Remote maintenance management system based on remote fault diagnosis

Abstract

The application discloses a remote maintenance management system based on remote fault diagnosis, belongs to the technical field of remote maintenance management, and comprises: a device client, which is used for acquiring device state real-time data and sending the device state real-time data to a remote server end based on data encryption technology; and the remote server end, which is used for processing and analyzing received device state real-time data, determining a remote fault diagnosis result, and adopting different remote maintenance management schemes to timely perform remote maintenance management on device faults. The application solves the problems that the existing device state cannot be monitored and managed in real time, device faults cannot be timely diagnosed and maintained remotely, device operation efficiency and reliability are reduced, and maintenance cost is increased. Through remote fault diagnosis and maintenance management, the application can realize real-time monitoring and management of devices, improve device operation efficiency and reliability, and reduce maintenance cost.

Description

Technical Field

[0001] This invention relates to the field of remote maintenance management technology, specifically a remote maintenance management system based on remote fault diagnosis. Background Technology

[0002] Equipment failures are having an increasingly significant impact on production; traditional maintenance methods often require waiting for equipment to stop or for obvious malfunctions to occur before repairs can be carried out, which not only wastes valuable time but may also lead to more serious consequences.

[0003] Chinese patent CN113569236A discloses a method for security monitoring and protection of IoT terminals, which identifies the type of IoT terminal devices; performs trusted verification of IoT terminal devices; constructs behavioral profiles of IoT terminal device behavior; creates security boundaries based on policies, divides permissions, and manages authorized resources for IoT terminals; and uses feature mapping to reduce the dimensionality of IoT terminal device feature data, displaying this data in a low-dimensional space through geometric relationships. However, this patent has the following drawbacks:

[0004] Existing technologies cannot monitor and manage equipment status in real time, nor can they perform timely remote diagnosis and maintenance management of equipment faults, which reduces equipment operating efficiency and reliability and increases maintenance costs. Summary of the Invention

[0005] The purpose of this invention is to provide a remote maintenance management system based on remote fault diagnosis. Through remote fault diagnosis and maintenance management, real-time monitoring and management of equipment can be achieved, improving equipment operating efficiency and reliability, and reducing maintenance costs, thus solving the problems mentioned in the background art.

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

[0007] A remote maintenance management system based on remote fault diagnosis includes:

[0008] The device client is used to monitor and manage the device nameplate, operation and working information in real time, obtain real-time device status data, and securely send the real-time device status data to the remote server based on data encryption technology. The remote server decrypts the encrypted data of the device status data based on a specific decryption value, combined with a decryption key and a decryption function to obtain the decrypted data.

[0009] The remote server is used to process and analyze the received real-time equipment status data and perform remote fault diagnosis, determine the remote fault diagnosis results, and, based on the remote fault diagnosis results, adopt different remote maintenance management schemes to perform timely remote maintenance management of equipment faults and resolve equipment faults in a timely manner.

[0010] Preferably, the device client includes:

[0011] The equipment status monitoring module is used to monitor equipment nameplate information, equipment operation information and equipment working information in real time based on Internet of Things technology;

[0012] The equipment status monitoring module includes:

[0013] The equipment nameplate acquisition unit is used to monitor and collect equipment name, equipment model, equipment parameters, serial number and manufacturing date in real time to determine the equipment nameplate information;

[0014] The equipment operation data acquisition unit is used to monitor and collect data on equipment operation time, equipment operation frequency, and equipment operation load in real time to determine equipment operation information;

[0015] The equipment operation data acquisition unit is used to monitor and collect equipment temperature, humidity, voltage, current and vibration in real time to determine equipment operation information.

[0016] Based on equipment nameplate information, equipment operation information, and equipment working information, real-time data on the status of IoT-based devices is determined.

[0017] Preferably, the device client sends real-time device status data based on the Internet of Things to a remote server and performs the following operations:

[0018] Based on access control and authentication protocols, secure authentication is performed on the access permissions of device clients;

[0019] The device client sends a connection request instruction to the remote server. After receiving the connection request instruction from the device client, the remote server performs security authentication on the device client's service port to verify whether the device client has permission to access the remote server. The service ports that have permission to access the remote server are [N, M].

[0020] If the service port of the device client is within [N, M], then the service port security authentication of the device client is successful.

[0021] If the service port of the device client is not within [N, M], the security authentication of the service port of the device client fails.

[0022] When the service port of the device client successfully completes the security authentication, the remote server sends an instruction to the device client to agree to establish a connection. After receiving the instruction from the remote server, the device client establishes a connection with the remote server.

