Circulating shared cable turnover disc and turnover method

Abstract

The invention discloses a circulation sharing cable turnover disc and turnover method.The circulation sharing cable turnover disc comprises a cable disc barrel and side plates arranged on the two sides of the cable disc barrel, the side plates are fixed to the side face of the cable disc barrel through screws, and the circulation sharing cable turnover disc further comprises supporting mechanisms symmetrically arranged on the two sides of the cable disc barrel and a weighing mechanism used for weighing a cable disc; a camera is arranged on the side plate, and a GPS sensor is arranged in the cable reel cylinder. Real-time position tracking and state monitoring are carried out on the cable reel through the Beidou positioning module and the sensor equipment, accurate positioning and circulation management of the cable reel are achieved, a unified cable reel sharing management platform is formed, and the utilization efficiency and the management level of cable resources on a construction site are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field, in particular to a circulating shared cable turnover tray and a turnover method. Background Art

[0002] As a crucial component of national infrastructure development, power project construction efficiency and resource allocation directly impact project progress and cost control. During power construction, cable drums, as key carriers of cable, are crucial for the smooth progress of the entire project. Their management and deployment efficiency are crucial.

[0003] When low-voltage cables leave the factory, they are usually equipped with solid wood or ironwood cable reels. After being used once at the construction site, the cable reels are easily damaged and discarded after being damaged, making them impossible to recycle and resulting in a waste of resources. In addition, the cable reels are large in size and inconvenient to transport.

[0004] Furthermore, current cable reel management relies primarily on the traditional model of manual record-keeping and telephone dispatch, which is subject to issues such as information lag and untimely deployment. Construction site environments are complex and ever-changing, and cable reels are frequently transferred between different work sites. Managers are unable to accurately grasp the specific location and status of each cable reel, resulting in blind resource deployment. This lack of location information further raises the technical challenges of monitoring remaining cable quantities. Traditional visual estimation methods are inaccurate and inefficient, and cannot provide reliable data support for construction plans. Inaccurate remaining cable quantity information directly impacts the rationality of resource allocation, making it difficult for construction personnel to select the most appropriate cable reel based on actual needs, resulting in wasted resources and project delays. Summary of the Invention

[0005] The purpose of the present invention is to solve the above problems and provide a circulating shared cable turnover tray and turnover method. The cable tray can be recycled and shared with project departments, thereby improving resource utilization efficiency, realizing efficient configuration and dynamic scheduling of cable tray resources, and improving the efficiency of power engineering construction.

[0006] The technical solution adopted by the present invention to solve the technical problem is: A circulating shared cable turnover drum, comprising a cable drum body and side panels arranged on both sides of the cable drum body, the side panels being fixed to the sides of the cable drum body by screws, and further comprising support mechanisms symmetrically arranged on both sides of the cable drum body and a weighing mechanism for weighing the cable drum, cameras being provided on the side panels, and a GPS sensor being provided within the cable drum body; The weighing mechanism includes a V-shaped groove and a weighing device arranged at the bottom of the V-shaped groove.

[0007] Furthermore, the support mechanism includes a base, a lifting hydraulic cylinder arranged on the base, an arc-shaped support plate is provided at the end of the piston rod of the lifting hydraulic cylinder, and a plurality of support wheels cooperating with the rotating shaft of the cable drum are provided in the arc-shaped support plate.

[0008] Furthermore, the base is provided with reinforcing ribs for supporting the lifting hydraulic cylinder.

[0009] Furthermore, a protective cover for protecting the camera is provided on the side panel.

[0010] Furthermore, a method for circulating a shared cable turnover tray, using the turnover tray, comprises the following steps: S101 obtains the original position information and state parameters of the cable drum through the GPS sensor to obtain a basic data set; S102 uses a clustering algorithm to group the original position information and determine the spatial distribution pattern; S103 uses time series analysis to detect state change characteristics based on the spatial distribution pattern and state parameters, and generates abnormal state records; S104 uses a filtering algorithm to filter the noise of the original position information to obtain smoothed position data; S105 constructs real-time trajectory visualization data based on the smoothed position data; S106 If the change amplitude of the position data exceeds the preset threshold, the moving distance and direction are calculated to generate a flow status mark; S107 uses image recognition technology to calculate the remaining cable and generate an early warning status mark; S108 uses a time series prediction algorithm to generate a demand forecast result based on the remaining cable and construction progress; S109 uses a clustering algorithm to generate a resource supply and demand matching matrix based on the demand forecast result to obtain an allocation optimization plan; S1010 generates a scheduling instruction and updates the resource status based on the allocation optimization plan.

