Information equipment full life cycle management method and device for power grid

Abstract

The invention discloses an information equipment full life cycle management method and device for a power grid, and relates to the technical field of equipment management, and the method comprises the steps: obtaining a non-increasing coefficient of first equipment through obtaining a current storage coefficient, a processing coefficient and a network load coefficient of the first equipment; comparing with a preset non-increasing coefficient gradient threshold to divide the first equipment into a non-increasing power grid operation information state, a temporarily-increasing power grid operation information state and a scrap state; for the first equipment which can temporarily increase the power grid data type state, obtaining the use time of the first equipment and the environment data of the first equipment, and combining the non-increasing coefficient to obtain a prediction state coefficient; the information equipment is managed in combination with a preset prediction state coefficient gradient threshold value; the possibility of confusion of data corresponding to power grid operation information originally stored by information equipment can be reduced, and accurate judgment of stable operation of a power grid is ensured; and the influence on the stability of a power grid system is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment management, and in particular to a method and device for full life cycle management of information equipment for power grids. Background Art

[0002] With the rapid development of the electric power industry, the scale and types of information equipment used in power grids are constantly increasing. These information equipment play a fundamental role in information operation and maintenance, covering a variety of asset types from data center servers to network equipment to terminal equipment, and the operation and maintenance scenarios are also extremely complex and diverse. Efficient and accurate management of information equipment has become a key problem that needs to be solved in information operation and maintenance. Traditional manual management alone can no longer meet the increasingly large and diverse needs of information equipment. Therefore, by setting up intelligent information equipment for power grids to store power grid operation data, and by using systematic and automated means to optimize the information equipment life cycle management process, improve management efficiency, reduce operation and maintenance costs, and ensure the safe and stable operation of information equipment;

[0003] Each existing information device used for the power grid will store one or some power grid operation information according to the settings of the staff, and determine whether the corresponding power grid operation information has any faults. In order to make full use of the existing information device resources for the power grid, some staff set up information devices that have already stored specific power grid operation information to add new power grid operation information to maximize the utilization of resources. However, the above method has the following problems:

[0004] 1. Not all information devices can add new grid operation information. For some information devices, the newly added grid operation information may exceed the original storage and processing capacity, resulting in resource bottlenecks for the devices, and even causing problems such as device overload, data loss or processing delays. More seriously, it may cause confusion in the data corresponding to the grid operation information originally stored in the information device;

[0005] 2. For some information equipment, even if these information equipment can add new grid operation information under the current situation, as time goes by, the information equipment cannot stably and accurately store the grid operation information. As a result, the information equipment will frequently fail, resulting in a reduction in service life, a waste of resources, and may also make maintenance more complicated, ultimately affecting the judgment of the stable operation of the grid;

[0006] 3. For the full life cycle of information equipment, generally speaking, when an information device fails and cannot be repaired, the corresponding information equipment is considered scrapped and the life of the information equipment ends. However, some information equipment may have hidden faults before this, and the hidden faults are no longer repairable. However, if the equipment is not scrapped until an obvious fault occurs, it may cause the equipment to frequently experience performance degradation, increased failures and other problems throughout its life cycle, and may even affect the stability of the power grid system before the equipment is completely scrapped. Summary of the invention

[0007] The purpose of the present invention is to solve the above-mentioned problems and to provide a method and device for the full life cycle management of information equipment for power grids.

[0008] In a first aspect of the present invention, a method for managing the entire life cycle of information equipment for a power grid is first proposed, the method comprising:

[0009] Each information device used in the power grid is recorded as a first device, and the current storage coefficient, processing coefficient and network load coefficient of the first device are obtained to obtain a non-incremental coefficient of the first device;

[0010] Comparing the non-increaseable coefficient of the first device with a preset non-increaseable coefficient gradient threshold, and classifying the first device into a non-increaseable grid operation information state, a temporarily increaseable grid operation information state, and a scrapped state according to the comparison result;

[0011] For a first device that can temporarily increase the state of a power grid data type, obtain the usage time and the environment data of the first device and obtain a predicted state coefficient in combination with a non-incremental coefficient;

[0012] The predicted state coefficient is compared with the preset predicted state coefficient gradient threshold, and the information device is managed according to the comparison result.

