Cable intelligent cutting and surplus management system and method for intelligent fusion warehouse

Abstract

The application relates to the technical field of cable and wire management, in particular to a cable intelligent cutting and surplus material management system and method for intelligent fusion warehouses. The system comprises the following steps: extracting order demand length, quantity, priority and specifications, calculating comprehensive priority weight, adopting a greedy genetic hybrid algorithm, taking raw material number arrangement as chromosome coding, performing greedy initialization, genetic global exploration and local refining, and generating a cutting distribution scheme; projecting a laser mark position according to the cutting scheme, collecting an image to identify sheath texture, matching a standard template to calculate axial, transverse and angle deviations; calculating adaptive compression force according to cable cross-sectional area and other parameters, monitoring the relative deviation of the compression force, and performing fine adjustment and early warning by PLC when the threshold is exceeded; calculating the actual length of the surplus material after cutting, combining the port flatness and axial deviation to obtain the comprehensive utilization rate through value evaluation, grading according to the comprehensive utilization rate, and re-storing the available surplus material. The application constructs a closed-loop optimization system, effectively reduces loss and improves cutting quality.

Description

Technical Field

[0001] This invention relates to the field of cable and wire management technology, specifically to a cable intelligent cutting and waste material management system and method for intelligent integrated warehouses. Background Technology

[0002] Power cables are core materials in warehouses, mainly including cross-linked polyethylene insulated power cables (such as YJV-10kV, YJV-35kV), control cables (such as KVV, KVVP), overhead insulated wires (such as JKLYJ series), and special cables such as flame-retardant, fire-resistant, and low-smoke halogen-free cables. These cables are stored by the meter or by reel. In scenarios such as power grid construction, emergency repairs, renovation of old residential areas, and new energy access, different lengths need to be cut in the warehouse according to actual needs. With the advancement of smart grid construction, the requirements for automation and precision in warehouse management are increasing, placing higher standards on the efficiency, accuracy, and data traceability of cutting operations.

[0003] Most cable cutting is still done manually. Although the emergence of intelligent equipment can improve efficiency and accuracy, the following technical problems still exist: Excessive manual measurement errors lead to high costs and material waste; leftover materials lack standardized labeling, resulting in unclear correspondence with raw material specifications, orders, and batches, leading to low secondary utilization rates; multiple orders are cut independently without overall material layout optimization, and different orders on the same raw material lack coordination, resulting in high total waste; cuts are prone to burrs, flattening, and unevenness, directly affecting the electrical performance and mechanical strength of cables, and the lack of process records makes it difficult to trace quality issues; manual records are incomplete, inaccurate, and untimely, failing to provide effective data support for warehouse optimization, procurement planning, and cost analysis. Summary of the Invention

[0004] This invention provides a cable intelligent cutting and scrap management system and method for intelligent fusion warehouses.

[0005] The technical solution of this invention is as follows: A method for intelligent cable cutting and waste management in a smart integrated warehouse includes the following steps: S1. Receive several cable demand orders and inventory raw material information in the warehouse, extract order and cable related data, calculate the comprehensive priority weight of the orders, sort the orders and use the raw materials with the best length to cut and generate an initial population, use the arrangement order of the cable raw material numbers as the chromosome code of the individual, use a greedy algorithm to initialize and compress the search space, use a standard genetic operator for global exploration, and then use a local refinement operator for local search to generate the optimal cutting model and obtain the cutting allocation scheme; S2. Combine the obtained cutting allocation scheme, and obtain the laser projection position and cable surface image of the cable surface to be cut, identify the texture features of the cable sheath surface, locate the cutting target position, obtain the position deviation, and obtain the adjusted deviation after matching with the standard texture template, including the axial deviation, the lateral deviation, and the angle deviation during cutting. S3. Combine cable-related data to obtain adaptive clamping force, and obtain the relative deviation of clamping force based on the actual clamping force. If the clamping force deviation threshold is exceeded, the pressure is finely adjusted according to the fine-tuning control quantity and the working condition warning is triggered. S4. After the cable is cut, the actual length of the remaining material is estimated for each section. The actual utilization rate of the remaining material is obtained by combining the flatness of the port and the axial deviation during cutting through the value evaluation of the remaining material. The remaining material is classified according to the actual utilization rate of the remaining material, and the remaining material other than waste material is put back into the warehouse.

[0006] Furthermore, the adjusted deviation is obtained after matching with the standard texture template, as follows: After identifying the sheath texture using a neural network, the texture feature point set on the cable surface is extracted and matched with a pre-stored standard texture template. The adjusted deviation is calculated by minimizing the matching error.

