Cord fabric dipping glue solution preparation management system

Abstract

The invention discloses a cord fabric dipping glue solution preparation management system, and relates to the technical field of glue solution production, the cord fabric dipping glue solution preparation management system comprises an order interaction unit, an order sequence making unit, a glue solution preparation management and control unit and a glue solution preparation monitoring unit; the order interaction unit is used for establishing connection between the enterprise resource planning system and the client to obtain a glue solution production order; the order sequence formulating unit is used for carrying out priority ranking on the glue solution production orders based on the client demand responsivity; and the glue solution preparation management and control unit is used for generating a raw material process formula based on a predefined raw material proportioning rule, and preparing a glue solution required by the glue solution production order according to the working procedures in the raw material process formula and the raw material combination. Through docking with an enterprise communication platform, important production information, abnormal alarms or task instructions can be pushed in real time, so that the response speed and the unmanned level of production management are greatly improved.

Description

Technical Field

[0001] The invention relates to the technical field of glue liquid production, in particular to a management system for preparing glue liquid for cord fabric dipping. Background Art

[0002] Tire fabric dipping glue is a chemical mixed liquid specially used for the dipping treatment of tire fabric (mainly used as the skeleton material of tires, conveyor belts and other products). Its main function is to improve the bonding performance between tire fabric and rubber, and at the same time give the tire fabric excellent mechanical properties and chemical stability. Tire fabric dipping glue preparation management refers to the systematic management of the entire process of glue formulation, preparation, use and storage in the tire fabric dipping process. Its core purpose is to ensure the quality and stability of the glue, thereby improving the bonding performance between tire fabric and rubber, and ensuring the quality consistency of the final product.

[0003] The glue liquid preparation management technology in the prior art is only based on PLC control to achieve automated production. It is impossible to achieve fully automated production for glue preparation with complex processes, and manual intervention is still required. Moreover, its formula can only change the weight and reaction time of the raw materials, and cannot achieve customized formula functions. The glue liquid preparation management technology in the prior art is not accurate enough and has insufficient detection points for data such as weight and temperature in the raw material production process, which affects its preparation accuracy and production stability, and thus cannot provide complete data support for later traceability.

[0004] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0005] In view of the problems in the related technology, the present invention proposes a tire cord fabric dipping glue solution preparation and management system to overcome the above technical problems existing in the existing related technology.

[0006] To this end, the specific technical solution adopted by the present invention is as follows:

[0007] A tire cord fabric dipping glue solution preparation management system, which includes an order interaction unit, an order sequence formulation unit, a glue solution configuration control unit, and a glue solution configuration monitoring unit;

[0008] An order interaction unit is used to establish a connection between the enterprise resource planning system and the client to obtain the glue production order and store the glue production order in the database;

[0009] An order sequence formulation unit is used to analyze the client demand responsiveness, prioritize the glue production orders based on the client demand responsiveness, and produce the corresponding glue production orders in the priority order;

[0010] The glue liquid preparation control unit is used to generate the raw material process formula based on the predefined raw material ratio rules, and prepare the glue liquid required for the glue liquid production order according to the process and raw material combination in the raw material process formula;

[0011] The glue liquid configuration monitoring unit is used to monitor the production parameters and production equipment during the glue liquid preparation process in real time, and trigger an early warning when an abnormal event is detected.

[0012] Preferably, the order sequence formulation unit includes a client demand analysis module, an order priority calculation module and a schedule generation module including:

[0013] The client demand analysis module is used to evaluate the importance of customers and the urgency of orders based on the client order demand, and calculate the responsiveness scores of the importance of customers and the urgency of orders according to the evaluation results, and weight the responsiveness scores to obtain a comprehensive responsiveness score;

[0014] The order priority calculation module is used to generate the priority ranking of the glue production orders using the optimization algorithm, and combine the priority ranking with the comprehensive responsiveness score to obtain the optimal glue production order sequence;

[0015] The schedule generation module is used to generate a glue production schedule based on the optimal glue production order sequence.

[0016] Preferably, the order priority calculation module generates the priority ranking of the glue production order by using the optimization algorithm, combines the priority ranking with the comprehensive responsiveness score, and obtains the optimal glue production order sequence, including:

[0017] The number of glue production orders is obtained, and the number of glue production order sequences is randomly generated based on the number of glue production orders as an initial solution;

[0018] Calculate the first fitness function of the glue production order sequence, and select glue production orders without conflicting orders from the glue production order sequence, and add the time node of the current glue production order to the graph network set;

[0019] Traverse the time nodes of the remaining glue production orders in the glue production order sequence, and compare them with the time nodes already occupied by the graph network set to determine whether there is a conflict;

[0020] If there is a conflict, the timetable corresponding to the glue production order of the non-conflicting path is modified; if there is no conflict, the time nodes of the remaining glue production orders are added to the graph network set, and the second fitness function of the glue production order sequence is obtained;

[0021] Based on the first fitness function and the second fitness function, determine the crossover rate of the glue production order sequence, and generate a new glue production order sequence based on the crossover rate;

[0022] The generation process of the glue liquid production order sequence is continuously iterated until the termination condition is met, and the iteration is stopped, and the finally generated glue liquid production order sequence is used as the optimal glue liquid production order sequence.

[0023] Preferably, the glue liquid preparation control unit includes a process formula customization module, a raw material demand prediction module, a raw material metering module, a mixing tank temperature control module, a glue liquid storage module and an automation control module;

[0024] The process formula customization module is used to customize the raw material process formula of the glue production order based on the predefined raw material ratio rules and client order requirements;

[0025] A raw material demand forecasting module is used to forecast the raw material demand based on the glue production order and store the raw material demand in the database;

[0026] The raw material metering module is used to collect the material discharge amount of the metering tank during the glue processing process using a weighing sensor, and to control the opening parameters of the regulating valve in real time according to the material discharge amount;

[0027] The temperature control module of the mixing tank is used to collect the temperature of the mixing tank using a temperature sensor and adjust the parameters of the mixing tank according to the temperature of the mixing tank to achieve two-way temperature control of the chilled water and the cooling water;

[0028] The glue storage module is used to store the prepared glue in the on-site tank, and use the weight sensor to monitor the glue usage and adjust the variable frequency stirring parameters according to the glue usage to achieve two-way temperature control of the chilled water and cooling water;

[0029] The automation control module is used to centrally control the production equipment in the glue processing process through the control cabinet configuration, and to perform remote signal acquisition using communication bus technology.

[0030] Preferably, the process recipe customization module includes a process recipe production module, a process recipe setting module and a process recipe customization module;

[0031] The process formula production module is used to generate the process formula based on the predefined raw material ratio rules and send the process formula to the production end by means of communication;

[0032] The process formula setting module is used to save the process formula that meets the glue production order into the database to realize the call of the process formula;

[0033] The formula customization module is used to customize the process formula according to the client's order requirements and match the generated process formula according to the finished glue product.

[0034] Preferably, the process formula setting module includes:

[0035] Based on the process flow setting program, call the sub-process and sub-process parameter settings, and generate the process formula that meets the glue production order after the settings are completed;

[0036] In the process flow setting program, the generated process formula is input into the formula name and stored in the database to realize the calling of the process formula during the liquid preparation process.

[0037] Preferably, the raw material metering module uses a weighing sensor to collect the discharge amount of the metering tank during the glue processing process, and controls the opening parameters of the regulating valve in real time according to the discharge amount, including:

[0038] The weighing sensor is used to collect the unloading amount of the metering tank during the glue liquid processing, and based on the glue liquid configuration completion cycle, the unloading cycle during the glue liquid processing is divided into a fast unloading period, an advance amount detection period, and a precise amount arrival period;

[0039] Extract the key characteristics of the raw materials, analyze the weight change law of the metering tank based on the key characteristics of the raw materials, correct the weight deviation in the weight change law of the metering tank, and obtain the raw material unloading rate of each unloading cycle;

[0040] A ridge regression model is established, and the raw material feeding rate of each feeding cycle is used as input. The ridge regression model is used to predict the opening parameters of the regulating valve in the fast feeding period, the advance quantity detection period and the precise quantity arrival period respectively.

[0041] Gradient adjustment mechanisms corresponding to the rapid material discharge period, advance quantity detection period and precise quantity arrival period are established respectively, and the PID controller is used to adjust the regulating valve opening parameters in the rapid material discharge period, advance quantity detection period and precise quantity arrival period that conform to the gradient adjustment mechanism respectively.

[0042] Preferably, a ridge regression model is established, and the raw material feeding rate of each feeding cycle is used as input. The ridge regression model is used to predict the regulating valve opening parameters in the fast feeding period, the advance amount detection period and the precise amount arrival period respectively, including:

[0043] The time series characteristics are established based on the raw material feeding rate of each feeding cycle, and the time sliding window conforming to the ridge regression model is established according to the time series characteristics;

[0044] The objective function of the ridge regression model is established, and the regularization parameter is optimized using the grid search algorithm. The deviation of the ridge regression model is optimized based on the regularization parameter to obtain the optimized ridge regression model.

[0045] Generate a training set for each feeding cycle according to the raw material feeding rate, and use the training set for each feeding cycle to train the ridge regression model to obtain the regression coefficient of each feeding cycle;

[0046] Based on the regression coefficient of each material unloading cycle, the opening parameters of the regulating valve in the fast material unloading period, the advance quantity detection period and the precise quantity arrival period are predicted.

[0047] Preferably, the temperature control module of the mixing tank collects the temperature of the mixing tank by using a temperature sensor, and adjusts the parameters of the mixing tank according to the temperature of the mixing tank to achieve two-way temperature control of the chilled water and the cooling water, including:

[0048] Collecting the temperature of the blending tank within a preset time period based on a temperature sensor, and analyzing the temperature trend of the blending tank based on the temperature of the blending tank within the preset time period;

[0049] According to the temperature trend of the mixing tank, a steady-state thermal balance model and a non-steady-state thermal balance model are established respectively, and the chilled water parameters and cooling water parameters in the process of mixing the glue liquid are adjusted by using the steady-state thermal balance model;

[0050] When the temperature trend of the deployment tank tends to be stable, the non-steady-state heat balance model is used to deploy the chilled water parameters and cooling water parameters under stable conditions.

[0051] Preferably, the objective function of the ridge regression model is expressed as:

[0052] ;

[0053] Where J(β) represents the objective function of the ridge regression model; N represents the number of samples; y i represents the true value of the i-th sample; β0 represents the intercept term; β j The coefficient representing the feeding rate of the jth raw material; x ij represents the jth raw material feeding rate value in the i-th feeding cycle; p represents the characteristic number of the raw material feeding rate; λ represents the regularization parameter.

