ERP (Enterprise Resource Planning) coprocessing method and system

Abstract

The invention is suitable for the technical field of cooperative processing, and particularly relates to an ERP cooperative processing method and system.The method comprises the steps that historical work order data in an ERP system are collected, cooperative nodes are read, processing flows are determined, each piece of historical work order data corresponds to one processing flow, and after real-time to-be-processed data uploaded to the ERP system by a user is received, the real-time to-be-processed data is sent to the ERP system; and comparing the similarity between the historical work order data and the real-time to-be-processed data, defining the historical work order data with the highest similarity as target data, and defining the corresponding collaborative node as a target node. By determining and selecting the popup window, human resources can be fully utilized, the waiting time of work order starting and circulation can be shortened, the smooth processing flow can be ensured, the overall cooperative processing efficiency of the ERP system can be improved, the smooth circulation of the data issued to the work order in real time can be ensured by calculating the unupdated duration, the flow blockage can be avoided, and the user experience can be improved. Therefore, intelligent circulation and automatic cooperation of the real-time work order are realized, and the office efficiency is remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of collaborative processing, in particular to an ERP collaborative processing method and system. BACKGROUND

[0002] ERP collaboration refers to the linkage between processes, data and roles in an ERP (Enterprise Resource Planning) system, which realizes data collaborative processing and decision-making among multiple departments, multiple positions and multiple systems.

[0003] In the prior art, there are still a large number of collaborative actions relying on manual review. For example, after the sales department enters a large quantity of urgent orders in the ERP system, the ERP system can automatically identify the current inventory shortage, but cannot trigger the collaborative processing flow, that is, the sales department can only publish the inventory shortage information in the ERP system, and cannot notify the head of the procurement department. After the procurement department determines the inventory shortage information, the procurement department manually creates a procurement application according to the actual situation in the ERP system, and the production planning department cannot obtain the new procurement demand information in the first time, which causes the production scheduling plan not to be adjusted in time, and ultimately causes the urgent order to be unable to be delivered within the agreed time.

[0004] Therefore, how to automatically trigger the collaborative processing flow is a technical problem to be solved by the present application. SUMMARY

[0005] The present application aims to provide an ERP collaborative processing method and system to solve the problem of how to automatically trigger the collaborative processing flow in the background art.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solutions: An ERP collaborative processing method, the method comprising: Collecting historical work order data in an ERP system, reading a collaborative node, and determining a processing flow, wherein each historical work order data corresponds to a processing flow, when receiving real-time to-be-done data uploaded by a user to the ERP system, comparing the similarity between the historical work order data and the real-time to-be-done data, defining the historical work order data with the highest similarity as target data, and defining the corresponding collaborative node as a target node; Setting evaluation indexes of task quantity, wherein each evaluation index corresponds to a weight value, and via the weight value, the task load level of each target node is configured, wherein the task load level includes high, medium and low, defining the target node with a low task load level as an available node, integrating the real-time to-be-done data and all target nodes, generating a selection pop-up window, and issuing it to the user terminal of the available node; Identify the interaction result of the selection pop-up window, and adjust the target node, wherein the adjustment at least includes: increase and delete, establish the processing order of the target node, create a key pair, use the private key to sign the real-time to-be-done data, and deliver to the target node according to the processing order; Receive the feedback data of the target node to the real-time to-be-done data, and perform signature verification to generate the flow progress, record the non-updated time length of the flow progress, when the non-updated time length is greater than the threshold, aggregate the real-time to-be-done data, the processing order and the flow progress, generate a collaborative processing report, and send it to a preset terminal.

[0007] Further, the step of collecting historical work order data in the ERP system, reading the collaborative node, and determining the processing flow includes: From the historical work order data, the start time and the completion time of each collaborative node are extracted; According to the order from the start time to the end, all the collaborative nodes are sorted to obtain the processing flow.