[0013] Optionally, the storage coefficient includes:

[0014] Obtaining a total storage capacity T and a used storage capacity of the first device, and subtracting the used storage capacity from the total storage capacity to obtain a remaining storage capacity A;

[0015] Obtaining a storage capacity corresponding to the fragmented space of the first device, and dividing the storage capacity corresponding to the fragmented space by the total storage capacity T to obtain a fragmentation coefficient F;

[0016] Obtain the storage capacity R occupied by the newly added power grid operation information and calculate the storage coefficient. The calculation formula is: Where SC is the storage coefficient;

[0017] The processing coefficients include:

[0018] Obtaining the maximum processing load of the first device and the current processing load of the device, and obtaining the processing load required by the newly added power grid operation information, and adding the current processing load of the device to the processing load required by the newly added power grid operation information to obtain a total processing load;

[0019] Divide the total processing load by the maximum processing load to obtain the processing factor.

[0020] Optionally, the network load factor includes:

[0021] Obtain the network bandwidth load of the current device at multiple times continuously to obtain the load sequence of the current device;

[0022] Calculate the standard deviation of the load sequence and use the standard deviation as the network load fluctuation value;

[0023] Calculate the mean of the load sequence as the current network bandwidth load, obtain the average network bandwidth load required for the newly added power grid operation information, and add the current network bandwidth load to the average network bandwidth load required for the newly added power grid operation information to obtain the total network bandwidth load;

[0024] The network load factor is obtained according to the total network bandwidth load and the network load fluctuation value.

[0025] Optionally, acquiring the current storage coefficient, processing coefficient, and network load coefficient of the first device to obtain the non-incremental coefficient of the first device includes:

[0026]

[0027] Wherein, HJU is the non-incremental coefficient of the first device, SC, CP, sd are respectively the current storage coefficient, processing coefficient and network load coefficient of the first device, b1, b2, b3 are respectively the preset proportional coefficients of SC, CP, sd, and b1, b2, b3 are all greater than 0.

[0028] Optionally, dividing the first device into a state where the grid operation information cannot be added, a state where the grid operation information can be temporarily added, and a scrapped state according to the comparison result includes:

[0029] Comparing the non-increaseable coefficient of the first device with a preset non-increaseable coefficient gradient threshold, if the non-increaseable coefficient is less than the preset non-increaseable coefficient first threshold, recording the first device as a temporarily increaseable power grid operation information state;

[0030] If the non-increaseable coefficient is not less than the preset non-increaseable coefficient first threshold value but less than the preset non-increaseable coefficient second threshold value, the first device is recorded as a non-increaseable power grid operation information state;

[0031] If the non-increaseable coefficient is not less than a preset non-increaseable coefficient second threshold value, the first device is recorded as scrapped and a replacement warning signal is issued.

[0032] Optionally, obtaining the usage time of the first device and the environment data thereof and combining the non-incremental coefficient to obtain the predicted state coefficient includes:

[0033] Obtain the used time and average usable time of the first device, and divide the used time by the average usable time to obtain a service life coefficient;

[0034] Obtain the number of times the first equipment is maintained and the duration of each maintenance, and calculate the average maintenance time;

[0035] Obtain an average maintenance time interval of the first device, obtain the time interval from the current time to the last maintenance, and divide the time interval from the current time to the last maintenance by the average maintenance time interval to obtain a maintenance interval coefficient;

[0036] A maintenance factor is obtained according to the service life factor, the mean maintenance time and the maintenance interval factor, and a predicted state factor is obtained according to the maintenance factor, the environmental data of the first device and the corresponding non-increase factor.

[0037] Optionally, obtaining the predicted state coefficient according to the maintenance coefficient, the environmental data of the first device and the corresponding non-incremental coefficient includes:

[0038] The real-time temperature T(t) and real-time humidity S(t) of the environment in which the first device is located from the time the first device is put into use to the current time period are obtained, and the environmental anomaly coefficient is calculated. The calculation expression is as follows:

[0039]

[0040] Wherein, [t1, t2] represents the time period when the real-time temperature is outside the preset optimal temperature range, and [t3, t4] represents the time period when the real-time humidity is outside the preset optimal humidity range; α and β are preset weight coefficients of temperature and humidity, respectively, and both α and β are greater than 0;

[0041] The predicted state coefficient is obtained according to the maintenance coefficient, the environmental abnormality coefficient and the corresponding non-increase coefficient.

