A high-efficiency service coordination platform system

Abstract

The application discloses a kind of efficient service coordination platform systems, specifically related to data processing technical field, the processing method includes the following steps: relevant information after target system is divided into module is monitored, collected, and the relevant information of user after model is constructed using load balancing algorithm.The present application provides a kind of efficient service coordination platform systems, the existing efficient service coordination platform system is divided into different modules according to function, builds the evaluation model of module, the priority of the start of target module is evaluated, according to the evaluation result, it is decided whether the target module needs to be started, for all the target module integrated after starting, load balancing algorithm model is constructed, and the load is regulated and distributed using polling method, output optimized system, and system efficiency evaluation model is constructed to evaluate system efficiency, so that the result obtained is more in line with the purpose of efficient service coordination platform system exists.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically, to a high-efficiency business collaboration platform system. Background Technology

[0002] Highly efficient business collaboration platform systems are typically based on technologies such as cloud computing, the Internet of Things (IoT), and artificial intelligence (AI). Cloud computing provides flexible resource management and data storage capabilities, IoT enables real-time data sharing between devices, and AI is used to optimize processes, analyze data, and improve user experience. Furthermore, APIs and microservice architectures facilitate integration between different systems, promoting information flow and improved collaboration efficiency.

[0003] Existing high-efficiency business collaboration platforms have evolved from the early 1990s, through the rise of collaborative office software and cloud computing, social collaboration, and the era of mobile and intelligent technologies, to the current stage of integrating multiple functions into unified platforms. They are gradually transforming towards 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. Existing high-efficiency business collaboration platforms are mainly classified according to different functions and application scenarios, providing enterprises with a variety of functional options.

[0004] However, existing high-efficiency 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 problems of high complexity and functional redundancy run counter to the original intention of a high-efficiency business collaboration platform system. Users need a simple, clear, customizable, more personalized, and more efficient business collaboration platform system. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a high-efficiency business collaboration platform system. This system divides existing high-efficiency business collaboration platform systems into different modules based on their functions, constructs a module evaluation model, assesses the priority of enabling target modules, and determines whether to enable a target module based on the evaluation results. After integrating all enabled target modules, a load balancing algorithm model is constructed to regulate and distribute the load using a round-robin method, outputting an optimized system. Finally, a system performance evaluation model is constructed to evaluate system performance, ensuring that the obtained results better align with the purpose of the high-efficiency business collaboration platform system.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A high-efficiency business collaboration platform system includes the following steps:

[0008] Step S1: Monitor and collect operational complexity information, module association information, and load feedback information after the target system is divided into modules, as well as user team collaboration information, flexible adaptation information, and recorded demand information after the model is built using a load balancing algorithm.

[0009] Step S2: Construct an evaluation model for the equipped module to assess the activation priority of the target module;

[0010] Step S3: Integrate the activated target modules, construct a load balancing algorithm model, use round-robin to regulate and distribute the load, and output the optimized system.

[0011] Step S4: Construct a system performance evaluation model to evaluate system performance and output a more personalized and efficient business collaboration platform system.

[0012] Specifically, in step S1, the different functions of the efficient business collaboration platform system are modularized. When different user requests are received, each module is evaluated to determine whether it should be activated. After the reorganized modules are integrated, the load is balanced through an algorithm. Specifically, the data is processed using the computer software Apache Spark, and the algorithm model is constructed and output using Python. In practical use, due to factors such as equipment cost and the complexity of real-time monitoring, this invention is based on the premise of relatively stable target user operations and platform system environment, and continuous monitoring of platform operation and user data. This may lead to changes in the rationality of a certain monitoring point.

[0013] In step S2, an evaluation model for the mounted modules is constructed. Specifically, this involves collecting operational complexity information, module association information, and burden feedback information for the modules divided within the target system. The operational complexity information includes operational complexity coefficients, calibrated as follows: Module association information includes module association coefficients, calibrated as follows: The burden feedback information includes the burden feedback coefficient, calibrated as follows: ;

[0014] Operational complexity By collecting data on the number of main functions provided by the module when users use the target module, it is calibrated as follows: The number of interactions required by users when using the module is counted and calibrated as... And the average time spent by the user in the past ten uses of the target module, calibrated as Then the operation complexity coefficient ;

[0015] Module correlation coefficient The number of times the target module is activated by users in the same industry is calibrated as... The total number of times the industry data was collected was calibrated as follows: Obtain the probability that the target module and the already enabled modules can be used together, and define it as... Let x be the activated module number, x = 0, 1, 2, 3, ..., y, where y is a positive integer. Then, the module correlation coefficient is... ;

