 is kn1.

[0082] S130: Determine the corresponding delivery production time for each product type based on the current time and the delivery time. Subtract the current time from the delivery time to obtain the delivery production time.

[0083] S140: Based on the product quantity of all product types and the unit time required for each production line to produce each product type, and with the actual production time being less than or equal to the delivery production time as a constraint, a production scheduling model is constructed to obtain production scheduling plan information.

[0084] Specifically, the above production scheduling model is:

[0085]

[0086] Among them, max() means taking the maximum value, k nn k represents the unit production time of the nth production line producing the nth type of product, (n-1)n A represents the unit production time of the n-1 production line producing the n-th type of product, nn A represents the number of products of type n produced by the n-th production line. (n-1)n represents the number of products of type n produced by the n-1 production line, t n represents the actual production time of the nth type of product, T n Indicates the delivery production time of the nth type of product.

[0087] In the above scheduling model, since one production line produces two products, in order to ensure that each type of product can be continuously produced, each production line has a product produced first and another product produced later. For the completion time of each product, the maximum production completion time is taken. For example, for the second product, it is produced first on the second production line and later on the first production line. Therefore, the completion time of the second product on the two production lines is k and k respectively. 22 A22 and k 11 A 11 +k 12 A 12 , the completion time on line 1 is the time to complete production A 11 The first product and A 12 Compare the completion times on the two lines and take the maximum of the two to determine the required completion time t2 for the second product. However, consider the number of products and the lead time for each product as constraints to determine a feasible production schedule. To optimize the production schedule, we can further minimize the actual production time for each product, thereby achieving the optimal schedule.

[0088] However, if the minimum actual production time analyzed by the production scheduling model is greater than the delivery production time, that is, even the optimal solution cannot complete the order within the delivery time, the minimum actual production time will be used as the delivery production time, and a delivery time exception message will be generated, where the delivery time exception message includes a delivery time exception reminder and the minimum actual production time. The delivery time exception message is fed back to the marketing personnel, who will then re-determine the delivery time with the customer based on the minimum actual production time, for example, by adding the minimum actual production time and the floating time to the current time to obtain the delivery time.

[0089] S2: Based on the parts demand information, purchasing information is issued to multiple suppliers, wherein each supplier has a supplier code.

[0090] Specifically, since each product is a mature product, each corresponds to a production parts list. By checking the factory's inventory information, parts demand information can be generated. For example, a product requires 50 bolts to assemble and secure its components. The order requires 20 sets of this product. There are 300 similar bolts in stock, so 700 more need to be purchased. Considering loss during use and spare parts, 720 can be purchased. The factory has multiple production lines, each of which may be suitable for different products, so production on each line needs to be rationally arranged.

[0091] S3: According to the production scheduling information, the purchased parts are transported to the corresponding production line. Each part has a part code, which corresponds to the supplier code.

[0092] Specifically, parts in inventory can be transferred to designated production lines according to the production schedule for pre-production, and then purchased parts that arrive in succession can be transferred to corresponding production lines for continuous production. Parts from different suppliers need to be coded before entering the warehouse and going into production.

[0093] S4: Record the installation position and installation parameters of each part, and encode the installation position, wherein the part code, position code and installation parameters are saved in correspondence.

[0094] Specifically, during actual production, each installation position is coded. For example, the bolt installation positions on a product are coded one by one. The bolt installation positions on similar products are coded the same, and similar parts have the same part codes. For example, the installation parameters can be installation temperature and installation torque, etc.

[0095] S5: Obtaining damaged parts information reported by the user, wherein the damaged parts information includes a damaged parts code and a usage time of the parts.

[0096] S6: Based on the damaged part codes of similar parts, periodically call the installation parameters, part usage time, location code and supplier code corresponding to each damaged part code for analysis to improve the production process.

[0097] S610: If the usage time of the part is greater than or equal to the preset service life, the damaged part corresponding to the part usage time is regarded as a normal damaged part, and the installation parameters, damaged part information, position code and supplier code corresponding to the normal damaged part are removed from the analysis information to obtain the information to be analyzed, wherein the analysis information includes the called installation parameters, part usage time, position code and supplier code corresponding to each damaged part code and the damaged part code.

[0098] Specifically, for parts that have been used for longer than the preset service life, this is considered normal damage and can be excluded from the analysis process of abnormal damage. The information of these normally damaged parts can be eliminated to reduce the amount of analysis data and improve the accuracy of the analysis results.