[0007] Furthermore, the relative deviation of the clamping force is obtained based on the actual clamping force, as follows: in, Indicates the relative deviation of the clamping force; This indicates the actual clamping force value collected in real time by the pressure sensor; This indicates adaptive clamping force.

[0008] Furthermore, after the cable is cut, the actual length of the remaining material for each section is estimated, as follows: in, Indicates the first The actual length of the leftover material after cutting the raw material; Indicates the first The length of the raw material before cutting; Indicates the first The required length for each order; This indicates that the actual cutting process starts from the first... Cut the first piece of raw material The actual quantity required for each order.

[0009] Furthermore, the relative deviation of the clamping force The process is as follows: Set clamping force deviation threshold ,when At that time, the equipment continued to operate normally; when If the clamping condition is deemed abnormal, the PLC will immediately activate the automatic fine-tuning cylinder to adjust the clamping force and trigger a condition warning.

[0010] Further, the control quantity is fine-tuned, as follows: in, This indicates the fine-tuning control value of the output; Indicates the proportional gain coefficient; This indicates the current deviation in clamping force. It is an integral variable; Indicates the current time; This represents the integral gain coefficient.

[0011] Furthermore, the optimal cropping model is generated, as follows: The iteration termination condition is defined as reaching the maximum number of generations or the convergence of the population fitness variance, outputting the optimal cutting model, and obtaining the cutting and layout plan, which includes the cutting allocation scheme and expected loss of each raw material.

[0012] Furthermore, expected losses The process is as follows: in, Indicates the first The length of the raw material, This indicates the total number of raw materials available for cutting in the warehouse. This represents the minimum total loss length under the optimal solution.

[0013] The intelligent cable cutting and waste management system for smart integrated warehouses includes the following: The trimming optimization scheduling and planning module receives several cable demand orders and inventory raw material information from the warehouse, extracts order and cable-related data, calculates the comprehensive priority weight of the orders, sorts the orders, and uses the raw materials with the best length matching to trim and generate an initial population. The order of the cable raw material numbers is used as the chromosome code of the individual. The search space is compressed by greedy initialization, global exploration is carried out using standard genetic operators, and local search is carried out through local refinement operators to generate the optimal trimming model and obtain the trimming allocation scheme. The visual positioning and deviation detection module combines the obtained cutting allocation scheme and obtains the laser projection position and cable surface image of the cable to be cut. It identifies the texture features of the cable sheath surface, locates the cutting target position, obtains the position deviation, and obtains the adjusted deviation after matching with the standard texture template. This includes the axial deviation, the lateral deviation, and the angular deviation during cutting. The adaptive clamping force control and early warning module combines cable-related data to obtain adaptive clamping force and calculates the relative deviation of clamping force based on the actual clamping force. If the clamping force deviation threshold is exceeded, the pressure is fine-tuned according to the fine-tuning control quantity and an operating condition early warning is triggered. The module for assessing and restocking surplus materials estimates the actual length of each remaining section after cable cutting. It then uses the flatness of the port and the axial deviation during cutting to evaluate the value of the surplus materials and obtain the actual comprehensive utilization rate. Based on the actual comprehensive utilization rate, the surplus materials are classified into grades, and all surplus materials except for waste materials are restocked.

[0014] Furthermore, the minimum error matching positioning module in the visual positioning and deviation detection module identifies the sheath texture through a neural network, extracts the texture feature point set on the cable surface, matches it with the pre-stored standard texture template, and calculates the adjusted deviation by minimizing the matching error.

[0015] The beneficial effects of this invention are as follows: 1. This invention effectively reduces the waste of cable raw materials by establishing an order priority calculation model and optimizing the cutting scheme using a genetic algorithm, thereby maximizing the comprehensive utilization rate of the cable. By using the arrangement order of the cable raw material numbers as chromosome encoding and employing a greedy algorithm to guide initialization and compress the search space, the search efficiency for the global optimal solution is significantly improved. At the same time, the generated expected cable cutting loss provides a reliable benchmark for subsequent quality evaluation, giving the entire cutting process a quantifiable and traceable optimization basis.

[0016] 2. This invention employs high-precision visual sensing technology, which can quickly identify the cutting marking lines on the cable surface and complete precise positioning; through matching calculation with a standard texture template, it accurately outputs the axial deviation, lateral deviation, and angular deviation during cutting, eliminating cutting errors caused by human factors and controlling cutting accuracy at a high level.

