Big data-based industrial internet data processing method and system

Abstract

The application provides an industrial internet data processing method and system based on big data, and relates to the technical field of industrial internet. According to order information, part requirements are identified and production scheduling is carried out, part requirement information is sent to suppliers, and parts purchased back are timely transported to the production line indicated by the production scheduling scheme information, the installation position and installation parameter of each part are recorded, the part, installation position and supplier are correspondingly coded, according to the damaged part information fed back by the user, the information corresponding to the damaged part code is periodically called and analyzed, so that the production process is improved. The method designs the part scheduling in the supplier and factory, production scheduling, assembly production of the production line and customers, etc., through unified coding of multiple ends, the data inside and outside the factory are associated with each other, so that the data can be deeply mined, the production process is improved, and the data utilization rate is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial internet, in particular to an industrial internet data processing method and system based on big data. BACKGROUND

[0002] Industrial internet is a product of the deep integration of new generation information technology and industrial system. Through the comprehensive interconnection of people, equipment, data and processes, it realizes the digitalization, networking and intelligent upgrading of the whole production process, and reconstructs the industrial value chain. It is not only an extension of industrial internet of things (IIoT), but also a comprehensive ecological system containing 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 devices, systems, business processes and user interactions are collected, transmitted and stored in real time through sensors, internet of things (IoT), cloud computing and big data technologies. Industrial internet data is the core resource driving intelligent manufacturing, optimizing production processes and improving decision-making efficiency.

[0004] However, in the prior art, the data sources are diverse, and the correlation between the data is weak, resulting in low data utilization. SUMMARY

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

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

[0007] Obtaining order information ordered by a customer, generating part demand information and production scheduling scheme information according to the order information;

[0008] According to the part demand information, issuing procurement information to a plurality of suppliers, wherein each supplier has a supplier code;

[0009] According to the production scheduling scheme information, transporting the procured parts to the corresponding production line, wherein each part has a part code, and the part code corresponds to the supplier code;

[0010] Recording the installation position and installation parameters of each part, and encoding the installation position, wherein the part code is saved corresponding to the position code and the installation parameters;

[0011] Obtaining damaged part information fed back by a user, wherein the damaged part information includes damaged part code and part use time;

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

[0013] Optionally, the periodically calling for analysis of the installation parameters, the part use time, the location code and the supplier code corresponding to each damage part code according to the damage part codes of the same type of parts to improve the production process comprises:

[0014] If the part use time is greater than or equal to the preset use life, the damage part corresponding to the part use time is regarded as a normal damage part, and the installation parameters, the damage part information, the location code and the supplier code corresponding to the normal damage part are excluded from the analysis information to obtain the to-be-analyzed information, wherein the analysis information comprises the called installation parameters, the part use time, the location code and the supplier code corresponding to each damage part code and the damage part code.

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

[0016] Optionally, the counting the installation parameters corresponding to the damage part codes and improving the installation parameters in the production process if the installation parameters meet the preset regular condition comprises:

[0017] The installation parameters corresponding to the damage part codes are counted, and the installation parameters are rounded to obtain a damage installation parameter sequence.

[0018] The installation parameters corresponding to all part codes are counted, and the installation parameters are rounded to obtain a full installation parameter sequence.

[0019] The ratio of the number of parameter types in the damage installation parameter sequence to the number of parameter types in the full installation parameter sequence is analyzed to obtain an abnormal parameter proportion.

[0020] If the abnormal parameter proportion is less than a preset parameter proportion, the number of each parameter type in the damage installation parameter sequence is counted to obtain a damage parameter number corresponding to each parameter type.

[0021] According to the damage parameter number, the screened installation parameters are marked as bad installation parameters, and the bad installation parameters are excluded in the production process.