[0011] Furthermore, step S106 includes: using a Gaussian filter algorithm to smooth the real-time position data to obtain smoothed position coordinates; calculating the Euclidean distance by comparing the previous and next position coordinates to obtain the moving distance; calculating the moving direction angle by the inverse tangent function; and calculating the path matching degree according to the moving distance and direction using the cosine similarity algorithm to generate a flow status identifier.

[0012] Furthermore, step S107 includes: A convolutional neural network model is used to extract features from the image to obtain cable reel diameter and layer number data; the remaining cable length is calculated based on the cable reel diameter and layer number data in combination with a geometric model; the remaining length is calibrated in combination with cable specification parameters to obtain calibrated remaining length data; if the calibrated remaining length data is lower than a preset threshold, a warning status indicator is generated.

[0013] Furthermore, step S109 includes: Based on the work site feature vectors, the K-means clustering algorithm is used to group the work sites to obtain resource demand clusters. The supply and demand difference is determined by comparing the total supply and total demand of the clusters. Based on the supply and demand difference and geographic location information, a resource supply and demand matching matrix is constructed, the allocation priority is calculated, and an allocation optimization plan is generated.

[0014] The beneficial effects of the present invention are: 1. The present invention uses Beidou positioning modules and sensor equipment to track the real-time position and monitor the status of cable reels, and adopts the Kalman filter algorithm to filter noise and smooth the trajectory of the position data to achieve accurate positioning and flow management of the cable reels. Image recognition technology is combined to accurately measure the cables, and a time series prediction algorithm is used to analyze the cable consumption trend and predict future cable demand. The cable demand of the work site is grouped by a cluster analysis algorithm to generate a resource supply and demand matching matrix and an allocation optimization plan to achieve intelligent scheduling among multiple work sites. The present invention adopts a real-time data synchronization mechanism to push the scheduling plan and resource status to each work site management terminal, forming a unified cable reel sharing management platform, which effectively improves the utilization efficiency and management level of cable resources at the construction site.

[0015] 2. In the present invention, the side panels are fixed to the sides of the cable drum body by screws. When transporting, the side panels are removed, which occupies less space and is convenient for transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0017] Figure 1 It is a schematic diagram of the structure of the present invention; Figure 2 It is a right side view of the present invention; Figure 3 It is a flow chart of the present invention.

[0018] In the figure: cable drum body 1, side plate 2, V-shaped groove 3, weighing device 4, camera 5, GPS sensor 6, base 7, lifting hydraulic cylinder 8, arc-shaped support plate 9, support wheel 10, rotating shaft 11, reinforcing rib 12, protective cover 13. DETAILED DESCRIPTION

[0019] like Figure 1 and Figure 2As shown, a revolving shared cable reel comprises a cable reel body 1 and side panels 2 mounted on either side of the reel body 1. The side panels 2 are screwed to the sides of the reel body 1 and can be removed for transport, minimizing space and facilitating transportation. The reel also includes support mechanisms symmetrically positioned on either side of the reel body 1 and a weighing mechanism for weighing the cable reel. Cameras 5 are mounted on the side panels 2, and a GPS sensor 6 is housed within the reel body 1. The support mechanism drives the cable reel upward and downward. When raised, the reel rotates, allowing cable to be withdrawn from the reel. When lowered, the reel rests on the weighing mechanism for weighing. The camera 5 captures a photo of the reel, and image recognition technology is used to calculate the remaining cable volume. The GPS sensor 6 acquires the reel's location information for convenient dispatching.

[0020] like Figure 1 As shown, the weighing mechanism includes a V-shaped groove 3 and a weighing device 4 arranged at the bottom of the V-shaped groove 3. After the cable drum is supported on the V-shaped groove 3, it will not fall, which makes it convenient to pull the rotating shaft out of the cable drum and also facilitate the replacement of the cable drum.

