Unmanned intelligent material distribution method and system

Abstract

The invention provides an unmanned intelligent material distribution method, which comprises the following steps of: S1, acquiring sensor data into a PLC (Programmable Logic Controller) in real time, and after data processing, generating logic control data and transmitting the logic control data to a PC (Personal Computer) end; s2, according to the logic control data, establishing a control model at a PC terminal, intelligently generating optimal material distribution parameters of each stock bin, and sending out a material distribution instruction; s3, according to the material distribution instruction, enabling a PLC logic controller to control material distribution equipment to walk, and enabling the material distribution equipment to arrive at a specified material distribution point for material distribution; and S3, repeating the steps S1 to S3, and circularly distributing until the heights of the materials in all the bins reach the optimal safe distribution height threshold value. The problems of signal transmission interruption caused by strong electromagnetic interference, electrical loop loosening caused by vibration, unbalanced material distribution caused by poor timeliness of control logic and the like in a mineral processing process section are solved, and unmanned and intelligent material distribution is realized by a diversified sensing advanced application technology.

Description

Technical Field

[0001] The present invention relates to the technical field of mineral processing, and in particular to an unmanned intelligent material distribution method and system. Background Art

[0002] The development of mineral processing engineering is closely related to the ever-increasing quality and quantity needs of human beings for natural mineral resources. The mineral processing process itself has a long history of development. In the mineral processing process, the traditional material distribution system has disadvantages such as difficult cable laying and high system cost. With the rapid development and widespread application of wireless networks around the world, the mineral processing industry is undergoing an unprecedented digital transformation. In the field of material distribution, the traditional manual material distribution system is not only inefficient and costly, but also difficult to meet the high requirements of modern industrial production for accuracy, flexibility and safety.

[0003] The patent document with the publication number CN118184103A discloses an automatic material distribution method, system, device and medium for a float glass raw material workshop. The method of the present invention includes obtaining preset material level height information, real-time material level image, and historical material discharging data; extracting the height information of each position of the real-time material level through image processing technology according to the real-time material level image; analyzing and calculating the target material distribution speed and material discharging position according to the preset material level height information, the real-time material level height information and the historical material discharging data, and generating a real-time material discharging control instruction according to the target material distribution speed and material discharging position, so that the material discharging machine can automatically distribute the material to realize real-time monitoring of the material distribution speed, and then accurately control the distribution process, avoid the distribution process of excessive or insufficient glass raw materials, reduce the waste of raw materials and the safety risks in the distribution process caused by manual operation, ensure the integrity and uniformity of the distribution process of the glass production line, and improve the stability and quality of the produced glass products. The patent does not customize the distribution plan for each silo, including the distribution height, etc., and cannot optimize the distribution strategy according to the differences of the silos.

[0004] In the existing mineral processing process, there are problems such as signal transmission interruption due to strong electromagnetic interference, loose electrical circuits due to strong vibration, and uneven material distribution due to poor timeliness of control logic. Therefore, it is necessary to replace traditional cable transmission with wireless network transmission technology as the core, optimize the control logic unit with dynamic matrix balance model, and realize unmanned and intelligent material distribution with diversified advanced sensing application technology. Summary of the invention

[0005] In view of the defects in the prior art, the object of the present invention is to provide an unmanned intelligent fabric method and system.

[0006] According to the present invention, a method for unmanned intelligent fabric production is provided, comprising:

[0007] Step S1: collect sensor data into the PLC logic controller in real time, generate logic control data after data processing and transmit it to the PC;

[0008] Step S2: According to the logical control data, a control model is established on the PC side, the optimal material distribution parameters for each silo are intelligently generated, and a material distribution instruction is issued;

[0009] Step S3: According to the material laying instruction, the PLC logic controller controls the material laying equipment to move so that it reaches the designated material laying point for material laying;

[0010] Repeat steps S1-S3 and circulate the material distribution until the material height in all silos reaches the optimal safe material distribution height threshold in the optimal material distribution parameters.

[0011] Preferably, step S1 comprises:

[0012] Step S1.1: The sensor sends the real-time collected data information to the PLC logic controller, and the PLC logic controller performs self-check processing on the material distribution equipment;

[0013] Step S1.2: When the equipment passes the self-test, a ready signal is sent to instruct the PLC logic controller to process the material distribution equipment and the silo; when the self-test fails, an alarm signal is sent and the PLC logic controller stops working;

[0014] The processing includes: sorting and coding the material bins, defining the direction of material distribution equipment movement, defining and coding the current parking space information;

[0015] Step S1.3: Instruct the PLC logic controller to integrate the processed parameters into logic control data and transmit it to the PC.

[0016] Preferably, step S2 comprises:

[0017] Step S2.1: According to the received logic control data, a control model is established on the PC side, and the material distribution points of all silos are coded; the control model includes local start-stop control rules, designated silo control rules, automatic material distribution control rules, and unattended control rules;

[0018] Step S2.2: When the working condition is normal, the control model is instructed to start the automatic operation mode according to the local start-stop control rules; when the working condition is abnormal, the control model is instructed to carry out manual instruction distribution according to the preset parameters of the specified silo control rules;

[0019] Step S2.3: After starting the automatic operation, the control model is made to automatically generate the position parameters of each material distribution point according to the automatic material distribution control rules, and after correction, the optimal material distribution point position is analyzed;

[0020] Step S2.4: The control model is instructed to establish a dynamic coefficient matrix table for each silo according to the unattended control rule, and the optimal height threshold of each silo is obtained by correcting the height threshold of the silo in real time;

[0021] Step S2.5: Integrate the optimal material distribution point position and the optimal material distribution height threshold to form the optimal material distribution parameters for each silo, and store them in the database of the control model;

[0022] Step S2.6: When the material distribution equipment distributes materials to the silo, the control model automatically searches for the corresponding optimal material distribution parameters in the database and issues material distribution instructions.

