Efficient business collaboration platform system

Abstract

The invention discloses an efficient business collaboration platform system, and particularly relates to the technical field of data processing, and the processing method comprises the following steps: monitoring and collecting the related information of a target system after being divided into modules, and the related information of a user after a load balancing algorithm is used for constructing a model; the invention provides an efficient business collaboration platform system which divides an existing efficient business collaboration platform system into different modules according to functions, constructs a carrying module evaluation model, evaluates the starting priority degree of a target module, and determines whether the target module needs to be started or not according to an evaluation result. A load balancing algorithm model is constructed after all started target modules are integrated, a polling method is used for carrying out regulation and control distribution on loads, an optimized system is output, a system efficiency evaluation model is constructed for evaluating system efficiency, and the purpose that an obtained result better conforms to existence of an efficient business collaboration platform system is achieved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and more specifically, to an efficient business collaboration platform system. Background Art

[0002] Efficient business collaboration platform systems are usually based on technologies such as cloud computing, the Internet of Things, and artificial intelligence. Cloud computing provides flexible resource management and data storage capabilities, the Internet of Things enables real-time data sharing between devices, and artificial intelligence is used to optimize processes, analyze data, and improve user experience. In addition, API and microservice architecture make integration between different systems more convenient, promoting the circulation of information and the improvement of collaboration efficiency.

[0003] The existing efficient business collaboration platform has evolved from the early stage in the 1990s, through the rise of collaborative office software, cloud computing, social collaboration, mobility and intelligence, to the current integrated platform stage that has begun to integrate multiple functions. It has gradually changed in the direction of intelligence and personalization, aiming to improve work efficiency, promote team collaboration, optimize resource utilization, enhance transparency and traceability, support decision-making, enhance adaptability and flexibility, promote knowledge management and innovation, and improve customer satisfaction. The existing efficient business collaboration platform is mainly classified according to different functions and application scenarios, providing enterprises with a variety of functional options.

[0004] However, the existing efficient business collaboration platforms still have many problems in practical applications, such as high complexity, integration issues, data storage security, functional redundancy and performance issues. In particular, the high complexity and functional redundancy problems run counter to the original intention of the efficient business collaboration platform system. Users need a concise, customized, more personalized and more efficient business collaboration platform system. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an efficient business collaboration platform system, which divides the existing efficient business collaboration platform system into different modules according to functions, constructs a module evaluation model, evaluates the activation priority of the target module, and decides whether to enable the target module based on the evaluation result. After integrating all enabled target modules, a load balancing algorithm model is constructed, and the load is regulated and distributed by polling. The optimized system is output, and a system performance evaluation model is constructed to evaluate the system performance, so that the obtained results are more in line with the purpose of the existence of the efficient business collaboration platform system.

[0006] To achieve the above object, the present invention provides the following technical solutions: An efficient business collaboration platform system comprises the following steps: Step S1, monitoring and collecting operation complexity information, module association information and burden feedback information after the target system is divided into modules, as well as user team collaboration information, flexible adaptation information and record demand information after the model is built using the load balancing algorithm; Step S2, constructing a module evaluation model to evaluate the activation priority of the target module; Step S3, integrating the enabled target modules, building a load balancing algorithm model, regulating and distributing the load by a round-robin method, and outputting an optimized system; Step S4, constructing a system performance evaluation model to evaluate the system performance and output a more personalized and efficient business collaboration platform system.

[0007] Specifically, in step S1, the different functions of the efficient business collaboration platform system are modularized, and when the usage requirements of different users are received, the modules are evaluated respectively to determine whether the modules are enabled. After the reorganized modules are integrated, the load is balanced by adjusting the algorithm. Specifically, the data is processed in the computer software Apache Spark, and the construction and output of the algorithm model are implemented by Python. In actual use, due to factors such as equipment cost and the complexity of real-time monitoring, the present invention is based on the premise of continuously monitoring the platform operation and user data under the condition that the target user operation and platform system environment are relatively stable, which may lead to the phenomenon of rationality change at a certain monitoring moment.

