Cable reel margin measuring method and system

Abstract

The invention discloses a cable reel allowance measuring method and system, and the method comprises the steps: obtaining measurement data captured by a plurality of laser distance measuring sensors disposed at preset positions of a target cable reel, calculating the difference value of the measurement data of the laser distance measuring sensors at two sides of each position, carrying out the error analysis according to the difference value, and obtaining the allowance of the target cable reel. Correcting the measurement data based on the error analysis result to obtain a first measurement value; calculating the cable thickness of each position according to the first measurement value, and calculating a first margin value of the target cable reel according to the cable thickness; acquiring a second measurement value captured by an angular velocity sensor arranged on the target cable reel, and calculating a second margin value of the target cable reel according to the second measurement value; and 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. According to the method provided by the invention, the real-time and accurate measurement of the allowance of the cable reel is realized.

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: 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; 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; 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; 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; 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.

[0007] As one preferred solution, performing error analysis according to the difference, and correcting the measurement data based on the error analysis result to obtain the first measurement value includes: The upper limit value of the fluctuation range of the difference of the measured data on both sides of the target cable drum under normal circumstances is determined as the error threshold; Performing an error analysis on the difference according to the error threshold, and if the difference is less than the error threshold, determining an average value of the measurement data of the laser ranging sensors 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 laser ranging sensors on both sides are input into a pre-constructed eccentricity correction model for correction, and the first measurement value of the position is calculated according to the correction result.

[0008] As one preferred solution, the step of 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, comprises: The cable thickness at each location is calculated based on the first measurement value, expressed as: in, Indicates the cable thickness at various locations, Indicates the total outer diameter of the target cable drum, Indicates the inner diameter of the target cable drum, is the first measurement value, is the angle corresponding to the position of the first measurement value; The cable length of the target cable drum is calculated according to the cable thickness, which is expressed as: in, Indicates the total cable length, Represents the detection interval formed by two adjacent positions, Indicates the cable length within the detection range, Indicates the number of cable turns in each detection interval, Indicates the number of cable layers in each detection interval, Indicates the cable diameter; A first margin of the target cable drum is determined according to the cable length.

[0009] As one preferred solution, before calculating the cable thickness at each position according to the first measurement value, the method further includes: The first measurement value of each position is judged. If the horizontal distance calculated according to the first measurement value is greater than the width of the target cable drum, the first measurement value is an invalid measurement value.

[0010] As one preferred solution, the step of calculating the second margin value of the target cable drum according to the second measured value includes: in, Indicates the second margin of the target cable drum, is the cumulative angle, is the rotation time, is the angular velocity, is the number of coils, is the total number of coils, is the current layer number, Indicates the cable diameter, Indicates the total number of cable layers. Indicates the width of the target cable drum, It is the number of uncircled circles remaining in the current circle-circled layer.

[0011] As one of the preferred solutions, the 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, including: Construct an initial multi-dimensional decision model based on a multi-layer perceptron neural network; Acquire historical measurement data of the target cable drum, the historical measurement data including rotation state data of the target cable drum, first historical margin data obtained based on measurement data of a double-sided laser ranging sensor, and second historical margin data obtained based on measurement data of an angular velocity sensor; Training the initial multi-dimensional decision model according to the historical measurement data to obtain a trained multi-dimensional decision model; In an actual measurement process, the acquired rotation state data of the target cable drum, 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 preferred solution, after obtaining the remaining value of the target cable drum, the method further includes: Acquire historical measurement data of the target cable drum, wherein the historical measurement data includes margin data and project data; Constructing an initial margin prediction model based on a long short-term memory network, inputting the historical measurement data into the initial margin prediction model for training, and during the training process, optimizing the parameters of the initial margin prediction model by a back propagation algorithm to obtain a trained margin prediction model; Inputting the surplus value obtained in real time and current project data into the surplus prediction model to obtain the surplus change trend of the target cable drum; An adjustment strategy for the target cable drum is generated based on the margin variation trend.

[0013] Another embodiment of the present application provides a cable drum remainder measurement system, which is applied to the cable drum remainder measurement method as described above, and the cable drum remainder measurement system includes: An acquisition module, used to acquire measurement data captured by a plurality of laser distance measuring sensors arranged 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; A correction module, used for 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; A first measuring module, configured to calculate the cable thickness at each position according to the first measurement value, and calculate a first margin value of the target cable drum according to the cable thickness; A second measurement module, configured to obtain 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; An output module is used to establish a multi-dimensional decision model based on the rotation state of the target cable drum, 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 drum.

