An execution resource management system based on big data

Abstract

The present invention relates to the field of data analysis technology, and in particular to an execution resource management system based on big data, the system comprising: a scanning module, which is used to scan the shape of a scanned object and generate a scanning model and generate a projection graphic according to the scanning model; a decision module, which is used to distribute the scanned object to different processors according to the scanning model; a processing module, which is used to receive the scanned object and calculate the scanned object to obtain a required space; an assembly module, which is used to assemble the scanned object according to the required space; the present invention accurately calculates the assembly volume of the scanned object by setting the above modules, effectively improving the detection accuracy while effectively improving the stability and practicality of the execution resource management system based on big data.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to an execution resource management system based on big data. Background Art

[0002] Big data refers to the extraction, processing, and analysis of useful information from very large data sets through computer technology and data analysis methods, and the discovery of new laws and insights. It has become an important part of the contemporary information society and is widely used in business, science, government management and other fields. The big data platform is a visual web page for developers or ordinary people to use. The purpose is to simplify the cost of using big data and shield various program commands when using big data functions. A cluster is usually a number of servers connected through the network and a complete set of big data software is installed on these machines. It is mainly used to organize and manage multiple machines to complete tasks in collaboration (big data technology is based on clusters as hardware). Each machine in the cluster plays a different role, including master node (management), backup node, slave node (execution), etc. In a macro sense, the cluster is the resource for big data execution. JAVA is a program development language used to develop ordinary program software and can also be used to develop big data application software. Big data technology is equivalent to a relatively new technology. It was born to complete the calculation of extremely large amounts of data. It is usually a technology that can integrate many machines to complete data storage and calculation. Big data engineers are technicians who can develop big data programs. A scheduler is a program that allocates and arranges tasks or resources according to certain rules and strategies. Big data computing technology is the computing technology for a large amount of data. Due to the huge amount of data, the conventional method of computing is very slow or cannot be completed, so it is necessary to use a cluster for computing. When computing, it is necessary to install special big data computing software in the cluster to organize various nodes to collaborate and complete the computing of big data applications. At present, the more popular big data computing technologies include Hadoop technology (Hadoop is a relatively broad big data technology system, mainly referring to MapReduce, Yarn, HDFS), Spark, Flink technology, etc. Usually, the application developed by big data developers refers to the application package developed based on the above big data computing technology. Big data application submission and computing is the process of submitting the application developed by the developer to the big data cluster for computing. For example: the developer develops a big data application based on the big data computing technology Spark. After the development is completed, it is just a static package (such as a file with the extension .jar). This package needs to be put on the cluster for execution (the execution result is not important, the main focus is on the submission process). The submission process is basically an existing fixed method, and the submission methods of different big data computing technologies are roughly the same. With the popularity of the Internet and the express delivery industry, people can now shop without leaving their homes. The packages of various commodities that enter the express delivery station for packing are large and small and of various shapes. Currently, effective packaging of express delivery is a good way to improve the efficiency of express delivery. Using big data to reasonably allocate the assembly space of express delivery can not only reasonably allocate the transportation of express delivery, but also reduce the cost of express delivery.

[0003] The Chinese patent application publication number: CN112330201A discloses an invention disclosure of a logistics intelligent distribution vehicle scheduling method based on big data analysis. The invention counts the number of express delivery points in the area and the number of parcels arriving at each express delivery point within a preset time period and the set delivery time, and counts the total delivery volume of each express delivery point within the set delivery time, and compares it with the number of parcels arriving at each express delivery point within the preset time period, thereby constructing a set of remaining express delivery points for vehicles and a set of remaining express delivery points for parcel arrivals, thereby selecting the corresponding remaining express delivery points for vehicles from the remaining express delivery points set of vehicles using the principle of scheduling by proximity for each remaining express delivery point in the set of remaining express delivery points for parcel arrivals, and performing distribution vehicle scheduling, thereby realizing intelligent and perfect scheduling of logistics distribution vehicles, overcoming the drawbacks of the current scheduling of logistics distribution vehicles at express delivery points, improving the scheduling efficiency of distribution vehicles, effectively reducing the logistics distribution cost, and improving the service quality of logistics distribution enterprises.