[0017] 3. This invention incorporates a pressure sensor for real-time monitoring, enabling dynamic adjustment of the clamping force based on subtle differences in cable outer diameter, internal strand hardness, and insulation thickness. When the actual clamping force exceeds the deviation threshold, the system automatically activates cylinder fine-tuning and triggers a working condition warning, preventing cutting deviations caused by unstable clamping and ensuring clamping stability for cables of different materials and specifications during the cutting process.

[0018] 4. This invention achieves precise quantification of the value of surplus materials by automatically evaluating each section of remaining material and calculating the comprehensive utilization rate of the surplus material based on the flatness of the port and the axial deviation. Simultaneously, it intelligently determines whether the surplus material should be re-entered into the warehouse, further improving the level of material resource recycling. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the cable cutting equipment of the present invention; Figure 2 This is a schematic diagram of the system interface of the present invention. Detailed Implementation

[0020] To better understand the above technical solutions, a detailed description of the technical solutions will be provided below in conjunction with the accompanying drawings and specific embodiments. It should also be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0021] This embodiment provides a method for intelligent cable cutting and waste management for intelligent fusion warehouses, including the following steps: S1. Receive several cable demand orders and inventory raw material information from the warehouse, extract order and cable-related data, calculate the comprehensive priority weight of the orders, sort the orders, use the raw materials with the best length matching to cut and generate an initial population, use the arrangement order of the cable raw material numbers as the chromosome code of the individuals, use a greedy algorithm to initialize and compress the search space, use standard genetic operators for global exploration, and then use local refinement operators for local search to generate the optimal cutting model and obtain the cutting allocation scheme.

[0022] In this embodiment of the invention, multiple cable demand orders are received from a warehouse, and the following parameters are extracted from each order: required length. Quantity required Delivery priority Material specifications An order information matrix is ​​constructed, in which the higher the value of the delivery priority, the higher the priority. Material specifications include cable type, voltage level, conductor material, insulation type, etc.

[0023] Assume there is a total The number of pending orders, the first Overall priority weight of each order As shown below: in, Indicates the first Delivery priority for each order (levels 1-5); This represents the maximum delivery priority among all orders, used for normalization. ; This represents the weighting coefficient, which is a priority factor. This represents the weighting coefficient, which is the urgency factor. This indicates the reference delivery period, determined based on historical data, and is expressed in days. Indicates the first The remaining days for delivery of each order; This represents the weighting coefficient, which indicates the contribution of the order size factor. Indicates the first The required quantity for each order, further... It's a temporary variable, iterated from 1 to... , Indicates the total number of orders; Indicates the first The required length for each order; This indicates the proportion of the current order's demand to the total demand of all orders.

[0024] Furthermore, based on the overall priority weight of the orders and a list of raw materials in stock. ;in, Indicates the first The number of the root raw material, This indicates the total number of raw materials available for cutting in the warehouse; Indicates the first Cross-sectional area specifications of the raw material; Indicates the first Based on the length of the raw material, an improved greedy-genetic hybrid algorithm is used to establish the optimal cutting model, the specific process of which is as follows: Define the decision matrix Its elements are , indicating the first The order demand was allocated to the first... The number of cut roots on the raw root material is mapped to a chromosome for genetic encoding purposes. ,in, Indicates the first The order number sequence carried on the root raw material. The corresponding cutting allocation matrix. Strict process constraints must be met. Indicates chromosome The clipping assignment matrix obtained after decoding.

[0025] Considering both material utilization rate and uniformity of waste distribution, a global minimum loss objective function is defined. The process is as follows: The constraints are: This includes ensuring that the total cut length of each raw material does not exceed its own length, and ensuring that the required quantity for each order is met.

[0026] The algorithm encodes the cable raw material numbers in a specific order as an individual chromosome, with each number representing a gene. A greedy algorithm is used to initialize and compress the search space. Standard genetic operators are used for global exploration, and local refinement operators are used to enhance local optimization capabilities. This ensures a balance between global optimality and computational efficiency in the trimming model. The algorithm flow is as follows: Utilizing order priority weights Orders are sorted from highest to lowest order. For each order, the raw material with the best length match is used for cutting first, generating an initial population. Define the greedy matching degree. , Define the fitness function , in, , , This represents the weighting coefficient of each penalty item; It represents the standard deviation of the maximum and minimum single scrap lengths in the cutting scheme, and is used to evaluate the uniformity of scrap distribution; This indicates the total loss of the chromosome cutting scheme; This indicates a penalty for violating material specification matching constraints; that is, a penalty is imposed if an order is assigned to raw materials that do not match the specifications.

