Enterprise resource planning management system based on cloud computing

Abstract

The invention discloses an enterprise resource planning management system based on cloud computing, and relates to the technical field of cloud computing, the enterprise resource planning management system comprises a management center, and the management center is connected with an enterprise acquisition module, a resource processing module, an intelligent analysis module and a comprehensive planning module; dividing resource use departments, and converting and classifying enterprise resource data according to a division result to obtain a current monitoring array and a front-end reference data block; performing pairing judgment on the front-end reference data block through the current monitoring array to obtain a front-end matching data segment, and performing path updating on the pre-filling flow path according to the front-end matching data segment to obtain a resource flow path; performing cyclic pairing supplement on the front-end matching data segment to obtain a supplement flow-through resource segment, and performing flow marking and resource proportion release on a resource flow-through path through the supplement flow-through resource segment to obtain an optimal release proportion resource; the optimal configuration of resources is realized, the resource use efficiency is improved, and the resource waste is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud computing, and specifically to an enterprise resource planning and management system based on cloud computing. Background Art

[0002] With the acceleration of the global informatization process, enterprises are facing increasingly fierce competitive pressures. How to enhance the core competitiveness of enterprises through effective management means has become the key to the development of enterprises. As a comprehensive information system integrating various business processes and management functions of enterprises, the enterprise resource planning system plays an important role in improving enterprise management efficiency, optimizing resource allocation, and reducing operating costs.

[0003] Traditional enterprise resource planning systems usually require enterprises to invest a large amount of hardware equipment and software resources, and at the same time, a dedicated operation and maintenance team is needed for system maintenance and upgrade. This undoubtedly increases the costs of enterprises and is difficult to adapt to the rapidly changing market demands of enterprises.

[0004] The emergence of cloud computing technology provides a new solution for enterprises. Therefore, an enterprise resource planning and management system based on cloud computing is provided. It makes full use of the computing resources in the cloud to achieve dynamic allocation and elastic scaling of resources, improve system efficiency and flexibility, and at the same time ensure the high availability and security of data, providing guarantee for the stable operation of enterprises. Summary of the Invention

[0005] The object of the present invention can be achieved through the following technical solutions:

[0006] An enterprise resource planning and management system based on cloud computing, including a management center, wherein the management center is connected with an enterprise collection module, a resource processing module, an intelligent analysis module, and a comprehensive planning module;

[0007] The enterprise collection module is used to collect enterprise resource data of resource-using departments;

[0008] The resource processing module is used to coordinate and convert enterprise resource data to obtain department resource data streams, perform combined replacement on the department resource data streams to obtain department scale arrays, divide resource-using departments to obtain current operation departments and front-end operation departments, and classify the department scale arrays according to the divided departments to obtain current monitoring arrays and front-end reference data blocks;

[0009] The intelligent analysis module is used to perform in-segment pairing on the front-end reference data blocks through the current monitoring arrays to obtain paired verification arrays, make result rulings on the paired verification arrays to obtain front-end matching data segments, and update the path of the pre-filled flow path according to the front-end matching data segments to obtain resource flow paths;

[0010] The comprehensive planning module is used to verify and match the front-end reference data block according to the front-end matching data segment, obtain the reference verification data segment, perform cyclic verification on the reference verification data segment, obtain the supplementary flowing resource segment, mark the flow of the resource flowing path through the supplementary flowing resource segment, and perform resource ratio allocation on the target enterprise according to the flow marking result to obtain the optimal allocation ratio resource.

[0011] Preferably, the process of the enterprise acquisition module acquiring the enterprise resource data of the resource using department includes:

[0012] Decompose the target enterprise by department to obtain the resource using department;

[0013] Based on the target enterprise, perform monitoring setting on the obtained resource using department to obtain the monitoring acquisition end;

[0014] Collect elements of the resource using department through the monitoring acquisition end to obtain enterprise resource data.

[0015] Preferably, the process of obtaining the department scale array includes:

[0016] Perform coordinated conversion on the enterprise resource data to obtain the department resource data stream, perform data segmentation on the department resource data stream to obtain the department resource data block;

[0017] Based on the department resource data stream, perform sequential combination on the department resource data blocks to obtain the department usage array, and perform status replacement on the obtained department usage array to obtain the department scale array.