[0023] Preferably, step S2.3 comprises:

[0024] Step S2.3.1: Based on the logic control data information, the control model is instructed to generate the position parameters of each material distribution point in the silo according to the automatic material distribution control rules;

[0025] Step S2.3.2: Compare the last actual material placement point position with the corresponding material placement point position parameter generated according to the automatic material placement control rule, and the difference obtained by subtraction is the material placement position deviation value;

[0026] Step S2.3.3: Correct the distribution point position, calculate the distribution point position generated this time and the distribution position deviation value, and obtain the optimal distribution point position of the current silo;

[0027] Specifically, the optimal cloth point position = the cloth point position generated this time + the cloth position deviation value.

[0028] Preferably, step S2.4 comprises:

[0029] Step S2.4.1: According to the logic control data information, the control model is instructed to set the material distribution height threshold for each silo according to the unattended control rules, including the priority material distribution height threshold and the safety material distribution height threshold;

[0030] Step S2.4.2: According to the historical data changes of the material height threshold, the control model is set up according to the unattended control rules to establish a dynamic matrix coefficient table for each silo, wherein the dynamic matrix coefficient table of the nth silo includes: material level height difference dynamic matrix coefficient table H n , Change rate dynamic matrix coefficient table V n , Dynamic matrix coefficient table S of change range n ;

[0031] Step S2.4.3: According to the dynamic matrix coefficient table, the set cloth height threshold is corrected to obtain the optimal cloth height threshold;

[0032] Specifically, according to the data generated by the dynamic matrix coefficient table of the silo, the maximum coefficient method is used to take the data with the largest absolute value in the dynamic matrix coefficient table to calculate the correction coefficient. The specific formula is as follows:

[0033] P n =Max{|H n , S n , V n ∣}

[0034] P n is the correction factor for the nth silo;

[0035] Calculate the optimal cloth height threshold, including: the optimal priority cloth height threshold and the optimal safety cloth height threshold;

[0036] Calculate the optimal priority cloth height threshold:

[0037] L n1 =L n1上 ×(α×P n +1)

[0038] L n1 is the optimal priority height threshold of the nth bin, L n1上 is the last priority height threshold of the nth bin; α is specified as 1 or -1 according to the actual situation;

[0039] Calculate the optimal safe cloth height threshold:

[0040] L n2 =L n2上 ×(α×P n +1)

[0041] L n2 is the optimal safe height threshold of the nth silo, L n2上 is the last safe height threshold of the nth bin; α is specified as 1 or -1 according to the actual situation;

[0042] Step S2.4.4: Instruct the sensor to measure the material height in the silo. If the actual material height in the silo is lower than the optimal priority material distribution height threshold, issue a priority material distribution instruction for the silo.

[0043] If the actual height of materials in all silos is above the optimal priority laying height threshold, the laying equipment will lay materials one by one according to the order of laying point coding.

[0044] According to the present invention, an unmanned intelligent fabric system is provided, comprising:

[0045] Module M1: collects sensor data into the PLC logic controller in real time, generates logic control data after data processing and transmits it to the PC;

[0046] Module M2: Based on the logical control data, a control model is established on the PC side to intelligently generate the optimal material distribution parameters for each silo and issue material distribution instructions;

[0047] Module M3: According to the material laying instruction, the PLC logic controller controls the material laying equipment to move to the designated material laying point for material laying;

[0048] Repeatedly trigger modules M1-M3 and circulate material distribution until the material height in all silos reaches the optimal safe material distribution height threshold in the optimal material distribution parameters.

[0049] Preferably, module M1 comprises:

[0050] Module M1.1: The sensor sends the real-time collected data to the PLC logic controller, which performs self-checking on the material distribution equipment.

[0051] Module M1.2: When the equipment passes the self-test, it sends a ready signal to instruct the PLC logic controller to process the material distribution equipment and silo; when the self-test fails, it sends an alarm signal and the PLC logic controller stops working;

[0052] The processing includes: sorting and coding the material bins, defining the direction of material distribution equipment movement, defining and coding the current parking space information;

[0053] Module M1.3: enables the PLC logic controller to integrate the processed parameters into logic control data and transmit it to the PC.

[0054] Preferably, module M2 comprises:

[0055] Module M2.1: According to the received logic control data, a control model is established on the PC side, and the material distribution points of all silos are coded; the control model includes local start-stop control rules, designated silo control rules, automatic material distribution control rules, and unattended control rules;

[0056] Module M2.2: When the working condition is normal, the control model starts the automatic operation mode according to the local start-stop control rules; when the working condition is abnormal, the control model is ordered to carry out manual instruction distribution according to the preset parameters of the specified silo control rules;

[0057] Module M2.3: After starting the automatic operation, the control model automatically generates the position parameters of each material distribution point according to the automatic material distribution control rules, and after correction, the optimal material distribution point position is analyzed;

[0058] Module M2.4: The control model is instructed to establish a dynamic coefficient matrix table for each silo according to the unattended control rules, and the optimal height threshold of each silo is obtained by correcting the height threshold of the silo in real time;

[0059] Module M2.5: Integrate the optimal material distribution point position and the optimal material distribution height threshold to form the optimal material distribution parameters for each silo and store them in the database of the control model;

[0060] Module M2.6: When the material distribution equipment distributes materials to the silo, the control model automatically searches for the corresponding optimal material distribution parameters in the database and issues material distribution instructions.

[0061] Preferably, module M2.3 includes:

[0062] Module M2.3.1: Based on the logical control data information, the control model generates the position parameters of each silo distribution point according to the automatic distribution control rules;

[0063] Module M2.3.2: Compare the last actual material placement point position with the corresponding material placement point position parameters generated according to the automatic material placement control rules, and the difference obtained by subtraction is the material placement position deviation value;

[0064] Module M2.3.3: Correct the placement point position, calculate the placement point position generated this time and the placement position deviation value, and obtain the optimal placement point position of the current silo;

[0065] Specifically, the optimal cloth point position = the cloth point position generated this time + the cloth position deviation value.

