Device operation and maintenance management method and system based on multi-layer permission control

Abstract

The application discloses a kind of based on multi-layer permission control's equipment operation management method and system, the method includes: obtaining target user's access control request to target equipment;Based on the preset historical access database, the user access permission corresponding to the target user is determined;Based on prediction model, according to the device data of the target equipment and the access control request, the task information corresponding to the access control request is predicted;According to the user access permission and the task information, based on the preset access determination rule, the pass permission of the access control request is determined. Visible, the present application can realize accurate access control based on user permission and task prediction, improve the accuracy and security of target equipment access management, reduce equipment safety accident.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a device operation and maintenance management method and system based on multi-layer permission control. BACKGROUND

[0002] With the growing demand for device access management security, more and more industries have begun to focus on precise control of target device access requests to ensure system security. The prior art usually obtains a user's access request to a device, determines the user's access permission in combination with a static permission database or a simple identity verification method, and processes the access control request based on fixed rules to maintain the security of device operation. The existing solution lacks dynamic analysis of historical access data and prediction ability of task information, and it is difficult to accurately judge the legality and task relevance of the user's request. The commonly used unified or overly simplified judgment rules cannot adapt to complex access scenarios, resulting in insufficient precision of access control, which easily leads to misauthorization or security incidents, limiting the security and reliability of device access management. It can be seen that the prior art has defects and needs to be solved. SUMMARY

[0003] The technical problem to be solved by the present application is to provide a device operation and maintenance management method and system based on multi-layer permission control, which can realize precise access control based on user permissions and task prediction, improve the accuracy and security of target device access management, and reduce device security incidents.

[0004] To solve the above technical problems, the first aspect of the present application discloses a device operation and maintenance management method based on multi-layer permission control, which comprises:

[0005] obtaining an access control request of a target user to a target device;

[0006] determining the user access permission corresponding to the target user based on a preset historical access database;

[0007] predicting task information corresponding to the access control request based on a prediction model according to device data of the target device and the access control request;

[0008] determining the pass permission of the access control request based on a preset access judgment rule according to the user access permission and the task information.

[0009] As an optional implementation, in the first aspect of the present application, the determination of the user access permission corresponding to the target user based on the preset historical access database comprises:

[0010] determining a plurality of historical access data identical or similar to the target user in the preset historical access database;

[0011] calculating a weighted average of access levels corresponding to all the historical access data, to obtain an access level parameter;

[0012] determining a user access permission corresponding to the target user according to a preset correspondence between access levels and access permissions, and the access level parameter.

[0013] As an optional implementation, in the first aspect of the present application, the determining of the multiple historical access data identical or similar to the target user in the preset historical access database comprises:

[0014] For each access data record in the preset historical access database, obtaining relevant user information corresponding to the access data record; the relevant user information comprises at least one of a sending user, a receiving user, an auditing user and a supervising user;

[0015] calculating a user similarity between the relevant user information and user information of the target user;

[0016] screening the access data records with a user similarity greater than a preset similarity threshold, to obtain the multiple historical access data identical or similar to the target user.

[0017] As an optional implementation, in the first aspect of the present application, when calculating the weighted average of access levels corresponding to all the historical access data, the calculation weight corresponding to each access level comprises a first weight and a second weight; the first weight is proportional to a data completeness degree of the corresponding historical access data; the data completeness degree is obtained by inputting the historical access data into a trained completeness prediction model; the second weight is proportional to the user similarity corresponding to the corresponding historical access data.

[0018] As an optional implementation, in the first aspect of the present application, the target device is an energy production device; the energy production device is a solar panel, a wind turbine, a nuclear reactor, a water turbine, a geothermal pump, a coal-fired furnace, a gas turbine or a biomass furnace.

[0019] As an optional implementation, in the first aspect of the present application, the predicting of the task information corresponding to the access control request based on the prediction model according to the device data of the target device and the access control request comprises:

[0020] obtaining device configuration parameters and historical energy production work data corresponding to the target device;

[0021] determining multiple historical work data related to the access control request in the historical energy production work data;

[0022] inputting all the historical work data, the device configuration parameters and the access control request into a trained task prediction neural network to obtain task information corresponding to the access control request; the task prediction neural network is trained by a training data set including a plurality of training historical work data and corresponding device parameter labels and access control request labels and work task labels.

