Engineering whole-process consultation platform management system

Abstract

The invention relates to the technical field of information, in particular to an engineering whole-process consultation platform management system which comprises a heartbeat monitoring module, an acquisition module, an encryption transmission unit, a user level analysis module, a deep mining module, a cloud computing platform, a resource allocation module, a redundancy processing platform, an automatic switching unit and a feedback module. The heartbeat monitoring module is connected with the consultation platform, the user grade analysis module is connected with the consultation platform through the encryption transmission unit and the acquisition module, the deep mining module is connected with the user grade analysis module, the cloud computing platform is connected with the deep mining module, and the resource allocation module is connected with the cloud computing platform. And the automatic switching unit is connected with the resource distribution module, so that the technical problem that the stability of the platform is influenced because the fault of the processor cannot be found and processed in time due to the fact that the working state and heartbeat signals of the platform processor cannot be monitored in real time by the engineering whole-process consultation platform management system in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and particularly to a management system for the whole-process engineering consulting platform. Background Art

[0002] With the booming development of the engineering consulting industry, the whole-process engineering consulting platform has become an important bridge connecting consulting agencies and clients. Such platforms integrate various services such as project management, technical consulting, and cost consulting, providing comprehensive support for the entire life cycle of engineering construction. However, with the growth of business volume and the diversification of consulting services, higher requirements are put forward for the management of the platform. In order to ensure the stable operation of the platform, efficient utilization of resources, and provision of high-quality consulting services, a comprehensive and intelligent management system is needed to monitor and manage the whole-process engineering consulting platform.

[0003] However, the existing management systems for the whole-process engineering consulting platform cannot real-time monitor the working status and heartbeat signal of the platform processor, resulting in the inability to detect and handle processor failures in a timely manner, affecting the stability of the platform. Summary of the Invention

[0004] The purpose of the present invention is to provide a management system for the whole-process engineering consulting platform, aiming to solve the technical problem that the existing management systems for the whole-process engineering consulting platform cannot real-time monitor the working status and heartbeat signal of the platform processor, resulting in the inability to detect and handle processor failures in a timely manner, affecting the stability of the platform.

[0005] To achieve the above purpose, a management system for the whole-process engineering consulting platform adopted by the present invention includes a heartbeat monitoring module, an acquisition module, an encrypted transmission unit, a user level analysis module, a deep mining module, a cloud computing platform, a resource allocation module, a redundancy processing platform, an automatic switching unit, and a feedback module;

[0006] The heartbeat monitoring module is connected to the consulting platform, the user level analysis module is connected to the consulting platform through the encrypted transmission unit and the acquisition module, the deep mining module is connected to the user level analysis module, the cloud computing platform is connected to the deep mining module, the resource allocation module is connected to the cloud computing platform, the automatic switching unit is connected to the resource allocation module, the redundancy processing platform is connected to the automatic switching unit, and the feedback module is connected to both the resource allocation module and the consulting platform;

[0007] The heartbeat monitoring module is used to acquire the heartbeat signal and operation status data of the consulting platform processor;

[0008] The cloud computing platform analyzes the data monitored by the heartbeat monitoring module to determine whether the consulting platform is operating abnormally;

[0009] When the work is abnormal, the resource allocation module controls the automatic switching unit to switch to the redundant processing platform for work;

[0010] When the user needs to consult manually, the acquisition module obtains user data from the consultation platform, and then encrypts and transmits the data to the user level analysis module through the encryption transmission unit;

[0011] The user level analysis module deeply mines the user data in combination with the deep mining module;

[0012] The resource allocation module formulates a resource allocation strategy according to the resource usage of the cloud computing platform and the analysis results of the deep mining module, and sends a resource allocation instruction to the feedback module;

[0013] The feedback module feeds back the result to the consultation platform, and the consultation platform matches the corresponding level of customer service personnel for docking according to the result.

[0014] Among them, the automatic switching unit includes a task pause module, a status saving module and a status migration module. The task pause module is connected to the consultation platform, the status saving module is connected to the task pause module, the status migration module is connected to the status saving module, and the status migration module is connected to the redundant processing platform.