[0004] Chinese patent application publication number: CN111389736A discloses an intelligent express delivery method and system based on big data and unmanned transportation, which invents an intelligent express delivery method and system based on big data and unmanned transportation, including: if an express delivery request is received, controlling the large unmanned transportation equipment to start and controlling the first camera to capture the first image in real time, controlling the large unmanned transportation equipment to fly to the top of the community residential building in sequence to hover and control the logistics warehouse to place the express storage box on the rooftop, controlling the express storage box to unfold and controlling the express sorting robot in the express sorting warehouse to sort and classify the express items stored in the express storage box, controlling the distribution mechanism to start and controlling the second camera to capture the second image in real time, controlling the distribution mechanism to move to the side of the express sorting robot through a moving track to queue and control the express delivery box to open, controlling the weight sensor to start to obtain weight information in real time and analyze whether the express delivery box has placed the express items, if so, controlling the express delivery box to close and controlling the distribution mechanism to move to the corresponding resident's house window through the moving track.

[0005] However, the above method has the following problems: the frequent deployment of vehicles increases the cost of using vehicles, the time cost of waiting for vehicles increases, the transportation cost of large-scale unmanned transportation equipment increases, and multiple logistics warehouses need to be established for coordinated use, which increases the cost of express logistics. Summary of the invention

[0006] To this end, the present invention provides an execution resource management system based on big data to overcome the problems in the prior art of frequent deployment of vehicles, increased vehicle use costs, increased waiting time costs for vehicles, increased transportation costs due to large-scale unmanned transportation equipment, and the need to establish multiple logistics warehouses for coordinated use, which increases the cost of express logistics.

[0007] To achieve the above object, the present invention provides an execution resource management system based on big data, which is provided with a decision-making module, including:

[0008] A projection unit, which is used to count the number of corners of the scanned model projection figure, and place the scanned model projection figure on the standard model projection figure with the same number of corners to compare and obtain a projection result;

[0009] A comparison unit, used for comparing the scanned model with each standard model whose projection results are passed to obtain a comparison result;

[0010] an allocating unit, configured to allocate the scanned object to different processors according to the comparison result;

[0011] Wherein, each of the processors corresponds to one of the standard models; the projection graph is a graph formed by projecting each model onto a horizontal plane;

[0012] The projection unit compares the angle contrast change rate with the angle setting change rate;

[0013] If the angle contrast change rate is less than the angle setting change rate, the projection unit obtains that the projection result is passed;

[0014] If the angle contrast change rate is not less than the angle setting change rate, the projection unit obtains that the projection result is not passed;

[0015] The comparison unit compares the scanned model with the standard models whose corresponding projection results are passed, calculates the volume change rate according to the comparison, and compares the volume change rate with the set change rate;

[0016] If the volume change rate is less than the set change rate, the comparison unit obtains the comparison result as passed;

[0017] If the volume change rate is not less than the set change rate, the comparison unit obtains the comparison result as failure;

[0018] The allocation unit allocates the scanned object with the comparison result of passing to a corresponding processor;

[0019] The angle setting change rate is positively correlated with the performance of the processor; the setting change rate is positively correlated with the performance of the processor.

[0020] Furthermore, an execution resource management system based on big data includes:

[0021] A scanning module, which is used to scan the shape of the scanned object and generate the scan model, and generate a projection image according to the scan model;

[0022] The decision module is connected to the scanning module and is used to allocate the scanned objects to different processors according to the scanning model;

[0023] A processing module, connected to the decision module, comprising a plurality of processors, configured to receive the scanned object and calculate the scanned object to obtain the required space;

[0024] An assembly module, connected to the processing module, for assembling the scanned object according to the required space;

[0025] Wherein, the required space is the space volume that the scanned object needs to occupy during the assembly process;

[0026] When the scanning module generates a projection figure, the projected arc is replaced by a straight line tangent to the arc and forms a closed polygon with the remaining projected straight lines.

