A method and system for measuring the remaining amount of a cable reel

Abstract

The present application discloses a method and system for measuring the remaining amount of a cable reel. The method includes obtaining measurement data captured by a plurality of laser range sensors provided at preset positions of a target cable reel, calculating the difference between the measurement data of the laser range sensors on both sides of each position, performing error analysis based on the difference, and correcting the measurement data based on the error analysis result to obtain a first measurement value; calculating the cable thickness at each position according to the first measurement value, and calculating a first remaining amount value of the target cable reel according to the cable thickness; obtaining a second measurement value captured by an angular velocity sensor provided on the target cable reel, and calculating a second remaining amount value of the target cable reel according to the second measurement value; establishing a multi-dimensional decision model based on the rotation state of the target cable reel, and inputting the first remaining amount value and the second remaining amount value into the multi-dimensional decision model to obtain the remaining amount value of the target cable reel. The method of the present application realizes real-time and accurate measurement of the remaining amount of the cable reel.

Description

Technical Field

[0001] The present application relates to the technical field of cable management, and in particular to a cable reel remainder measurement method and system. Background Art

[0002] Cable reels are widely used in industrial production, power transmission, construction and other fields to store and manage cables. With the popularization of cable applications, the demand for real-time monitoring of cable reel surplus is increasing.

[0003] Traditional cable drum remainder measurement methods mostly rely on manual estimation or single sensor measurement, which has problems such as inaccurate measurement, poor real-time performance, and complex operation. Taking the weighing method as an example, its implementation process requires each cable drum to be equipped with a specially customized rack, which not only increases the equipment cost and floor space, but is also extremely inconvenient in operation. Each measurement requires cumbersome lifting or handling operations, which seriously affects the measurement efficiency.

[0004] It can be seen that how to optimize the existing cable drum remainder measurement method to improve the efficiency of cable drum remainder measurement has become a technical problem that needs to be solved urgently by those skilled in the art. Summary of the invention

[0005] The present application provides a cable reel remainder measurement method and system to solve the technical problem of how to improve the efficiency of cable reel remainder measurement.

[0006] In order to solve the above technical problems, an embodiment of the present application provides a cable drum remainder measurement method, which includes:

[0007] Acquiring measurement data captured by a plurality of laser distance measuring sensors disposed at preset positions of a target cable drum, wherein the laser distance measuring sensors are symmetrically arranged at preset angles on both sides of the outer edges of the target cable drum;

[0008] Calculating the difference of the measurement data of the laser ranging sensors on both sides of each position, performing error analysis according to the difference, and correcting the measurement data based on the error analysis result to obtain a first measurement value;

[0009] Calculating the cable thickness at each position according to the first measurement value, and calculating the first margin value of the target cable drum according to the cable thickness;

[0010] Acquire a second measurement value captured by an angular velocity sensor disposed on the target cable drum, and calculate a second margin value of the target cable drum according to the second measurement value;

[0011] A multi-dimensional decision model is established based on the rotation state of the target cable drum, and the first margin value and the second margin value are input into the multi-dimensional decision model to obtain the margin value of the target cable drum.

[0012] As one of the preferred solutions, performing error analysis based on the difference, and correcting the measurement data based on the error analysis result to obtain a first measurement value, includes:

[0013] Determining the upper limit value of the fluctuation range of the difference between the measurement data on both sides of the target cable reel under normal conditions as the error threshold;

[0014] Performing error analysis on the difference according to the error threshold. If the difference is less than the error threshold, determining the average value of the measurement data of the two laser distance sensors as the first measurement value of the position;

[0015] If the difference is greater than the error threshold, inputting the measurement data of the two laser distance sensors into a pre-constructed eccentricity correction model for correction, and calculating the first measurement value of the position according to the correction result.

[0016] As one of the preferred solutions, calculating the cable thickness at each position according to the first measurement value, and calculating the first margin value of the target cable reel according to the cable thickness, includes:

[0017] Calculating the cable thickness at each position according to the first measurement value, expressed as:

[0018]

[0019] Wherein, represents the cable thickness at each position, represents the total outer diameter of the target cable reel, represents the inner diameter of the target cable reel, is the first measurement value, is the angle corresponding to the position of the first measurement value;

[0020] Calculating the cable length of the target cable reel according to the cable thickness, expressed as:

[0021]

[0022]

[0023]

[0024]

[0025] Wherein, represents the total cable length, represents the detection interval formed by two adjacent positions, represents the cable length within the detection interval, Indicates the number of turns of the cable in each detection interval. Indicates the number of layers of the cable in each detection interval. Indicates the cable diameter;

[0026] Determine the first margin of the target cable reel according to the cable length.

