Power station material management method, device and equipment based on smart storage

Abstract

The embodiment of the invention provides a power station material management method, device and equipment based on intelligent storage, relates to the technical field of computers, is suitable for a power station material management system, and comprises the steps: employing multi-dimensional real-time monitoring data to generate a monitoring data set; the multi-dimensional real-time monitoring data comprises time parameter data, behavior information data and material information data, the time parameter data represents a time response parameter generated in system operation, and the behavior information data represents the authority level of a post role of a user and the use condition of the system; the material information data represents access control strength of different categories of materials in the system, a dynamic permission model is adopted to perform permission allocation calculation on the monitoring data set, permission allocation parameters are generated in combination with environmental fluctuation parameters, and the permission allocation parameters are adopted to update a permission allocation mechanism in the power station material management system; and managing power station materials based on the updated authority distribution mechanism in the system. The flexibility, the accuracy, the response speed, the safety and the like of the system are improved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular, to a power station material management method, device, and equipment based on intelligent warehousing. Background Art

[0002] In the field of modern industrial management, the importance of the power station material management system is self-evident. It is directly related to the resource security, operation efficiency of the power station, and the protection of the core assets of the power station enterprise. Especially in the management of key resources and key infrastructure of the power station, the power station material management system not only needs to play a basic maintenance role in ensuring the daily operation of the power station, but also is a key link in preventing risks and improving overall safety during the power station material management process. For example, power station materials usually include flammable and explosive items such as batteries and energy, as well as precision instruments. It can be seen that the power station material management system needs to manage the access and storage of power station materials and the permissions of staff safely, efficiently, quickly, and accurately. However, current solutions often have limitations. Many power station material management systems are too rigid in material management and permission allocation, and it is difficult to adapt to complex and changing actual needs. At the same time, the response speed and accuracy in abnormal situations are often insufficient, resulting in low resource utilization efficiency and even potential safety hazards.

[0003] Therefore, there is an urgent need for a power station material management method, device, and equipment based on intelligent warehousing to improve the flexibility, accuracy, response speed, and safety of the power station material management system. Summary of the Invention

[0004] Embodiments of this application provide a power station material management method, device, and equipment based on intelligent warehousing to improve the flexibility, accuracy, response speed, and safety of the power station material management system.

[0005] In a first aspect, embodiments of this application provide a power station material management method based on intelligent warehousing, which is applicable to a power station material management system. The method includes: Obtain multi-dimensional real-time monitoring data and generate a monitoring data set; the multi-dimensional real-time monitoring data includes time parameter data, behavior information data, and material information data obtained in real time. The time parameter data is used to represent the time response parameters generated during the operation of the power station material management system. The behavior information data is used to represent the permission levels of the user's job roles and the usage of the power station material management system. The material information data is used to represent the access control intensity of different categories of materials in the power station material management system. The access control intensity represents the material priority information and material access conditions of the corresponding category of materials; The dynamic permission model is used to calculate the permission allocation for the monitored data set, generate permission allocation parameters in combination with environmental fluctuation parameters, and update the permission allocation mechanism in the power station material management system with the permission allocation parameters. The environmental fluctuation parameters include at least one of environmental temperature, humidity, wind force, sunny or cloudy weather, rainy day, and snowy day; Manage the power station materials based on the updated permission allocation mechanism in the power station material management system.

[0006] In a second aspect, an embodiment of the present application provides a power station material management device based on intelligent warehousing, which is applicable to a power station material management system. The device includes: A data acquisition module, configured to acquire multi-dimensional real-time monitoring data and generate a monitored data set; the multi-dimensional real-time monitoring data includes time parameter data, behavior information data, and material information data acquired in real time. The time parameter data is used to represent the time response parameters generated during the operation of the power station material management system, the behavior information data is used to represent the permission levels of the user's job roles and the usage of the power station material management system, and the material information data is used to represent the access control intensity of different categories of materials in the power station material management system. The access control intensity represents the material priority information and material access conditions of the corresponding category of materials; An update module, configured to use the dynamic permission model to calculate the permission allocation for the monitored data set, generate permission allocation parameters in combination with environmental fluctuation parameters, and update the permission allocation mechanism in the power station material management system with the permission allocation parameters. The environmental fluctuation parameters include at least one of environmental temperature, humidity, wind force, sunny or cloudy weather, rainy day, and snowy day; A management module, configured to manage the power station materials based on the updated permission allocation mechanism in the power station material management system.

[0007] Optionally, the dynamic permission model includes the preset data conditions of each dimension of monitoring data in the monitored data set and the correlation relationships between the dimensions of monitoring data. The preset data conditions and the correlation relationships are obtained according to the power station material management standards, job role permissions, and historical monitored data sets implemented in the industry; The update module is further configured to, Perform the following operations on each dimension of monitoring data respectively: Based on the dynamic permission model, use the preset data conditions of the current dimension data to perform a comparison process on the current dimension data to obtain a comparison result; Use the dynamic permission model to analyze the correlation situation between the dimensions of monitoring data, and compare and analyze the correlation relationship and the correlation situation to obtain a correlation result; Determine that no abnormal results occur in the obtained multiple comparison results and the correlation result.

[0008] Optionally, the update module is further configured to Determine that abnormal results occur in the multiple comparison results and the association result; Determine that the dimension monitoring data corresponding to the abnormal result is abnormal dimension monitoring data, and obtain the risk dimension monitoring data associated with the abnormal dimension monitoring data; Extract the abnormal association features of the abnormal dimension monitoring data and the risk dimension monitoring data by using the dynamic permission model; Determine at least one risk node according to the abnormal association features, and generate an emergency control message including an emergency control plan according to the determined at least one risk node; Send the emergency control message to the corresponding account and display the emergency control plan.