[0066] Preferably, module M2.4 comprises:

[0067] Module M2.4.1: Based on the logic control data information, the control model sets the material height threshold for each silo according to the unattended control rules, including the priority material height threshold and the safety material height threshold;

[0068] Module M2.4.2: According to the historical data changes of the material height threshold, the control model is set up according to the unattended control rules to establish a dynamic matrix coefficient table for each silo. The dynamic matrix coefficient table of the nth silo includes: material level height difference dynamic matrix coefficient table H n , Change rate dynamic matrix coefficient table V n , Dynamic matrix coefficient table S of change range n ;

[0069] Module M2.4.3: According to the dynamic matrix coefficient table, the set cloth height threshold is corrected to obtain the optimal cloth height threshold;

[0070] Specifically, according to the data generated by the dynamic matrix coefficient table of the silo, the maximum coefficient method is used to take the data with the largest absolute value in the dynamic matrix coefficient table to calculate the correction coefficient. The specific formula is as follows:

[0071] P n=Max{|H n , S n , V n ∣}

[0072] P n is the correction factor for the nth silo;

[0073] Calculate the optimal cloth height threshold, including: the optimal priority cloth height threshold and the optimal safety cloth height threshold;

[0074] Calculate the optimal priority cloth height threshold:

[0075] L n1 =L n1上 ×(α×P n +1)

[0076] L n1 is the optimal priority height threshold of the nth bin, L n1上 is the last priority height threshold of the nth bin; α is specified as 1 or -1 according to the actual situation;

[0077] Calculate the optimal safe cloth height threshold:

[0078] L n2 =L n2上 ×(α×P n +1)

[0079] L n2 is the optimal safe height threshold of the nth silo, L n2上 is the last safe height threshold of the nth bin; α is specified as 1 or -1 according to the actual situation;

[0080] Module M2.4.4: Instruct the sensor to measure the material height in the silo. If the actual material height in the silo is lower than the optimal priority material distribution height threshold, a priority material distribution instruction is issued for the silo.

[0081] If the actual height of materials in all silos is above the optimal priority laying height threshold, the laying equipment will lay materials one by one according to the order of laying point coding.

[0082] Compared with the prior art, the present invention has the following beneficial effects:

[0083] 1. The present invention adopts a wireless network to replace the traditional wired transmission mode, thereby improving the stability, timeliness and reliability of the system, saving labor, improving work efficiency, realizing precise positioning, unmanned material distribution, intelligent operation and maintenance, improving the level of intelligent production, promoting safe production, and improving overall production efficiency; at the same time, it has the characteristics of visual maintenance and easy fault judgment, improving the working environment and optimizing resource utilization;

[0084] 2. The present invention adopts a dynamic matrix logic control unit to track the material distribution in real time based on high-quality detection data and corrections, dynamically analyze the working conditions and optimize the material distribution strategy, thus achieving unmanned intelligent and precise material distribution;

[0085] 3. The present invention improves the accuracy of material distribution, avoids abnormal situations such as empty warehouse and full warehouse caused by uneven material distribution and poor timeliness, maximizes the transportation capacity of the material distribution system, ensures the stability of ore, and reduces the material distribution error caused by human error operation;

[0086] 4. The present invention solves the problem that the optimal cloth height threshold of each silo cannot be the same due to the different silo capacities and discharge port sizes, and the different feeding and discharging speeds, but the model can only set the cloth strategy in a universal way. Specifically, the present invention uses dynamic matrix coefficients to continuously monitor and correct, customize the cloth height threshold of each silo, and obtain the current optimal cloth height threshold. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0088] Figure 1 A flow chart of the unmanned intelligent fabric method of the present invention;

[0089] Figure 2 This is a topological diagram of the unmanned intelligent fabric system in Example 2 of the present invention;

[0090] Figure 3 This is the distribution map of unmanned smart fabric sensors. DETAILED DESCRIPTION

[0091] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0092] Example 1

[0093] A method for unmanned intelligent fabric production comprises the following steps:

[0094] Step S1: Use wireless network communication technology and wireless AP technology to collect real-time sensor data into the PLC logic control unit. After edge computing, generate logic control data: including material distribution equipment position data, displacement data, limit protection data, silo material height data, and hydraulic brake device status data.

[0095] Specifically, based on the collected data information, before operation, the material distribution equipment runs a self-check of the motor, hydraulic brake device, communication network loading, wireless AP network status, profinet network status, etc. based on the sensor signal. After the self-check is successful, the prerequisites are met and a ready signal is issued; the silos are sorted and encoded; the moving direction of the material distribution equipment is defined; the current parking space information is defined and encoded; the target parking space and the current parking space are coupled, and according to the logical instructions issued by the PC, the moving direction of the material distribution equipment is controlled to make it reach the specified position.

[0096] Step S2: Transmit the sensor data, motor status, brake status and other logical control data to the PC and store them in the data center. According to the cloth equipment position data in the logical control data, sort all cloth openings according to the distance from the PC, and encode them in order from near to far, which is the code of the cloth point. Establish a control model on the PC according to the dynamic matrix table, set the cloth point position for each silo, set the cloth height threshold for each silo, and record the cloth point position of each silo in the database. When the cloth equipment moves to a certain position, the system automatically searches for the corresponding data in the database, and finally determines the code of the cloth point where the cloth equipment is located;

[0097] Step S2.1: The control model starts the operation mode according to the local start-stop control rules when the sensor status is normal, the motor is normal, the brake device is normal, and the logic control data is normal;

[0098] Step S2.2: If there is an abnormality, the control model starts manual material distribution according to the parameters set by the specified silo control rules;

[0099] Step S2.3: Start the operation mode, initialize the model, and according to the actual material distribution point position, control the model to automatically adjust the material distribution point position according to the automatic material distribution control rule, thereby coupling the optimal material distribution point position;

[0100] According to the automatic material laying rules, the material laying point position is automatically adjusted to correct the material laying position deviation caused by inertia or mechanical wear of the material laying equipment. The deviation value obtained by subtracting the actual position from the target position is the correction value, which achieves precise positioning and precise material laying.