[0027] The algorithm employs a roulette wheel selection operator combined with an elite retention strategy; it uses a partially matched crossover (PMX) operator to perform crossover operations, randomly selecting two chromosomes from the parent population and randomly generating crossover intervals; and it uses a crossover mutation operator to perform crossover mutations on chromosomes with mutation probabilities, randomly selecting two gene loci for mutation operations.

[0028] For the best elite individual in each generation, the neighborhood search space is defined as the combination of cutting and allocation by exchanging any two raw materials. After each iteration, a greedy local search is performed on the best individual in the population; it is checked whether there is a solution that can further reduce the total loss by exchanging cutting and allocation, and if so, it is replaced.

[0029] The iteration termination condition is defined as reaching the maximum number of generations or the convergence of the population fitness variance, outputting the optimal pruning model. , This yields a cutting and layout plan, including the cutting allocation scheme for each raw material and the expected wastage rate. The process is as follows: , This represents the minimum total loss length under the optimal solution.

[0030] S2. Combine the obtained cutting allocation scheme, obtain the laser projection position and cable surface image of the cable to be cut, identify the texture features of the cable sheath surface, locate the cutting target position, obtain the position deviation, and obtain the adjusted deviation after matching with the standard texture template, including the axial deviation, the lateral deviation, and the angle deviation during cutting.

[0031] In this embodiment of the invention, a three-roller mechanism is used to clamp and straighten the cable. The front roller group is used for introduction and coarse positioning, the middle roller group is used for straightening and fine positioning, and the rear roller group is used for support and guidance. The roller spacing is adjustable to accommodate cables of different lengths. A linear laser is projected onto the cable surface to mark the cutting points.

[0032] After receiving the cutting allocation plan generated by S1, a linear laser is projected onto the surface of the cable to be cut to accurately mark the cutting position. The laser projection mechanism has the following performance indicators: projection accuracy better than ±1mm; response time less than 100ms; and supports two modes: continuous projection and pulse projection. The continuous projection mode is suitable for conventional cutting operations, while the pulse projection mode is suitable for cutting positioning scenarios with high precision requirements.

[0033] An industrial camera automatically acquires images of the cable surface after laser projection. An adaptive threshold segmentation algorithm extracts laser line features while filtering ambient light interference. Furthermore, the system automatically extracts the cable area, eliminating background interference and reducing the image processing area to the cable surface to improve computational speed. An adaptive histogram equalization (CLAHE) algorithm enhances texture contrast, while Gaussian filtering removes ambient light noise and the effects of oil and dust on the cable surface. Based on camera intrinsic parameters and calibration parameters, image distortion caused by lens distortion is corrected to ensure a linear correspondence between pixel coordinates and physical coordinates. Finally, a convolutional neural network (CNN) is used to recognize the sheath texture, identifying features such as text, patterns, and colors on the cable sheath surface, and establishing a texture coordinate system.

[0034] Furthermore, an improved MobileNetV3 lightweight network is employed to extract multiple types of features from the sheath surface at three scales. The shallow network layers 1-3 extract basic visual features such as edges, lines, and color blocks for quickly locating the cable outline and laser line position. The middle network layers 4-8 extract intermediate features such as embossed textures, printed text strokes, and insulation layer boundaries. These features are unique and stable, serving as the core benchmark for establishing the coordinate system. The deep network layers 9-12 extract global semantic features to distinguish cables of different specifications and manufacturers, avoiding feature confusion. From the selected stable feature points, three non-collinear feature points are chosen as the coordinate system reference points, denoted as the origin. X-axis direction point Y-axis direction point , Select the stable feature point closest to the cable's starting end as the origin of the texture coordinate system, with the X-axis along the cable axis. point to , for Adjacent feature points along the cable axis, with the Y-axis along the tangent to the cable circumference. point to , for Adjacent feature points perpendicular to the X-axis direction.

[0035] The affine transformation matrix is ​​established by fitting the correspondence between the pixel coordinates and physical coordinates of the reference point using the least squares method. This achieves the conversion from pixel coordinates to texture physical coordinates. The physical scale of the Y-axis is calibrated using the known diameter D of the cable, and the physical scale of the X-axis is calibrated using the known standard length of the laser projection, ensuring the absolute accuracy of the coordinate system.