[0023] Based on data encryption technology, the device client encrypts and sends real-time device status data based on the Internet of Things to the remote server.

[0024] Preferably, based on data encryption technology, the device client encrypts and sends real-time device status data based on the Internet of Things to the remote server, specifically including:

[0025] Based on the device identifier, status attributes, and basic data content of the device status data, a data vector of the device status data is established.

[0026] Based on the data vector, combined with the encryption key and encryption function, a specific encryption value for the device status data is determined according to the following formula;

[0027]

[0028] Where L represents the encrypted specific value of the device status data, n represents the number of elements in the data vector, and X i Let represent the value of the i-th element in the data vector, C represent a constant, k represent the key representation value of the encryption key, and H represent the algorithm representation value of the encryption function;

[0029] The device client generates ciphertext data of the device status data based on a specific encrypted value, combined with an encryption key and an encryption function;

[0030] Based on the encrypted specific value, the decrypted specific value is calculated according to the following formula;

[0031]

[0032] Where S represents decrypting a specific value, δ K δ represents the key representation value of the decryption key corresponding to the encryption key. H This represents the algorithmic representation of the decryption function corresponding to the encryption function;

[0033] The remote server decrypts the encrypted data of the device status data based on the specific decryption value, combined with the decryption key and decryption function, to obtain the decrypted data.

[0034] Preferably, the remote server includes:

[0035] The device data processing module is used to clean and transform real-time device status data based on the Internet of Things, and to integrate and store the real-time device status data into the database.

[0036] The remote fault diagnosis module is used to remotely diagnose equipment faults based on deep learning technology and determine the remote fault diagnosis results.

[0037] The remote maintenance management module is used to perform timely remote maintenance management of equipment faults based on remote fault diagnosis results, and to resolve equipment faults in a timely manner.

[0038] Preferably, the device data processing module includes:

[0039] The equipment data cleaning unit is used to clean the real-time equipment status data based on the Internet of Things (IoT) and remove duplicate, missing, and outlier values ​​that are not useful for remote maintenance management.

[0040] The device data conversion unit is used to convert real-time device status data based on the Internet of Things (IoT), remove the dimensional differences between real-time device status data based on IoT, determine standardized real-time device status data, and integrate and store the standardized real-time device status data.

[0041] Preferably, the remote fault diagnosis module includes:

[0042] Diagnostic model training unit, used to train remote fault diagnosis models based on deep learning technology;

[0043] Based on the needs of remote maintenance management based on remote fault diagnosis, historical equipment status data is collected and divided into training set and test set.

[0044] Based on deep learning technology, a training set is used to train the deep learning model, enabling the deep learning model to autonomously learn the remote fault diagnosis process and remotely diagnose equipment faults, thus determining the deep learning-based remote fault diagnosis model.

[0045] Based on the test set, the performance of the trained deep learning-based remote fault diagnosis model is tested, and the precision and recall are used to determine whether the trained deep learning-based remote fault diagnosis model can achieve the expected results.

[0046] When the trained deep learning-based remote fault diagnosis model fails to achieve the expected results, the parameters and structure of the deep learning-based remote fault diagnosis model are adjusted, and the optimal remote fault diagnosis model is determined through continuous iterative optimization.

[0047] Preferably, the remote fault diagnosis module further includes:

[0048] The fault analysis and prediction unit is used to analyze and predict real-time equipment status data and remotely diagnose equipment faults.

[0049] The process involves obtaining the optimal remote fault diagnosis model, deploying the optimal remote fault diagnosis model in the actual equipment fault remote diagnosis environment, inputting real-time equipment status data into the optimal remote fault diagnosis model, analyzing and predicting the real-time equipment status data based on the optimal remote fault diagnosis model, remotely diagnosing equipment faults, and determining the remote fault diagnosis results.

[0050] Preferably, the remote maintenance management module includes:

[0051] The maintenance plan development unit is used to develop remote maintenance management plans in a timely manner based on the results of remote fault diagnosis.

[0052] The remote maintenance management unit is used to perform timely remote maintenance management of equipment failures according to the remote maintenance management plan.

[0053] Among them, maintenance suggestions are automatically sent to maintenance management personnel based on remote fault diagnosis results, and equipment faults are automatically resolved; or maintenance personnel are dispatched to the site based on remote fault diagnosis results to promptly resolve equipment faults through manual intervention.