[0101] Step S2.4: Based on the optimal placement point position, the control model establishes a dynamic matrix coefficient table for each silo according to the unattended control rule, which is used to correct the placement height threshold of each silo. The silo placement height threshold includes the priority placement height threshold and the safety placement height threshold, which are the material level height difference dynamic matrix coefficient table H, the change rate dynamic matrix coefficient table V, and the change amplitude dynamic matrix coefficient table S, which are used to determine the optimal priority placement height threshold and the safety placement height threshold of each silo, thereby coupling the target placement height of each silo. The dynamic matrix coefficient table of the nth silo is as follows:

[0102]

[0103] The subscript mn represents the element in the mth row and nth column of the matrix. The feeding and discharging rates of each silo are different and are affected by many factors. Therefore, the optimal material height threshold of each silo is also different. The initial priority material height threshold and the safety material height threshold are set manually. The dynamic matrix coefficient table is established to correct the manually set thresholds to achieve material balance.

[0104] The dynamic coefficient matrix table of the nth silo includes the material level height difference dynamic matrix coefficient table H n , Dynamic matrix coefficient table S of change range n , Change rate dynamic matrix coefficient table V n The coefficients in the dynamic coefficient matrix table are automatically generated according to the unattended control rules and the changes in historical data. The dynamic coefficient matrix table corresponds to coefficients from -1.0 to 1.0 from top to bottom. m in different matrix coefficient tables represents different values. n In: m = [|h n上 -h n ∣ / △h)],h n上 is the material level height of the nth silo in the cycle, h n is the target material level height of the nth silo in this cycle, △h is the preset reference height difference, and m is calculated by rounding to obtain a positive integer; the dynamic coefficient table S of the change range n In: m = [|s n上 -s n ∣ / △s)],s n上 is the periodic variation amplitude of the nth bin, s n is the target change amplitude value of the nth silo in this cycle, △s is the preset benchmark change amplitude difference, and m is calculated by rounding to obtain a positive integer; the change rate dynamic matrix coefficient table V n In: m = [|v n上 -v n ∣ / △v)],v n上 is the periodic change rate value of the nth bin, v n is the target change rate value of the nth silo in this cycle, △v is the preset benchmark change rate difference, and m is calculated by rounding to a positive integer; thus H is obtained n , S n 、V n Three sets of coefficient matrices are set, and then the maximum coefficient method is used, that is, the data with the largest absolute value in the dynamic matrix coefficient table is taken, and the correction coefficient P is calculated based on the data generated by the dynamic matrix coefficient table of each silo. n :P n =Max{|H n, S n , V n ∣},P n is the correction coefficient of the nth silo. The optimal height threshold of the material distribution includes the optimal priority height threshold and the optimal safety height threshold. Specifically, L n1 =L n1上 ×(α×P n +1), L n1 is the optimal priority height threshold of the nth bin, L n1上 L is the last priority height threshold of the nth bin; n2 =L n2上 ×(α×P n +1), L n2 is the optimal safe height threshold of the nth silo, L n2上 is the last safe height threshold of the nth silo, and α is specified as 1 or -1 according to the actual situation. α is the positive or negative correlation (1 / -1) between the difference between the current cycle and the previous cycle. If P n If the original value corresponding to the maximum absolute value of the corresponding dynamic coefficient matrix is ​​positive, α is 1; if the original value is negative, α is -1. n The maximum absolute value of the corresponding dynamic coefficient matrix is ​​S n ∣s mn ∣, if s mn is positive, then α is 1; if s mn If α is negative, then α is -1.

[0105] When the material height in the silo is lower than the optimal priority distribution height threshold, priority distribution is given to distribution until the optimal priority distribution height threshold is reached; when the material heights in all silos are above the optimal priority distribution height threshold, distribution is carried out one by one according to the silo code and prefabricated rules until the height reaches the optimal safe distribution height threshold.

[0106] Step S3: The target parking space sent by the PC-side control model is transmitted to the PLC controller. According to the parking space coding signal in the PLC, the running direction of the material distribution equipment is controlled to reach the target distribution point for distribution. The level sensor detects the height of the material in the silo, reaches the optimal safe distribution height threshold, and returns the end signal. Step S3.1: The target instruction sent by the PC-side control model is transmitted to the PLC logic controller. According to the preset silo coding signal in the PLC logic controller, the material distribution equipment is controlled to reach the target distribution point and distribute the material; Step S3.2: The level sensor is used to detect the height of the material in the silo to detect whether the height of the material in the silo reaches the optimal safe distribution height threshold; if the optimal safe distribution height threshold is reached, the end signal is returned; if the optimal safe distribution height threshold is not reached, step S3.1 is repeated.

[0107] Repeat steps S1-S3 to cycle the fabric.

[0108] In addition to the above work steps, in order to improve the reliability of the system and realize unmanned operation, it is necessary to have the characteristics of pre-action, pre-maintenance, and real-time alarm. The position sensor, laser sensor, and time estimation model are coupled and corrected with each other to intervene in the fault point in advance to ensure the stability of the system. Vibration sensors, temperature sensors, and torque sensors predict the maintenance of the hydraulic brake device. When the hydraulic torque is too small or the hydraulic oil temperature is too high or the amplitude is too large, a warning is issued in time and an alarm message is issued. The alarm information issued is divided into levels according to the severity. Class A is a shutdown fault. When a Class A alarm is issued, the system automatically stops running. Class B is a warning fault. When a Class B fault is issued, the centralized control center automatically pops up an alarm message to remind the inspection personnel to conduct timely inspection and maintenance.