[0036] Specifically, images of the cable surface are acquired using an industrial camera, and the sheath texture is identified using a convolutional neural network (CNN). The identified texture features of the cable sheath surface serve as a positioning reference. The deviation between the laser projection position and the cutting target position is defined. The specific process is as follows: in, This indicates the deviation between the laser projection position and the target position; Indicates the positional deviation along the cable axis; Indicates the positional deviation perpendicular to the cable axis; This indicates the angular deviation of the projected line relative to the target cutting line.

[0037] After industrial cameras acquire images, the texture of the cable sheath is identified using convolutional neural networks such as CNNs, and the texture feature point set of the cable surface is extracted. With pre-stored standard texture templates Perform a matching process. Calculate the adjusted deviation by minimizing the matching error. The process is as follows: in, Represents the rotation matrix; Represents the first in the standard texture template Reference coordinates of each feature point; Indicates the total number of feature points in the standard texture template; Indicates the positional deviation along the cable axis; Indicates the positional deviation perpendicular to the cable axis; This indicates the angular deviation of the projected line relative to the target cutting line.

[0038] Based on the calculated adjusted deviation Real-time error compensation is performed, and the correction value is fed back to the laser projection mechanism to adjust the laser projection position for precise positioning. The corrected actual projection position. With the target location satisfy: in, Represents the compensation vector, taking This achieves closed-loop error elimination.

[0039] Specifically Indicates the first The axial deviation corrected by laser projection during the second cut directly affects the accuracy of the cut length; Indicates the first The lateral deviation corrected by laser projection during the second cut affects the port flatness index. It will have an impact; Indicates the first The angular deviation between the laser projection marking line and the cable axis during the second cutting affects the port flatness index. It has an impact.

[0040] S3. Combine cable-related data to obtain adaptive clamping force, and obtain the relative deviation of clamping force based on the actual clamping force. If the clamping force deviation threshold is exceeded, the pressure is fine-tuned according to the fine-tuning control quantity and the working condition warning is triggered.

[0041] In this embodiment of the invention, the clamping force is adaptively matched during the cutting process based on the characteristics of the cable cross-sectional area, adapting to the clamping operation requirements of all cable specifications.

[0042] Define the cable cross-sectional area as (mm) 2 The hardness coefficient of the cable sheath material is The cutting speed coefficient is determined by the material type; for example, PVC uses 1.0, XLPE uses 1.2, and rubber uses 0.8. The adaptive clamping force is determined by the current feed speed of the cutting equipment, and is taken as 1.0 below the base speed. The preset reference cross-sectional area is The specific process is as follows: in, This indicates the basic clamping force, which varies depending on the equipment model. Indicates the cross-sectional area of ​​the cable conductor; Indicates the hardness coefficient of the sheath material; Indicates the cutting speed coefficient; The cross-sectional area contribution coefficient represents the slope of the static clamping force that needs to be added due to the increase in the cross-sectional area of ​​the cable. This represents the hardness compensation coefficient, which indicates the sensitivity of the sheath material's hardness to the clamping force and reflects the additional pressure required due to the difference in hardness. The dynamic impact coefficient represents the degree of dynamic disturbance of the cutting speed to the clamping force, reflecting the destructive force of vibration and impact on cable stability during high-speed cutting. , , After normalization, the unit is unified as Newton (N).

[0043] Furthermore, to monitor the quality of the compaction operation in real time, a relative deviation of the compaction force is defined. The specific process for using this as a quality assessment indicator is as follows: in, This indicates the actual clamping force value collected in real time by the pressure sensor.

[0044] Set clamping force deviation threshold (Usually 5%-10%). When At that time, the equipment continued to operate normally; when If the clamping condition is found to be abnormal, the PLC will immediately start the automatic fine-tuning cylinder to adjust the clamping force and trigger the condition warning to ensure the stability of the clamping process quality.

[0045] Specifically, the fine-tuning control quantity of the PLC A proportional-integral control law is used to ensure that the clamping force matches the cable cross-section, preventing damage to the cable. The specific process is as follows: in, This represents the fine-tuning control quantity of the output, which is the increment of the clamping force that the PLC needs to output to the proportional valve of the cylinder at time t. Indicates the proportional gain coefficient; This indicates the current deviation in clamping force. ; It is an integral variable; Indicates the current time; This represents the integral gain coefficient.

[0046] In this embodiment of the invention, the total depth of the progressive trimming is satisfied, as shown in the following process: in, Indicates the first Single cut depth; This indicates the number of steps in the progressive cutting process. These correspond to pre-cutting, main cutting, and final cutting, respectively.