[0027] As one of the preferred solutions, before calculating the cable thickness at each position according to the first measurement value, it further includes:

[0028] Judge the first measurement value at each position. If the horizontal distance calculated according to the first measurement value is greater than the width of the target cable reel, the first measurement value is an invalid measurement value.

[0029] As one of the preferred solutions, calculating the second margin value of the target cable reel according to the second measurement value includes:

[0030]

[0031]

[0032]

[0033]

[0034]

[0035] Wherein, Indicates the second margin of the target cable reel, Is the cumulative angle, Is the rotation time, Is the angular velocity, Is the number of coil layers released, Is the total number of coil layers released, Is the current layer number, Indicates the cable diameter, Indicates the total number of cable layers, Indicates the width of the target cable reel, Is the number of remaining non-uncoiled turns in the currently released layer.

[0036] As one of the preferred solutions, establishing a multi-dimensional decision model based on the rotation state of the target cable reel, and inputting the first margin value and the second margin value into the multi-dimensional decision model to obtain the margin value of the target cable reel, includes:

[0037] Construct an initial multi-dimensional decision model based on a multi-layer perceptron neural network;

[0038] Obtain the historical measurement data of the target cable reel, where the historical measurement data includes the rotation state data of the target cable reel, the first historical remaining amount data obtained based on the measurement data of the bilateral laser range sensors, and the second historical remaining amount data obtained based on the measurement data of the angular velocity sensors;

[0039] Train the initial multi-dimensional decision model according to the historical measurement data to obtain a trained multi-dimensional decision model;

[0040] During the actual measurement process, input the obtained rotation state data, the first remaining amount value, and the second remaining amount value of the target cable reel into the multi-dimensional decision model to obtain the remaining amount value of the target cable reel.

[0041] As one preferred solution, after obtaining the remaining amount value of the target cable reel, it further includes:

[0042] Obtain the historical measurement data of the target cable reel, where the historical measurement data includes remaining amount data and project data;

[0043] Construct an initial remaining amount prediction model based on a long short-term memory network, input the historical measurement data into the initial remaining amount prediction model for training, and during the training process, optimize the parameters of the initial remaining amount prediction model through the backpropagation algorithm to obtain a trained remaining amount prediction model;

[0044] Input the obtained remaining amount value and the current project data in real time into the remaining amount prediction model to obtain the remaining amount change trend of the target cable reel;

[0045] Generate an adjustment strategy for the target cable reel based on the remaining amount change trend.

[0046] Another embodiment of the present application provides a cable reel remaining amount measurement system, which is applied to the cable reel remaining amount measurement method as described above. The cable reel remaining amount measurement system includes:

[0047] An acquisition module, configured to acquire measurement data captured by a plurality of laser range sensors provided at preset positions of a target cable reel, where the laser range sensors are symmetrically arranged at a preset angle along the outer edges on both sides of the target cable reel;

[0048] A correction module, configured to calculate the difference between the measurement data of the laser range sensors on both sides at each position, perform error analysis based on the difference, and correct the measurement data based on the error analysis result to obtain a first measurement value;

[0049] A first measurement module, configured to calculate the cable thickness at each position according to the first measurement value, and calculate the first remaining amount value of the target cable reel according to the cable thickness;

[0050] A second measurement module, configured to obtain a second measurement value captured by an angular velocity sensor disposed on the target cable reel, and calculate a second margin value of the target cable reel according to the second measurement value;

[0051] An output module, configured to establish a multi-dimensional decision model based on the rotation state of the target cable reel, and input the first margin value and the second margin value into the multi-dimensional decision model to obtain the margin value of the target cable reel.

[0052] As one of the preferred solutions, the correction module is further configured to:

[0053] Determine the upper limit value of the fluctuation range of the difference between the measurement data on both sides of the target cable reel under normal conditions as the error threshold;

[0054] Perform error analysis on the difference according to the error threshold. If the difference is less than the error threshold, determine the average value of the measurement data of the two-sided laser range sensors as the first measurement value of the position;

[0055] If the difference is greater than the error threshold, input the measurement data of the two-sided laser range sensors into a pre-constructed eccentricity correction model for correction, and calculate the first measurement value of the position according to the correction result.

[0056] As one of the preferred solutions, the output module is further configured to:

[0057] Construct an initial multi-dimensional decision model based on a multi-layer perceptron neural network;

[0058] Obtain the historical measurement data of the target cable reel, where the historical measurement data includes the rotation state data of the target cable reel, the first historical margin data obtained based on the measurement data of the two-sided laser range sensors, and the second historical margin data obtained based on the measurement data of the angular velocity sensor;

[0059] Train the initial multi-dimensional decision model according to the historical measurement data to obtain a trained multi-dimensional decision model;

[0060] In the actual measurement process, input the rotation state data, the first margin value, and the second margin value of the obtained target cable reel into the multi-dimensional decision model to obtain the margin value of the target cable reel.