[0023] As an optional implementation, in the first aspect of the present application, a time similarity between a work time point corresponding to the historical work data and a request time point corresponding to the access control request is greater than a preset second similarity threshold; the time similarity is a sum of a time difference and a time period type similarity; the time difference is a time difference between the work time point and the request time point; the time period type similarity is a similarity between time type data corresponding to the work time point and the request time point respectively; the time type data includes one or more of a date type, a holiday type, a month type, a season type and a work time period type to which the time point belongs.

[0024] As an optional implementation, in the first aspect of the present application, the determining of the pass permission of the access control request based on the user access authority and the task information according to a preset access determination rule comprises:

[0025] determining a user authority limit corresponding to the task information based on a preset correspondence between a task and an authority threshold;

[0026] determining whether the user access authority meets the user authority limit to obtain a determination result;

[0027] when the determination result is yes, determining that the pass permission of the access control request is allowed to pass;

[0028] when the determination result is no, determining that the pass permission of the access control request is not allowed to pass.

[0029] The second aspect of the embodiment of the present application discloses a device operation and maintenance management system based on multi-layer authority control, which comprises:

[0030] an acquisition module configured to acquire an access control request of a target user to a target device;

[0031] a determination module configured to determine a user access authority corresponding to the target user based on a preset historical access database;

[0032] a prediction module configured to predict task information corresponding to the access control request based on a prediction model according to device data of the target device and the access control request;

[0033] a judging module, configured to determine, according to the user access right and the task information, a pass permission of the access control request based on a preset access judging rule.

[0034] As an optional implementation, in the second aspect of the present application, the specific manner in which the determining module determines the user access right corresponding to the target user based on the preset historical access database comprises:

[0035] determining a plurality of historical access data identical or similar to the target user in the preset historical access database;

[0036] calculating a weighted average value of the access levels corresponding to all the historical access data to obtain an access level parameter;

[0037] determining the user access right corresponding to the target user according to a preset correspondence between access levels and access rights and the access level parameter.

[0038] As an optional implementation, in the second aspect of the present application, the specific manner in which the determining module determines a plurality of historical access data identical or similar to the target user in the preset historical access database comprises:

[0039] for each access data record in the preset historical access database, obtaining relevant user information corresponding to the access data record; the relevant user information comprises at least one of a sending user, a receiving user, an auditing user and a supervising user;

[0040] calculating a user similarity between the relevant user information and user information of the target user;

[0041] screening the access data records with a user similarity greater than a preset similarity threshold to obtain a plurality of historical access data identical or similar to the target user.

[0042] As an optional implementation, in the second aspect of the present application, when calculating the weighted average value of the access levels corresponding to all the historical access data, the calculation weight corresponding to each access level comprises a first weight and a second weight; the first weight is directly proportional to a data completeness degree of the corresponding historical access data; the data completeness degree is obtained by inputting the historical access data into a trained completeness prediction model; and the second weight is directly proportional to the user similarity corresponding to the corresponding historical access data.

[0043] As an optional implementation, in the second aspect of the present application, the target device is an energy production device; and the energy production device is a solar panel, a wind turbine, a nuclear reactor, a water turbine, a geothermal pump, a coal-fired furnace, a gas turbine or a biomass furnace.

[0044] As an optional implementation, in the second aspect of the present application, the prediction module predicts the specific manner of the task information corresponding to the access control request based on a prediction model according to the device data of the target device and the access control request, which includes:

[0045] obtaining device configuration parameters and historical energy production work data corresponding to the target device;

[0046] determining a plurality of historical work data related to the access control request in the historical energy production work data;

[0047] inputting all the historical work data, the device configuration parameters and the access control request into a trained task prediction neural network to obtain the task information corresponding to the access control request; the task prediction neural network is trained by a training data set including a plurality of training historical work data and corresponding device parameter labels, access control request labels and work task labels.

[0048] As an optional implementation, in the second aspect of the present application, the time similarity between the work time point corresponding to the historical work data and the request time point corresponding to the access control request is greater than a preset second similarity threshold; the time similarity is the sum of a time difference and a time period type similarity; the time difference is the time difference between the work time point and the request time point; the time period type similarity is the similarity between the time type data corresponding to the work time point and the request time point respectively; the time type data includes one or more of date type, holiday type, month type, season type and work time period type.