[0014] As one preferred solution, the correction module is further used for: The upper limit value of the fluctuation range of the difference of the measured data on both sides of the target cable drum under normal circumstances is determined as the error threshold; Performing an error analysis on the difference according to the error threshold, and if the difference is less than the error threshold, determining an average value of the measurement data of the laser ranging sensors 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 laser ranging sensors on both sides are input into a pre-constructed eccentricity correction model for correction, and the first measurement value of the position is calculated according to the correction result.

[0015] As one preferred solution, the output module is further used for: Construct an initial multi-dimensional decision model based on a multi-layer perceptron neural network; Acquire historical measurement data of the target cable drum, the historical measurement data including rotation state data of the target cable drum, first historical margin data obtained based on measurement data of a double-sided laser ranging sensor, and second historical margin data obtained based on measurement data of an angular velocity sensor; Training the initial multi-dimensional decision model according to the historical measurement data to obtain a trained multi-dimensional decision model; In an actual measurement process, the acquired rotation state data of the target cable drum, 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.

[0016] Compared with the prior art, the beneficial effects of the embodiments of the present application are at least one of the following: (1) This application calculates the difference in measurement data from the double-sided laser ranging sensor to perform error analysis and correction, which can effectively reduce measurement deviations caused by factors such as the sensor's own accuracy error, installation error, and environmental interference, thereby improving measurement accuracy.

[0017] (2) This application combines the data from the laser ranging sensor and the angular velocity sensor, and performs comprehensive processing through a multi-dimensional decision model. It can measure and calculate the cable reel surplus from different dimensions. It is more accurate than the measurement of a single sensor and can be accurate to a smaller surplus unit, meeting the demand for accurate control of the cable surplus. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic flow chart of a cable drum remainder measurement method in one embodiment of the present application; Figure 2 This is a schematic diagram of the installation layout of the sensor on the outer edge of the cable drum in one embodiment of the present application; Figure 3 is a schematic diagram of a cable drum remainder measurement system in one embodiment of the present application; Figure 4 It is a schematic diagram of a cable drum remainder measuring device in one embodiment of the present application. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. The purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0020] In the description of this application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0021] In the description of the present application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" 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 it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used in this article are only for illustrative purposes, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on this application. The term "and / or" used in this article includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0022] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this application have the same meaning as those commonly understood by those skilled in the art. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood by specific circumstances.

[0023] An embodiment of the present application provides a cable drum remainder measurement method. For details, see Figure 1 , Figure 1 The figure shows a schematic flow chart of a cable drum remainder measurement method in one embodiment of the present application, which includes steps S1-S5: S1: obtaining 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; First, a number of laser distance measuring sensors with preset angles are symmetrically arranged on the outer edges of both sides of the target cable drum. Figure 2 As shown, Figure 2 This is a schematic diagram of the installation layout of the sensors provided in this application on the outer edge of the cable drum. Six laser ranging sensors are installed on the outer edge of the same side of the cable drum, respectively set at angular positions of 0 degrees, 15 degrees, 30 degrees, 45 degrees, 60 degrees and 75 degrees, and the other side is arranged accordingly. Each sensor measures the distance to the surface of the cable drum, which is used to calculate the cable thickness of the cable drum and its corresponding laying area. In practical applications, considering the small size of the optical fiber sensor, the error caused by the position arrangement of the optical fiber sensor is not considered, so the sensor deployment order is not restricted.

[0024] These sensors measure the corresponding positions of the cable drum and obtain measurement data. For example, suppose a laser ranging sensor is installed symmetrically on the left and right sides of the cable drum. At a specific moment, the data measured by the left sensor is , the data measured by the sensor on the right is .

[0025] S2: Calculate the difference of the measurement data of the laser ranging sensors on both sides of each position, perform error analysis according to the difference, and correct the measurement data based on the error analysis result to obtain a first measurement value; Preferably, in one embodiment of the present application, error analysis is performed according to the difference, and the measurement data is corrected based on the error analysis result to obtain the first measurement value, including: The upper limit value of the fluctuation range of the difference of the measured data on both sides of the target cable drum under normal circumstances is determined as the error threshold; 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 laser ranging sensors 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 laser ranging sensors on both sides are input into the pre-built eccentricity correction model for correction, and the first measurement value of the position is calculated according to the correction result.