[0021] like Figure 1 As shown, the support mechanism includes a base 7 and a lifting hydraulic cylinder 8 mounted on the base 7. The piston rod end of the lifting hydraulic cylinder 8 is provided with an arc-shaped support plate 9, within which are mounted a number of support wheels 10 that cooperate with the cable drum's rotating shaft 11. When the piston rod of the lifting hydraulic cylinder 8 retracts, it drives the cable drum downward, supported on the V-shaped groove 3. Further retraction of the piston rod of the lifting hydraulic cylinder 8 allows the cable drum's rotating shaft 11 to be withdrawn, thereby facilitating cable drum replacement.

[0022] like Figure 1 As shown, the base 7 is provided with a reinforcing rib 12 for supporting the lifting hydraulic cylinder 8 .

[0023] like Figure 1 As shown, a protective cover 13 for protecting the camera 5 is provided on the side panel 2 .

[0024] like Figure 3 As shown, a method for circulating a shared cable turnover tray, using the turnover tray, includes the following steps: S101 obtains the original position information and state parameters of the cable drum through the GPS sensor to obtain a basic data set; S102 uses a clustering algorithm to group the original position information and determine the spatial distribution pattern; S103 uses time series analysis to detect state change characteristics based on the spatial distribution pattern and state parameters, and generates abnormal state records; S104 uses a filtering algorithm to filter the noise of the original position information to obtain smoothed position data; S105 constructs real-time trajectory visualization data based on the smoothed position data; S106 If the change amplitude of the position data exceeds the preset threshold, the moving distance and direction are calculated to generate a flow status mark; S107 uses image recognition technology to calculate the remaining cable and generate an early warning status mark; S108 uses a time series prediction algorithm to generate a demand forecast result based on the remaining cable and construction progress; S109 uses a clustering algorithm to generate a resource supply and demand matching matrix based on the demand forecast result to obtain an allocation optimization plan; S1010 generates a scheduling instruction and updates the resource status based on the allocation optimization plan.

[0025] Steps S101 to S103 include: collecting data from all cable reels at the construction site through the Beidou positioning module and sensor equipment, obtaining original location information including latitude and longitude coordinates, timestamps, and equipment numbers, and collecting cable reel weight parameters at the same time to form a multi-dimensional basic data set for subsequent location information processing and status monitoring analysis.

[0026] The K-means clustering algorithm is used to group the longitude and latitude coordinates to determine the spatial distribution pattern of the cable reels. If the spatial distribution pattern shows abnormal clustering, a correlation analysis is performed using the timestamp data and the device number identifier to determine the movement trajectory of the abnormal cable reel, generating a trajectory analysis result. Based on the trajectory analysis result and the cable reel weight parameters, a time series analysis method is used to detect weight change trends and obtain state change characteristics. If the state change characteristic exceeds a preset threshold, the abnormal cable reel is located using the device number identifier, and an abnormal state record is generated.

[0027] Step S104 involves using a Kalman filter algorithm to filter noise from the pre-sorted location dataset, dynamically correcting for signal interference using state parameters, and determining smoothed location data points. Based on these smoothed location data points, interpolation processing is performed through time series analysis to fill in missing data and obtain the complete cable drum movement path. By comparing the trajectory patterns with those in the historical database, it is determined whether the outliers in the complete movement path conform to normal movement patterns. If not, the outliers are corrected to obtain optimized trajectory information. Based on this optimized trajectory information, combined with timestamps and location coordinates, real-time cable drum trajectory visualization data is constructed and synchronously updated to the historical database to determine the long-term storage of location records.

[0028] When processing cable drum position data, the Kalman filter algorithm can be used to reduce the effects of noise caused by signal interference or equipment accuracy issues.

[0029] When performing interpolation processing in time series analysis, reasonable inferences can be made for the missing data.

[0030] When comparing the trajectory patterns with those in the historical database, it is possible to determine whether there are any abnormal points by comparing the similarity between the current path and the past regular paths.

