A smart production scheduling method and device based on blockchain technology

Abstract

The present invention relates to the technical field of production management, and discloses an intelligent production scheduling method and device based on blockchain technology; the method comprises constructing a blockchain network, extracting demand characteristics from production scheduling information, calculating production scheduling demand, acquiring production characteristics of production lines, screening target production lines from production lines, calculating production scheduling priority coefficients of target production lines, dividing production scheduling demand into sub-demands as required, and controlling intelligent production scheduling of target production lines; compared with the prior art, the present invention constructs a blockchain network for management systems and production lines in industrial production through blockchain technology, realizes transparent interaction and real-time query of data between management systems and production lines, identifies target production lines in combination with demand characteristics and production characteristics, and calculates production scheduling priority coefficients through comprehensive production data, so that industrial production demand can be reasonably and accurately allocated to target production lines for scheduling.

Description

Technical Field

[0001] The present invention relates to the field of production management technology, and more specifically, to an intelligent production scheduling method and device based on blockchain technology. Background Art

[0002] With the rapid development of the manufacturing industry, production scheduling plays an increasingly important role in corporate operations. Traditional production scheduling methods often rely on experience and manual operations, resulting in low scheduling efficiency, serious waste of resources and other problems. In this context, by introducing blockchain technology, production information can be shared in real time among all links of industrial production, and all participants can work together to improve the efficiency of information flow and improve the efficiency of intelligent scheduling.

[0003] The patent application with reference publication number CN118822232A discloses an intelligent production scheduling method and an intelligent production scheduling system, which sorts out multiple factors affecting the production plan, such as production capacity, process steps, delivery priority, beat, production cycle, etc., and introduces artificial intelligence algorithms to realize forward and reverse production scheduling of the production plan, and dynamically adjusts the production schedule by monitoring the process of the production plan, and finally realizes the intelligentization and wisdom of the production plan, effectively solves the important problems that the production plan is overly dependent on the factory's production capacity and cannot be dynamically adjusted during the production process, and improves the efficiency and accuracy of intelligent production scheduling;

[0004] The existing intelligent scheduling method obtains the dynamic correlation and matching degree between production demand and scheduling demand, and schedules the production information as a whole according to the correlation and matching degree, so that the production information is directly correlated with the production equipment and scheduled. Due to the differences in the operating status and production performance of each production equipment, the matching correlation degree between each production equipment and the production demand is inconsistent. When the production equipment with a low matching correlation degree participates in the scheduling of production demand, abnormal operation of the production equipment is likely to occur, resulting in unreasonable scheduling operation, thereby reducing scheduling efficiency.

[0005] In view of this, the present invention proposes an intelligent production scheduling method and device based on blockchain technology to solve the above problems. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solutions: an intelligent production scheduling method based on blockchain technology, applied to an intelligent production scheduling controller, comprising:

[0007] S1: Convert the management system and production line of industrial production into transmission nodes, which include primary nodes and secondary nodes, and build a blockchain network based on the transmission nodes;

[0008] S2: Obtain demand information from the management system through the blockchain network, and extract demand features from the production scheduling information. The demand features include the lower limit of production and the busy waiting value, and calculate the production scheduling demand;

[0009] S3: Obtain the production characteristics of the production line, which include the production lower limit value and the production waiting value, identify the effectiveness of the production characteristics and the demand characteristics, identify the attribute status of the production line, and screen out the target production line from the production lines;

[0010] S4: Obtain comprehensive production data of the target production line, which includes fault trigger rate, production qualification value and effective output value, calculate the production scheduling priority coefficient of the target production line, and arrange and number the target production lines in order;

[0011] S5: Divide the production demand into sub-demands as needed, and allocate the sub-demands to the corresponding target production lines to control the intelligent production scheduling of the target production lines.

[0012] Furthermore, the conversion method of the primary node and the secondary node includes:

[0013] A basic network template is selected from the database, and nodes are established in the basic network template with the number of nodes consistent with the number of the management system and the number of production lines, and a first node and A second nodes are obtained respectively;

[0014] The model of the production line is found through the technical parameter table, and the model of the production line is marked on A second nodes accordingly to obtain A marked nodes;

[0015] The management system shares data with the first node through blockchain technology to obtain a first-level node;

[0016] Through blockchain technology, data of A production lines are shared with A marking nodes one by one to obtain A secondary nodes.

[0017] Furthermore, the method for constructing a blockchain network includes:

[0018] Three application layers are sequentially established in the basic network template, and are recorded as the upper layer, the middle layer and the lower layer from top to bottom.

[0019] Mark A lower points at equal intervals in the lower layer, import A secondary nodes into the A lower points in sequence, and establish a data channel between two adjacent lower points;

[0020] Mark the median point in the median layer, import the first-level node to the median point, and establish a data channel between the median point and A lower points;

[0021] The upper points are marked in the upper layer, and a data channel is established between the upper points and the middle points to generate a blockchain network.