[0054] Preferably, the raw material metering module further includes:

[0055] Obtain glue configuration parameters during glue processing;

[0056] According to the analysis of the glue liquid configuration parameters, the glue liquid configuration feeding deviation coefficient is obtained;

[0057] Analyze whether to adjust the glue liquid configuration and material feeding according to the glue liquid configuration and material feeding deviation coefficient;

[0058] The specific analysis process of analyzing whether to adjust the glue solution configuration and feeding is as follows:

[0059] Compare the glue liquid configuration unloading deviation coefficient with the glue liquid configuration unloading threshold value preset in the database to determine whether the glue liquid configuration unloading deviation coefficient is greater than the glue liquid configuration unloading threshold value preset in the database;

[0060] When the glue liquid configuration unloading deviation coefficient is not greater than the glue liquid configuration unloading threshold preset in the database, the glue liquid configuration unloading adjustment is not performed;

[0061] When the glue liquid configuration feeding deviation coefficient is greater than the glue liquid configuration feeding threshold value preset in the database, the glue liquid configuration feeding adjustment is performed, and after the glue liquid configuration feeding adjustment, when the monitored glue liquid configuration feeding deviation coefficient is not greater than the glue liquid configuration feeding threshold value preset in the database, the glue liquid configuration feeding adjustment is stopped.

[0062] Preferably, the blending tank temperature control module further comprises:

[0063] Obtain the temperature control parameters of the two-way temperature control of the mixing tank;

[0064] The temperature control deviation coefficient of the glue mixing tank is obtained by analyzing the temperature control parameters;

[0065] When the obtained glue liquid mixing tank temperature control deviation coefficient is greater than the glue liquid mixing tank temperature control first threshold value preset in the database and less than the glue liquid mixing tank temperature control second threshold value, no temperature control adjustment is performed;

[0066] When the obtained glue solution mixing tank temperature control deviation coefficient is not greater than the glue solution mixing tank temperature control first threshold preset in the database, the temperature control is adjusted to the first level;

[0067] When the obtained temperature control deviation coefficient of the glue solution mixing tank is not less than the second temperature control threshold of the glue solution mixing tank preset in the database, the second level adjustment of the temperature control is performed.

[0068] Preferably, the method further includes analyzing the qualified degree of the mixing of the glue solution, and the specific process is as follows:

[0069] Obtain glue solution stirring quality parameters of glue solution;

[0070] The glue liquid stirring quality deviation coefficient is obtained according to the glue liquid stirring quality parameter analysis;

[0071] When the obtained glue liquid stirring quality deviation coefficient is greater than the first glue liquid stirring quality threshold value preset in the database and less than the second glue liquid stirring quality threshold value, no glue liquid stirring adjustment is performed;

[0072] In other cases, the glue solution should be stirred and adjusted.

[0073] The beneficial effects of the present invention are:

[0074] 1. The tire cord fabric dipping glue solution preparation and management system provided by the present invention adopts sensors and control technology to ensure the accuracy of the raw material ratio, thereby reducing the order preparation accuracy error. The present invention can reduce manual intervention, reduce labor intensity, improve production efficiency, and reduce the workload of staff, thereby being able to adapt to different process formulas and production scales, meet diversified production needs, and continuously optimize the formula process flow according to production data.

[0075] 2. The present invention not only improves production efficiency but also significantly improves the stability of product quality by integrating automation technology. A data interface is embedded in the system design, which can achieve seamless connection with the enterprise ERP system, upload and share production data in real time, and form a complete intelligent production closed loop. At the same time, by connecting with the enterprise communication platform, it can push important production information, abnormal alarms or task instructions in real time, thereby greatly improving the response speed and the unmanned level of production management.

[0076] 3. The present invention obtains the glue liquid configuration parameters in the glue liquid processing process, and obtains the glue liquid configuration feeding deviation coefficient according to the glue liquid configuration parameter analysis, thereby analyzing whether to adjust the glue liquid configuration feeding according to the glue liquid configuration feeding deviation coefficient, thereby achieving improved accuracy of glue liquid feeding configuration.

[0077] 4. The present invention obtains the temperature control parameters of the bidirectional temperature control of the mixing tank, and obtains the temperature control deviation coefficient of the glue liquid mixing tank by analyzing the temperature control parameters, thereby performing temperature control adjustment according to the temperature control deviation coefficient of the glue liquid mixing tank, thereby achieving improvement in the accuracy of the bidirectional temperature control of the glue liquid mixing tank.

[0078] 5. The present invention obtains the glue liquid stirring quality parameters of the glue liquid, and obtains the glue liquid stirring quality deviation coefficient according to the glue liquid stirring quality parameter analysis, thereby comparing the glue liquid stirring quality deviation coefficient with the glue liquid stirring quality threshold to analyze whether to adjust the glue liquid stirring, thereby improving the accuracy of glue liquid stirring, which is conducive to obtaining glue liquid with more qualified quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0080] Figure 1 It is a principle block diagram of a tire cord fabric dipping glue solution preparation and management system according to an embodiment of the present invention;

[0081] Figure 2It is a principle block diagram of an order sequence formulation unit in a tire cord fabric dipping glue solution preparation management system according to an embodiment of the present invention;

[0082] Figure 3 It is a principle block diagram of a glue solution configuration control unit in a tire cord fabric dipping glue solution preparation management system according to an embodiment of the present invention;

[0083] Figure 4 It is a schematic diagram of the expected warning information prompt in the tire cord fabric dipping glue solution preparation management system according to an embodiment of the present invention;

[0084] Figure 5 This is a schematic diagram of the intelligent control principle in the tire cord fabric dipping glue solution preparation and management system according to an embodiment of the present invention;

[0085] Figure 6 It is a schematic diagram of a production interface in a tire cord fabric dipping glue solution preparation management system according to an embodiment of the present invention;

[0086] Figure 7 It is a schematic diagram of an order process operation history information interface in a tire cord fabric dipping glue solution preparation management system according to an embodiment of the present invention;

[0087] Figure 8 2. It is a schematic diagram of an ordering interface in a tire cord fabric dipping glue solution preparation and management system according to an embodiment of the present invention;

[0088] Fig. 9 2. It is a schematic diagram of a process recipe design interface in a tire cord fabric dipping glue solution preparation management system according to an embodiment of the present invention;

[0089] Fig.10 It is a principle diagram of a raw material demand prediction module in a tire cord fabric dipping glue solution preparation and management system according to an embodiment of the present invention;

[0090] Fig.11 It is a schematic diagram of a raw material metering module in a tire cord fabric dipping glue solution preparation and management system according to an embodiment of the present invention;

[0091] Fig.12 It is a schematic diagram of a temperature control module of a mixing tank in a tire cord fabric dipping glue solution preparation and management system according to an embodiment of the present invention;

[0092] Fig.13 The present invention is a schematic diagram of a glue storage module in a tire cord fabric dipping glue preparation and management system according to an embodiment of the present invention.

[0093] In the figure:

[0094] 1. Order interaction unit; 2. Order sequence formulation unit; 201. Client demand analysis module; 202. Order priority calculation module; 203. Schedule generation module; 3. Glue liquid configuration control unit; 301. Process formula customization module; 302. Raw material demand prediction module; 303. Raw material metering module; 304. Mixing tank temperature control module; 305. Glue liquid storage module; 306. Automation control module; 307. Cost statistics module; 4. Glue liquid configuration monitoring unit. DETAILED DESCRIPTION

[0095] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments. They can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in this field should be able to understand other possible implementation methods and advantages of the present invention.

[0096] According to an embodiment of the present invention, a tire cord fabric dipping glue solution preparation and management system is provided.

[0097] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to the tire cord fabric dipping glue solution preparation management system of an embodiment of the present invention, the tire cord fabric dipping glue solution preparation management system includes an order interaction unit 1, an order sequence formulation unit 2, a glue solution configuration control unit 3 and a glue solution configuration monitoring unit 4.

[0098] The order interaction unit 1 is used to establish a connection between the enterprise resource planning system and the client to obtain the glue production order, and store the glue production order in the database.

[0099] It should be noted that the ERP system (Enterprise Resource Planning System) is an integrated management software that helps enterprises integrate and manage information and processes of multiple key business departments. ERP systems usually include the following core functional modules:

[0100] Financial management: including accounting, asset management, budgeting and financial reporting.

[0101] Supply Chain Management: covers procurement, inventory management, logistics and supplier relationship management.

[0102] Human Resource Management: including employee information management, compensation and benefits, recruitment and employee development.

[0103] Customer Relationship Management: involves customer information management, sales support and marketing activities.

[0104] Production and operations management: involves production planning, quality control and equipment maintenance.

[0105] The steps of establishing a connection between the enterprise resource planning system and the client to obtain the glue production order and storing the glue production order in the database include:

[0106] Step 1: Demand analysis and planning: Clarify the needs for data exchange between the client and the ERP system, including order information, production requirements, etc.; design the data transmission path from the client to the ERP system, and how to store this data in the database.

[0107] Step 2: System Integration: Develop an application programming interface (API) so that clients can send order requests securely; design a user interface so that clients can easily enter and submit orders.

[0108] Step 3: Database design: Design the database model, including table structure, fields, relationships, etc., to store glue production orders; ensure the security of the database, including access control, data encryption and backup strategies.

[0109] Step 4: Client operation:

[0110] Order input: The client enters glue production order information through the user interface.

[0111] Order submission: The client sends the order information to the ERP system through the API.

[0112] ERP system operation:

[0113] Receiving data: The ERP system receives order data sent by the client through the API.

[0114] Data validation: Verify that the received data complies with the predetermined format and business rules.

[0115] Data storage: Store the verified order data in the database.

[0116] Database operations:

[0117] Order Record: Create a new order record and store it in the corresponding table.

[0118] Index and backup: Create indexes for order data to improve query efficiency and back up data regularly.

[0119] The order for glue dipping for cord fabric (equivalent to glue production order) includes the following:

[0120] Product specifications: including the material, thickness, width, length and other parameters of the cord fabric.

[0121] Glue type: Specify the type of glue required, such as natural latex, resorcinol formaldehyde resin, etc.

[0122] Dipping process requirements: including the number of dipping times, drying temperature, thermal stretching conditions, etc.

[0123] Quality standards: Physical and mechanical properties requirements of cord fabrics, such as strength, elongation, durability, etc.

[0124] Quantity: The total amount or batch size of cord fabric in the order.

[0125] Delivery deadline: The delivery time or date requested by the customer.

[0126] The order sequence formulation unit 2 is used to analyze the client demand responsiveness, prioritize the glue production orders based on the client demand responsiveness, and produce the corresponding glue production orders in the priority order;

[0127] Among them, Figure 2 As shown, the order sequence formulation unit 2 includes a client demand analysis module 201, an order priority calculation module 202 and a schedule generation module 203 including:

[0128] The client demand analysis module 201 is used to evaluate the importance of the client and the urgency of the order based on the client order demand, and calculate the responsiveness scores of the importance of the client and the urgency of the order according to the evaluation results, and weightedly integrate the responsiveness scores to obtain a comprehensive responsiveness score.

[0129] It should be noted that based on the client order demand, the customer importance and order urgency are evaluated respectively, and the responsiveness scores of customer importance and order urgency are calculated respectively according to the evaluation results. The responsiveness scores are weighted and integrated to obtain the comprehensive responsiveness score including:

[0130] 1. Customer importance assessment:

[0131] Determine the evaluation indicators: historical order volume; customer value (such as annual spending); customer loyalty (such as repeat purchase rate); customer satisfaction; customer potential growth potential.