[0047] Furthermore, if during the cutting process, when the cable cross-sectional area... mm 2 At this stage, it is considered a large-section cable, and it is generally cut in three progressive steps to avoid cable deformation. The process is as follows: The first cut is a pre-cut: cutting depth. ,in, The total thickness of the cable sheath, its main function is to form the cutting guide groove; The second cut is the main cut: cutting depth. The guide groove is further deepened. The third cut is the final cut: cutting depth. Completely cut the sheath.

[0048] Interval between each cut This releases the stress on the cable material and prevents cable deformation caused by a single deep cut.

[0049] S4. After the cable is cut, the actual length of the remaining material is estimated for each section. The actual utilization rate of the remaining material is obtained by combining the flatness of the port and the axial deviation during cutting through the value evaluation of the remaining material. The remaining material is classified according to the actual utilization rate of the remaining material, and the remaining material other than waste material is put back into the warehouse.

[0050] In this embodiment of the invention, after cutting, the remaining material for each segment is estimated. The definition of the first... The actual length of the leftover material after cutting the raw material is The specific process is as follows: in, Indicates the first The length of the raw material before cutting; Indicates the first The required length for each order; This indicates that the actual cutting process starts from the first... Cut the first piece of raw material The actual quantity required for each order.

[0051] The value of the leftover material is evaluated based on factors such as its actual length, axial deviation during cutting, and the flatness of the cut ends. The process is as follows: And, when hour, This means that if the remaining material just meets the benchmark requirement within the measurement accuracy range, that is, when it is equal to the standard reusable length threshold, the full length base score is given. From the above, it can be seen that when When the actual effective length, i.e. the net length after deducting the cumulative positioning deviation, is greater than the standard reusable length threshold, the value of the surplus material is increased. when When the actual effective length is less than the standard reusable length threshold, the value of the surplus material is deducted.

[0052] in, This represents the actual comprehensive utilization rate of surplus materials, which is the [number]th [unit]. The actual utilization rate of leftover materials after the second cutting Indicates the total number of cuts; This represents the quality weighting coefficient, indicating the port flatness. The importance proportion in the evaluation of the comprehensive utilization rate of waste materials; The length weighting coefficient represents the importance of the effective length utilization rate in the evaluation of the comprehensive utilization rate of scrap materials. Indicates the first The port flatness index after the second cut is measured by a visual or laser sensor; This indicates the set standard reusable length threshold, which is the minimum standard cut length of cables for this purpose in historical orders. For example, communication cables are often set to 50 meters. Indicates the actual length of the remaining material; Indicates the preceding The cumulative sum of positioning deviations during each cut; Indicates the first The axial deviation corrected by laser projection during the second cut.

[0053] if Minimum available length threshold Then directly order , marked as waste.

[0054] Combined with the actual comprehensive utilization rate of surplus materials The process of classifying the surplus materials into grades is as follows: if If so, it means that the scrap material is rated as Grade I, which is a high-quality scrap material; if If so, it means that the remaining material is rated as Grade II, which is good quality; if If so, it means that the leftover material is rated as Grade III, which is a general product and can be reused. if If so, it means that the remaining material is rated as Grade IV, which is waste material; in, This represents the first threshold for the comprehensive utilization rate of surplus materials; This represents the second threshold for the comprehensive utilization rate of surplus materials; This represents the third threshold for the comprehensive utilization rate of surplus materials.

[0055] Then, the usable surplus materials are put back into storage. Furthermore, the set of surplus materials put back into storage is defined as... .

[0056] In this embodiment of the invention, a QR code label is printed synchronously behind the cutting position. The label information includes: the leftover material number. Source raw material number Raw material specifications Actual length of surplus material The label includes information such as the cutting date and time, and the grade of the leftover material. The QR code label records the complete link between the leftover material and the original raw material, ensuring traceability of the leftover material.

[0057] Add the available surplus materials as new available inventory entries to the raw materials inventory list and update the inventory database: At the same time, calculate the actual losses generated by this batch of cable cutting. The specific process is as follows: in, This represents a collection of surplus materials that have been re-entered into the warehouse. Indicates the number of items re-entered into the warehouse. The actual length of the remaining material; Indicates the first The actual length of the leftover material after cutting the raw material.

[0058] Preferably, the system will periodically re-inspect the quality of the Grade I, Grade II, and Grade III surplus materials that have been put into storage. When the flatness index of the end of a surplus material is found to be lower than the usable threshold, the system will automatically trigger the end-re-processing process.