[0022] Furthermore, the method for extracting the output lower limit value and the busy waiting value includes:

[0023] All industrial production data is queried from the database through the blockchain network, and the type values ​​of all data are identified one by one, and the data with the type value of demand is recorded as demand information;

[0024] The demand semantics of demand information are identified one by one through natural language processing technology, and the demand semantics are split into demand text and demand value through word segmentation technology;

[0025] The demand semantics in which the demand text is the lower limit of the output is recorded as the first semantics, and the demand value in the first semantics is recorded as the lower limit of the output;

[0026] Recording the demand semantics of the demand text as busy waiting as the second semantics, and recording the demand value in the second semantics as the busy waiting value;

[0027] The methods for extracting production demand include:

[0028] The demand semantics of demand texts such as daily demand, weekly demand and monthly demand are recorded as the third semantics, and the demand value in the third semantics is recorded as the single-point demand. Single point demand;

[0029] Will After the single-point demands are accumulated one by one, the production scheduling demand is obtained;

[0030] The expression of production demand is:

[0031] ;

[0032] In the formula, To schedule the demand, For the A single point demand.

[0033] Furthermore, the attribute state includes a valid state and an invalid state, and the identification method of the valid state and the invalid state includes:

[0034] Compare the yield lower limit value with the production lower limit value;

[0035] When the output lower limit value is greater than the production lower limit value, or the output lower limit value is less than or equal to the production lower limit value, and the busy waiting value is less than the production waiting value, the attribute state of the production line is identified as an invalid state;

[0036] When the output lower limit value is less than or equal to the production lower limit value, and the busy waiting value is greater than or equal to the production waiting value, the attribute state of the production line is identified as a valid state.

[0037] Furthermore, the method for obtaining the fault trigger rate includes:

[0038] Based on the preset fault trigger cycle, all safety operation logs of B target production lines are queried from the database in sequence;

[0039] Query all the remark words of the safety operation logs, record the safety operation logs with the remark word "fault shutdown" as fault logs, and count the number of fault logs to obtain B fault trigger values;

[0040] Compare the B fault trigger values ​​with the durations corresponding to the preset fault trigger cycles in sequence to obtain B fault trigger rates;

[0041] The expression of fault trigger rate is:

[0042] ;

[0043] In the formula, For the The failure trigger rate of the target production line, =1,2...B, For the The fault trigger value of the target production line, The duration corresponding to the preset fault trigger cycle.

[0044] Furthermore, the method for obtaining the production qualified value includes:

[0045] Query the production line numbers of all incoming products in the warehouse through the database, and record the products whose production line numbers are consistent with the production line numbers of B target production lines as valid products;

[0046] Mark the first moment and the last moment of the preset fault trigger cycle respectively, and record them as the cycle start moment and the cycle end moment;

[0047] The entry time of the effective products of B target production lines is queried one by one through the timestamp, and the effective products whose entry time is between the start time and the end time of the cycle are recorded as qualified products. The number of qualified products of B target production lines is counted in turn to obtain B qualified total values;

[0048] Compare B qualified total values ​​one by one with the duration corresponding to the preset fault trigger cycle to obtain B production qualified values;

[0049] The expression for the production qualified value is:

[0050] ;

[0051] In the formula, For the The production qualification value of the target production line, For the The total qualified value of the target production line.

[0052] Furthermore, the method for obtaining the effective output value includes:

[0053] Take the duration of a preset production cycle as the standard, take the current moment as the starting point, work backwards and mark the Production period;

[0054] Mark them one by one The first moment and the last moment of the production period are obtained respectively. The starting time and Ending moment;

[0055] Count the storage time of B target production lines respectively. Starting time to The total amount of qualified products between the end times is obtained The value of each period;

[0056] Will The values ​​of each time period are accumulated one by one and averaged to obtain B effective output values;

[0057] The expression of effective output value is:

[0058] ;

[0059] In the formula, For the The effective output value of the target production line, For the The target production line The value of each period;

[0060] The expression of scheduling priority coefficient is:

[0061] ;

[0062] In the formula, For the The production scheduling priority coefficient of each target production line, , , are all weight factors greater than 0.

[0063] Furthermore, the method for allocating sub-demands includes:

[0064] Record the effective output values ​​of B target production lines as segmentation criteria, obtain B segmentation criteria, and assign the B segmentation criteria the same number as the target production line respectively;

[0065] According to the order of numbering from small to large, with the segmentation standard as the segmentation point, the production demand quantity is segmented into sub-demand quantities one by one until all the production demand quantities are segmented, and E sub-demand quantities are obtained;

[0066] The E sub-demands are bound to the corresponding E target production lines one by one, and at the same time, the bound sub-demands are allocated to the corresponding target production lines.

[0067] A device includes a device body and an intelligent production scheduling controller. The intelligent production scheduling controller integrates a network construction module, a feature extraction module, an identification and screening module, a coefficient calculation module and an allocation and scheduling module. The network construction module, the feature extraction module, the identification and screening module, the coefficient calculation module and the allocation and scheduling module are jointly used to realize an intelligent production scheduling method based on blockchain technology.