[0132] Composite importance score: Assign a weight to each indicator; normalize based on the data range and importance of each indicator; calculate the customer's composite importance score.

[0133] 2. Order urgency assessment:

[0134] Determine the evaluation indicators: order delivery time requirements; the urgency of the order (such as whether it is an express order); the deviation of the order quantity from the regular order quantity; the impact of the order on the supply chain.

[0135] Scoring methodology: assign weights to each metric; standardize data for each metric; calculate an order's urgency score.

[0136] 3. Weighted fusion:

[0137] Determine the weights Based on the company's business strategy and goals, determine the relative importance of customer importance and order urgency in the comprehensive responsiveness score, assign corresponding weights, and ensure that the sum of the weights is 1.

[0138] Calculate the composite responsiveness score (CRS): CRS = (weighted customer importance × customer importance responsiveness score) + (weighted order urgency × order urgency responsiveness score), for example:

[0139] Assume that the customer importance weight is 0.7 and the order urgency weight is 0.3.

[0140] The Customer Importance Responsiveness Score (CIRS) is 0.85.

[0141] The Order Urgency Responsiveness Score (OIRS) is 0.6.

[0142] The Comprehensive Responsiveness Score (CRS) is calculated as follows:

[0143] CRS=(0.7×0.85)+(0.3×0.6)=0.595+0.18=0.775.

[0144] The priority calculation module 202 is used to generate the priority ranking of the glue production orders by using an optimization algorithm, and combine the priority ranking with the comprehensive responsiveness score to obtain the optimal glue production order sequence.

[0145] The order priority calculation module 202 generates the priority ranking of the glue production order by using the optimization algorithm, combines the priority ranking with the comprehensive responsiveness score, and obtains the optimal glue production order sequence, including:

[0146] The number of glue production orders is obtained, and the number of glue production order sequences is randomly generated based on the number of glue production orders as an initial solution;

[0147] Calculate the first fitness function of the glue production order sequence, and select glue production orders without conflicting orders from the glue production order sequence, and add the time node of the current glue production order to the graph network set;

[0148] Traverse the time nodes of the remaining glue production orders in the glue production order sequence, and compare them with the time nodes already occupied by the graph network set to determine whether there is a conflict;

[0149] If there is a conflict, the timetable corresponding to the glue production order of the non-conflicting path is modified; if there is no conflict, the time nodes of the remaining glue production orders are added to the graph network set, and the second fitness function of the glue production order sequence is obtained;

[0150] Based on the first fitness function and the second fitness function, determine the crossover rate of the glue production order sequence, and generate a new glue production order sequence based on the crossover rate;

[0151] The generation process of the glue liquid production order sequence is continuously iterated until the termination condition is met, and the iteration is stopped, and the finally generated glue liquid production order sequence is used as the optimal glue liquid production order sequence.

[0152] In order to facilitate understanding of the above technical scheme of the present invention, the present invention uses an optimization algorithm to generate priority ranking of glue production orders in the actual process, combines the priority ranking with the comprehensive responsiveness score, and obtains the optimal glue production order sequence. A detailed description is given below.

[0153] Step 1: Glue production order sequence: Assume that the production order is represented by a set O = {o1, o2, ..., o n} means that each order o i Contains the following information:

[0154] :Order o i The scheduled start time.

[0155] :Order o i The planned end time of .

[0156] p i :Order o i The priority or weight of (used to calculate the fitness function).

[0157] Graph network collection: used to record the currently assigned time node collection G={[ , ],[ , ], …}.

[0158] Step 2: Define the fitness function F1 to represent the initial fitness of the current order sequence O, and calculate the first fitness function:

[0159] Weight summation: For example, F1=∑ oi∈O p i , that is, the priorities of all orders are accumulated.

[0160] Completion rate measures: for example, the proportion of orders that can be completed without conflict.

[0161] Other custom metrics such as time utilization, delivery delays, etc.

[0162] After calculating the initial fitness F1, the order screening stage begins.

[0163] Step 3: Filter non-conflicting orders:

[0164] Initialize an empty set G= , used to record the allocated conflict-free time periods.

[0165] Traverse each order o in the production order sequence i ∈O, execute the following logic:

[0166] Compare the time nodes of order oi[ , ] whether it conflicts with the existing time period in G.

[0167] Conflict conditions: , ] and any time period in set G[ , ]One of the following conditions is met: < and > .

[0168] If there is no conflict, [ , ] is added to set G, indicating that this order has been assigned.

[0169] If a conflict occurs, the order is temporarily skipped or put into the conflict set C.

[0170] Step 4: Modify the schedule of the conflict-free path:

[0171] For the order C={o k , o m , …}, try to adjust its schedule to avoid conflicts, and the adjustment logic includes the following strategies:

[0172] Delay start time: Adjust o k of and , so that it does not conflict with the time periods in the current graph network set G.

[0173] Early start time: If the order allows early execution, try to advance it. k The start time of the

[0174] Reorder: According to priority p i , rearrange conflicting orders and give priority to high-priority orders.

[0175] After adjusting the schedule, recheck its conflict with G:

[0176] If there is no conflict after adjustment, add it to G.

[0177] If there is still a conflict after adjustment, you can choose to abandon the order or mark it as undispatched.

[0178] Step 5: Update the graph network collection:

[0179] Add all non-conflicting order time nodes to the graph network set G to form a complete non-conflicting schedule, for example:

[0180] If the initial order sequence time is [ , ],[ , ]After adjustment, G may become [ , ],[ , ],[ , ].

[0181] Step 6: Calculate the second fitness function:

[0182] Based on the adjusted conflict-free order set G, the second fitness function F2 is calculated, and its calculation formula is: F2=∑o i ∈ G p i .

[0183] To measure time utilization, utilization rate = total occupied time and total available time. Utilization rate = total available time / total occupied time.

[0184] Compare the changes in F1 and F2 to determine the optimization effect; if F2>F1, it means that the fitness has improved after adjustment; otherwise, readjustment or optimization may be required.

[0185] The schedule generation module 203 is used to generate a glue production schedule based on the optimal glue production order sequence.

[0186] The glue liquid preparation control unit 3 is used to generate a raw material process formula based on a predefined raw material ratio rule, and prepare the glue liquid required for the glue liquid production order according to the process and raw material combination in the raw material process formula.

[0187] Among them, Figure 3As shown, the glue liquid preparation control unit 3 includes a process formula customization module 301, a raw material demand prediction module 302, a raw material metering module 303, a mixing tank temperature control module 304, a glue liquid storage module 305 and an automation control module 306;

[0188] The process formula customization module 301 is used to customize the raw material process formula of the glue production order based on the predefined raw material ratio rules and the client order requirements.

[0189] Among them, the process recipe customization module 301 includes a process recipe production module 3011, a process recipe setting module 3012 and a process recipe customization module 3013;

[0190] The process formula production module 3011 is used to generate a process formula based on a predefined raw material ratio rule and send the process formula to the production end using a communication method.

[0191] The process formula setting module 3012 is used to save the process formula that meets the glue production order into the database to realize the call of the process formula.

[0192] The process formula setting module 3012 saves the process formula that meets the glue production order into the database to realize the call of the process formula, including:

[0193] Based on the process flow setting program, call the sub-process and sub-process parameter settings, and generate the process formula that meets the glue production order after the settings are completed;

[0194] In the process flow setting program, the generated process formula is input into the formula name and stored in the database to realize the calling of the process formula during the liquid preparation process.

[0195] The recipe customization module 3013 is used to customize the process recipe according to the client's order requirements and match the generated process recipe with the finished glue product.

[0196] The raw material demand prediction module 302 is used to predict the required raw material demand based on the glue production order and store the raw material demand in the database.

[0197] The raw material metering module 303 is used to collect the material discharge amount of the metering tank during the glue processing process using a weighing sensor, and to control the opening parameters of the regulating valve in real time according to the material discharge amount.

[0198] The raw material metering module 303 uses a weighing sensor to collect the material discharge amount of the metering tank during the glue processing process, and controls the opening parameters of the regulating valve in real time according to the material discharge amount, including:

[0199] The weighing sensor is used to collect the unloading amount of the metering tank during the glue liquid processing, and based on the glue liquid configuration completion cycle, the unloading cycle during the glue liquid processing is divided into a fast unloading period, an advance amount detection period, and a precise amount arrival period;

[0200] The key characteristics of the raw materials are extracted, and the weight change pattern of the metering tank is analyzed based on the key characteristics of the raw materials. The weight deviation is corrected in the weight change pattern of the metering tank to obtain the raw material unloading rate of each unloading cycle.

[0201] It should be noted that the weighing sensor is used to collect the unloading amount of the metering tank during the glue processing process, and based on the glue configuration completion cycle, the unloading cycle during the glue processing process is divided into a fast unloading period, an advance amount detection period, and a precise amount arrival period, including:

[0202] A high-precision weighing sensor is installed at the bottom of the metering tank to collect the weight change data of the metering tank in real time, set the sampling frequency, and adjust the sampling interval according to the material discharge rate (for example, once every 50-100ms); collect and record the weight of the metering tank in real time, and store the data in the form of time series.

[0203] According to the characteristics of the glue configuration and the processing requirements, the blanking cycle can be divided into the following three stages:

[0204] 1. Rapid unloading period: This is the initial stage of unloading with large amounts of materials. The goal is to quickly bring the raw materials in the metering tank close to 90%-95% of the target total amount.

[0205] accomplish:

[0206] Analyze real-time weighing data and automatically enter the next stage when the weight is close to 90%-95% of the target value.

[0207] 2. Advance detection period: In this stage, the material feeding speed is slowed down and the material feeding amount is accurately controlled to avoid overshoot (material feeding exceeds the target value).

[0208] accomplish:

[0209] The difference (advance amount) between the current weight and the target value is calculated in real time based on the weighing data.

[0210] When the difference is lower than a certain threshold (such as 5%-10% of the target value), switch to the precise quantity arrival period.

[0211] 3. Accurate quantity arrival period:

[0212] Definition: In the final stage of approaching the target weight, a very slow feeding rate is used to achieve accurate feeding.

[0213] accomplish:

[0214] Real-time calculation of the slight changes in each material discharge (weight change per unit time).

[0215] Stop unloading when the target weight is reached within the allowable error range (such as ±0.1%).

[0216] Extract the key characteristics of the raw materials to analyze the law of their weight changes in the measuring tank and provide a basis for correcting weight deviations. The key characteristics include fluidity, density-volume relationship, fluctuation of feeding rate and environmental factors;

[0217] Among them, fluidity: The fluidity of the raw material directly affects the fluctuation of the feeding rate. The fluidity can be estimated by analyzing the weight change rate (weight / time) during the rapid feeding period.

[0218] Density and volume relationship: Measure the volume change of the material per unit weight, and combine it with the raw material density to evaluate whether there is any deviation in the raw material.

[0219] Fluctuation of feeding rate: Extract the statistical characteristics of the real-time feeding rate curve, such as mean and standard deviation, to determine the uniformity of the raw materials.