[0059] Specifically, the system sacrifices a small number of defective ends to improve the grade and restore the usability of the remaining material. First, based on the difference between the current flatness and the standard, the system calculates the length of the end to be removed. The worse the flatness, the larger the amount to remove, while ensuring that the remaining length still has recycling value. After trimming, the length of the remaining material is shortened accordingly, the flatness of the end is restored to above the acceptable standard by default, and the accumulated trimming deviation is updated accordingly.

[0060] Subsequently, the system re-evaluates the value of the remaining materials by processing the new parameters of the repaired cable, calculates the new comprehensive utilization rate of the remaining materials, and performs a secondary classification of the remaining materials accordingly. If the repaired cable meets the Class I, Class II, or Class III standards, the QR code label and inventory database are updated, and the cable is re-entered into the warehouse; if the repaired cable still does not meet the standards or the remaining length is too short, it is directly classified as waste.

[0061] At the same time, economic verification will be carried out manually. The repair operation will only be performed when the value increase brought by the repair exceeds the processing cost; otherwise, the item will be scrapped directly.

[0062] This embodiment provides a smart cable cutting and scrap management system for intelligent fusion warehouses, including the following: The trimming optimization scheduling and planning module receives several cable demand orders and inventory raw material information from the warehouse, extracts order and cable-related data, calculates the comprehensive priority weight of the orders, sorts the orders, and uses the raw materials with the best length matching to trim and generate an initial population. The order of the cable raw material numbers is used as the chromosome code of the individual. The search space is compressed by greedy initialization, global exploration is carried out using standard genetic operators, and local search is carried out through local refinement operators to generate the optimal trimming model and obtain the trimming allocation scheme. The visual positioning and deviation detection module combines the obtained cutting allocation scheme and obtains the laser projection position and cable surface image of the cable to be cut. It identifies the texture features of the cable sheath surface, locates the cutting target position, obtains the position deviation, and obtains the adjusted deviation after matching with the standard texture template. This includes the axial deviation, the lateral deviation, and the angular deviation during cutting. The adaptive clamping force control and early warning module combines cable-related data to obtain adaptive clamping force and calculates the relative deviation of clamping force based on the actual clamping force. If the clamping force deviation threshold is exceeded, the pressure is fine-tuned according to the fine-tuning control quantity and an operating condition early warning is triggered. The module for assessing and restocking surplus materials estimates the actual length of each remaining section after cable cutting. It then uses the flatness of the port and the axial deviation during cutting to evaluate the value of the surplus materials and obtain the actual comprehensive utilization rate. Based on the actual comprehensive utilization rate, the surplus materials are classified into grades, and all surplus materials except for waste materials are restocked.

[0063] Reference Appendix Figure 1 Schematic diagram of cable cutting equipment; Reference Appendix Figure 2 This is a schematic diagram of the interface of the intelligent cable cutting and scrap management system for intelligent integrated warehouses.

Claims

1. A method for intelligent cable cutting and waste material management in an intelligent integrated warehouse, characterized in that, Includes the following steps: S1. Receive several cable demand orders and inventory raw material information in the warehouse, extract order and cable related data, calculate the comprehensive priority weight of the orders, sort the orders and use the raw materials with the best length to cut and generate an initial population, use the arrangement order of the cable raw material numbers as the chromosome code of the individual, use a greedy algorithm to initialize and compress the search space, use a standard genetic operator for global exploration, and then use a local refinement operator for local search to generate the optimal cutting model and obtain the cutting allocation scheme; S2. Combine the obtained cutting allocation scheme, and obtain the laser projection position and cable surface image of the cable surface to be cut, identify the texture features of the cable sheath surface, locate the cutting target position, obtain the position deviation, and obtain the adjusted deviation after matching with the standard texture template, including the axial deviation, the lateral deviation, and the angle deviation during cutting. S3. Combine cable-related data to obtain adaptive clamping force, and obtain the relative deviation of clamping force based on the actual clamping force. If the clamping force deviation threshold is exceeded, the pressure is finely adjusted according to the fine-tuning control quantity and the working condition warning is triggered. S4. After the cable is cut, the actual length of the remaining material is estimated for each section. The actual utilization rate of the remaining material is obtained by combining the flatness of the port and the axial deviation during cutting through the value evaluation of the remaining material. The remaining material is classified according to the actual utilization rate of the remaining material, and the remaining material other than waste material is put back into the warehouse.