[0054] Preferably, the maintenance plan formulation unit includes:

[0055] The data acquisition unit is used to determine the equipment fault type and equipment fault value from the remote fault diagnosis results;

[0056] The fault analysis unit is used to determine a first fault degree based on the fault range of the equipment fault type, determine a second fault degree based on the difference between the equipment fault value and the standard value, and determine the numerical matching relationship between the first fault degree and the second fault degree based on the matching relationship between the equipment fault type and the equipment fault value.

[0057] The scheme determination unit is used to determine an initial maintenance scheme based on the first fault degree and the second fault degree, as well as the numerical matching relationship, and in conjunction with the scheme database.

[0058] The historical data determination unit is used to obtain target historical maintenance data from historical maintenance data that has a similarity greater than a preset similarity with the equipment fault type and equipment fault value, and to determine the maintenance effect of each corresponding historical maintenance scheme based on the target historical maintenance data.

[0059] The scheme extraction unit is used to determine the local optimal scheme based on each historical maintenance scheme and its corresponding maintenance effect, obtain the scheme attributes of the local optimal scheme, and determine the initial local scheme in the initial maintenance scheme based on the scheme attributes.

[0060] The scheme optimization unit, based on the aforementioned local optimal scheme and combined with the specific equipment environment, adaptively optimizes the initial local scheme to obtain a remote maintenance management scheme;

[0061] The scheme determination unit includes:

[0062] The fault assessment unit is used to calculate the fault assessment value of the equipment based on the first fault degree and the second fault degree, as well as the numerical matching relationship, according to the following formula;

[0063]

[0064] Where K represents the equipment's fault assessment value, m represents the number of equipment fault types, and δ i H represents the type weight of the fault type of the i-th device. i1 H represents the first degree of failure for the i-th device failure type. i2 This indicates the second degree of failure for the i-th device failure type;

[0065] The type determination unit is used to determine the solution type when the fault assessment value is greater than the preset assessment value, and dispatch maintenance personnel to the site for timely maintenance through manual intervention; otherwise, the solution type is to send maintenance suggestions to maintenance management personnel.

[0066] The scheme selection unit is used to select candidate maintenance schemes that meet the fault repair requirements from the scheme database based on the scheme type, combined with the equipment fault type and equipment fault value.

[0067] The performance analysis unit is used to calculate the performance ratio of the alternative repair schemes based on the repair costs of the alternative repair schemes according to the following formula;

[0068]

[0069] Where F represents the performance ratio of the alternative maintenance scheme, e represents the natural constant with a value of 2.72, M represents the maintenance cost of the alternative maintenance scheme, and Y represents the predicted maintenance effect value of the alternative maintenance scheme.

[0070] The scheme selection unit is also used to select the candidate maintenance scheme with the highest performance ratio as the initial maintenance scheme.

[0071] Compared with the prior art, the beneficial effects of the present invention are:

[0072] This invention acquires real-time device status data through a device client and securely transmits this data to a remote server. The remote server processes and analyzes the received real-time device status data and performs remote fault diagnosis to determine the results. Based on these results, different remote maintenance management schemes are adopted to promptly manage and resolve equipment malfunctions remotely. Through remote fault diagnosis and maintenance management, real-time monitoring and management of equipment can be achieved, saving time, improving equipment operating efficiency and reliability, and reducing maintenance costs. Furthermore, the intelligent analysis and decision-making of the remote server can more quickly and accurately identify and resolve problems, thereby improving user experience. In addition, seamless integration and data sharing between the device client and the remote server facilitate management and collaborative work. Attached Figure Description

[0073] Figure 1 This is a block diagram of the remote maintenance management system based on remote fault diagnosis according to the present invention. Detailed Implementation

[0074] 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.

[0075] To address the current issues of inability to monitor and manage equipment status in real time, the inability to perform timely remote diagnosis and maintenance of equipment faults, which reduces equipment operating efficiency and reliability and increases maintenance costs, please refer to [link to relevant documentation]. Figure 1 This embodiment provides the following technical solution:

[0076] The remote maintenance management system based on remote fault diagnosis includes: equipment client and remote server.

[0077] It should be noted that the device client enables real-time monitoring and management of device nameplate information, device operation information, and device working information, acquiring real-time device status data and securely transmitting this data to a remote server using data encryption technology. The remote server processes and analyzes the received real-time device status data and performs remote fault diagnosis to determine the results. Based on these results, different remote maintenance management solutions are adopted to promptly resolve equipment malfunctions. Through remote fault diagnosis and maintenance management, real-time monitoring and management of equipment can be achieved, saving time, improving equipment operating efficiency and reliability, and reducing maintenance costs. Furthermore, the intelligent analysis and decision-making of the remote server can more quickly and accurately identify and resolve problems, thereby improving user experience. In addition, seamless integration and data sharing between the device client and the remote server facilitate management and collaborative work.