[0220] Environmental influencing factors: record temperature and humidity data and analyze their interference with weight changes, especially for moisture-sensitive raw materials.

[0221] Among them, the raw material metering module also includes:

[0222] Obtain glue configuration parameters during glue processing;

[0223] According to the analysis of the glue liquid configuration parameters, the glue liquid configuration feeding deviation coefficient is obtained;

[0224] Analyze whether to adjust the glue liquid configuration and material feeding according to the glue liquid configuration and material feeding deviation coefficient;

[0225] The specific analysis process of analyzing whether to adjust the glue solution configuration and feeding is as follows:

[0226] Compare the glue liquid configuration unloading deviation coefficient with the glue liquid configuration unloading threshold value preset in the database to determine whether the glue liquid configuration unloading deviation coefficient is greater than the glue liquid configuration unloading threshold value preset in the database;

[0227] When the glue liquid configuration unloading deviation coefficient is not greater than the glue liquid configuration unloading threshold preset in the database, the glue liquid configuration unloading adjustment is not performed;

[0228] When the glue liquid configuration feeding deviation coefficient is greater than the glue liquid configuration feeding threshold value preset in the database, the glue liquid configuration feeding adjustment is performed, and after the glue liquid configuration feeding adjustment, when the monitored glue liquid configuration feeding deviation coefficient is not greater than the glue liquid configuration feeding threshold value preset in the database, the glue liquid configuration feeding adjustment is stopped.

[0229] The unloading process of the glue solution is divided into multiple time periods, and the time periods are numbered. D, It represents the number of the time period, and D represents the total number of time periods. Multiple observation time points are set within the time period and the observation time points are numbered. , It represents the number of the observation time point. It is expressed as the total number of observation time points. The specific limiting expression of the glue solution configuration feeding deviation coefficient is:

[0230] ;

[0231] ;

[0232] ;

[0233] ;

[0234] ;

[0235] In the formula, Expressed as Deviation coefficient of glue liquid configuration and feeding of glue liquid in each time period, Expressed as The time period The weight change rate at each observation time point, Expressed as The time period The material feeding speed at each observation time point, Expressed as The time period The raw material density at each observation time point, Expressed as The time period The humidity at each observation time point, Expressed as The reference weight change rate for a period of time, Expressed as The reference feeding speed for each time period is: Expressed as Reference raw material density for a time period, Expressed as Reference humidity for each time period, Expressed as The time period The weight factor for each observation time point, Expressed as The time period The feeding factor at each observation time point is Expressed as The time period The density factor of each observation time point, Expressed as The time period The humidity factor at each observation time point, Expressed as a natural constant.

[0236] Among them, the weight change rate indicates the rate of change of weight when the raw material is fed; the feeding speed indicates the speed when the raw material is fed; the raw material density indicates the density when the raw material is fed; the humidity indicates the ambient humidity when the raw material is fed; the reference weight change rate indicates the maximum value of the weight change rate when the raw material is fed qualified; the reference feeding speed indicates the maximum value of the speed when the raw material is fed qualified; the reference raw material density indicates the maximum value of the density when the raw material is fed qualified; the reference humidity indicates the maximum value of the ambient humidity when the raw material is fed qualified.

[0237] The weight change data of the measuring tank is collected in real time by a weighing sensor, and the weight change rate is obtained by calculating the weight change per unit time. The weight change data of the measuring tank is collected in real time by a weighing sensor, and the material discharge speed is obtained by combining the time record of the material discharge process. A clear raw material image is captured by a high-resolution camera, and the collected image is preprocessed by graying, noise removal, contrast enhancement and other operations. The raw material is separated from the background by image segmentation technology to obtain the accurate outline of the raw material, and the texture characteristics of the raw material image are analyzed, such as particle size, distribution uniformity, etc. The three-dimensional shape information of the raw material is obtained by three-dimensional reconstruction technology or stereo vision technology, and then its volume is estimated. The weight of the measuring tank is collected in real time by a weighing sensor, and the density of the raw material is calculated by the density formula ρ=m / V (where ρ represents density, m represents mass, and V represents volume). The humidity of the environment is measured by a humidity sensor.

[0238] The weight change rate, feeding speed, raw material density and humidity of the glue solution when the feeding is qualified in the historical time period are obtained from the database, and the corresponding maximum values ​​are obtained to obtain the reference weight change rate, reference feeding speed, reference raw material density and reference humidity.

[0239] The fluidity of the raw material directly affects its feeding rate in the metering tank. The weight change rate of raw materials with good fluidity may be more stable and rapid, resulting in a faster feeding speed; while raw materials with poor fluidity may cause the feeding rate to fluctuate, resulting in a slower feeding speed, which in turn affects the stability of the weight change rate. Ambient humidity has a significant impact on the weight change of raw materials, especially moisture-sensitive raw materials, whose weight may fluctuate with changes in ambient humidity, thereby causing changes in raw material density. Raw materials with poor fluidity may cause unstable feeding rates, resulting in feeding deviations. Especially during the rapid feeding period, if the raw material has insufficient fluidity, it may not be able to quickly reach 90%-95% of the target weight, thus affecting the precise control in the subsequent stages. If the density of the raw material changes (such as due to moisture, etc.), it may cause the actual feeding amount to be inconsistent with the theoretical value. The fluctuation of the feeding rate may cause unevenness in the feeding process, resulting in feeding deviations. Changes in humidity may cause changes in the weight of the raw material, thereby affecting the accuracy of the feeding amount. Especially in humidity-sensitive environments, serious feeding deviations may occur if the raw materials are not properly protected or calibrated.

[0240] By combining the analysis of weight change rate, feeding speed, raw material density and humidity, the glue liquid configuration feeding deviation coefficient is obtained, so that the glue liquid configuration feeding deviation coefficient is more accurate, which is conducive to more accurate glue liquid configuration feeding adjustment according to the glue liquid configuration feeding deviation coefficient.

[0241] The glue liquid configuration feeding threshold is the maximum glue liquid configuration feeding deviation coefficient when the glue liquid configuration feeding is qualified within the historical time period.

[0242] Adjustment of glue liquid configuration and feeding refers to lowering the ambient humidity of glue liquid configuration and feeding. The air conditioner dehumidification function or dehumidification equipment can be turned on to reduce the ambient humidity and reduce the feeding deviation caused by excessive humidity.

[0243] The weight change rate is used as the independent variable, and the glue liquid configuration feeding deviation coefficient is used as the dependent variable. For example, the time period is the third time period, the observation time point is 1, the reference weight change rate is 50 g / s, the reference feeding speed is 10 kg / min, the reference raw material density is 2 g / cm3, and the reference humidity is 60%. Through the increase and decrease analysis of curves 1, 2, and 3, it can be seen that when the weight change rate is greater, the glue liquid configuration feeding deviation coefficient is greater; when the weight change rate is smaller, the glue liquid configuration feeding deviation coefficient is smaller. The relevant data statistics of the glue liquid configuration feeding deviation coefficient are shown in Table 1:

[0244] Table 1 Statistical table of relevant data of glue solution configuration and feeding deviation coefficient

[0245]

[0246] When the parameters of curves 1, 2 and 3 are as shown in Table 1, it can be seen that when the density and humidity of the raw materials are greater and other data are consistent, the glue liquid configuration feeding deviation coefficient is greater, and the glue liquid configuration feeding adjustment is more needed to improve the accuracy of the glue liquid feeding configuration.

[0247] A ridge regression model is established, and the raw material feeding rate of each feeding cycle is taken as input. The ridge regression model is used to predict the opening parameters of the regulating valve in the rapid feeding period, advance quantity detection period and precise quantity arrival period respectively.

[0248] Among them, a ridge regression model is established, and the raw material feeding rate of each feeding cycle is used as input. The ridge regression model is used to predict the opening parameters of the regulating valve in the fast feeding period, the advance quantity detection period and the precise quantity arrival period, including:

[0249] The time series characteristics are established based on the raw material feeding rate of each feeding cycle, and the time sliding window conforming to the ridge regression model is established according to the time series characteristics;

[0250] The objective function of the ridge regression model is established, and the regularization parameter is optimized using the grid search algorithm. The deviation of the ridge regression model is optimized based on the regularization parameter to obtain the optimized ridge regression model.

[0251] Generate a training set for each feeding cycle according to the raw material feeding rate, and use the training set for each feeding cycle to train the ridge regression model to obtain the regression coefficient of each feeding cycle;

[0252] Based on the regression coefficient of each material unloading cycle, the opening parameters of the regulating valve in the fast material unloading period, the advance quantity detection period and the precise quantity arrival period are predicted.

[0253] In order to facilitate understanding of the above technical scheme of the present invention, the present invention establishes a ridge regression model in the actual process, takes the raw material feeding rate of each feeding cycle as input, and uses the ridge regression model to predict the regulating valve opening parameters in the fast feeding period, the advance detection period and the precise amount arrival period.

[0254] Step 1: Establish time series features and sliding windows based on raw material feeding rate:

[0255] The raw material feeding rate of each feeding cycle is time series data. When establishing a ridge regression model, the time series needs to be converted into features that can be used by the model.

[0256] For each time step t, construct a feature matrix of a sliding window w. Assuming the window size is k, for each time step, the input feature is the feed rate x at the past k time points t−k+1 , x t−k+2 , ..., x t .

[0257] Let X represent the time series of the raw material feeding rate, with the dimension of [N, p], where N represents the number of samples and p represents the number of features of the feeding rate; a new feature matrix X is generated by sliding the window train , each row is the material feeding rate value in window w, the target value y train Indicates the valve opening parameter at the current time step.

[0258] For example, if the sliding window size is 3, then X train and train The expression is as follows:

[0259] ;

[0260] ;

[0261] After generating features through the time series sliding window, the training set and test set are divided into training set and test set, which are used to train the ridge regression model and evaluate the model performance, respectively.

[0262] Step 2: Establish a ridge regression model:

[0263] The ridge regression model adds a regularization term to the ordinary linear regression model to limit the model complexity and prevent overfitting. The objective function of the ridge regression model is expressed as:

[0264] ;

[0265] Where J(β) represents the objective function of the ridge regression model; N represents the number of samples; y i represents the true value of the i-th sample; β0 represents the intercept term; β j The coefficient representing the feeding rate of the jth raw material; x ij represents the jth raw material feeding rate value in the i-th feeding cycle; p represents the characteristic number of the raw material feeding rate; λ represents the regularization parameter.

[0266] The regularization parameter λ is optimized using a grid search algorithm. During the grid search process, a set of possible λ values ​​(e.g., 0.1, 1, 10, 1000.1, 1, 10, 100) are defined, the ridge regression model is trained one by one, and the optimal λ is determined by cross-validation.

[0267] Step 3: Train the ridge regression model:

[0268] Through the time series feature matrix X train and the target value y train , the ridge regression model is used to fit the data; the optimized regression coefficient β includes the weight of each raw material feeding rate and the intercept term β0.