[0026] The error threshold is a standard for judging whether the measured data has a large error. Under normal circumstances, due to the installation accuracy of the cable drum, the accuracy of the sensor itself, and environmental factors, there will be a certain difference fluctuation in the measured data on both sides, but this fluctuation should be within a reasonable range. The upper limit of this fluctuation range is determined as the error threshold.

[0027] In one embodiment of the present application, the error threshold can be determined through a large number of experiments and data analysis. For example, when the cable drum is operating normally and there is no obvious interference, the measurement data of the laser ranging sensors on both sides are recorded multiple times, and their differences are calculated. The distribution of these differences is statistically analyzed, and the upper limit of the fluctuation range is taken as the error threshold.

[0028] If the calculated difference between the measurement data on both sides is less than the error threshold, it is considered that the difference between the measurement data on both sides is caused by normal measurement errors, and the average value of the measurement data on both sides is determined as the first measurement value of the position. If the calculated difference between the measurement data on both sides is greater than the error threshold, it is considered that there is a large error in the measurement data, which may be caused by cable drum eccentricity, sensor installation deviation or external interference. The measurement data of the laser ranging sensors on both sides are input into the pre-built eccentricity correction model for correction.

[0029] This model is obtained by analyzing and modeling a large amount of experimental data. It can correct the error 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 measurement value of the position is calculated according to the correction result, and the average value method can also be used.

[0030] S3: Calculate the cable thickness at each position according to the first measurement value, and calculate the first margin value of the target cable drum according to the cable thickness; In one embodiment of the present application, 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, includes: The cable thickness at each location is calculated based on the first measurement value and is expressed as: in, Indicates the cable thickness at various locations, Indicates the total outer diameter of the target cable drum, Indicates the inner diameter of the target cable drum, 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 drum according to the cable thickness, expressed as: in, Indicates the total cable length, Represents the detection interval formed by two adjacent positions, Indicates the cable length within the detection range, Indicates the number of cable turns in each detection interval, Indicates the number of cable layers in each detection interval, Indicates the cable diameter; A first margin of the target cable drum is determined based on the cable length.

[0031] In one embodiment of the present application, before calculating the cable thickness at each position according to the first measurement value, the method further includes: The first measurement value of each position is judged. If the horizontal distance calculated according to the first measurement value is greater than the width of the target cable drum, the first measurement value is an invalid measurement value.

[0032] Specifically, each laser ranging sensor is responsible for measuring the cable length in a specific interval on the cable drum, and the interval is defined as [ , ].

[0033] When 6>i>0, the interval is defined as: , when i=0, the lower limit of the interval =0, the 0th sensor measures 0 to When the cable margin is small, the sensor with a large measurement angle may measure the distance to the cable drum on the other side instead of the distance to the cable. In this case, the measured point is calculated as =K, the measurement result of this sensor is no longer considered. Correspondingly, the sensor with a smaller angle than this sensor (for example, if the measurement result of sensor No. 6 is invalid , and the measurement result of sensor 5 is valid), then the interval that this sensor is responsible for measuring is [ , K], that is When i=6, the upper limit of the interval =K.

[0034] S4: acquiring a second measurement value captured by an angular velocity sensor disposed on the target cable drum, and calculating a second margin value of the target cable drum according to the second measurement value; In one embodiment of the present application, calculating the second margin value of the target cable drum according to the second measurement value includes: in, Indicates the second margin of the target cable drum, is the cumulative angle, is the rotation time, is the angular velocity, is the number of coils, is the total number of coils, is the current layer number, Indicates the cable diameter, Indicates the total number of cable layers. Indicates the width of the target cable drum, It is the number of uncircled circles remaining in the current circle-circled layer.

[0035] S5: establishing a multi-dimensional decision model based on the rotation state of the target cable drum, 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 drum.

[0036] In one embodiment of the present application, 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, including: Construct an initial multi-dimensional decision model based on a multi-layer perceptron neural network; Acquire historical measurement data of the target cable drum, the historical measurement data including rotation state data of the target cable drum, first historical margin data obtained based on measurement data of the double-sided laser ranging sensor, and second historical margin data obtained based on measurement data of the angular velocity sensor; The initial multi-dimensional decision model is trained according to the historical measurement data to obtain a trained multi-dimensional decision model; In an actual measurement process, the acquired rotation state data, the first margin value, and the second margin value of the target cable drum are input into a multi-dimensional decision model to obtain the margin value of the target cable drum.

[0037] Among them, the Multilayer Perceptron (MLP) is a feedforward artificial neural network model, which consists 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, which mainly include the rotation state data of the target cable drum, the first margin value, and the second margin value related features. 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 is usually one neuron, which is used to output the margin value of the target cable drum.