[0008] Further, the step of setting the evaluation index of the task amount, wherein each evaluation index corresponds to a weight value, and the task load level of each target node is configured through the weight value includes: Collect the real-time value of the target node under each evaluation index, establish the corresponding relationship between the real-time value and the weight value, and draw a comparison table; Query the comparison table, superimpose the weight values corresponding to all evaluation indexes to obtain a load score, and divide the load score into several intervals, wherein each interval corresponds to a task load level.

[0009] Further, the step of integrating the real-time to-be-done data and all target nodes, generating a selection pop-up window, and delivering to the user terminal of the available node includes: Integrate the time item into the selection pop-up window, calculate the processing time using the start time and the completion time; When the processing time exceeds the corresponding time item, activate the pre-edited emergency handling rule.

[0010] Further, the step of creating a key pair, using a private key to sign real-time to-be-done data, and delivering to the target node according to the processing order includes: According to a preset frequency, dynamically update the key pair; Generate a version corresponding to the key pair one by one, and embed a time stamp.

[0011] Further, the step of receiving the feedback data of the target node to the real-time to-be-done data, and performing signature verification to generate the flow progress, recording the non-updated time length of the flow progress includes: The non-updated duration is divided into several segments, and a coping strategy corresponding to each segment is edited; The flow progress is published to an ERP system and is dynamically updated.

[0012] Further, the system comprises: The collection module is configured to collect historical work order data in the ERP system, read a collaboration node, and determine a processing flow. Each historical work order data corresponds to a processing flow. When receiving real-time to-do data uploaded by a user to the ERP system, the collection module is configured to compare the similarity between the historical work order data and the real-time to-do data, define the historical work order data with the highest similarity as target data, and define the corresponding collaboration node as a target node. The setting module is configured to set evaluation indexes of a task amount, wherein each evaluation index corresponds to a weight value. Via the weight value, a task load level of each target node is configured, wherein the task load level comprises high, medium, and low. The target node with a low task load level is defined as an available node. The real-time to-do data and all target nodes are integrated to generate a selection pop-up window and are sent to a user terminal of the available node. The sending module is configured to receive feedback data of the target node on the real-time to-do data, and to sign the feedback data. The processing order of the target node is established, a key pair is created, and the real-time to-do data is signed by using a private key. The real-time to-do data is sent to the target node according to the processing order. The sending module is configured to receive feedback data of the target node on the real-time to-do data, and to sign the feedback data. The processing order of the target node is established, a key pair is created, and the real-time to-do data is signed by using a private key. The real-time to-do data is sent to the target node according to the processing order.

[0013] Further, the collection module comprises: The extraction unit is configured to extract the start time and the end time of each collaboration node from the historical work order data. The sorting unit is configured to sort all collaboration nodes in the order of the start time from early to late to obtain the processing flow.

[0014] Further, the setting module comprises: The drawing unit is configured to collect real-time values of the target node under each evaluation index, establish a corresponding relationship between the real-time values and the weight values, and draw a comparison table. The division unit is configured to query the comparison table, superimpose the weight values corresponding to all evaluation indexes, obtain a load score, and divide the load score into several intervals, wherein each interval corresponds to a task load level. An integration unit is used to integrate time items into the selection pop-up window and calculate the processing time using the start time and completion time; The activation unit is used to activate the pre-edited emergency response rules when the processing time exceeds the corresponding time item.

[0015] Furthermore, the distribution module includes: An update unit is used to dynamically update the key pair according to a preset frequency; An embedding unit is used to generate a version that corresponds one-to-one with the key pair and embed a timestamp.

[0016] Compared with the prior art, the beneficial effects of the present invention are: By defining the processing flow of historical work order data, mature processes can be quickly reused, improving the processing efficiency of real-time pending data. By calculating similarity and identifying target nodes, it is possible to avoid relying entirely on manual experience to trigger corresponding processing flows, reducing process startup time and cross-departmental communication costs, and further improving collaborative processing efficiency. Through the selection pop-up window, human resources can be fully utilized, shortening the startup and flow time of real-time pending data, ensuring efficient and orderly operation of the processing flow, and improving the overall collaborative processing efficiency of the ERP system. By calculating the time without updates, process blockage can be avoided, thereby realizing the intelligent flow and automated collaborative processing of real-time pending data, significantly improving office efficiency. Attached Figure Description