[0068] The technical effects and advantages of the intelligent production scheduling method and device based on blockchain technology of the present invention are as follows:

[0069] The present invention converts the management system and production line of industrial production into transmission nodes, and constructs a blockchain network based on the transmission nodes, obtains the demand information of the management system through the blockchain network, extracts the demand characteristics from the production scheduling information, calculates the production scheduling demand, obtains the production characteristics of the production line, identifies the effectiveness of the production characteristics and the demand characteristics, identifies the attribute status of the production line, and screens the target production line from the production line, obtains the comprehensive production data of the target production line, calculates the production scheduling priority coefficient of the target production line, arranges and numbers the target production lines in order, divides the production scheduling demand into sub-demands as needed, and allocates the sub-demands to the corresponding target production line, and controls the intelligent scheduling of the target production line. Compared with the prior art, the present invention converts the management system and production line of industrial production into transmission nodes through the blockchain technology, extracts the demand characteristics from the production scheduling information, calculates the production scheduling demand, obtains the production characteristics of the production line, and identifies the production characteristics of the production line. A blockchain network is built by combining the management system and the production line to achieve transparent interaction and real-time query of data between the management system and the production line, avoiding the phenomenon of blocked data interaction or poor monitoring between different levels in industrial production, achieving the effect of decentralized access control and real-time traceability of data, and identifying the attribute status of the production line in combination with demand characteristics and production characteristics, which can accurately screen out target production lines that meet the requirements of intelligent scheduling and narrow the equipment range of intelligent scheduling. At the same time, the scheduling priority coefficient is calculated through comprehensive production data, which can provide multi-dimensional, accurate and reasonable data support for the scheduling sequence of the target production line, so that industrial production demand can be reasonably and accurately allocated to the target production line for scheduling, thereby achieving the goal of efficient intelligent scheduling and improving industrial production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A flowchart of an intelligent production scheduling method based on blockchain technology provided in Embodiment 1 of the present invention;

[0071] Figure 2 A schematic diagram of the structure of a device provided in Embodiment 2 of the present invention;

[0072] Figure 3 A schematic diagram of a module of an intelligent production scheduling controller provided in Embodiment 2 of the present invention;

[0073] In the figure: 1. Device body; 2. Intelligent production scheduling controller. DETAILED DESCRIPTION

[0074] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0075] Example 1: Please refer to Figure 1 As shown, the intelligent production scheduling method based on blockchain technology described in this embodiment is applied to an intelligent production scheduling controller, including:

[0076] S1: Convert the management system and production line of industrial production into transmission nodes, and build a blockchain network based on the transmission nodes;

[0077] In order to achieve data interaction and sharing between the management system and the production line in industrial production management, it is necessary to connect the management system and the production line to the blockchain network through blockchain technology to achieve transparency, sharing and decentralization of production data in industrial production management, and provide a solid foundation for subsequent intelligent production scheduling;

[0078] The transmission node is the corresponding node used to convert the management system and production line in industrial production management to blockchain technology, and serves as the basis for the subsequent construction of the blockchain network to achieve two-way data interaction and transmission control effects between the management system and the production line;

[0079] Transmission nodes include primary nodes and secondary nodes; primary nodes refer to nodes where management systems transmit and interact with each other in blockchain technology, and secondary nodes refer to nodes where production lines transmit and interact with each other in blockchain technology;

[0080] The conversion method between the primary node and the secondary node includes:

[0081] A basic network template is selected from the database, and nodes are established in the basic network template with the same number as the management system and the number of production lines, and the first node and A second nodes are obtained respectively; the basic network template refers to a blank network architecture without substantial content, and serves as the basis for the subsequent construction of the blockchain network;

[0082] The model of the production line is found through the technical parameter table, and the model of the production line is marked on A second nodes accordingly to obtain A marked nodes;

[0083] The management system shares data with the first node through blockchain technology to obtain a first-level node;

[0084] Through blockchain technology, data of A production lines are shared with A marking nodes one by one to obtain A secondary nodes.

[0085] When the management system and production line are converted into corresponding transmission nodes, the basic network template can be used as a benchmark to build a blockchain network, so that the management system and A production lines can store, transmit, record and query related data in the blockchain network, and provide a transparent and shared data foundation for subsequent intelligent production scheduling;

[0086] The blockchain network is a network model that integrates the management system and the production line with blockchain technology, providing a front-end architecture for the subsequent intelligent production scheduling and ensuring the effective implementation of intelligent production scheduling.

[0087] The construction methods of blockchain network include:

[0088] Three application layers are sequentially established in the basic network template, and are recorded as the upper layer, the middle layer and the lower layer from top to bottom.

[0089] Mark A lower points at equal intervals in the lower layer, import A secondary nodes into A lower points in sequence, and establish a data channel between two adjacent lower points; a data channel refers to a channel that can meet the needs of data transmission and interaction in blockchain technology, and provides a transparent and visual basis for the construction of blockchain networks, thereby ensuring the convenience and accuracy of data transmission within the blockchain network;

[0090] Mark the median point in the median layer, import the first-level node to the median point, and establish a data channel between the median point and A lower points;

[0091] The upper points are marked in the upper layer, and a data channel is established between the upper points and the middle points to generate a blockchain network.