[0061] Compared with the prior art, the beneficial effects of the embodiments of the present application are at least one of the following:

[0062] (1) This application conducts error analysis and correction by calculating the difference in the measurement data of bilateral laser rangefinder sensors, which can effectively reduce measurement deviations caused by factors such as the inherent accuracy error of the sensors, installation errors, and environmental interference, and improve the accuracy of measurement.

[0063] (2) This application combines the data of laser rangefinder sensors and angular velocity sensors and conducts comprehensive processing through a multi-dimensional decision-making model, which can measure and calculate the remaining amount of the cable reel from different dimensions. It is more accurate than single-sensor measurement and can be accurate to a smaller remaining amount unit, meeting the need for precise control of the cable remaining amount. Description of the Drawings

[0064] Figure 1 is a schematic flow chart of the method for measuring the remaining amount of the cable reel in one embodiment of this application;

[0065] Figure 2 is a schematic layout diagram of the installation of sensors on the outer edge of the cable reel in one embodiment of this application;

[0066] Figure 3 is a schematic diagram of the system for measuring the remaining amount of the cable reel in one embodiment of this application;

[0067] Figure 4 is a schematic diagram of the device for measuring the remaining amount of the cable reel in one embodiment of this application. Specific Embodiments

[0068] Next, the technical solutions in the embodiments of this application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. The purpose of providing these embodiments is to make the disclosure of this application more thorough and comprehensive. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.

[0069] In the description of this application, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first", "second", "third", etc. may explicitly or implicitly include one or more of such features. In the description of this application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0070] In the description of the present application, it should be noted that unless otherwise clearly specified and defined, the terms "installation", "connection", and "coupling" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the communication inside two components. The terms "vertical", "horizontal", "left", "right", "up", "down" and similar expressions used herein are only for the purpose of illustration, rather than indicating or implying that the device or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present application. The term "and / or" used herein includes any and all combinations of one or more of the related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0071] In the description of the present application, it should be noted that unless otherwise defined, all technical and scientific terms used in the present application have the same meanings as those commonly understood by those skilled in the technical field to which the present application belongs. The terms used in the specification of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0072] An embodiment of the present application provides a method for measuring the remaining amount of a cable reel. Specifically, please refer to Figure 1 , Figure 1 which shows a schematic flowchart of the method for measuring the remaining amount of a cable reel in one of the embodiments of the present application, and it includes steps S1 - S5:

[0073] S1: Obtain the measurement data captured by a plurality of laser ranging sensors disposed at preset positions on the target cable reel, wherein the laser ranging sensors are symmetrically arranged at a preset angle on the outer edges on both sides of the target cable reel;

[0074] First, a plurality of laser ranging sensors at a preset angle are symmetrically arranged on the outer edges on both sides of the target cable reel. The arrangement is as Figure 2 shown, Figure 2 which is a schematic diagram of the installation layout of the sensors provided by the present application on the outer edge of the cable reel. Six laser ranging sensors are installed on the outer edge on the same side of the cable reel, and are respectively set at the angular positions of 0°, 15°, 30°, 45°, 60° and 75°, and the corresponding arrangement is made on the other side. Each sensor measures the distance from the surface of the cable reel, which is used to calculate the cable thickness of the cable reel and its corresponding wire releasing area. In practical applications, considering the small size of the fiber optic sensor, the error caused by the position arrangement of the fiber optic sensor is not considered, so the deployment order of the sensors is not limited.

[0075] These sensors will measure corresponding positions of the cable reel to obtain measurement data. For example, assume that a laser ranging sensor is installed at each of the symmetric positions on the left and right sides of the cable reel. At a specific moment, the data measured by the left sensor is and the data measured by the right sensor is .

[0076] S2: Calculate the difference between the measurement data of the laser ranging sensors on both sides of each position, perform error analysis based on the difference, and correct the measurement data based on the error analysis result to obtain the first measurement value;

[0077] Preferably, in an embodiment of the present application, performing error analysis based on the difference and correcting the measurement data based on the error analysis result to obtain the first measurement value includes:

[0078] Determine the upper limit value of the fluctuation range of the difference between the measurement data on both sides of the target cable reel under normal conditions as the error threshold;

[0079] Perform error analysis on the difference according to the error threshold. If the difference is less than the error threshold, determine the average value of the measurement data of the laser ranging sensors on both sides as the first measurement value of the position;

[0080] If the difference is greater than the error threshold, input the measurement data of the laser ranging sensors on both sides into a pre-constructed eccentric correction model for correction, and calculate the first measurement value of the position according to the correction result.

[0081] Among them, the error threshold is a standard for judging whether there are large errors in the measurement data. Under normal circumstances, due to the influence of factors such as the installation accuracy of the cable reel, the accuracy of the sensor itself, and environmental factors, there will be a certain difference fluctuation in the measurement data on both sides, but this fluctuation should be within a reasonable range. Determine the upper limit value of this fluctuation range as the error threshold.