[0099] S620: Count the installation parameters corresponding to the damaged part code, and if the installation parameters meet the preset regularity conditions, improve the installation parameters in the production process. The preset regularity conditions are pre-set analysis conditions, and two preset regularity conditions are given below.

[0100] like Figure 2 As shown in FIG, the specific process of analyzing according to the first preset regular condition is as follows:

[0101] S6211: Count the installation parameters corresponding to the damaged part codes, and round the installation parameters to obtain a damaged installation parameter sequence.

[0102] S6212: Count the installation parameters corresponding to all part codes, and round the installation parameters to obtain a full installation parameter sequence.

[0103] Specifically, sometimes the installation parameters obtained during installation and measurement have certain deviations or small fluctuations, so rounding is performed here. On the one hand, rounding can reduce the volume of analysis data, and on the other hand, it can also improve the analysis accuracy.

[0104] S6213: Analyze the ratio of the number of parameter types in the damaged installation parameter sequence to the number of parameter types in the full installation parameter sequence to obtain a proportion of abnormal parameters.

[0105] Specifically, for example, the installation temperature. The types of installation temperatures in the damaged installation parameter sequence are 15°C, 11°C, 13°C and 16°C, but in the full installation parameter sequence, in addition to the above four types, there may also be 19°C, 20°C, 23°C, 29°C and 32°C, etc. The number of parameter types in the two sequences is divided to obtain the proportion of abnormal parameters.

[0106] S6214: If the proportion of abnormal parameters is less than the proportion of preset parameters, it indicates that under most installation parameters, the part is not susceptible to damage, but is susceptible to damage only under certain installation parameters. In this case, the number of each parameter type in the damaged installation parameter sequence can be further counted to obtain the number of damaged parameters corresponding to each parameter type. If the proportion of abnormal parameters is less than the proportion of preset parameters, it indicates that the installation parameters meet the preset regularity condition; if the proportion of abnormal parameters is greater than or equal to the proportion of preset parameters, it indicates that the installation parameters do not meet the preset regularity condition.

[0107] S6215: Based on the number of damaged parameters, screen the installation parameters corresponding to the two largest numbers of damaged parameters, mark the screened installation parameters as bad installation parameters, and eliminate the bad installation parameters during the production process, thereby reducing the damage rate or failure rate of subsequent products and improving product performance and service life.

[0108] like Figure 3 As shown in FIG, the specific process of analyzing according to the second preset regular condition is as follows:

[0109] S6221: Count the installation parameters corresponding to the damaged part codes and sort them in ascending order to obtain a damaged installation parameter sequence.

[0110] S6222: Analyze the distance between two adjacent installation parameters in the damaged installation parameter sequence to obtain a parameter distance.

[0111] S6223: If the parameter distance is greater than or equal to a preset parameter distance, disconnect the two installation parameters corresponding to the parameter distance to divide the damaged installation parameter sequence into multiple damaged installation parameter subsequences.

[0112] Specifically, for example, the installation temperature sequence is {3, 5, 11, 12, 13, 13, 14, 20, 21, 21, 23, 24, 24, 25, 25, 26, 26, 27}, among which the parameter distances between 5 and 11, and 14 and 20 are greater than the preset parameter distance 5. At this time, three subsequences are obtained by disconnecting from these two places, namely {3, 5}, {11, 12, 13, 13, 14} and {20, 21, 21, 23, 24, 24, 25, 25, 26, 26, 27}.

[0113] S6224: Count the number of elements of the damaged installation parameter in the subsequence of the damaged installation parameters.

[0114] S6225: If the number of elements is less than the first preset number of elements, discard the damaged installation parameter subsequence.

[0115] Specifically, if the number of the first preset elements is 3, the subsequence {3, 5} is discarded. This subsequence is discarded because the damage caused by the installation parameters corresponding to this subsequence is regarded as an accidental event and does not need to be analyzed.

[0116] S6226: If the number of elements is greater than or equal to the first preset number of elements, retain the damaged installation parameter subsequence.

[0117] S6227: Pick up the top three retained damaged installation parameter subsequences with the largest number of elements, and divide the number of elements in each picked up damaged installation parameter subsequence by the total number of elements in all retained damaged installation parameter subsequences to obtain the damaged parameter ratio corresponding to each picked up damaged installation parameter subsequence.