[0269] The obtained β is a parameter of a linear regression equation, based on which the opening of the regulating valve during the rapid material discharge period, the advance quantity detection period and the precise quantity arrival period can be predicted.

[0270] Step 4: Model-based prediction:

[0271] Using the trained ridge regression model, the test set X test Make a prediction and get the predicted value y of the control valve opening pred ; According to the division of the test set, separate predictions are made for the rapid material unloading period, the advance quantity detection period and the precise quantity arrival period, and the prediction performance of the ridge regression model is evaluated using indicators such as mean square error and determination coefficient.

[0272] Gradient adjustment mechanisms corresponding to the rapid material discharge period, advance quantity detection period and precise quantity arrival period are established respectively, and the PID controller is used to adjust the regulating valve opening parameters in the rapid material discharge period, advance quantity detection period and precise quantity arrival period that conform to the gradient adjustment mechanism respectively.

[0273] In order to facilitate understanding of the above technical scheme of the present invention, the present invention will establish a gradient adjustment mechanism corresponding to the rapid material discharge period, the advance detection period and the precise amount arrival period in the actual process, and use the PID controller to adjust the regulating valve opening parameters in the rapid material discharge period, the advance detection period and the precise amount arrival period that conform to the gradient adjustment mechanism in detail as follows.

[0274] Step 1: Segmented gradient adjustment mechanism:

[0275] 1. Gradient adjustment mechanism during rapid unloading period: Set the target unloading rate to R target , the current feeding rate is R current , define the valve opening adjustment rate: ΔO fast =K fast ⋅(R target −R current ) where K fast It represents the gradient coefficient of the rapid feeding period. A larger value will speed up the response.

[0276] 2. Gradient adjustment mechanism during the advance detection period: During the advance detection period, the adjustment speed of the regulating valve opening needs to be reduced to avoid overshoot (i.e. the material discharge rate exceeds the target value).

[0277] The target value is gradually approached through a gentler gradient, while reducing the fluctuation of the opening. For example, if the target feeding rate is R target , the current feeding rate is R current , the gradient adjustment rate is ΔO advance =K advance ⋅(R target −R current ) where K advanceRepresents the gradient coefficient of the advance detection period, and the value should be small to avoid drastic adjustments.

[0278] 3. Gradient adjustment mechanism during the precise quantity arrival period: The precise quantity arrival period requires fine adjustment of the valve opening to ensure the stability of the target material discharge rate.

[0279] Reduce the adjustment error and maintain a stable state as much as possible. Set the target feeding rate as R target , the current feeding rate is R current , the adjustment rate is: ΔO precision =K precision ⋅(R target −R current ) where K precision It is the gradient coefficient of the precision quantity arrival period, and the minimum value is used for fine adjustment.

[0280] Step 2: Adjust the valve opening based on the PID controller:

[0281] The output formula of the PID controller is:

[0282] ;

[0283] Where, O(t) represents the current opening of the regulating valve; e(t)=R target −R current Represents the current error (the difference between the target value and the actual value); K p , K i , K d Respectively represent the proportional, integral, and differential gain coefficients; It represents the cumulative value of the error, that is, the degree to which the system deviates from the target in the long term; Indicates the rate of change of error, used to offset fluctuations caused by rapid changes.

[0284] Select the appropriate PID parameter K according to the characteristics of each stage p , K i , K d :

[0285] 1. Rapid unloading period:

[0286] Goal: Respond to errors quickly, allowing for some oscillation.

[0287] Parameter design: proportional gain K p Larger, integral gain K i Smaller or 0, differential gain K d Smaller, output formula:

[0288] ;

[0289] 2. Advance quantity detection period:

[0290] Goal: Gradually approach the target value and reduce error accumulation.

[0291] Parameter design: proportional gain K p Moderate, integral gain K i Increase, differential gain K d It can be increased appropriately, and the output formula is:

[0292] ;

[0293] 3. Accurate quantity arrival period:

[0294] Goal: Maintain target rate precisely and avoid fluctuations.

[0295] Parameter design: proportional gain K p Smaller, integral gain K i Larger, differential gain K d Larger to suppress noise, output formula:

[0296] ;

[0297] Step 3: Dynamic adjustment based on PID controller:

[0298] Initialize PID parameters K for each stage p , K i , K d , and set the stage switching threshold:

[0299] The end condition of the rapid unloading period: the current rate reaches 80% of the target rate.

[0300] End condition of the advance detection period: The current rate reaches 95% of the target rate.

[0301] End condition of the precision quantity arrival period: entering the tolerance range of the target rate (such as ±0.5%).

[0302] Real-time error calculation and adjustment:

[0303] At each time step t, the current error e(t) is calculated.

[0304] According to the current stage, the corresponding PID controller formula is called to calculate the opening adjustment value O(t).

[0305] When the end condition of a certain stage is reached, it automatically switches to the next stage and reinitializes the PID parameters of the corresponding stage.

[0306] The mixing tank temperature control module 304 is used to collect the mixing tank temperature using a temperature sensor and adjust the mixing tank parameters according to the mixing tank temperature to achieve two-way temperature control of the chilled water and the cooling water;

[0307] The glue storage module 305 is used to store the prepared glue in the on-site tank, and use the weight sensor to monitor the use of the glue and adjust the variable frequency stirring parameters according to the use of the glue to achieve two-way temperature control of the chilled water and the cooling water;

[0308] The automation control module 306 is used to centrally control the production equipment in the glue processing process through the control cabinet configuration, and to perform remote signal acquisition using the communication bus technology.

[0309] The mixing tank temperature control module 304 collects the mixing tank temperature using the temperature sensor and adjusts the mixing tank parameters according to the mixing tank temperature to achieve two-way temperature control of the chilled water and the cooling water, including:

[0310] Collecting the temperature of the blending tank within a preset time period based on a temperature sensor, and analyzing the temperature trend of the blending tank based on the temperature of the blending tank within the preset time period;

[0311] According to the temperature trend of the mixing tank, a steady-state thermal balance model and a non-steady-state thermal balance model are established respectively, and the chilled water parameters and cooling water parameters in the process of mixing the glue liquid are adjusted by using the steady-state thermal balance model;

[0312] When the temperature trend of the deployment tank tends to be stable, the non-steady-state heat balance model is used to deploy the chilled water parameters and cooling water parameters under stable conditions.

[0313] Steady-state heat balance model: According to the temperature trend of the mixing tank, if the temperature tends to be stable, a steady-state heat balance model is established. This model is used to calculate and adjust the parameters of chilled water and cooling water to ensure that the temperature of the glue processing process remains stable.

[0314] Non-steady-state heat balance model: If the temperature fluctuates greatly (non-stable state), a non-steady-state heat balance model is established to dynamically adjust the parameters of chilled water and cooling water so that the mixing tank can gradually reach a stable operating condition.

[0315] Among them, the blending tank temperature control module also includes:

[0316] Obtain the temperature control parameters of the two-way temperature control of the mixing tank;

[0317] The temperature control deviation coefficient of the glue mixing tank is obtained by analyzing the temperature control parameters;

[0318] When the obtained glue liquid mixing tank temperature control deviation coefficient is greater than the glue liquid mixing tank temperature control first threshold value preset in the database and less than the glue liquid mixing tank temperature control second threshold value, no temperature control adjustment is performed;

[0319] When the obtained glue solution mixing tank temperature control deviation coefficient is not greater than the glue solution mixing tank temperature control first threshold preset in the database, the temperature control is adjusted to the first level;

[0320] When the obtained temperature control deviation coefficient of the glue solution mixing tank is not less than the second temperature control threshold of the glue solution mixing tank preset in the database, the second level adjustment of the temperature control is performed.

[0321] Among them, the specific limiting expression of the temperature control deviation coefficient of the glue mixing tank is:

[0322] ;

[0323] ;

[0324] ;

[0325] ;

[0326] ;

[0327] In the formula, It is expressed as the temperature control deviation coefficient of the glue mixing tank. Expressed as Deviation coefficient of glue liquid configuration and feeding of glue liquid in a time period, Indicates the volume of chilled water used in the temperature control process, Indicates the volume of cooling water used in the temperature control process, Indicates the system response time of the temperature control process, Indicates the electromagnetic interference intensity of the temperature control process, Indicates the reference chilled water usage volume of the temperature control process, Indicates the reference cooling water usage volume of the temperature control process, Indicates the reference system response time of the temperature control process, Indicates the reference electromagnetic interference intensity of the temperature control process, Expressed as the chilled water factor, Expressed as the cooling water factor, Expressed as the response time factor, Expressed as electromagnetic interference factor.

[0328] Among them, at the end of each time period, whether to adjust the glue liquid configuration is analyzed according to the glue liquid configuration feeding deviation coefficient. After the glue liquid configuration feeding adjustment, the next time period is carried out. After the end of the feeding in this time period, the glue liquid configuration feeding deviation coefficient is analyzed, and the above process is repeated. Therefore, the glue liquid configuration feeding deviation coefficient in the restriction expression does not need to specifically indicate the glue liquid configuration feeding deviation coefficient before or after the adjustment.

[0329] Among them, the volume of chilled water usage represents the volume of chilled water used in the mixing tank; the volume of cooling water usage represents the volume of cooling water used in the mixing tank; the system response time represents the response time when the system performs two-way temperature control; the electromagnetic interference intensity represents the electromagnetic interference intensity when the system performs two-way temperature control; the reference volume of chilled water usage represents the average volume of chilled water used in the mixing tank when the system performs two-way temperature control qualified; the reference volume of cooling water usage represents the average volume of cooling water used in the mixing tank when the system performs two-way temperature control qualified; the reference system response time represents the maximum response time when the system performs two-way temperature control qualified; the reference electromagnetic interference intensity represents the maximum electromagnetic interference intensity when the system performs two-way temperature control qualified.

[0330] The volume of chilled water and cooling water actually used is obtained by flow meters installed on the supply pipes of chilled water and cooling water, and the chilled water usage volume and cooling water usage volume are obtained. The timer measures the time difference from the system receiving the adjustment instruction to the actual execution of the adjustment operation to obtain the system response time. The electromagnetic interference intensity when the system performs two-way temperature control is measured by the electromagnetic interference monitor to obtain the electromagnetic interference intensity.

[0331] The chilled water usage volume, cooling water usage volume, system response time and electromagnetic interference intensity when the two-way temperature control of the deployment tank is qualified in the historical time period are obtained from the database, the average of the chilled water usage volume and the cooling water usage volume are obtained to obtain the reference chilled water usage volume and the reference cooling water usage volume, and the maximum value of the system response time and the electromagnetic interference intensity is obtained to obtain the reference system response time and the reference electromagnetic interference intensity.

[0332] During the temperature control process of the mixing tank, the volume of chilled water and cooling water used often needs to be dynamically adjusted according to the temperature in the mixing tank. When the temperature in the mixing tank is too high, the volume of chilled water used may need to be increased to lower the temperature; conversely, when the temperature is too low, the volume of cooling water used may need to be increased to increase the temperature. Therefore, the volume of chilled water used and the volume of cooling water used are interrelated and work together to maintain the temperature stability in the mixing tank. A longer system response time may cause a lag in the temperature control effect and affect the stability of the temperature in the mixing tank. A shorter system response time can respond to temperature control instructions more quickly and improve the accuracy and stability of the temperature control system. Electromagnetic interference may cause errors or failures in the temperature control system, affecting the accuracy and stability of temperature control. The greater the intensity of electromagnetic interference, the more significant the impact on the temperature control system.