[0082] In an embodiment of the present application, the error threshold can be determined through a large number of experiments and data analyses. For example, when the cable reel is operating normally and there is no obvious interference, record the measurement data of the laser ranging sensors on both sides multiple times and calculate their differences. Statistically analyze the distribution of these differences, and take the upper limit value of the fluctuation range as the error threshold.

[0083] If the difference between the measured data on both sides is less than the error threshold, it is considered at this time that the difference in the measured data on both sides is caused by normal measurement errors, and the average value of the measured data on both sides is determined as the first measured value at this position. If the calculated difference between the measured data on both sides is greater than the error threshold, it is considered at this time that there are large errors in the measured data, which may be caused by factors such as cable reel eccentricity, sensor installation deviation, or external interference. The measured data of the two laser distance sensors on both sides are input into a pre-constructed eccentricity correction model for correction.

[0084] This model is obtained by analyzing and modeling a large amount of experimental data, and can correct the errors caused by factors such as eccentricity according to the difference in the measured data on both sides. Assume that the corrected measured data is obtained after being processed by the eccentricity correction model and , then the first measured value at this position is calculated according to the correction result, and the average value method can also be used.

[0085] S3: Calculate the cable thickness at each position according to the first measured value, and calculate the first margin value of the target cable reel according to the cable thickness;

[0086] In an embodiment of the present application, calculating the cable thickness at each position according to the first measured value and calculating the first margin value of the target cable reel includes:

[0087] Calculating the cable thickness at each position according to the first measured value, which is expressed as:

[0088]

[0089] where represents the cable thickness at each position, represents the total outer diameter of the target cable reel, represents the inner diameter of the target cable reel, is the first measured value, is the angle corresponding to the position of the first measured value;

[0090] Calculating the cable length of the target cable reel according to the cable thickness, which is expressed as:

[0091]

[0092]

[0093]

[0094]

[0095] where represents the total cable length, Indicates the detection interval formed by two adjacent positions. Indicates the cable length within the detection interval. Indicates the number of cable turns in each detection interval. Indicates the number of cable layers in each detection interval. Indicates the cable diameter;

[0096] Determine the first margin of the target cable reel according to the cable length.

[0097] In an embodiment of the present application, before calculating the cable thickness at each position according to the first measurement value, it further includes:

[0098] Judge the first measurement value at each position. If the horizontal distance calculated according to the first measurement value is greater than the width of the target cable reel, the first measurement value is an invalid measurement value.

[0099] Specifically, each laser distance sensor is responsible for measuring the cable length in a specific interval on the cable reel, and this interval is defined as , .

[0100] When 6 > i > 0, the interval is defined as: , when i = 0, the lower limit of the interval = 0, and the 0th sensor measures the cable length within the range from 0 to . When the cable margin is small, it may cause the distance measured by the sensor with a large measurement angle to be the distance to the other side of the cable reel instead of the distance to the cable. At this time, the calculated measurement point is = K, and then the measurement result of this sensor is no longer considered. Correspondingly, for the sensor with a smaller angle than this sensor (for example, if the measurement result of the 6th sensor is invalid , and the measurement result of the 5th sensor is valid), then the interval responsible for measurement by this sensor is , K], that is

[0101]

[0102] When i = 6, the upper limit of the interval = K.

[0103] S4: Obtain the second measurement value captured by the angular velocity sensor provided on the target cable reel, and calculate the second margin value of the target cable reel according to the second measurement value;

[0104] In an embodiment of the present application, calculating the second margin value of the target cable reel according to the second measurement value includes:

[0105]

[0106]

[0107]

[0108]

[0109]

[0110] Among them, represents the second margin of the target cable reel, is the cumulative angle, is the rotation time, is the angular velocity, is the number of unwound coils, is the total number of unwound coils, is the current layer number, represents the cable diameter, represents the total number of cable layers, represents the width of the target cable reel, is the number of remaining unrolled turns in the currently unwound layer.

[0111] S5: Establish a multi-dimensional decision model based on the rotation state of the target cable reel, input the first margin value and the second margin value into the multi-dimensional decision model, and obtain the margin value of the target cable reel.

[0112] In an embodiment of the present application, establishing a multi-dimensional decision model based on the rotation state of the target cable reel, inputting the first margin value and the second margin value into the multi-dimensional decision model, and obtaining the margin value of the target cable reel includes:

[0113] Construct an initial multi-dimensional decision model based on a multi-layer perceptron neural network;

[0114] Obtain the historical measurement data of the target cable reel, where the historical measurement data includes the rotation state data of the target cable reel, the first historical margin data obtained based on the measurement data of the bilateral laser ranging sensor, and the second historical margin data obtained based on the measurement data of the angular velocity sensor;

[0115] Train the initial multi-dimensional decision model according to the historical measurement data to obtain a trained multi-dimensional decision model;

[0116] During the actual measurement process, input the obtained rotation state data, the first margin value, and the second margin value of the target cable reel into the multi-dimensional decision model to obtain the margin value of the target cable reel.