[0038] Historical measurement data includes the following categories: Rotation status data: such as the cable drum’s rotation speed, acceleration, rotation direction and other information. These data can reflect the working status of the cable drum.

[0039] First historical margin data: historical margin value obtained based on the measurement data of the double-sided laser ranging sensor, which is obtained through the steps of calculating the cable thickness and cable length mentioned above.

[0040] Second historical margin data: a historical margin value obtained based on the angular velocity sensor measurement data, which is calculated using information captured by the angular velocity sensor.

[0041] The acquired historical measurement data is preprocessed, including data cleaning (removing outliers, missing values, etc.), normalization (mapping data to a specific range, such as [0, 1]), and other operations to improve the training effect and stability of the model. The preprocessed historical measurement data is divided into a training set, a validation set, and a test set. The training set is used for model parameter learning, the validation set is used to adjust the model's hyperparameters (such as the number of hidden layer neurons, learning rate, etc.), and the test set is used to evaluate the performance of the trained model.

[0042] The initial multi-dimensional decision model is trained using the training set. During the training process, the model continuously adjusts its weights and bias parameters according to the input feature data and the corresponding target value (known historical margin value) to minimize the error between the predicted value and the true value. Commonly used loss functions include Mean Squared Error (MSE). Through multiple iterations of training, until the performance of the model reaches a satisfactory level, a trained multi-dimensional decision model is obtained.

[0043] In actual measurement, the current rotation state data, the first margin value, and the second margin value of the target cable drum are obtained, and these data are input 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 drum.

[0044] In one embodiment of the present application, after obtaining the margin value of the target cable drum, the method further includes: Obtain historical measurement data of the target cable drum, the historical measurement data including margin data and project data; An initial margin prediction model based on a long short-term memory network is constructed, and historical measurement data is input into the initial margin prediction model for training. During the training process, the parameters of the initial margin prediction model are optimized through a back propagation algorithm to obtain a trained margin prediction model. Input the real-time surplus value and current project data into the surplus prediction model to obtain the surplus change trend of the target cable drum; Generate the adjustment strategy of the target cable drum based on the margin change trend.

[0045] Among them, historical measurement data includes: Margin data: the historical margin value of the target cable drum calculated previously.

[0046] Project data: Project information related to cable usage, such as construction progress, cable usage frequency, and estimated usage.

[0047] Long Short-Term Memory (LSTM) is a special recurrent neural network that can effectively process sequence data and capture long-term dependencies in data. When building the initial margin prediction model, the input layer receives a sequence of margin data and project data, and the hidden layer consists of multiple LSTM units that can memorize and process historical information in the sequence. The output layer is used to predict future margin values.

[0048] Input historical measurement data into the initial margin prediction model for training. Since LSTM processes sequence data, the data needs to be organized in chronological order. During the training process, the backpropagation algorithm (such as Backpropagation Through Time, BPTT) is used to optimize the parameters of the model. By calculating the error between the predicted value and the true value, the error is backpropagated to each parameter of the model, and the parameters are adjusted according to the gradient information of the error to minimize the error. The training is iterated continuously until the prediction performance of the model reaches a satisfactory level, and a trained margin prediction model is obtained.

[0049] The real-time surplus value and current project data are input into the trained surplus prediction model. The model predicts the future surplus value based on the patterns and rules learned from the historical data, thereby obtaining the surplus change trend of the target cable drum. According to the obtained surplus change trend, combined with the actual project needs and cable usage, the corresponding adjustment strategy is generated. For example, if it is predicted that the surplus will decrease rapidly, it may be necessary to arrange for additional cables in time; if the surplus remains sufficient for a long time, the cable usage plan can be appropriately adjusted. The generation of the adjustment strategy requires comprehensive consideration of multiple factors to ensure that the supply and use of cables can meet the requirements of the project.

[0050] Another embodiment of the present application provides a cable drum remainder measurement system. For details, see Figure 3 , Figure 3 The diagram shows a cable drum remainder measurement system in one embodiment of the present application, which includes: An acquisition module 11 acquires 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; A correction module 12 is used to calculate the difference of the measurement data of the laser ranging sensors on both sides of each position, perform error analysis according to the difference, and correct the measurement data based on the error analysis result to obtain a first measurement value; A first measuring module 13, used to calculate the cable thickness at each position according to the first measurement value, and calculate a first margin value of the target cable drum according to the cable thickness; A second measuring module 14 is used to obtain 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; The output module 15 is used to establish a multi-dimensional decision model based on the rotation state of the target cable drum, 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 drum.