[0015] Among them, the encryption transmission unit includes a key generation module, a key management module, an encryption module, a transmission module and a decryption module. The encryption module and the decryption module are respectively connected to the consultation platform and the redundant processing platform. The transmission module is arranged between the consultation platform and the redundant processing platform. The key management module is connected to the encryption module, the decryption module and the key generation module.

[0016] Among them, the whole-process consultation platform management system of the project further includes a self-repair module, and the self-repair module is connected to the consultation platform.

[0017] Among them, the whole-process consultation platform management system of the project further includes an alarm module and a management terminal, and the alarm module is connected to the cloud computing platform and the management terminal.

[0018] Among them, the whole-process consultation platform management system of the project further includes a login module and an identity verification module. The login module is used to log in to the management terminal, and the identity verification module is used to verify the identity of the management personnel logging in to the management terminal.

[0019] Among them, the whole-process consultation platform management system of the project further includes a permission allocation module, and the permission allocation module is connected to the identity verification module.

[0020] Among them, the whole-process engineering consulting platform management system further includes a log recording module and a performance optimization module, and both the log recording module and the performance optimization module are connected to the cloud computing platform.

[0021] In the specific use of a whole-process engineering consulting platform management system of the present invention, users conduct whole-process engineering consulting through the consulting platform. The consulting platform utilizes natural language processing technology and machine learning algorithms to implement an intelligent customer service function, which can automatically answer users' questions and provide preliminary consulting and assistance. During this process, the heartbeat monitoring module is used to obtain the heartbeat signal and operating status data of the consulting platform processor; the cloud computing platform analyzes the data monitored by the heartbeat monitoring module to determine whether the consulting platform is working abnormally; when working abnormally, the resource allocation module controls the automatic switching unit to switch to the redundant processing platform for work.

[0022] When a user needs manual consultation, the acquisition module obtains user data from the consulting platform, and then encrypts and transmits the data to the user level analysis module through the encryption transmission unit; the user level analysis module deeply mines the user data in combination with the deep mining module; the resource allocation module formulates a resource allocation strategy according to the resource usage situation of the cloud computing platform and the analysis results of the deep mining module, and sends a resource allocation instruction to the feedback module; the feedback module feeds back the result to the consulting platform, and the consulting platform matches customer service personnel at the corresponding level for docking according to the result.

[0023] In this way, the technical problem in the prior art that the whole-process engineering consulting platform management system cannot real-time monitor the working status and heartbeat signal of the platform processor, resulting in the inability to timely discover and handle when the processor fails and affecting the stability of the platform is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0025] Figure 1 It is a principle block diagram of the first embodiment of the present invention.

[0026] Figure 2 It is a principle block diagram of the second embodiment of the present invention.

[0027] Figure 3 It is a principle block diagram of the third embodiment of the present invention.

[0028] 101 - Heartbeat Monitoring Module, 102 - Acquisition Module, 103 - Encrypted Transmission Unit, 104 - User Level Analysis Module, 105 - Deep Mining Module, 106 - Cloud Computing Platform, 107 - Resource Allocation Module, 108 - Redundancy Processing Platform, 109 - Automatic Switching Unit, 110 - Feedback Module, 111 - Self - Repair Module, 112 - Task Pause Module, 113 - Status Saving Module, 114 - Status Migration Module, 115 - Key Generation Module, 116 - Key Management Module, 117 - Encryption Module, 118 - Transmission Module, 119 - Decryption Module, 201 - Alarm Module, 202 - Management Terminal, 203 - Login Module, 204 - Identity Authentication Module, 205 - Permission Allocation Module, 301 - Storage Module, 302 - Data Backup Module, 303 - Data Recovery Module, 304 - Compression Module. Detailed Embodiment

[0029] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present invention, and should not be construed as a limitation to the present invention.

[0030] The first embodiment of the present application is as follows:

[0031] Please refer to Figure 1 , Figure 1 which is the principle block diagram of the first embodiment of the present invention.

[0032] The present invention provides an engineering whole - process consulting platform management system, including a heartbeat monitoring module 101, an acquisition module 102, an encrypted transmission unit 103, a user level analysis module 104, a deep mining module 105, a cloud computing platform 106, a resource allocation module 107, a redundancy processing platform 108, an automatic switching unit 109, a feedback module 110 and a self - repair module 111; the automatic switching unit 109 includes a task pause module 112, a status saving module 113 and a status migration module 114, and the encrypted transmission unit 103 includes a key generation module 115, a key management module 116, an encryption module 117, a transmission module 118 and a decryption module 119. The foregoing solution solves the technical problem that the existing engineering whole - process consulting platform management system cannot monitor the working state and heartbeat signal of the platform processor in real time, resulting in the inability to detect and handle the processor failure in time, affecting the stability of the platform.