[0117] Among them, the Multilayer Perceptron (MLP) is a feedforward artificial neural network model, consisting of an input layer, multiple hidden layers, and an output layer. In this scenario, when constructing the initial multi-dimensional decision model, the number of neurons in the input layer depends on the number of input features, mainly including the rotation state data of the target cable reel, and the features related to the first margin value and the second margin value. The number of hidden layers and the number of neurons in each hidden layer need to be adjusted and optimized according to the actual situation to achieve better model performance. The output layer usually has one neuron, which is used to output the margin value of the target cable reel.

[0118] The historical measurement data includes the following categories:

[0119] Rotation state data: For example, information such as the rotation speed, acceleration, and rotation direction of the cable reel. These data can reflect the working state of the cable reel.

[0120] The first historical margin data: The historical margin value obtained based on the measurement data of the bilateral laser rangefinder sensor. This data is obtained through the steps of calculating the cable thickness and cable length mentioned above.

[0121] The second historical margin data: The historical margin value obtained based on the measurement data of the angular velocity sensor, which is calculated using the information captured by the angular velocity sensor.

[0122] Preprocess the obtained historical measurement data, including data cleaning (removing outliers, missing values, etc.), normalization (mapping the data to a specific range, such as [0, 1]), etc., to improve the training effect and stability of the model. Divide the preprocessed historical measurement data into a training set, a validation set, and a test set. The training set is used for parameter learning of the model, the validation set is used to adjust the hyperparameters of the model (such as the number of neurons in the hidden layer, learning rate, etc.), and the test set is used to evaluate the performance of the trained model.

[0123] Use the training set to train the initial multi-dimensional decision model. During the training process, the model will continuously adjust its own weight and bias parameters according to the input feature data and the corresponding target values (known historical margin values) to minimize the error between the predicted value and the true value. Commonly used loss functions include Mean Squared Error (MSE), etc. Through multiple iterative trainings, until the performance of the model reaches a satisfactory level, a trained multi-dimensional decision model is obtained.

[0124] During actual measurement, obtain the current rotation state data, the first margin value, and the second margin value of the target cable reel, and input these data into the trained multi-dimensional decision model. The model processes and calculates the input data according to the learned patterns and rules, and finally outputs the margin value of the target cable reel.

[0125] In one embodiment of the present application, after obtaining the remaining value of the target cable reel, it further includes:

[0126] Obtain the historical measurement data of the target cable reel, where the historical measurement data includes remaining data and project data;

[0127] Construct an initial remaining value prediction model based on the Long Short-Term Memory (LSTM) network, input the historical measurement data into the initial remaining value prediction model for training. During the training process, optimize the parameters of the initial remaining value prediction model through the backpropagation algorithm to obtain a trained remaining value prediction model;

[0128] Input the real-time obtained remaining value and the current project data into the remaining value prediction model to obtain the remaining value change trend of the target cable reel;

[0129] Generate an adjustment strategy for the target cable reel based on the remaining value change trend.

[0130] Among them, the historical measurement data includes:

[0131] Remaining data: That is, the historical remaining value of the target cable reel calculated previously.

[0132] Project data: Project information related to cable use, such as construction progress, cable use frequency, estimated usage, etc.

[0133] The Long Short-Term Memory (LSTM) network is a special recurrent neural network that can effectively process sequential data and capture long-term dependencies in the data. When constructing the initial remaining value prediction model, the input layer receives the sequence composed of the remaining data and the project data, and the hidden layer consists of multiple LSTM units, which can remember and process the historical information in the sequence. The output layer is used to predict the future remaining value.

[0134] Input the historical measurement data into the initial remaining value prediction model for training. Since LSTM processes sequential data, the data needs to be organized in chronological order. During the training process, use the backpropagation algorithm (such as Backpropagation Through Time, BPTT) to optimize the parameters of the model. Calculate the error between the predicted value and the real value, then backpropagate the error to each parameter of the model, and adjust the parameters according to the gradient information of the error to minimize the error. Continuously iterate the training until the prediction performance of the model reaches a satisfactory level to obtain a trained remaining value prediction model.