[0016] Burden feedback coefficient The system memory size occupied by the target module after it is enabled is calibrated as follows. After the statistics were enabled, the average time users received feedback in the system was extended by a certain number of times, which was calibrated as follows: Then the burden feedback coefficient ;

[0017] The module evaluation model is constructed by weighting three aspects: operational complexity information, module association information, and burden feedback information of the modules divided in the target system, and generates a module evaluation index. The corresponding coefficients are the operational complexity coefficients. Module correlation coefficient Burden feedback coefficient The formula formed is ;

[0018] at the same time, , , All values ​​are greater than 0 and are set according to actual conditions. For example, the expert weighting method can be used, which involves inviting experts in relevant fields to determine the weights of each indicator through professional opinion surveys and comprehensive evaluations, ensuring that the weight coefficients accurately reflect the importance of each indicator in the evaluation of the mounted module. In addition, methods such as the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation can also be considered to determine the weight coefficients to ensure their objectivity and scientific validity. These will not be elaborated upon here.

[0019] In step S2, the module evaluation index obtained from the module evaluation model is used to reflect the activation priority of the modules divided by the target system. The larger the value, the more complex the operation required by the target module to solve the user's problem, the lower the correlation with the user's problem, the heavier the burden on the system, and the lower the activation priority of the module.

[0020] In step S2, when the module evaluation index is mounted... If the value exceeds the set activation threshold, it indicates that the activation priority of the module in the target system is low and has little impact on meeting user needs. The target module will not be used for the time being. The evaluation result will be output and the module will be replaced for re-evaluation.

[0021] When the module evaluation index is equipped If the value is less than or equal to the set activation threshold, it indicates that the module assigned to the target system has a high activation priority. Activate the target module, output the evaluation results, and replace the module for re-evaluation.

[0022] In step S3, after evaluating all modules, the information of the modules with high activation priority among all evaluation results is obtained, and the efficient business collaboration platform system is re-integrated and rebuilt. Through the load balancing algorithm in the scheduling algorithm, the operation of the efficient business collaboration platform is optimized.

[0023] Furthermore, the method for constructing the load balancing algorithm model of this invention is as follows:

[0024] Step S3.1, Requirements Analysis and Model Building: Collect user request data, analyze the load characteristics of different modules, and define the load function for each server. , representing the current load of the i-th server: ,in, This represents the resources required by the i-th server to process the j-th request;

[0025] Step S3.2: Implement load balancing by distributing requests to each server sequentially using a round-robin method. Assuming there are N servers, the request distribution will proceed as follows: ,in, The target server for the k-th request;

[0026] Step S3.3, dynamically adjust and define performance metrics, such as average response time. And system throughput H: and It monitors the load and response time of each server in real time, and triggers load balancing strategy adjustments and updates the load function when performance metrics exceed defined performance targets. : ,in, It is a smoothing factor. Based on real-time load, the request distribution strategy is dynamically adjusted, such as reallocating requests to high-load servers and pausing the acceptance of new requests, thus optimizing the system output.

[0027] In step S4, after applying the optimization system, a system performance evaluation model is constructed. Specifically, this involves collecting user team collaboration information, flexibility adaptation information, and recording demand information. The team collaboration information includes a team collaboration coefficient, calibrated as follows: Flexible adaptation information includes the flexible adaptation coefficient, calibrated as follows: Recording demand information includes recording demand coefficients, calibrated as follows: ;

[0028] Team collaboration coefficient The increase in interactions among team members after the system was optimized by collecting data was calibrated as... The percentage increase in task completion rate after system optimization is defined as... The increase in team members' satisfaction ratings regarding collaboration was obtained through a questionnaire survey and calibrated as... Let p be the member number, p = 0, 1, 2, 3, ..., q, where q is a positive integer. Then the team collaboration coefficient is... ;

[0029] Flexible Adaptation Coefficient By collecting the number of times user policies and strategies change, the time period of the collection is calibrated as... The number of times the strategy and policy changes are denoted as The flexible adaptation coefficient ;

[0030] Record demand coefficient During the user task collection process, the existing document support provided by the user is counted, and the total word count of the document is categorized as follows: Let z be the task number, z = 0, 1, 2, 3, ..., n, where n is a positive integer. Then record the demand coefficient. ;

[0031] The system performance evaluation model is constructed by weighting three aspects from the algorithm model: team collaboration information, flexible adaptation information, and recorded requirements information, and generates a system performance evaluation index. The corresponding coefficients are the team collaboration coefficient. Flexible adaptability coefficient and record demand coefficient The formula formed is ;

[0032] In step S4, the system performance evaluation index obtained from the system performance evaluation model is used to reflect the effect and collaborative capability of the optimized system after application. The larger the system performance evaluation index, the closer the team collaboration after the system is applied, the more varied the user strategies and policies, the more suitable the environment provided for the system, and the greater the user's recording needs. Therefore, the better the effect and collaborative capability of the optimized system after application.