[0033] For this specific embodiment, the heartbeat monitoring module 101 is used to obtain the heartbeat signal and operation status data of the consulting platform processor; and perform pre - processing operations such as data cleaning and denoising on the collected data to ensure the accuracy and reliability of the data

[0034] The cloud computing platform 106 analyzes and judges whether the consultation platform is working abnormally based on the data monitored by the heartbeat monitoring module 101;

[0035] The cloud computing platform 106 receives the data transmitted by the heartbeat monitoring module 101 and analyzes the data using methods such as statistical analysis and machine learning (such as anomaly detection algorithms); The specific fault judgment is as follows: According to the preset threshold or model, it is judged whether the consultation platform is working abnormally. For example, when the CPU usage rate continuously exceeds 90%, the memory occupancy rate exceeds 80%, or the response time exceeds the set value, it is determined to be working abnormally.

[0036] Early warning and decision-making: Once it is determined that there is an abnormal operation, a fault warning is immediately issued, and the resource allocation module 107 controls the automatic switching unit 109 to switch to the redundant processing platform 108 for operation

[0037] When there is an abnormal operation, the resource allocation module 107 controls the automatic switching unit 109 to switch to the redundant processing platform 108 for operation;

[0038] When a user needs to consult manually, the acquisition module 102 obtains user data from the consultation platform, and then the encryption transmission unit 103 encrypts and transmits the data to the user level analysis module 104;

[0039] The user level analysis module 104 deeply mines the user data in combination with the deep mining module 105; Specifically, according to information such as the user's historical consultation records, consumption behavior, and satisfaction evaluation, machine learning algorithms such as clustering algorithms and decision trees are used to classify the users.

[0040] Algorithm description of the deep mining module 105:

[0041] Feature extraction: Extract valuable features from user data, such as consultation frequency, question type, focus, etc.

[0042] Pattern mining: Use algorithms such as association rule mining and time series analysis to mine information such as the user's behavior patterns and demand trends.

[0043] The resource allocation module 107 formulates a resource allocation strategy using optimization algorithms (such as linear programming and integer programming) in combination with the analysis results of the deep mining module 105 (such as user level, demand trend, etc.), and sends a resource allocation instruction to the feedback module 110;

[0044] The feedback module 110 feeds back the result to the consultation platform, and the consultation platform matches the corresponding level of customer service personnel for docking according to the result.

[0045] Among them, the heartbeat monitoring module 101 is connected to the consultation platform, the user level analysis module 104 is connected to the consultation platform through the encryption transmission unit 103 and the acquisition module 102, the in-depth mining module 105 is connected to the user level analysis module 104, the cloud computing platform 106 is connected to the in-depth mining module 105, the resource allocation module 107 is connected to the cloud computing platform 106, the automatic switching unit 109 is connected to the resource allocation module 107, the redundancy processing platform 108 is connected to the automatic switching unit 109, and the feedback module 110 is connected to both the resource allocation module 107 and the consultation platform. In specific use, the user conducts full-process engineering consultation through the consultation platform. The consultation platform uses natural language processing technology and machine learning algorithms to implement the intelligent customer service function, which can automatically answer the user's questions and provide preliminary consultation and assistance. During this process, the heartbeat monitoring module 101 is used to obtain the heartbeat signal and operating status data of the consultation platform processor; the cloud computing platform 106 analyzes the data monitored by the heartbeat monitoring module 101 to determine whether the consultation platform is working abnormally; when it works abnormally, the automatic switching unit 109 is controlled through the resource allocation module 107 to switch to the redundancy processing platform 108 for work.