[0031] When building real-time trajectory visualization data, the optimized trajectory information can be displayed graphically on the monitoring platform.

[0032] When dynamically correcting for signal interference, auxiliary adjustments can be made based on state parameters such as equipment operating status or environmental factors. For example, if the location data of a cable reel is subject to electromagnetic interference from nearby large machinery, analyzing the equipment's state parameters can appropriately adjust the filter weights to reduce the impact of interference on the data, ultimately resulting in smoother location data points. This correction method can adapt to data fluctuations in different scenarios.

[0033] Step S105 and step S106 include: using a Gaussian filter algorithm to smooth the real-time position data to obtain smoothed position coordinates; calculating the Euclidean distance by comparing the previous and next position coordinates to obtain the moving distance; calculating the moving direction angle by the inverse tangent function; and calculating the path matching degree based on the moving distance and direction using the cosine similarity algorithm to generate a flow status identifier.

[0034] The cable reel's real-time position data is acquired through a positioning sensor. This data is smoothed using a Gaussian filter to obtain smoothed position coordinates. Based on these smoothed position coordinates, the coordinate data collected twice before and after are compared to calculate the Euclidean distance to obtain the travel distance. The calculation formula is: d = sqrt((x²x¹)² + (y²y¹)²), where x¹ and y¹ are the coordinates at the previous moment, x² and y² are the coordinates at the current moment, and d is the travel distance. The inverse tangent function is used to calculate the travel direction angle using the formula: θ = atan²(y²y¹, x²x¹), where θ is the travel direction angle, x¹ and y¹ are the coordinates at the previous moment, and x² and y² are the coordinates at the current moment. This provides the travel direction. Based on the travel distance and direction, and in combination with a preset normal flow path template, the cosine similarity algorithm is used to calculate the degree of match between the actual travel path and the template path, yielding a path match score. If the path matching score is below a preset threshold, the cable reel is determined to be in an abnormal movement state, and an abnormal movement state indicator is generated. If the path matching score is above or equal to the preset threshold, the cable reel is determined to be in a normal flow state, and a normal flow state indicator is generated. Based on the location change time, the movement path, and the flow state indicator, a time series analysis method is used to generate movement trajectory data. Using this movement trajectory data, a clustering algorithm is used to classify the cable reel's movement patterns to obtain movement pattern categories.

[0035] In the field of real-time cable drum location tracking and flow management, obtaining location data through positioning sensors is the foundation of the entire process. Positioning sensors can be GPS-based devices installed on the cable drum, collecting location information at fixed intervals such as 5 seconds.

[0036] To smooth position data, a Gaussian filter can be used to process the collected coordinate points. For example, suppose the cable reel's position data exhibits multiple small fluctuations within a short period of time. These fluctuations may be caused by signal interference. A Gaussian filter generates a more continuous coordinate sequence by weighted averaging the surrounding data points, reducing the error caused by these fluctuations.

[0037] To calculate the distance and direction of movement, the cable drum's movement can be inferred from the change in coordinates between the previous and subsequent locations. Assuming the coordinates were 100, 200 at the previous moment and 110, 210 at the current moment, the calculated distance is approximately 14.1 units, and the direction angle can be calculated using the inverse tangent function to be approximately 45 degrees northeast. This method can help determine whether the cable drum is moving according to the planned route, providing a basis for subsequent path matching.

[0038] Path matching can be calculated using a cosine similarity algorithm. For example, assume the default normal flow path is from warehouse A to warehouse B, and the path template is a coordinate sequence that approximates a straight line. However, the actual collected path shows that the cable drum deviates from the main route, forming a large curve. By comparing the vector directions of the actual path and the template path, a matching score of, for example, 0.75 is obtained. If the preset threshold is 0.85, it is determined to be an abnormal movement state.

[0039] For the generation of flow status identification, the status of the cable reel can be directly marked according to the matching score. If the matching score of a certain transport is 0.9, which is higher than the threshold, it is marked as a normal flow status; conversely, if the score is 0.7, it is marked as abnormal.

[0040] The generation of movement trajectory data can be achieved through time series analysis. Assuming that the location data of the cable drum in a day is arranged in chronological order, combined with the movement path and status identification, a complete trajectory data including time, location and status can be generated.