[0018] Preferably, the process of classifying the data of the department scale array according to the divided departments includes:

[0019] Arbitrarily select one of the resource using departments as the current operation department, obtain the department resource data stream of the current operation department, and denote the department scale array of the obtained department resource data stream as the current monitoring array;

[0020] Based on the current operation department, mark the remaining resource using departments as the front-end operation departments;

[0021] Obtain the department resource data stream of the front-end operation department, and mark the department resource data block corresponding to the department resource data stream as the front-end reference data block.

[0022] Preferably, the process of performing intra-segment pairing on the front-end reference data block through the current monitoring array includes:

[0023] Upload the front-end reference data block to the current monitoring array, perform front-end correction on the current monitoring array to obtain the corrected monitoring array;

[0024] Perform assimilation transformation based on the obtained front-end reference data block and the correction monitoring array to obtain a paired check matrix.

[0025] Preferably, the process of obtaining the front-end matching data segment includes:

[0026] Perform result judgment on the paired check matrix to obtain a data pairing result;

[0027] Based on the obtained data pairing result, make a judgment and ruling on the paired check matrix to obtain a front-end matching data segment;

[0028] Construct a pre-filled flow path based on the current job department.

[0029] Preferably, the process of updating the path of the pre-filled flow path according to the front-end matching data segment includes:

[0030] Locate resources for the current job department according to the obtained front-end matching data segment to obtain a matching resource location node;

[0031] Based on the current job department, fill and update the pre-filled flow path according to the obtained matching resource location node to obtain a resource flow path.

[0032] Preferably, the process of circularly checking the reference verification data segment includes:

[0033] Obtain the front-end reference data block corresponding to the front-end matching data segment, and record the obtained front-end reference data block as the reference verification data segment;

[0034] Based on the department resource data flow of the current job department, with the reference verification data segment as the center, circularly check the front-end reference data blocks on both sides of the reference verification data segment with the front-end matching data segment to obtain a supplementary flowing-through resource segment.

[0035] Preferably, the process of obtaining the best placement ratio resources includes:

[0036] Perform flow statistics on the supplementary flowing-through resource segment to obtain the flowing-through resource usage;

[0037] Obtain the matching resource location node corresponding to the front-end matching data segment, and associate the obtained flowing-through resource usage with the corresponding matching resource location node;

[0038] Based on the matching resource location node, mark the flow of the resource flow path according to the obtained flowing-through resource usage to obtain a resource flow path axis;

[0039] Perform resource ratio placement on the target enterprise according to the obtained resource flow path axis to obtain the best placement ratio resources.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] 1. Divide the target enterprise into departments, collect the resource data used by each department, perform coordinated conversion on the resource data to obtain department resource data streams. Group the department resource data streams to obtain monitoring arrays and data blocks. Match whether the data streams of the current department contain corresponding data segments through the data blocks of other departments, and mark the position information of the corresponding other departments for the partial data segments of other departments contained in the current department, thereby generating a resource flow path. Divide the overall resource data of an enterprise into parts, improving the efficiency and accuracy of data processing. At the same time, generating a resource flow path can visually monitor the resource flow directions between departments within the enterprise, promoting department collaboration.

[0042] 2. Statistically analyze the data volume of the resource data containing other departments within the current department to obtain the usage amount of resources in each department, and update it in combination with the generated resource flow path to obtain a resource flow path axis. Through the resource flow path axis, the proportion of resource data used by each department can be directly obtained, facilitating resource allocation and investment for the target enterprise according to the resource flow path axis, and obtaining the optimal resource amount to be allocated to the department. This allocation can not only maximize the reasonable allocation of the required resource data for each department, but also avoid resource waste caused by excessive resource allocation, greatly improving work efficiency, achieving the optimal allocation of resources, and improving resource utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0044] Figure 1 is the schematic diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] The following will clearly and completely describe the technical solutions of the present invention in combination with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0046] As Figure 1 shown, an enterprise resource planning management system based on cloud computing includes a management center, and the management center is connected to an enterprise collection module, a resource processing module, an intelligent analysis module, and a comprehensive planning module;

[0047] The enterprise collection module is used to collect enterprise resource data of the resource-using departments;