[0078] In this embodiment, the device client includes:

[0079] The equipment status monitoring module is used to monitor equipment nameplate information, equipment operation information and equipment working information in real time based on Internet of Things (IoT) technology, and determine real-time equipment status data based on IoT.

[0080] The equipment status monitoring module includes:

[0081] The equipment nameplate acquisition unit is used to monitor and collect equipment name, equipment model, equipment parameters, serial number and manufacturing date in real time to determine the equipment nameplate information;

[0082] The equipment operation data acquisition unit is used to monitor and collect data on equipment operation time, equipment operation frequency, and equipment operation load in real time to determine equipment operation information;

[0083] The equipment operation data acquisition unit is used to monitor and collect data on equipment temperature, humidity, voltage, current, and vibration in real time to determine equipment operation information.

[0084] Specifically, the temperature of internal components can cause malfunctions; for example, overheating may be caused by a short circuit, overload, or fan failure. The voltage and current levels of the equipment may reflect potential problems; for example, unstable voltage may indicate a power supply failure or excessive load. Vibration patterns generated by the equipment during operation can be used to identify malfunctions; for example, abnormal vibration patterns may indicate bearing failure or loose connections. For some electrical equipment, humidity is an important indicator; for example, moisture on circuit boards may cause malfunctions.

[0085] It should be noted that the device client sends real-time device status data based on the Internet of Things to the remote server to perform the following operations:

[0086] Based on access control and authentication protocols, secure authentication is performed on the access permissions of device clients;

[0087] The device client sends a connection request instruction to the remote server. After receiving the connection request instruction from the device client, the remote server performs security authentication on the device client's service port to verify whether the device client has permission to access the remote server. The service ports that have permission to access the remote server are [N, M].

[0088] If the service port of the device client is within [N, M], then the service port security authentication of the device client is successful.

[0089] If the service port of the device client is not within [N, M], the security authentication of the service port of the device client fails.

[0090] When the service port of the device client successfully completes the security authentication, the remote server sends an instruction to the device client to agree to establish a connection. After receiving the instruction from the remote server, the device client establishes a connection with the remote server.

[0091] Based on data encryption technology, the device client encrypts and sends real-time device status data based on the Internet of Things to the remote server.

[0092] In one embodiment, based on data encryption technology, the device client encrypts and sends real-time device status data based on the Internet of Things to the remote server, specifically including:

[0093] Based on the device identifier, status attributes, and basic data content of the device status data, a data vector of the device status data is established.

[0094] Based on the data vector, combined with the encryption key and encryption function, a specific encryption value for the device status data is determined according to the following formula;

[0095]

[0096] Where L represents the encrypted specific value of the device status data, n represents the number of elements in the data vector, and X iLet represent the value of the i-th element in the data vector, C represent a constant, k represent the key representation value of the encryption key, and H represent the algorithm representation value of the encryption function;

[0097] The device client generates ciphertext data of the device status data based on a specific encrypted value, combined with an encryption key and an encryption function;

[0098] Based on the encrypted specific value, the decrypted specific value is calculated according to the following formula;

[0099]

[0100] Where S represents decrypting a specific value, δ K δ represents the key representation value of the decryption key corresponding to the encryption key. H This represents the algorithmic representation of the decryption function corresponding to the encryption function;

[0101] The remote server decrypts the encrypted data of the device status data based on the specific decryption value, combined with the decryption key and decryption function, to obtain the decrypted data.

[0102] In this embodiment, the key representation value of the encryption key is used to represent the characteristics of the encryption key, and its key representation value is a key representation value pre-assigned to multiple encryption keys.

[0103] In this embodiment, the algorithmic representation value of the encryption function is determined based on the function characteristics, and its algorithmic representation value is the corresponding algorithmic representation value pre-assigned to various encryption functions.

[0104] In this embodiment, data encryption based on a specific encrypted value ensures that each transmitted data corresponds to a single encrypted value, thereby guaranteeing the security of data transmission.

[0105] In this embodiment, the data vector is used to represent the state data of the device, such as time, content, device characteristics, etc., and is unique, thereby ensuring that specific data values ​​are unique.

[0106] The beneficial effects of the above design scheme are: by establishing a data vector of the device status data based on the device identifier, status attributes and basic data content of the device status data, specific encrypted values ​​and decrypted values ​​are designed. Combined with encryption keys and encryption functions, decryption keys and decryption functions, one encrypted value is achieved for each transmitted data, ensuring the security of data transmission.