[0051] In one embodiment of the present application, the correction module is further used to: The upper limit value of the fluctuation range of the difference of the measured data on both sides of the target cable drum under normal circumstances is determined as the error threshold; 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 laser ranging sensors 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 laser ranging sensors on both sides are input into the pre-built eccentricity correction model for correction, and the first measurement value of the position is calculated according to the correction result.

[0052] In one embodiment of the present application, the output module is further used to: Construct an initial multi-dimensional decision model based on a multi-layer perceptron neural network; Acquire historical measurement data of the target cable drum, the historical measurement data including rotation state data of the target cable drum, first historical margin data obtained based on measurement data of the double-sided laser ranging sensor, and second historical margin data obtained based on measurement data of the angular velocity sensor; The initial multi-dimensional decision model is trained according to the historical measurement data to obtain a trained multi-dimensional decision model; In an actual measurement process, the acquired rotation state data, the first margin value, and the second margin value of the target cable drum are input into a multi-dimensional decision model to obtain the margin value of the target cable drum.

[0053] Another embodiment of the present application provides a cable drum remainder measurement device, specifically, see Figure 4, which is a structural block diagram of a cable drum remainder measuring device provided in an embodiment of the present application. The cable drum remainder measuring device provided in an embodiment of the present application comprises 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 above-mentioned cable drum remainder measuring method embodiment are implemented, for example Figure 1 or, when the processor 21 executes the computer program, the functions of the modules in the above-mentioned device embodiments are implemented, such as the acquisition module 11.

[0054] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory 22 and executed by the processor 21 to complete the present application. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the cable drum remainder measurement device. For example, the computer program may be divided into an acquisition module 11, a correction module 12, a first measurement module 13, etc.

[0055] The cable reel remainder measurement device may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art may understand that the schematic diagram is only an example of the cable reel remainder measurement device and does not constitute a limitation on the cable reel remainder measurement device. The cable reel remainder measurement device may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the cable reel remainder measurement device may also include an input and output device, a network access device, a bus, etc.

[0056] The processor 21 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor 21 is the control center of the cable tray remainder measurement device, and uses various interfaces and lines to connect various parts of the entire cable tray remainder measurement device.

[0057] The memory 22 can be used to store the computer program and / or module. The processor 21 realizes various functions of the cable drum remainder measuring device by running or executing the computer program and / or module stored in the memory 22 and calling the data stored in the memory 22. The memory 22 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory 22 can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0058] Wherein, if the module integrated in the cable drum surplus measurement device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, 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.

[0059] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0060] Accordingly, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to perform the steps in the cable drum remainder measurement method of the above embodiment, for example Figure 1 Steps S1 to S5 described in .

[0061] Compared with the prior art, the beneficial effects of the embodiments of the present application are at least one of the following: (1) This application calculates the difference in measurement data from the double-sided laser ranging sensor to perform error analysis and correction, which can effectively reduce measurement deviations caused by factors such as the sensor's own accuracy error, installation error, and environmental interference, thereby improving measurement accuracy.

[0062] (2) This application combines the data from the laser ranging sensor and the angular velocity sensor, and performs comprehensive processing through a multi-dimensional decision model. It can measure and calculate the cable reel surplus from different dimensions. It is more accurate than the measurement of a single sensor and can be accurate to a smaller surplus unit, meeting the demand for accurate control of the cable surplus.

[0063] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A cable drum remainder measurement method, characterized in that: include: 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; 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; 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; 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; 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.

2. The cable drum remainder measurement method according to claim 1, characterized in that: The performing error analysis according to the difference, and correcting the measurement data based on the error analysis result to obtain the first measurement value, includes: The upper limit value of the fluctuation range of the difference of the measured data on both sides of the target cable drum under normal circumstances is determined as the error threshold; Performing an error analysis on the difference according to the error threshold, and if the difference is less than the error threshold, determining an average value of the measurement data of the laser ranging sensors 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 laser ranging sensors on both sides are input into a pre-constructed eccentricity correction model for correction, and the first measurement value of the position is calculated according to the correction result.