[0033] 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 optimized system has excellent performance and collaborative capabilities. The evaluation result is output directly, and the current algorithm model is continued to be used.

[0034] When the system performance evaluation index If the value is less than the set application threshold, it indicates that the effect and collaborative capability of the optimized system after application are not excellent. Return to step S2, shuffle the module evaluation order, adjust the activation threshold, and rebuild the model.

[0035] A high-efficiency business collaboration platform system, used in the aforementioned high-efficiency business collaboration platform method, includes:

[0036] The data acquisition module monitors and collects operational complexity information, module association information, and load feedback information after the target system is divided into modules, as well as user team collaboration information, flexible adaptation information, and recorded demand information after the model is built using load balancing algorithms.

[0037] Equipped with a module evaluation module, a module evaluation model is constructed to assess the activation priority of target modules;

[0038] The load balancing algorithm module integrates the enabled target modules, constructs a load balancing algorithm model, uses a round-robin method to regulate and distribute the load, and outputs an optimized system.

[0039] The performance evaluation module constructs a system performance evaluation model to assess system performance and output a more personalized and efficient business collaboration platform system.

[0040] The technical effects and advantages of this invention are as follows:

[0041] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a high-efficiency business collaboration platform system. This system divides existing high-efficiency business collaboration platform systems into different modules based on their functions, constructs a module evaluation model, assesses the priority of enabling target modules, and determines whether to enable a target module based on the evaluation results. After integrating all enabled target modules, a load balancing algorithm model is constructed to regulate and distribute the load using a round-robin method, outputting an optimized system. Finally, a system performance evaluation model is constructed to evaluate system performance, ensuring that the obtained results better align with the purpose of the high-efficiency business collaboration platform system. Attached Figure Description

[0042] Figure 1 This is a flowchart of a method for an efficient business collaboration platform according to the present invention.

[0043] Figure 2 This is a flowchart of a high-efficiency business collaboration platform system according to the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] This invention provides a high-efficiency business collaboration platform system. It divides existing high-efficiency business collaboration platform systems into different modules according to function, constructs a module evaluation model, assesses the priority of target module activation, and determines whether a target module needs to be activated based on the evaluation results. After integrating all activated target modules, a load balancing algorithm model is constructed to regulate and distribute the load using a round-robin method, outputting an optimized system. Finally, a system performance evaluation model is constructed to evaluate system performance, ensuring that the obtained results better align with the purpose of the high-efficiency business collaboration platform system.

[0046] Example 1

[0047] like Figure 1 As shown, the steps of an efficient business collaboration platform system are as follows:

[0048] Step S1: Monitor and collect operational complexity information, module association information, and load feedback information after the target system is divided into modules, as well as user team collaboration information, flexible adaptation information, and recorded demand information after the model is built using a load balancing algorithm.

[0049] Step S2: Construct an evaluation model for the equipped module to assess the activation priority of the target module;

[0050] Step S3: Integrate the activated target modules, construct a load balancing algorithm model, use round-robin to regulate and distribute the load, and output the optimized system.

[0051] Step S4: Construct a system performance evaluation model to evaluate system performance and output a more personalized and efficient business collaboration platform system.

[0052] Specifically, in step S1, the different functions of the efficient business collaboration platform system are modularized. When different user requests are received, each module is evaluated to determine whether it should be activated. After the reorganized modules are integrated, the load is balanced through an algorithm. Specifically, the data is processed using the computer software Apache Spark, and the algorithm model is constructed and output using Python. In practical use, due to factors such as equipment cost and the complexity of real-time monitoring, this invention is based on the premise of relatively stable target user operations and platform system environment, and continuous monitoring of platform operation and user data. This may lead to changes in the rationality of a certain monitoring point.