[0107] It should be noted that data encryption technology refers to transforming information into meaningless ciphertext using an encryption key and encryption function. The recipient then uses a decryption function and decryption key to restore the plaintext. This enhances the security of real-time data transmission of IoT-based device status. Specifically, the device client uses an encryption key to encrypt the real-time device status data and transmits the encrypted data to a remote server. Upon receiving the encrypted data, the remote server uses a decryption key to decrypt it, obtaining the true real-time device status data and thus ensuring secure transmission of the IoT-based device status data.

[0108] In this embodiment, the remote server includes: a device data processing module, a remote fault diagnosis module, and a remote maintenance management module.

[0109] It should be noted that the device data processing module cleans and transforms the real-time device status data based on the Internet of Things, and integrates and stores the real-time device status data in the database; the remote fault diagnosis module remotely diagnoses device faults and determines the remote fault diagnosis results; and the remote maintenance management module performs timely remote maintenance management of device faults and resolves device faults in a timely manner.

[0110] In this embodiment, the device data processing module includes:

[0111] The equipment data cleaning unit is used to clean the real-time equipment status data based on the Internet of Things (IoT) and remove duplicate, missing, and outlier values ​​that are not useful for remote maintenance management.

[0112] The device data conversion unit is used to convert real-time device status data based on the Internet of Things (IoT), remove the dimensional differences between real-time device status data based on IoT, determine standardized real-time device status data, and integrate and store the standardized real-time device status data.

[0113] In this embodiment, the remote fault diagnosis module includes:

[0114] Diagnostic model training unit, used to train remote fault diagnosis models based on deep learning technology;

[0115] Based on the needs of remote maintenance management based on remote fault diagnosis, historical equipment status data is collected and divided into training set and test set.

[0116] Based on deep learning technology, a training set is used to train the deep learning model, enabling the deep learning model to autonomously learn the remote fault diagnosis process and remotely diagnose equipment faults, thus determining the deep learning-based remote fault diagnosis model.

[0117] Based on the test set, the performance of the trained deep learning-based remote fault diagnosis model is tested, and the precision and recall are used to determine whether the trained deep learning-based remote fault diagnosis model can achieve the expected results.

[0118] When the trained deep learning-based remote fault diagnosis model fails to achieve the expected results, the parameters and structure of the deep learning-based remote fault diagnosis model are adjusted, and the optimal remote fault diagnosis model is determined through continuous iterative optimization.

[0119] In this embodiment, the remote fault diagnosis module further includes:

[0120] The fault analysis and prediction unit is used to analyze and predict real-time equipment status data and remotely diagnose equipment faults.

[0121] The process involves obtaining the optimal remote fault diagnosis model, deploying the optimal remote fault diagnosis model in the actual equipment fault remote diagnosis environment, inputting real-time equipment status data into the optimal remote fault diagnosis model, analyzing and predicting the real-time equipment status data based on the optimal remote fault diagnosis model, remotely diagnosing equipment faults, and determining the remote fault diagnosis results.

[0122] In this embodiment, the remote maintenance management module includes:

[0123] The maintenance plan development unit is used to develop remote maintenance management plans in a timely manner based on the results of remote fault diagnosis.

[0124] The remote maintenance management unit is used to perform timely remote maintenance management of equipment failures according to the remote maintenance management plan.

[0125] Among them, maintenance suggestions are automatically sent to maintenance management personnel based on remote fault diagnosis results, and equipment faults are automatically resolved; or maintenance personnel are dispatched to the site based on remote fault diagnosis results to promptly resolve equipment faults through manual intervention.

[0126] In one embodiment, the maintenance plan formulation unit includes:

[0127] The data acquisition unit is used to determine the equipment fault type and equipment fault value from the remote fault diagnosis results;

[0128] The fault analysis unit is used to determine a first fault degree based on the fault range of the equipment fault type, determine a second fault degree based on the difference between the equipment fault value and the standard value, and determine the numerical matching relationship between the first fault degree and the second fault degree based on the matching relationship between the equipment fault type and the equipment fault value.

[0129] The scheme determination unit is used to determine an initial maintenance scheme based on the first fault degree and the second fault degree, as well as the numerical matching relationship, and in conjunction with the scheme database.

[0130] The historical data determination unit is used to obtain target historical maintenance data from historical maintenance data that has a similarity greater than a preset similarity with the equipment fault type and equipment fault value, and to determine the maintenance effect of each corresponding historical maintenance scheme based on the target historical maintenance data.

