44 results about "Industrial land" patented technology

The invention discloses an industrial land intelligent site selection method based on spatial big data and an artificial intelligence big model. The method comprises the following steps: 1) obtaining a feature matrix containing spatial topology constraints and economic association rules; 2) extracting key analysis elements in user site selection requirements; 3) inputting the spatial key analysis elements into CNN convolutional neural network branches to obtain spatial features; the policy text type key analysis elements are input into a Transform branch, and policy features are obtained; inputting the industrial chain type key analysis elements into GNN graph neural network branches to obtain industrial chain features; 4) generating an industrial land analysis conclusion; and 5) generating a comparison report, a site selection proposal, a risk avoidance strategy and an emergency plan of each feasible scheme. According to the invention, through fusion of multi-source spatial data (industrial layout, traffic network and regional assessment) and an artificial intelligence large model, dynamic planning constraint matching, full-life-cycle risk assessment and multi-objective optimization site selection are realized.

The invention discloses a method for intelligently investigating industrial land information. The method comprises the following steps: 1) creating a unique association code for the industrial land information by adopting an association code identification system; 2) periodically identifying newly-built buildings and idle lands in the industrial lands; and 3) planning an optimal path of the unmanned aerial vehicle by using a Voronoi graph algorithm, carrying out evidence collection on the abnormal area through the unmanned aerial vehicle carrying a high-definition camera and a laser radar, and generating an evidence collection report associated with the land parcel according to the association code. According to the invention, an intelligent sensing network is constructed by using satellite remote sensing, the unmanned aerial vehicle and Internet of Things equipment, and 24-hour automatic monitoring and real-time monitoring and early warning are realized.

The invention discloses a cellular automaton-based land utilization and urban element collaborative simulation method. The method comprises the following steps of: calculating the development probability of each element; the method comprises the following steps of: constructing a cellular automata (CA) model of each element; space collaborative simulation calculation; and evaluating the result precision. According to the method, the urban land use change simulation precision can be effectively improved, the land use change in reality can be better simulated, the simulation precision of population and economic elements can be improved, and the interaction and synergistic effect between different urban elements are effectively revealed. By means of multi-element collaborative simulation, small changes of urban development elements can be caught more finely, and the method has good perception ability for residential land, industrial land, commercial land, public management land and the like and has important significance for simulating urban collaborative development and reasonably planning urban layout.

1.The name of the design product: intelligent control system box. 2.The use of the design product: the design product belongs to the intelligent robot motion control hardware system platform, which can be used for commercial or industrial floor cleaning robots, patrol robots, sweeping robots, carrying robots and other mobile robot control system boxes. 3.The design points of the design product: in shape. 4.The picture or photo that best indicates the design points: perspective view 1.

The invention relates to the field of industrial land analysis, and particularly discloses an industrial land intelligent analysis system and method based on a spatial coupling degree model.The industrial land intelligent analysis method comprises the steps that firstly, by collecting basic geography, resource environment, social economy and other multi-source index data of an industrial land, the problem that high-dimensional heterogeneous data is difficult to directly and effectively utilize is solved; a low-dimensional embedded coding and aggregation analysis algorithm is innovatively introduced to carry out intelligent dimension reduction, feature extraction and aggregation on index data of each subsystem, and an efficacy value capable of accurately reflecting intrinsic characteristics of each subsystem is generated. On the basis, a coupling weight matrix (fusing geography and economic distance) is constructed, the interaction strength among different space units or elements is quantified, the overall efficacy and the overall coupling degree are calculated in combination with the efficacy value of each subsystem, and then the overall coordination degree of the industrial land is obtained. And finally, according to the comprehensive coordination degree, intelligent analysis of the industrial land is realized, and a proper industrial chain link is accurately matched.

The application discloses a high-protection-grade industrial floor drain device, which comprises a floor drain body, a sewer pipe, a first adsorption assembly, a first sealing assembly, a sensing assembly and a control and warning assembly. The first adsorption assembly is arranged in the sewer pipe. The first sealing assembly comprises a sealing piece and a floor drain sealing liquid. The floor drain sealing liquid is arranged in a containing space of the floor drain body. The sewer pipe is communicated with the containing space and leaks into the floor drain sealing liquid. The sealing piece is located in the containing space and covers the outside of the sewer pipe. The open end of the sealing piece extends into the floor drain sealing liquid. The sensing assembly comprises a liquid level sensing piece and a gas sensing piece. The liquid level sensing piece is used for monitoring the liquid level. The gas sensing piece is used for monitoring the leakage gas. The control and warning assembly is electrically connected with the liquid level sensing piece and the gas sensing piece. When a small amount of leakage occurs, the application can isolate the backflow of contaminated wastewater and toxic and harmful gas, so as to avoid the pollution of the external environment. When a large amount of leakage occurs, the application can warn the relevant personnel to perform emergency treatment, so as to avoid greater safety risks.