[0017] Figure 1 A flowchart of the ERP collaborative processing method provided in an embodiment of the present invention; Figure 2 This is a first sub-process flowchart of the ERP collaborative processing method provided in an embodiment of the present invention; Figure 3 This is a second sub-process flowchart of the ERP collaborative processing method provided in an embodiment of the present invention; Figure 4 This is a flowchart of the third sub-process of the ERP collaborative processing method provided in an embodiment of the present invention; Figure 5 This is a flowchart of the fourth sub-process of the ERP collaborative processing method provided in the embodiments of the present invention; Figure 6 This is a block diagram of the ERP collaborative processing system provided in an embodiment of the present invention; Figure 7 This is a block diagram of the data acquisition module in the ERP collaborative processing system provided in an embodiment of the present invention; Figure 8 A block diagram showing the composition of the setting module in the ERP collaborative processing system provided in this embodiment of the invention; Figure 9This is a block diagram of the distribution module in the ERP collaborative processing system provided in this embodiment of the invention; Figure 10 This is a block diagram of the sending module in the ERP collaborative processing system provided in an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0019] In Example 1, Figure 1 The implementation flow of the ERP collaborative processing method provided in this embodiment of the invention is illustrated below, and is described in detail below: S100: Collects historical work order data from the ERP system, reads collaborative nodes, and determines the processing flow. Each historical work order data corresponds to a processing flow. When it receives real-time pending data uploaded by the user to the ERP system, it compares the similarity between the historical work order data and the real-time pending data, defines the historical work order data with the highest similarity as the target data, and defines the corresponding collaborative node as the target node.

[0020] The system reads historical work order data accumulated over a long period in the ERP system, determining the creation time, work order type, processing steps, participating personnel, and processing results for each historical work order. It traces each node the historical work order passed through during its actual flow, identifying collaborative nodes. These collaborative nodes refer to specific business personnel or positions. Based on the sequence and dependencies between collaborative nodes, and considering the duration, processing method, and flow results of historical work orders at each collaborative node, the collaborative nodes are linked and summarized to generate a processing flow. Each historical work order corresponds to one processing flow. Upon receiving real-time pending data uploaded to the ERP system by users, the system performs standardized preprocessing, including keyword extraction and determining the data type of the real-time pending data. Candidate data belonging to the same business domain as the real-time pending data is extracted from the historical work order data. The same business domain refers to categories such as finance, human resources, and technology.

[0021] By utilizing existing semantic matching models, the similarity between real-time pending data and each of the aforementioned candidate data items is calculated. The historical work order data with the highest similarity to the real-time pending data is defined as the target data, and the corresponding collaborative node is defined as the target node. The advantage of this approach is that it provides a data foundation for the reuse of real-time order data, thereby enabling automated collaborative processing.

[0022] S200: Set the evaluation indicators for task volume, where each evaluation indicator corresponds to a weight value. Based on the weight value, configure the task load level of each target node, where the task load level includes: high, medium and low. Define the target node with a low task load level as an available node. Integrate real-time to-do data and all target nodes, generate a selection pop-up window, and send it to the user terminal of the available node.

[0023] Each target node's workload evaluation metrics are determined, including the number of pending tasks, average task processing time, task complexity, and historical completion rate. A corresponding weight value is assigned to each metric, and the target node's weight values ​​for each metric are summed to obtain a load score, which represents the target node's current workload. The load scores are then categorized into high, medium, and low workload levels, with each level corresponding to a specific load score range. A high workload level indicates a large current workload and high processing pressure, while a low workload level indicates a smaller current workload and stronger idle processing capacity, allowing the node to prioritize and process new real-time pending data.