2. The intelligent cable cutting and waste material management method for intelligent fusion warehouses according to claim 1, characterized in that, The process of obtaining the adjusted deviation after matching with the standard texture template is as follows: After identifying the sheath texture using a neural network, the texture feature point set on the cable surface is extracted and matched with a pre-stored standard texture template. The adjusted deviation is calculated by minimizing the matching error.

3. The intelligent cable cutting and waste material management method for intelligent integrated warehouses according to claim 1, characterized in that, The process of obtaining the relative deviation of the clamping force based on the actual clamping force is as follows: in, Indicates the relative deviation of the clamping force; This indicates the actual clamping force value collected in real time by the pressure sensor; This indicates adaptive clamping force.

4. The intelligent cable cutting and waste material management method for intelligent fusion warehouses according to claim 1, characterized in that, After the cable is cut, the actual length of the remaining material for each section is estimated, as follows: in, Indicates the first The actual length of the leftover material after cutting the raw material; Indicates the first The length of the raw material before cutting; Indicates the first The required length for each order; This indicates that the actual cutting process starts from the first... Cut the first piece of raw material The actual quantity required for each order.

5. The intelligent cable cutting and waste material management method for intelligent fusion warehouses according to claim 3, characterized in that, The relative deviation of the clamping force The process is as follows: Set clamping force deviation threshold ,when At that time, the equipment continued to operate normally; when If the clamping condition is deemed abnormal, the PLC will immediately activate the automatic fine-tuning cylinder to adjust the clamping force and trigger a condition warning.

6. The intelligent cable cutting and waste material management method for intelligent fusion warehouses according to claim 1, characterized in that, The process for fine-tuning the control quantity is as follows: in, This indicates the fine-tuning control value of the output; Indicates the proportional gain coefficient; This indicates the current deviation in clamping force. It is an integral variable; Indicates the current time; This represents the integral gain coefficient.

7. The intelligent cable cutting and waste material management method for intelligent fusion warehouses according to claim 1, characterized in that, The process of generating the optimal cropping model is as follows: The iteration termination condition is defined as reaching the maximum number of generations or the convergence of the population fitness variance, outputting the optimal cutting model, and obtaining the cutting and layout plan, which includes the cutting allocation scheme and expected loss of each raw material.

8. The intelligent cable cutting and waste material management method for intelligent fusion warehouses according to claim 7, characterized in that, The expected loss The process is as follows: in, Indicates the first The length of the raw material, This indicates the total number of raw materials available for cutting in the warehouse. This represents the minimum total loss length under the optimal solution.

9. A cable intelligent cutting and waste management system for intelligent integrated warehouses, used to implement the cable intelligent cutting and waste management method for intelligent integrated warehouses as described in claim 1, characterized in that, Includes the following: The trimming optimization scheduling and planning module receives several cable demand orders and inventory raw material information from the warehouse, extracts order and cable-related data, calculates the comprehensive priority weight of the orders, sorts the orders, and uses the raw materials with the best length matching to trim and generate an initial population. The order of the cable raw material numbers is used as the chromosome code of the individual. The search space is compressed by greedy initialization, global exploration is carried out using standard genetic operators, and local search is carried out through local refinement operators to generate the optimal trimming model and obtain the trimming allocation scheme. The visual positioning and deviation detection module combines the obtained cutting allocation scheme and obtains the laser projection position and cable surface image of the cable to be cut. It identifies the texture features of the cable sheath surface, locates the cutting target position, obtains the position deviation, and obtains the adjusted deviation after matching with the standard texture template. This includes the axial deviation, the lateral deviation, and the angular deviation during cutting. The adaptive clamping force control and early warning module combines cable-related data to obtain adaptive clamping force and calculates the relative deviation of clamping force based on the actual clamping force. If the clamping force deviation threshold is exceeded, the pressure is fine-tuned according to the fine-tuning control quantity and an operating condition early warning is triggered. The module for assessing and restocking surplus materials estimates the actual length of each remaining section after cable cutting. It then uses the flatness of the port and the axial deviation during cutting to evaluate the value of the surplus materials and obtain the actual comprehensive utilization rate. Based on the actual comprehensive utilization rate, the surplus materials are classified into grades, and all surplus materials except for waste materials are restocked.

10. The intelligent cable cutting and scrap management system for intelligent fusion warehouses according to claim 9, characterized in that, The minimum error matching positioning module in the visual positioning and deviation detection module identifies the sheath texture through a neural network, extracts the texture feature point set on the cable surface, matches it with a pre-stored standard texture template, and calculates the adjusted deviation by minimizing the matching error.

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