[0027] Furthermore, the projection unit aligns the geometric center point of the scanned model projection figure with the geometric center point of the standard model projection figure, rotates the scanned model projection figure with the geometric center point as the center so that each corner corresponds to each corner of the standard model projection figure one by one, calculates the size of each corresponding angle and compares them with each other to obtain the angle change rate, and selects the result with the smallest angle change rate as the angle comparison change rate; the scanned model projection figure is placed in turn on the standard model projection figure with the same number of corners as it, and the angle comparison change rates are calculated respectively.

[0028] Furthermore, the scanning module places the scanned object in a stable state and scans it to obtain the scanned model;

[0029] The stable state is that the scanned object does not deflect in a static state.

[0030] Furthermore, the comparison unit inserts the scanned model into the standard model, adjusts the scanned model until the overlap between the two is maximized, and calculates the volume change rate.

[0031] Furthermore, if the comparison result is not passed, the comparison unit continues to compare the scanned object with the remaining standard models in sequence until the comparison result is passed;

[0032] If the comparison result is passed, the allocation unit allocates the scanned object to a processor corresponding to the standard model compared with the scanned object.

[0033] Furthermore, the decision module obtains the progress of each processor in processing the scanned object;

[0034] If the number of the scanned objects waiting to be processed in the processor exceeds the optimal processing number, the allocation unit reallocates the scanned objects to an idle processor;

[0035] The idle processor is a processor whose queue number of scanned objects waiting to be processed does not exceed the optimal processing number; the optimal processing number is positively correlated with the performance of the processor.

[0036] Furthermore, the scanned object can be allocated to a number of the idle processors;

[0037] The standard model corresponds to a processor, and a single processor corresponds to a single standard model.

[0038] Furthermore, the decision module compares the processing time of a single scanned object in different processors to obtain the fastest processor; the allocation unit allocates the scanned object with time requirements to the fastest processor;

[0039] The fastest processor is the processor that takes the shortest time to process the same scanned object; and the time requirement is that the time required to process the scanned object is the shortest.

[0040] Furthermore, the assembly module scans the assembled collection of the plurality of scanned objects and inserts the scanned objects that fit the remaining space;

[0041] The assembly is composed of a plurality of the scanned objects; and the remaining space is the volume of space in the assembly that is not filled by the scanned objects when the plurality of the scanned objects are assembled according to the required space.

[0042] Compared with the prior art, the beneficial effect of the present invention is that the system of the present invention utilizes the method of setting up the above-mentioned modules to accurately detect the volume of the parcels after express packaging, thereby improving the accuracy of the detection and effectively improving the stability and practicality of the execution resource management system based on big data.

[0043] Furthermore, by setting up a comparison unit and utilizing the models that can be processed by existing processors, the most suitable processor is accurately found through comparison. This effectively improves the detection of the size of express packages and further improves the stability and practicality of the execution resource management system based on big data.

[0044] Furthermore, by setting up allocation units, express parcels can be accurately distributed, which effectively improves the distribution efficiency and further enhances the stability and practicality of the execution resource management system based on big data.

[0045] Furthermore, by setting up an assembly module to scan the remaining space size, the utilization rate of the assembly space during the assembly process of express packages can be improved. While effectively saving space, the stability and practicality of the execution resource management system based on big data is further improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a structural schematic diagram of a decision module of an execution resource management system based on big data of the present invention;

[0047] Figure 2 It is a structural diagram of the execution resource management system based on big data of the present invention;

[0048] Figure 3 A schematic diagram of the structure of calculating the volume change rate by comparing the first standard model with the embodiment of the present invention;

[0049] Figure 4 A schematic diagram of the structure of calculating the volume change rate by comparing the second standard model with the embodiment of the present invention;

[0050] Figure 5 A structural diagram of a scanning module generating a projection pattern according to an embodiment of the present invention;

[0051] Figure 6 A structural diagram of calculating the angle contrast change rate according to an embodiment of the present invention;