The utility model provides a soil sampling device for industrial land, which relates to the technical field of sampling devices, and comprises a branch pipe, the bottom of the branch pipe is fixedly connected with a branch shell, the inner wall of the branch shell is fixedly connected with two limiting frames, a sliding block is connected between the inner walls of the two limiting frames in a sliding manner, the inner walls of the limiting frames are provided with sliding chutes, and the sliding chutes are communicated with the branch pipe. A sliding plate is slidably connected to the inner wall of the sliding groove, and a shaft hole is formed in one side of the sliding plate. The digging spoon is driven to rotate by pulling the pull ring, so that the digging spoon can dig soil, the supporting rod is matched with the fixing ring, the sliding stability of the supporting rod in the supporting pipe can be improved, the stability of the supporting rod in the supporting pipe can be secondarily improved through the limiting ring, and the supporting rod can serve as a linkage structure. The center of the pull rod and the center of the connecting rod are located on the same plane all the time, so that the connecting rod is driven to move when the pull rod is pulled, a stress point is provided when the pull ring is pulled through the supporting plate, and the convenience of equipment is improved.

The invention discloses an intelligent storage device and system for heavy goods, and the device comprises a sinking gridding groove type warehouse structure which is internally divided into different storage areas according to goods types, and is used for constructing a vertically and horizontally through moving channel for hoisting equipment, carrying tools and transport vehicles; photovoltaic power generation equipment and energy storage equipment are deployed and installed on a ceiling of the sinking gridding groove type storage plant, and non-gantry heavy-load object conveying channels are arranged in the transverse direction and the longitudinal direction of the sinking gridding groove type storage plant structure; the lattice groove transverse carrying and communicating structure is matched with the sinking gridding groove type warehouse structure, and article carrying and communicating of all lattice grooves in the warehouse in the transverse direction are achieved. The belt-shaped axis carrying and communicating structure is matched with the sinking gridding groove type warehouse structure, and goods carrying and communicating of the warehouse in the belt-shaped axis direction are achieved. And more heavy objects can be stored in a limited plant space, so that the utilization efficiency of land resources is greatly improved, and the problem of shortage of industrial land is effectively relieved.

The invention discloses a low-efficiency industrial land identification method, device and equipment and a storage medium, and the method comprises the steps: obtaining the average land business income, the average land tax, the unit area investment amount and the unit area energy consumption, and obtaining the current industry type, the planned land type, the land plot ratio and the current service life; when the local average business income is lower than the average business income, the local average tax is lower than the average tax, the unit area investment is lower than the average investment, and the unit area energy consumption is higher than the average energy consumption, at least three items are satisfied; when the current industrial type is a preset industrial type, the planned land type is a non-industrial land, the land plot ratio is lower than a preset plot ratio, and the current service life is greater than a preset service life, identifying the to-be-identified industrial land as a first low-efficiency industrial land; and when at least one item is satisfied, identifying the to-be-identified industrial land as a second low-efficiency industrial land. The method is beneficial for improving the identification efficiency of the low-efficiency industrial land.

The invention relates to the technical field of data processing, and provides a contiguous industrial land identification method based on deep learning and multi-source data fusion, and the method comprises the steps: converting the text information of each interest point in a to-be-identified region into an interest point feature; constructing a to-be-recognized semantic feature map based on the features of the interest points and the segmented plots in the remote sensing image of the to-be-recognized area; inputting the remote sensing image of the to-be-recognized area and the to-be-recognized semantic feature map into a contiguous industrial land recognition model to obtain an initial contiguous industrial land recognition map; the model is obtained by training a U-Net model based on a remote sensing image, a semantic feature map and a grid label of a sample contiguous industrial land; each sample contiguous industrial land comprises remote sensing images in different seasons; and performing post-processing such as kernel density statistics, semantic similarity verification and geometric simplification on the initial contiguous industrial land identification map to obtain a target contiguous industrial land identification map. According to the invention, the identification efficiency of the contiguous industrial land is obviously improved.