[0008] In step S2, the evaluation model of the installed modules is constructed. The specific process is to collect the operation complexity information, module association information and burden feedback information of the modules divided by the target system. The operation complexity information includes the operation complexity coefficient, which is calibrated as , the module association information includes the module association coefficient, calibrated as , the burden feedback information includes the burden feedback coefficient, calibrated as ; Operation complexity coefficient By collecting the number of main functions provided by the module when users use the target module, it is calibrated as , count the number of interactions that users need to make when using the module, calibrated as , and the average time spent by the user in using the target module in the past ten times, calibrated as , then the operation complexity coefficient ; Module correlation coefficient The number of activations of the target module when used by users in the same industry is calibrated as , the total number of activation times of the collected industries is marked as , get the probability of the target module being used together with the enabled modules, calibrated as , x is the enabled module number, x=0,1,2,3,..., y, y is a positive integer, then the module association coefficient ; Burden feedback coefficient The size of the system memory occupied by the acquisition target module after it is enabled, calibrated as , after the statistics are enabled, the average time extension of users getting feedback in the system is marked as , then the burden feedback coefficient ; The module evaluation model is constructed by weighting the operation complexity information, module association information and burden feedback information of the modules divided by the target system to generate the module evaluation index. , and the corresponding coefficients are respectively the operation complexity coefficient , module correlation coefficient , burden feedback coefficient , the formula is ; at the same time, , , All are greater than 0 and are set according to actual conditions. For example, the expert weighting method is adopted, that is, experts in related fields are invited to determine the weights of various indicators through professional opinion surveys and comprehensive evaluations to ensure that the weight coefficients can accurately reflect the importance of various indicators in the evaluation of the installed modules. In addition, various methods such as hierarchical analysis method and fuzzy comprehensive evaluation method can also be considered to determine the weight coefficients to ensure the objectivity and scientificity of the weight coefficients. I will not go into details here.

[0009] In step S2, the installed module evaluation index obtained from the installed module evaluation model is used to reflect the activation priority of the modules divided by the target system. The larger its value is, the more complex the operation required by the target module to solve the user problem is, the lower the correlation with the problem required by the user is, the heavier the burden on the system is, and the lower the activation priority of the module is.

[0010] In step S2, when the module evaluation index When it is greater than the set activation threshold, it means that the activation priority of the module divided by the target system is low, which has little impact on the realization of user needs. The target module is not used temporarily, the evaluation result is output, and the module is replaced for re-evaluation; When equipped with module evaluation index When it is less than or equal to the set activation threshold, it means that the module divided by the target system has a high activation priority. The target module is enabled, the evaluation result is output, and the module is replaced for re-evaluation.

[0011] In step S3, after all modules are evaluated, the information of modules with high priority in all evaluation results is obtained, and the efficient business collaboration platform system is re-integrated and constructed. The operation optimization of the efficient business collaboration platform is realized through the load balancing algorithm in the scheduling algorithm; Furthermore, the load balancing algorithm model of the present invention is constructed as follows: Step S3.1, demand analysis and model building, collect user request data, analyze the load characteristics of different modules, and define the load function of each server , which represents the current load of the i-th server: ,in, represents the resources required by the i-th server to process the j-th request; Step S3.2, load balancing is achieved by allocating requests to each server in turn through the polling method. Assuming there are N servers, the requests are allocated in turn as follows: ,in, is the target server for the kth request; Step S3.3, dynamic adjustment, defining performance indicators, such as average response time And the system throughput H: and , real-time monitoring of the load and response time of each server, when the performance index exceeds the defined performance index, trigger the load balancing strategy adjustment and update the load function : ,in, is the smoothing factor, ,According to the real-time load, the request distribution strategy is dynamically adjusted, such as reallocating requests to high-load servers and suspending the acceptance of new requests, to output the optimized system.

[0012] In step S4, after applying the optimization system, a system effectiveness evaluation model is constructed. The specific process is to collect the user's team collaboration information, flexible adaptation information and record demand information. The team collaboration information includes the team collaboration coefficient, which is calibrated as , the flexible adaptation information includes the flexible adaptation coefficient, calibrated as , recording demand information includes recording demand coefficient, calibrated as ; Teamwork coefficient The increase in the number of interactions between team members after collecting and applying the optimized system is calibrated as , the task completion rate improvement ratio after applying the optimization system is calibrated as , the increase in the satisfaction score of team members on collaboration is obtained through questionnaire survey and calibrated as , p is the member number, p=0,1,2,3,...,q,q is a positive integer, then the teamwork coefficient ; Flexible adaptability factor By collecting the number of times the user's policy changes, the collection time is marked as , the number of policy changes is calibrated as , then the flexible adaptation coefficient ; Recording Demand Factors During the user task collection process, the user provides existing document support, counts the total number of words in the document, and marks it as , z is the task number, z=0,1,2,3,...,n, n is a positive integer, then record the demand coefficient ; The system effectiveness evaluation model is constructed by weighting the team collaboration information, flexible adaptation information and record demand information in the algorithm model to generate a system effectiveness evaluation index. , and the corresponding coefficients are teamwork coefficient , Flexible Adaptability Coefficient and record demand factors , the formula is ; In step S4, the system performance evaluation index obtained in the system performance evaluation model is used to reflect the effect and coordination ability of the optimized system after application. The larger the system performance evaluation index, the closer the team collaboration is after the system is applied, the more varied the user strategies and policies are, the more suitable the environment provided for the system is, and the greater the user's recording needs are. Therefore, the effect and coordination ability of the optimized system after application are better.