[0024] Integrate real-time to-do data and all target nodes, generate a selection pop-up, and send it to the user terminals of available nodes. Available nodes then adjust the target nodes and their corresponding order. For example, consider historical work order data: Order number: H001, expedited purchase quantity: 100 pieces, processing flow: Sales → Purchasing → Warehouse → Production → Finance, time limit: 2 days. This historical work order data has a high similarity to real-time to-do data. Find the position with the least workload among all the above positions (target nodes), which is the available node. Send the real-time to-do data and the aforementioned processing flow (target node) to the available node, where the available node corrects the target node and processing order.

[0025] S300: Identify the interaction result of the selection pop-up window and adjust the target node, wherein the adjustment includes at least: adding and deleting, establishing the processing order of the target node, creating a key pair, using the private key to sign the real-time pending data, and sending it to the target node according to the processing order.

[0026] The system receives interaction results uploaded by available nodes, adjusts target nodes and processing order, and allows for the addition or deletion of target nodes. After determining the processing order, a key pair is created, and the real-time pending data is signed using the private key and distributed to each target node according to the processing order. In this application, signing the real-time pending data prevents its tampering and facilitates the traceability of the processing flow, enabling timely detection of process blockages and anomalies.

[0027] S400: Receive feedback data from the target node on real-time pending data, verify signatures, generate a workflow progress, record the unupdated duration of the workflow progress, and when the unupdated duration exceeds a threshold, summarize the real-time pending data, processing order, and workflow progress, generate a collaborative processing report, and send it to a preset terminal.

[0028] When a target node receives feedback data from the previous target node in the ERP system, it uses a pre-configured public key to verify the signature of the real-time pending data, determining the flow progress of the real-time pending data in the ERP system. The feedback data includes the completion result, task status, and processing progress of the previous target node. This flow progress is monitored, recording the duration of stagnation (i.e., the time without update). When the time without update for a target node exceeds a threshold, it indicates a potential bottleneck or blockage in the real-time pending data. This target node is then included in the monitoring scope. A collaborative processing report is generated, summarizing the current real-time pending data, the processing order of each target node, and the flow progress information of each target node. The collaborative processing report includes not only the processing status of real-time pending data at each target node but also latency and time without update. The collaborative processing report is sent to a preset terminal, such as the administrator's mobile terminal, allowing for timely intervention by the administrator to eliminate bottlenecks and ensure that the real-time pending data is processed promptly.

[0029] In Example 2, Figure 2 The first sub-process flowchart of the ERP collaborative processing method provided in this embodiment of the invention is shown. The steps of collecting historical work order data from the ERP system, reading collaborative nodes, and determining the processing flow are described in detail below: S101: Extract the start time and completion time of each collaborative node from historical work order data.

[0030] The start time and completion time of each historical work order data are read. The start time refers to the time when the target node begins processing the historical work order data, and the completion time refers to the time when the historical work order data is completed.

[0031] S102: Sort all collaborative nodes in order of their startup time from earliest to latest to obtain the processing flow.

[0032] In the same historical work order data, the start time of each collaborative node is determined, and the collaborative nodes are sorted in order of their start times to obtain the processing flow.

[0033] In Example 3, Figure 3The second sub-process flowchart of the ERP collaborative processing method provided in this embodiment of the invention is shown. The following details the step of setting the evaluation indicators for the task load, where each evaluation indicator corresponds to a weight value, and configuring the task load level of each target node based on the weight values: S201: Collect the real-time values ​​of the target node under each evaluation indicator, establish the correspondence between the real-time values ​​and the weight values, and draw a comparison table.

[0034] Collect the real-time values ​​of the target node under each evaluation indicator, integrate the correspondence between the real-time values ​​and the weight values, and generate a lookup table. For example, if the number of pending tasks for a target node is 5, and the evaluation indicator is the number of pending tasks with a real-time value of 5, the weight value is 1 when the real-time value is 0-5, and the weight value is 5 when the real-time value is 6-10.

[0035] S202: Query the comparison table, superimpose the weight values ​​corresponding to all evaluation indicators to obtain the load score, and divide the load score into several intervals, where each interval corresponds to a task load level.

[0036] By consulting the reference table, the sum of the weight values ​​of each target node under all evaluation indicators is calculated, which is the load score. The load score is divided into several intervals, and each interval corresponds to a task load level. For example, when the load score is 1-10, the task load level is low.