[0053] In step S2, an evaluation model for the mounted modules is constructed. Specifically, this involves collecting operational complexity information, module association information, and burden feedback information for the modules divided within the target system. The operational complexity information includes operational complexity coefficients, calibrated as follows: Module association information includes module association coefficients, calibrated as follows: The burden feedback information includes the burden feedback coefficient, calibrated as follows: ;

[0054] Operational complexity By collecting data on the number of main functions provided by the module when users use the target module, it is calibrated as follows: The number of interactions required by users when using the module is counted and calibrated as... And the average time spent by the user in the past ten uses of the target module, calibrated as Then the operation complexity coefficient ;

[0055] Module correlation coefficient The number of times the target module is activated by users in the same industry is calibrated as... The total number of times the industry data was collected was calibrated as follows: Obtain the probability that the target module and the already enabled modules can be used together, and define it as... Let x be the activated module number, x = 0, 1, 2, 3, ..., y, where y is a positive integer. Then, the module correlation coefficient is... ;

[0056] Burden feedback coefficient The system memory size occupied by the target module after it is enabled is calibrated as follows. After the statistics were enabled, the average time users received feedback in the system was extended by a certain number of times, which was calibrated as follows: Then the burden feedback coefficient ;

[0057] The module evaluation model is constructed by weighting three aspects: operational complexity information, module association information, and burden feedback information of the modules divided in the target system, and generates a module evaluation index. The corresponding coefficients are the operational complexity coefficients. Module correlation coefficient Burden feedback coefficient The formula formed is ;

[0058] at the same time, , , All values ​​are greater than 0 and are set according to actual conditions. For example, the expert weighting method can be used, which involves inviting experts in relevant fields to determine the weights of each indicator through professional opinion surveys and comprehensive evaluations, ensuring that the weight coefficients accurately reflect the importance of each indicator in the evaluation of the mounted module. In addition, methods such as the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation can also be considered to determine the weight coefficients to ensure their objectivity and scientific validity. These will not be elaborated upon here.

[0059] In step S2, the module evaluation index obtained from the module evaluation model is used to reflect the activation priority of the modules divided by the target system. The larger the value, the more complex the operation required by the target module to solve the user's problem, the lower the correlation with the user's problem, the heavier the burden on the system, and the lower the activation priority of the module.

[0060] In step S2, when the module evaluation index is mounted... If the value exceeds the set activation threshold, it indicates that the activation priority of the module in the target system is low and has little impact on meeting user needs. The target module will not be used for the time being. The evaluation result will be output and the module will be replaced for re-evaluation.

[0061] When the module evaluation index is equipped If the value is less than or equal to the set activation threshold, it indicates that the module assigned to the target system has a high activation priority. Activate the target module, output the evaluation results, and replace the module for re-evaluation.

[0062] In step S3, after evaluating all modules, the information of the modules with high activation priority among all evaluation results is obtained, and the efficient business collaboration platform system is re-integrated and rebuilt. Through the load balancing algorithm in the scheduling algorithm, the operation of the efficient business collaboration platform is optimized.

[0063] Furthermore, the method for constructing the load balancing algorithm model of this invention is as follows:

[0064] Step S3.1, Requirements Analysis and Model Building: Collect user request data, analyze the load characteristics of different modules, and define the load function for each server. , representing the current load of the i-th server: ,in, This represents the resources required by the i-th server to process the j-th request;

[0065] Step S3.2: Implement load balancing by distributing requests to each server sequentially using a round-robin method. Assuming there are N servers, the request distribution will proceed as follows: ,in, The target server for the k-th request;

[0066] Step S3.3, dynamically adjust and define performance metrics, such as average response time. And system throughput H: and It monitors the load and response time of each server in real time, and triggers load balancing strategy adjustments and updates the load function when performance metrics exceed defined performance targets. : ,in, It is a smoothing factor. Based on real-time load, the request distribution strategy is dynamically adjusted, such as reallocating requests to high-load servers and pausing the acceptance of new requests, thus optimizing the system output.

[0067] In step S4, after applying the optimization system, a system performance evaluation model is constructed. Specifically, this involves collecting user team collaboration information, flexibility adaptation information, and recording demand information. The team collaboration information includes a team collaboration coefficient, calibrated as follows: Flexible adaptation information includes the flexible adaptation coefficient, calibrated as follows: Recording demand information includes recording demand coefficients, calibrated as follows: ;

[0068] Team collaboration coefficient The increase in interactions among team members after the system was optimized by collecting data was calibrated as... The percentage increase in task completion rate after system optimization is defined as... The increase in team members' satisfaction ratings regarding collaboration was obtained through a questionnaire survey and calibrated as... Let p be the member number, p = 0, 1, 2, 3, ..., q, where q is a positive integer. Then the team collaboration coefficient is... ;