[0048] The resource processing module is used to coordinate and transform the enterprise resource data to obtain department resource data streams, combine and replace the department resource data streams to obtain department scale arrays, divide the resource-using departments to obtain the current operation department and the front-end operation department, classify the data of the department scale arrays according to the divided departments to obtain the current monitoring array and the front-end reference data block;

[0049] The intelligent analysis module is used to perform in-segment pairing on the front-end reference data block through the current monitoring array to obtain a pairing verification array, make a result ruling on the pairing verification array to obtain the front-end matching data segment, and update the path of the pre-filled flow path according to the front-end matching data segment to obtain the resource flow path;

[0050] The comprehensive planning module is used to verify and match the front-end reference data block according to the front-end matching data segment to obtain a reference verification data segment, perform a cyclic check on the reference verification data segment to obtain a supplementary flowing resource segment, mark the flow of the resource flow path through the supplementary flowing resource segment, and perform resource ratio allocation on the target enterprise according to the flow marking result to obtain the best allocation ratio resources.

[0051] In actual application, the process of the enterprise collection module collecting enterprise resource data includes:

[0052] Decompose the target enterprise by department to obtain the resource-using departments;

[0053] Furthermore, the target enterprise refers to an enterprise that needs to conduct resource planning and management, and department decomposition means decomposing the internal composition of the target enterprise to obtain individual sub-units that need to use resources, that is, the resource-using departments;

[0054] Based on the target enterprise, set monitoring for the obtained resource-using departments to obtain monitoring and collection terminals. The monitoring and collection terminals are set according to the resource-using departments and can collect the data information generated by the corresponding resource-using departments, that is, each resource-using department has a corresponding monitoring and collection terminal;

[0055] Build a transmission link between the monitoring and collection terminals and the management center. The transmission link means transmitting the data information of the monitoring and collection terminals and the management center to each other, and there is also a corresponding transmission link between any two monitoring and collection terminals;

[0056] Collect elements of the resource-using departments through the monitoring and collection terminals to obtain enterprise resource data. The enterprise resource data includes basic data, financial data, operation data, human resource data, and supply chain data. Among them, the basic data represents the basic information of each department;

[0057] Associate the obtained enterprise resource data with the resource-using departments at the monitoring and acquisition end.

[0058] The resource processing module is used to divide the resource-using departments, and convert and classify the enterprise resource data according to the division results to obtain the current monitoring array and the front-end reference data block. The specific process includes:

[0059] Obtain the enterprise resource data, perform coordinated conversion on the obtained enterprise resource data to obtain the department resource data stream;

[0060] Furthermore, the coordinated conversion means converting the enterprise resource data into a data stream composed of binary code elements. For each enterprise resource data, the same coding criterion is followed;

[0061] Perform traffic statistics on the obtained department resource data stream to obtain the resource usage amount;

[0062] The traffic statistics means counting the data length of the department resource data stream, that is, counting the number of binary code elements in the department resource data stream segment, which is the resource usage amount, representing the data volume of the enterprise resource data used by the resource-using department;

[0063] Perform data segmentation on the obtained department resource data stream to obtain department resource data blocks;

[0064] The data segmentation means dividing the obtained department resource data stream into groups of 7 binary code elements to obtain department resource data blocks. If the last group has less than 7 binary code elements, fill 0 at the front of the last group of department resource data blocks to make it reach 7 binary code elements;

[0065] Mark the obtained department resource data blocks as , where n represents the number of the resource-using department corresponding to the department resource data stream, n = 1, 2, 3, ……, v1, v1 is a positive integer, i represents the number of the department resource data block, i = 1, 2, 3, ……, v2, v2 is a positive integer, and "n - i" represents the i-th department resource data block corresponding to the n-th department resource data stream;

[0066] Based on the department resource data stream, perform sequential combination according to the obtained department resource data blocks to obtain the department usage array, and mark the obtained department usage array as , where k represents the number of the department usage array, k = 1, 2, 3, ……, v3, v3 is a positive integer, and "n - k" represents the k-th department usage array corresponding to the n-th department resource data stream;

[0067] Furthermore, the process of the sequential combination includes:

[0068] In the department resource data stream, group it in groups of three department resource data blocks, and construct a matrix according to the three grouped department resource data blocks, which is the department usage array. The obtained department usage array is a matrix with three rows and seven columns. In particular, if the last group has less than three department resource data blocks, supplement it from the previous group until it meets the requirement of three department resource data blocks in a group.