[0009] Optionally, the update module is specifically configured to Based on the trust evaluation sub-model in the dynamic permission model, extract the user behavior characteristics of each user in the behavior information data in the monitoring data set, and obtain the respective permission trust degrees of the users according to the user behavior characteristics of each user; Based on the scheduling sub-model in the dynamic permission model, extract the material usage characteristics of each category of materials in the material information data in the monitoring data set, and obtain the respective importance degrees of the categories of materials according to the material usage characteristics of each category of materials; Based on the response configuration sub-model in the dynamic permission model, extract the access response characteristics of each system module in the time parameter data in the monitoring data set, and obtain the configuration parameters of each system module according to the access response characteristics of each system module; Based on the permission allocation sub-model in the dynamic permission model, obtain the permission allocation parameters of the respective permission trust degrees of the users, the respective importance degrees of the categories of materials, the configuration parameters of each system module, and the environmental fluctuation parameters, and update the permission allocation mechanism by using the permission allocation parameters.

[0010] Optionally, the user behavior characteristics include at least one of user operation characteristics, position permission role characteristics, and identity authentication characteristics; Wherein, the user operation characteristics are used to represent the operation information of the corresponding user's operating system in terms of time, region, accessed material category, accessed material quantity, and operation frequency, the position permission role characteristics are used to represent the position role of the corresponding user and the permission information of the position role for accessing and storing materials in the system, and the identity authentication characteristics are used to represent the authentication analysis information of the basic identity information of the corresponding user by the system, and the degree of consistency between the historical behavior information data and the current behavior information data of the corresponding user.

[0011] Optionally, the material usage characteristics include at least one of a material usage frequency characteristic, a material property characteristic, a material demand characteristic, a material extraction time characteristic, a material extraction area characteristic, and a material priority characteristic; Among them, the material usage frequency characteristic is used to characterize the usage frequency of the corresponding category of materials, the material property characteristic is used to characterize the physical and chemical characteristics of the corresponding category of materials, the material demand characteristic is used to characterize the predetermined required quantity of the corresponding category of materials in the power station material management system, the material extraction time characteristic is used to characterize the distribution of the extraction time of the corresponding category of materials, the material extraction area characteristic is used to characterize the distribution of the extraction area of the corresponding category of materials, and the material priority characteristic is used to characterize the priority of providing the corresponding category of materials to different job roles.

[0012] Optionally, the access response characteristics include at least one of a request response time characteristic, an authorization verification time characteristic, and an identity authentication time characteristic; Among them, the request response time characteristic is used to characterize the processing efficiency of the power station material management system and its internal subsystems and corresponding modules for access requests, the authorization verification time characteristic is used to characterize the verification efficiency of the power station physical management system for user authorization information in access requests, and the identity authentication time characteristic is used to characterize the authentication efficiency of the power station physical management system for user identity information in access requests.

[0013] Optionally, the update module is further configured to update the authorization allocation mechanism in the test system using the authorization allocation parameters, and simulate the access process of the corresponding job role to the updated test system, and obtain the access result of the updated test system during the access process; confirm that the access result is normal.

[0014] In a third aspect, an embodiment of the present application provides a computer device, including: a memory and a processor; the memory is used to store a computer program; the processor is used to call the computer program stored in the memory, so that the computer device executes the power station physical management method in any possible design of the first aspect.

[0015] Advantages of the present application: In the power station material management method provided in the embodiments of the present application, multi-dimensional real-time monitoring data generated in the power station material management system is obtained. The multi-dimensional real-time monitoring data includes time parameter data, behavior information data, and material information data obtained in real time. A monitoring data set is generated based on the multi-dimensional real-time monitoring data, and a dynamic permission model is used to perform permission allocation analysis and calculation on the monitoring data set, and permission allocation parameters are generated in combination with environmental fluctuation parameters. Thus, the obtained permission allocation parameters are obtained by comprehensively considering the current environmental aspects, system performance aspects, user behavior aspects, and material aspects. The permission allocation mechanism in the system is updated using the permission allocation parameters, so that the system can perform material management according to the latest permission allocation mechanism, improving the flexibility, accuracy, response speed, and security of the power station material management system. Moreover, since the permission allocation mechanism in the power station material management system is always in a dynamic adjustment state, the power station physical management system can maintain high flexibility, high accuracy, a relatively fast response speed, and high security.

[0016] These implementation manners of the present application or other implementation manners will be more clearly understood in the following description of the embodiments. Brief Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is a schematic diagram of an application scenario provided for the embodiments of the present application; Figure 2 It is a schematic flowchart of a power station material management method based on intelligent warehousing provided for the embodiments of the present application; Figure 3 It is a schematic architecture diagram of a dynamic permission model provided for the embodiments of the present application; Figure 4 It is a schematic diagram of a power station material management device provided for the embodiments of the present application. Detailed Embodiments

[0019] In order to make the purpose, technical solutions, and advantages of the present application clearer, the following will further describe the present application in detail with reference to the drawings. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0020] The power station material management system is an information management tool designed for the power industry (including thermal power, hydropower, photovoltaic, etc.), aiming to optimize the links of material procurement, inventory, distribution, full life cycle management, etc., improve operation efficiency, and reduce resource waste. Generally speaking, since power station materials include high-precision instruments, micro items, etc., power station materials need to be managed restrictively. However, due to the factors such as user behavior, environmental conditions, and material importance faced by the power station material management system changing at all times, the static permission allocation method of the traditional permission management of the power station material management system cannot reflect and respond to these changes in a timely manner, resulting in overly loose or overly strict permissions, affecting the security and efficiency of the system.

[0021] In view of this, the embodiment of the present application provides a power station material management method based on intelligent warehousing. By using multi-dimensional real-time monitoring data generated in the power station material management system (the multi-dimensional real-time monitoring data includes time parameter data, behavior information data, and material information data obtained in real time, and according to the multi-dimensional real-time monitoring data), a monitoring data set is generated, and a dynamic permission model is used to perform permission allocation analysis and calculation on the monitoring data set, generate permission allocation parameters in combination with environmental fluctuation parameters, and use the permission allocation parameters to update the permission allocation mechanism in the power station material management system. In this way, the permission allocation mechanism in the power station material management system is in a dynamic adjustment state, and then the power station physical management system can maintain high flexibility, high accuracy, a relatively fast response speed, and high security.

[0022] As Figure 1 shown, it is a schematic diagram of an application scenario in the embodiment of the present application. This application scenario diagram includes at least one terminal device 110 and at least one server 120.