The invention discloses a low-efficiency industrial land transformation strategy optimization method based on industrial chain analysis. The method comprises the following steps: acquiring related information data in a research area; calculating a target industrial land benefit index based on the preprocessed related information data, and identifying the low-efficiency industrial land according to the industrial land benefit index to obtain a low-efficiency industrial land identification result; the method comprises the following steps: constructing an enterprise association network based on a social network analysis method, calculating network feature values of enterprise nodes, identifying key enterprises in key industrial links from an industrial chain dimension, and carrying out land type division according to low-efficiency industrial lands in low-efficiency industrial land identification results related to the key enterprises, and carrying out transformation strategy optimization according to the division result. The method aims at overcoming the defect that in the prior art, a low-efficiency industrial land transformation strategy lacks consideration of the influence of an industrial chain on land benefits.

The invention discloses an industrial land evaluation system based on WebGIS, belongs to the technical field of WebGIS information, and aims to solve the technical problem of how to realize automatic evaluation of industrial lands and form a low-efficiency industrial land database. The technical scheme is that the system comprises a data layer, a control layer and a view layer which are sequentially deployed from bottom to top; the data layer is used for completing storage and response of system data through a MySQL relational database and a GeoServer server; the control layer is used for judging the type of a received view layer request through a Vue framework and sending a request to a Spinr Boot back-end processing program according to the type of the view layer request, the Spinr Boot back-end processing program initiates a data request to the data layer as required, performs format arrangement on received response data and finally responds the data to the view layer; and the view layer is used for developing a front-end page through front-end UI technologies of Echarts, Openlayers and ElementUI, sending a corresponding request to the control layer according to user request interaction content, receiving response data and visualizing the data on the front-end page.

The invention discloses a multi-source data analysis city production and city integration and job-housing balance city physical examination method, which belongs to the technical field of city physical examination methods, integrates city scale, traffic accessibility and terrain multi-source data, selects a proper reference radius according to a city type needing to be evaluated, considers the influence of population density, congestion index, gradient and the like on actual commuting, and provides an evaluation result for the city production and city integration and job-housing balance. Correcting the reference radius through data standardization calculation and a three-dimensional correction formula to obtain a final buffer area radius, performing buffer area analysis on industrial land and residential land data by using a GIS tool to serve as the buffer area radius, calculating an overlapping area of the industrial land and residential land buffer areas, calculating a proximity index through a formula, and obtaining a final buffer area. And quantifying the job-housing balance fluctuation degree of the space unit by calculating the proximity index standard deviation, setting a stability threshold value, and judging whether the threshold value is met or not according to whether the threshold value is met or not, or triggering a three-stage iteration mechanism.

The invention discloses an intelligent industrial land evaluation system and method based on artificial intelligence, and the system obtains enterprise demands and land resource data in real time through a data collection module, guarantees the timeliness of evaluation information, and effectively avoids information lag. The dynamic decision-making module fuses an improved decision-making tree and a DQN reinforcement learning algorithm, can optimize a decision-making strategy online according to a real-time data stream, adapts to market and policy changes, and solves the problem that a traditional method depends on static data and is difficult to deal with a dynamic scene; the decision tree is improved to improve the adaptability of the model to a complex scene, the DQN algorithm automatically learns an optimal strategy, manual intervention is reduced, subjective deviation is reduced, and decision scientificity and generalization ability are enhanced; the index generation module calculates a land efficiency index and an industrial gathering index, provides a multi-dimensional evaluation basis, breaks through the limitation of a single index, more accurately reflects the economic, environmental and industrial values of the land parcels, and enables the evaluation result to better meet the actual demands.

The invention belongs to the technical field of ecological environment, and relates to a method for determining specific pollutants in construction land soil pollution condition investigation. The method comprises the following steps: S1) determining specific pollutants according to the industrial land history of an investigated land; (2) determining specific pollutants according to pollution accidents in the industrial land history of the investigated plot; (3) investigating pollution traces according to the distribution condition of the industrial production devices on the investigated land parcel, and determining specific pollutants; s2, drawing a groundwater equal-water-level map of the investigated plot; s3, combining the groundwater equal-water-level graph of the investigated land parcel, combing enterprises existing in history and existing around the boundary of the investigated land parcel, and determining related characteristic pollutants; and S4, combining the same specific pollutants in the steps S1 to S3 to obtain the sum of the specific pollutants of the investigated plot.