[0046] When the user needs manual consultation, the acquisition module 102 obtains user data from the consultation platform, and then encrypts and transmits the data to the user level analysis module 104 through the encryption transmission unit 103; the user level analysis module 104 deeply mines the user data in combination with the in-depth mining module 105; the resource allocation module 107 formulates a resource allocation strategy according to the resource usage situation of the cloud computing platform 106 and the analysis result of the in-depth mining module 105, and sends a resource allocation instruction to the feedback module 110; the feedback module 110 feeds back the result to the consultation platform, and the consultation platform matches the corresponding level of customer service personnel for docking according to the result.

[0047] In this way, it solves the technical problem in the prior art that the full-process engineering consultation platform management system cannot monitor the working state and heartbeat signal of the platform processor in real time, resulting in the inability to detect and handle the processor failure in time, affecting the stability of the platform.

[0048] Secondly, the task suspension module 112 is connected to the consultation platform, the status saving module 113 is connected to the task suspension module 112, the status migration module 114 is connected to the status saving module 113, and the status migration module 114 is connected to the redundancy processing platform 108;

[0049] When the task suspension module 112 detects a failure or maintenance need on the consultation platform, it quickly suspends the current task to avoid data loss or task anomalies;

[0050] The status saving module 113 copies the task status data multiple times and stores them in different locations. When the data in a certain storage location fails, the data can be restored from other copies;

[0051] The status migration module 114 migrates the task status from the consultation platform to the redundant processing platform 108, enabling the redundant processing platform 108 to continue executing the processing task.

[0052] Meanwhile, the encryption module 117 and the decryption module 119 are respectively connected to the consultation platform and the redundant processing platform 108. The transmission module 118 is arranged between the consultation platform and the redundant processing platform 108. The key management module 116 is connected to the encryption module 117, the decryption module 119, and the key generation module 115;

[0053] When the consultation platform needs to send data to the redundant processing platform 108, the encryption module 117 uses the RSA encryption algorithm or the AES encryption algorithm to obtain the plaintext data. Then, it obtains the encryption key from the key management module 116. According to the preset encryption algorithm, it encrypts the plaintext data and converts the plaintext into ciphertext. The encrypted ciphertext data is sent to the redundant processing platform 108 through the transmission module 118; after the redundant processing platform 108 receives the ciphertext data sent by the encryption module 117 through the transmission module 118, the decryption module 119 obtains the decryption key from the key management module 116. Then, it uses the decryption algorithm corresponding to the encryption module 117 to decrypt the ciphertext data and restore the ciphertext to plaintext data for subsequent processing and use.

[0054] In addition, the self - repair module 111 is connected to the consultation platform;

[0055] The self - repair module 111 selects the most suitable repair plan according to the results of fault diagnosis, such as evaluating and comparing different repair strategies and selecting the strategy that can quickly and effectively solve the problem.

[0056] Subsequently, it performs specific repair operations, such as redeploying services, updating configurations, installing patches, etc.

[0057] Meanwhile, the self - repair module 111 continuously optimizes the fault detection, diagnosis, and repair strategies by learning from historical fault data and repair processes, improving the self - repair ability.

[0058] When using a management system for the whole-process engineering consulting platform of this embodiment, in specific use, users conduct whole-process engineering consulting through the consulting platform. The consulting platform utilizes natural language processing technology and machine learning algorithms to implement an intelligent customer service function, which can automatically answer users' questions and provide preliminary consulting and assistance. During this process, the heartbeat monitoring module 101 is used to obtain the heartbeat signal and operating status data of the consulting platform processor; the cloud computing platform 106 analyzes the data monitored by the heartbeat monitoring module 101 to determine whether the consulting platform is working abnormally; when working abnormally, the resource allocation module 107 controls the automatic switching unit 109 to switch to the redundant processing platform 108 for work.

[0059] When users need manual consultation, the acquisition module 102 obtains user data from the consulting platform, and then the encryption transmission unit 103 encrypts and transmits the data to the user level analysis module 104; the user level analysis module 104 deeply mines the user data in combination with the in-depth mining module 105; the resource allocation module 107 formulates a resource allocation strategy according to the resource usage of the cloud computing platform 106 and the analysis results of the in-depth mining module 105, and sends a resource allocation instruction to the feedback module 110; the feedback module 110 feeds back the result to the consulting platform, and the consulting platform matches the corresponding level of customer service personnel for docking according to the result.

[0060] In this way, it solves the technical problem in the prior art that the management system of the whole-process engineering consulting platform cannot real-time monitor the working state and heartbeat signal of the platform processor, resulting in the inability to detect and handle in time when the processor fails, affecting the stability of the platform.