[0049] As an optional implementation, in the second aspect of the present application, the judgment module determines the specific manner of the pass permission of the access control request based on a preset access judgment rule according to the user access right and the task information, which includes:

[0050] determining the user right limit corresponding to the task information based on a preset correspondence between task and right threshold values;

[0051] judging whether the user access right meets the user right limit to obtain a judgment result;

[0052] when the judgment result is yes, determining that the pass permission of the access control request is allowed to pass.

[0053] When the determination result is no, it is determined that the pass permission of the access control request is not allowed to pass.

[0054] The third aspect of the present application discloses another device operation and maintenance management system based on multi-layer permission control, the system comprises:

[0055] A memory storing executable program codes;

[0056] A processor coupled with the memory;

[0057] The processor invokes the executable program codes stored in the memory to execute part or all steps of the device operation and maintenance management method based on multi-layer permission control disclosed in the first aspect of the present application.

[0058] The fourth aspect of the present application discloses a computer storage medium storing computer instructions, when the computer instructions are invoked, part or all steps of the device operation and maintenance management method based on multi-layer permission control disclosed in the first aspect of the present application are executed.

[0059] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0060] The present application can realize precise access control based on user permission and task prediction, improve the accuracy and security of target device access management, and reduce device safety accidents by obtaining the access control request of the target user to the target device, combining the preset historical access database to determine the user access permission, using the prediction model to predict task information according to device data and request, and determining the pass permission of the access control request according to the access permission and task information according to the preset determination rule. BRIEF DESCRIPTION OF DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0062] Figure 1 is a flow diagram of a device operation and maintenance management method based on multi-layer permission control disclosed by the embodiments of the present application.

[0063] Figure 2 is a structural diagram of a device operation and maintenance management system based on multi-layer permission control disclosed by the embodiments of the present application.

[0064] Figure 3is a structural schematic diagram of another device operation and maintenance management system based on multi-layer permission control disclosed by the embodiment of the present application. DETAILED DESCRIPTION

[0065] In order for those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0066] The terms "first", "second", and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish different objects, not to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or equipment including a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed or can optionally include other steps or units inherent to the process, method, product or equipment.

[0067] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The phrase appears at various places in the specification does not necessarily all refer to the same embodiment, nor is it necessarily mutually exclusive of other embodiments. It is explicitly and implicitly understood that the embodiments described herein can be combined with other embodiments.

[0068] The present application discloses a device operation and maintenance management method and system based on multi-layer permission control, which determines user access permission by obtaining the target user's access control request to the target device and combining the preset historical access database, and simultaneously uses a prediction model to predict task information according to device data and requests, determines the passing permission of the access control request according to the access permission and the task information according to the preset determination rule, so as to realize accurate access control based on user permission and task prediction, improve the accuracy and security of target device access management, and reduce device safety accidents. The following will be described in detail.

[0069] Embodiment one

[0070] Please refer to Figure 1 , Figure 1 is a flowchart of a device operation and maintenance management method based on multi-layer permission control disclosed by the embodiment of the present application. Among them, Figure 1The device operation and maintenance management method based on multi-layer permission control can be applied in a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). As shown in Figure 1 The device operation and maintenance management method based on multi-layer permission control can include the following operations:

[0071] 101. Obtain an access control request of a target user to a target device.

[0072] 102. Determine the user access permission corresponding to the target user based on a preset historical access database.

[0073] 103. Based on a prediction model, predict task information corresponding to the access control request according to device data of the target device and the access control request.

[0074] 104. Determine the passing permission of the access control request based on a preset access determination rule according to the user access permission and the task information.

[0075] It can be seen that the above-mentioned embodiments of the application obtain an access control request of a target user to a target device, determine the user access permission in combination with a preset historical access database, predict task information according to device data and the request by using a prediction model, and determine the passing permission of the access control request according to the access permission and the task information according to a preset determination rule, so as to realize precise access control based on user permission and task prediction, improve the accuracy and security of target device access management, and reduce device security incidents.