[0037] In Example 4, Figure 3 The second sub-process flowchart of the ERP collaborative processing method provided in this embodiment of the invention is shown. The following details the steps of integrating real-time to-do data and all target nodes, generating selection pop-ups, and sending them to user terminals of available nodes: S203: Integrate the time item into the selection pop-up window, and calculate the processing time using the start time and completion time.

[0038] Write a time item to the target node corresponding to the selection pop-up window. The time item is the time limit. The difference between the start time and the completion time is defined as the processing time. When the processing time exceeds the time limit, it indicates that the corresponding target node has not processed the real-time pending data in a timely manner.

[0039] S204: When the processing time exceeds the corresponding time item, activate the pre-edited emergency response rules.

[0040] When the processing time exceeds the corresponding time item, the pre-edited emergency response rules are activated. These rules include temporarily adding collaborating personnel or providing cross-departmental support.

[0041] In Example 5,Figure 4 The fourth sub-process flowchart of the ERP collaborative processing method provided in this embodiment of the invention is shown. The following details the steps of creating a key pair, signing the real-time pending data using the private key, and sending it to the target node according to the processing order: S301: The key pair is dynamically updated according to a preset frequency.

[0042] The key pairs are rotated at a preset frequency to ensure the backtracking efficiency of the key pairs.

[0043] S302: Generate a version that corresponds one-to-one with the key pair and embed a timestamp.

[0044] When a key pair is updated, it is retained according to the version and the generation time of the version is recorded using a timestamp, which facilitates subsequent version verification, permission verification and auditing.

[0045] In Example 6, Figure 5 The fourth sub-process flowchart of the ERP collaborative processing method provided in this embodiment of the invention is shown. The following details the steps of receiving feedback data from the target node on real-time pending data, verifying signatures, generating workflow progress, and recording the unupdated duration of the workflow progress: S401: Divide the unupdated duration into several segments and edit the corresponding response strategy for each segment.

[0046] The time without updates is divided into several segments, such as normal segments, slightly delayed segments, moderately delayed segments, and severely delayed segments. A corresponding response strategy is set for each segment. The response strategy is the method of urging action when real-time pending data is not processed in a timely manner. For example, in the ERP system, the real-time pending data is highlighted or given priority in being pushed.

[0047] S402: Publish the aforementioned workflow progress to the ERP system and update it dynamically.

[0048] The real-time pending data, including its processing status, start time, and current progress percentage at each target node, is synchronized to the ERP system and dynamically updated.

[0049] Figure 6 This invention provides a block diagram of the composition of an ERP collaborative processing system 1, which includes: The data acquisition module 11 is used to collect historical work order data in the ERP system, read collaborative nodes, and determine the processing flow. Each historical work order data corresponds to a processing flow. When the real-time pending data uploaded by the user to the ERP system is received, the similarity between the historical work order data and the real-time pending data is compared, and the historical work order data with the highest similarity is defined as the target data, and the corresponding collaborative node is defined as the target node. The setting module 12 is used to set the evaluation indicators of task volume, wherein each evaluation indicator corresponds to a weight value. Based on the weight value, the task load level of each target node is configured, wherein the task load level includes: high, medium and low. The target node with a low task load level is defined as an available node. Real-time to-do data and all target nodes are integrated to generate a selection pop-up window and send it to the user terminal of the available node. The distribution module 13 is used to identify the interaction result of the selection pop-up and adjust the target node. The adjustment includes at least: adding and deleting, establishing the processing order of the target node, creating a key pair, using the private key to sign the real-time pending data, and distributing it to the target node according to the processing order. The sending module 14 is used to receive feedback data from the target node on the real-time pending data, verify the signature, generate the flow progress, record the unupdated duration of the flow progress, and when the unupdated duration is greater than the threshold, summarize the real-time pending data, processing order and flow progress, generate a collaborative processing report, and send it to the preset terminal.