[0022] Optionally, the counting the installation parameters corresponding to the damage part codes and improving the installation parameters in the production process if the installation parameters meet the preset regular condition comprises:

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

[0024] analyzing distances between adjacent installation parameters in the damaged installation parameter sequence to obtain parameter distances;

[0025] if the parameter distance is greater than or equal to a preset parameter distance, the damaged installation parameter sequence is divided into a plurality of damaged installation parameter subsequences by cutting off the two installation parameters corresponding to the parameter distance;

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

[0027] if the number of elements is less than a first preset number of elements, the damaged installation parameter subsequence is discarded;

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

[0029] picking up the top three retained damaged installation parameter subsequences, dividing the number of elements of each picked up damaged installation parameter subsequence by the total number of elements of all retained damaged installation parameter subsequences to obtain a damaged parameter proportion corresponding to each picked up damaged installation parameter subsequence;

[0030] if the damaged parameter proportion corresponding to the picked up damaged installation parameter subsequence is greater than or equal to a preset damaged parameter proportion and the number of elements corresponding thereto is greater than or equal to a second preset number of elements, the damaged installation parameter subsequence is marked as a defective installation parameter sequence;

[0031] picking up the first and last installation parameters of the defective installation parameter sequence to obtain a defective installation parameter range, and removing the defective installation parameter range in the production process.

[0032] Optionally, the counting of installation parameters corresponding to the damaged part codes, if the installation parameters satisfy the preset regularity condition, the industrial internet data processing method based on big data includes:

[0033] if the installation parameters do not satisfy the preset regularity condition, counting position codes corresponding to all damaged part codes to obtain the number of codes of each position code;

[0034] dividing the number of codes of each position code by the total number of codes of all position codes to obtain a damaged part proportion corresponding to each position code;

[0035] if the damaged part proportion is greater than a preset damaged part proportion, the part at the position code corresponding to the damaged part proportion is marked as a selection error part, and a replacement part selection reminder information is generated.

[0036] Optionally, the big data-based industrial internet data processing method further comprises:

[0037] According to the supplier code in the information to be analyzed, the number of each supplier code is determined;

[0038] According to the number of each supplier code and the total number of all supplier codes, the damaged part supply proportion corresponding to each supplier code is obtained;

[0039] If the damaged part supply proportion is greater than the preset damaged part supply proportion, the supplier corresponding to the supplier code is listed as a supplier to be investigated;

[0040] If a supplier is listed as a supplier to be investigated for a plurality of consecutive periods, the supplier is reduced or cancelled to issue procurement information.

[0041] Optionally, the order information ordered by the customer is obtained, and the part demand information and the production scheduling scheme information are generated according to the order information, which comprises:

[0042] According to the order information, the product type, the product quantity and the delivery time corresponding to each product type are determined;

[0043] The product type that each production line can produce and the unit production time of each production line to produce each product type are obtained, 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;

[0044] According to the current time and the delivery time, the delivery production time corresponding to each product type is determined;

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

[0046] Optionally, it is assumed that the first production line can produce the first product and the second product, the second production line can produce the second product and the third product, the third production line can produce the third product and the fourth product, and so on, the n-1th production line can produce the n-1th product and the nth product, and the nth production line can produce the nth product and the first product; the first production line produces the first product, the second production line produces the second product, the third production line produces the third product, and so on, the nth production line produces the nth product;

[0047] The production scheduling model is:

[0048]

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

[0050] Optionally, the scheduling model is constructed according to 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 delivery production time as a constraint condition, to obtain scheduling scheme information, which includes:

[0051] If the minimum actual production time analyzed according to the scheduling model is greater than the delivery production time, the minimum actual production time is taken as the delivery production time, and delivery time abnormal information is generated, wherein the delivery time abnormal information includes delivery time abnormality reminders and the minimum actual production time.

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

[0053] An order information analysis module is configured to obtain order information of a customer, and generate part demand information and scheduling scheme information according to the order information.

[0054] A procurement information issuing module is configured to issue procurement information to a plurality of suppliers according to the part demand information, wherein each supplier has a supplier code.

[0055] A part scheduling module is configured to dispatch the procured parts to corresponding production lines according to the scheduling scheme information, wherein each part has a part code, and the part code corresponds to the supplier code.