[0052] Among them: 1, scanned model; 2, first standard model; 3, second standard model; 4, original projection; 5, projection figure; 6, standard model projection figure; 7, first position; 8, second position. DETAILED DESCRIPTION

[0053] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0054] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0055] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0056] In addition, it should be noted that in the description of the present invention, 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 the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0057] See also Figure 1 As shown, it is a structural diagram of the decision module of the execution resource management system based on big data of the present invention, and the decision module includes:

[0058] A projection unit, which is used to count the number of corners of the scanned model projection figure, and place the scanned model projection figure on the standard model projection figure with the same number of corners to compare and obtain a projection result;

[0059] A comparison unit, which is used to compare the scanned model with each standard model whose projection results pass to obtain a comparison result;

[0060] an allocating unit, which is used to allocate the scanned objects to different processors according to the comparison results;

[0061] Wherein, each processor corresponds to a standard model; the projection graph is a graph formed by projecting each model onto a horizontal plane;

[0062] The projection unit compares the angle contrast change rate with the angle setting change rate;

[0063] If the angle contrast change rate is less than the angle setting change rate, the projection unit obtains a projection result that is passed;

[0064] If the angle contrast change rate is not less than the angle setting change rate, the projection unit obtains a projection result that fails;

[0065] The comparison unit compares the scanned model with each standard model whose corresponding projection results are passed, calculates the volume change rate according to the comparison, and compares the volume change rate with the set change rate;

[0066] If the volume change rate is less than the set change rate, the comparison unit obtains a comparison result of passing;

[0067] If the volume change rate is not less than the set change rate, the comparison unit obtains a comparison result of failure;

[0068] The allocation unit allocates the scanned object with a comparison result of passing to a corresponding processor;

[0069] Example 1: In this example, the angle setting change rate is 10%. If the projection unit obtains an angle contrast change rate of 5%, the projection result is passed;

[0070] If the projection unit obtains an angular contrast change rate of 15%, the projection result is not passed;

[0071] The change rate is set to 10%, and the comparison unit compares the scanned model with the first standard model. If the comparison unit obtains a volume change rate of 5%, the comparison result is passed;

[0072] If the comparison unit obtains a volume change rate of 15%, the comparison result is failed.

[0073] The rate of change of the angle setting is positively correlated with the performance of the processor; the rate of change of the setting is positively correlated with the performance of the processor.

[0074] In one embodiment, the processor processing power is 1 MHz and the angle setting change rate is 5%;

[0075] The processor has a processing power of 2 MHz and an angle setting change rate of 10%;

[0076] The processor power is 3 MHz and the angle setting variation rate is 15%.

[0077] In one embodiment, the processor processing power is 1 MHz and the setting change rate is 10%;

[0078] The processor has a processing power of 2 MHz and a set change rate of 15%;

[0079] The processor power is 3 MHz and the setting change rate is 20%.

[0080] Specifically, by setting up a comparison unit and utilizing the models that can be processed by existing processors, the most suitable processor is accurately found through comparison. This effectively improves the size detection of express packages and further improves the stability and practicality of the execution resource management system based on big data.

[0081] See also Figure 2 As shown, it is a structural diagram of an execution resource management system based on big data of the present invention, an execution resource management system based on big data, including:

[0082] A scanning module, which is used to scan the shape of the scanned object and generate a scanning model, and to generate a projection image according to the scanning model;

[0083] A decision module, connected to the scanning module, for allocating scanned objects to different processors according to the scanning model;

[0084] A processing module, which is connected to the decision module and includes a plurality of processors, for receiving the scanned object and calculating the scanned object to obtain the required space;

[0085] An assembly module, which is connected to the processing module and is used to assemble the scanned object according to the required space;

[0086] Among them, the required space is the volume of space that the scanned object needs to occupy during the assembly process;

[0087] When the scanning module generates a projection figure, the projected arc is replaced by a straight line tangent to it and forms a closed polygon with the remaining projected straight lines.