[0050] Figure 7 This diagram illustrates the composition of the data acquisition module 11 in the ERP collaborative processing system provided by an embodiment of the present invention. The data acquisition module 11 includes: Extraction unit 111 is used to extract the start time and completion time of each collaborative node from historical work order data; The sorting unit 112 is used to sort all the collaborative nodes in order of their start times to obtain the processing flow.

[0051] Figure 8 This diagram illustrates the structural composition of the setting module 12 in the ERP collaborative processing system provided by an embodiment of the present invention. The setting module 12 includes: The drawing unit 121 is used to collect the real-time values ​​of the target node under each evaluation index, establish the correspondence between the real-time values ​​and the weight values, and draw a comparison table; Division unit 122 is used to query the lookup table, superimpose the weight values ​​corresponding to all evaluation indicators to obtain the load score, and divide the load score into several intervals, where each interval corresponds to a task load level. Integration unit 123 is used to integrate time items into the selection pop-up window and calculate the processing time using the start time and completion time; The activation unit 124 is used to activate the pre-edited emergency response rules when the processing time exceeds the corresponding time item.

[0052] Figure 9 This diagram illustrates the structural composition of the distribution module 13 in the ERP collaborative processing system provided by an embodiment of the present invention. The distribution module 13 includes: The update unit 131 is used to dynamically update the key pair according to a preset frequency; Embedding unit 132 is used to generate a version that corresponds one-to-one with the key pair and embed a timestamp.

[0053] Figure 10 This diagram illustrates the structural composition of the sending module 14 in the ERP collaborative processing system provided by an embodiment of the present invention. The sending module 14 includes: Editing unit 141 is used to divide the unupdated duration into several segments and edit the corresponding response strategy for each segment; The publishing unit 142 is used to publish the workflow progress to the ERP system and update it dynamically.

[0054] The acquisition module 11 is mainly used to complete step S100, the setting module 12 is mainly used to complete step S200, the sending module 13 is mainly used to complete step S300, and the sending module 14 is mainly used to complete step S400. Extraction unit 111 is mainly used to complete step S101, and sorting unit 112 is mainly used to complete step S102; The drawing unit 121 is mainly used to complete step S201, the dividing unit 122 is mainly used to complete step S202, the integrating unit 123 is mainly used to complete step S203, and the activation unit 124 is mainly used to complete step S204. The updating unit 131 is mainly used to complete step S301, and the embedding unit 132 is mainly used to complete step S302. The editing unit 141 is mainly used to complete step S401, and the publishing unit 142 is mainly used to complete step S402.

[0055] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0056] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0057] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An ERP collaboration processing method, characterized by, The method comprises: Collecting historical work order data in an ERP system, reading collaborative nodes, and determining processing procedures, wherein each historical work order data corresponds to a processing procedure, comparing the similarity between historical work order data and real-time to-be-done data after receiving real-time to-be-done data uploaded by a user into the ERP system, defining the historical work order data with the highest similarity as target data, and defining the corresponding collaborative node as a target node; Setting evaluation indexes of task quantity, wherein each evaluation index corresponds to a weight value, configuring the task load level of each target node through the weight value, wherein the task load level comprises high, medium, and low, defining the target node with a low task load level as an available node, integrating real-time to-be-done data and all target nodes, generating a selection pop-up window, and delivering it to the user terminal of the available node; Identifying the interaction result of the selection pop-up window and adjusting the target node, wherein the adjustment at least includes adding and deleting, establishing a processing order of the target node, creating a key pair, signing the real-time to-be-done data using a private key, and delivering it to the target node according to the processing order; Receiving feedback data of the target node on the real-time to-be-done data, performing signature verification, generating a flow progress, recording the non-updated duration of the flow progress, when the non-updated duration is greater than a threshold, summarizing the real-time to-be-done data, the processing order, and the flow progress, generating a collaborative processing report, and sending it to a preset terminal.

2. The ERP collaborative processing method of claim 1, wherein, The step of collecting historical work order data in an ERP system, reading collaborative nodes, and determining processing procedures comprises: Extracting the start time and completion time of each collaborative node from the historical work order data; According to the start time from early to late, all collaborative nodes are sorted to obtain the processing procedure.