[0092] It should be noted that the constructed blockchain network is used as the network foundation for the collection, analysis and application of relevant data in the subsequent intelligent production scheduling, and enables the relevant data in intelligent production scheduling to be transparently, shared and efficiently transmitted and flowed within the blockchain network.

[0093] S2: Obtain demand information of the management system through the blockchain network, extract demand characteristics from the production scheduling information, and calculate the production scheduling demand;

[0094] Demand information refers to the overall representation of data such as industrial production equipment and processes that require intelligent scheduling, that is, the overall representation of industrial production information to be scheduled. Since demand information contains many types and a large amount of data, in order to facilitate the accurate operation of subsequent intelligent sorting, it is necessary to further screen and optimize the demand information;

[0095] Demand characteristics refer to the characteristics of demand information that can directly and clearly express industrial production demand, and serve as the basis for subsequent analysis and judgment of the production line. At the same time, they can also concisely and accurately express the basic requirements of the production line in the demand information;

[0096] The demand characteristics include the output lower limit and the busy waiting value; the output lower limit is used to set the minimum production volume per unit time of a single production line required in the industrial production demand, that is, to limit the minimum unit output in the industrial production demand; the busy waiting value is used to set the maximum waiting time that can be tolerated in the industrial production demand, that is, to limit the maximum waiting time for the production line in the industrial production demand;

[0097] Methods for extracting the lower limit value of production and the busy waiting value include:

[0098] All industrial production data are queried from the database through the blockchain network, and the type values ​​of all data are identified one by one, and the data with the type value of demand is recorded as demand information; the type value is used to specifically represent the data type, so as to achieve an effective distinction between different types of data. For example, when the type value is demand, it indicates that the type of the data is demand data for industrial production;

[0099] The demand semantics of the demand information are identified one by one through natural language processing technology, and the demand semantics are split into demand text and demand value through word segmentation technology; the demand semantics is used to concisely and accurately express the true meaning of the demand information, and provides a real basis for the subsequent splitting and identification of demand text and demand value;

[0100] The demand semantics in which the demand text is the lower limit of the output is recorded as the first semantics, and the demand value in the first semantics is recorded as the lower limit of the output;

[0101] The demand semantics in which the demand text is busy waiting is recorded as the second semantics, and the demand value in the second semantics is recorded as the busy waiting value.

[0102] After obtaining the demand characteristics, in order to meet the needs of subsequent intelligent production scheduling, it is also necessary to extract the production scheduling demand from the demand information, so that the production scheduling demand can represent the total production scheduling in the industrial production demand and serve as the total amount of intelligent production scheduling when the subsequent production line manufactures products;

[0103] The methods for extracting production demand include:

[0104] The demand semantics of demand texts such as daily demand, weekly demand and monthly demand are recorded as the third semantics, and the demand value in the third semantics is recorded as the single-point demand. Single point demand;

[0105] Will After the single-point demands are accumulated one by one, the production scheduling demand is obtained;

[0106] The expression of production demand is:

[0107] ;

[0108] In the formula, To schedule the demand, For the A single point demand.

[0109] After obtaining the demand characteristics and production scheduling requirements, the management system can now comprehensively collect relevant demand information that affects the intelligent scheduling of industrial production, and use the demand characteristics and production scheduling requirements as a basis to meet the subsequent intelligent scheduling needs, ultimately achieving efficient and intelligent scheduling effects in industrial production.

[0110] S3: Obtain the production characteristics of the production line, identify the effectiveness of the production characteristics and demand characteristics, identify the attribute status of the production line, and select the target production line from the production lines;

[0111] Production characteristics refer to the characteristics that can indicate the strength of the production and manufacturing performance of each production line in industrial production, and serve as the basis for subsequent comparison and screening of each production line. At the same time, they can also indicate the production situation of each production line;

[0112] Production characteristics include production lower limit and production waiting value; the production lower limit is used to set the minimum production volume of a single production line in industrial production per unit time, that is, to limit the minimum unit output of the production line in industrial production; the production waiting value is used to set the maximum waiting time in industrial production, that is, to limit the maximum waiting time in industrial production; both the production lower limit and the production waiting value are obtained by querying the equipment management system through the blockchain network;

[0113] After obtaining the production characteristics, the effectiveness of the production characteristics and demand characteristics can be identified, so that the matching degree between the production line and the production demand can be compared and judged one by one, and the attribute status of each production line can be represented according to the comparison and judgment results;

[0114] The attribute status is used to represent the basic judgment result of whether the production line meets the production demand, so as to effectively distinguish the basic conditions of different production lines, so as to select the target production line that can meet the subsequent intelligent scheduling from a large number of production lines.