[0056] An installation information recording module is configured to record the installation position and installation parameters of each part, and encode the installation position, wherein the part code, the position code and the installation parameters are saved correspondingly.

[0057] A feedback information collection module is configured to obtain damaged part information fed back by a user, wherein the damaged part information includes damaged part code and part use time.

[0058] The feedback information analysis module periodically calls and analyzes the installation parameters, part use time, location code and supplier code corresponding to each damaged part code according to the damaged part codes of the same type of parts, so as to improve the production process.

[0059] The application provides an industrial internet data processing method and system based on big data.

[0060] According to the order information, the part demand is identified and production scheduling is performed, reasonable production and procurement are performed, the part demand information is sent to the supplier, and the returned parts are timely transported to the production line indicated by the production scheduling information, the installation position and installation parameter of each part are recorded during production and assembly, the parts, installation positions and suppliers are correspondingly coded, the data from different data sources are associated, then according to the damaged part information fed back by the user, the information corresponding to the damaged part code is periodically called and analyzed, so as to improve the production process. The method designs the supplier, part scheduling in the factory, production scheduling, assembly production line production and multiple terminals of customers, through unified coding of multiple terminals, the data inside and outside the factory are associated, so as to facilitate deep mining of data, thereby improving the production process and improving the data utilization rate. BRIEF DESCRIPTION OF DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0062] Figure 1 The flowchart of the industrial internet data processing method based on big data provided by the embodiment of the application is shown.

[0063] Figure 2 The damaged part coding analysis flowchart provided by the embodiment of the application is shown.

[0064] Figure 3 Another damaged part coding analysis flowchart provided by the embodiment of the application is shown.

[0065] Figure 4 The structure diagram of the industrial internet data processing system based on big data provided by the embodiment of the application is shown. DETAILED DESCRIPTION

[0066] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application are described clearly and completely. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0067] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0068] like Figure 1 As shown in the embodiment of this application, an industrial internet data processing method based on big data is provided, including:

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

[0070] S2: Based on the part requirement information, issue procurement information to multiple suppliers, where each supplier has a supplier code.

[0071] S3: Based on the production scheduling information, the purchased parts are transferred to the corresponding production lines. 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. The part code, position code, and installation parameters are saved in correspondence.

[0073] S5: Obtain information on damaged parts reported by users, including the damaged part code and the part's usage time.

[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 in order to improve the production process.

[0075] In this optional embodiment, the part requirements in the order information are identified and production scheduling is performed according to the order information, reasonable production and procurement are performed, the part requirement information is sent to the supplier, and the parts purchased are timely transported to the production line indicated by the production scheduling information. In the process of production and assembly, the installation position and installation parameter of each part are recorded, and the parts, installation positions, and suppliers are correspondingly coded. The data from different data sources are associated, and then the information corresponding to the damaged part code is periodically called and analyzed according to the user feedback of the damaged part information, so as to improve the production process. This method designs the supplier, the part scheduling in the factory, the production scheduling, the assembly production line, and the customer, etc. Through unified coding of multiple ends, the data inside and outside the factory are associated, so as to facilitate deep mining of data, thereby improving the production process and improving the data utilization rate.

[0076] The following will be described in detail.

[0077] S1: Obtain order information of a customer, and generate part requirement information and production scheduling information according to the order information. Specifically, the following sub-steps are included.

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

[0079] Specifically, the product type, product quantity, and customer expected delivery time can be identified in the order information of the customer. It should be noted that the customer expected delivery time may not be satisfied, and needs to be negotiated after actual production scheduling.

[0080] S120: Obtain the product type that each production line can produce and the unit production time of each production line for 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 of type 1 and type 2, production line 2 can produce products of type 2 and type 3, production line 3 can produce products of type 3 and type 4, and so on. Production line (n-1) can produce products of type n-1 and type n, and production line n can produce products of type n and type 1. Production line 1 produces type 1 products first, production line 2 produces type 2 products first, production line 3 produces type 3 products first, and so on. Production line n produces type n products first. On production line 1, the unit production time for producing one type 1 product is k11, the unit production time for producing one type 2 product is k12, and so on. On production line n, the unit production time for producing one type n product is knn, and the unit production time for producing one type 1 product is kn1.