[0069] Flexible Adaptation Coefficient By collecting the number of times user policies and strategies change, the time period of the collection is calibrated as... The number of times the strategy and policy changes are denoted as The flexible adaptation coefficient ;

[0070] Record demand coefficient During the user task collection process, the existing document support provided by the user is counted, and the total word count of the document is categorized as follows: Let z be the task number, z = 0, 1, 2, 3, ..., n, where n is a positive integer. Then record the demand coefficient. ;

[0071] The system performance evaluation model is constructed by weighting three aspects from the algorithm model: team collaboration information, flexible adaptation information, and recorded requirements information, and generates a system performance evaluation index. The corresponding coefficients are the team collaboration coefficient. Flexible adaptability coefficient and record demand coefficient The formula formed is ;

[0072] In step S4, the system performance evaluation index obtained from the system performance evaluation model is used to reflect the effect and collaborative capability of the optimized system after application. The larger the system performance evaluation index, the closer the team collaboration after the system is applied, the more varied the user strategies and policies, the more suitable the environment provided for the system, and the greater the user's recording needs. Therefore, the better the effect and collaborative capability of the optimized system after application.

[0073] 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 optimized system has excellent performance and collaborative capabilities. The evaluation result is output directly, and the current algorithm model is continued to be used.

[0074] When the system performance evaluation index If the value is less than the set application threshold, it indicates that the effect and collaborative capability of the optimized system after application are not excellent. Return to step S2, shuffle the module evaluation order, adjust the activation threshold, and rebuild the model.

[0075] Example 2

[0076] A high-efficiency business collaboration platform system, used in the aforementioned high-efficiency business collaboration platform method, includes:

[0077] The data acquisition module monitors and collects operational complexity information, module association information, and load feedback information after the target system is divided into modules, as well as user team collaboration information, flexible adaptation information, and recorded demand information after the model is built using load balancing algorithms.

[0078] Equipped with a module evaluation module, a module evaluation model is constructed to assess the activation priority of target modules;

[0079] The load balancing algorithm module integrates the enabled target modules, constructs a load balancing algorithm model, uses a round-robin method to regulate and distribute the load, and outputs an optimized system.

[0080] The performance evaluation module constructs a system performance evaluation model to assess system performance and output a more personalized and efficient business collaboration platform system.

[0081] The above formulas are all dimensionless calculations. Dimensionless calculations can be performed using various methods such as standardization, which will not be elaborated here. The formulas are derived from software simulations based on a large amount of collected data, and the preset parameters in the formulas can be set by those skilled in the art according to the actual situation.

[0082] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as 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, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, ATA hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state ATA hard disk.

[0083] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply 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 this application.

[0084] Those skilled in the art will recognize that the units and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0085] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0086] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0087] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0088] If the aforementioned functions are implemented as 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 this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable ATA hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0089] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A high-efficiency business collaboration platform system, characterized in that, Includes the following steps: Step S1: Monitor and collect operational complexity information, module association information, and load feedback information after the target system is divided into modules, as well as user team collaboration information, flexible adaptation information, and recorded demand information after the model is built using a load balancing algorithm. Step S2: Construct an evaluation model for the equipped module to assess the activation priority of the target module; Step S3: Integrate the activated target modules, construct a load balancing algorithm model, use round-robin to regulate and distribute the load, and output the optimized system. Step S4: Construct a system performance evaluation model to evaluate system performance and output a more personalized and efficient business collaboration platform system. In step S2, an evaluation model for the mounted modules is constructed. Specifically, this involves collecting operational complexity information, module association information, and burden feedback information for the modules divided within the target system. The operational complexity information includes operational complexity coefficients, calibrated as follows: Module association information includes module association coefficients, calibrated as follows: The burden feedback information includes the burden feedback coefficient, calibrated as follows: ; Operational complexity By collecting data on the number of functions provided by the module when users use the target module, it is calibrated as... The number of interactions required by users when using the module is counted and calibrated as... And the average time spent by the user in the past ten uses of the target module, calibrated as Then the operation complexity coefficient ; Module correlation coefficient The number of times the target module is activated by users in the same industry is calibrated as... The total number of times the industry data was collected was calibrated as follows: Obtain the probability that the target module and the already enabled modules can be used together, and define it as... Let x be the activated module number, x = 0, 1, 2, 3, ..., y, where y is a positive integer. Then, the module correlation coefficient is... ; Burden feedback coefficient The system memory size occupied by the target module after it is enabled is calibrated as follows. After the statistics were enabled, the average time users received feedback in the system was extended by a certain number of times, which was calibrated as follows: Then the burden feedback coefficient ; The module evaluation model is constructed by weighting three aspects: operational complexity information, module association information, and burden feedback information of the modules divided in the target system, and generates a module evaluation index. The corresponding coefficients are the operational complexity coefficients. Module correlation coefficient Burden feedback coefficient The formula formed is , These are the weighting coefficients of the corresponding indicators, all of which are greater than 0.