[0109] By adopting sensor technology and wireless network data transmission, problems such as signal transmission interruption caused by strong electromagnetic interference, poor sensor stability, and poor timeliness have been solved, thereby enhancing data stability and ensuring timeliness.

[0110] By adopting the structure of PLC logic controller combined with wireless AP, the signal loss and failure of execution instructions caused by loose electrical circuits due to harsh working conditions such as strong vibration, heavy dust and fog are solved, and the stability, safety and effectiveness of the signal and the timeliness of command execution are enhanced.

[0111] The built-in hard disk housing and dynamic matrix control logic unit on the industrial PC end solve the problem that the static control model of the common logic unit only collects the height of the silo in real time, and cannot meet the complex working conditions of multi-point unloading and irregular unloading. The dynamic matrix control logic unit solves the drawbacks of the general control model. On the basis of the general control mode, a dynamic matrix coefficient table is established for each material distribution point, which are the material level height difference dynamic matrix table H, the change rate dynamic matrix table V, and the change amplitude dynamic matrix table S, respectively, to realize the dynamic control, pre-action control, system pre-diagnosis, and time estimation judgment of the material distribution system, improve the system reliability and timeliness, and provide the necessary conditions for unmanned operation.

[0112] The present invention also provides an unmanned intelligent fabric system, which can be implemented by utilizing the steps of the unmanned intelligent fabric method, that is, those skilled in the art can understand the unmanned intelligent fabric system as a preferred implementation of the unmanned intelligent fabric method.

[0113] Specifically, an unmanned intelligent material distribution system provided by the present invention includes: module M1: collecting sensor data into a PLC logic controller in real time, generating logic control data after data processing and transmitting it to a PC; module M2: establishing a control model on the PC according to the logic control data, intelligently generating the optimal material distribution parameters for each silo, and issuing material distribution instructions; module M3: according to the material distribution instructions, the PLC logic controller controls the material distribution equipment to move so that it reaches a designated material distribution point for material distribution; repeatedly triggering modules M1-M3, cyclically distributing materials until the material height in all silos reaches the optimal safe material distribution height threshold in the optimal material distribution parameters.

[0114] Preferably, module M1 includes: module M1.1: the sensor sends the real-time collected data information to the PLC logic controller, and the PLC logic controller performs self-inspection on the material distribution equipment; module M1.2: when the equipment self-inspection is qualified, a ready signal is issued, and the PLC logic controller processes the material distribution equipment and the silo; when the self-inspection fails, an alarm signal is issued, and the PLC logic controller stops working; the processing includes: sorting and encoding the silos, defining the walking direction of the material distribution equipment, defining and encoding the current parking space information; module M1.3: the PLC logic controller integrates the parameters obtained after processing into logic control data, and transmits it to the PC.

[0115] Preferably, module M2 includes: module M2.1: according to the received logic control data, a control model is established on the PC side, and the material distribution points of all silos are coded; the control model includes local start-stop control rules, designated silo control rules, automatic material distribution control rules, and unattended control rules; module M2.2: when the working condition is normal, the control model is made to start the automatic operation mode according to the local start-stop control rules; when the working condition is abnormal, the control model is made to carry out manual instruction material distribution according to the preset parameters of the designated silo control rules; module M2.3: after starting the automatic operation, the control model is made to automatically start the automatic material distribution control rules. Generate the position parameters of each distribution point, and after correction, analyze the optimal distribution point position; Module M2.4: Let the control model establish a dynamic coefficient matrix table for each silo according to the unattended control rules, and obtain the optimal distribution height threshold of each silo by correcting the distribution height threshold of the silo in real time; Module M2.5: Let the optimal distribution point position and the optimal distribution height threshold be integrated to form the optimal distribution parameters of each silo, and store them in the database of the control model; Module M2.6: When the distribution equipment distributes the silo, let the control model automatically search for the corresponding optimal distribution parameters in the database and issue distribution instructions.

[0116] Preferably, module M2.3 includes: module M2.3.1: according to the logical control data information, the control model is made to generate the position parameters of each material distribution point in the silo according to the automatic material distribution control rules; module M2.3.2: the actual distribution point position of the last time is compared with the corresponding distribution point position parameters generated according to the automatic material distribution control rules, and the difference obtained by subtraction is the distribution position deviation value; module M2.3.3: correcting the distribution point position, calculating the distribution point position generated this time and the distribution position deviation value, and obtaining the optimal distribution point position of the current silo; specifically, the optimal distribution point position = the distribution point position generated this time + the distribution position deviation value.

[0117] Preferably, module M2.4 includes: module M2.4.1: according to the logic control data information, the control model is set according to the unattended control rules for each silo cloth height threshold, including the priority cloth height threshold, the safety cloth height threshold; module M2.4.2: according to the historical data change of the cloth height threshold, the control model is set according to the unattended control rules for each silo dynamic matrix coefficient table, wherein the dynamic matrix coefficient table of the nth silo includes: material level height difference dynamic matrix coefficient table H n , Change rate dynamic matrix coefficient table V n , Dynamic matrix coefficient table S of change range n ; Module M2.4.3: According to the dynamic matrix coefficient table, correct the set cloth height threshold to obtain the optimal cloth height threshold; Specifically, according to the data generated by the dynamic matrix coefficient table of the silo, use the maximum coefficient method to take the data with the largest absolute value in the dynamic matrix coefficient table to calculate the correction coefficient. The specific formula is as follows: P n =Max{|H n , S n , V n ∣},P n is the correction coefficient of the nth silo; calculate the optimal cloth height threshold, including: the optimal priority cloth height threshold and the optimal safety cloth height threshold; calculate the optimal priority cloth height threshold: L n1 =L n1上 ×(α×P n +1), L n1 is the optimal priority height threshold of the nth bin, L n1上 is the last priority height threshold of the nth silo; α is specified as 1 or -1 according to the actual situation; calculate the optimal safe height threshold of the cloth: L n2 =L n2上 ×(α×P n +1), L n2 is the optimal safe height threshold of the nth silo, L n2上is the last safe height threshold of the nth silo; α is specified as 1 or -1 according to the actual situation; Module M2.4.4: Let the sensor measure the material height in the silo. If the actual material height in the silo is lower than the optimal priority height threshold, a priority laying instruction is issued for the silo; if the actual material height in all silos is above the optimal priority height threshold, the laying equipment lays the materials one by one according to the order of the laying point coding.