The invention discloses a multi-planning-in-one urban and rural intelligent planning auxiliary method and system. The method comprises the following steps: acquiring historical data of urban and rural land planning; according to the method, the neural network model is used for training and learning, the complex relation of land attributes can be more accurately captured, and the planning accuracy is improved; the data deduplication and filling step ensures the quality and consistency of input data, avoids the problem of inaccurate model training caused by data redundancy or missing, extracts detailed attribute features of residential land, commercial land, industrial land, agricultural land and the like from historical data, facilitates more detailed land resource planning, and improves the land utilization rate. The method is advantaged in that diversified land demands are satisfied, the VGG16 network is utilized to carry out land area detection, the network has excellent performance in the image identification field, the land area in the remote sensing image can be accurately identified, a reliable basis is provided for subsequent attribute matching, corresponding land attributes are automatically endowed, and an intelligent planning decision process is realized.

The application relates to the technical field of data processing, and provides a method for identifying contiguous industrial land based on deep learning and multi-source data fusion, which comprises the following steps: converting text information of each interest point in a region to be identified into interest point features; constructing a to-be-identified semantic feature map based on the interest point features and each segmented land plot in a remote sensing image of the region to be identified; inputting the remote sensing image of the region to be identified and the to-be-identified semantic feature map into a contiguous industrial land identification model to obtain an initial contiguous industrial land identification map; the model is obtained by training a U-Net model based on remote sensing images, semantic feature maps and grid labels of sample contiguous industrial land; each sample contiguous industrial land contains remote sensing images of different seasons; and post-processing such as kernel density statistics, semantic similarity verification and geometric simplification is performed on the initial contiguous industrial land identification map to obtain a target contiguous industrial land identification map. The application significantly improves the efficiency of contiguous industrial land identification.

The invention discloses a heavy metal pollution assessment method for industrial land in the technical field of heavy metal pollution assessment, which comprises the following steps: determining the sampling point density of a prediction area by adopting an improved space Kriging interpolation method to obtain the number of sampling points per hectare; constructing a weight coefficient model by using the multi-dimensional pollution index; establishing a soil and underground water double-medium migration evolution model; constructing a heavy metal pollution factor diffusion prediction system based on an LSTM neural network; and the operation target prediction system outputs prediction data of heavy metal pollution diffusion in a certain period in the future. The density of the sampling points is determined by adopting the improved space Kriging interpolation method, the variation characteristics of the space can be reflected more accurately, the weight coefficient model is constructed by utilizing the multi-dimensional pollution index, the weight distribution of the heavy metal pollution factor can be more reasonable, and by establishing the soil and groundwater double-medium migration evolution model, the heavy metal pollution factor can be more accurately distributed. The migration and transformation process of pollutants in soil and underground water can be comprehensively simulated.

The invention discloses an industrial land use efficiency data analysis system and method based on big data, and relates to the technical field of data processing. Compared with a traditional industrial land restoration method, the industrial land use efficiency data analysis system and method can accurately position a target polluted land parcel and improve the use efficiency of an industrial land; according to the method, the industrial land is divided according to the production workshops, the production workshops are monitored, the target land parcels are accurately positioned, the situation that the unpolluted land parcels are repaired is avoided, and the repairing cost is reduced; polluted land parcels with high use efficiency are remedied preferentially, hindrance of pollution to industrial production can be reduced to the maximum extent, and normal production and operation of enterprises are maintained; a pollution remediation path is automatically generated according to the correlation degree between the polluted land parcels, the polluted land parcels with the high correlation degree may have a relation in the aspects of pollutant diffusion, migration and the like, and systematic treatment can be achieved by planning the pollution remediation path according to the correlation degree.

The invention discloses a low-efficiency industrial land two-dimensional fusion identification and evaluation method based on deep learning. The method comprises the following steps: S1, acquiring industrial land data and enterprise data of a research city, and associating the industrial land data and the enterprise data to obtain an industrial land enterprise database; s2, constructing a multi-dimensional index system to carry out weighted evaluation on the industrial land enterprise database and screening out low-efficiency industrial land units; s3, constructing a waste building recognition model for performing waste building recognition training through the street view image sample data, obtaining street view image data associated with the low-efficiency industrial land unit, inputting the street view image data into the waste building recognition model, and extracting and recognizing a waste building entity and a boundary region in the waste building recognition model; and S4, correspondingly displaying the boundary area of the low-efficiency industrial land unit in the research urban road map. According to the method, the low-efficiency industrial land is screened and re-checked and identified by fusing the multi-dimensional indexes including economy, income and the like with the two-dimensional fusion of the real scene features of the building, so that the accurate identification and classification judgment of the low-efficiency industrial land are realized.