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

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

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

[0085]

[0086] Where max() represents taking the maximum value, k nn k represents the unit production time for the nth production line to produce the nth type of product. (n-1)n A represents the unit production time for the (n-1)th production line to produce the nth type of product. nn A represents the quantity of product of type n produced by the nth production line. (n-1)n t represents the quantity of product of type n produced by the (n-1)th production line. n T represents the actual production time for producing product type n. n This represents the delivery and production time for the nth type of product.

[0087] In the above production scheduling model, since each production line produces two types of products, to ensure a continuous output of each type of product, each production line has one product produced first and another produced later. For each product's completion time, the longest production completion time is chosen. For example, for the second product, it is produced first on the second production line and last on the first production line. Therefore, the completion times for the second product on the two production lines are k and k, respectively. 22 A22 and k 11 A 11 +k 12 A 12 The completion time on the first line is the total time for producing A 11 first products and A 12 second products. By comparing the completion time on the two lines, the maximum of the two is the completion time t2 required for the second product. However, the product quantity and delivery production time of each product are taken as constraints to analyze the feasible production scheduling scheme. In order to obtain the optimal production scheduling scheme, the actual production time of each product can be further minimized to obtain the optimal production scheduling scheme.

[0088] However, if the minimum actual production time obtained according to the production scheduling model is greater than the delivery production time, i.e., the optimal scheme cannot complete the order within the delivery time, the minimum actual production time is taken as the delivery production time, and delivery time exception information is generated, wherein the delivery time exception information includes delivery time exception reminder and minimum actual production time. The delivery time exception information is fed back to the market personnel, so that the market personnel can re-determine the delivery time with the customer according to the minimum actual production time, for example, adding the minimum actual production time and the floating time to the current time to obtain the delivery time.

[0089] S2: According to the part demand information, issuing procurement information to a plurality of suppliers, wherein each supplier has a supplier code.

[0090] Specifically, since each product is a mature product, each product corresponds to a production part list, and by checking the inventory information in the factory, part demand information can be generated, for example, 50 bolts are needed in a product to assemble and fix the components of the product, and 20 sets of the product are needed in the order, and there are 300 similar bolts in stock, so 700 need to be purchased, considering the loss in the use process and spare parts, 720 can be purchased. There are multiple production lines in the factory, and each production line may be suitable for different products, so it is necessary to reasonably arrange the production of each production line.

[0091] S3: According to the production scheduling scheme information, the parts purchased back are transported to the corresponding production line, wherein each part has a part code, and the part code corresponds to the supplier code.

[0092] Specifically, the parts in the inventory can be first transported to the designated production line according to the production scheduling scheme information for early production, and then the purchased parts arriving successively are transferred to the corresponding production line for continuous production. Before the parts are put into the warehouse and put into production, the parts of different suppliers need to be coded.

[0093] S4: record the installation position and installation parameter of each part, and encode the installation position, wherein the part code corresponds to the position code and installation parameter.

[0094] Specifically, in actual production, each installation position is encoded, for example, the bolt installation position on a product is encoded one by one, the bolt installation position on the same product is encoded in the same way, and the same part has the same part code, for example, the installation parameters can be installation temperature and installation torque.

[0095] S5: obtain user feedback damage part information, wherein the damage part information includes damage part code and part service time.

[0096] S6: periodically call and analyze the installation parameters, part service time, position code and supplier code corresponding to each damage part code according to the damage part code of the same part to improve the production process.

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

[0098] Specifically, for parts with part service time greater than the preset service life, normal damage can be excluded from the analysis process of abnormal damage, and the part information of this part of normal damage is excluded to reduce the amount of analysis data and improve the accuracy of the analysis result.