[0088] See also Figure 5 As shown, it is a structural diagram of a scanning module generating a projection pattern according to an embodiment of the present invention;

[0089] In one embodiment, the original projection 4 is an irregular shape, and the scanning module replaces the projected arc with a straight line tangent to it and forms a closed polygon with the remaining projected straight lines, which is the projection shape 5.

[0090] Specifically, the system of the present invention utilizes the above-mentioned modules to perform accurate volume detection on parcels after express packaging, thereby improving the accuracy of detection and effectively improving the stability and practicality of the execution resource management system based on big data.

[0091] Specifically, the projection unit aligns the geometric center point of the scanned model projection figure with the geometric center point of the standard model projection figure, rotates the scanned model projection figure with the geometric center point as the center so that each corner corresponds to the corner of the standard model projection figure one by one, calculates the size of each corresponding angle and compares them with each other to obtain the angle change rate, and selects the result with the smallest angle change rate as the angle comparison change rate; places the scanned model projection figure on the standard model projection figure with the same number of corners in turn, and calculates the angle comparison change rates respectively.

[0092] See also Figure 6 As shown, it is a structural diagram of calculating the angle contrast change rate according to an embodiment of the present invention;

[0093] In one embodiment, the geometric center point of the projection figure 5 is aligned with the geometric center point of the standard model projection figure 6, the four angles of the projection figure 5 are 120 degrees, 120 degrees, 60 degrees and 60 degrees respectively, and the four angles of the standard model projection figure 6 are 120 degrees, 120 degrees, 60 degrees and 60 degrees respectively. Since the projection figure 5 and the standard model projection figure 6 are both centrally symmetrical figures, the angular change rates calculated at the first position 7 and the second position 8 are 0 and 4 respectively, then the angular contrast change rate is 0.

[0094] Specifically, the scanning module places the scanned object in a stable state and scans it to obtain a scanned model;

[0095] The stable state is that the scanned object does not deflect in a static state.

[0096] Specifically, the comparison unit fits the scanned model into the standard model, adjusts the scanned model until the overlap between the two is maximized, and calculates the volume change rate.

[0097] Embodiment 2:

[0098] Please cooperate Figure 3 See also Figure 4 As shown, they are respectively a schematic diagram of a structure in which an embodiment of the present invention compares with a first standard model to calculate a volume change rate, and a schematic diagram of a structure in which an embodiment of the present invention compares with a second standard model to calculate a volume change rate;

[0099] On the basis of Example 1, this embodiment is different from it in that the scanning model 1 is a cube with a side length of 2.5 meters; the first standard model 2 is a cube with a side length of 3 meters; the second standard model 3 is a diamond column with a side length of 4 meters, angles of 60 degrees and 120 degrees respectively, and a height of 3 meters.

[0100] The volume of the scanned model 1 is 15.625 cubic meters; the volume of the first standard model is 27 cubic meters; the volume of the second standard model is 41.568 cubic meters;

[0101] The first volume change rate is 72.8%; the second volume change rate is 166%.

[0102] Specifically, by setting up allocation units, express parcels can be accurately distributed, which effectively improves the distribution efficiency and further enhances the stability and practicality of the execution resource management system based on big data.

[0103] Specifically, if the comparison result is not passed, the comparison unit continues to compare the scanned object with the remaining standard models in sequence until the comparison result is passed;

[0104] If the comparison result is passed, the allocation unit allocates the scanned object to a processor corresponding to the standard model compared with the scanned object.

[0105] Embodiment 3:

[0106] Based on Example 1, this embodiment is different from it in that when the comparison result is not passed, the comparison unit continues to compare the scanned model with the second standard model. If the comparison unit obtains a volume change rate of 5%, the comparison result is passed.

[0107] Specifically, the decision module obtains the progress of each processor in processing the scanned object;

[0108] If the number of scanned objects waiting to be processed in the processor exceeds the optimal processing number, the allocation unit reallocates the scanned objects to an idle processor;

[0109] Embodiment 4:

[0110] Based on Example 3, this embodiment is different from it in that the optimal processing number is 10, the decision module obtains the progress of the processor in processing the scanned objects, and if the number of scanned objects processed by the first processor is 20, which exceeds the optimal processing number, the allocation unit reallocates the scanned objects to the idle processor.