[0333] The volume of chilled water used, the volume of cooling water used, the system response time and the electromagnetic interference intensity jointly affect the temperature stability in the deployment tank. Reasonable volumes of chilled water and cooling water used and a short system response time can maintain the temperature stability in the deployment tank. Excessive electromagnetic interference intensity may cause fluctuations or errors in the temperature control system and reduce temperature stability. Therefore, when the volume of chilled water used is close to the reference volume of chilled water used, the volume of cooling water used is close to the reference volume of cooling water used, the system response time is less than the reference system response time, and the electromagnetic interference intensity is less than the reference electromagnetic interference intensity, the deployment tank accurately performs two-way temperature control. In other cases, the two-way temperature control of the deployment tank is inaccurate.

[0334] The temperature control deviation coefficient of the glue liquid mixing tank is obtained by combining the analysis of the chilled water usage volume, the cooling water usage volume, the system response time and the electromagnetic interference intensity. This makes the temperature control deviation coefficient of the glue liquid mixing tank more comprehensively considered, which is conducive to more accurately analyzing whether to make temperature control adjustments based on the temperature control deviation coefficient of the glue liquid mixing tank.

[0335] The preset first temperature control threshold of the glue liquid mixing tank and the second temperature control threshold of the glue liquid mixing tank are obtained from the database. The first temperature control threshold of the glue liquid mixing tank and the second temperature control threshold of the glue liquid mixing tank are respectively the minimum temperature control deviation coefficient of the glue liquid mixing tank and the maximum temperature control deviation coefficient of the glue liquid mixing tank when the two-way temperature control of the glue liquid mixing tank is qualified in the historical time period.

[0336] Among them, the first-level adjustment of temperature control refers to increasing the power of the pump that calls chilled water and increasing the power of the pump that calls cooling water. When the temperature control deviation coefficient of the monitored glue liquid mixing tank is greater than the first temperature control threshold of the glue liquid mixing tank preset in the database, the first-level adjustment of temperature control is stopped; the second-level adjustment of temperature control refers to reducing the intensity of electromagnetic interference. When the temperature control deviation coefficient of the monitored glue liquid mixing tank is less than the second temperature control threshold of the glue liquid mixing tank preset in the database, the second-level adjustment of temperature control is stopped.

[0337] The first level adjustment of temperature control refers to gradually increasing the power of the pump that calls the chilled water to provide more cooling capacity and reduce the temperature in the glue mixing tank. At the same time, increase the power of the pump that calls the cooling water as needed to enhance the cooling effect. After increasing the pump power, continue to monitor the temperature control deviation coefficient. If the temperature control deviation coefficient gradually decreases and approaches the target range, continue to maintain the current pump power. If the temperature control deviation coefficient is still large, further increase the pump power until a satisfactory control effect is achieved. When the monitored temperature control deviation coefficient is less than or equal to the first threshold, stop the first level adjustment. At this point, it can be considered that the temperature control has achieved initial results, but it is still necessary to continue monitoring and make fine adjustments as appropriate.

[0338] The secondary adjustment of temperature control refers to checking and adjusting the electromagnetic equipment around the glue mixing tank, such as motors, transformers, etc., to reduce their electromagnetic interference to the temperature control system. Shielding, grounding and other measures can be used to reduce the impact of electromagnetic interference. After reducing the intensity of electromagnetic interference, continue to monitor the temperature control deviation coefficient. If the temperature control deviation coefficient remains stable or further decreases, continue to maintain the current measures. If the temperature control deviation coefficient fluctuates or increases, it is necessary to recheck the source of electromagnetic interference and take corresponding adjustment measures. When the monitored temperature control deviation coefficient is stable and less than or equal to the second threshold, stop the secondary adjustment. At this point, it can be considered that the temperature control has achieved a high degree of accuracy and stability.

[0339] The glue liquid configuration monitoring unit 4 is used to monitor the production parameters and production equipment during the glue liquid preparation process in real time, and trigger an early warning when an abnormal event is detected.

[0340] In order to facilitate understanding of the above technical solutions of the present invention, the following is a detailed description of the management system for preparing glue solution for cord fabric dipping in the actual process of the present invention.

[0341] The cord fabric dipping glue solution preparation and management system provided by the present invention comprises: obtaining orders by interacting with the order-placer; according to the process formula in the order, using the glue solution preparation and management system to measure, stir, and react the raw materials to obtain the glue solution required in the order; customizing the process flow of glue solution preparation and the parameters corresponding to each process, so as to generate a process formula, and each module of the preparation and management system automatically works according to the process in the formula and the combination of various raw materials without manual intervention; the information related to each device and the processing process in the preparation and management system is managed accordingly to achieve traceability, and the information of each node is uploaded to the ERP system.

[0342] The present invention obtains orders by interacting with the ordering party; according to the customized formula in the order, the glue liquid preparation management system measures, stirs, and reacts the raw materials to obtain the glue liquid required in the order, and stores the glue liquid in the on-site tank. To achieve the above purpose, the glue liquid preparation management system includes:

[0343] Order interaction unit 1 and order sequence formulation unit 2:

[0344] The ERP system stores individual orders in the order database according to the formula name and quantity. The glue preparation management system completes the orders in the database one by one in a first-in-first-out production manner, and can also realize the adjustment function of waiting orders.

[0345] The glue liquid configuration control unit 3 includes a process formula customization module 301, a raw material demand prediction module 302, a raw material metering module 303, a mixing tank temperature control module 304, a glue liquid storage module 305 and an automation control module 306;

[0346] Among them, the raw material demand prediction module 302:

[0347] like Fig.10 As shown, it includes storage tanks for storing different raw materials, equipped with weight sensors to monitor the usage of raw materials in real time. At the same time, the demand for each raw material is calculated according to the order scheduling in the system, and the raw material data is stored in the raw material database for the ERP system to make inventory warnings. The monthly usage is estimated based on historical inventory changes, and the warehouse can provide procurement plans.

[0348] like Figure 4 As shown (for example in Figure 4 In the formula number HWR1X1-5000kg, HWR indicates the client code of the formula; 1X1 indicates the code corresponding to the glue parameters PH, solid content and viscosity; 5000KG indicates the total amount of glue of the formula; HLDF indicates a special test formula, which is not named according to this rule. The same applies to the other formula numbers). When the monthly order is determined, the monthly raw material consumption is directly calculated based on the demand for each raw material in the order, and an expected warning prompt message is sent. The total volume of the raw material tank T4112 is 1500L, and the total raw material weight is 1500kg.

[0349] Threshold = (current remaining total - order raw material demand) / 1500KG, the warning threshold is 20%. When the threshold is <20%, a warning message will be prompted.

[0350] When the same raw material is stored in multiple raw material tanks, other raw material tanks will be automatically called when the raw material in the tank specified in the process formula is used up.

[0351] The raw material storage tank is equipped with variable frequency stirring. When the raw material stirring process is called in the process formula, its variable frequency speed is automatically associated with the stirring parameters of the process.

[0352] Raw material metering module 303:

[0353] like Fig.11 As shown, various raw materials are weighed. In order to meet the accuracy requirements, the metering tank is equipped with a high-precision weighing sensor. At the same time, the feeding speed can be adjusted in real time through the feed regulating valve. When the metering process is called, the total weight, advance amount and regulating valve opening parameters are included. When the feed reaches the advance amount, the regulating valve adjusts the opening to slow down the feeding speed and achieve its feeding accuracy.

[0354] The total weight after metering is compared with the weight reduction of the corresponding raw material tank to achieve precise closed-loop control.

[0355] The metering tank is equipped with variable frequency stirring. When the stirring process of the metering module is called in the process formula, its variable frequency speed is automatically associated with the stirring parameters of the process.

[0356] Mixing tank temperature control module 304:

[0357] like Fig.12 As shown, it is used for glue preparation and reaction. The mixing tank is equipped with a high-precision weighing sensor to monitor the weight and accuracy of raw materials in real time. The mixing tank also has a raw material import, and its accuracy control algorithm is consistent with the import mode of the metering module.

[0358] The mixing tank is equipped with chilled water and cooling water for two-way temperature control. The temperature parameters of the mixing process are adjusted and the reaction temperature of the mixing process is ensured by temperature sensors.

[0359] Glue storage module 305:

[0360] like Fig.13 As shown, it is used to store the prepared glue. The on-site tank is equipped with a weight sensor to monitor the usage status of the glue in real time.

[0361] Equipped with variable frequency stirring, two-way temperature control of chilled water and cooling water, its control logic is consistent with the mixing tank.

[0362] exist Figure 10-13 In the figure, A represents valve, B represents cooling water valve, C represents raw material tank No. T4112, D represents pipeline, E represents metering tank No. T4121, F represents peristaltic pump, G represents field tank No. T4143, and H represents blending tank No. T4131; among them, Fig.10 The raw material tank No. T4112 is connected to the metering tank No. T4121 through the bottom valve, and the raw materials are accurately fed through the regulating valve and tank weighing sensor through the metering tank No. T4121. The raw materials are weighed by the metering tank No. T4121 and enter the blending tank No. T4131. After blending, they are sent to the field tank No. T4143 through the peristaltic pump, and the cooling water valve is controlled by the set temperature. In the attached figure, 0.0℃ represents the current temperature and set temperature of the liquid in the tank; 0.0% represents the percentage of the tank liquid in the total tank; KG represents the weight of the tank liquid; M represents the stirring motor, and 0.0% at the top of the stirring motor represents the percentage of the motor speed.

[0363] Automation control module 306:

[0364] like Figure 5 As shown, it is used to drive the variable frequency motor and various valves on site, as well as collect various sensor data. The control cabinet is equipped with Siemens PLC for centralized control, and remote signal acquisition is performed through PROFINET bus technology to ensure the accuracy of its control and data acquisition.

[0365] Process recipe customization module 301:

[0366] like Figure 6-Figure 9 As shown (in Figure 7In the table, ID indicates the database number that is automatically produced after the process is completed; GH indicates the tank number stored after the order glue is prepared; MC indicates the recipe number; LX indicates the system location of the order production, and the glue preparation system is divided into a one-bath and two-bath dual system; V indicates the parameters related to the process operation; RQ indicates the process completion time; Figure 8 In the figure, ID indicates the database number for automatic production of the formula; MC indicates the formula number; BZ indicates the formula note information, which is optional; LX indicates the system location of the formula production; SX indicates the sub-process of the formula). The glue preparation system is divided into two independent process flow settings, one bath and two baths. The process flow contains more than 100 sub-processes. The process flow generates a step by calling the sub-process and setting the sub-process parameters. Each process can be called repeatedly multiple times. The process flow has no limit on the number of steps. The entire process flow is executed from top to bottom according to the number of steps. After the formula is saved in the database, it is called for production by the process formula production module 3011.