[0069] Perform status replacement on the obtained department usage array to obtain the department scale array.

[0070] It should be further noted that in the specific implementation process, the process of the status replacement includes:

[0071] Obtain the department usage array, perform data block grouping on the obtained department usage array to obtain a regulation grouping and a reference grouping. Among them, data block grouping means performing internal grouping in the department usage array. Denote the first four columns of the array as the regulation grouping, which is a matrix with three rows and four columns, and denote the last three columns of the array as the reference grouping, which is a matrix with three rows and three columns.

[0072] Perform terminal transformation on the obtained department usage array based on the reference grouping to obtain the department scale array.

[0073] The terminal transformation means that in the department usage array, perform row transformation inside the array to make the reference grouping become the identity matrix, that is, perform modulo-2 addition operation on the rows of the department usage array until the reference grouping part becomes the identity matrix. At the same time, record the transformed regulation grouping, and the transformed identity matrix of the reference grouping constitutes the department scale array. Mark the obtained department scale array as ;

[0074] Select any one of the resource usage departments as the current operation department, obtain the department resource data stream of the current operation department, and denote the department scale array of the obtained department resource data stream as the current monitoring array. Among them, the current operation department means selecting one of the numerous resource usage departments as the department where the current resource is used, and also denote the department scale array of the department resource data stream corresponding to the current operation department as the currently used array, which is the current monitoring array.

[0075] Based on the current operation department, mark the remaining resource usage departments as the front-end operation departments.

[0076] Obtain the department resource data stream of the front-end operation departments, and mark the department resource data blocks corresponding to the department resource data stream as the front-end reference data blocks.

[0077] The intelligent analysis module conducts paired adjudication on the front-end reference data blocks through the current monitoring array to obtain the front-end matching data segments, and updates the pre-filled flow path according to the front-end matching data segments to obtain the resource flow path. The specific process is as follows:

[0078] Based on the order of data segmentation, the obtained front-end reference data blocks are uploaded to the current monitoring array. Here, the upload order means uploading the front-end reference data blocks to the current monitoring array of the current operation department in sequence according to the order of data segmentation, and only selecting the front-end reference data blocks corresponding to the department resource data flow of one front-end operation department among the front-end operation departments;

[0079] The current monitoring array conducts intra-segment pairing on the front-end reference data blocks to obtain a paired check matrix;

[0080] It should be further noted that in the specific implementation process, the process of intra-segment pairing includes:

[0081] The front part of the current monitoring array is corrected to obtain a corrected monitoring array. The front part correction means performing a transpose operation on the current monitoring array, and the obtained corrected monitoring array is the transpose form of the current monitoring array;

[0082] Based on the order of the front-end reference data blocks, assimilation transformation is performed according to the obtained front-end reference data blocks and the corrected monitoring array to obtain a paired check matrix, and the obtained paired check matrix is marked as where, , represents the front-end reference data block, represents the corrected monitoring array, "T" represents the transpose symbol, and "·" represents matrix multiplication of the front-end reference data block and the corrected monitoring array;

[0083] Specifically, when the front-end reference data block and the correction monitoring array are assimilated and transformed, the following order is followed: after the first front-end reference data block of the department resource data stream of the front-end operation department is assimilated and transformed with the first paired check matrix of the current operation department, then the first front-end reference data block of the department resource data stream of the front-end operation department is assimilated and transformed with the second paired check matrix of the current operation department, the first front-end reference data block of the department resource data stream of the front-end operation department is assimilated and transformed with the third paired check matrix of the current operation department, until it is assimilated and transformed with the last paired check matrix of the current operation department. Then, the second front-end reference data block of the department resource data stream of the front-end operation department is successively assimilated and transformed with each paired check matrix of the current operation department, the third front-end reference data block of the department resource data stream of the front-end operation department is successively assimilated and transformed with each paired check matrix of the current operation department, until each front-end reference data block of the department resource data stream of the front-end operation department is successively assimilated and transformed with each paired check matrix of the current operation department, and the obtained paired check matrices are statistically analyzed;

[0084] Perform result judgment on the obtained paired check matrices to obtain data pairing results, where the data pairing results include matching data blocks and abnormal data blocks;