[0118] S6228: If the second preset number of elements is 6, and the proportion of damaged parameters corresponding to the picked damaged installation parameter subsequence is greater than or equal to the preset damaged parameter proportion, and the number of elements corresponding thereto is greater than or equal to the second preset number of elements, this indicates that the number of parameters in the subsequence is large and the proportion is large, and the installation parameters of the damaged parts are mainly concentrated in the installation parameters corresponding to the subsequence. In this case, the installation parameters meet the preset regularity conditions, and the damaged installation parameter subsequence is marked as a poor installation parameter sequence. If the proportion of damaged parameters corresponding to the picked damaged installation parameter subsequence is less than the preset damaged parameter proportion, or the number of elements corresponding to the picked damaged installation parameter subsequence is less than the second preset number of elements, this indicates that the number of elements in the picked damaged installation parameter subsequence is small or the proportion is not large. In other words, there is no particularly prominent subsequence among all subsequences, and all subsequences are relatively average, that is, part damage occurs evenly within each installation parameter range. In this case, the installation parameters do not meet the preset regularity conditions, and parameter improvement is of little significance.

[0119] S6229: Pick the first and last installation parameters of the bad installation parameter sequence to obtain the bad installation parameter range, and eliminate the bad installation parameter range during the production process. For example, if the final bad installation parameter sequence is {20, 21, 21, 23, 24, 24, 25, 25, 26, 26, 27}, then the bad installation parameter range is [20, 27], which is the installation parameter range where parts are often damaged.

[0120] S630: If the installation parameters do not meet the preset regular conditions, it means that the damage to the parts is not concentrated in certain installation parameters or certain parameter ranges, but appears evenly on the parts after executing different installation parameters. At this time, the position where the parts are installed may have a certain impact on the installation and use of the parts. At this time, the position codes corresponding to all the damaged part codes are counted to obtain the number of codes for each position code.

[0121] S640: Divide the number of codes for each position code by the total number of codes for all position codes to obtain a percentage of damaged parts corresponding to each position code.

[0122] S650: If the damaged parts ratio is greater than the preset damaged parts ratio, the part at the position code corresponding to the damaged parts ratio is marked as an incorrectly selected part, and a reminder message is generated to prompt the designer to select a better replacement part. If the damaged parts ratio is less than the preset damaged parts ratio, no improvement is made to the part at that position code. This allows for personalized selection of the same part at different locations, avoiding a one-size-fits-all selection design that leads to frequent damage in localized locations, thereby extending the overall product lifespan through small improvements.

[0123] S7: Screening suppliers according to the supplier codes in the information to be analyzed.

[0124] S710: Determine the quantity of each supplier code according to the supplier code in the information to be analyzed.

[0125] S720: Obtain the supply ratio of damaged parts corresponding to each supplier code based on the quantity of each supplier code and the total quantity of all supplier codes.

[0126] S730: If the damaged parts supply ratio is greater than the preset damaged parts supply ratio, the supplier corresponding to the supplier code is listed as a supplier to be inspected.

[0127] S740: If a supplier is listed as a supplier to be reviewed for multiple consecutive periods, the purchase information sent to the supplier will be reduced or cancelled.

[0128] Specifically, by analyzing damaged parts in multiple past cycles, if a supplier appears at the top of the damaged parts ranking every time, it means that the parts provided by this supplier have poorer performance than the same parts provided by other suppliers. Therefore, if other suppliers can supply in sufficient quantity, the purchase of such parts from this supplier can be cancelled, thereby screening out high-quality suppliers.

[0129] like Figure 4 As shown, an industrial Internet data processing system based on big data provided by an embodiment of the present application includes:

[0130] The order information parsing module 100 is used to obtain the order information of the customer and generate parts demand information and production scheduling plan information according to the order information.

[0131] The purchasing information sending module 200 is used to send purchasing information to multiple suppliers based on the parts demand information, wherein each supplier has a supplier code.

[0132] The parts scheduling module 300 is used to transport the purchased parts to the corresponding production line according to the production scheduling plan information, wherein each part has a part code, and the part code corresponds to the supplier code.

[0133] The installation information recording module 400 is used to record the installation position and installation parameters of each part and encode the installation position, wherein the part code, position code and installation parameters are stored in correspondence.

[0134] The feedback information collection module 500 is used to obtain damaged parts information fed back by users, wherein the damaged parts information includes the damaged parts code and the usage time of the parts.

[0135] The feedback information analysis module 600 periodically calls the installation parameters, part usage time, location code and supplier code corresponding to each damaged part code for analysis based on the damaged part code of the same type of parts to improve the production process.

[0136] In this embodiment, the beneficial effects of the industrial Internet data processing system based on big data are similar to the beneficial effects of the above-mentioned industrial Internet data processing method based on big data, and will not be repeated here.

[0137] An embodiment of the present application provides an electronic device comprising a memory and a processor; the memory is used to store a computer program; and the processor is used to implement the above-mentioned big data-based industrial Internet data processing method when executing the computer program.