[0076] As an optional embodiment, in the above-mentioned step of determining the user access permission corresponding to the target user based on the preset historical access database, the step includes:

[0077] Determine a plurality of historical access data same as or similar to the target user in the preset historical access database;

[0078] Calculate a weighted average value of the access levels corresponding to all the historical access data to obtain an access level parameter;

[0079] Determine the user access permission corresponding to the target user according to a preset correspondence between the access level and the access permission and the access level parameter.

[0080] It can be seen that, through the above optional embodiments, the access level parameter is obtained by screening the same or similar historical access data of the target user in the preset historical access database and calculating the weighted average value of the access levels thereof, and the access permission of the target user is determined according to the correspondence between the preset access levels and the permissions, thereby realizing accurate permission determination based on historical access behavior, assisting in realizing accurate access control based on user permission and task prediction, improving the accuracy and security of target device access management, and reducing device security incidents.

[0081] As an optional embodiment, in the above step, the plurality of historical access data same or similar to the target user is determined in the preset historical access database, comprising:

[0082] For each access data record in the preset historical access database, the related user information corresponding to the access data record is obtained; optionally, the related user information includes at least one of the sending user, the receiving user, the auditing user and the supervising user;

[0083] The user similarity between the related user information and the user information of the target user is calculated.

[0084] The access data records with a user similarity greater than a preset similarity threshold are screened out to obtain the plurality of historical access data same or similar to the target user.

[0085] It can be seen that, through the above optional embodiments, by analyzing the related user information of each access data record in the preset historical access database and calculating the similarity between the related user information and the user information of the target user, the records with a similarity greater than a threshold are screened out as historical access data same or similar to the target user, thereby realizing accurate historical data screening based on multi-dimensional user information comparison, improving the accuracy of target user access permission evaluation, assisting in realizing accurate access control based on user permission and task prediction, improving the accuracy and security of target device access management, and reducing device security incidents.

[0086] As an optional embodiment, in the above step, when calculating the weighted average value of the access levels corresponding to all historical access data, the calculation weight corresponding to each access level includes a first weight and a second weight; the first weight is proportional to the data completeness of the corresponding historical access data; the data completeness is obtained by inputting the historical access data into a trained completeness prediction model; and the second weight is proportional to the user similarity corresponding to the corresponding historical access data.

[0087] It can be seen that, through the above optional embodiments, the target user access right is determined by defining the weight composed of the first weight proportional to the data integrity and the second weight proportional to the user similarity, thereby realizing precise right determination based on data integrity and user similarity, assisting in realizing precise access control based on user right and task prediction, improving the accuracy and security of target device access management, and reducing device security incidents.

[0088] As an optional embodiment, in the above step, the target device is an energy production device; and the energy production device is a solar panel, a wind turbine, a nuclear reactor, a water turbine, a geothermal pump, a coal-fired furnace, a gas turbine, or a biomass furnace.

[0089] It can be seen that, through the above optional embodiments, the device type of the target device is defined to effectively represent the device characteristics, assist in realizing precise access control based on user right and task prediction, improve the accuracy and security of target device access management, and reduce device security incidents.

[0090] As an optional embodiment, in the above step, based on the prediction model, the task information corresponding to the access control request is predicted according to the device data of the target device and the access control request, including:

[0091] Obtaining the device configuration parameters and historical energy production work data corresponding to the target device;

[0092] Determining a plurality of historical work data related to the access control request in the historical energy production work data;

[0093] Inputting all the historical work data, device configuration parameters, and access control request into the trained task prediction neural network to obtain the task information corresponding to the access control request; the task prediction neural network is trained by a training data set including a plurality of training historical work data and corresponding device parameter labels, access control request labels, and work task labels.

[0094] It can be seen that, through the above optional embodiments, by obtaining the device configuration parameters and historical energy production work data of the target device and screening out the historical work data related to the access control request, the device configuration parameters and the request are input into the trained task prediction neural network to determine the task information of the access control request, thereby realizing precise task prediction based on device parameters and historical work data, improving the accuracy of access control decision and the efficiency of safe management of target device energy production tasks.