[0023] In the embodiment of the present application, the terminal device 110 includes, but is not limited to, devices such as mobile phones, tablet computers, laptop computers, desktop computers, e-book readers, intelligent voice interaction devices, intelligent home appliances, in-vehicle terminals, etc.; relevant clients can be installed on the terminal device, and the client can be software (for example, an application that can remotely control the power station material management system, the client of the power station material management system, etc.), or a web page, a small program, etc. The server 120 is the background server corresponding to the software, web page, small program, etc., or a server specifically used to provide data related to power station physical management, etc., and the present application does not make specific limitations. The server 120 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery network (Content Delivery Network, CDN), and big data and artificial intelligence platforms.

[0024] It should be noted that the power station material management method in each embodiment of this application can be executed by an electronic device, which can be a terminal device 110 or a server 120. That is, this method can be executed independently by the terminal device 110 or the server 120, or can be jointly executed by the terminal device 110 and the server 120. Taking the joint action of the terminal device 110 and the server 120 as an example, an application APP can be installed on the terminal device 110. The user logs in to the power station material management system by operating the application. After authentication, corresponding operations are performed based on the user's own job role; alternatively, the user can also remotely control the power station material management system to enter the update state by operating the application, generate permission allocation parameters using multi-dimensional real-time monitoring data, a dynamic permission model, and environmental fluctuation parameters, and update the permission allocation mechanism in the power station material management system to complete the permission update. After that, when the user accesses the terminal again, the server 120 determines the relevant data that the user can access according to the corresponding permission allocation parameters and transmits it to the terminal device 110 for presentation to the user.

[0025] In an alternative embodiment, the terminal device 110 and the server 120 can communicate through a communication network.

[0026] In an alternative embodiment, the communication network is a wired network or a wireless network.

[0027] It should be noted that Figure 1 The above is only an example. In fact, the number of terminal devices and servers is not limited and is not specifically limited in the embodiments of this application.

[0028] In the embodiments of this application, when the number of servers is multiple, the multiple servers can form a blockchain, and the server is a node on the blockchain; as the power station physical management method for the power station material management system disclosed in the embodiments of this application, the relevant data involved can be stored on the blockchain. For example, multi-dimensional real-time monitoring data, monitoring data sets, permission allocation parameters, etc.

[0029] Next, in combination with the above-described application scenarios, the power station material management method based on intelligent warehousing provided by the exemplary embodiments of this application will be described with reference to the accompanying drawings. It should be noted that the above application scenarios are only shown for the convenience of understanding the spirit and principle of this application, and the embodiments of this application are not limited in this regard.

[0030] Refer to Figure 2 As shown, it is a flowchart of a power station material management method based on intelligent warehousing in the embodiments of this application. Taking the terminal device or server running the power station material management system as the execution subject, the specific implementation process of this method is as follows in steps 201 - 203: Step 201: Obtain multi-dimensional real-time monitoring data and generate a monitoring data set; the multi-dimensional real-time monitoring data includes time parameter data, behavior information data, and material information data obtained in real time. The time parameter data is used to represent the time response parameters generated during the operation of the power station material management system. The behavior information data is used to represent the permission level of the user's job role and the usage of the power station material management system. The material information data is used to represent the access control intensity of different categories of materials in the power station material management system. The access control intensity represents the material priority information and material access conditions of the corresponding category of materials.

[0031] In one embodiment, the multi-dimensional real-time monitoring data can obtain the time parameter data, behavior information data, and material information data from the logs in the power station material management system. It should be noted that the acquisition method here can be direct acquisition or indirect acquisition. For example, if the material information data is not recorded in the logs, it can be obtained from the relevant material records in the power station physical management system.

[0032] In one embodiment, before generating the monitoring data set, the multi-dimensional real-time monitoring data is cleaned, such as filling in missing data, correcting or deleting outliers (note that for some sensitive data items, this correction or deletion operation cannot be performed to prevent the problem of ignoring risk points), and then the cleaned multi-dimensional real-time monitoring data is used to generate the monitoring data set.

[0033] In one embodiment, during the implementation of the power station material management method, first, a comprehensive monitoring data set is constructed through the acquisition of multi-dimensional real-time monitoring data. For example, the system automatically collects time parameter data every minute and records the response time during the operation of the power station material management system. For example, the response time of a certain operation is 0.5 seconds. At the same time, behavior information data is collected. By analyzing the user login log, it is found that the job role of user A is a warehouse administrator, the permission level is 3, and the operation frequency is 10 times of material access records per day. The material information data records the access control intensity of different categories of materials. For example, the material priority of generator components is high, and the access condition requires double authentication, while the priority of office supplies is low and only single authentication is required. These data are integrated into a monitoring data set through the database to form a data foundation.

[0034] Step 202: Use a dynamic permission model to calculate the permission allocation for the monitoring data set, generate permission allocation parameters in combination with environmental fluctuation parameters, and update the permission allocation mechanism in the power station material management system with the permission allocation parameters. The environmental fluctuation parameters include at least one of environmental temperature, humidity, wind force, sunny or cloudy weather, rainy day, and snowy day.

[0035] In one embodiment, the dynamic permission model processes the monitoring data set, analyzes and obtains the user behavior characteristics of each user, the access response characteristics of each system module in the system, and the material usage characteristics of each category of materials. Based on this, preliminary permission allocation parameters are obtained, and then the preliminary permission allocation parameters are adjusted by the environmental fluctuation parameters to obtain the final permission allocation parameters. The final permission allocation parameters are used to update the permission allocation mechanism. For example, if the environmental fluctuation parameter exceeds a certain level, the permission of the warehouse administrator can be urgently released, allowing the warehouse administrator to monitor and urgently protect the materials.

[0036] In one embodiment, based on the updated permission allocation mechanism, the power station materials are managed. The system automatically adjusts the access and storage rules according to the permission allocation parameters. For example, when the humidity exceeds 70%, the access and storage permissions of high-priority materials are automatically locked, and a log is generated to record the reason for the adjustment to ensure the safety of the materials.

[0037] In one embodiment, it should be noted that the triggering conditions of the power station material management method are not specifically limited and can be set according to needs. For example, if the inventory of a certain category of materials is less than a certain quantity, the update process is triggered to generate permission allocation parameters and adjust the corresponding permissions in the permission allocation mechanism to enhance the extraction limit for this category of materials.