[0135] Input the real-time obtained remaining value and the current project data into the trained remaining value prediction model. Based on the patterns and rules learned from historical data, the model predicts the future remaining value, thereby obtaining the remaining value change trend of the target cable reel. According to the obtained remaining value change trend, combined with the actual project requirements and cable usage situation, generate corresponding adjustment strategies. For example, if it is predicted that the remaining value will decrease rapidly, it may be necessary to arrange for cable replenishment in a timely manner; if the remaining value remains sufficient for a long time, the cable usage plan can be appropriately adjusted, etc. The generation of adjustment strategies needs to comprehensively consider various factors to ensure that the cable supply and usage can meet the project requirements.

[0136] Another embodiment of the present application provides a cable reel remaining value measurement system. Specifically, please refer to Figure 3 , Figure 3 which shows a schematic diagram of the cable reel remaining value measurement system in one of the embodiments of the present application, and it includes:

[0137] An acquisition module 11 that acquires measurement data captured by a plurality of laser range sensors provided at preset positions of the target cable reel, wherein the laser range sensors are symmetrically arranged at a preset angle along the outer edges on both sides of the target cable reel;

[0138] A correction module 12, which is used to calculate the difference between the measurement data of the laser range sensors on both sides of each position, perform error analysis based on the difference, and correct the measurement data based on the error analysis result to obtain the first measurement value;

[0139] A first measurement module 13, which is used to calculate the cable thickness at each position according to the first measurement value, and calculate the first remaining value of the target cable reel according to the cable thickness;

[0140] A second measurement module 14, which is used to acquire a second measurement value captured by an angular velocity sensor provided on the target cable reel, and calculate the second remaining value of the target cable reel according to the second measurement value;

[0141] An output module 15, which is used to establish a multi-dimensional decision model based on the rotation state of the target cable reel, and input the first remaining value and the second remaining value into the multi-dimensional decision model to obtain the remaining value of the target cable reel.

[0142] In an embodiment of the present application, the correction module is further used to:

[0143] Determine the upper limit value of the fluctuation range of the difference between the measurement data on both sides of the target cable reel under normal conditions as the error threshold;

[0144] Perform error analysis on the difference according to the error threshold. If the difference is less than the error threshold, determine the average value of the measurement data of the laser range sensors on both sides as the first measurement value of the position;

[0145] If the difference is greater than the error threshold, the measurement data of the two-sided laser range sensors are input into a pre-constructed eccentricity correction model for correction, and a first measurement value of the position is calculated according to the correction result.

[0146] In an embodiment of the present application, the output module is further configured to:

[0147] Construct an initial multi-dimensional decision model based on a multi-layer perceptron neural network;

[0148] Obtain historical measurement data of the target cable reel, where the historical measurement data includes the rotation state data of the target cable reel, the first historical margin data obtained based on the measurement data of the two-sided laser range sensors, and the second historical margin data obtained based on the measurement data of the angular velocity sensors;

[0149] Train the initial multi-dimensional decision model according to the historical measurement data to obtain a trained multi-dimensional decision model;

[0150] During the actual measurement process, input the obtained rotation state data, the first margin value, and the second margin value of the target cable reel into the multi-dimensional decision model to obtain the margin value of the target cable reel.

[0151] Another embodiment of the present application provides a cable reel margin measurement device. Specifically, see Figure 4 , which is the structural block diagram of the cable reel margin measurement device provided by the embodiment of the present application. The cable reel margin measurement device provided by the embodiment of the present application includes a processor 21, a memory 22, and a computer program stored in the memory 22 and configured to be executed by the processor 21. When the processor 21 executes the computer program, the steps in the cable reel margin measurement method embodiment described above are implemented, such as Figure 1 the steps S1 to S5 described in; or, when the processor 21 executes the computer program, the functions of each module in the above device embodiments are implemented, such as the acquisition module 11.

[0152] Exemplarily, the computer program can be divided into one or more modules. The one or more modules are stored in the memory 22 and executed by the processor 21 to complete the present application. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the cable reel margin measurement device. For example, the computer program can be divided into an acquisition module 11, a correction module 12, a first measurement module 13, etc.

[0153] The cable reel remaining amount measuring device may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art can understand that the schematic diagram is only an example of the cable reel remaining amount measuring device, and does not constitute a limitation on the cable reel remaining amount measuring device. It may include more or fewer components than those shown in the figure, or combine certain components, or different components. For example, the cable reel remaining amount measuring device may also include an input / output device, a network access device, a bus, etc.

[0154] The processor 21 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor 21 is the control center of the cable reel remaining amount measuring device, and connects various parts of the entire cable reel remaining amount measuring device through various interfaces and lines.

[0155] The memory 22 can be used to store the computer programs and / or modules. The processor 21 realizes various functions of the cable reel remaining amount measuring device by running or executing the computer programs and / or modules stored in the memory 22, and by calling the data stored in the memory 22. The memory 22 may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area may store data created according to the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0156] Among them, if the modules integrated in the cable reel margin measuring device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present application, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0157] Those of ordinary skill in the art can understand that to implement all or part of the processes in the above-mentioned embodiment methods, it can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned method embodiments. Among them, the storage medium can be a magnetic disk, an optical disc, a read-only memory (ROM, Read-Only Memory), or a random access memory (Random Access Memory), etc.