The invention relates to the technical field of data updating, and discloses an urban industrial land updating method and system based on spatial planning elements, a terminal and a storage medium, and the method comprises the steps: obtaining urban industrial land information of a target city, determining key index information according to the urban industrial land information, and constructing a multi-target evaluation framework; acquiring an urban industrial land updating demand, performing variable formulation to obtain a plurality of decision variables, and constructing an industrial land updating scheme; matching a plurality of optimization objectives according to the multi-objective evaluation framework, setting constraint conditions, and performing scheme optimization according to all the optimization objectives and the constraint conditions to obtain a scheme optimization result; and performing scheme confirmation on the scheme optimization result to obtain a target update scheme, and updating the urban industrial land to obtain a land update result. According to the method, quantitative correlation between the spatial planning elements and the targets can be effectively constructed, so that an optimal updating scheme is provided, and the utilization rate of urban industrial land is improved.

The invention discloses a low-efficiency industrial land data identification method, and relates to the technical field of land resource management, and the method comprises the steps: collecting the business income, tax, power consumption, employees, land area, building area and other basic information of an industrial land through on-site treading, and calculating the land average evaluation index of each land block. A natural discontinuity point grading method is adopted to carry out grading assignment on each index data, and an analytic hierarchy process is adopted to determine the evaluation weight of each index. And calculating the efficiency comprehensive score of each land parcel in combination with the grading assignment and the evaluation weight, and judging whether the land parcel is a low-efficiency industrial land or not through a preset score threshold value. And finally, performing coupling verification on a model judgment result and a preliminary result of field exploration of a professional, and establishing a final low-efficiency industrial land database after taking a union set. According to the method, the quantitative model and field verification are integrated, and effective support is provided for fine management and efficiency improvement of land resources.

The application provides a low-efficiency industrial land intelligent identification method and system based on multi-dimensional index fusion, relates to the technical field of intelligent identification, obtains industrial land original data from a multi-source platform; determines the comprehensive score of the industrial land based on the industrial land original data, determines the low-efficiency industrial land according to the comprehensive score; establishes an input-output data set by constructing a virtual input variable, determines the slack efficiency of the low-efficiency industrial land, and determines the dominant type of the low-efficiency industrial land according to the slack efficiency; and matches and outputs a strategy tool combination from a preset strategy tool library based on the dominant type. The application can realize accurate identification and cause classification of the low-efficiency industrial land, and improve the objectivity of identification and the pertinence of strategy matching.

The invention discloses a low-efficiency industrial land identification and classification system based on a multi-dimensional index, and relates to the technical field of land classification, and the method comprises the steps: obtaining original data from a multi-source heterogeneous platform, and carrying out the cleaning and complementation; the industrial land multi-dimensional index calculation module is used for calculating industrial land multi-dimensional indexes according to data obtained by the data acquisition and preprocessing module; establishing a scale adaptive mapping model to obtain industrial land multi-dimensional index mapping values with a unified scale; and sending the comprehensive score of the industrial land into a gray zone judgment strategy according to a historical labeling sample, dynamically adjusting a gray zone boundary, obtaining a classification index weight, summarizing a low-efficiency membership degree and a high-efficiency membership degree, and judging low-efficiency land, high-efficiency land and gray land according to a set threshold value. According to the method, the problems of multi-scale mismatching, hard threshold splitting, time sequence lag and environmental protection compliance weakening in a traditional low-efficiency industrial land identification method are solved, and high-precision, interpretable and sustainable low-efficiency identification and classification of the industrial land are realized.

The invention discloses an industrial land management and control index and investment attraction admission optimization method based on machine learning, and the method comprises the steps: collecting the historical data of an established industrial land, including land parcel features, location features, enterprise development attributes and the output performance over the years; and constructing an industrial land output performance prediction model by using the data and a machine learning algorithm. The planning data of the industrial land to be transferred is obtained and then input into the model, and the output performance of the industrial land in the target year can be predicted. Furthermore, a plurality of different investment attracting condition combinations (involving different industrial development attributes or space management and control indexes) are set for the same plot, and performance prediction is performed respectively, so that quantitative simulation and comparison of different investment attracting schemes are realized. Finally, an optimal investment attracting scheme can be selected based on comparative evaluation of prediction results. According to the invention, the scientificity and accuracy of industrial land performance evaluation and investment attraction decision making are effectively improved.