[0099] S620: count the installation parameters corresponding to the damage part code, and improve the installation parameters in the production process if the installation parameters meet the preset rule condition. Wherein the preset rule condition is the analysis condition set in advance, and two preset rule conditions are given below.

[0100] As shown in the following formula (1), the process of analyzing according to the first preset rule condition is specifically: Figure 2

[0101] S6211: count the installation parameters corresponding to the damage part code, and take the installation parameters to obtain the damage installation parameter sequence.

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

[0103] ​Specifically, sometimes the installation parameters obtained during installation and measurement have certain deviations or small fluctuations, and therefore rounding is performed here. Rounding can reduce the amount of analysis data on the one hand, and can improve analysis accuracy on the other hand.

[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 an abnormal parameter proportion.

[0105] Specifically, for example, the installation temperature, the types of installation temperature in the damaged installation parameter sequence are 15℃, 11℃, 13℃ and 16℃ respectively, but in addition to the above four types, the types of installation temperature in the full installation parameter sequence can also include 19℃, 20℃, 23℃, 29℃ and 32℃, etc. Divide the number of parameter types in the two sequences to obtain the abnormal parameter proportion.

[0106] S6214: If the abnormal parameter proportion is less than the preset parameter proportion, it means that under most installation parameters, the part is not easy to be damaged, and the part is only easy to be damaged under individual installation parameters. At this time, the number of each parameter type in the damaged installation parameter sequence can be further counted to obtain the damaged parameter number corresponding to each parameter type. If the abnormal parameter proportion is less than the preset parameter proportion, it means that the installation parameters meet the preset rule condition; if the abnormal parameter proportion is greater than or equal to the preset parameter proportion, it means that the installation parameters do not meet the preset rule condition.

[0107] S6215: According to the damaged parameter number, the installation parameters corresponding to the top two damaged parameter numbers in the order are screened out, and the screened installation parameters are marked as bad installation parameters. The bad installation parameters are eliminated in the production process, so as to reduce the damage rate or failure rate of subsequent products, improve the performance and service life of the products.

[0108] As shown in Figure 3 , the process of analyzing according to the second preset rule condition is specifically:

[0109] S6221: Count the installation parameters corresponding to the damaged part code, 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, the two installation parameters corresponding to the parameter distance are disconnected, and the damaged installation parameter sequence is divided into a plurality of 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}, wherein the parameter distance between 5 and 11, 14 and 20 is greater than the preset parameter distance 5, at this time, the three sub-sequences are obtained by cutting off from these two places respectively, which are {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 installation parameter in the damaged installation parameter sub-sequence.

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

[0115] Specifically, if the first preset number of elements is 3, the sub-sequence {3, 5} is discarded. The sub-sequence is discarded because the damage caused by the installation parameter corresponding to this sequence is considered as an occasional 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, keep the damaged installation parameter sub-sequence.

[0117] S6227: Pick up the top three kept damaged installation parameter sub-sequences. Divide the number of elements of each picked up damaged installation parameter sub-sequence by the total number of elements of all kept damaged installation parameter sub-sequences to obtain the damaged parameter proportion corresponding to each picked up damaged installation parameter sub-sequence.

[0118] S6228: If the second preset number of elements is 6, and the damaged parameter proportion corresponding to the picked up damaged installation parameter sub-sequence 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, it indicates that the number of parameters in the sub-sequence 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 sub-sequence. At this time, the installation parameter satisfies the preset rule condition, and the damaged installation parameter sub-sequence is marked as a bad installation parameter sequence. If the damaged parameter proportion corresponding to the picked up damaged installation parameter sub-sequence is less than the preset damaged parameter proportion, or the number of elements corresponding to the picked up damaged installation parameter sub-sequence is less than the second preset number of elements, it indicates that the number of elements in the picked up damaged installation parameter sub-sequence is small or the proportion is not large, that is, there is no particularly outstanding sub-sequence in all sub-sequences, and all sub-sequences are relatively average, that is, the part damage occurs uniformly in each installation parameter range. At this time, the installation parameter does not satisfy the preset rule condition, and it is not meaningful to improve the parameter.

