A multi-business management and control method and system

Abstract

The present invention relates to a management and control method and system for multi-business operations, which obtains the current nodes of multiple business processes at the current time point from a shared platform; determines whether there are relevant process nodes based on a pre-built node relationship database, and if so, initiates a plan reporting process to two target business departments corresponding to the relevant process nodes; upon receiving the current node plans uploaded from the two target business departments, compares the current node plans of the two business departments to obtain a correlation comparison result; and manages the current nodes of the relevant process nodes based on the correlation comparison result. The present application can predict whether there is a correlation between the task nodes of different business departments at the same time, and if so, compares the plans uploaded by the two departments, and performs collaborative control when there is a correlation. The present application can effectively realize information interaction between multiple business departments, avoiding information islands and waste of resources.

Description

Technical Field

[0001] The present invention relates to the field of business management and control, and specifically to a multi-business management and control method and system. Background Art

[0002] Power supporting enterprises are involved in a variety of businesses, such as power equipment manufacturing, power technology research and development, etc. According to the operating conditions of power enterprises, some businesses are core businesses and some businesses are marginal businesses.

[0003] The business data between different business departments are generally independent, so there may be information islands and departmental barriers, resulting in the inability to effectively share and collaborate resources. Summary of the invention

[0004] In view of this, an object of the present invention is to provide a multi-business operation management and control method and system to solve the information island problem existing in the multi-business operation in the prior art.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A multi-business management and control method of the present invention comprises the following steps:

[0007] Obtain the current nodes of multiple business processes at the current time point from the shared platform;

[0008] Based on the pre-built node relationship database, it is determined whether there is a related process node. If so, a plan reporting process is initiated to two target business departments corresponding to the related process nodes, wherein the related process nodes are two process nodes with the same business elements;

[0009] When receiving the current node plans uploaded by the two target business departments, comparing the current node plans of the two business departments to obtain a correlation comparison result, wherein the current node plans are generated based on the plan reporting process;

[0010] Based on the correlation comparison result, the current node of the relevant process node is controlled.

[0011] In one embodiment of the present application, the method for constructing the node relationship database includes:

[0012] S1, obtaining historical business data of multiple business departments, wherein the historical business data includes business content of multiple process nodes, and the business content includes text descriptions and related personnel;

[0013] S2, taking one of the process nodes of the current business as the current process node, and calculating the matching degree between the business content of the current process node and the business content of each process node of other businesses;

[0014] S3, taking the next process node of the current business as the current process node, and returning to S2 until the matching degree calculation between all process nodes of the current business and all other process nodes is completed;

[0015] S4, when the matching degree calculation of all process nodes of the current business and all other process nodes is completed, the next business is taken as the current business, and the process returns to S2 until the matching degree calculation of all process nodes of all business types and all other process nodes is completed;

[0016] S5, building a node relationship database based on the matching degree between all process nodes of all business types, wherein nodes with a matching degree greater than a preset matching degree threshold are related process nodes.

[0017] In an embodiment of the present application, calculating the matching degree between the business content of the current process node and the business content of each process node of other businesses includes:

[0018] Extract the key entities corresponding to the text description of each process node, and build a key entity library and a related personnel library for each process node of each business based on the key entities and related personnel, wherein the key entities are extracted based on a pre-built extraction model;

[0019] Calculate the occurrence frequency f(w i ) and the frequency of occurrence of each relevant person in each relevant person database f(p j ), f(w i )=N(w i ) / N w , f(p j )=N(p j ) / N p , where N(w i ) is the number of occurrences of the i-th key entity, N w is the total number of occurrences of all key entities in the key entity library, N(p j ) is the number of occurrences of the jth related person, N p is the total number of occurrences of all relevant persons in the relevant person database;

[0020] Extract the frequency of occurrence f(w i ) is greater than the first preset frequency threshold and the occurrence frequency f(p j ) target related personnel whose frequency is greater than a second preset frequency threshold;

[0021] Based on the target key entity corresponding to the current process node, the target related personnel corresponding to the current process node, the target key entities of all other process nodes, and the target related personnel of all other process nodes, a correlation calculation is performed to obtain a matching degree M. k .