[0138] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the industrial Internet data processing method based on big data as described above is implemented.

[0139] In this embodiment, the beneficial effects of the electronic device and the computer-readable storage medium are similar to the beneficial effects of the above-mentioned industrial Internet data processing method based on big data, and will not be repeated here.

[0140] An electronic device that can serve as a server or client of the present application will now be described, which is an example of a hardware device that can be applied to various aspects of the present application. Electronic devices are intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0141] An electronic device includes a computing unit that can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) or a computer program loaded from a storage unit into a random access memory (RAM). Various programs and data required for device operation can also be stored in the RAM. The computing unit, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0142] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM). In this application, the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme of this application. In addition, the functional units in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0143] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0144] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for processing industrial Internet data based on big data, characterized in that: include: Obtain customer order information and generate parts demand information and production scheduling information based on the order information; issuing procurement information to multiple suppliers based on the parts demand information, wherein each supplier has a supplier code; According to the production scheduling information, the purchased parts are transported to the corresponding production line. Each part has a part code, which corresponds to the supplier code. Record the installation position and installation parameters of each part, and encode the installation position, where the part code, position code and installation parameters are saved in correspondence; Obtaining damaged parts information reported by users, wherein the damaged parts information includes the damaged parts code and the time the parts have been used; According to the damaged part codes of similar parts, the installation parameters, part usage time, location code and supplier code corresponding to each damaged part code are periodically called for analysis to improve the production process.

2. The industrial Internet data processing method based on big data according to claim 1, characterized in that: The step of periodically analyzing the installation parameters, part usage time, location code, and supplier code corresponding to each damaged part code based on the damaged part code of similar parts to improve the production process includes: If the part usage time is greater than or equal to the preset service life, the damaged part corresponding to the part usage time is regarded as a normal damaged part, and the installation parameters, damaged part information, location code, and supplier code corresponding to the normal damaged part are removed from the analysis information to obtain information to be analyzed, wherein the analysis information includes the called installation parameters, part usage time, location code, and supplier code corresponding to each damaged part code, as well as the damaged part code; The installation parameters corresponding to the damaged part codes are counted, and if the installation parameters meet the preset regular conditions, the installation parameters in the production process are improved.

3. The industrial Internet data processing method based on big data according to claim 2, characterized in that: The statistically analyzing the installation parameters corresponding to the damaged part codes, and if the installation parameters meet the preset regularity conditions, improving the installation parameters in the production process includes: Counting the installation parameters corresponding to the damaged part codes, and rounding the installation parameters to obtain a damaged installation parameter sequence; Count the installation parameters corresponding to all part codes, and round the installation parameters to obtain a complete installation parameter sequence; Analyzing the ratio of the number of parameter types in the damaged installation parameter sequence to the number of parameter types in the full installation parameter sequence to obtain a proportion of abnormal parameters; If the abnormal parameter ratio is less than the preset parameter ratio, the number of each parameter type in the damaged installation parameter sequence is counted to obtain the number of damaged parameters corresponding to each parameter type; According to the number of damaged parameters, the screened installation parameters are marked as bad installation parameters, and the bad installation parameters are eliminated during the production process.

4. The industrial Internet data processing method based on big data according to claim 2, characterized in that: The statistically analyzing the installation parameters corresponding to the damaged part codes, and if the installation parameters meet the preset regularity conditions, improving the installation parameters in the production process includes: Count the installation parameters corresponding to the damaged part codes and sort them in ascending order to obtain a damaged installation parameter sequence; Analyze the distance between two adjacent installation parameters in the damaged installation parameter sequence to obtain the parameter distance; If the parameter distance is greater than or equal to the preset parameter distance, disconnecting from the two installation parameters corresponding to the parameter distance, and dividing the damaged installation parameter sequence into multiple damaged installation parameter subsequences; Count the number of elements of the installation parameters in the damaged installation parameter subsequence; If the number of elements is less than the first preset number of elements, discard the damaged installation parameter subsequence; If the number of elements is greater than or equal to the first preset number of elements, retain the damaged installation parameter subsequence; Pick up the top three retained damaged installation parameter subsequences with the largest number of elements, divide the number of elements in each picked damaged installation parameter subsequence by the total number of elements in all retained damaged installation parameter subsequences, and obtain the damaged parameter ratio corresponding to each picked damaged installation parameter subsequence; If the damaged parameter ratio corresponding to the picked damaged installation parameter subsequence is greater than or equal to the preset damaged parameter ratio and the number of elements corresponding thereto is greater than or equal to the second preset number of elements, then the damaged installation parameter subsequence is marked as a bad installation parameter sequence; The first and last installation parameters of the bad installation parameter sequence are picked up to obtain a bad installation parameter range, and the bad installation parameter range is eliminated during the production process.