3. The cable drum remainder measurement method according to claim 1, characterized in that: The step of 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, comprises: The cable thickness at each location is calculated based on the first measurement value, expressed as: in, Indicates the cable thickness at various locations, Indicates the total outer diameter of the target cable drum, Indicates the inner diameter of the target cable drum, is the first measurement value, is the angle corresponding to the position of the first measurement value; The cable length of the target cable drum is calculated according to the cable thickness, which is expressed as: in, Indicates the total cable length, Represents the detection interval formed by two adjacent positions, Indicates the cable length within the detection range, Indicates the number of cable turns in each detection interval, Indicates the number of cable layers in each detection interval, Indicates the cable diameter; A first margin of the target cable drum is determined according to the cable length.

4. The cable drum remainder measurement method according to claim 3, characterized in that: Before calculating the cable thickness at each position according to the first measurement value, the method further comprises: The first measurement value of each position is judged. If the horizontal distance calculated according to the first measurement value is greater than the width of the target cable drum, the first measurement value is an invalid measurement value.

5. The cable drum remainder measurement method according to claim 1, characterized in that: The step of calculating the second margin value of the target cable drum according to the second measured value comprises: in, Indicates the second margin of the target cable drum, is the cumulative angle, is the rotation time, is the angular velocity, is the number of coils, is the total number of coils, is the current layer number, Indicates the cable diameter, Indicates the total number of cable layers. Indicates the width of the target cable drum, It is the number of uncircled circles remaining in the current circle-circled layer.

6. The cable drum remainder measurement method according to claim 1, characterized in that: The step of establishing a multidimensional decision model based on the rotation state of the target cable drum, inputting the first margin value and the second margin value into the multidimensional decision model, and obtaining the margin value of the target cable drum includes: Construct an initial multi-dimensional decision model based on a multi-layer perceptron neural network; Acquire historical measurement data of the target cable drum, the historical measurement data including rotation state data of the target cable drum, first historical margin data obtained based on measurement data of a double-sided laser ranging sensor, and second historical margin data obtained based on measurement data of an angular velocity sensor; Training the initial multi-dimensional decision model according to the historical measurement data to obtain a trained multi-dimensional decision model; In an actual measurement process, the acquired rotation state data of the target cable drum, 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.

7. The cable drum remainder measurement method according to claim 1, characterized in that: After obtaining the remaining value of the target cable drum, the method further includes: Acquire historical measurement data of the target cable drum, wherein the historical measurement data includes margin data and project data; Constructing an initial margin prediction model based on a long short-term memory network, inputting the historical measurement data into the initial margin prediction model for training, and during the training process, optimizing the parameters of the initial margin prediction model by a back propagation algorithm to obtain a trained margin prediction model; Inputting the surplus value obtained in real time and current project data into the surplus prediction model to obtain the surplus change trend of the target cable drum; An adjustment strategy for the target cable drum is generated based on the margin variation trend.

8. A cable drum remainder measurement system, characterized in that: The cable drum remainder measurement method according to any one of claims 1 to 7 is applied, and the cable drum remainder measurement system comprises: An acquisition module, used to acquire measurement data captured by a plurality of laser distance measuring sensors arranged 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; A correction module, used for 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; A first measuring module, configured to calculate the cable thickness at each position according to the first measurement value, and calculate a first margin value of the target cable drum according to the cable thickness; A second measurement module, configured to obtain 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; An output module is used to establish a multi-dimensional decision model based on the rotation state of the target cable drum, 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 drum.

9. The cable drum remainder measurement system according to claim 8, characterized in that: The correction module is further used for: The upper limit value of the fluctuation range of the difference of the measured data on both sides of the target cable drum under normal circumstances is determined as the error threshold; Performing an error analysis on the difference according to the error threshold, and if the difference is less than the error threshold, determining an average value of the measurement data of the laser ranging sensors 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 laser ranging sensors on both sides are input into a pre-constructed eccentricity correction model for correction, and the first measurement value of the position is calculated according to the correction result.

10. The cable drum remainder measurement system according to claim 8, characterized in that: The output module is further used for: Construct an initial multi-dimensional decision model based on a multi-layer perceptron neural network; Acquire historical measurement data of the target cable drum, wherein the historical measurement data includes rotation state data of the target cable drum, first historical margin data obtained based on measurement data of a double-sided laser ranging sensor, and second historical margin data obtained based on measurement data of an angular velocity sensor; Training the initial multi-dimensional decision model according to the historical measurement data to obtain a trained multi-dimensional decision model; In an actual measurement process, the acquired rotation state data of the target cable drum, 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.

11. A cable drum remainder measuring device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the cable drum remainder measurement method according to any one of claims 1 to 7 when executing the computer program.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the cable drum remainder measurement method according to any one of claims 1 to 7 is implemented.

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