3. The ERP collaborative processing method of claim 1, wherein, The step of setting evaluation indexes of task quantity, wherein each evaluation index corresponds to a weight value, and configuring the task load level of each target node through the weight value comprises: Collecting real-time values of the target node under each evaluation index, establishing a correspondence between the real-time values and the weight values, and drawing a comparison table; Querying the comparison table, superimposing the weight values corresponding to all evaluation indexes to obtain a load score, and dividing the load score into several intervals, wherein each interval corresponds to a task load level.

4. The ERP collaborative processing method of claim 2, wherein, The step of integrating real-time to-be-done data and all target nodes, generating a selection pop-up window, and delivering it to the user terminal of the available node comprises: Integrating time items into the selection pop-up window, calculating the processing duration using the start time and completion time; When the processing duration exceeds the corresponding time item, activating the pre-edited emergency handling rule.

5. The ERP collaboration processing method of claim 1, wherein, The step of creating a key pair, signing the real-time to-be-done data using a private key, and delivering it to the target node according to the processing order comprises: According to a preset frequency, dynamically updating the key pair; Generating a version corresponding to the key pair one by one and embedding a time stamp.

6. The ERP collaborative processing method of claim 2, wherein, The step of receiving feedback data of the target node on the real-time to-be-done data, performing signature verification, generating a flow progress, and recording the non-updated duration of the flow progress comprises: The non-updated duration is divided into several segments, and a coping strategy corresponding to each segment is edited; The flow progress is published to the ERP system and is dynamically updated.

7. An ERP collaboration system, characterized by The system comprises: A collection module configured to collect historical work order data in an ERP system, read a collaboration node, and determine a processing flow, wherein each historical work order data corresponds to a processing flow, and when receiving real-time to-do data uploaded by a user to the ERP system, compare the similarity between the historical work order data and the real-time to-do data, define the historical work order data with the highest similarity as target data, and define the corresponding collaboration node as a target node; A setting module configured to set evaluation indexes of task quantity, wherein each evaluation index corresponds to a weight value, and via the weight value, a task load level of each target node is configured, wherein the task load level comprises high, medium, and low, and a target node with a low task load level is defined as an available node, real-time to-do data and all target nodes are integrated, a selection pop-up window is generated, and is sent to a user terminal of the available node; A sending module configured to identify an interaction result of the selection pop-up window, and adjust the target node, wherein the adjustment at least comprises adding and deleting, establishing a processing order of the target node, creating a key pair, signing the real-time to-do data by using a private key, and sending the real-time to-do data to the target node according to the processing order; A sending module configured to receive feedback data of the target node on the real-time to-do data, and perform signature verification to generate a flow progress, record a non-updated duration of the flow progress, when the non-updated duration is greater than a threshold value, integrate the real-time to-do data, the processing order, and the flow progress to generate a collaborative processing report, and send the collaborative processing report to a preset terminal.

8. The ERP collaboration system of claim 7, wherein, The collection module comprises: An extraction unit configured to extract a start time and an end time of each collaboration node from the historical work order data; An ordering unit configured to sort all collaboration nodes according to the start time from early to late to obtain the processing flow.

9. The ERP collaboration system of claim 8, wherein, The setting module comprises: A drawing unit configured to collect real-time values of the target node under each evaluation index, establish a corresponding relationship between the real-time values and the weight values, and draw a comparison table; A division unit configured to query the comparison table, superimpose the weight values corresponding to all evaluation indexes to obtain a load score, and divide the load score into several intervals, wherein each interval corresponds to a task load level; An integration unit configured to integrate a time item into the selection pop-up window, and calculate a processing duration by using the start time and the end time; An activation unit configured to activate an emergency disposal rule edited in advance when the processing duration exceeds the corresponding time item.

10. The ERP collaboration system of claim 7, wherein, The sending module comprises: An updating unit configured to dynamically update the key pair according to a preset frequency; An embedding unit configured to generate a version corresponding to the key pair one by one, and embed a time stamp.

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