[0022] In one embodiment of the present application, the mathematical expression of the matching degree M is:

[0023] M k =α×N w (node c ,node k )+β×N p (node c ,node k )

[0024] In the formula, α is the first unit score, β is the second unit score, The current process node node c With the kth process node node k The number of common target key entities, N p (node c ,node k ) is the current process node node c With the kth process node node k The number of people involved in a common goal.

[0025] In an embodiment of the present application, the current node plans of two business departments are compared to obtain a correlation comparison result, including:

[0026] Extract the key entities and relevant personnel of the current node plan of the two business units;

[0027] Compare the key entities and related personnel of the current node plans of the two business departments to obtain common key entities and common related personnel;

[0028] When the number of the common key entities exceeds a preset first quantity threshold, or the number of the common related personnel exceeds a preset second quantity threshold, it is determined that the current node plans of the two business departments are correlated, otherwise, it is determined that there is no correlation.

[0029] In an embodiment of the present application, the current node of the relevant process node is controlled based on the correlation comparison result, including:

[0030] When the current node plans of two business departments are related, a collaborative conference process is initiated to the two business departments, wherein the collaborative conference process is an online meeting or an offline meeting.

[0031] In one embodiment of the present application, it also includes:

[0032] When the meeting minutes uploaded by the two business departments are received, the collaborative meeting process ends.

[0033] The present application also provides a multi-business management and control system, including:

[0034] An acquisition module is used to obtain the current nodes of multiple business processes at the current time point from the sharing platform;

[0035] A prediction module, used to determine whether there is a related process node based on the historical data of the current nodes of multiple business processes, and if so, initiate a plan reporting process to two target business departments corresponding to the related process nodes, wherein the related process nodes are two businesses with the same business elements;

[0036] A comparison module is used to compare the current node plans of the two business departments when receiving the current node plans uploaded by the two target business departments to obtain a correlation comparison result, wherein the current node plan is generated based on the plan report process;

[0037] A management and control module is used to manage and control the current node of the relevant process nodes based on the correlation comparison result.

[0038] The present application also provides an electronic device, the electronic device comprising:

[0039] one or more processors;

[0040] The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the electronic device implements the multi-business operation management and control method as described above.

[0041] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor of a computer, the computer is enabled to execute the multi-business operation management and control method as described above.

[0042] The beneficial effects of the present invention are as follows: a multi-business management and control method and system of the present invention obtains the current nodes of multiple business processes at the current time point from a shared platform; determines whether there are relevant process nodes based on a pre-built node relationship database, and if so, initiates a plan reporting process to the two target business departments corresponding to the relevant process nodes; upon receiving the current node plans uploaded from the two target business departments, compares the current node plans of the two business departments to obtain a correlation comparison result; and manages the current nodes of the relevant process nodes based on the correlation comparison result. The present application can predict whether there is a correlation between the task nodes of different business departments at the same time. If so, compares the plans uploaded by the two departments, and performs collaborative control when there is a correlation. The present application can effectively realize information interaction between multiple business departments, avoiding information islands and waste of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0044] Figure 1 It is a flow chart of a multi-business operation management and control method shown in an embodiment of the present application;

[0045] Figure 2 A business process diagram in an embodiment of the present application;

[0046] Figure 3 It is a structural diagram of a multi-business management and control system shown in one embodiment of the present application. DETAILED DESCRIPTION

[0047] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0048] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show the layers related to the present invention rather than being drawn according to the number, shape and size of the layers in actual implementation. In actual implementation, the type, quantity and proportion of each layer may be changed arbitrarily, and the layer layout may also be more complicated.

[0049] In the following description, numerous details are discussed to provide a more thorough explanation of embodiments of the present invention; however, it is apparent to one skilled in the art that embodiments of the present invention may be practiced without these specific details.

[0050] Figure 1 is a flow chart of a multi-business management and control method shown in an embodiment of the present application, such as Figure 1 As shown: A multi-business operation management and control method of this embodiment may include steps S110 to S140:

[0051] S110, obtaining current nodes of multiple business processes at the current time point from the sharing platform;

[0052] Figure 2 This is a business process diagram in an embodiment of the present application, such as Figure 2 As shown, in this application, the power supporting enterprise includes multiple project groups, each of which is engaged in the research and development and production of different power equipment, for example:

[0053] Transformer R&D business: core optimization R&D - winding optimization R&D - testing, where core optimization R&D and winding optimization R&D can be carried out in parallel;

[0054] Motor R&D business: stator optimization R&D-rotor optimization R&D-testing, among which stator optimization R&D and rotor optimization R&D can be carried out in parallel. The above nodes are not typical processes, and are uploaded to a shared platform such as an enterprise resource planning (ERP) system by the relevant project team based on R&D calculations.