[0131] The scheme extraction unit is used to determine the local optimal scheme based on each historical maintenance scheme and its corresponding maintenance effect, obtain the scheme attributes of the local optimal scheme, and determine the initial local scheme in the initial maintenance scheme based on the scheme attributes.

[0132] The scheme optimization unit, based on the local optimal scheme and combined with the specific equipment environment, adaptively optimizes the initial local scheme to obtain a remote maintenance management scheme.

[0133] In this embodiment, there are multiple first fault levels and second fault levels, and the numerical matching relationship between the first fault level and the second fault level is used to determine the correspondence between the two.

[0134] In this embodiment, the scheme attributes include, for example, maintenance route planning, maintenance tools, and maintenance personnel types.

[0135] In this embodiment, adaptive optimization of the initial local solution, combined with the specific equipment environment, can either replace the initial local solution with a locally optimal solution, or it can adaptively adjust the initial local solution based on the locally optimal solution, depending on the characteristics of the specific equipment environment.

[0136] In this embodiment, the scheme database is pre-designed based on specific circumstances and historical experience.

[0137] The beneficial effects of the above design scheme are: by determining the equipment fault type and equipment fault value from the remote fault diagnosis results, the maintenance scheme is set, and the mechanical energy scheme is optimized by combining historical maintenance data, so as to ensure the accuracy of the obtained remote maintenance management scheme and achieve accurate and efficient remote maintenance management.

[0138] In one embodiment, the scheme determination unit includes:

[0139] The fault assessment unit is used to calculate the fault assessment value of the equipment based on the first fault degree and the second fault degree, as well as the numerical matching relationship, according to the following formula;

[0140]

[0141] Where K represents the equipment's fault assessment value, m represents the number of equipment fault types, and δ i H represents the type weight of the fault type of the i-th device. i1 H represents the first degree of failure for the i-th device failure type. i2 This indicates the second degree of failure for the i-th device failure type;

[0142] The type determination unit is used to determine the solution type when the fault assessment value is greater than the preset assessment value, and dispatch maintenance personnel to the site for timely maintenance through manual intervention; otherwise, the solution type is to send maintenance suggestions to maintenance management personnel.

[0143] The scheme selection unit is used to select candidate maintenance schemes that meet the fault repair requirements from the scheme database based on the scheme type, combined with the equipment fault type and equipment fault value.

[0144] The performance analysis unit is used to calculate the performance ratio of the alternative repair schemes based on the repair costs of the alternative repair schemes according to the following formula;

[0145]

[0146] Where F represents the performance ratio of the alternative maintenance scheme, e represents the natural constant with a value of 2.72, M represents the maintenance cost of the alternative maintenance scheme, and Y represents the predicted maintenance effect value of the alternative maintenance scheme.

[0147] The scheme selection unit is also used to select the candidate maintenance scheme with the highest performance ratio as the initial maintenance scheme.

[0148] In this embodiment, the predicted maintenance effect value of the selected maintenance scheme is determined according to a pre-set prediction model.

[0149] The beneficial effects of the above design scheme are as follows: By obtaining the equipment fault assessment value based on the first fault degree and the second fault degree, as well as the numerical matching relationship, when the fault assessment value is greater than the preset assessment value, the scheme type is determined and maintenance personnel are dispatched to the site for timely maintenance through manual intervention. Otherwise, the scheme type is determined and maintenance suggestions are sent to maintenance management personnel, thereby realizing the determination of the scheme type. Then, based on the scheme type, combined with the equipment fault type and equipment fault value, candidate maintenance schemes that meet the fault maintenance requirements are selected from the scheme database. The initial maintenance scheme is selected based on the performance ratio of the candidate maintenance schemes, ensuring the scheme matching and performance ratio of the obtained initial maintenance scheme, and providing a scheme basis for remote maintenance management.

[0150] Therefore, by acquiring real-time device status data through the device client and securely sending this data to a remote server, the remote server can process and analyze the received data, perform remote fault diagnosis, determine the fault diagnosis results, and, based on these results, adopt different remote maintenance management schemes to promptly manage and resolve equipment malfunctions. Through remote fault diagnosis and maintenance management, real-time monitoring and management of equipment can be achieved, saving time, improving equipment operating efficiency and reliability, and reducing maintenance costs. Furthermore, the intelligent analysis and decision-making of the remote server can more quickly and accurately identify and resolve problems, thereby improving user experience. In addition, seamless integration and data sharing between the device client and the remote server facilitate management and collaborative work.