2. The efficient business collaboration platform system according to claim 1, characterized in that: In step S2, when the module evaluation index is mounted... If the value exceeds the set activation threshold, the target module will not be used temporarily, the evaluation result will be output, and the module will be replaced for re-evaluation. When the module evaluation index is equipped When the threshold value is less than or equal to the set activation threshold, the target module is activated, the evaluation result is output, and the module is replaced for re-evaluation.

3. The efficient business collaboration platform system according to claim 1, characterized in that: In step S3, after evaluating all modules, the information of the modules with high activation priority among all evaluation results is obtained, and the efficient business collaboration platform system is re-integrated and rebuilt. Through the load balancing algorithm in the scheduling algorithm, the operation of the efficient business collaboration platform is optimized. Furthermore, the method for constructing the load balancing algorithm model is as follows: Step S3.1, Requirements Analysis and Model Building: Collect user request data, analyze the load characteristics of different modules, and define the load function for each server; Step S3.2: Implement load balancing by distributing requests to each server sequentially using a round-robin method. Step S3.3: Dynamically adjust and define performance metrics. Monitor the load and response time of each server in real time. When the performance metrics exceed the defined performance metrics, 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.

4. The efficient business collaboration platform system according to claim 3, characterized in that: In step S4, after applying the optimization system, a system performance evaluation model is constructed. Specifically, this involves collecting user team collaboration information, flexibility adaptation information, and recording demand information. The team collaboration information includes a team collaboration coefficient, calibrated as follows: Flexible adaptation information includes the flexible adaptation coefficient, calibrated as follows: Recording demand information includes recording demand coefficients, calibrated as follows: ; Team collaboration coefficient The increase in interactions among team members after the system was optimized by collecting data was calibrated as... The percentage increase in task completion rate after system optimization is defined as... The increase in team members' satisfaction ratings regarding collaboration was obtained through a questionnaire survey and calibrated as... Let p be the member number, p = 0, 1, 2, 3, ..., q, where q is a positive integer. Then the team collaboration coefficient is... ; Flexible Adaptation Coefficient By collecting the number of times user policies and strategies change, the time period of the collection is calibrated as... The number of times the strategy and policy changes are denoted as The flexible adaptation coefficient ; Record demand coefficient During the user task collection process, the existing document support provided by the user is counted, and the total word count of the document is categorized as follows: Let z be the task number, z = 0, 1, 2, 3, ..., n, where n is a positive integer. Then record the demand coefficient. .

5. The efficient business collaboration platform system according to claim 4, characterized in that: The system performance evaluation model is constructed by weighting three aspects from the algorithm model: team collaboration information, flexible adaptation information, and recorded requirements information, and generates a system performance evaluation index. The corresponding coefficients are the team collaboration coefficient. Flexible adaptability coefficient and record demand coefficient The formula formed is , These are the weighting coefficients of the corresponding indicators, all of which are greater than 0.

6. The efficient business collaboration platform system according to claim 5, characterized in that: In step S4, when the system performance evaluation index If the value is greater than or equal to the set application threshold, the evaluation result is output directly, and the current algorithm model is continued to be used. When the system performance evaluation index If the value is less than the set application threshold, return to step S2, shuffle the module evaluation order, adjust the activation threshold, and rebuild the model.

7. A high-efficiency business collaboration platform system, used to implement the high-efficiency business collaboration platform method according to any one of claims 1-6, characterized in that, include: The data acquisition module monitors and collects operational complexity information, module association information, and load feedback information after the target system is divided into modules, as well as user team collaboration information, flexible adaptation information, and recorded demand information after the model is built using load balancing algorithms. Equipped with a module evaluation module, a module evaluation model is constructed to assess the activation priority of target modules; The load balancing algorithm module integrates the enabled target modules, constructs a load balancing algorithm model, uses a round-robin method to regulate and distribute the load, and outputs an optimized system. The performance evaluation module constructs a system performance evaluation model to assess system performance and output a more personalized and efficient business collaboration platform system.

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