[0115] The attribute status includes valid status and invalid status. The valid status means that the current basic situation of the production line meets the basic requirements of subsequent intelligent production scheduling, and the invalid status means that the current basic situation of the production line does not meet the basic requirements of subsequent intelligent production scheduling.

[0116] Methods for identifying valid and invalid states include:

[0117] Compare the yield lower limit value with the production lower limit value;

[0118] When the output lower limit is greater than the production lower limit, the minimum output per unit time of the production line cannot meet the minimum output requirement of industrial production demand, and the attribute state of the production line is identified as an invalid state;

[0119] When the output lower limit is less than or equal to the production lower limit, and the busy waiting value is less than the production waiting value, the waiting time of the production line cannot meet the waiting time requirement of industrial production demand, and the attribute state of the production line is identified as invalid;

[0120] When the output lower limit value is less than or equal to the production lower limit value, and the busy waiting value is greater than or equal to the production waiting value, the waiting time of the production line and the minimum output per unit time meet the requirements of industrial production needs, and the attribute status of the production line is identified as a valid state.

[0121] After the attribute status is identified, each production line can be further screened according to the attribute status of the production line, and a target production line that can meet the subsequent intelligent production scheduling needs is obtained. Therefore, the target production line is a production line whose attribute status is valid, thereby obtaining B target production lines.

[0122] S4: Obtain comprehensive production data of the target production line, which includes fault trigger rate, production qualification value and effective output value, calculate the production scheduling priority coefficient of the target production line, and arrange and number the target production lines in order;

[0123] Comprehensive production data refers to comprehensive data that can affect the priority order of the target production line in intelligent production scheduling, that is, it can comprehensively represent the corresponding changes of the target production line during production scheduling, so as to serve as the data basis for subsequent intelligent production scheduling;

[0124] Comprehensive production data includes fault trigger rate, production qualification value and unit output value;

[0125] The fault trigger rate refers to the number of fault downtime events that occur in the target production line within a unit time period, which can be used to represent the production operation reliability of the target production line. The larger the fault trigger rate, the more fault downtime events that occur in the target production line within a unit time period, and the lower the operation reliability of the target production line, the later the intelligent production scheduling order of the target production line, and the smaller the production scheduling priority coefficient;

[0126] Methods for obtaining the fault trigger rate include:

[0127] Based on the preset fault trigger cycle, all the safety operation logs of B target production lines are queried from the database in sequence; the preset fault trigger cycle refers to the maximum time span that a fault downtime event can affect the operational reliability of the production line, thereby limiting the collection time period of the fault downtime situation and avoiding the problem of too much data involved in the calculation due to a large time span;

[0128] Query all the remark words of the safety operation logs, record the safety operation logs with the remark word "fault shutdown" as fault logs, and count the number of fault logs to obtain B fault trigger values; the remark word is used to truly represent the operation status corresponding to the safety operation log, and the remark words include fault shutdown, safe operation and idle shutdown; fault shutdown indicates that the production line has experienced a fault shutdown phenomenon, and safe operation and idle shutdown both indicate that the production line has not experienced a fault shutdown phenomenon;

[0129] Compare the B fault trigger values ​​with the durations corresponding to the preset fault trigger cycles in sequence to obtain B fault trigger rates;

[0130] The expression of fault trigger rate is:

[0131] ;

[0132] In the formula, For the The failure trigger rate of the target production line, =1,2...B, For the The fault trigger value of the target production line, The duration corresponding to the preset fault trigger cycle.

[0133] The production qualification value refers to the number of qualified products manufactured by the target production line in a unit time period, which can represent the production performance of the target production line. The larger the production qualification value, the more qualified products the target production line produces in a unit time, the stronger the production performance of the target production line, the higher the intelligent production scheduling order of the target production line, and the larger the production scheduling priority coefficient;

[0134] The methods for obtaining production qualified values ​​include:

[0135] Query the production line numbers of all incoming products in the warehouse through the database, and record the products whose production line numbers are consistent with the production line numbers of B target production lines as valid products;

[0136] Mark the first moment and the last moment of the preset fault trigger cycle respectively, and record them as the cycle start moment and the cycle end moment;

[0137] The entry time of the effective products of B target production lines is queried one by one through the timestamp, and the effective products whose entry time is between the start time and the end time of the cycle are recorded as qualified products. The number of qualified products of B target production lines is counted in turn to obtain B qualified total values;

[0138] Compare B qualified total values ​​one by one with the duration corresponding to the preset fault trigger cycle to obtain B production qualified values;

[0139] The expression for the production qualified value is:

[0140] ;

[0141] In the formula, For the The production qualification value of the target production line, For the The total qualified value of the target production line.