[0119] S6229: picking the first and last installation parameters of the sequence of poor installation parameters to obtain a poor installation parameter range, and eliminating the poor installation parameter range in the production process. For example, if the sequence of final poor installation parameters is {20, 21, 21, 23, 24, 24, 25, 25, 26, 26, 27}, the range of poor installation parameters is [20, 27], which is the most common installation parameter range of part damage.

[0120] S630: If the installation parameter does not meet the preset rule condition, it means that the damage of the part is not concentrated on certain installation parameters or certain parameter ranges, but occurs on the parts after executing different installation parameters. At this time, it is possible that the position of the part installation has a certain influence on the part installation and use, and at this time, the number of codes of each position code is obtained by counting all the damaged part codes corresponding to the position codes.

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

[0122] S650: If the proportion of damaged parts is greater than the preset proportion of damaged parts, the part at the position code corresponding to the proportion of damaged parts is marked as a selection error part, and a replacement part selection reminder information is generated to remind the designer to select a better part for replacement. If the proportion of damaged parts is less than the preset proportion of damaged parts, the part at the position code is not improved. Thus, the same part at different positions is personalized in selection, avoiding the one-size-fits-all selection design leading to frequent damage at local positions, and thus improving the overall service life of the product with small improvements.

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

[0124] S710: Determining the number of each supplier code according to the supplier codes in the information to be analyzed.

[0125] S720: Obtaining the proportion of damaged part supply corresponding to each supplier code according to the number of each supplier code and the total number of all supplier codes.

[0126] S730: If the proportion of damaged part supply is greater than the preset proportion of damaged part supply, the supplier corresponding to the supplier code is listed as a supplier to be investigated.

[0127] S740: If a supplier is listed as a supplier to be investigated for a plurality of consecutive periods, the procurement information to the supplier is reduced or cancelled.

[0128] Specifically, through analysis of damaged parts in past periods, if a supplier appears in the top of the list of damaged parts every time, it indicates that the parts provided by the supplier are poorer in performance than the same parts provided by other suppliers, and therefore, the procurement of such parts from the supplier can be cancelled on the premise that other suppliers can supply sufficient parts, so as to screen out high-quality suppliers.

[0129] As shown in Figure 4 The embodiment of the application provides an industrial internet data processing system based on big data, which comprises:

[0130] An order information analysis module 100 is configured to acquire order information of a customer, and generate part demand information and production scheduling scheme information according to the order information.

[0131] A procurement information issuing module 200 is configured to issue procurement information to a plurality of suppliers according to the part demand information, wherein each supplier has a supplier code.

[0132] A part scheduling module 300 is configured to dispatch the parts purchased back to corresponding production lines according to the production scheduling scheme information, wherein each part has a part code, and the part code corresponds to the supplier code.

[0133] An installation information recording module 400 is configured to record installation positions and installation parameters of each part, and encode the installation positions, wherein the part code is saved in correspondence with the position code and the installation parameters.

[0134] A feedback information collection module 500 is configured to acquire damaged part information fed back by a user, wherein the damaged part information comprises a damaged part code and a part use time.

[0135] A feedback information analysis module 600 is configured to periodically call and analyze installation parameters, part use times, position codes and supplier codes corresponding to each damaged part code according to the damaged part codes of the same type of parts, so as to improve the production process.

[0136] In the embodiment, the industrial internet data processing system based on big data has similar beneficial effects to the industrial internet data processing method based on big data, and details are not repeated here.

[0137] The embodiment of the application provides an electronic device, which comprises a memory and a processor; the memory is configured to store a computer program; and the processor is configured to implement the industrial internet data processing method based on big data when the computer program is executed.

[0138] The embodiment of the present application provides a computer readable storage medium, and the storage medium stores a computer program. When the computer program is executed by a processor, the industrial internet data processing method based on big data is realized.