[0367] After the completed process flow enters the recipe name, it is saved in the recipe database for later use in production. It can also be used when the process recipe is modified. The process recipe can be searched and deleted by the set recipe name.

[0368] The formula customization function can customize the process formula according to the customer's process requirements for the product; it can also match the perfect process formula according to similar products, or further improve it on this basis, greatly reducing the workload of formula design.

[0369] Implementation steps: The formula name is named according to the specific values ​​of the characteristics of the glue, such as density, viscosity, transparency, etc. When a new formula is needed, it is matched with the formula with close parameters in the database according to the customer's requirements for the characteristics of the glue, and the process library required by the characteristic requirements can also be adapted.

[0370] Process recipe production module 3011:

[0371] Each step in the process recipe in production is automatically displayed, including the process name, parameters and the step being executed.

[0372] For example, select the recipe number HWR1X1-5000KG, and the order will start production. The cursor is the currently running sub-process, and the completed sub-process is displayed in the prompt information bar. After the entire recipe is completed, the on-site employees are reminded by pushing the work number through the enterprise communication platform; if there are any abnormalities in the production process, they will be reminded by pop-up windows.

[0373] The production module can run one or two baths at the same time, and the button order demand automatically calls production.

[0374] The interface between the production module and the enterprise communication platform sends the fault information to the employee's enterprise communication platform through the employee's work number in the production module through the HTTP PUT request.

[0375] After a single step is completed, the completion information, including operating parameters, weight and other data, will be saved to the historical information database and can be traced back through the order number.

[0376] After a single order is completed, the order completion related data will be sent to the order database, and the ERP system will form an order closed loop.

[0377] After the order is completed, the operating parameters of each process are stored in the process recipe database for subsequent process recipe optimization.

[0378] The feeding accuracy is optimized and adjusted in relation to the advance amount and valve opening.

[0379] Implementation steps: When the remaining amount of material unloading is less than or equal to the advance amount, the valve opening will decrease in linear proportion to the reduction of the remaining amount until it is closed; the advance amount will be automatically adjusted according to the unloading accuracy each time, and the adjustment basis is that the smaller the advance amount, the greater the positive deviation of unloading.

[0380] For example, when the formula HWR1X1-5000KG runs to the sub-process (KG) T4113 to T4121, the parameter is 200KG. The raw material in the raw material tank T4113 is sent to the metering tank T4121 through the regulating valve V4113104, and the accuracy requirement is ±0.5kg. Its accuracy is adjusted by the lead time (kg) and the regulating valve opening (%).

[0381] As shown in Table 2, the quality of the glue solution is optimized and adjusted in correlation with the stirring speed, temperature and reaction time.

[0382] Table 2: Optimization and adjustment of adhesive parameters

[0383]

[0384] The adjustment is made by matching the linear trend relationship among the pH, solid content and viscosity of the glue solution to the stirring speed, temperature and reaction time.

[0385] This also includes analyzing the qualified degree of mixing of the glue solution. The specific process is as follows:

[0386] Obtain glue solution stirring quality parameters of glue solution;

[0387] The glue liquid stirring quality deviation coefficient is obtained according to the glue liquid stirring quality parameter analysis;

[0388] When the obtained glue liquid stirring quality deviation coefficient is greater than the first glue liquid stirring quality threshold value preset in the database and less than the second glue liquid stirring quality threshold value, no glue liquid stirring adjustment is performed;

[0389] In other cases, the glue solution should be stirred and adjusted.

[0390] Among them, the specific limiting expression of the glue stirring quality deviation coefficient is:

[0391] ;

[0392] ;

[0393] ;

[0394] ;

[0395] In the formula, It is expressed as the glue stirring quality deviation coefficient, ' represents the temperature control deviation coefficient of the glue mixing tank when the mixing tank temperature is adjusted properly. Expressed as Deviation coefficient of glue liquid configuration and feeding of glue liquid in a time period, It is expressed as the pH value of the glue solution after the glue solution is stirred. It is expressed as the solid content of the glue liquid after stirring. It is expressed as the viscosity of the glue after the glue is stirred. It is expressed as the reference pH value of the glue solution after the glue solution is stirred. It is expressed as the solid content of the reference glue solution after the glue solution is stirred. It is expressed as the reference viscosity of the glue after the glue is stirred. Expressed as a pH factor, Expressed as the solid content factor, Expressed as the viscosity factor, Expressed as a natural constant.

[0396] Among them, the pH value of the glue solution indicates the pH value of the glue solution; the solid content of the glue solution indicates the solid content of the glue solution; the viscosity of the glue solution indicates the viscosity of the glue solution; the reference pH value of the glue solution indicates the average of the minimum pH value and the maximum pH value when the glue solution quality is qualified; the reference solid content of the glue solution indicates the average of the minimum solid content and the maximum solid content when the glue solution quality is qualified; the reference viscosity of the glue solution indicates the average of the minimum viscosity and the maximum viscosity when the glue solution quality is qualified.

[0397] The pH value of the glue solution is measured by a pH meter to obtain the pH value of the glue solution; the solid content of the glue solution is measured by a halogen rapid moisture meter to obtain the solid content of the glue solution; the viscosity of the glue solution is measured by a rotational viscometer or an oscillating viscometer to obtain the viscosity of the glue solution.

[0398] The minimum pH value and the maximum pH value, the minimum solid content and the maximum solid content, and the minimum viscosity and the maximum viscosity are obtained from the parameter range of the glue solution configuration, and the mean value is processed to obtain the reference glue solution pH value, the reference glue solution solid content and the reference glue solution viscosity.

[0399] Changes in pH may affect the solubility and stability of solid components in the glue, thereby affecting the solid content. Changes in pH may cause structural changes in the chemical components in the glue, which in turn affects the viscosity. An increase in solid content usually leads to an increase in viscosity because the interaction between the solid components is enhanced. The pH value of the glue directly affects its chemical stability and reactivity. Too high or too low pH values ​​may cause adverse reactions of the chemical components in the glue, such as hydrolysis, cross-linking or degradation, thereby affecting the physical properties and stability of the glue. Solid content is the proportion of solid components in the glue, which directly affects the concentration and viscosity of the glue. Too high solid content may cause difficulty in stirring, while too low solid content may affect the adhesion and strength of the glue. Viscosity is an important indicator for measuring the fluidity of the glue. Too high viscosity may cause difficulty in stirring, while too low viscosity may affect the adhesion and stability of the glue.

[0400] During the stirring process, if the stirring is not sufficient, the pH value may deviate from the reference value (i.e., the pH value of the reference glue solution), reflecting that the chemical components in the glue solution are unevenly distributed, and local over-reaction or under-reaction occurs, thereby affecting the uniformity and stability of the glue solution. During the stirring process, if the stirring is not sufficient, the solid content may deviate from the reference value (i.e., the solid content of the reference glue solution), and the concentration and viscosity of the glue solution may change, thereby affecting the stirring effect and the final performance of the glue solution. During the stirring process, if the stirring is not sufficient, the viscosity may deviate from the reference value (i.e., the viscosity of the reference glue solution), reflecting that the stirring efficiency is reduced and the stirring is uneven, thereby affecting the uniformity and stability of the glue solution.

[0401] Therefore, when the pH value, solid content and viscosity of the glue solution are respectively lower than the corresponding reference pH value, solid content and viscosity of the glue solution, the smaller the glue solution stirring quality deviation coefficient is, and when the glue solution stirring quality deviation coefficient is less than or equal to the first threshold of the glue solution stirring quality, the more glue solution stirring adjustment is needed; when the glue solution stirring quality deviation coefficient is greater than or equal to the second threshold of the glue solution stirring quality, the more glue solution stirring adjustment is needed.

[0402] The glue solution stirring quality deviation coefficient is obtained by analyzing the glue solution pH value, glue solution solid content and glue solution viscosity, so that the glue solution stirring quality deviation coefficient is more reliable and is conducive to more accurate glue solution stirring adjustment based on the glue solution stirring quality deviation coefficient.

[0403] The first glue liquid stirring quality threshold and the second glue liquid stirring quality threshold are the minimum glue liquid stirring quality deviation coefficient and the maximum glue liquid stirring quality deviation coefficient when the glue liquid is stirred properly in the historical time period obtained from the database.

[0404] The glue stirring adjustment includes increasing the glue stirring speed, increasing the glue reaction temperature and increasing the glue reaction time.

[0405] Increase the stirring speed of the glue: gradually increase the speed of the agitator to improve the stirring effect. It should be noted that the increase in stirring speed should be controlled within a certain range to avoid excessive load on the equipment or adverse effects on the glue. Increase the reaction temperature of the glue: according to the properties of the glue and production requirements, the reaction temperature should be appropriately increased. The increase in temperature helps to accelerate the chemical reaction rate, thereby improving the stirring efficiency. However, it should also be noted that the increase in temperature should be controlled within a safe range to avoid causing safety accidents or affecting product quality. Increase the reaction time of the glue: Under the premise of ensuring production efficiency, appropriately increasing the reaction time can make the glue more fully mixed and react, thereby improving the stirring quality. However, it should be noted that too long a reaction time may increase production costs and energy consumption.

[0406] When the monitored stirring quality deviation coefficient gradually approaches the preset first threshold value of the glue liquid stirring quality and the second threshold value of the glue liquid stirring quality, it means that the adjustment has achieved results. At this time, the adjustment can be stopped in time to avoid the negative impact of excessive adjustment on the stirring quality.

[0407] Cost statistics module 307:

[0408] The raw material usage of a single order is statistically summarized and the raw material cost is calculated to facilitate profit settlement of the production department.

[0409] Extract the raw material dosage parameters of the process formula, summarize and calculate the cost, and use it for cost optimization when designing the formula.

[0410] This module uses the electricity data in the energy consumption database to calculate the total electricity consumption of a single order, the raw material cost plus the energy consumption cost, making the order cost calculation more accurate.

[0411] To sum up, with the help of the above-mentioned technical scheme of the present invention, the cord fabric dipping glue preparation and management system provided by the present invention adopts sensors and control technology to ensure the accuracy of the raw material ratio, thereby reducing the order preparation accuracy error, and the present invention can reduce manual intervention, reduce labor intensity, improve production efficiency, and reduce the workload of staff, so as to adapt to different process formulas and production scales, meet diversified production needs, and continuously optimize the formula process flow according to production data; the present invention not only improves production efficiency through integrated automation technology, but also significantly improves the stability of product quality, and embeds a data interface in the system design, which can achieve seamless connection with the enterprise ERP system, upload and share production data in real time, and form a complete intelligent production closed loop, and at the same time, by docking with the enterprise communication platform, it can push important production information, abnormal alarms or task instructions in real time, thereby greatly improving the response speed and the unmanned level of production management.