[0038] In one embodiment, the dynamic permission model can be used to calculate the permission allocation for the monitoring data set. Combining environmental fluctuation parameters such as an environmental temperature of 25.5 degrees Celsius, a humidity of 60%, and a wind force of level 3, the permission allocation parameters are calculated through a weighted algorithm. Among them, the environmental fluctuation parameters can be represented in a vector manner, and the user behavior characteristics of each user, the access response characteristics of each system module in the system, and the material usage characteristics of each category of materials obtained by calculating the permission allocation for the monitoring data set can also be represented in a vector manner. The vectors of the environmental fluctuation parameters and the matrices of the user behavior characteristics of each user, the access response characteristics of each system module in the system, and the material usage characteristics of each category of materials are unified in length, and then the permission allocation parameters are obtained through calculation. Based on the obtained permission allocation parameters, the permission allocation mechanism is updated.

[0039] In one embodiment, the power station material management system includes a permission configuration file, which may include relevant parameters such as job role configuration, system access configuration, and material category quantity configuration. Then, the permission allocation parameters can be used to update the corresponding parameters in the permission configuration file.

[0040] Step 203: Manage the power station materials based on the updated permission allocation mechanism in the power station material management system.

[0041] In one embodiment, an adjusted permission allocation mechanism is adopted, combined with real-time monitoring data, to dynamically manage the access control of power station materials, continuously update the status of material information, and obtain a real-time updated management result.

[0042] In one embodiment, a large number of permission allocation parameters and their respective management results obtained through real-time updates can also be used to regularly analyze the impact of environmental fluctuations on access control, and then the permission allocation model can be further optimized and adjusted according to the impact situation.

[0043] Through the above process, the system realizes a complete management process from data collection to dynamic permission adjustment and then to material management, ensuring the security and efficiency of power station material management.

[0044] Based on the above Figure 2 method flow, an embodiment of the present application provides a data verification method before permission allocation calculation. The dynamic permission model includes the preset data conditions of each dimension of monitoring data in the monitoring data set and the correlation relationship between the monitoring data of each dimension. The preset data conditions and the correlation relationship are obtained according to the power station material management standards, job role permissions, and historical monitoring data sets implemented in the industry; In step 202, before using the dynamic permission model to perform permission allocation calculation on the monitoring data set and generating permission allocation parameters in combination with environmental fluctuation parameters, it further includes: Step a: Perform the following operations on the monitoring data of each dimension respectively: Based on the dynamic permission model, use the preset data conditions of the current dimension data to perform comparison processing on the current dimension data to obtain a comparison result; Step b: Use the dynamic permission model to analyze the correlation situation between the monitoring data of each dimension, and compare and analyze the correlation relationship and the correlation situation to obtain a correlation result; Step c: Determine that no abnormal results appear in the obtained multiple comparison results and correlation results.

[0045] In one embodiment, the preset data conditions of the monitoring data in the user dimension may include, for example, if the user has relevant professional certificates, then the user can perform operations on a certain type of dangerous goods, and the extraction can only be carried out within a fixed time, and the number of extractions per day can only be 12 times. Therefore, it is necessary to analyze the user's job role, whether there is a certificate, the category of materials extracted, the extraction time, and the number of extractions in the monitoring data of the user dimension. Another example is that for a certain type of photosensitive material that needs to be stored specially, only a set number of extractions are allowed within a set time period, and the operation time for each extraction, that is, the opening time of the storage device, shall not exceed the preset duration. In this way, it is verified whether the monitoring data is abnormal.

[0046] In one embodiment, the preset data conditions for the monitoring data of the material dimension may include, for example, the daily consumption of a certain type of dangerous material, or the minimum storage quantity of a certain type of general material, etc. The preset data conditions are set such that the daily consumption of dangerous materials is not allowed to exceed the set consumption, and the minimum storage quantity of a certain type of general material is not less than the set storage quantity.

[0047] In one embodiment, the preset data conditions for the monitoring data of the system access dimension may include, for example, controlling the access volume within a certain time period within the set access volume, the response speed of access requests occurring shall not be lower than the set response speed, the success rate of access requests shall not be lower than the set success rate, etc.

[0048] In one embodiment, for the monitoring data of each dimension, statistical analysis can be performed on its historical monitoring data set to obtain the possible fluctuation range of each piece of monitoring data, and some allowable release amounts can also be set on this fluctuation range. When the monitoring data of the corresponding item exceeds this fluctuation range, an abnormal result is generated.

[0049] In one embodiment, taking the time parameter data as an example, the system compares the currently collected data with the response time condition (such as the standard response time should be less than 1.2 seconds) in the preset data conditions of the dynamic permission model. It is found that the response time of a certain operation is 1.5 seconds, exceeding the standard value, and the comparison result is marked as "abnormal delay".

[0050] The system can also analyze the behavior information data. For the operation records of users, it is found that the user's permission level is 4, but the operation frequency reaches 15 times per day, exceeding the upper limit of 12 times preset in the model, and the comparison result is marked as "frequency overlimit". At the same time, the access control intensity requirement for a certain type of material in the material information data is "three-level verification required", while the current data record shows that only two-level verification has been performed, and the comparison result is marked as "insufficient verification".

[0051] In one embodiment, the association situation between the monitoring data of each dimension may include: relevant data such as the job role, permission level, operation data, etc. of the user included in the monitoring data of the user dimension, and the material dimension includes relevant data such as the daily consumption of dangerous materials, the inventory quantity of materials, etc. If the user's job role is biased towards document work and the access permission for dangerous materials corresponding to the permission level is small, when the daily consumption of dangerous materials is already relatively close to the set daily consumption, there should be rejected operations in the corresponding operation data of the user dimension.

[0052] In one embodiment, the correlation between monitoring data in each dimension is analyzed based on a dynamic permission model. For example, the correlation degree between time parameter data and behavior information data is calculated through an algorithm. The formula is: Correlation degree = (Response time outlier value 1.5 - Standard value 1.2) / Standard value 1.2 × Behavior frequency overrun value (15 - 12) / 12. The obtained correlation degree is 0.31, which is lower than the threshold 0.5 of the preset correlation relationship in the model, and the correlation result is marked as "weak correlation".