[0013] In step S4, when the system performance evaluation index When the value is greater than or equal to the set application threshold, it indicates that the effect and coordination ability of the optimized system after application are excellent, and the evaluation result is directly output, and the current algorithm model continues to be used; When the system performance evaluation index When it is less than the set application threshold, it means that the effect and coordination ability of the optimized system after application are not good, and the process returns to step S2, disrupts the module evaluation order, adjusts the activation threshold, and rebuilds the model.

[0014] An efficient business collaboration platform system, used for the above-mentioned efficient business collaboration platform method, comprises: The data collection module monitors and collects the operation complexity information, module association information and burden feedback information after the target system is divided into modules, as well as the user's team collaboration information, flexible adaptation information and record demand information after the model is built using the load balancing algorithm; Equipped module evaluation module, builds an equipped module evaluation model, and evaluates the activation priority of the target module; The load algorithm module integrates the enabled target modules, builds a load balancing algorithm model, regulates and distributes the load using the round-robin method, and outputs an optimized system. The performance evaluation module builds a system performance evaluation model to evaluate the system performance and output a more personalized and efficient business collaboration platform system.

[0015] Technical effects and advantages of the present invention: In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an efficient business collaboration platform system, which divides the existing efficient business collaboration platform system into different modules according to functions, constructs a module evaluation model, evaluates the activation priority of the target module, and decides whether to enable the target module based on the evaluation result. After integrating all enabled target modules, a load balancing algorithm model is constructed, and the load is regulated and distributed by polling. The optimized system is output, and a system performance evaluation model is constructed to evaluate the system performance, so that the obtained results are more in line with the purpose of the existence of the efficient business collaboration platform system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of an efficient business collaboration platform method of the present invention.

[0017] Figure 2 This is a flow chart of an efficient business collaboration platform system of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] The present invention provides an efficient business collaboration platform system, which divides the existing efficient business collaboration platform system into different modules according to functions, constructs a module evaluation model, evaluates the activation priority of the target module, and decides whether to enable the target module based on the evaluation result. After integrating all enabled target modules, a load balancing algorithm model is constructed, and the load is regulated and distributed by polling method. The optimized system is output, and a system performance evaluation model is constructed to evaluate the system performance, so that the obtained results are more in line with the purpose of the existence of the efficient business collaboration platform system.

[0020] Example 1 like Figure 1As shown, the steps of an efficient business collaboration platform system are as follows: Step S1, monitoring and collecting operation complexity information, module association information and burden feedback information after the target system is divided into modules, as well as user team collaboration information, flexible adaptation information and record demand information after the model is built using the load balancing algorithm; Step S2, constructing a module evaluation model to evaluate the activation priority of the target module; Step S3, integrating the enabled target modules, building a load balancing algorithm model, regulating and distributing the load by a round-robin method, and outputting an optimized system; Step S4, constructing a system performance evaluation model to evaluate the system performance and output a more personalized and efficient business collaboration platform system.

[0021] Specifically, in step S1, the different functions of the efficient business collaboration platform system are modularized, and when the usage requirements of different users are received, the modules are evaluated respectively to determine whether the modules are enabled. After the reorganized modules are integrated, the load is balanced by adjusting the algorithm. Specifically, the data is processed in the computer software Apache Spark, and the construction and output of the algorithm model are implemented by Python. In actual use, due to factors such as equipment cost and the complexity of real-time monitoring, the present invention is based on the premise of continuously monitoring the platform operation and user data under the condition that the target user operation and platform system environment are relatively stable, which may lead to the phenomenon of rationality change at a certain monitoring moment.