[0111] Among them, the idle processor is a processor whose queue number of scanned objects waiting to be processed does not exceed the optimal processing number; the optimal processing number is positively correlated with the processor performance.

[0112] In one embodiment, the processor processing power is 1 MHz and the optimal processing number is 10;

[0113] The processor has a processing power of 2 MHz and an optimal processing number of 20;

[0114] The processor has a processing power of 3 MHz and an optimal processing number of 30.

[0115] Specifically, the scanned objects can be assigned to several idle processors;

[0116] A standard model corresponds to a processor, and a single processor corresponds to a single standard model.

[0117] Specifically, the decision module compares the processing time of a single scanned object in different processors to obtain the fastest processor; the allocation unit allocates the scanned object with time requirements to the fastest processor;

[0118] The fastest processor is the processor that takes the least time to process the same scanned object; the time requirement is that the time required to process the scanned object is the shortest.

[0119] Embodiment 5:

[0120] Based on Example 4, this embodiment is different from it in that the processing time of the first scanned object in the first processor, the second processor and the third processor are 5 seconds, 6 seconds and 10 seconds respectively. If the first scanned object has a time requirement, the allocation module will allocate it to the first processor.

[0121] Specifically, the assembly module scans the assembly of several scanned objects and inserts the scanned objects that fit the remaining space size;

[0122] The aggregate is composed of a number of scanned objects; the remaining space is the space volume in the aggregate that is not filled by the scanned objects when the several scanned objects are assembled according to the required space.

[0123] Specifically, by setting up an assembly module to scan the size of the remaining space, the utilization rate of the assembly space during the assembly process of express packages can be improved. While effectively saving space, the stability and practicality of the execution resource management system based on big data can be further improved.

[0124] Example 6: In this example, the developer has developed a big data application package and is ready to submit it to a cluster for execution. There are multiple clusters.

[0125] Developers upload the application package on the big data platform and can select the cluster to be submitted on the page. The cluster options are added by the operation and maintenance personnel when the platform is initialized. There are usually multiple clusters. A scheduler with decision-making functions is pre-deployed on the big data platform. The cluster is pre-connected to the cluster for detection (the cluster usually provides an information interface to the outside world). The CPU, memory, network, queue load and other information of each cluster detected are recorded and returned to the "decision-making" module. After analysis by the "decision-making" module, the appropriate cluster is selected for submission.

[0126] How does the "decision-making" module of the scheduler determine whether it is the optimal cluster? First, the user provides the required number of resources, urgency and other information as reference data. Second, based on the detection results, it determines which cluster's resources are closest to the queue load, and submits it to the cluster for execution.

[0127] After determining the optimal submission cluster, the "decision-making" module hands over the task submitted by the big data program to the "task submission" module. The logic of module submission is as follows:

[0128] The program submission module is deployed on a machine that is not related to any cluster. The configuration files required for each cluster submission are placed on an independent configuration management machine (downloaded in advance by the operation and maintenance personnel). Each time the "decision-making" module determines the cluster to be submitted, the "task submission" program is temporarily downloaded as the configuration of the submission program (the configuration describes the corresponding cluster information) for submission.

[0129] If the developer knows which cluster is suitable for executing their program, they can also choose to customize the cluster on the platform without supporting smart selection;

[0130] You can also select several more suitable clusters to execute tasks simultaneously, and the cluster that produces results first will be given priority. This is the strategy adopted in the previous point when all clusters have sufficient resources.