[0053] Comparing the correlation between the control material information data and the behavior information, it is found that the matching degree of insufficient verification and frequency overrun is 0.8, which is higher than the threshold 0.6, and it is marked as "strong correlation".

[0054] Finally, the system summarizes all the comparison results and correlation results to detect whether there are any abnormalities. If abnormal results such as "abnormal delay", "frequency overrun", "insufficient verification", etc. are found, the subsequent logic processing mechanism is triggered to automatically generate an analysis report and record the details of the abnormalities to ensure the data accuracy before the subsequent permission allocation calculation.

[0055] Through the above method process, the system realizes the process from data comparison to correlation analysis and then to anomaly detection, providing a reliable data basis for the power station material management.

[0056] Based on the above Figure 2 data verification method, the embodiment of the present application provides a data verification method before permission allocation calculation, further including: Step (1), determining that abnormal results appear in multiple comparison results and correlation results; Step (2), determining the dimension monitoring data corresponding to the abnormal results as abnormal dimension monitoring data, and obtaining the risk dimension monitoring data associated with the abnormal dimension monitoring data; Step (3), using a dynamic permission model to extract the abnormal correlation features of the abnormal dimension monitoring data and the risk dimension monitoring data; Step (4), determining at least one risk node according to the abnormal correlation features, and generating an emergency control message including an emergency control plan according to the determined at least one risk node; Step (5), sending the emergency control message to the corresponding account and displaying the emergency control plan.

[0057] In one embodiment, the abnormal result may be a certain item of monitoring data in the monitoring data of the corresponding dimension. If this monitoring data appears abnormal, the monitoring data of the corresponding dimension can be determined as abnormal monitoring data to obtain risk dimension monitoring data associated with the abnormal dimension monitoring data (the method of obtaining risk dimension monitoring here can be knowledge graph, association analysis tool, clustering algorithm, etc., and the specific method of obtaining risk dimension monitoring data is not specifically limited here). Here, the abnormal result may be caused by other risk dimension monitoring data associated with the abnormal dimension monitoring data. For example, a certain category of materials in the monitoring data of the material dimension is out of stock for a long time, resulting in a large amount of data with unsuccessful operations in the monitoring data of the user dimension.

[0058] In one embodiment, feature extraction is performed on the abnormal dimension monitoring data and the risk dimension monitoring data through a dynamic permission model to obtain abnormal association features. If the distribution of the abnormal association features conforms to a preset abnormal pattern, they are classified as key abnormal features to determine a set of key abnormal features. According to the set of key abnormal features, at least one risk node is identified, and a corresponding emergency control plan is generated through a preset mapping rule. By associating the risk node with the emergency control plan, the preliminary content of the emergency control message is obtained. The preliminary content of the emergency control message is obtained, and in combination with the account position role and relevant permission rules, the emergency control message is sent to the corresponding communication account.

[0059] In the above process, through data-driven and algorithmic, analytical and other methods of processing, the abnormal detection to risk control is all automatically completed by the system, ensuring high efficiency and accuracy.

[0060] Based on the above data verification method, an embodiment of the present application provides a method for generating permission allocation parameters. In step 201, a dynamic permission model is used to perform permission allocation calculation on the monitoring data set, and permission allocation parameters are generated in combination with environmental fluctuation parameters, including: Step ①: Based on the trust evaluation sub-model in the dynamic permission model, extract the user behavior characteristics of each user in the behavior information data in the monitoring data set, and obtain the permission trust degree of each user according to the user behavior characteristics of each user; Step ②: Based on the scheduling sub-model in the dynamic permission model, extract the material usage characteristics of each category of materials in the material information data in the monitoring data set, and obtain the importance of each category of materials according to the material usage characteristics of each category of materials; Step ③: Based on the response configuration sub-model in the dynamic permission model, extract the access response characteristics of each system module in the time parameter data in the monitoring data set, and obtain the configuration parameters of each system module according to the access response characteristics of each system module; Step ④: Based on the permission allocation sub-model in the dynamic permission model, obtain the permission trust degrees of each user, the importance degrees of each category of materials, the configuration parameters of each system module, and the permission allocation parameters of the environmental fluctuation parameters, and update the permission allocation mechanism using the permission allocation parameters.

[0061] In one embodiment, the trust evaluation sub-model, the scheduling sub-model, and the response configuration sub-model can adopt neural network models. That is to say, the trust evaluation sub-model obtained by the neural network model trained with behavior information data, the scheduling sub-model obtained by the neural network model trained with material information data, and the response configuration sub-model obtained by the neural network model trained with time parameter data. The permission allocation sub-model can adopt a support vector machine model, as Figure 3 shown, which is a schematic diagram of the architecture of a dynamic permission model provided by an embodiment of the present application. It should be noted that there is no limitation on the specific algorithm models adopted by the trust evaluation sub-model, the scheduling sub-model, the response configuration sub-model, and the permission allocation sub-model. For example, the trust evaluation sub-model, the scheduling sub-model, the response configuration sub-model, and the permission allocation sub-model can also adopt the random forest algorithm, etc. The algorithms adopted among the trust evaluation sub-model, the scheduling sub-model, and the response configuration sub-model can be the same or different.

[0062] Based on the above method for generating permission allocation parameters, an embodiment of the present application provides a user behavior feature, which includes at least one of user operation features, position permission role features, and identity authentication features; Among them, the user operation feature is used to represent the operation information of the corresponding user operating the system, including the time, area, accessed material category, accessed material quantity, and operation frequency. The position permission role feature is used to represent the position role of the corresponding user and the permission information of the position role for accessing materials in the system. The identity authentication feature is used to represent the authentication analysis information of the basic identity information of the corresponding user by the system, and the degree of consistency between the historical behavior information data and the current behavior information data of the corresponding user.

[0063] In one embodiment, for user operation characteristics, the system can record the user's behavior data in the operating system. For example, a user logs in to the system from 8:00 to 10:00 a.m. every weekday, the operation area is limited to a regional headquarters, the category of materials stored and accessed is office supplies, the number of items stored and accessed at a time does not exceed 50, and the operation frequency is 3 times a week. The system obtains user operation characteristics based on these data by counting data from the past 30 days. For job authority role characteristics, the system can preset the scope of authority according to the user's job role (such as warehouse manager). For example, only specific categories of materials can be stored and accessed (such as office supplies but not electronic equipment). The authority information is stored in the database, and the job authority role characteristics are obtained based on these data. For identity authentication characteristics, the user's basic identity information (such as work number, fingerprint data), historical behavior data, etc., are used to obtain identity authentication characteristics based on these data.