[0095] As an optional embodiment, in the above step, the time similarity between the work time point corresponding to the historical work data and the request time point corresponding to the access control request is greater than a preset second similarity threshold; the time similarity is a sum of a time difference and a time period type similarity; the time difference is a time difference between the work time point and the request time point; the time period type similarity is a similarity between time type data corresponding to the work time point and the request time point respectively; the time type data includes one or more of a date type, a holiday type, a month type, a season type and a work time period type to which the time point belongs.

[0096] It can be seen that, through the above optional embodiment, the details of screening related historical work data are limited, so that more time-related work data is used for task prediction, thereby realizing precise task prediction based on multi-dimensional time similarity and device data, and improving the accuracy of access control decision and the efficiency of safety management of energy production devices.

[0097] As an optional embodiment, in the above step, the pass permission of the access control request is determined based on the user access permission and the task information according to a preset access determination rule, including:

[0098] determining the user permission limit corresponding to the task information based on a preset correspondence between the task and the permission threshold;

[0099] determining whether the user access permission meets the user permission limit to obtain a determination result;

[0100] when the determination result is yes, determining that the pass permission of the access control request is allowed to pass;

[0101] when the determination result is no, determining that the pass permission of the access control request is not allowed to pass.

[0102] It can be seen that, through the above optional embodiment, the user permission limit corresponding to the task information is determined based on the preset correspondence between the task and the permission threshold, and it is judged whether the user access permission meets the limit, and when it meets, the access control request is allowed to pass, otherwise it is rejected, thereby realizing precise access control based on task permission matching, improving the accuracy and safety of target device access management, and reducing the risk of unauthorized access to energy production.

[0103] Embodiment two

[0104] Please refer to Figure 2 , Figure 2 is a structural schematic diagram of a device operation and maintenance management system based on multi-layer permission control disclosed by the embodiment of the application. Among them, Figure 2The device operation and maintenance management system based on multi-layer permission control can be applied in a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). As shown in Figure 2 The device operation and maintenance management system based on multi-layer permission control can include:

[0105] The acquisition module 201 is configured to acquire an access control request of a target user to a target device.

[0106] The determination module 202 is configured to determine a user access permission corresponding to the target user based on a preset historical access database.

[0107] The prediction module 203 is configured to predict task information corresponding to the access control request based on a prediction model according to device data of the target device and the access control request.

[0108] The judgment module 204 is configured to determine a pass permission of the access control request based on a preset access judgment rule according to the user access permission and the task information.

[0109] It can be seen that the above-mentioned embodiments of the application can acquire an access control request of a target user to a target device, determine a user access permission in combination with a preset historical access database, predict task information according to device data and the request by using a prediction model, and determine a pass permission of the access control request according to the user access permission and the task information according to a preset judgment rule, so as to realize precise access control based on user permission and task prediction, improve the accuracy and security of target device access management, and reduce device security incidents.

[0110] As an optional embodiment, the specific manner in which the determination module determines a user access permission corresponding to the target user based on a preset historical access database includes:

[0111] A plurality of historical access data identical or similar to the target user is determined in the preset historical access database.

[0112] A weighted average value of access levels corresponding to all the historical access data is calculated to obtain an access level parameter.

[0113] The user access permission corresponding to the target user is determined according to a preset correspondence between access levels and access permissions and the access level parameter.

[0114] It can be seen that, through the above optional embodiments, the access level parameter is obtained by screening the same or similar historical access data of the target user in the preset historical access database and calculating the weighted average value of the access level thereof, and the access permission of the target user is determined according to the correspondence between the preset access level and the permission, so as to realize accurate permission determination based on historical access behavior, assist in realizing accurate access control based on user permission and task prediction, improve the accuracy and security of target device access management, and reduce device security incidents.

[0115] As an optional embodiment, the specific manner in which the determining module determines the plurality of historical access data same or similar to the target user in the preset historical access database includes:

[0116] For each access data record in the preset historical access database, the relevant user information corresponding to the access data record is obtained; optionally, the relevant user information includes at least one of a sending user, a receiving user, an auditing user, and a supervising user;

[0117] The user similarity between the relevant user information and the user information of the target user is calculated;

[0118] The access data records with a user similarity greater than a preset similarity threshold are screened out to obtain the plurality of historical access data same or similar to the target user.