[0085] The result judgment means analyzing the state of the paired check matrix. When all elements in the paired check matrix are zero, the front-end reference data block corresponding to the paired check matrix is denoted as a matching data block, indicating that the front-end reference data block assimilated and transformed with the correction monitoring array is correspondingly matched with the correction monitoring array;

[0086] When there are non-zero elements in the paired check matrix, the front-end reference data block corresponding to the paired check matrix is denoted as an abnormal data block, indicating that the front-end reference data block assimilated and transformed with the correction monitoring array does not match the correction monitoring array;

[0087] Make a judgment and ruling on the paired check matrix according to the obtained data pairing results to obtain the front-end matching data segment;

[0088] The process of the judgment and ruling includes:

[0089] Obtain the data pairing result of the paired check matrix corresponding to the front-end reference data block of the front-end operation department. When the data pairing results of six consecutive front-end reference data blocks are all matching data blocks, record the department resource data block of the current operation department corresponding to the correction monitoring array of the six consecutive matching data blocks as the front-end matching data segment, indicating that there is a part of the data in the front-end operation department undergoing assimilation transformation that coincides with the data of the current operation department. That is, the part of the enterprise resource data of the front-end operation department that coincides with the enterprise resource data of the current operation department is the front-end matching data segment. Among them, the front-end matching data segment is to mark the overlapping part of the data of the front-end operation department undergoing assimilation transformation in the current operation department;

[0090] Construct a pre-filled flow path based on the department resource data stream of the current operation department. Among them, the pre-filled flow path is a blank data band similar to the department resource data stream. There is no data information in this data band, and it is only used to mark whether the enterprise resource data of the current operation department comes from other front-end operation departments. If there is data from other front-end operation departments, according to the position of the front-end matching data segment determined from other front-end operation departments in the department resource data stream of the current operation department, and at the same time determine the order of resource flows of multiple front-end operation departments, which is the pre-filled flow path;

[0091] Locate the resources of the current operation department according to the obtained front-end matching data segment to obtain the matching resource position node. The matching resource position node includes the number of the correction monitoring array of the front-end matching data segment and the number of the front-end operation department undergoing assimilation transformation. Among them, resource location means that according to the number of the correction monitoring array corresponding to the front-end matching data segment, the position of the front-end matching data segment in the department resource data stream can be obtained, which is the matching resource position node, indicating the position of the part of the resource data of the front-end operation department that coincides with the current operation department in the enterprise resource data of the current operation department. At the same time, marking the number of the front-end operation department undergoing assimilation transformation can directly obtain which resource usage department the part of the resource data that coincides with the current operation department comes from;

[0092] It should be further noted that in the specific implementation process, for each front-end operation department, it is necessary to successively perform assimilation transformation with the correction monitoring array of the current operation department, and make a result judgment on the obtained paired check matrix to obtain the data pairing result. When the data pairing result is judged and determined to be the front-end matching data segment, the current operation department corresponding to the front-end matching data segment is resource-located to obtain the matching resource position node, indicating the enterprise resource data of the front-end operation department with the resource data part that coincides with the current operation department, and the corresponding resource data position can be located, that is, the position information of the part of the resource data that matches the current operation department in the front-end operation department can be obtained through the matching resource position node; in particular, not every front-end operation department has a corresponding matching resource data part in the current operation department. Only when it meets the condition of being judged and determined to be the front-end matching data segment can the front-end matching data segment be located to obtain the matching resource position node, that is, the matching resource position node represents the resource data part that coincides with the enterprise resource data of the current operation department;

[0093] Based on the current operation department, the pre-filled flow path is filled and updated according to the obtained matching resource position node to obtain the resource flow path;

[0094] It should be further noted that in the specific implementation process, the process of the filling and updating includes:

[0095] According to the position order of the matching resource position nodes, the matching resource position nodes are uploaded to the corresponding positions of the pre-filled flow path of the current operation department to obtain the resource flow path. Among them, the resource flow path is composed of the matching resource position nodes, indicating which front-end operation department the part of the resources that coincides with the enterprise resource data of the current operation department comes from. That is, for the enterprise resource data obtained by the current operation department in the target enterprise, whether there is enterprise resource data from other departments, and the enterprise resource data from other departments can be obtained through the resource flow path, that is, which front-end operation department it comes from can be obtained according to the matching resource position node.