[0055] S120, judging whether there is a related process node based on a pre-built node relationship database, and if so, initiating a plan report process to two target business departments corresponding to the related process nodes, wherein the related process nodes are two process nodes with the same business elements;

[0056] Under normal circumstances, different nodes of different businesses are not related. However, in some special cases, for example, the rotor itself has windings, but different winding methods will bring about incorrect effects. Therefore, if the transformer R&D project team and the motor R&D project team are respectively in the winding optimization R&D node and the rotor optimization R&D node at the current time point, the system automatically determines that the two nodes are related.

[0057] At this time, the system automatically initiates the planning report process, thereby automatically obtaining the R&D plans for winding optimization and rotor optimization from the two project teams. This application uses a pre-built node relationship database to determine whether all task nodes being executed at the current time point are associated, and only initiates the planning report process when there is an association. This avoids wasting too much time and energy on the R&D project team.

[0058] Specifically, the method for constructing the node relationship database includes:

[0059] S1, obtain historical business data of multiple business departments,

[0060] The historical business data includes business contents of multiple process nodes, and the business contents include text descriptions and related personnel;

[0061] Historical business data is data that is sorted and uploaded to the management system by relevant personnel, including a text description of the business and the name and employee number of the person in charge.

[0062] S2, taking one of the process nodes of the current business as the current process node, and calculating the matching degree between the business content of the current process node and the business content of each process node of other businesses;

[0063] The calculation of matching degree is based on the key entities and handlers in the business content, including:

[0064] (1) extracting key entities corresponding to the text description of each process node, and constructing a key entity library and a relevant personnel library for each process node of each business based on the key entities and relevant personnel, wherein the key entities are extracted based on a pre-constructed extraction model;

[0065] Entity Extraction, also known as Named Entity Recognition (NER), is an important task in natural language processing, which aims to identify entities with specific meanings from text, such as names of people, places, and organizations. In this application, a pre-built entity extraction model is used to extract key entities in text descriptions. The key entities in this application are feature words in business descriptions, such as "rotor", "winding", "transformer", "magnetic flux", etc. The model construction process includes:

[0066] Collect data: First, you need to collect a large amount of annotated text data as a training set. This data should contain various types of entities and be accurately annotated.

[0067] Data cleaning: remove irrelevant information to ensure data quality.

[0068] Data enhancement: The amount of data can be increased through synonym replacement, sentence reorganization, etc., to improve the generalization ability of the model.

[0069] Word embedding: Use pre-trained word vectors (such as Word2Vec, GloVe) or obtain distributed representations of words through deep learning models such as Bert.

[0070] Contextual information: In addition to the features of a single word, the contextual information of the word can also be considered, such as the features of the previous or next word.

[0071] Part-of-speech tagging: Part-of-speech information is sometimes very helpful for entity recognition.

[0072] Select the model: such as RNN (recurrent neural network), LSTM (long short-term memory network), GRU (gated recurrent unit), BiLSTM-CRF (bidirectional LSTM combined with CRF), etc.

[0073] Pre-trained model fine-tuning: Fine-tune pre-trained models such as BERT and RoBERTa, and use their powerful semantic understanding capabilities to improve the NER effect.

[0074] Divide the dataset: Divide the dataset into training set, validation set, and test set.

[0075] Set parameters: Adjust hyperparameters such as learning rate, batch size, etc. according to the selected model.

[0076] Iterative optimization: Use optimization algorithms such as gradient descent to continuously iteratively update model parameters until the model performs satisfactorily on the validation set.

[0077] The model performance is evaluated using metrics such as precision, recall, and F1 score.

[0078] A final performance evaluation is performed on the test set to ensure the generalization ability of the model.