[0131] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0132] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An execution resource management system based on big data, characterized in that: It is equipped with a decision-making module, including: A projection unit, which is used to count the number of corners of the scanned model projection figure, and place the scanned model projection figure on the standard model projection figure with the same number of corners to compare and obtain a projection result; A comparison unit, used for comparing the scanned model with each standard model whose projection results are passed to obtain a comparison result; an allocating unit, configured to allocate the scanned object to different processors according to the comparison result; Wherein, each of the processors corresponds to one of the standard models; the projection graph is a graph formed by projecting each model onto a horizontal plane; The projection unit compares the angle contrast change rate with the angle setting change rate; If the angle contrast change rate is less than the angle setting change rate, the projection unit obtains that the projection result is passed; The comparison unit compares the scanned model with the standard models whose corresponding projection results are passed, calculates the volume change rate according to the comparison, and compares the volume change rate with the set change rate; If the volume change rate is less than the set change rate, the comparison unit obtains the comparison result as passed; The allocation unit allocates the scanned object with the comparison result of passing to a corresponding processor; The angle setting change rate is positively correlated with the performance of the processor; the setting change rate is positively correlated with the performance of the processor.

2. The execution resource management system based on big data according to claim 1, characterized in that: include: A scanning module, which is used to scan the shape of the scanned object and generate the scan model, and generate a projection image according to the scan model; The decision module is connected to the scanning module and is used to allocate the scanned objects to different processors according to the scanning model; A processing module, connected to the decision module, comprising a plurality of processors, configured to receive the scanned object and calculate the scanned object to obtain the required space; An assembly module, connected to the processing module, for assembling the scanned object according to the required space; Wherein, the required space is the space volume that the scanned object needs to occupy during the assembly process; When the scanning module generates a projection figure, the projected arc is replaced by a straight line tangent to the arc and forms a closed polygon with the remaining projected straight lines.

3. The execution resource management system based on big data according to claim 2 is characterized in that: The projection unit aligns the geometric center point of the scanned model projection figure with the geometric center point of the standard model projection figure, rotates the scanned model projection figure with the geometric center point as the center so that each corner corresponds to each corner of the standard model projection figure one by one, calculates the size of each corresponding angle and compares them with each other to obtain the angle change rate, and selects the result with the smallest angle change rate as the angle comparison change rate; the scanned model projection figure is placed on the standard model projection figure with the same number of corners in turn to obtain the angle comparison change rate.

4. The execution resource management system based on big data according to claim 3 is characterized in that: The scanning module places the scanned object in a stable state and scans it to obtain the scanned model; The stable state is that the scanned object does not deflect in a static state.

5. The execution resource management system based on big data according to claim 4 is characterized in that: The comparison unit inserts the scanned model into the standard model, adjusts the scanned model until the overlap between the two is maximized, and calculates the volume change rate.

6. The execution resource management system based on big data according to claim 5, characterized in that: If the comparison result is not passed, the comparison unit continues to compare the scanned object with the remaining standard models in sequence until the comparison result is passed; If the comparison result is passed, the allocation unit allocates the scanned object to a processor corresponding to the standard model compared with the scanned object.

7. The execution resource management system based on big data according to claim 6, characterized in that: The decision module obtains the progress of each processor in processing the scanned object; If the number of the scanned objects waiting to be processed in the processor exceeds the optimal processing number, the allocation unit reallocates the scanned objects to an idle processor; The idle processor is a processor whose queue number of scanned objects waiting to be processed does not exceed the optimal processing number; the optimal processing number is positively correlated with the performance of the processor.

8. The execution resource management system based on big data according to claim 7, characterized in that: The scanned object can be allocated to a number of the idle processors; The standard model corresponds to a processor, and a single processor corresponds to a single standard model.

9. The execution resource management system based on big data according to claim 8, characterized in that: The decision module compares the processing time of a single scanned object in different processors to obtain the fastest processor; the allocation unit allocates the scanned object with time requirements to the fastest processor; The fastest processor is the processor that takes the shortest time to process the same scanned object; and the time requirement is that the time required to process the scanned object is the shortest.

10. The execution resource management system based on big data according to claim 9, characterized in that: The assembly module scans the assembled collection of the plurality of scanned objects and inserts the scanned objects that fit the remaining space; The assembly is composed of a plurality of the scanned objects; and the remaining space is the volume of space in the assembly that is not filled by the scanned objects when the plurality of the scanned objects are assembled according to the required space.

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