[0118] Example 2

[0119] Unmanned intelligent fabric sensors are distributed as follows: Figure 3 As shown. The core sensors include: position sensor, level sensor, laser sensor, limit sensor, displacement sensor, vibration sensor, temperature sensor, and torque sensor.

[0120] The position sensor is electromagnetic, with a sensing distance of 0-10.0cm and multiple communication protocols such as PROFINET and TCP / IP. The sensor is installed in the middle position and over-limit position of the silo for automatic correction of the material distribution point and over-travel protection.

[0121] Laser sensor, diffuse reflection type, measuring range 0-200.0m, accuracy 0.3mm, with PROFINET, ModbusTCP communication protocol, installed at the head and tail ends of the material distribution equipment, the laser center is aligned with the white target of the material distribution equipment, and the position of the material distribution equipment is measured in real time;

[0122] The level sensor adopts self-purging and self-heating type, with a detection distance of 0-150.0m and PROFINET and TCP / IP communication protocols. The working conditions inside the silo are harsh, with a lot of dust and a lot of water vapor on rainy days. Traditional level timing is often interfered by dust and water mist, resulting in system inaccuracy. The self-purging type is used to reduce the influence of dust, and the self-heating system effectively avoids the influence of water mist, thereby improving the stability of the level detection system.

[0123] The limit sensor is mechanical multi-turn, explosion-proof, with an instantaneous action time of ≤100.0ms, and has PROFINET and TCP / IP communication protocols. It is installed at the forward and reverse ends of the material distribution equipment track and is used in conjunction with the displacement sensor for safety protection to prevent the material distribution equipment from derailing longitudinally.

[0124] The displacement sensor adopts contact type linear explosion-proof type, with instantaneous time ≤100.0ms, and has PROFINET and TCP / IP communication protocols. It is installed at the positive and reverse ends of the material distribution equipment track and used in conjunction with the limit sensor for safety protection to prevent the material distribution equipment from derailing laterally.

[0125] Vibration sensors, temperature sensors, and torque sensors, which have PROFINET and TCP / IP communication protocols, are installed on the hydraulic brake device. The vibration sensor is used to detect whether the mechanical part of the brake device is reliable, the temperature sensor is used to detect whether its hydraulic oil is normal and effective, and the torque sensor is used to detect whether the locking force meets the braking conditions of the fabric equipment.

[0126] This application provides an unmanned intelligent fabric network topology diagram, such as Figure 2 As shown:

[0127] The 5G communication gateway is used to achieve remote control monitoring and control, completely solving the problem of signal transmission interruption caused by strong electromagnetic interference, which leads to system failure and inaccuracy.

[0128] The wireless AP is used for profinet-5G protocol conversion. It is the communication link between sensors and PLC controllers. It is the transmission bridge in the wireless network architecture. It also has security protocols, enforces network security management, and further improves network stability.

[0129] The PLC logic controller has strong anti-interference ability, large data processing capacity, reliable and stable operation, and a PROFINET communication protocol interface. The PLC controller replaces the traditional electrical circuit and can achieve wireless transmission with the sensor PROFINET and 5G communication with the PC. The system hard disk casing has a signal digital-to-analog conversion processing unit and a data acquisition processing unit to pre-process the position sensor data and perform edge computing to improve data quality, effectively utilize storage space, and further enhance system stability.

[0130] The industrial PC has a built-in hard disk housing and a built-in dynamic matrix control logic unit, which centrally processes the signals of the PLC logic controller, generates a dynamic matrix coefficient table, and outputs the target height threshold through the balanced model processing.

[0131] According to the information data pre-processed in the PLC controller, it is transmitted to the PC through the 5G gateway and stored in the data center. The PC establishes a control model, which includes: local start-stop control rules, designated silo control rules, automatic material distribution control rules, and unattended control rules;

[0132] Local start-stop control rules include rules for defining equipment fault repair, unit trial operation, operating status detection, and start-up preconditions;

[0133] The specified silo control rules include specifying the manually set target material distribution point height threshold, selecting the corresponding height threshold rule, and the material distribution equipment will distribute the material at the corresponding height threshold. The rules for emergency use are in the event of inaccurate material height detection at the distribution point and positioning system failure.

[0134] The automatic material distribution control rules include that when all detection links are normal, the material distribution point position is automatically adjusted according to the actual material distribution point position in the silo according to the automatic material distribution control rules, thereby coupling the rule of the optimal material distribution point position; when automatically distributing materials, the material distribution point position is automatically adjusted to correct the material distribution position deviation caused by inertia or mechanical wear of the material distribution equipment; the deviation value is the difference between the actual material distribution point position of the last distribution equipment and the set material distribution point position, and the deviation value obtained by subtracting the actual position from the target position is the correction value, realizing the rules of precise positioning and precise material distribution;