[0096] For a resource flow path, the sequence of the resource usage departments through which the enterprise resource data can flow within the target enterprise can be obtained according to the matching resource position nodes in the path;

[0097] If it is necessary to reasonably allocate the resource information required by each resource - using department, it is necessary to distribute the best resource data to each department through the resource flow path, that is, to maximize the use of resources to achieve the purpose of saving resources. Then, according to the matching resource position nodes included in the resource flow path, obtain the actual used resource quantity of each front - end operation department passing through, and upload the obtained resource flow path to the comprehensive planning module. The comprehensive planning module distributes the best - placed ratio resources to each resource - using department within the target enterprise according to the resource flow path. The specific process includes:

[0098] Obtain the front - end reference data block corresponding to the front - end matching data segment, and denote the obtained front - end reference data block as the reference verification data segment;

[0099] The reference verification data block is composed of front - end reference data blocks whose data pairing results of six consecutive front - end reference data blocks are all matching data blocks;

[0100] Based on the department resource data stream of the current operation department, with the reference verification data segment as the center, circularly check the front - end reference data blocks on both sides of the reference verification data segment with the front - end matching data segment to obtain the supplementary flowing - through resource segment;

[0101] It should be further noted that in the specific implementation process, the process of the circular check includes:

[0102] Obtain the first front - end reference data block adjacent to the left side of the reference verification data segment, and perform in - segment check on the obtained front - end reference data block with the front - end matching data segment to obtain the verification pairing check matrix;

[0103] The in - segment check means that the obtained front - end reference data block and the correction monitoring array in the front - end matching data segment are sequentially subjected to assimilation transformation, that is, in the order of the numbers of the correction monitoring array, sequentially perform assimilation transformation with the obtained front - end reference data block to obtain the pairing check matrix, and denote the obtained pairing check matrix as the verification pairing check matrix;

[0104] Traverse and judge the obtained verification pairing check matrix to obtain the normalized pairing result, and the normalized pairing result includes the matching judgment block and the abnormal judgment block;

[0105] The traverse and judgment means that since the front - end reference data block and the correction monitoring array in the front - end matching data segment are sequentially subjected to assimilation transformation, several verification pairing check matrices are obtained. The results of the obtained several verification pairing check matrices are sequentially judged. If the result judgments of at least three verification pairing check matrices are matching data blocks, the front - end reference data block for which the in - segment check is performed is denoted as the matching judgment block, otherwise, it is denoted as the abnormal judgment block;

[0106] When the normalization pairing result is a matching decision block, obtain the second front-end reference data block adjacent to the left side of the reference verification data segment. Perform in-segment verification between the obtained front-end reference data block and the front-end matching data segment, and traverse and judge the obtained verification pairing check matrix. If the normalization pairing result is still a matching decision block, continue to obtain the third front-end reference data block adjacent to the left side of the reference verification data segment, and perform traversal and judgment with the front-end matching data segment until the normalization pairing result of traversing and judging the verification pairing check matrix is an abnormal decision block, then end the in-segment verification process to obtain the left verification matching segment. Among them, the left verification matching segment means that the first front-end reference data block adjacent to the left side of the reference verification data segment to the front-end reference data block before the traversal judgment result is an abnormal decision block are all recorded as the left verification matching segment, that is, the front-end reference data block before obtaining the abnormal decision block is the data segment that matches the front-end matching data segment, indicating that the left verification matching segment still has a part that coincides with the department resource data flow of the current operation department. Increase the actually matching data segment by obtaining a matching decision block through in-segment verification;

[0107] Specifically, the abnormal decision block represents the end point of the in-segment verification. If the in-segment verification between the first front-end reference data block and the front-end matching data segment and the traversal judgment of the verification pairing check matrix obtain an abnormal decision block, directly end the in-segment verification process, indicating that there is no data segment on the left side of the reference verification data segment that matches the front-end matching data segment;

[0108] Obtain the first front-end reference data block adjacent to the right side of the reference verification data segment, perform in-segment verification between the obtained front-end reference data block and the front-end matching data segment, obtain the verification pairing check matrix, and repeat the process of obtaining the normalization pairing result for the verification pairing check matrix until the normalization pairing result is an abnormal decision block, then end the in-segment verification process to obtain the right verification matching segment;

[0109] Record the obtained left verification matching segment, reference verification data segment, and right verification matching segment as the supplementary flowing-through resource segment, indicating the supplement of the data of the front-end matching segment that matches the department resource data flow of the current operation department, and obtain the complete flowing-through resource data segment within the front-end operation department that flows through the current operation department.