[0079] (2) Calculate the occurrence frequency f(w) of each key entity in each key entity library i ) and the frequency of occurrence of each relevant person in each relevant person database f(p j ), f(w i )=N(w i ) / N w , f(p j )=N(p j ) / N p , where N(w i ) is the number of occurrences of the i-th key entity, N w is the total number of occurrences of all key entities in the key entity library, N(p j ) is the number of occurrences of the jth related person, N p is the total number of occurrences of all relevant persons in the relevant person database;

[0080] (3) Extract the frequency of occurrence f(w) in each process node i) is greater than the first preset frequency threshold and the occurrence frequency f(p j ) target related personnel whose frequency is greater than a second preset frequency threshold;

[0081] The key entity library contains the key entities corresponding to each node when each business is executed multiple times, and the related personnel library contains the related personnel corresponding to each node when each business is executed multiple times. Therefore, high-frequency key entities and high-frequency related personnel are filtered out as the basis for subsequent predictions.

[0082] (4) Based on the target key entity corresponding to the current process node, the target related personnel corresponding to the current process node, the target key entities of all other process nodes, and the target related personnel of all other process nodes, a correlation calculation is performed to obtain a matching degree M. k ;

[0083] M k =α×N w (node c ,node k )+β×N p (node c ,node k )

[0084] In the formula, α is the first unit score, β is the second unit score, The current process node node c With the kth process node node k The number of common target key entities, N p (node c ,node k ) is the current process node node c With the kth process node node k The number of people involved in a common goal.

[0085] The matching degree in this application is based on the common target key entities and common target related personnel of the current process node and other process nodes at the same time. If the repetition is high. It means that either the corresponding nodes of the two groups of projects have a large number of the same technical keywords, and there is a certain overlap in technical content. At this time, there may be the same technology in the corresponding nodes of the two projects, where communication and exchange can be carried out. If there is a large overlap of personnel, it is more likely that the two projects have similar technologies. For example, in the process nodes "winding optimization research and development" and "rotor optimization research and development" mentioned above, there may be a large number of key entities "winding", "magnetic flux", "coil", etc., and there may also be some R&D personnel in both project groups at the same time. Therefore, the node matching degree at this time is relatively high.

[0086] S3, taking the next process node of the current business as the current process node, and returning to S2 until the matching degree calculation between all process nodes of the current business and all other process nodes is completed;

[0087] S4, when the matching degree calculation of all process nodes of the current business and all other process nodes is completed, the next business is taken as the current business, and the process returns to S2 until the matching degree calculation of all process nodes of all business types and all other process nodes is completed;

[0088] S5, building a node relationship database based on the matching degree between all process nodes of all business types, wherein nodes with a matching degree greater than a preset matching degree threshold are related process nodes.

[0089] By correcting S3-S4, all nodes that have appeared are subjected to entity extraction and personnel information extraction, thereby building a node relationship database. The two nodes with the largest matching degree in the node relationship database are related process nodes.

[0090] S130, when receiving the current node plans uploaded by the two target business departments, comparing the current node plans of the two business departments to obtain a correlation comparison result, wherein the current node plan is generated based on the plan reporting process;

[0091] Existing cross-departmental communication methods are generally based on shared platforms, but the actual effect is not good. Since cross-departmental communication also has a certain time cost and requires subjective initiative. Therefore, in most cases, few people will take the initiative to check the shared platform to obtain cross-departmental information and apply it in their daily work. Therefore, this application actively seeks information from different business departments during operation, and when there is a correlation, the two will communicate further at the same time, which can play a positive role in cross-departmental information communication.

[0092] In the previous article, if two related nodes are matched at the current time point, the planning report process is automatically initiated in the system. The two business departments upload the planning reports of the current nodes to the system respectively, and the system automatically compares the correlation between the two planning reports. To determine whether there is a correlation between the two, the judgment method includes:

[0093] (1) Extract the key entities (indicating work content) and relevant personnel (handling personnel) of the current node plans of the two business departments;

[0094] (2) Compare the key entities and relevant personnel of the current node plans of the two business departments to obtain common key entities and common relevant personnel;

[0095] (3) When the number of the common key entities exceeds a preset first quantity threshold, or the number of the common related personnel exceeds a preset second quantity threshold, it is determined that the current node plans of the two business departments are correlated; otherwise, it is determined that there is no correlation.