[0061] The second embodiment of this application is as follows:

[0062] On the basis of the first embodiment, please refer to Figure 2 , Figure 2 which is the principle block diagram of the second embodiment of the present invention.

[0063] The present invention provides a management system for the whole-process engineering consulting platform, further including an alarm module 201, a management terminal 202, a login module 203, an identity authentication module 204, and a permission allocation module 205.

[0064] For this specific embodiment, the alarm module 201 is connected to the cloud computing platform 106 and the management terminal 202. When the cloud computing platform 106 determines that the consulting platform is abnormal, it sends alarm information to the management terminal 202 of relevant management personnel through means such as text messages, emails, and APP push, and at the same time records an alarm log for subsequent query and analysis.

[0065] Among them, the login module 203 is used to log in to the management terminal 202, and the authentication module 204 is used to authenticate the management personnel who log in to the management terminal 202; the authentication module 204 ensures that only authorized legitimate users can log in to the system, prevents illegal users from intruding, and protects the security and integrity of the management system data.

[0066] Secondly, the permission allocation module 205 is connected to the authentication module 204. The permission allocation module 205 can allocate corresponding operation permissions according to the user's role, responsibilities, and work requirements, ensuring that the user can only access and operate the functions and data within their permission scope, and avoiding misoperations and data leakage.

[0067] The following algorithms can be adopted: Role definition and permission mapping algorithm: Different roles in the system are predefined in advance, and a corresponding set of permissions is allocated to each role to establish the mapping relationship between roles and permissions.

[0068] User role binding algorithm: According to the user's identity and job responsibilities, the user is bound to the corresponding role, so as to automatically obtain the permissions corresponding to that role.

[0069] Permission dynamic adjustment algorithm: According to the user's operation behavior, work progress, or system-set rules, the user's permissions are dynamically adjusted. For example, the permissions of certain users are temporarily elevated at the critical stage of the project, or the relevant permissions are withdrawn after the task is completed.

[0070] When using the management system of the whole-process engineering consulting platform of this embodiment, when the cloud computing platform 106 determines that the consulting platform is abnormal, it sends alarm information to the management terminal 202 of relevant management personnel through means such as text messages, emails, and APP push, and at the same time records the alarm log for subsequent query and analysis;

[0071] The authentication module 204 ensures that only authorized legitimate users can log in to the system, prevents illegal users from intruding, and protects the security and integrity of the management system data.

[0072] The third embodiment of this application is:

[0073] On the basis of the second embodiment, please refer to Figure 3 , Figure 3 which is the principle block diagram of the third embodiment of the present invention.

[0074] The present invention provides a management system for the whole-process engineering consulting platform, which further includes a storage module 301, a data backup module 302, a data recovery module 303, and a compression module 304.

[0075] For this specific embodiment, the storage module 301 is connected to the cloud computing platform 106, the data backup module 302 is connected to the storage module 301, the data recovery module 303 is connected to the data backup module 302, and the data recovery module 303 is also connected to the storage module 301. The storage module 301 centrally stores various types of data generated by the cloud computing platform 106 in the storage module 301, facilitating unified management and maintenance, and improving the availability and consistency of the data. The data backup module 302 regularly backs up the data stored in the storage module 301 to ensure timely recovery in case of data loss or damage, guaranteeing the security and integrity of the data. The data recovery module 303 can quickly recover data from the backup in case of data loss or damage, reducing the service interruption time and improving the availability and reliability of the system.

[0076] Recovery process: Start from the most recent backup and gradually recover the database until the target state is reached. During recovery, the backup data will be read and copied to the target storage device. Multiple recovery methods are supported, such as recovering to the original location, recovering to a new location, partial recovery, etc., to meet the data recovery requirements in different situations.

[0077] Among them, the compression module 304 is connected to the storage module 301. The compression module 304 compresses the data in the storage module 301, reducing the occupation of storage space and improving the storage efficiency. Common compression algorithms include LZW, DEFLATE, etc.