[0110] Perform flow statistics on the obtained supplementary flowing-through resource segment to obtain the flowing-through resource usage;

[0111] The said flow statistics means counting the number of binary code elements in the supplementary flowing-through resource segment to obtain the data volume of the enterprise resource data flowing through the current operation department by the front-end operation department, which is the flowing-through resource usage;

[0112] Obtain the matching resource position node corresponding to the front-end matching data segment, and associate the obtained flowing-through resource usage with the corresponding matching resource position node;

[0113] Based on the matching resource position nodes, flow marking is performed on the resource flow path according to the obtained resource usage during flow, and a resource flow path axis is obtained;

[0114] The flow marking means that in the resource flow path, according to the resource usage during flow associated with each matching resource position node, the obtained resource usage during flow is marked at the position of the matching resource position node, and the marked resource flow path is equivalently replaced to obtain a resource flow path axis;

[0115] Further, the process of the equivalent replacement includes:

[0116] Construct a blank flow axis. The blank flow axis is a blank scale axis that is the same as the resource flow path and is flexibly transformed for each scale. The size of each scale represents the size of the resource usage during flow at the corresponding matching resource position node. The scales are generated according to the ratio of different resource usages during flow on a path, that is, the obtained resource usage during flow is proportionally converted to obtain a resource usage ratio during flow, and the obtained resource usage ratio during flow is marked as W, where, , represents the resource usage during flow of the matching resource position node for proportional conversion, represents the sum of all resource usages during flow in the resource flow path;

[0117] The scale of the blank flow axis is updated according to the obtained resource usage ratio during flow to obtain a resource flow path axis, that is, according to the ratio of the resource usage ratio during flow, the scale of each matching resource position node is generated. Then, each obtained scale represents the size of the resource usage during flow. Based on the obtained resource flow path axis, the flow volume of the resource data of each front-end operation department flowing through the current operation department can be obtained, and thus it is convenient to allocate the resource quantity to each resource usage department;

[0118] Resource ratio allocation is performed on the target enterprise according to the obtained resource flow path axis to obtain the best allocated ratio resources.

[0119] It should be further noted that in the specific implementation process, the process of the resource ratio allocation includes:

[0120] Obtain the resource flow path axis. Since the resource flow path axis is the proportion of an enterprise's resource data among the resource usage departments within the target enterprise, the resource ratio can be divided according to the resource flow volume of each resource usage department within the resource flow path axis to obtain the best allocated ratio resources;

[0121] The optimal allocation ratio resources refer to equally dividing the resources of the target enterprise according to the order of the resource-using departments through which the resource data flows within the resource flow path axis and the amount of resources flowing through, obtaining the amount of resources with the optimal allocation ratio for each resource-using department, which is the optimal allocation ratio resources. It means that the resources allocated to each department are the enterprise resource data with corresponding ratios sequentially allocated to the corresponding resource-using departments according to the passing order of the resource flow path axis, so that the resource data received by each resource-using department just matches the required amount of resources, that is, there will be no waste and shortage, greatly improving the resource allocation speed and accuracy, maximizing the reasonable planning of the resource data required by each department, being conducive to saving resource costs, and avoiding resource waste caused by excessive allocated resources.