[0142] The effective output value refers to the average number of qualified products that the target production line can produce in a production period, which can be used to indicate the production capacity of the target production line. The larger the effective output value, the more average number of qualified products the target production line can produce in a production period, the higher the operating reliability of the target production line, the higher the intelligent production scheduling order of the target production line, and the greater the production scheduling priority coefficient;

[0143] Methods for obtaining effective yield values ​​include:

[0144] Take the duration of a preset production cycle as the standard, take the current moment as the starting point, work backwards and mark the The preset production cycle is used to represent the time span corresponding to the production period, which can ensure that each production period can maintain consistency in duration, and is also convenient for subsequent calculation and analysis. The production period is used to evaluate the duration of the production line when it can continuously and efficiently produce industrial products. The duration of the specific production period is set according to the specific industrial production and industrial equipment. For example, a production period can be 1 day or 7 days.

[0145] Mark them one by one The first moment and the last moment of the production period are obtained respectively. The starting time and Ending moment;

[0146] Count the storage time of B target production lines respectively. Starting time to The total amount of qualified products between the end times is obtained The value of each period;

[0147] Will The values ​​of each time period are accumulated one by one and averaged to obtain B effective output values;

[0148] The expression of effective output value is:

[0149] ;

[0150] In the formula, For the The effective output value of the target production line, For the The target production line The value of a period of time.

[0151] After the fault trigger rate, production qualification value and unit output value of the target production line are obtained, the production scheduling priority coefficient of the target production line can be calculated according to the fault trigger rate, production qualification value and unit output value, so that the production scheduling priority coefficient can provide a numerical basis for the sequence of intelligent production scheduling of the target production line and ensure that the target production line can be sorted in an orderly and accurate manner;

[0152] The expression of scheduling priority coefficient is:

[0153] ;

[0154] In the formula, For the The production scheduling priority coefficient of each target production line, , , All are weight factors greater than 0;

[0155] in, , , , The setting is to balance the ratio of fault trigger rate, production qualification value and unit output value in the production scheduling priority coefficient, and to ensure that changes in fault trigger rate, production qualification value and unit output value can have a corresponding impact on changes in the production scheduling priority coefficient, so as to ensure the calculation accuracy of the production scheduling priority coefficient.

[0156] After calculating the production scheduling priority coefficient, the target production lines can be arranged in order according to the size of the production scheduling priority coefficient of each target production line. Therefore, the target production line with a larger production scheduling priority coefficient, the more it meets the industrial production needs, and the target production line is arranged earlier. Conversely, the target production line with a smaller production scheduling priority coefficient, the less it meets the industrial production needs, and the target production line is arranged later. After arranging the B target production lines in order, the B target production lines can be numbered in ascending order according to the production scheduling priority coefficient from large to small. For example, the target production line with the largest production scheduling priority coefficient is numbered 1, followed by 2, and the last target production line is numbered B.

[0157] S5: Split the production demand into sub-demands as needed, and allocate the sub-demands to the corresponding target production lines to control the intelligent production scheduling of the target production lines;

[0158] Sub-demand refers to the production volume divided according to the effective output value of the target production line. It can divide the total production demand into smaller units, so that each target production line can perform a reasonable number of production operations as needed. After dividing the sub-demand, the sub-demand needs to be allocated to the corresponding target production line, so that each sub-demand can produce a corresponding number of industrial products.

[0159] The methods for allocating sub-demands include:

[0160] Record the effective output values ​​of B target production lines as segmentation criteria, obtain B segmentation criteria, and assign the B segmentation criteria the same number as the target production line respectively;

[0161] According to the order of numbering from small to large, with the segmentation standard as the segmentation point, the production demand is segmented into sub-demands one by one until all the production demand is segmented to obtain E sub-demands; after the sub-demands are segmented, the E sub-demands may all be consistent with the effective output values ​​of the E target production lines, or the first E-1 sub-demands may be consistent with the effective output values ​​of the corresponding E-1 target production lines, and the last sub-demand may be smaller than the effective output value of the corresponding target production line;

[0162] The E sub-demands are bound to the corresponding E target production lines one by one, and at the same time, the bound sub-demands are allocated to the corresponding target production lines.

[0163] After being divided into multiple sub-demand quantities, the production scheduling demand quantity at this time will be divided into multiple sub-demand quantities in an orderly and complete manner, and the multiple sub-demand quantities can be allocated to the corresponding target production lines for industrial production scheduling processing, so that the sub-demand quantities can be used as the basis for controlling the intelligent scheduling of the target production line.

[0164] It should be noted that in actual industrial production, after the sub-demand is allocated to the corresponding target production line, the production raw materials corresponding to the sub-demand need to be transported to the location of the corresponding target production line by a transport vehicle, so that the industrial production demand can be connected with the actual production and manufacturing, and the intelligent production scheduling control effect of industrial production and manufacturing can be achieved.