[0158] Correspondingly, the embodiment of the present application provides a computer-readable storage medium, and the computer-readable storage medium includes a stored computer program. Among them, when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the steps in the cable reel margin measuring method in the above-mentioned embodiment, for example Figure 1 the steps S1 to S5 described above.

[0159] Compared with the prior art, the beneficial effects of the embodiment of the present application are at least one of the following:

[0160] (1) By calculating the difference between the measurement data of the bilateral laser ranging sensors, the present application conducts error analysis and correction, which can effectively reduce the measurement deviation caused by factors such as the accuracy error of the sensor itself, installation error, and environmental interference, and improve the measurement accuracy.

[0161] (2) This application combines the data of the laser ranging sensor and the angular velocity sensor, and conducts comprehensive processing through a multi-dimensional decision-making model. It can measure and calculate the remaining amount of the cable reel from different dimensions, which is more accurate than the measurement by a single sensor. It can be accurate to a smaller remaining amount unit, meeting the requirement of accurately grasping the cable remaining amount.

[0162] The above-described embodiments only represent several implementation manners of this application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of this application. It should be noted that for those of ordinary skill in the art, without departing from the concept of this application, several deformations and improvements can still be made, and these all belong to the protection scope of this application. Therefore, the protection scope of the patent of this application shall be subject to the appended claims.

Claims

1. A method for measuring the remaining amount of a cable reel, characterized in that, Including: Obtain measurement data captured by a number of laser range sensors disposed at preset positions of a target cable reel, wherein the laser range sensors are symmetrically arranged at a preset angle along the outer edges on both sides of the target cable reel; Calculate the difference between the measurement data of the laser range sensors on both sides of each position, perform error analysis based on the difference, and correct the measurement data based on the error analysis result to obtain a first measurement value; Calculate the cable thickness at each position according to the first measurement value, and calculate a first margin value of the target cable reel according to the cable thickness; Obtain a second measurement value captured by an angular velocity sensor disposed on the target cable reel, and calculate a second margin value of the target cable reel according to the second measurement value; Establish a multi-dimensional decision model based on the rotation state of the target cable reel, and input the first margin value and the second margin value into the multi-dimensional decision model to obtain the margin value of the target cable reel; The calculating the cable thickness at each position according to the first measurement value and calculating the first margin value of the target cable reel according to the cable thickness includes: Calculate the cable thickness at each position according to the first measurement value, expressed as: Among them, represents the cable thickness at each position, represents the total outer diameter of the target cable reel, represents the inner diameter of the target cable reel, is the first measurement value, is the angle corresponding to the position of the first measurement value; Calculate the cable length of the target cable reel according to the cable thickness, expressed as: Among them, represents the total cable length, represents the detection interval formed by two adjacent positions, represents the cable length within the detection interval, represents the number of cable turns in each detection interval, represents the number of cable layers in each detection interval, represents the cable diameter; Determine the first margin of the target cable reel according to the cable length; The calculating the second margin value of the target cable reel according to the second measurement value includes: Among them, represents the second margin of the target cable reel, is the cumulative angle, is the rotation time, is the angular velocity, is the number of unwound coils, is the total number of unwound coils, is the current layer number, represents the cable diameter, represents the total number of cable layers, represents the width of the target cable reel, is the number of remaining uncoiled turns in the currently unwound layer.

2. The cable reel allowance measurement method according to claim 1, wherein The performing error analysis based on the difference and correcting the measurement data based on the error analysis result to obtain a first measurement value includes: Determine the upper limit value of the fluctuation range of the difference between the measurement data on both sides of the target cable reel under normal conditions as the error threshold; Perform error analysis on the difference according to the error threshold. If the difference is less than the error threshold, determine the average value of the measurement data of the laser range sensors on both sides as the first measurement value of the position; If the difference is greater than the error threshold, input the measurement data of the laser range sensors on both sides into a pre-constructed eccentricity correction model for correction, and calculate the first measurement value of the position according to the correction result.

3. The cable reel allowance measurement method according to claim 1, wherein, Before calculating the cable thickness at each position according to the first measurement value, it further includes: Judge the first measurement value of each position. If the horizontal distance calculated according to the first measurement value is greater than the width of the target cable reel, the first measurement value is an invalid measurement value.