[0022] In step S2, the evaluation model of the installed modules is constructed. The specific process is to collect the operation complexity information, module association information and burden feedback information of the modules divided by the target system. The operation complexity information includes the operation complexity coefficient, which is calibrated as , the module association information includes the module association coefficient, calibrated as , the burden feedback information includes the burden feedback coefficient, calibrated as ; Operation complexity coefficient By collecting the number of main functions provided by the module when users use the target module, it is calibrated as , count the number of interactions that users need to make when using the module, calibrated as , and the average time spent by the user in using the target module in the past ten times, calibrated as , then the operation complexity coefficient ; Module correlation coefficient The number of activations of the target module when used by users in the same industry is calibrated as , the total number of activation times of the collected industries is marked as , get the probability of the target module being used together with the enabled modules, calibrated as , x is the enabled module number, x=0,1,2,3,..., y, y is a positive integer, then the module association coefficient ; Burden feedback coefficient The size of the system memory occupied by the acquisition target module after it is enabled, calibrated as , after the statistics are enabled, the average time extension of users getting feedback in the system is marked as , then the burden feedback coefficient ; The module evaluation model is constructed by weighting the operation complexity information, module association information and burden feedback information of the modules divided by the target system to generate the module evaluation index. , and the corresponding coefficients are respectively the operation complexity coefficient , module correlation coefficient , burden feedback coefficient , the formula is ; at the same time, , , All are greater than 0 and are set according to actual conditions. For example, the expert weighting method is adopted, that is, experts in related fields are invited to determine the weights of various indicators through professional opinion surveys and comprehensive evaluations to ensure that the weight coefficients can accurately reflect the importance of various indicators in the evaluation of the installed modules. In addition, various methods such as hierarchical analysis method and fuzzy comprehensive evaluation method can also be considered to determine the weight coefficients to ensure the objectivity and scientificity of the weight coefficients. I will not go into details here.

[0023] In step S2, the installed module evaluation index obtained from the installed module evaluation model is used to reflect the activation priority of the modules divided by the target system. The larger its value is, the more complex the operation required by the target module to solve the user problem is, the lower the correlation with the problem required by the user is, the heavier the burden on the system is, and the lower the activation priority of the module is.

[0024] In step S2, when the module evaluation index When it is greater than the set activation threshold, it means that the activation priority of the module divided by the target system is low, which has little impact on the realization of user needs. The target module is not used temporarily, the evaluation result is output, and the module is replaced for re-evaluation; When equipped with module evaluation index When it is less than or equal to the set activation threshold, it means that the module divided by the target system has a high activation priority. The target module is enabled, the evaluation result is output, and the module is replaced for re-evaluation.

[0025] In step S3, after all modules are evaluated, the information of modules with high priority in all evaluation results is obtained, and the efficient business collaboration platform system is re-integrated and constructed. The operation optimization of the efficient business collaboration platform is realized through the load balancing algorithm in the scheduling algorithm; Furthermore, the load balancing algorithm model of the present invention is constructed as follows: Step S3.1, demand analysis and model building, collect user request data, analyze the load characteristics of different modules, and define the load function of each server , which represents the current load of the i-th server: ,in, represents the resources required by the i-th server to process the j-th request; Step S3.2, load balancing is achieved by allocating requests to each server in turn through the polling method. Assuming there are N servers, the requests are allocated in turn as follows: ,in, is the target server for the kth request; Step S3.3, dynamic adjustment, defining performance indicators, such as average response time And the system throughput H: and , real-time monitoring of the load and response time of each server, when the performance index exceeds the defined performance index, trigger the load balancing strategy adjustment and update the load function : ,in, is the smoothing factor, ,According to the real-time load, the request distribution strategy is dynamically adjusted, such as reallocating requests to high-load servers and suspending the acceptance of new requests, to output the optimized system.