[0139] In the embodiment, the electronic device and the computer readable storage medium have similar advantages to the advantages of the industrial internet data processing method based on big data, which will not be repeated here.

[0140] Now, an electronic device which can be a server or a client of the present application will be described, which is an example of a hardware device that can be applied to various aspects of the present application. The electronic device is intended to represent a variety of forms of digital electronic computing devices, such as a laptop computer, a desktop computer, a workstation, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as a personal digital processing, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown in the present application, 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 claimed herein.

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

[0142] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed 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, the processes of the above-mentioned embodiment methods can be included. The storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM) or a random access memory (RAM), etc. In the present application, the units described as separate components can be or can not be physically separated, and the components displayed as units can be or can not be physical units, i.e., they can 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 embodiments of the present application. In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0143] It should be noted that, in the present document, the terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of another identical element in the process, method, article or device including the element.

[0144] The above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A big data-based industrial internet data processing method, characterized in that, The method comprises the following steps: obtaining order information of a customer, generating part demand information and production scheduling scheme information according to the order information; issuing procurement information to multiple suppliers according to the part demand information, wherein each supplier has a supplier code; delivering parts purchased back to corresponding production lines according to the production scheduling scheme information, wherein each part has a part code, and the part code corresponds to the supplier code; recording the installation position and installation parameters of each part, and encoding the installation position, wherein the part code corresponds to the position code and the installation parameters and is saved accordingly; obtaining damaged part information fed back by a user, wherein the damaged part information includes damaged part code and part service time; periodically calling and analyzing the installation parameters, part service time, position code and supplier code corresponding to each damaged part code according to the damaged part code of the same type of parts to improve the production process, including: if the part service time is greater than or equal to the preset service life, regarding the damaged part corresponding to the part service time as a normal damaged part, eliminating the installation parameters, damaged part information, position code and supplier code corresponding to the normal damaged part from the analysis information to obtain to-be-analyzed information, wherein the analysis information includes the called installation parameters, part service time, position code and supplier code corresponding to each damaged part code and damaged part code; counting the installation parameters corresponding to the damaged part code and rounding the installation parameters to obtain a damaged installation parameter sequence; counting the installation parameters corresponding to all part codes and rounding the installation parameters to obtain a full 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 an abnormal parameter proportion; if the abnormal parameter proportion is less than a preset parameter proportion, counting the number of each parameter type in the damaged installation parameter sequence to obtain the damaged parameter number corresponding to each parameter type; according to the damaged parameter number, marking the screened installation parameters as poor installation parameters, and eliminating the poor installation parameters in the production process; if the abnormal parameter proportion is greater than or equal to the preset parameter proportion, counting the installation parameters corresponding to the damaged part code and sorting them in ascending order to obtain the damaged installation parameter sequence; analyzing the distance between adjacent installation parameters in the damaged installation parameter sequence to obtain a parameter distance; if the parameter distance is greater than or equal to a preset parameter distance, disconnecting the two installation parameters corresponding to the parameter distance, and dividing the damaged installation parameter sequence into multiple damaged installation parameter subsequences; counting the number of elements of the installation parameters in the damaged installation parameter subsequence; if the number of elements is less than a first preset number of elements, discarding the damaged installation parameter subsequence; if the number of elements is greater than or equal to the first preset number of elements, retaining the damaged installation parameter subsequence; picking the top three reserved damaged installation parameter subsequences in terms of the number of elements, dividing the number of elements of each picked damaged installation parameter subsequence by the total number of elements of all reserved damaged installation parameter subsequences to obtain a damaged parameter proportion corresponding to each picked damaged installation parameter subsequence; if the damaged parameter proportion corresponding to the picked damaged installation parameter subsequence is greater than or equal to a preset damaged parameter proportion and the number of elements corresponding to the picked damaged installation parameter subsequence is greater than or equal to a second preset number of elements, marking the damaged installation parameter subsequence as a defective installation parameter sequence; picking the first and last installation parameters of the defective installation parameter sequence to obtain a defective installation parameter range, and eliminating the defective installation parameter range in the production process; if the damaged parameter proportion 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, counting the position codes corresponding to all damaged parts codes to obtain a code number of each position code; dividing the code number of each position code by the total number of codes of all position codes to obtain a damaged part proportion corresponding to each position code; if the damaged part proportion is greater than a preset damaged part proportion, marking the part at the position code corresponding to the damaged part proportion as a selection error part, and generating a replacement part selection reminder information. 