[0119] It can be seen that, through the above optional embodiments, by analyzing the relevant user information of each access data record in the preset historical access database and calculating the similarity between the relevant user information and the user information of the target user, the records with a similarity greater than a threshold are screened out as historical access data same or similar to the target user, so as to realize accurate historical data screening based on multi-dimensional user information comparison, improve the accuracy of target user access permission evaluation, assist in realizing accurate access control based on user permission and task prediction, improve the accuracy and security of target device access management, and reduce device security incidents.

[0120] As an optional embodiment, when calculating the weighted average value of the access level corresponding to all historical access data, the calculation weight corresponding to each access level includes a first weight and a second weight; the first weight is proportional to the data completeness of the corresponding historical access data; the data completeness is obtained by inputting the historical access data into a trained completeness prediction model; and the second weight is proportional to the user similarity corresponding to the corresponding historical access data.

[0121] It can be seen that, through the above optional embodiments, the target user access right is determined by defining the weight composed of the first weight proportional to the data integrity and the second weight proportional to the user similarity, so as to realize the accurate right determination based on data integrity and user similarity, assist to realize the accurate access control based on user right and task prediction, improve the accuracy and security of target device access management, and reduce device security incidents.

[0122] As an optional embodiment, the target device is an energy production device; the energy production device is a solar panel, a wind turbine, a nuclear reactor, a water turbine, a geothermal pump, a coal-fired furnace, a gas turbine, or a biomass furnace.

[0123] It can be seen that, through the above optional embodiments, the device type of the target device is defined to effectively represent the device characteristics, assist to realize the accurate access control based on user right and task prediction, improve the accuracy and security of target device access management, and reduce device security incidents.

[0124] As an optional embodiment, the prediction module predicts the specific manner of the task information corresponding to the access control request based on the prediction model according to the device data of the target device and the access control request, including:

[0125] Obtaining the device configuration parameters and historical energy production work data corresponding to the target device;

[0126] Determining a plurality of historical work data related to the access control request in the historical energy production work data;

[0127] Inputting all the historical work data, device configuration parameters, and access control request into the trained task prediction neural network to obtain the task information corresponding to the access control request; the task prediction neural network is trained by a training data set including a plurality of training historical work data and corresponding device parameter labels, access control request labels, and work task labels.

[0128] It can be seen that, through the above optional embodiments, by obtaining the device configuration parameters and historical energy production work data of the target device and screening out the historical work data related to the access control request, the device configuration parameters and the request are input into the trained task prediction neural network to determine the task information of the access control request, so as to realize the accurate task prediction based on the device parameters and historical work data, improve the accuracy of the access control decision, and improve the efficiency of the safety management of the target device energy production task.

[0129] As an optional embodiment, the time similarity between the working time point corresponding to the historical working data and the request time point corresponding to the access control request is greater than a preset second similarity threshold; the time similarity is the sum of the time difference and the time period type similarity; the time difference is the time difference between the working time point and the request time point; the time period type similarity is the similarity between the time type data corresponding to the working time point and the request time point respectively; the time type data includes one or more of the date type, holiday type, month type, season type and working time period type to which the time point belongs.

[0130] As can be seen, the above optional embodiments limit the details of screening relevant historical work data so that more time-related work data can be used for task prediction, thereby achieving accurate task prediction based on multi-dimensional time similarity and equipment data, improving the accuracy of access control decisions and the efficiency of energy production equipment safety management.

[0131] As an optional embodiment, the determination module determines the specific method for granting permission to the access control request based on user access permissions and task information, according to preset access determination rules, including:

[0132] Based on the preset correspondence between tasks and permission thresholds, determine the user permission restrictions corresponding to task information;

[0133] Determine whether the user's access permissions meet the user's permission restrictions, and obtain the determination result;

[0134] If the judgment result is yes, the access control request is granted permission.

[0135] If the result of the judgment is negative, the access control request is determined to be denied.

[0136] As can be seen, through the above optional embodiments, the user permission restrictions corresponding to the task information are determined based on the correspondence between preset tasks and permission thresholds, and it is determined whether the user's access permission meets the restriction. When it meets the restriction, the access control request is allowed to pass; otherwise, it is rejected. This achieves precise access control based on task permission matching, improves the accuracy and security of target device access management, and reduces the energy production risks caused by unauthorized access.