[0064] Based on the above method for generating permission allocation parameters, the embodiment of the present application provides a material usage feature, which includes at least one of a material usage frequency feature, a material property feature, a material demand feature, a material extraction time feature, a material extraction area feature, and a material priority feature; Among them, the material usage frequency feature is used to characterize the frequency of use of materials of the corresponding category, the material property feature is used to characterize the physical and chemical properties of materials of the corresponding category, the material demand feature is used to characterize the required quantity of materials of the corresponding category in the power plant material management system, the material extraction time feature is used to characterize the distribution of extraction time of materials of the corresponding category, the material extraction area feature is used to characterize the distribution of extraction areas of materials of the corresponding category, and the material priority feature is used to characterize the priority of materials of the corresponding category provided to different job roles.

[0065] In one embodiment, for the material usage frequency characteristics: for example, by counting the usage records of a certain category of materials in the past 12 months, the average monthly usage is calculated to be 15 times, combined with the historical peak data of 20 times and the trough data of 10 times, and the weighted average algorithm (weights are 0.5, 0.3, and 0.2 respectively) is used to obtain a comprehensive frequency score of 14.5 times / month. For the material characteristics, the system automatically reads the physical and chemical properties recorded in the material database. For example, the temperature resistance range of a certain material is -20°C to 80°C. Combined with the power station ambient temperature data (average 25°C), the matching algorithm is used to determine that its applicability score is 90%, ensuring the availability of materials in specific environments. For material demand characteristics, the system calculates the scheduled demand for a certain category of materials in the next three months as 500 units based on historical demand data and forecasting models (such as time series analysis), and compares it with the current inventory of 400 units to generate a replenishment warning. The time characteristics of material extraction are analyzed by analyzing the distribution of extraction time in the past 6 months, and the peak period is identified as 8:00-10:00 am every day, accounting for 60%. The system optimizes the extraction time scheduling algorithm based on this to reduce waiting time. The regional characteristics of material extraction are analyzed based on the geographic information system. Statistics show that the concentration of a certain material extraction area is 80% in area A. The distribution path can be optimized through the clustering algorithm (K-means) to shorten the distribution time by 20%. The priority characteristics of materials can be calculated through the role demand matrix. Assuming that the priority of a certain material for maintenance positions is 0.8 and for management positions is 0.3, the system automatically adjusts the allocation strategy to ensure that high-priority positions are supplied when materials are in short supply.

[0066] Based on the above method for generating permission allocation parameters, an embodiment of the present application provides an access response feature, wherein the access response feature includes at least one of a request response time feature, a permission verification time feature, and an identity authentication time feature; Among them, the request response time feature is used to characterize the processing efficiency of the power plant material management system and its internal subsystems and corresponding modules for access requests; the authority verification time feature is used to characterize the efficiency of the power plant physical management system in verifying the user authority information in the access request; the identity authentication time feature is used to characterize the efficiency of the power plant physical management system in authenticating the user identity information in the access request.

[0067] In one embodiment, for the request response time feature, assume that when a certain module of a power station material management system processes an access request, the system records that the total time taken from receiving the request to returning the response is 2.5 seconds. Among them, the data query time of the internal subsystem is 1.2 seconds, the logic processing time is 0.8 seconds, and the response generation time is 0.5 seconds. By statistically analyzing these data, the request response time feature is obtained. For the permission verification time feature, the system records that the permission verification time for a certain access request is 0.3 seconds, and the average historical time is 0.2 seconds. By statistically analyzing these data, the permission verification time feature is obtained. For the identity authentication time feature, assume that the user identity authentication time for a certain time is 0.4 seconds. By analyzing the authentication process, the system finds that the encryption and decryption algorithm time accounts for 60%. By statistically analyzing these data, the identity authentication time feature is obtained.

[0068] Based on the above method for generating permission allocation parameters, an embodiment of the present application provides a method for testing permission allocation parameters. Before obtaining the permission allocation parameters of each user's respective permission trust degree, each category of materials' respective importance, the configuration parameters of each system module, and the environmental fluctuation parameters based on the permission allocation sub-model in the dynamic permission model and using the permission allocation parameters to update the permission allocation mechanism, it includes: Use the permission allocation parameters to update the permission allocation mechanism in the test system, simulate the access process of the updated test system for the corresponding job roles, and obtain the access results of the updated test system during the access process; confirm that there are no abnormalities in the access results.

[0069] In one embodiment, through the permission allocation sub-model, obtain the permission trust degree of each user, the importance of each material, the configuration parameter values of the system module, and the environmental fluctuation values, and generate initial permission allocation parameters. According to the generated permission allocation parameters, update the permission allocation mechanism in the test system, construct the updated test system environment, and obtain the basic framework for simulated access. Use the updated test system environment to execute the simulated access process for job role categories, record various data during the access process, and obtain a complete set of access results. If there are no abnormal data in the access result set, the permission allocation mechanism in the power station material management system can be updated using the permission allocation parameters.

[0070] If there is abnormal data in the access result set, classify and process the abnormal data through the abnormal confirmation method to determine the specific category and influence range of the abnormal data. If the influence range of the abnormal data exceeds the preset threshold, adjust the relevant parameters in the permission allocation mechanism of the test system to obtain the adjusted test system environment. Through the adjusted test system environment, execute the simulated access process again to obtain a new access result set, and determine whether there is still abnormal data. If there is no abnormal data in the new access result set, apply the adjusted permission allocation parameters to the power station material management system to complete the final update of the permission allocation mechanism.