[0135] The unmanned control rules include establishing a dynamic matrix coefficient table for each silo according to the unmanned control rules, which is used to correct the material distribution height threshold of each silo, namely the material level height difference dynamic matrix coefficient table H, the change rate dynamic matrix coefficient table V, and the change amplitude dynamic matrix coefficient table S, which are used to determine each optimal priority material distribution height threshold and the optimal safety material distribution height threshold, thereby coupling out the rules for the optimal material distribution height of each silo. Specifically, the dynamic matrix coefficient table of the nth silo includes: material level height difference dynamic matrix coefficient table H n , Change rate dynamic matrix coefficient table V n , Dynamic matrix coefficient table S of change range n ; According to the dynamic matrix coefficient table, the set cloth height threshold is corrected to obtain the optimal cloth height threshold; according to the data generated by the dynamic matrix coefficient table of the silo, the maximum coefficient method is used to take the data with the largest absolute value in the dynamic matrix coefficient table to calculate the correction coefficient. The specific formula is as follows: P n =Max{|H n , S n , V n ∣},P n is the correction coefficient of the nth bin; calculate the optimal priority height threshold of the material distribution: L n1 =L n1上 ×(α×P n +1), L n1 is the optimal priority height threshold of the nth bin, L n1上 is the last priority height threshold of the nth silo; α is specified as 1 or -1 according to the actual situation; calculate the optimal safe height threshold of the cloth: L n2 =L n2上 ×(α×P n +1), L n2 is the optimal safe height threshold of the nth silo, L n2上 is the last safe height threshold of the nth bin; α is specified as 1 or -1 according to the actual situation.

[0136] The feeding and discharging rates of each silo are different and are affected by many factors. Therefore, the optimal material height threshold of each silo is also different. The initial priority material height threshold and safety material height threshold are set manually. The dynamic matrix coefficient table is established to correct the manually set height threshold, so as to achieve material balance.

[0137] Those skilled in the art know that, in addition to realizing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code, it is entirely possible to realize the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for realizing various functions can also be regarded as both software modules for realizing the method and structures within the hardware component.

[0138] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A method for unmanned intelligent fabric production, characterized in that: include: Step S1: collect sensor data into the PLC logic controller in real time, generate logic control data after data processing and transmit it to the PC; Step S2: According to the logical control data, a control model is established on the PC side, the optimal material distribution parameters for each silo are intelligently generated, and material distribution instructions are issued; Step S3: According to the material laying instruction, the PLC logic controller controls the material laying equipment to move so that it reaches the designated material laying point for material laying; Repeat steps S1-S3 and circulate the material distribution until the material height in all silos reaches the optimal safe material distribution height threshold in the optimal material distribution parameters.

2. The unmanned intelligent fabric method according to claim 1, characterized in that: The step S1 comprises: Step S1.1: The sensor sends the real-time collected data information to the PLC logic controller, and the PLC logic controller performs self-check processing on the material distribution equipment; Step S1.2: When the equipment passes the self-test, a ready signal is sent to instruct the PLC logic controller to process the material distribution equipment and the silo; when the self-test fails, an alarm signal is sent and the PLC logic controller stops working; The processing includes: sorting and coding the material bins, defining the walking direction of the material distribution equipment, defining and coding the current parking space information; Step S1.3: Instruct the PLC logic controller to integrate the processed parameters into logic control data and transmit it to the PC.

3. The unmanned intelligent fabric method according to claim 1, characterized in that: The step S2 comprises: Step S2.1: According to the received logic control data, a control model is established on the PC side, and the material distribution points of all silos are coded; the control model includes local start-stop control rules, designated silo control rules, automatic material distribution control rules, and unattended control rules; Step S2.2: When the working condition is normal, the control model is instructed to start the automatic operation mode according to the local start-stop control rules; when the working condition is abnormal, the control model is instructed to carry out manual instruction distribution according to the preset parameters of the specified silo control rules; Step S2.3: After starting the automatic operation, the control model is made to automatically generate the position parameters of each material distribution point according to the automatic material distribution control rules, and after correction, the optimal material distribution point position is analyzed; Step S2.4: The control model is instructed to establish a dynamic coefficient matrix table for each silo according to the unattended control rule, and the optimal height threshold of each silo is obtained by correcting the height threshold of the silo in real time; Step S2.5: Integrate the optimal material distribution point position and the optimal material distribution height threshold to form the optimal material distribution parameters for each silo, and store them in the database of the control model; Step S2.6: When the material distribution equipment distributes materials to the silo, the control model automatically searches for the corresponding optimal material distribution parameters in the database and issues material distribution instructions.

4. The unmanned intelligent fabric method according to claim 3, characterized in that: The step S2.3 comprises: Step S2.3.1: Based on the logic control data information, the control model is instructed to generate the position parameters of each material distribution point in the silo according to the automatic material distribution control rules; Step S2.3.2: Compare the last actual material placement point position with the corresponding material placement point position parameter generated according to the automatic material placement control rule, and the difference obtained by subtracting them is the material placement position deviation value; Step S2.3.3: Correct the distribution point position, calculate the distribution point position generated this time and the distribution position deviation value, and obtain the optimal distribution point position of the current silo; Specifically, the optimal cloth point position = the cloth point position generated this time + the cloth position deviation value.

5. The unmanned intelligent fabric method according to claim 3, characterized in that: The step S2.4 comprises: Step S2.4.1: According to the logic control data information, the control model is instructed to set the material distribution height threshold for each silo according to the unattended control rules, including the priority material distribution height threshold and the safety material distribution height threshold; Step S2.4.2: According to the historical data changes of the material height threshold, the control model is set up according to the unattended control rules to establish a dynamic matrix coefficient table for each silo, wherein the dynamic matrix coefficient table of the nth silo includes: material level height difference dynamic matrix coefficient table H n , Change rate dynamic matrix coefficient table V n , Dynamic matrix coefficient table S of change range n ; Step S2.4.3: According to the dynamic matrix coefficient table, the set cloth height threshold is corrected to obtain the optimal cloth height threshold; Specifically, according to the data generated by the dynamic matrix coefficient table of the silo, the maximum coefficient method is used to take the data with the largest absolute value in the dynamic matrix coefficient table to calculate the correction coefficient. The specific formula is as follows: P n =Max{∣H n ,S n ,V n ∣} P n is the correction factor for the nth silo; Calculate the optimal cloth height threshold, including: the optimal priority cloth height threshold and the optimal safety cloth height threshold; Calculate the optimal priority cloth height threshold: L n1 =L n1上 ×(α×P n +1) L n1 is the optimal priority height threshold of the nth bin, L n1上 is the last priority height threshold of the nth bin; α is specified as 1 or -1 according to the actual situation; Calculate the optimal safe cloth height threshold: L n2 =L n2上 ×(α×P n +1) L n2 is the optimal safe height threshold of the nth silo, L n2上 is the last safe height threshold of the nth bin; α is specified as 1 or -1 according to the actual situation; Step S2.4.4: Instruct the sensor to measure the material height in the silo. If the actual material height in the silo is lower than the optimal priority material distribution height threshold, issue a priority material distribution instruction for the silo. If the actual height of materials in all silos is above the optimal priority laying height threshold, the laying equipment will lay materials one by one according to the order of laying point coding.