[0165] In this embodiment, the management system and production line of industrial production are converted into transmission nodes, and a blockchain network is constructed based on the transmission nodes. The demand information of the management system is obtained through the blockchain network, the demand characteristics are extracted from the production scheduling information, and the production scheduling demand is calculated, the production characteristics of the production line are obtained, the effectiveness of the production characteristics and the demand characteristics are identified, the attribute status of the production line is identified, and the target production line is screened from the production line, the comprehensive production data of the target production line is obtained, the production scheduling priority coefficient of the target production line is calculated, and the target production lines are arranged and numbered in order, the production scheduling demand is divided into sub-demands as needed, and the sub-demands are allocated to the corresponding target production lines, and the intelligent scheduling of the target production line is controlled; compared with the prior art, the blockchain technology is used to convert the industrial production in The management system and the production line build a blockchain network, which realizes the transparent interaction and real-time query of data between the management system and the production line, avoids the phenomenon of blocked data interaction or poor monitoring between different levels in industrial production, and achieves the effect of decentralized access control and real-time traceability of data. It also identifies the attribute status of the production line by combining demand characteristics and production characteristics, and can accurately screen out target production lines that meet the requirements of intelligent scheduling, narrow the equipment range of intelligent scheduling, and calculate the scheduling priority coefficient through comprehensive production data. It can provide multi-dimensional, accurate and reasonable data support for the scheduling sequence of the target production line, so that industrial production demand can be reasonably and accurately allocated to the target production line for scheduling, thereby achieving the goal of efficient intelligent scheduling and improving industrial production efficiency.

[0166] Example 2: Please refer to Figure 2 and Figure 3 As shown, the part not described in detail in this embodiment is described in the first embodiment. A device is provided, which includes a device body 1 and an intelligent production scheduling controller 2. The intelligent production scheduling controller 2 integrates a network construction module, a feature extraction module, an identification and screening module, a coefficient calculation module and an allocation and scheduling module, wherein the network construction module, the feature extraction module, the identification and screening module, the coefficient calculation module and the allocation and scheduling module are used together to realize an intelligent production scheduling method based on blockchain technology;

[0167] When the intelligent production scheduling controller performs intelligent production scheduling for industrial production needs, it is necessary to combine the network construction module, feature extraction module, identification and screening module, coefficient calculation module and allocation scheduling module into a whole and jointly achieve the effect of intelligent production scheduling. In addition, each module is connected through a wired or wireless network, and each module has an independent function, as follows:

[0168] The network construction module is used to convert the management system and production line of industrial production into transmission nodes. The transmission nodes include primary nodes and secondary nodes, and the blockchain network is constructed based on the transmission nodes.

[0169] The feature extraction module is used to obtain the demand information of the management system through the blockchain network, and extract the demand features from the production scheduling information. The demand features include the lower limit of production and the busy waiting value, and calculate the production scheduling demand;

[0170] The identification and screening module is used to obtain the production characteristics of the production line, including the production lower limit value and the production waiting value, to identify the effectiveness of the production characteristics and the demand characteristics, to identify the attribute status of the production line, and to screen the target production line from the production line;

[0171] The coefficient calculation module is used to obtain the comprehensive production data of the target production line, which includes the fault trigger rate, the production qualification value and the effective output value, calculate the production scheduling priority coefficient of the target production line, and arrange and number the target production lines in order;

[0172] The production scheduling module is used to divide the production demand into sub-demands as needed, and allocate the sub-demands to the corresponding target production lines to control the intelligent scheduling of the target production lines.

[0173] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An intelligent production scheduling method based on blockchain technology, applied to an intelligent production scheduling controller, characterized in that: include: S1: Convert the management system and production line of industrial production into transmission nodes, which include primary nodes and secondary nodes, and build a blockchain network based on the transmission nodes; The conversion method between the primary node and the secondary node includes: A basic network template is selected from the database, and nodes are established in the basic network template with the number of nodes consistent with the number of the management system and the number of production lines, and a first node and A second nodes are obtained respectively; The model of the production line is found through the technical parameter table, and the model of the production line is marked on A second nodes accordingly to obtain A marked nodes; The management system shares data with the first node through blockchain technology to obtain a first-level node; Through blockchain technology, data of A production lines are shared with A marking nodes one by one to obtain A secondary nodes; The construction methods of blockchain network include: Three application layers are sequentially established in the basic network template, and are recorded as the upper layer, the middle layer and the lower layer from top to bottom. Mark A lower points at equal intervals in the lower layer, import A secondary nodes into the A lower points in sequence, and establish a data channel between two adjacent lower points; Mark the median point in the median layer, import the first-level node to the median point, and establish a data channel between the median point and A lower points; Mark the upper point in the upper layer, and establish a data channel between the upper point and the middle point to generate a blockchain network; S2: Obtain demand information from the management system through the blockchain network, and extract demand features from the production scheduling information. The demand features include the lower limit of production and the busy waiting value, and calculate the production scheduling demand; S3: Obtain the production characteristics of the production line, which include the production lower limit value and the production waiting value, identify the effectiveness of the production characteristics and the demand characteristics, identify the attribute status of the production line, and screen out the target production line from the production lines; S4: Obtain comprehensive production data of the target production line, which includes fault trigger rate, production qualification value and effective output value, calculate the production scheduling priority coefficient of the target production line, and arrange and number the target production lines in order; S5: Divide the production demand into sub-demands as needed, and allocate the sub-demands to the corresponding target production lines to control the intelligent production scheduling of the target production lines.