This utility model discloses a sampling device for industrial land soil testing. The key technical features include a column with a hollow cylindrical structure and an embedded support rod; both the column and the support rod have through-grooves; the support rod has a hollow main column structure and an embedded lifting assembly; and a drone is also included, which is connected to the upper end of a drone lifting ring and used for lifting and flying the lifting assembly. The soil sampling instrument mounted on the lifting assembly can be adjusted by the lifting assembly to move up and down. The user can set the sampling depth as needed to ensure accurate sampling layers and representative data. Whether on flat ground, slopes, or uneven areas, the combination of the drone's flight and the lifting assembly's adjustment of the soil sampling instrument allows the sampling device to quickly complete the sampling task and improve the overall testing progress.

The invention discloses an industrial chain intelligent land selection recommendation method based on a complex network, and the method comprises the following steps: 1), obtaining industrial chain enterprise land data, and carrying out the preprocessing; 2) constructing an industrial space association network based on the preprocessed industrial chain enterprise land data; 3) based on the industrial space association network, constructing an industrial chain intelligent land selection index system; 4) obtaining the index data of one or more available industrial lands, and determining the weight of the index by adopting a weight distribution method; 5) constructing an intelligent recommendation algorithm model which takes the index system data as input and takes the recommendation score of the existing available industrial land as output; and 6) processing the index data of the existing available industrial land by using the intelligent recommendation algorithm model to obtain a recommendation score of the current available industrial land, and sorting the current available industrial land according to the recommendation score. According to the invention, through five steps of data acquisition, complex network construction, index calculation, intelligent algorithm and interactive display, comprehensive evaluation of a regional site selection decision is realized.

The invention discloses a prediction method for an industrial land output performance standard, and the method comprises the steps: integrating the industrial land of a target city, the built environment over the years, and enterprise economic data, and constructing a basic spatial database; further extracting land parcel features (such as area, plot ratio, shape regularity and the like) of each land parcel, and analyzing time evolution and location association features of the land parcel by combining space-time superposition; meanwhile, land dominant industries are matched according to enterprise economic data, and historical output performance is calculated. On the basis, plot features, space-time indexes, industry classification and performance data are fused, a'feature-performance 'associated data set is constructed, and a prediction model is established by taking the'feature-performance' associated data set as a training sample and adopting a machine learning algorithm. And aiming at the industrial land to be given, calculating each characteristic index and inputting the characteristic indexes into the model, so that a predicted value of the land output performance standard can be output. According to the method, quantitative mapping from multi-dimensional features to output performance is realized, and data-driven decision support is provided for industrial land planning and management.

The invention discloses a land utilization conflict analysis method based on land space planning, and relates to the technical field of land planning, and the method comprises the steps: obtaining planning data of a target region, the planning data comprising a space boundary and a population bearing scale of a residential land, and a space boundary and a pollution emission grade of an industrial land; generating a corresponding population density grade and an initial pollution influence intensity grade based on the population bearing scale and the pollution emission grade; on the basis of geographical and traffic data of the target area, a composite diffusion coefficient is calculated through a diffusion model, and the initial pollution influence intensity grade is corrected, so that the one-sidedness that only space boundaries are seen in traditional analysis is solved, conflict identification of living and industrial land is more accurate, and the method is suitable for popularization and application. And the rectification cost caused by conflicts after planning landing is reduced.

A plurality of plastic extruding machines are arrayed on a workshop floor, a bottom rail is erected above the plastic extruding machines, a longitudinal moving force piece is suitable for driving a longitudinal moving frame to longitudinally move along the bottom rail, a transverse moving force piece is suitable for driving a transverse moving sliding table to transversely move along the longitudinal moving frame, and a crane is connected with the transverse moving sliding table. The crane is suitable for hoisting a finished product frame output by the plastic extruding machine and is also suitable for placing a material frame to a preparation station of the plastic extruding machine, the temporary storage bin is connected with the transverse moving sliding table, a discharging opening of the temporary storage bin is provided with an opening and closing structure, the filling telescopic pipe extends in the vertical direction, and the telescopic power piece is suitable for driving the lower end of the filling telescopic pipe to stretch into or be away from a material barrel of the plastic extruding machine. Compared with the prior art, raw material supply and finished product transfer-out can be carried out above a workshop, so that a channel between plastic extruding machines can be narrowed, more plastic extruding machines can be arrayed in the workshop, and the utilization rate of industrial land in the workshop is increased.