2.The big data based industrial internet data processing method of claim 1, wherein, Further comprising: determining the number of each supplier code according to the supplier code in the information to be analyzed; obtaining a damaged part supply proportion corresponding to each supplier code according to the number of each supplier code and the total number of all supplier codes; if the damaged part supply proportion is greater than a preset damaged part supply proportion, listing the supplier corresponding to the supplier code as a supplier to be investigated; if a supplier is listed as a supplier to be investigated for a plurality of consecutive periods, reducing or canceling the procurement information issued to the supplier. 3.The big data based industrial internet data processing method of claim 1, wherein, The order information ordered by the customer is obtained, and part demand information and production scheduling scheme information are generated according to the order information, comprising: determining product types, product quantities, and delivery times corresponding to each product type according to the order information; obtaining product types that can be produced by each production line and unit production time for each production line to produce 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; determining a delivery production time length corresponding to each product type according to the current time and the delivery time; constructing a production scheduling model according to the product quantities of all product types and the unit time required for each production line to produce each product type, with the actual production time length being less than or equal to the delivery production time length as a constraint condition, to obtain production scheduling scheme information. 4.The big data based industrial internet data processing method of claim 3, wherein, Assuming that the first production line can produce the first type of product and the second type of product, the second production line can produce the second type of product and the third type of product, the third production line can produce the third type of product and the fourth type of product, and so on, the n-1th production line can produce the n-1th type of product and the nth type of product, and the nth production line can produce the nth type of product and the first type of product; the first production line produces the first type of product, the second production line produces the second type of product, the third production line produces the third type of product, and so on, and the nth production line produces the nth type of product; The production scheduling model is: wherein max() represents taking the maximum value, represents the unit production time of the nth production line producing the nth type of product, represents the unit production time of the (n-1)th production line producing the nth type of product, represents the number of the nth type of product produced by the nth production line, represents the number of the nth type of product produced by the (n-1)th production line, represents the actual production duration of producing the nth type of product, represents the delivery production duration of the nth type of product. 5.The big data based industrial internet data processing method of claim 3, wherein, The production scheduling model is constructed according to the product quantity of all product types and the unit time required by each production line to produce each product type, with the actual production time being less than or equal to the delivery production time as a constraint condition, to obtain production scheduling scheme information including: 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 is taken as the delivery production time, and delivery time abnormal information is generated, wherein the delivery time abnormal information includes delivery time abnormal reminder and minimum actual production time.

6. A big data-based industrial internet data processing system, characterized by, The industrial internet data processing system based on big data comprises: An order information analysis module is configured to obtain order information of a customer, and generate part demand information and production scheduling scheme information according to the order information; A procurement information issuing module is configured to issue procurement information to a plurality of suppliers according to the part demand information, wherein each supplier has a supplier code; A part scheduling module is configured to dispatch the procured parts to corresponding production lines according to the production scheduling scheme information, wherein each part has a part code, and the part code corresponds to the supplier code; An installation information recording module is configured to record the installation position and installation parameters of each part, and encode the installation position, wherein the part code, the position code and the installation parameters are saved correspondingly; A feedback information collection module is configured to obtain damaged part information fed back by a user, wherein the damaged part information includes damaged part code and part use time; A feedback information analysis module is configured to periodically call and analyze the installation parameters, part use time, position code and supplier code corresponding to each damaged part code according to the damaged part code of the same type of part, so as to improve the production process.

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