2. According to claim 1, a smart production scheduling method based on blockchain technology is characterized in that: Methods for extracting the lower limit value of production and the busy waiting value include: All industrial production data is queried from the database through the blockchain network, and the type values ​​of all data are identified one by one, and the data with the type value of demand is recorded as demand information; The demand semantics of demand information are identified one by one through natural language processing technology, and the demand semantics are split into demand text and demand value through word segmentation technology; The demand semantics in which the demand text is the lower limit of the output is recorded as the first semantics, and the demand value in the first semantics is recorded as the lower limit of the output; Recording the demand semantics of the demand text as busy waiting as the second semantics, and recording the demand value in the second semantics as the busy waiting value; The methods for extracting production demand include: The demand semantics of demand texts such as daily demand, weekly demand and monthly demand are recorded as the third semantics, and the demand value in the third semantics is recorded as the single-point demand. Single point demand; Will After the single-point demands are accumulated one by one, the production scheduling demand is obtained; The expression of production demand is: ； In the formula, To schedule the demand, For the A single point demand.

3. According to claim 2, a smart production scheduling method based on blockchain technology is characterized in that: The attribute status includes a valid status and an invalid status. The identification methods of the valid status and the invalid status include: Compare the yield lower limit value with the production lower limit value; When the output lower limit value is greater than the production lower limit value, or the output lower limit value is less than or equal to the production lower limit value, and the busy waiting value is less than the production waiting value, the attribute state of the production line is identified as an invalid state; When the output lower limit value is less than or equal to the production lower limit value, and the busy waiting value is greater than or equal to the production waiting value, the attribute state of the production line is identified as a valid state.

4. According to claim 3, a smart production scheduling method based on blockchain technology is characterized in that: Methods for obtaining the fault trigger rate include: Based on the preset fault trigger cycle, all safety operation logs of B target production lines are queried from the database in sequence; Query all the remark words of the safety operation logs, record the safety operation logs with the remark word "fault shutdown" as fault logs, and count the number of fault logs to obtain B fault trigger values; Compare the B fault trigger values ​​with the durations corresponding to the preset fault trigger cycles in sequence to obtain B fault trigger rates; The expression of fault trigger rate is: ； In the formula, For the The failure trigger rate of the target production line, =1,2...B, For the The fault trigger value of the target production line, The duration corresponding to the preset fault trigger cycle.

5. According to claim 4, a smart production scheduling method based on blockchain technology is characterized in that: The methods for obtaining production qualified values ​​include: The production line numbers of all incoming products in the warehouse are queried through the database, and the products whose production line numbers are consistent with the production line numbers of B target production lines are recorded as valid products; Mark the first moment and the last moment of the preset fault trigger cycle respectively, and record them as the cycle start moment and the cycle end moment; The entry time of effective products of B target production lines is queried one by one through the timestamp, and the effective products whose entry time is between the start time and the end time of the cycle are recorded as qualified products. The number of qualified products of B target production lines is counted in turn to obtain B qualified total values; Compare B qualified total values ​​one by one with the duration corresponding to the preset fault trigger cycle to obtain B production qualified values; The expression for the production qualified value is: ； In the formula, For the The production qualification value of the target production line, For the The total qualified value of each target production line.

6. According to claim 5, a smart production scheduling method based on blockchain technology is characterized in that: Methods for obtaining effective yield values ​​include: Take the duration of a preset production cycle as the standard, take the current moment as the starting point, work backwards and mark the Production period; Mark them one by one The first moment and the last moment of the production period are obtained respectively. The starting time and Ending moment; Count the storage time of B target production lines respectively. Starting time to The total amount of qualified products between the end times is obtained The value of each period; Will The values ​​of each time period are accumulated one by one and averaged to obtain B effective output values; The expression of effective output value is: ； In the formula, For the The effective output value of the target production line, For the The target production line The value of each period; The expression of scheduling priority coefficient is: ； In the formula, For the The production scheduling priority coefficient of each target production line, , , are all weight factors greater than 0.

7. According to claim 6, a smart production scheduling method based on blockchain technology is characterized in that: The methods for allocating sub-demands include: Record the effective output values ​​of B target production lines as segmentation criteria, obtain B segmentation criteria, and assign the B segmentation criteria the same number as the target production line respectively; According to the order from small to large, with the segmentation standard as the segmentation point, the production demand quantity is segmented into sub-demand quantities one by one until all the production demand quantities are segmented to obtain E sub-demand quantities; The E sub-demands are bound to the corresponding E target production lines one by one, and at the same time, the bound sub-demands are allocated to the corresponding target production lines.

8. A device, comprising a device body (1) and an intelligent production scheduling controller (2), wherein the intelligent production scheduling controller (2) integrates a network construction module, a feature extraction module, an identification and screening module, a coefficient calculation module and an allocation and scheduling module, wherein: The network construction module, feature extraction module, identification and screening module, coefficient calculation module and allocation and scheduling module are used together to implement the intelligent scheduling method based on blockchain technology as described in any one of claims 1-7.

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