[0071] Based on the same concept, an embodiment of the present application provides a power station material management device, which is applicable to a power station material management system. Figure 4 As shown in the schematic diagram of a power station material management device provided by an embodiment of the present application, Figure 4 it includes: A data acquisition module 401, configured to acquire multi-dimensional real-time monitoring data and generate a monitoring data set; the multi-dimensional real-time monitoring data includes time parameter data, behavior information data, and material information data acquired in real time. The time parameter data is used to represent the time response parameters generated during the operation of the power station material management system. The behavior information data is used to represent the permission level of the user's position role and the usage of the power station material management system. The material information data is used to represent the access control intensity of different categories of materials in the power station material management system. The access control intensity represents the material priority information and material access conditions of the corresponding category of materials. An update module 402, configured to perform permission allocation calculation on the monitoring data set using a dynamic permission model, generate permission allocation parameters in combination with environmental fluctuation parameters, and update the permission allocation mechanism in the power station material management system using the permission allocation parameters. The environmental fluctuation parameters include at least one of environmental temperature, humidity, wind force, weather sunny or cloudy, rainy day, and snowy day. A management module 403, configured to manage power station materials based on the updated permission allocation mechanism in the power station material management system.

[0072] Optionally, the dynamic permission model includes preset data conditions for each dimension of monitoring data in the monitoring data set, and the correlation relationship between each dimension of monitoring data. The preset data conditions and the correlation relationship are obtained according to the power station material management standards, position role permissions, and historical monitoring data sets implemented in the industry. The update module 402 is further configured to Perform the following operations for each dimension of monitoring data respectively: Based on the dynamic permission model, perform comparison processing on the current dimension data using the preset data conditions of the current dimension data to obtain a comparison result. Analyze the association between the monitoring data of each dimension using the dynamic permission model, and compare and analyze the association relationship and the association situation to obtain an association result; Determine that no abnormal results occur among the multiple comparison results and the association result obtained.

[0073] Optionally, the update module 402 is further configured to, Determine that abnormal results occur among the multiple comparison results and the association result; Determine that the dimension monitoring data corresponding to the abnormal result is abnormal dimension monitoring data, and obtain the risk dimension monitoring data associated with the abnormal dimension monitoring data; Extract the abnormal association features of the abnormal dimension monitoring data and the risk dimension monitoring data using the dynamic permission model; Determine at least one risk node according to the abnormal association features, and generate an emergency control message including an emergency control plan according to the determined at least one risk node; Send the emergency control message to the corresponding account and display the emergency control plan.

[0074] Optionally, the update module 402 is specifically configured to, Based on the trust evaluation sub-model in the dynamic permission model, extract the user behavior characteristics of each user in the behavior information data in the monitoring data set, and obtain the respective permission trust degrees of each user according to the user behavior characteristics of each user; Based on the scheduling sub-model in the dynamic permission model, extract the material usage characteristics of each category of materials in the material information data in the monitoring data set, and obtain the respective importance degrees of each category of materials according to the material usage characteristics of each category of materials; Based on the response configuration sub-model in the dynamic permission model, extract the access response characteristics of each system module in the time parameter data in the monitoring data set, and obtain the configuration parameters of each system module according to the access response characteristics of each system module; Based on the permission allocation sub-model in the dynamic permission model, obtain the permission allocation parameters of the respective permission trust degrees of each user, the respective importance degrees of each category of materials, the configuration parameters of each system module, and the environmental fluctuation parameters, and update the permission allocation mechanism using the permission allocation parameters.

[0075] Optionally, the user behavior characteristics include at least one of user operation characteristics, position permission role characteristics, and identity authentication characteristics; Among them, the user operation characteristics are used to characterize the operation information of the time, area, type of materials accessed, quantity of materials accessed, and operation frequency of the corresponding user's operation system; the job authority role characteristics are used to characterize the job role of the corresponding user and the job role's authority information for accessing materials in the system; the identity authentication characteristics are used to characterize the system's authentication analysis information of the corresponding user's basic identity information, as well as the degree of consistency between the corresponding user's historical behavior information data and current behavior information data.

[0076] Optionally, the material usage characteristics include at least one of material usage frequency characteristics, material property characteristics, material demand characteristics, material extraction time characteristics, material extraction area characteristics, and material priority characteristics; Among them, the material usage frequency feature is used to characterize the frequency of use of materials of the corresponding category, the material property feature is used to characterize the physical and chemical properties of materials of the corresponding category, the material demand feature is used to characterize the required quantity of materials of the corresponding category reserved in the power station material management system, the material extraction time feature is used to characterize the distribution of extraction time of materials of the corresponding category, the material extraction area feature is used to characterize the distribution of material extraction areas of the corresponding category, and the material priority feature is used to characterize the priority of materials of the corresponding category provided to different job roles.

[0077] Optionally, the access response feature includes at least one of a request response time feature, an authority verification time feature, and an identity authentication time feature; Among them, the request response time characteristic is used to characterize the processing efficiency of the power plant material management system and its internal subsystems and corresponding modules for access requests; the authority verification time characteristic is used to characterize the verification efficiency of the power plant physical management system for user authority information in access requests; the identity authentication time characteristic is used to characterize the authentication efficiency of the power plant physical management system for user identity information in access requests.

[0078] Optionally, the updating module 402 is further configured to: The permission allocation parameter is used to update the permission allocation mechanism in the test system, and a corresponding position role is simulated to access the updated test system, and an access result of the updated test system is obtained during the access process; Confirm that there is no abnormality in the access result.

[0079] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0080] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0081] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0082] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0083] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A power station material management method based on intelligent warehousing, characterized in that, Applicable to a power station material management system, the method includes: Obtain multi-dimensional real-time monitoring data and generate a monitoring data set; the multi-dimensional real-time monitoring data includes time parameter data, behavior information data, and material information data obtained in real time. The time parameter data is used to represent the time response parameters generated during the operation of the power station material management system. The behavior information data is used to represent the permission levels of the user's job roles and the usage of the power station material management system. The material information data is used to represent the access control intensity of different categories of materials in the power station material management system. The access control intensity represents the material priority information and material access conditions of the corresponding category of materials; Use a dynamic permission model to perform permission allocation calculations on the monitoring data set, generate permission allocation parameters in combination with environmental fluctuation parameters, and update the permission allocation mechanism in the power station material management system with the permission allocation parameters. The environmental fluctuation parameters include at least one of environmental temperature, humidity, wind force, and weather conditions; Manage power station materials based on the updated permission allocation mechanism in the power station material management system.