[0151] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0152] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A remote maintenance management system based on remote fault diagnosis, characterized in that, include: The device client is used to monitor and manage the device nameplate, operation and working information in real time, obtain real-time device status data, and securely send the real-time device status data to the remote server based on data encryption technology. The remote server decrypts the encrypted data of the device status data based on a specific decryption value, combined with a decryption key and a decryption function to obtain the decrypted data. The remote server is used to process and analyze the received real-time equipment status data and perform remote fault diagnosis, determine the remote fault diagnosis results, and, based on the remote fault diagnosis results, adopt different remote maintenance management schemes to perform timely remote maintenance management of equipment faults and resolve equipment faults in a timely manner. The remote server includes: The device data processing module is used to clean and transform real-time device status data based on the Internet of Things, and to integrate and store the real-time device status data into the database. The remote fault diagnosis module is used to remotely diagnose equipment faults based on deep learning technology and determine the remote fault diagnosis results. The remote maintenance management module is used to perform timely remote maintenance management of equipment faults based on remote fault diagnosis results, and to resolve equipment faults in a timely manner. The remote maintenance management module includes: The maintenance plan development unit is used to develop remote maintenance management plans in a timely manner based on the results of remote fault diagnosis. The remote maintenance management unit is used to perform timely remote maintenance management of equipment failures according to the remote maintenance management plan. Among them, maintenance suggestions are automatically sent to maintenance management personnel based on remote fault diagnosis results, and equipment faults are automatically resolved; or maintenance personnel are automatically dispatched to the site based on remote fault diagnosis results, and equipment faults are resolved in a timely manner through manual intervention. The maintenance plan formulation unit includes: The data acquisition unit is used to determine the equipment fault type and equipment fault value from the remote fault diagnosis results; The fault analysis unit is used to determine a first fault degree based on the fault range of the equipment fault type, determine a second fault degree based on the difference between the equipment fault value and the standard value, and determine the numerical matching relationship between the first fault degree and the second fault degree based on the matching relationship between the equipment fault type and the equipment fault value. The scheme determination unit is used to determine an initial maintenance scheme based on the first fault degree and the second fault degree, as well as the numerical matching relationship, and in conjunction with the scheme database. The historical data determination unit is used to obtain target historical maintenance data from historical maintenance data that has a similarity greater than a preset similarity with the equipment fault type and equipment fault value, and to determine the maintenance effect of each corresponding historical maintenance scheme based on the target historical maintenance data. The scheme extraction unit is used to determine the local optimal scheme based on each historical maintenance scheme and its corresponding maintenance effect, obtain the scheme attributes of the local optimal scheme, and determine the initial local scheme in the initial maintenance scheme based on the scheme attributes. The scheme optimization unit, based on the aforementioned local optimal scheme and combined with the specific equipment environment, adaptively optimizes the initial local scheme to obtain a remote maintenance management scheme; The scheme determination unit includes: The fault assessment unit is used to calculate the fault assessment value of the equipment based on the first fault degree and the second fault degree, as well as the numerical matching relationship, according to the following formula; ； in, This represents the equipment's fault assessment value, where m represents the number of equipment fault types. This represents the type weight of the fault type of the i-th device. This indicates the first degree of failure for the i-th device failure type. This indicates the second degree of failure for the i-th device failure type; The type determination unit is used to determine the solution type when the fault assessment value is greater than the preset assessment value, and dispatch maintenance personnel to the site for timely maintenance through manual intervention; otherwise, the solution type is to send maintenance suggestions to maintenance management personnel. The scheme selection unit is used to select candidate maintenance schemes that meet the fault repair requirements from the scheme database based on the scheme type, combined with the equipment fault type and equipment fault value. The performance analysis unit is used to calculate the performance ratio of the alternative repair schemes based on the repair costs of the alternative repair schemes according to the following formula; ； in, This represents the performance ratio of the alternative repair options, where e represents the natural constant with a value of 2.

72. This indicates the repair cost of the alternative repair options. This represents the predicted repair effect value of the alternative repair solutions; The scheme selection unit is also used to select the candidate maintenance scheme with the highest performance ratio as the initial maintenance scheme.