[0412] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. The system for preparing and managing the glue solution for cord fabric dipping is characterized by: The tire cord fabric dipping glue solution preparation management system includes an order interaction unit, an order sequence formulation unit, a glue solution configuration control unit and a glue solution configuration monitoring unit; The order interaction unit is used to establish a connection between the enterprise resource planning system and the client to obtain the glue production order, and store the glue production order in the database; The order sequence formulation unit is used to analyze the client demand responsiveness, prioritize the glue production orders based on the client demand responsiveness, and produce the corresponding glue production orders in the priority order; The glue liquid preparation control unit is used to generate a raw material process formula based on a predefined raw material ratio rule, and prepare the glue liquid required for the glue liquid production order according to the process and raw material combination in the raw material process formula; The glue liquid configuration monitoring unit is used to monitor the production parameters and production equipment during the glue liquid preparation process in real time, and trigger an early warning when an abnormal event is monitored.

2. The tire cord fabric dipping glue solution preparation and management system according to claim 1, characterized in that: The order sequence formulation unit includes a client demand analysis module, an order priority calculation module and a timetable generation module. The client demand analysis module is used to evaluate the importance of customers and the urgency of orders based on the client order demand, and calculate the responsiveness scores of the importance of customers and the urgency of orders according to the evaluation results, and weightedly fuse the responsiveness scores to obtain a comprehensive responsiveness score; The order priority calculation module is used to generate the priority ranking of the glue production order by using the optimization algorithm, and combine the priority ranking with the comprehensive responsiveness score to obtain the optimal glue production order sequence; The schedule generation module is used to generate a glue production schedule based on the optimal glue production order sequence.

3. The tire cord fabric dipping glue solution preparation and management system according to claim 2, characterized in that: The order priority calculation module generates a priority ranking of the glue production order by using an optimization algorithm, combines the priority ranking with the comprehensive responsiveness score, and obtains the optimal glue production order sequence, including: The number of glue production orders is obtained, and the number of glue production order sequences is randomly generated based on the number of glue production orders as an initial solution; Calculate the first fitness function of the glue production order sequence, and select glue production orders without conflicting orders from the glue production order sequence, and add the time node of the current glue production order to the graph network set; Traverse the time nodes of the remaining glue production orders in the glue production order sequence, and compare them with the time nodes already occupied by the graph network set to determine whether there is a conflict; If there is a conflict, the timetable corresponding to the glue production order of the non-conflicting path is modified; if there is no conflict, the time nodes of the remaining glue production orders are added to the graph network set, and the second fitness function of the glue production order sequence is obtained; Based on the first fitness function and the second fitness function, determine the crossover rate of the glue production order sequence, and generate a new glue production order sequence based on the crossover rate; The generation process of the glue liquid production order sequence is continuously iterated until the termination condition is met, and the iteration is stopped, and the finally generated glue liquid production order sequence is used as the optimal glue liquid production order sequence.

4. The tire cord fabric dipping glue solution preparation and management system according to claim 3, characterized in that: The glue liquid preparation control unit includes a process formula customization module, a raw material demand prediction module, a raw material metering module, a mixing tank temperature control module, a glue liquid storage module and an automation control module; The process formula customization module is used to customize the raw material process formula of the glue production order based on the predefined raw material ratio rules and the client order requirements; The raw material demand prediction module is used to predict the raw material demand based on the glue production order and store the raw material demand in the database; The raw material metering module is used to collect the discharge amount of the metering tank during the glue processing process using a weighing sensor, and to control the opening parameter of the regulating valve in real time according to the discharge amount; The mixing tank temperature control module is used to collect the mixing tank temperature using a temperature sensor and adjust the mixing tank parameters according to the mixing tank temperature to achieve two-way temperature control of chilled water and cooling water; The glue storage module is used to store the prepared glue in an on-site tank, and use a weight sensor to monitor the use of the glue and adjust the variable frequency stirring parameters according to the use of the glue to achieve two-way temperature control of the chilled water and the cooling water; The automation control module is used to centrally control the production equipment in the glue processing process through the control cabinet configuration, and to perform remote signal acquisition using the communication bus technology.

5. The tire cord fabric dipping glue solution preparation and management system according to claim 4, characterized in that: The process recipe customization module includes a process recipe production module, a process recipe setting module and a process recipe customization module; The process formula production module is used to generate a process formula based on a predefined raw material ratio rule and send the process formula to the production end by using a communication method; The process formula setting module is used to save the process formula that meets the glue production order into the database to realize the call of the process formula; The formula customization module is used to customize the process formula according to the client's order requirements and match the generated process formula with the finished glue product.

6. The tire cord fabric dipping glue solution preparation and management system according to claim 5, characterized in that: The process formula setting module saves the process formula that meets the glue production order into the database to realize the call of the process formula, including: Based on the process flow setting program, call the sub-process and sub-process parameter settings, and generate the process formula that meets the glue production order after the settings are completed; In the process flow setting program, the generated process formula is input into the formula name and stored in the database to realize the calling of the process formula during the liquid preparation process.

7. The tire cord fabric dipping glue solution preparation and management system according to claim 6, characterized in that: The raw material metering module uses a weighing sensor to collect the discharge amount of the metering tank during the glue processing process, and controls the opening parameters of the regulating valve in real time according to the discharge amount, including: The weighing sensor is used to collect the unloading amount of the metering tank during the glue liquid processing, and based on the glue liquid configuration completion cycle, the unloading cycle during the glue liquid processing is divided into a fast unloading period, an advance amount detection period, and a precise amount arrival period; Extract the key characteristics of the raw materials, analyze the weight change law of the metering tank based on the key characteristics of the raw materials, correct the weight deviation in the weight change law of the metering tank, and obtain the raw material unloading rate of each unloading cycle; A ridge regression model is established, and the raw material feeding rate of each feeding cycle is used as input. The ridge regression model is used to predict the opening parameters of the regulating valve in the fast feeding period, the advance quantity detection period and the precise quantity arrival period respectively. Gradient adjustment mechanisms corresponding to the rapid material discharge period, advance quantity detection period and precise quantity arrival period are established respectively, and the PID controller is used to adjust the regulating valve opening parameters in the rapid material discharge period, advance quantity detection period and precise quantity arrival period that conform to the gradient adjustment mechanism respectively.

8. The tire cord fabric dipping glue solution preparation and management system according to claim 7, characterized in that: The ridge regression model is established, the raw material feeding rate of each feeding cycle is used as input, and the ridge regression model is used to predict the regulating valve opening parameters in the fast feeding period, the advance amount detection period and the accurate amount arrival period respectively, including: The time series characteristics are established based on the raw material feeding rate of each feeding cycle, and the time sliding window conforming to the ridge regression model is established according to the time series characteristics; The objective function of the ridge regression model is established, and the regularization parameter is optimized using the grid search algorithm. The deviation of the ridge regression model is optimized based on the regularization parameter to obtain the optimized ridge regression model. Generate a training set for each feeding cycle according to the raw material feeding rate, and use the training set for each feeding cycle to train the ridge regression model to obtain the regression coefficient of each feeding cycle; Based on the regression coefficient of each material unloading cycle, the opening parameters of the regulating valve in the fast material unloading period, the advance quantity detection period and the precise quantity arrival period are predicted.

9. The tire cord fabric dipping glue solution preparation and management system according to claim 8, characterized in that: The temperature control module of the mixing tank collects the temperature of the mixing tank by using the temperature sensor and adjusts the parameters of the mixing tank according to the temperature of the mixing tank to realize the two-way temperature control of the chilled water and the cooling water, including: Collecting the temperature of the blending tank within a preset time period based on a temperature sensor, and analyzing the temperature trend of the blending tank based on the temperature of the blending tank within the preset time period; According to the temperature trend of the mixing tank, a steady-state thermal balance model and a non-steady-state thermal balance model are established respectively, and the chilled water parameters and cooling water parameters in the process of mixing the glue liquid are adjusted by using the steady-state thermal balance model; When the temperature trend of the deployment tank tends to be stable, the non-steady-state heat balance model is used to deploy the chilled water parameters and cooling water parameters under stable conditions.

10. The tire cord fabric dipping glue solution preparation and management system according to claim 9, characterized in that: The objective function of the ridge regression model is expressed as: ； Where J(β) represents the objective function of the ridge regression model; N represents the number of samples; y i represents the true value of the i-th sample; β0 represents the intercept term; β j The coefficient representing the feeding rate of the jth raw material; x ij represents the jth raw material feeding rate value in the i-th feeding cycle; p represents the characteristic number of the raw material feeding rate; λ represents the regularization parameter.

11. The tire cord fabric dipping glue solution preparation and management system according to claim 7, characterized in that: The raw material metering module also includes: Obtain glue configuration parameters during glue processing; According to the analysis of the glue liquid configuration parameters, the glue liquid configuration feeding deviation coefficient is obtained; Analyze whether to adjust the glue liquid configuration and material feeding according to the glue liquid configuration and material feeding deviation coefficient; The specific analysis process of analyzing whether to adjust the glue solution configuration and feeding is as follows: Compare the glue liquid configuration unloading deviation coefficient with the glue liquid configuration unloading threshold value preset in the database to determine whether the glue liquid configuration unloading deviation coefficient is greater than the glue liquid configuration unloading threshold value preset in the database; When the glue liquid configuration unloading deviation coefficient is not greater than the glue liquid configuration unloading threshold preset in the database, the glue liquid configuration unloading adjustment is not performed; When the glue liquid configuration feeding deviation coefficient is greater than the glue liquid configuration feeding threshold value preset in the database, the glue liquid configuration feeding adjustment is performed, and after the glue liquid configuration feeding adjustment, when the monitored glue liquid configuration feeding deviation coefficient is not greater than the glue liquid configuration feeding threshold value preset in the database, the glue liquid configuration feeding adjustment is stopped.

12. The tire cord fabric dipping glue solution preparation and management system according to claim 9, characterized in that: The blending tank temperature control module also includes: Obtain the temperature control parameters of the two-way temperature control of the mixing tank; The temperature control deviation coefficient of the glue mixing tank is obtained by analyzing the temperature control parameters; When the obtained glue liquid mixing tank temperature control deviation coefficient is greater than the glue liquid mixing tank temperature control first threshold value preset in the database and less than the glue liquid mixing tank temperature control second threshold value, no temperature control adjustment is performed; When the obtained glue solution mixing tank temperature control deviation coefficient is not greater than the glue solution mixing tank temperature control first threshold preset in the database, the temperature control is adjusted to the first level; When the obtained temperature control deviation coefficient of the glue solution mixing tank is not less than the second temperature control threshold of the glue solution mixing tank preset in the database, the second level adjustment of the temperature control is performed.

13. The tire cord fabric dipping glue solution preparation and management system according to claim 9, further comprising analyzing the qualified degree of glue solution configuration and stirring, the specific process is: Obtain glue solution stirring quality parameters of glue solution; The glue liquid stirring quality deviation coefficient is obtained according to the glue liquid stirring quality parameter analysis; When the obtained glue liquid stirring quality deviation coefficient is greater than the first glue liquid stirring quality threshold value preset in the database and less than the second glue liquid stirring quality threshold value, no glue liquid stirring adjustment is performed; In other cases, the glue solution should be stirred and adjusted.

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