[0041] Clustering algorithms can be used to classify movement patterns. For example, if analysis of trajectory data for multiple cable reels reveals that some reels frequently move in straight lines between warehouses, while others frequently move repeatedly within a specific area, these patterns can be classified into linear transport patterns and intra-area adjustment patterns. This classification helps optimize cable reel scheduling strategies and improve turnover efficiency.

[0042] Step S107 includes: using image recognition technology to accurately measure the cables on the cable reel, measuring the change in cable reel diameter and identifying the number of cable layers, and calculating the remaining cable length in combination with cable specification parameters. If the remaining amount is lower than the preset safety threshold, an early warning message is generated to obtain accurate cable remaining amount data and early warning status identification.

[0043] A pre-trained convolutional neural network model is used to extract features from the captured images to obtain preliminary data on the cable reel diameter and the number of cable layers. Based on the cable reel diameter data, combined with historical record data, a comparison is performed to determine the exact value of the diameter change. The cable layer data is combined with the diameter change value and a preset geometric model is used for calculation to obtain the remaining length value of the cable. The remaining length value is calibrated in combination with the cable specification parameters and adjusted using a preset calibration formula to obtain the calibrated remaining length data. If the calibrated remaining length data is lower than the preset threshold, an early warning signal is triggered and corresponding status identification information is generated. Based on the early warning signal and the status identification, the remaining data record is automatically updated and stored synchronously in the database to obtain the latest inventory status information.

[0044] A pre-trained convolutional neural network model is used to extract features from the captured images, aiming to accurately identify the physical characteristics of the cable reels. Through multiple layers of convolution and pooling, the convolutional neural network extracts features such as edges and textures from the images, generating preliminary data on the cable reel diameter and number of layers.

[0045] For example, historical data indicates that a cable reel's diameter was 0.82 meters last week, but this measurement shows it's 0.8 meters, indicating a 0.02-meter decrease. This change could be due to cable usage. The system compares the change to eliminate the effects of measurement noise. This approach more reliably reflects the reel's actual condition than relying solely on a single measurement.

[0046] The calibration formula adjusts the remaining length data based on cable specifications (such as material and cross-sectional area). If a cable's actual thickness is slightly higher than the standard, the system will calibrate the estimated length to 480 meters. This calibration effectively reduces errors caused by cable model variations and improves data reliability.

[0047] If the remaining length after calibration falls below a preset threshold (e.g., 200 meters), the system triggers an alert and generates a low-stock status indicator. For example, if a cable reel has 180 meters remaining, the system automatically sends an alert to the management terminal, prompting the need to restock. This automated alert allows for more timely detection of problems compared to regular manual checks.

[0048] Step S108 includes: analyzing the cable consumption trend based on the cable remaining data and the warning status indicator using a time series prediction algorithm, predicting future cable demand through historical usage data and the current construction progress, and triggering a resource allocation request if the predicted demand exceeds the current remaining quantity, thereby obtaining a cable demand prediction result and an allocation trigger signal.

[0049] Based on structured basic cable usage data, a time series analysis method is used to extract consumption trend characteristics from the basic data and determine a cable consumption trend model. The cable consumption trend model uses current remaining quantity data and construction progress updates as input to predict cable demand over a period of time, generating forecast data. If the future demand in the forecast data exceeds the current remaining quantity, a warning status indicator is generated. This warning status indicator triggers a resource allocation signal, generating allocation request information. Based on this allocation request information and combined with resource allocation rules, the required cable replenishment quantity is calculated to generate resource allocation solution data.

[0050] Step S109 includes: grouping the cable demand prediction results of all work sites through a cluster analysis algorithm, dividing the work sites into different resource demand clusters according to factors such as geographical location, construction progress, and cable specifications, calculating the resource supply and demand matching degree between each cluster, judging which work sites have excess resources and which work sites have insufficient resources, and obtaining a resource supply and demand matching matrix and an allocation optimization plan.