[0137] Example 3

[0138] Please see Figure 3 , Figure 3 This is another device operation and maintenance management system based on multi-level access control disclosed in the embodiments of the present invention. Figure 3 The described multi-level access control-based equipment operation and maintenance management system is applied to data processing systems / data processing equipment / data processing servers (wherein, the server includes local processing servers or cloud processing servers). For example...Figure 3 As shown, the device operation and maintenance management system based on multi-layer permission control can include:

[0139] The memory 301 stores executable program codes.

[0140] The processor 302 is coupled to the memory 301.

[0141] The processor 302 invokes the executable program codes stored in the memory 301 to execute the steps of the device operation and maintenance management method based on multi-layer permission control described in Embodiment One.

[0142] Embodiment Four

[0143] The computer readable storage medium stores a computer program for electronic data exchange, wherein the computer program causes a computer to execute the steps of the device operation and maintenance management method based on multi-layer permission control described in Embodiment One.

[0144] Embodiment Five

[0145] The computer program product includes a non-transitory computer readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute the steps of the device operation and maintenance management method based on multi-layer permission control described in Embodiment One.

[0146] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that in the embodiments and still achieve the desired result. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order shown or in consecutive order to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

[0147] The system, device, module or unit illustrated in the above embodiments can be specifically implemented by a computer chip or entity, or by a product with certain functions. A typical implementation device is a computer. Specifically, the computer may, for example, be a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0148] For the convenience of description, the above device is described as various units divided by functions. Of course, the functions of each unit can be implemented in the same or more software and / or hardware when implementing the present specification.

[0149] Those skilled in the art will appreciate that embodiments of the present description can be readily used as a method, an apparatus (system) or a computer program product. Accordingly, embodiments of the present description can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present description can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, and the like) embodying computer readable program code.

[0150] The present description is described in reference to flow diagrams and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present description. It will be understood that each block of the flow diagrams and / or block diagrams, and combinations of blocks in the flow diagrams and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processing device or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flow diagrams and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flow diagram and / or block diagram block or blocks. Figure 1 one or more functions specified in the flow diagram and / or block diagram block or blocks.

[0151] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the flow diagrams and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flow diagram and / or block diagram block or blocks. Figure 1 one or more functions specified in the flow diagram and / or block diagram block or blocks.

[0152] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flow diagrams and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flow diagram and / or block diagram block or blocks. Figure 1 one or more functions specified in the flow diagram and / or block diagram block or blocks.

[0153] In one typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0154] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) and / or cache memory. The memory can also include non-volatile memory, such as read-only memory (ROM), electrically programmable read-only memory (EPROM), electrically erasable read-only memory (EEPROM), flash memory, or a combination of non-volatile memories in different forms. The memory is an example of computer-readable media.

[0155] Computer-readable media includes permanent and non-permanent, movable and non-movable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory media such as modulated data signals and carriers.

[0156] It should also be noted that the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, so that processes, methods, articles or devices that include a series of elements not only include those elements, but also include other elements not explicitly listed or inherent to such processes, methods, articles or devices. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or device that includes the element.

[0157] The specification can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The specification can also be practiced in a distributed computing environment, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0158] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. Each embodiment focuses on the difference from other embodiments. In particular, for system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiment.

[0159] Finally, it should be noted that: the embodiment of the application discloses a kind of device operation and maintenance management method and system based on multi-layer permission control, only for the preferred embodiment of the application, only for describing the technical solutions of the application, not for its limitation;Although the application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that;It can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part of the technical features;And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the application.