2. The remote maintenance management system based on remote fault diagnosis as described in claim 1, characterized in that, The device client includes: The equipment status monitoring module is used to monitor equipment nameplate information, equipment operation information and equipment working information in real time based on Internet of Things technology; The equipment status monitoring module includes: The equipment nameplate acquisition unit is used to monitor and collect equipment name, equipment model, equipment parameters, serial number and manufacturing date in real time to determine the equipment nameplate information; The equipment operation data acquisition unit is used to monitor and collect data on equipment operation time, equipment operation frequency, and equipment operation load in real time to determine equipment operation information; The equipment operation data acquisition unit is used to monitor and collect equipment temperature, humidity, voltage, current and vibration in real time to determine equipment operation information. Based on equipment nameplate information, equipment operation information, and equipment working information, real-time data on the status of IoT-based devices is determined.

3. The remote maintenance management system based on remote fault diagnosis as described in claim 2, characterized in that, The device client sends real-time device status data based on the Internet of Things to the remote server and performs the following operations: Based on access control and authentication protocols, secure authentication is performed on the access permissions of device clients; The device client sends a connection request command to the remote server. Upon receiving this command, the remote server performs security authentication on the device client's service port, verifying whether the device client has permission to access the remote server. The service port with permission to access the remote server is... ; When the service port of the device client is If the device client's service port security authentication is successful, then the authentication is successful. When the device client's service port is not... If the device client's service port security authentication fails, then the authentication fails. When the service port of the device client successfully completes the security authentication, the remote server sends an instruction to the device client to agree to establish a connection. After receiving the instruction from the remote server, the device client establishes a connection with the remote server. Based on data encryption technology, the device client encrypts and sends real-time device status data based on the Internet of Things to the remote server.

4. The remote maintenance management system based on remote fault diagnosis according to claim 3, characterized in that, Based on data encryption technology, the device client encrypts and sends real-time device status data based on the Internet of Things to the remote server, specifically including: Based on the device identifier, status attributes, and basic data content of the device status data, a data vector of the device status data is established. Based on the data vector, combined with the encryption key and encryption function, a specific encryption value for the device status data is determined according to the following formula; ； in, The encrypted specific value represents the device status data, and n represents the number of elements in the data vector. This represents the value of the i-th element in the data vector. Represents a constant. The key representation value representing the encryption key. The algorithmic representation value of the encryption function; The device client generates ciphertext data of the device status data based on a specific encrypted value, combined with an encryption key and an encryption function; Based on the encrypted specific value, the decrypted specific value is calculated according to the following formula; ； in, This indicates that a specific value has been decrypted. This represents the key representation value of the decryption key corresponding to the encryption key. This represents the algorithmic representation of the decryption function corresponding to the encryption function; The remote server decrypts the encrypted data of the device status data based on the specific decryption value, combined with the decryption key and decryption function, to obtain the decrypted data.

5. The remote maintenance management system based on remote fault diagnosis as described in claim 1, characterized in that, The device data processing module includes: The equipment data cleaning unit is used to clean the real-time equipment status data based on the Internet of Things (IoT) and remove duplicate, missing, and outlier values ​​that are not useful for remote maintenance management. The device data conversion unit is used to convert real-time device status data based on the Internet of Things (IoT), remove the dimensional differences between real-time device status data based on IoT, determine standardized real-time device status data, and integrate and store the standardized real-time device status data.

6. The remote maintenance management system based on remote fault diagnosis as described in claim 1, characterized in that, The remote fault diagnosis module includes: Diagnostic model training unit, used to train remote fault diagnosis models based on deep learning technology; Based on the needs of remote maintenance management based on remote fault diagnosis, historical equipment status data is collected and divided into training set and test set. Based on deep learning technology, a training set is used to train the deep learning model, enabling the deep learning model to autonomously learn the remote fault diagnosis process and remotely diagnose equipment faults, thus determining the deep learning-based remote fault diagnosis model. Based on the test set, the performance of the trained deep learning-based remote fault diagnosis model is tested, and the precision and recall are used to determine whether the trained deep learning-based remote fault diagnosis model can achieve the expected results. When the trained deep learning-based remote fault diagnosis model fails to achieve the expected results, the parameters and structure of the deep learning-based remote fault diagnosis model are adjusted, and the optimal remote fault diagnosis model is determined through continuous iterative optimization.

7. The remote maintenance management system based on remote fault diagnosis as described in claim 6, characterized in that, The remote fault diagnosis module also includes: The fault analysis and prediction unit is used to analyze and predict real-time equipment status data and remotely diagnose equipment faults. The process involves obtaining the optimal remote fault diagnosis model, deploying the optimal remote fault diagnosis model in the actual equipment fault remote diagnosis environment, inputting real-time equipment status data into the optimal remote fault diagnosis model, analyzing and predicting the real-time equipment status data based on the optimal remote fault diagnosis model, remotely diagnosing equipment faults, and determining the remote fault diagnosis results.

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