4. The cable reel allowance measurement method according to claim 1, characterized in that, The establishing a multi-dimensional decision model based on the rotation state of the target cable reel and inputting the first margin value and the second margin value into the multi-dimensional decision model to obtain the margin value of the target cable reel includes: Construct an initial multi-dimensional decision model based on a multi-layer perceptron neural network; Obtain the historical measurement data of the target cable reel, where the historical measurement data includes the rotation state data of the target cable reel, the first historical margin data obtained based on the measurement data of the bilateral laser range sensors, and the second historical margin data obtained based on the measurement data of the angular velocity sensor; Train the initial multi-dimensional decision model according to the historical measurement data to obtain a trained multi-dimensional decision model; During the actual measurement process, input the obtained rotational state data, the first margin value, and the second margin value of the target cable reel into the multi-dimensional decision model to obtain the margin value of the target cable reel.

5. The cable reel margin measurement method according to claim 1, characterized in that After obtaining the margin value of the target cable reel, it further includes: Obtain the historical measurement data of the target cable reel, where the historical measurement data includes margin data and project data; Construct an initial margin prediction model based on a long short-term memory network, input the historical measurement data into the initial margin prediction model for training, and during the training process, optimize the parameters of the initial margin prediction model through the backpropagation algorithm to obtain a trained margin prediction model; Input the obtained margin value and the current project data into the margin prediction model in real time to obtain the margin change trend of the target cable reel; Generate an adjustment strategy for the target cable reel based on the margin change trend.

6. A cable reel surplus measurement system, characterized in that, Apply the cable reel margin measurement method according to any one of claims 1-5, and the cable reel margin measurement system includes: An acquisition module for acquiring measurement data captured by a plurality of laser rangefinders provided at preset positions of a target cable reel, where the laser rangefinders are symmetrically arranged at a preset angle on both outer edges of the target cable reel; A correction module for calculating the difference between the measurement data of the laser rangefinders on both sides of each position, performing error analysis based on the difference, and correcting the measurement data based on the error analysis result to obtain a first measurement value; A first measurement module for calculating the cable thickness at each position according to the first measurement value and calculating the first margin value of the target cable reel according to the cable thickness; A second measurement module for obtaining a second measurement value captured by an angular velocity sensor provided on the target cable reel and calculating the second margin value of the target cable reel according to the second measurement value; An output module for establishing a multi-dimensional decision model based on the rotational state of the target cable reel, inputting the first margin value and the second margin value into the multi-dimensional decision model to obtain the margin value of the target cable reel; The calculating the cable thickness at each position according to the first measurement value and calculating the first margin value of the target cable reel according to the cable thickness includes: Calculating the cable thickness at each position according to the first measurement value, expressed as: Among them, represents the cable thickness at each position, represents the total outer diameter of the target cable reel, represents the inner diameter of the target cable reel, is the first measurement value, is the angle corresponding to the position of the first measurement value; Calculating the cable length of the target cable reel according to the cable thickness, expressed as: Among them, represents the total cable length, represents the detection interval formed by two adjacent positions, represents the cable length within the detection interval, represents the number of cable turns in each detection interval, represents the number of cable layers in each detection interval, represents the cable diameter; Determining the first margin of the target cable reel according to the cable length; The calculating the second margin value of the target cable reel according to the second measurement value includes: Among them, represents the second margin of the target cable reel, is the cumulative angle, is the rotation time, is the angular velocity, is the number of unwound coils, is the total number of unwound coils, is the current layer number, represents the cable diameter, represents the total number of cable layers, represents the width of the target cable reel, is the number of remaining uncoiled turns in the currently unwound layer.

7. The cable reel margin measurement system according to claim 6, characterized in that, The correction module is further used for: Determining the upper limit value of the fluctuation range of the difference between the measurement data on both sides of the target cable reel under normal conditions as the error threshold; Performing error analysis on the difference according to the error threshold, and if the difference is less than the error threshold, determining the average value of the measurement data of the laser rangefinders on both sides as the first measurement value of the position; If the difference is greater than the error threshold, the measurement data of the two-sided laser range sensors are input into a pre-constructed eccentricity correction model for correction, and a first measurement value of the position is calculated according to the correction result.

8. The cable reel allowance measurement system according to claim 6, characterized in that, The output module is further configured to: Construct an initial multi-dimensional decision model based on a multi-layer perceptron neural network; Obtain historical measurement data of the target cable reel, where the historical measurement data includes rotation state data of the target cable reel, first historical margin data obtained based on measurement data of bilateral laser range sensors, and second historical margin data obtained based on measurement data of angular velocity sensors; Train the initial multi-dimensional decision model according to the historical measurement data to obtain a trained multi-dimensional decision model; During the actual measurement process, input the obtained rotation state data, the first margin value, and the second margin value of the target cable reel into the multi-dimensional decision model to obtain the margin value of the target cable reel.

9. A cable reel surplus measurement device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the cable reel margin measurement method according to any one of claims 1 to 5 is implemented.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program. When the device where the computer-readable storage medium is located executes the computer program, the cable reel margin measurement method according to any one of claims 1 to 5 is implemented.

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