[0122] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not elaborate on all details, nor limit the present invention to the specific implementation manners. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principle and practical application of the present invention, so that those skilled in the relevant technical field can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. An enterprise resource planning management system based on cloud computing, comprising a management center, characterized in that: The management center is connected to an enterprise acquisition module, a resource processing module, an intelligent analysis module and a comprehensive planning module; The enterprise collection module is used to collect enterprise resource data of resource-using departments; The resource processing module is used to coordinate and convert enterprise resource data, obtain department resource data streams, combine and replace department resource data streams, obtain department scale arrays, divide resource-using departments, obtain current operating departments and front-end operating departments, classify department scale arrays according to the divided departments, and obtain current monitoring arrays and front-end reference data blocks; The intelligent analysis module is used to perform intra-segment pairing on the front-end reference data block through the current monitoring array to obtain a pairing check array, conduct result determination on the pairing check array to obtain a front-end matching data segment, and perform path update on the pre-filled flow path according to the front-end matching data segment to obtain a resource flow path; The comprehensive planning module is used to verify and match the front-end reference data block according to the front-end matching data segment, obtain the reference verification data segment, perform cyclic verification on the reference verification data segment, obtain the supplementary flow resource segment, mark the flow of resource flow paths through the supplementary flow resource segment, and allocate resources to the target enterprise according to the flow marking results to obtain the best allocation ratio of resources.

2. The cloud computing-based enterprise resource planning management system according to claim 1, characterized in that: The process of the enterprise collection module collecting enterprise resource data of resource-using departments includes: Decompose the target enterprise into departments to obtain resource-using departments; Based on the target enterprise, monitor and set the resource-using departments to obtain the monitoring collection terminal; By monitoring and collecting data from resource-using departments, we can obtain enterprise resource data.

3. The cloud computing-based enterprise resource planning management system according to claim 1, characterized in that: The process of obtaining a sector-scale array includes: Coordinate and convert enterprise resource data to obtain department resource data stream, and segment the department resource data stream to obtain department resource data blocks; The department resource data blocks are sequentially combined based on the department resource data stream to obtain a department usage array, and the state of the obtained department usage array is replaced to obtain a department scale array.

4. The cloud computing-based enterprise resource planning management system according to claim 3, characterized in that: The process of classifying data in the department scale array according to the divided departments includes: Select one of the resource-using departments as the current operating department, obtain the department resource data stream of the current operating department, and record the department scale array of the obtained department resource data stream as the current monitoring array; Based on the current operation department, mark the remaining resource usage departments as front-end operation departments; The department resource data stream of the front-end operation department is obtained, and the department resource data block corresponding to the department resource data stream is marked as a front-end reference data block.

5. The cloud computing-based enterprise resource planning management system according to claim 1, characterized in that: The process of intra-segment matching of the front-end reference data blocks through the current monitoring array includes: Upload the front-end reference data block to the current monitoring array, perform front correction on the current monitoring array, and obtain a corrected monitoring array; An assimilation transformation is performed based on the obtained front-end reference data block and the corrected monitoring array to obtain a paired verification array.

6. The cloud computing-based enterprise resource planning management system according to claim 1, characterized in that: The process of obtaining the front-end matching data segment includes: The result of pairing check array is judged to obtain data pairing result; According to the obtained data pairing result, the pairing check array is judged and determined to obtain the front-end matching data segment; Builds a pre-populated flow path based on the current job department.

7. The cloud computing-based enterprise resource planning management system according to claim 1, characterized in that: The process of updating the pre-filled flow path according to the front-end matching data segment includes: Locate the resources of the current operation department according to the obtained front-end matching data segment to obtain the matching resource location node; Based on the current operation department, the pre-filled flow path is filled and updated according to the obtained matching resource location nodes to obtain the resource flow path.

8. The cloud computing-based enterprise resource planning management system according to claim 1, characterized in that: The process of cyclically checking the reference verification data segment includes: Acquire a front-end reference data block corresponding to the front-end matching data segment, and record the obtained front-end reference data block as a reference verification data segment; Based on the department resource data flow of the current operating department, with the reference verification data segment as the center, the front-end reference data blocks on both sides of the reference verification data segment are cyclically checked with the front-end matching data segment to obtain a supplementary flow-through resource segment.

9. The cloud computing-based enterprise resource planning management system according to claim 1, characterized in that: The process of obtaining the best delivery ratio resources includes: Perform traffic statistics on the supplementary flow-through resource segment to obtain the flow-through resource usage; Obtaining a matching resource location node corresponding to the front-end matching data segment, and associating the obtained flow resource usage with the corresponding matching resource location node; Based on the matching resource location node, flow marking is performed on the resource flow path according to the obtained flow resource usage, so as to obtain the resource flow path axis; According to the obtained resource flow path axis, resources are allocated to the target enterprise to obtain the best allocation of resources.

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