[0051] Based on the worksite data, a worksite feature vector is constructed. The worksites are grouped using the K-means clustering algorithm to generate resource demand clusters. For each resource demand cluster, the total cable demand and supply are calculated. The difference is compared to determine resource surplus or shortage, thereby determining the supply and demand status of the cluster. If the total supply exceeds the total demand, the cluster is marked as being in resource surplus. Based on the supply-demand difference and geographic location information, a resource supply-demand matching matrix is constructed, allocation priorities are calculated, and the resource allocation path is generated.

Claims

1. A circulating shared cable turnover drum, comprising a cable drum body (1) and side plates (2) arranged on both sides of the cable drum body (1), characterized in that: The side panels (2) are fixed to the sides of the cable drum body (1) by screws, and further include support mechanisms symmetrically arranged on both sides of the cable drum body (1) and a weighing mechanism for weighing the cable drum. A camera (5) is provided on the side panels (2), and a GPS sensor (6) is provided inside the cable drum body (1). The weighing mechanism comprises a V-shaped groove (3) and a weighing device (4) arranged at the bottom of the V-shaped groove (3).

2. A circulating shared cable turnover tray as claimed in claim 1, characterized in that: The supporting mechanism comprises a base (7), a lifting hydraulic cylinder (8) arranged on the base (7), an arc-shaped supporting plate (9) is provided at the piston rod end of the lifting hydraulic cylinder (8), and a plurality of supporting wheels (10) cooperating with the rotating shaft (11) of the cable drum are provided in the arc-shaped supporting plate (9).

3. A circulating shared cable turnover tray as claimed in claim 2, characterized in that: The base (7) is provided with a reinforcing rib (12) for supporting the lifting hydraulic cylinder (8).

4. A circulating shared cable turnover tray as claimed in claim 1, characterized in that: A protective cover (13) for protecting the camera (5) is provided on the side panel (2).

5. A method for circulating a shared cable turnover tray, using the turnover tray according to any one of claims 1 to 4, characterized in that: The following steps are involved: S101 obtains the original position information and state parameters of the cable drum through the GPS sensor to obtain a basic data set; S102 uses a clustering algorithm to group the original position information and determine the spatial distribution pattern; S103 uses time series analysis to detect state change characteristics based on the spatial distribution pattern and state parameters, and generates abnormal state records; S104 uses a filtering algorithm to filter the noise of the original position information to obtain smoothed position data; S105 constructs real-time trajectory visualization data based on the smoothed position data; S106 If the change amplitude of the position data exceeds the preset threshold, the moving distance and direction are calculated to generate a flow status mark; S107 uses image recognition technology to calculate the remaining cable and generate an early warning status mark; S108 uses a time series prediction algorithm to generate a demand forecast result based on the remaining cable and construction progress; S109 uses a clustering algorithm to generate a resource supply and demand matching matrix based on the demand forecast result to obtain an allocation optimization plan; S1010 generates a scheduling instruction and updates the resource status based on the allocation optimization plan.

6. A method for circulating shared cable turnover trays as claimed in claim 5, characterized in that: Step S106 includes: using a Gaussian filter algorithm to smooth the real-time position data to obtain smoothed position coordinates; calculating the Euclidean distance by comparing the previous and next position coordinates to obtain the moving distance; calculating the moving direction angle by an inverse tangent function; and calculating the path matching degree based on the moving distance and direction using a cosine similarity algorithm to generate a flow status identifier.

7. A method for circulating shared cable turnover trays as claimed in claim 5, characterized in that: Step S107 includes: A convolutional neural network model is used to extract features from the image to obtain cable reel diameter and layer number data; the remaining cable length is calculated based on the cable reel diameter and layer number data in combination with a geometric model; the remaining length is calibrated in combination with cable specification parameters to obtain calibrated remaining length data; if the calibrated remaining length data is lower than a preset threshold, a warning status indicator is generated.

8. A method for circulating shared cable turnover trays as claimed in claim 5, characterized in that: Step S109 includes: Based on the work site feature vectors, the K-means clustering algorithm is used to group the work sites to obtain resource demand clusters. The supply and demand difference is determined by comparing the total supply and total demand of the clusters. Based on the supply and demand difference and geographic location information, a resource supply and demand matching matrix is constructed, the allocation priority is calculated, and an allocation optimization plan is generated.

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