5. The industrial Internet data processing method based on big data according to claim 2, characterized in that: The statistically determined installation parameters corresponding to the damaged part codes, if the installation parameters meet the preset regularity conditions, after improving the installation parameters in the production process, include: If the installation parameters do not meet the preset regular conditions, counting the position codes corresponding to all damaged part codes to obtain the number of codes for each position code; Divide the number of codes for each position code by the total number of codes for all position codes to obtain the percentage of damaged parts corresponding to each position code; If the damaged parts ratio is greater than the preset damaged parts ratio, the part at the position code corresponding to the damaged parts ratio is marked as a wrongly selected part, and a reminder message for selecting a replacement part is generated.

6. The industrial Internet data processing method based on big data according to claim 2, characterized in that: Also includes: Determining the quantity of each supplier code according to the supplier code in the information to be analyzed; Based on the quantity of each supplier code and the total quantity of all supplier codes, the proportion of damaged parts supplied corresponding to each supplier code is obtained; If the proportion of damaged parts supplied is greater than the preset proportion of damaged parts supplied, the supplier corresponding to the supplier code will be listed as a supplier to be inspected; If a supplier is listed as a supplier to be reviewed for multiple consecutive periods, the procurement information sent to this supplier will be reduced or cancelled.

7. The industrial Internet data processing method based on big data according to claim 1, characterized in that: The step of obtaining customer order information and generating parts demand information and production scheduling information based on the order information includes: Determine the product type, product quantity, and delivery time for each product type based on the order information; Obtain the product types that each production line can produce and the unit production time of each product type on each production line, where each production line can produce two product types, each product type can be produced on two production lines, and the number of product types is equal to the number of production lines; Determine the corresponding delivery production time for each product type based on the current time and delivery time; Based on the product quantity of all product types and the unit time required for each production line to produce each product type, and with the actual production time being less than or equal to the delivery production time as the constraint condition, a production scheduling model is constructed to obtain production scheduling plan information.

8. The industrial Internet data processing method based on big data according to claim 7, characterized in that: Assume that the first production line can produce products of category 1 and category 2, the second production line can produce products of category 2 and category 3, the third production line can produce products of category 3 and category 4, and so on. The n-1th production line can produce products of category n-1 and category n, and the nth production line can produce products of category n and category 1. The first production line produces products of category 1 first, the second production line produces products of category 2 first, the third production line produces products of category 3 first, and so on. The nth production line produces products of category n first. The production scheduling model is: Among them, max() means taking the maximum value, k nn k represents the unit production time of the nth production line producing the nth type of product, (n-1)n A represents the unit production time of the n-1 production line producing the n-th type of product, nn A represents the number of products of type n produced by the n-th production line. (n-1)n represents the number of products of type n produced by the n-1 production line, t n represents the actual production time of the nth type of product, T n Indicates the delivery production time of the nth type of product.

9. The industrial Internet data processing method based on big data according to claim 7, characterized in that: According to the quantity of all product types and the unit time required for each production line to produce each product type, and with the actual production time being less than or equal to the delivery production time as the constraint condition, a production scheduling model is constructed to obtain the following production scheduling information: If the minimum actual production time analyzed according to the production scheduling model is greater than the delivery production time, the minimum actual production time will be used as the delivery production time, and delivery time exception information will be generated, wherein the delivery time exception information includes a delivery time exception reminder and the minimum actual production time.

10. An industrial Internet data processing system based on big data, characterized in that: include: An order information parsing module is used to obtain customer order information and generate parts demand information and production scheduling information based on the order information; A purchasing information issuing module is used to issue purchasing information to multiple suppliers based on the parts demand information, wherein each supplier has a supplier code; The parts scheduling module is used to transfer purchased parts to the corresponding production line according to the production scheduling plan information. Each part has a part code, which corresponds to the supplier code. An installation information recording module is used to record the installation position and installation parameters of each part and encode the installation position, wherein the part code is saved in correspondence with the position code and the installation parameters; A feedback information collection module is used to obtain damaged parts information reported by users, wherein the damaged parts information includes the damaged parts code and the time the parts have been used; The feedback information analysis module periodically calls the installation parameters, part usage time, location code and supplier code corresponding to each damaged part code for analysis based on the damaged part code of the same type of parts to improve the production process.

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