Claims

1. A device operation and maintenance management method based on multi-layer permission control, characterized in that, The method comprises: obtaining an access control request of a target user to a target device; determining a user access right corresponding to the target user based on a preset historical access database, comprising: for each access data record in the preset historical access database, obtaining relevant user information corresponding to the access data record; the relevant user information comprises at least one of a sending user, a receiving user, an auditing user and a supervising user; calculating a user similarity between the relevant user information and user information of the target user; filtering out the access data records with a user similarity greater than a preset similarity threshold to obtain a plurality of historical access data identical or similar to the target user; calculating a weighted average value of access levels corresponding to all the historical access data to obtain an access level parameter; when calculating the weighted average value of the access levels corresponding to all the historical access data, the calculation weight corresponding to each access level comprises a first weight and a second weight; the first weight is proportional to the data integrity of the corresponding historical access data; the data integrity is obtained by inputting the historical access data into a trained integrity prediction model; the second weight is proportional to the user similarity corresponding to the corresponding historical access data; determining the user access right corresponding to the target user according to a preset correspondence between access levels and access rights and the access level parameter; based on a prediction model, predicting task information corresponding to the access control request according to device data of the target device and the access control request; determining the pass permission of the access control request based on a preset access determination rule according to the user access right and the task information.

2. The device operation and maintenance management method based on multi-layer permission control according to claim 1, characterized in that, The target device is an energy production device; the energy production device is a solar panel, a wind turbine, a nuclear reactor, a water turbine, a geothermal pump, a coal-fired furnace, a gas turbine or a biomass furnace.

3. The device operation and maintenance management method based on multi-layer permission control according to claim 2, characterized in that, Based on the prediction model, the task information corresponding to the access control request is predicted according to the device data of the target device and the access control request, comprising: obtaining device configuration parameters and historical energy production work data corresponding to the target device; determining a plurality of historical work data related to the access control request in the historical energy production work data; inputting all the historical work data, the device configuration parameters and the access control request into a trained task prediction neural network to obtain the task information corresponding to the access control request; the task prediction neural network is trained by a training data set comprising a plurality of training historical work data and corresponding device parameter labels, access control request labels and work task labels.

4. The device operation and maintenance management method based on multi-layer permission control according to claim 3, characterized in that, The time similarity between the work time point corresponding to the historical work data and the request time point corresponding to the access control request is greater than a preset second similarity threshold; the time similarity is a sum of a time difference and a time period type similarity; the time difference is a time difference between the work time point and the request time point; the time period type similarity is a similarity between time type data corresponding to the work time point and the request time point respectively; the time type data includes one or more of a date type, a holiday type, a month type, a season type and a work time period type to which the time point belongs.

5. The device operation and maintenance management method based on multi-layer permission control according to claim 1, characterized in that, The determining, according to the user access authority and the task information, of the pass permission of the access control request based on a preset access determination rule comprises: determining a user authority limit corresponding to the task information based on a preset correspondence between a task and an authority threshold; determining whether the user access authority meets the user authority limit to obtain a determination result; when the determination result is yes, determining that the pass permission of the access control request is allowed to pass; when the determination result is no, determining that the pass permission of the access control request is not allowed to pass.

6. A device operation and maintenance management system based on multi-layer permission control, characterized in that, The system comprises: an acquisition module configured to acquire an access control request of a target user to a target device; a determination module configured to determine a user access authority corresponding to the target user based on a preset historical access database, comprising: for each access data record in the preset historical access database, acquiring related user information corresponding to the access data record; the related user information comprises at least one of a sending user, a receiving user, an auditing user and a supervising user; calculating a user similarity between the related user information and user information of the target user; filtering out the access data records with a user similarity greater than a preset similarity threshold to obtain a plurality of historical access data identical or similar to the target user; calculating a weighted average value of access levels corresponding to all the historical access data to obtain an access level parameter; in calculating the weighted average value of the access levels corresponding to all the historical access data, a calculation weight corresponding to each access level comprises a first weight and a second weight; the first weight is directly proportional to a data completeness degree of the corresponding historical access data; the data completeness degree is obtained by inputting the historical access data into a trained completeness prediction model; the second weight is directly proportional to the user similarity corresponding to the corresponding historical access data; determining a user access authority corresponding to the target user according to a preset correspondence between an access level and an access authority, and the access level parameter; a prediction module configured to predict task information corresponding to the access control request based on a prediction model according to device data of the target device and the access control request; a judgment module configured to determine a pass permission of the access control request based on a preset access determination rule according to the user access authority and the task information.

7. A device operation and maintenance management system based on multi-layer permission control, characterized in that, The system comprises: a memory storing executable program code; a processor coupled to the memory; The processor invokes the executable program code stored in the memory to perform the device operation and maintenance management method based on multi-layer permission control according to any one of claims 1-5.

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