6. An unmanned intelligent fabric system, characterized in that: include: Module M1: collects sensor data into the PLC logic controller in real time, generates logic control data after data processing and transmits it to the PC; Module M2: Based on the logical control data, a control model is established on the PC side to intelligently generate the optimal material distribution parameters for each silo and issue material distribution instructions; Module M3: According to the material laying instruction, the PLC logic controller controls the material laying equipment to move to the designated material laying point for material laying; Repeatedly trigger modules M1-M3 and circulate material distribution until the material height in all silos reaches the optimal safe material distribution height threshold in the optimal material distribution parameters.

7. The unmanned intelligent fabric system according to claim 6, characterized in that: The module M1 comprises: Module M1.1: enables the sensor to send the real-time collected data information to the PLC logic controller, and the PLC logic controller performs self-check processing on the material distribution equipment; Module M1.2: When the equipment passes the self-test, it sends a ready signal to instruct the PLC logic controller to process the material distribution equipment and silo; when the self-test fails, it sends an alarm signal and the PLC logic controller stops working; The processing includes: sorting and coding the material bins, defining the walking direction of the material distribution equipment, defining and coding the current parking space information; Module M1.3: enables the PLC logic controller to integrate the processed parameters into logic control data and transmit it to the PC.

8. The unmanned intelligent fabric system according to claim 6, characterized in that: The module M2 comprises: Module M2.1: According to the received logic control data, a control model is established on the PC side, and the material distribution points of all silos are coded; the control model includes local start-stop control rules, designated silo control rules, automatic material distribution control rules, and unattended control rules; Module M2.2: When the working condition is normal, the control model starts the automatic operation mode according to the local start-stop control rules; when the working condition is abnormal, the control model is ordered to carry out manual instruction distribution according to the preset parameters of the specified silo control rules; Module M2.3: After starting the automatic operation, the control model automatically generates the position parameters of each material distribution point according to the automatic material distribution control rules, and after correction, the optimal material distribution point position is analyzed; Module M2.4: Let the control model establish a dynamic coefficient matrix table for each silo according to the unattended control rules, and obtain the optimal height threshold of each silo by correcting the height threshold of the silo in real time; Module M2.5: Integrate the optimal material distribution point position and the optimal material distribution height threshold to form the optimal material distribution parameters for each silo and store them in the database of the control model; Module M2.6: When the material distribution equipment distributes materials to the silo, the control model automatically searches for the corresponding optimal material distribution parameters in the database and issues material distribution instructions.

9. The unmanned intelligent fabric system according to claim 8, characterized in that: The module M2.3 includes: Module M2.3.1: Based on the logical control data information, the control model generates the position parameters of each silo distribution point according to the automatic distribution control rules; Module M2.3.2: Compare the last actual material placement point position with the corresponding material placement point position parameters generated according to the automatic material placement control rules, and the difference obtained by subtraction is the material placement position deviation value; Module M2.3.3: Correct the material distribution point position, calculate the material distribution point position generated this time and the material distribution position deviation value, and obtain the optimal material distribution point position of the current silo; Specifically, the optimal cloth point position = the cloth point position generated this time + the cloth position deviation value.

10. The unmanned intelligent fabric system according to claim 8, characterized in that: The module M2.4 includes: Module M2.4.1: Based on the logic control data information, the control model sets the material height threshold for each silo according to the unattended control rules, including the priority material height threshold and the safety material height threshold; Module M2.4.2: According to the historical data changes of the material height threshold, the control model is set up according to the unattended control rules to establish a dynamic matrix coefficient table for each silo. The dynamic matrix coefficient table of the nth silo includes: material level height difference dynamic matrix coefficient table H n , Change rate dynamic matrix coefficient table V n , Dynamic matrix coefficient table S of change range n ; Module M2.4.3: According to the dynamic matrix coefficient table, the set cloth height threshold is corrected to obtain the optimal cloth height threshold; Specifically, according to the data generated by the dynamic matrix coefficient table of the silo, the maximum coefficient method is used to take the data with the largest absolute value in the dynamic matrix coefficient table to calculate the correction coefficient. The specific formula is as follows: P n =Max{∣H n ,S n ,V n ∣} P n is the correction factor for the nth silo; Calculate the optimal cloth height threshold, including: the optimal priority cloth height threshold and the optimal safety cloth height threshold; Calculate the optimal priority cloth height threshold: L n1 =L n1上 ×(α×P n +1) L n1 is the optimal priority height threshold of the nth bin, L n1上 is the last priority height threshold of the nth bin; α is specified as 1 or -1 according to the actual situation; Calculate the optimal safe cloth height threshold: L n2 =L n2上 ×(α×P n +1) L n2 is the optimal safe height threshold of the nth silo, L n2上 is the last safe height threshold of the nth bin; α is specified as 1 or -1 according to the actual situation; Module M2.4.4: Instruct the sensor to measure the material height in the silo. If the actual material height in the silo is lower than the optimal priority material distribution height threshold, a priority material distribution instruction is issued for the silo. If the actual height of materials in all silos is above the optimal priority laying height threshold, the laying equipment will lay materials one by one according to the order of laying point coding.

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