[0078] Using the engineering whole-process consulting platform management system of this embodiment, the storage module 301 centrally stores various types of data generated by the cloud computing platform 106 in the storage module 301, facilitating unified management and maintenance, and improving the availability and consistency of the data. The data backup module 302 regularly backs up the data stored in the storage module 301 to ensure timely recovery in case of data loss or damage, guaranteeing the security and integrity of the data. The data recovery module 303 can quickly recover data from the backup in case of data loss or damage, reducing the service interruption time and improving the availability and reliability of the system. The compression module 304 compresses the data in the storage module 301, reducing the occupation of storage space and improving the storage efficiency. Common compression algorithms include LZW, DEFLATE, etc.

[0079] What is disclosed above is only a preferred embodiment of the present invention. Of course, it cannot be used to limit the scope of the rights of the present invention. Those of ordinary skill in the art can understand the whole or part of the processes of implementing the above embodiments, and the equivalent changes made according to the claims of the present invention still fall within the scope covered by the invention.

Claims

1. An engineering whole-process consulting platform management system, characterized in that it includes a heartbeat monitoring module, an acquisition module, an encrypted transmission unit, a user level analysis module, a deep mining module, a cloud computing platform, a resource allocation module, a redundancy processing platform, an automatic switching unit, and a feedback module; The heartbeat monitoring module is connected to the consulting platform, the user level analysis module is connected to the consulting platform through the encrypted transmission unit and the acquisition module, the deep mining module is connected to the user level analysis module, the cloud computing platform is connected to the deep mining module, the resource allocation module is connected to the cloud computing platform, the automatic switching unit is connected to the resource allocation module, the redundancy processing platform is connected to the automatic switching unit, and the feedback module is connected to both the resource allocation module and the consulting platform; The heartbeat monitoring module is used to obtain the heartbeat signal and operation status data of the consulting platform processor; The cloud computing platform analyzes the data monitored by the heartbeat monitoring module to determine whether the consulting platform is operating abnormally; When an abnormal operation occurs, the automatic switching unit is controlled by the resource allocation module to switch to the redundancy processing platform for operation; When a user needs manual consultation, the acquisition module obtains user data from the consulting platform and then encrypts and transmits the data to the user level analysis module through the encrypted transmission unit; The user level analysis module deeply mines the user data in combination with the deep mining module; The resource allocation module formulates a resource allocation strategy according to the resource usage situation of the cloud computing platform and the analysis result of the deep mining module, and sends a resource allocation instruction to the feedback module; The feedback module feeds back the result to the consulting platform, and the consulting platform matches the corresponding level of customer service personnel for docking according to the result.

2. The engineering whole-process consulting platform management system according to claim 1, characterized in that The automatic switching unit includes a task suspension module, a status saving module, and a status migration module. The task suspension module is connected to the consulting platform, the status saving module is connected to the task suspension module, the status migration module is connected to the status saving module, and the status migration module is connected to the redundancy processing platform.

3. The engineering whole-process consulting platform management system according to claim 2, characterized in that The encrypted transmission unit includes a key generation module, a key management module, an encryption module, a transmission module, and a decryption module. The encryption module and the decryption module are respectively connected to the consulting platform and the redundancy processing platform. The transmission module is arranged between the consulting platform and the redundancy processing platform. The key management module is connected to the encryption module, the decryption module, and the key generation module.

4. The engineering whole-process consulting platform management system according to claim 3, characterized in that The engineering whole-process consulting platform management system further includes a self-repair module, and the self-repair module is connected to the cloud computing platform and the consulting platform.

5. The engineering whole-process consulting platform management system according to claim 4, characterized in that The whole-process engineering consulting platform management system further includes an alarm module and a management terminal, and the alarm module is connected to the cloud computing platform and the management terminal.

6. The whole-process engineering consulting platform management system according to claim 5, characterized in that the whole-process engineering consulting platform management system further includes a login module and an identity verification module. The login module is used to log in to the management terminal, and the identity verification module is used to verify the identity of the management personnel logging in to the management terminal.

7. The whole-process engineering consulting platform management system according to claim 6, characterized in that the whole-process engineering consulting platform management system further includes a permission allocation module, and the permission allocation module is connected to the identity verification module.

8. The whole-process engineering consulting platform management system according to claim 7, characterized in that the whole-process engineering consulting platform management system further includes a log recording module and a performance optimization module, and both the log recording module and the performance optimization module are connected to the cloud computing platform.

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