[0096] S140, managing and controlling the current node of the relevant process nodes based on the correlation comparison result.

[0097] When the current node plans of two business departments are related, a collaborative conference process is initiated to the two business departments, wherein the collaborative conference process is an online meeting or an offline meeting.

[0098] In the process, the two departments are agreed to further communicate through online or offline meetings. In order to ensure the communication effect, in this application, the collaborative meeting process is ended when the meeting minutes uploaded by the two business departments are received.

[0099] A multi-business management and control method of the present invention obtains the current nodes of multiple business processes at the current time point from a shared platform; determines whether there are relevant process nodes based on a pre-constructed node relationship database, and if so, initiates a plan reporting process to the two target business departments corresponding to the relevant process nodes; upon receiving the current node plans uploaded from the two target business departments, compares the current node plans of the two business departments to obtain a correlation comparison result; and manages the current nodes of the relevant process nodes based on the correlation comparison result. The present application can predict whether there is a correlation between the task nodes of different business departments at the same time, and if so, compares the plans uploaded by the two departments, and performs collaborative control when there is a correlation. The present application can effectively realize information interaction between multiple business departments, avoiding information islands and waste of resources.

[0100] like Figure 3 As shown, the present application also provides a multi-business management and control system, including:

[0101] An acquisition module is used to obtain the current nodes of multiple business processes at the current time point from the sharing platform;

[0102] A prediction module, used to determine whether there is a related process node based on the historical data of the current nodes of multiple business processes, and if so, initiate a plan reporting process to two target business departments corresponding to the related process nodes, wherein the related process nodes are two businesses with the same business elements;

[0103] A comparison module is used to compare the current node plans of the two business departments when receiving the current node plans uploaded by the two target business departments to obtain a correlation comparison result, wherein the current node plan is generated based on the plan report process;

[0104] A management and control module is used to manage and control the current node of the relevant process nodes based on the correlation comparison result.

[0105] A multi-business management and control method and system of the present invention obtains the current nodes of multiple business processes at the current time point from a shared platform; determines whether there are relevant process nodes based on a pre-built node relationship database, and if so, initiates a plan reporting process to the two target business departments corresponding to the relevant process nodes; upon receiving the current node plans uploaded from the two target business departments, compares the current node plans of the two business departments to obtain a correlation comparison result; and manages the current nodes of the relevant process nodes based on the correlation comparison result. The present application can predict whether there is a correlation between the task nodes of different business departments at the same time, and if so, compares the plans uploaded by the two departments, and performs collaborative control when there is a correlation. The present application can effectively realize information interaction between multiple business departments, avoiding information islands and waste of resources.

[0106] This embodiment further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, any one of the methods in this embodiment is implemented, wherein the method is the execution logic of this system.

[0107] This embodiment also provides an electronic terminal, including: a processor and a memory;

[0108] The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal executes any one of the methods in this embodiment.

[0109] The computer-readable storage medium in this embodiment can be understood by ordinary technicians in this field: all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes.

[0110] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication with each other. The memory is used to store computer programs, the communication interface is used to communicate, and the processor and the transceiver are used to run computer programs so that the electronic terminal executes each step of the above method.

[0111] In this embodiment, the memory may include a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0112] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0113] In the above-mentioned embodiments, although the present invention has been described in conjunction with the specific embodiments of the present invention, many replacements, modifications and variations of these embodiments will be apparent to those of ordinary skill in the art based on the foregoing description. The embodiments of the present invention are intended to cover all such replacements, modifications and variations falling within the broad scope of the appended claims.

[0114] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A multi-business management and control method, characterized in that: Includes steps: Obtain the current nodes of multiple business processes at the current time point from the shared platform; Based on the pre-built node relationship database, it is determined whether there is a related process node. If so, a plan reporting process is initiated to two target business departments corresponding to the related process nodes, wherein the related process nodes are two process nodes with the same business elements; When receiving the current node plans uploaded by the two target business departments, comparing the current node plans of the two business departments to obtain a correlation comparison result, wherein the current node plans are generated based on the plan reporting process; Based on the correlation comparison result, the current node of the relevant process node is controlled.