[0026] In step S4, after applying the optimization system, a system effectiveness evaluation model is constructed. The specific process is to collect the user's team collaboration information, flexible adaptation information and record demand information. The team collaboration information includes the team collaboration coefficient, which is calibrated as , the flexible adaptation information includes the flexible adaptation coefficient, calibrated as , recording demand information includes recording demand coefficient, calibrated as ; Teamwork coefficient The increase in the number of interactions between team members after collecting and applying the optimized system is calibrated as , the task completion rate improvement ratio after applying the optimization system is calibrated as , the increase in the satisfaction score of team members on collaboration is obtained through questionnaire survey and calibrated as , p is the member number, p=0,1,2,3,...,q,q is a positive integer, then the teamwork coefficient ; Flexible adaptability factor By collecting the number of times the user's policy changes, the collection time is marked as , the number of policy changes is calibrated as , then the flexible adaptation coefficient ; Recording Demand Factors During the user task collection process, the user provides existing document support, counts the total number of words in the document, and marks it as , z is the task number, z=0,1,2,3,...,n, n is a positive integer, then record the demand coefficient ; The system effectiveness evaluation model is constructed by weighting the team collaboration information, flexible adaptation information and record demand information in the algorithm model to generate a system effectiveness evaluation index. , and the corresponding coefficients are teamwork coefficient , Flexible Adaptability Coefficient and record demand factors , the formula is ; In step S4, the system performance evaluation index obtained in the system performance evaluation model is used to reflect the effect and coordination ability of the optimized system after application. The larger the system performance evaluation index, the closer the team collaboration is after the system is applied, the more varied the user strategies and policies are, the more suitable the environment provided for the system is, and the greater the user's recording needs are. Therefore, the effect and coordination ability of the optimized system after application are better.

[0027] In step S4, when the system performance evaluation index When the value is greater than or equal to the set application threshold, it indicates that the effect and coordination ability of the optimized system after application are excellent, and the evaluation result is directly output, and the current algorithm model continues to be used; When the system performance evaluation index When it is less than the set application threshold, it means that the effect and coordination ability of the optimized system after application are not good, and the process returns to step S2, disrupts the module evaluation order, adjusts the activation threshold, and rebuilds the model.

[0028] Example 2 An efficient business collaboration platform system, used for the above-mentioned efficient business collaboration platform method, comprises: The data collection module monitors and collects the operation complexity information, module association information and burden feedback information after the target system is divided into modules, as well as the user's team collaboration information, flexible adaptation information and record demand information after the model is built using the load balancing algorithm; Equipped module evaluation module, builds an equipped module evaluation model, and evaluates the activation priority of the target module; The load algorithm module integrates the enabled target modules, builds a load balancing algorithm model, regulates and distributes the load using the round-robin method, and outputs an optimized system. The performance evaluation module builds a system performance evaluation model to evaluate the system performance and output a more personalized and efficient business collaboration platform system.

[0029] The above formulas are all dimensionless and calculated numerically. Specific dimension removal can be achieved by various means such as standardization, which will not be elaborated here. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0030] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, an ATA hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state ATA hard disk.

[0031] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0032] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0033] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0034] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0035] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0036] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile ATA hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0037] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An efficient business collaboration platform system, characterized in that: The steps include: Step S1, monitoring and collecting operation complexity information, module association information and burden feedback information after the target system is divided into modules, as well as user team collaboration information, flexible adaptation information and record demand information after the model is built using the load balancing algorithm; Step S2, constructing a module evaluation model to evaluate the activation priority of the target module; Step S3, integrating the enabled target modules, building a load balancing algorithm model, regulating and distributing the load by a round-robin method, and outputting an optimized system; Step S4, constructing a system performance evaluation model to evaluate the system performance and output a more personalized and efficient business collaboration platform system.

2. According to claim 1, an efficient business collaboration platform system is characterized by: In step S2, the evaluation model of the installed modules is constructed. The specific process is to collect the operation complexity information, module association information and burden feedback information of the modules divided by the target system. The operation complexity information includes the operation complexity coefficient, which is calibrated as , the module association information includes the module association coefficient, calibrated as , the burden feedback information includes the burden feedback coefficient, calibrated as ; Operational complexity coefficient By collecting the number of functions provided by the module when the user uses the target module, it is calibrated as , count the number of interactions that users need to make when using the module, calibrated as , and the average time spent by the user in using the target module in the past ten times, calibrated as , then the operation complexity coefficient ; Module correlation coefficient The number of activations of the target module when used by users in the same industry is calibrated as , the total number of activation times collected in the industry is marked as , get the probability of the target module being used together with the enabled modules, calibrated as , x is the enabled module number, x=0,1,2,3,..., y, y is a positive integer, then the module association coefficient ; Burden feedback coefficient The size of the system memory occupied by the acquisition target module after it is enabled, calibrated as , after the statistics are enabled, the average time extension of users getting feedback in the system is marked as , then the burden feedback coefficient .