2. The method according to claim 1, wherein The dynamic permission model includes the preset data conditions of each dimension of monitoring data in the monitoring data set and the correlation relationship between the dimensions of monitoring data. The preset data conditions and the correlation relationship are obtained according to the power station material management standards, job role permissions, and historical monitoring data sets implemented in the industry; Before using the dynamic permission model to perform permission allocation calculations on the monitoring data set and generate permission allocation parameters in combination with environmental fluctuation parameters, it also includes: Perform the following operations on each dimension of monitoring data respectively: Based on the dynamic permission model, use the preset data conditions of the current dimension data to perform comparison processing on the current dimension data to obtain a comparison result; Use the dynamic permission model to analyze the correlation between the dimensions of monitoring data, and compare and analyze the correlation relationship and the correlation situation to obtain a correlation result; Determine that no abnormal results appear in the obtained multiple comparison results and the correlation result.

3. The method according to claim 2, wherein It also includes: Determine that abnormal results appear in the multiple comparison results and the correlation result; Determine the dimension monitoring data corresponding to the abnormal result as abnormal dimension monitoring data, and obtain the risk dimension monitoring data associated with the abnormal dimension monitoring data; Use the dynamic permission model to extract the abnormal correlation features of the abnormal dimension monitoring data and the risk dimension monitoring data; Determine at least one risk node according to the abnormal correlation features, and generate an emergency control message including an emergency control plan according to the determined at least one risk node; Send the emergency control message to the corresponding account and display the emergency control plan.

4. The method according to claim 1, wherein The use of the dynamic permission model to perform permission allocation calculations on the monitoring data set and generate permission allocation parameters in combination with environmental fluctuation parameters includes: Based on the trust evaluation sub-model in the dynamic permission model, extract the user behavior characteristics of each user in the behavior information data in the monitoring data set, and obtain the permission trust degree of each user according to the user behavior characteristics of each user; Based on the scheduling sub-model in the dynamic permission model, extract the material usage characteristics of each category of materials in the material information data of the monitoring dataset, and obtain the importance of each category of materials according to the material usage characteristics of each category of materials; Based on the response configuration sub-model in the dynamic permission model, extract the access response characteristics of each system module in the time parameter data of the monitoring dataset, and obtain the configuration parameters of each system module according to the access response characteristics of each system module; Based on the permission allocation sub-model in the dynamic permission model, use the respective permission trust degrees of each user, the importance of each category of materials, the configuration parameters of each system module, and the environmental fluctuation parameters to obtain permission allocation parameters, and use the permission allocation parameters to update the permission allocation mechanism.

5. The method according to claim 4, characterized in that, The user behavior characteristics include at least one of user operation characteristics, position permission role characteristics, and identity authentication characteristics; Among them, the user operation characteristics are used to represent the operation information of the corresponding user operating the system, including the time, area, stored and retrieved material categories, stored and retrieved material quantities, and operation frequencies. The position permission role characteristics are used to represent the position role of the corresponding user and the permission information of the position role for storing and retrieving materials in the system. The identity authentication characteristics are used to represent the authentication analysis information of the basic identity information of the corresponding user by the system, and the degree of consistency between the historical behavior information data and the current behavior information data of the corresponding user.

6. The method according to claim 4, wherein The material usage characteristics include at least one of material usage frequency characteristics, material property characteristics, material demand characteristics, material extraction time characteristics, material extraction area characteristics, and material priority characteristics; Among them, the material usage frequency characteristics are used to represent the usage frequency of the corresponding category of materials. The material property characteristics are used to represent the physical and chemical characteristics of the corresponding category of materials. The material demand characteristics are used to represent the predetermined required quantity of the corresponding category of materials in the power station material management system. The material extraction time characteristics are used to represent the distribution of the extraction time of the corresponding category of materials. The material extraction area characteristics are used to represent the distribution of the extraction area of the corresponding category of materials. The material priority characteristics are used to represent the priority provided by the corresponding category of materials to different position roles.

7. The method according to claim 4, characterized in that, The access response characteristics include at least one of request response time characteristics, permission verification time characteristics, and identity authentication time characteristics; Among them, the request response time characteristics are used to represent the processing efficiency of the power station material management system and its internal subsystems and corresponding modules for access requests. The permission verification time characteristics are used to represent the verification efficiency of the power station material management system for the user permission information in the access request. The identity authentication time characteristics are used to represent the authentication efficiency of the power station material management system for the user identity information in the access request.

8. The method according to claim 4, wherein Before obtaining the permission allocation parameters of each user's respective permission trust degree, each category of materials' respective importance degree, the configuration parameters of each system module, and the environmental fluctuation parameters based on the permission allocation sub-model in the dynamic permission model and using the permission allocation parameters to update the permission allocation mechanism, it includes: Using the permission allocation parameters to update the permission allocation mechanism in the test system, simulating the access process of the corresponding job roles to the updated test system, and obtaining the access results of the updated test system during the access process; Confirming that the access results are normal.

9. A power station material management device based on intelligent warehousing, characterized in that, Applicable to the power station material management system, the device includes: A data acquisition module, configured to acquire multi-dimensional real-time monitoring data and generate a monitoring data set; the multi-dimensional real-time monitoring data includes time parameter data, behavior information data, and material information data acquired in real time. The time parameter data is used to represent the time response parameters generated during the operation of the power station material management system, the behavior information data is used to represent the permission levels of the user's job roles and the usage of the power station material management system, and the material information data is used to represent the access control intensity of different categories of materials in the power station material management system. The access control intensity represents the material priority information and material access conditions of the corresponding category of materials; An update module, configured to perform permission allocation calculations on the monitoring data set using the dynamic permission model, generate permission allocation parameters in combination with environmental fluctuation parameters, and use the permission allocation parameters to update the permission allocation mechanism in the power station material management system. The environmental fluctuation parameters include at least one of environmental temperature, humidity, wind force, and weather conditions; A management module, configured to manage power station materials based on the updated permission allocation mechanism in the power station material management system.

10. A computer device, characterized in that, It includes: A memory, configured to store a computer program; A processor, configured to call the computer program stored in the memory and execute the method according to any one of claims 1 to 8 according to the obtained program.

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