2. A multi-business management and control method according to claim 1, characterized in that: The method for constructing the node relationship database includes: S1, obtaining historical business data of multiple business departments, wherein the historical business data includes business content of multiple process nodes, and the business content includes text descriptions and related personnel; S2, taking one of the process nodes of the current business as the current process node, and calculating the matching degree between the business content of the current process node and the business content of each process node of other businesses; S3, taking the next process node of the current business as the current process node, and returning to S2 until the matching degree calculation between all process nodes of the current business and all other process nodes is completed; S4, when the matching degree calculation of all process nodes of the current business and all other process nodes is completed, the next business is taken as the current business, and the process returns to S2 until the matching degree calculation of all process nodes of all business types and all other process nodes is completed; S5, building a node relationship database based on the matching degree between all process nodes of all business types, wherein nodes with a matching degree greater than a preset matching degree threshold are related process nodes.

3. A multi-business management and control method according to claim 2, characterized in that: Calculate the matching degree between the business content of the current process node and the business content of each process node of other businesses, including: Extract the key entities corresponding to the text description of each process node, and build a key entity library and a related personnel library for each process node of each business based on the key entities and related personnel, wherein the key entities are extracted based on a pre-built extraction model; Calculate the occurrence frequency f(w i ) and the frequency of occurrence of each relevant person in each relevant person database f(p j ), f(w i )=N(w i ) / N w , f(p j )=N(p j ) / N p , where N(w i ) is the number of occurrences of the i-th key entity, N w is the total number of occurrences of all key entities in the key entity library, N(p j ) is the number of occurrences of the jth related person, N p is the total number of occurrences of all relevant persons in the relevant person database; Extract the frequency of occurrence f(w i ) is greater than the first preset frequency threshold and the occurrence frequency f(p j ) target related personnel whose frequency is greater than a second preset frequency threshold; Based on the target key entity corresponding to the current process node, the target related personnel corresponding to the current process node, the target key entities of all other process nodes, and the target related personnel of all other process nodes, a correlation calculation is performed to obtain a matching degree M. k .

4. A multi-business management and control method according to claim 3, characterized in that: The mathematical expression of the matching degree M is: M k =α×N w (node c ,node k )+β×N p (node c ,node k ) In the formula, α is the first unit score, β is the second unit score, The current process node node c With the kth process node node k The number of common target key entities, N p (node c ,node k ) is the current process node node c With the kth process node node k The number of people involved in a common goal.

5. A multi-business management and control method according to claim 4, characterized in that: Compare the current node plans of the two business departments to obtain correlation comparison results, including: Extract the key entities and relevant personnel of the current node plan of the two business units; Compare the key entities and related personnel of the current node plans of the two business departments to obtain common key entities and common related personnel; When the number of the common key entities exceeds a preset first quantity threshold, or the number of the common related personnel exceeds a preset second quantity threshold, it is determined that the current node plans of the two business departments are correlated, otherwise, it is determined that there is no correlation.

6. A multi-business management and control method according to claim 5, characterized in that: Based on the correlation comparison result, the current node of the relevant process node is controlled, including: When the current node plans of two business departments are related, a collaborative conference process is initiated to the two business departments, wherein the collaborative conference process is an online meeting or an offline meeting.

7. A multi-business management and control method according to claim 6, characterized in that: Also includes: When the meeting minutes uploaded by the two business departments are received, the collaborative meeting process ends.

8. A multi-business management and control system, characterized in that: include: An acquisition module is used to obtain the current nodes of multiple business processes at the current time point from the sharing platform; A prediction module, used to determine whether there is a related process node based on the historical data of the current nodes of multiple business processes, and if so, initiate a plan reporting process to two target business departments corresponding to the related process nodes, wherein the related process nodes are two businesses with the same business elements; A comparison module is used to compare the current node plans of the two business departments when receiving the current node plans uploaded by the two target business departments to obtain a correlation comparison result, wherein the current node plan is generated based on the plan report process; A management and control module is used to manage and control the current node of the relevant process node based on the correlation comparison result.

9. An electronic device, characterized in that: The electronic device comprises: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the electronic device to implement the multi-business operation management and control method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor of a computer, the computer is enabled to execute the multi-business operation management and control method according to any one of claims 1 to 7.

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