3. The efficient business collaboration platform system according to claim 2, characterized in that: The module evaluation model is constructed by weighting the operation complexity information, module association information and burden feedback information of the modules divided by the target system to generate the module evaluation index. , and the corresponding coefficients are respectively the operation complexity coefficient , module correlation coefficient , burden feedback coefficient , the formula is , are the weight coefficients of the corresponding indicators, all of which are greater than 0.

4. The efficient business collaboration platform system according to claim 3, characterized in that: In step S2, when the module evaluation index When it is greater than the set activation threshold, the target module is temporarily not used, the evaluation results are output, and the module is replaced for re-evaluation; When equipped with module evaluation index When it is less than or equal to the set activation threshold, the target module is enabled, the evaluation result is output, and the module is replaced and evaluated again.

5. The efficient business collaboration platform system according to claim 1, characterized in that: In step S3, after all modules are evaluated, the information of modules with high priority in all evaluation results is obtained, and the efficient business collaboration platform system is re-integrated and constructed. The operation optimization of the efficient business collaboration platform is realized through the load balancing algorithm in the scheduling algorithm; Furthermore, the load balancing algorithm model is constructed as follows: Step S3.1, demand analysis and model building, collect user request data, analyze the load characteristics of different modules, and define the load function of each server; Step S3.2, load balancing is achieved by allocating requests to each server in turn through a round-robin method; Step S3.3, dynamic adjustment, define performance indicators, monitor the load and response time of each server in real time, when the performance indicators exceed the defined performance indicators, trigger the load balancing strategy adjustment, update the load function, dynamically adjust the request distribution strategy according to the real-time load, and finally output the optimized system.

6. The efficient business collaboration platform system according to claim 5, characterized in that: In step S4, after applying the optimization system, a system effectiveness evaluation model is constructed. The specific process is to collect the user's team collaboration information, flexible adaptation information and record demand information. The team collaboration information includes the team collaboration coefficient, which is calibrated as , the flexible adaptation information includes the flexible adaptation coefficient, calibrated as , recording demand information includes recording demand coefficient, calibrated as ; Teamwork coefficient The increase in the number of interactions between team members after collecting and applying the optimized system is calibrated as , the task completion rate improvement ratio after applying the optimization system is calibrated as , the increase in the satisfaction score of team members on collaboration is obtained through questionnaire survey and calibrated as , p is the member number, p=0,1,2,3,...,q,q is a positive integer, then the teamwork coefficient ; Flexible adaptability factor By collecting the number of times the user's policy changes, the collection time is marked as , the number of policy changes is calibrated as , then the flexible adaptation coefficient ; Recording Demand Factors During the user task collection process, the user provides existing document support, counts the total number of words in the document, and marks it as , z is the task number, z=0,1,2,3,...,n, n is a positive integer, then record the demand coefficient .

7. The efficient business collaboration platform system according to claim 6, characterized in that: The system effectiveness evaluation model is constructed by weighting the team collaboration information, flexible adaptation information and record demand information in the algorithm model to generate a system effectiveness evaluation index. , and the corresponding coefficients are teamwork coefficient , Flexible Adaptability Coefficient and record demand factors , the formula is , are the weight coefficients of the corresponding indicators, all of which are greater than 0.

8. The efficient business collaboration platform system according to claim 7, characterized in that: In step S4, when the system performance evaluation index When the value is greater than or equal to the set application threshold, the evaluation result is directly output and the current algorithm model continues to be used; When the system performance evaluation index When it is less than the set application threshold, return to step S2, disrupt the module evaluation order, adjust the activation threshold and rebuild the model.

9. An efficient business collaboration platform system, used to implement an efficient business collaboration platform method according to any one of claims 1 to 8, characterized in that: include: The data collection module monitors and collects the operation complexity information, module association information and burden feedback information after the target system is divided into modules, as well as the user's team collaboration information, flexible adaptation information and record demand information after the model is built using the load balancing algorithm; Equipped module evaluation module, builds an equipped module evaluation model, and evaluates the activation priority of the target module; The load algorithm module integrates the enabled target modules, builds a load balancing algorithm model, regulates and distributes the load using the round-robin method, and outputs an optimized system. The performance evaluation module builds a system performance evaluation model to evaluate the system performance and output a more personalized and efficient business collaboration platform system.

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