[0042] Optionally, the predicted state coefficient obtained according to the maintenance coefficient, the environmental abnormality coefficient and the corresponding non-increase coefficient includes:

[0043]

[0044] Wherein, Dax is the predicted state coefficient of the first equipment, DF, BH, HJU are the maintenance coefficient, environmental abnormality coefficient and non-increase coefficient of the first equipment respectively, f1, f2, f3 are the preset proportional coefficients of DF, BH, HJU respectively, and f1, f2, f3 are all greater than 0.

[0045] Optionally, comparing the predicted state coefficient with a preset predicted state coefficient gradient threshold, and managing the information device according to the comparison result includes:

[0046] If the preset prediction state coefficient of the first device is less than the preset prediction state coefficient first threshold, the corresponding first device may add the newly added power grid operation information;

[0047] If the preset prediction state coefficient of the first device is not less than the preset prediction state coefficient first threshold value but less than the preset prediction state coefficient second threshold value, the corresponding first device cannot add the newly added power grid operation information;

[0048] If the preset prediction state coefficient of the first device is not less than the preset prediction state coefficient second threshold value, the corresponding first device cannot add the newly added power grid operation information, and at the same time sends a maintenance warning signal.

[0049] In a second aspect of the present invention, a device for managing the entire life cycle of information equipment for a power grid is provided, the device comprising:

[0050] Non-incremental module: each information device used in the power grid is recorded as a first device, and the current storage coefficient, processing coefficient and network load coefficient of the first device are obtained to obtain the non-incremental coefficient of the first device;

[0051] A state classification module: comparing the non-increase coefficient of the first device with a preset non-increase coefficient gradient threshold, and classifying the first device into a non-increase grid operation information state, a temporary increase grid operation information state, and a scrapped state according to the comparison result;

[0052] Prediction state module: for a first device that can temporarily increase the state of a power grid data type, obtain the usage time and the environment data of the first device and combine them with the non-incremental coefficient to obtain a prediction state coefficient;

[0053] Management module: compares the predicted state coefficient with the preset predicted state coefficient gradient threshold, and manages the information equipment according to the comparison result.

[0054] Beneficial effects of the present invention:

[0055] The present invention proposes a full life cycle management method and device for information equipment for power grids, which can specifically determine which information equipment can add new power grid operation information, ensure that the equipment will not encounter resource bottlenecks, equipment overload, data loss or processing delays, and reduce the possibility of confusion in the data corresponding to the power grid operation information originally stored in the information equipment; in addition, it can determine whether the information equipment can add new power grid operation information even under the current circumstances, and whether the information equipment can store the power grid operation information stably and accurately over time, ensure that the information equipment will not frequently fail, ensure its service life, reduce waste of resources, and ensure accurate judgment of the stable operation of the power grid; in addition, it can determine in advance whether the information equipment is scrapped, reduce the frequent performance degradation and increased failures of the equipment throughout its life cycle, and reduce the impact on the stability of the power grid system. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The present invention will be further described below in conjunction with the accompanying drawings.

[0057] Figure 1 A flowchart of a method for managing the entire life cycle of information equipment used in a power grid;

[0058] Figure 2 The present invention is a framework diagram of a device for managing the entire life cycle of information equipment used in power grids. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0061] The embodiment of the present invention provides a method for managing the entire life cycle of information equipment for a power grid. Figure 1 , Figure 1 A flowchart of a method for managing the entire life cycle of information equipment for a power grid provided by an embodiment of the present invention. The method comprises the following steps:

[0062] Each information device used in the power grid is recorded as a first device, and the current storage coefficient, processing coefficient and network load coefficient of the first device are obtained to obtain a non-incremental coefficient of the first device;

[0063] Comparing the non-increaseable coefficient of the first device with a preset non-increaseable coefficient gradient threshold, and classifying the first device into a non-increaseable grid operation information state, a temporarily increaseable grid operation information state, and a scrapped state according to the comparison result;

[0064] For a first device that can temporarily increase the state of a power grid data type, obtain the usage time and the environment data of the first device and obtain a predicted state coefficient in combination with a non-incremental coefficient;

[0065] The predicted state coefficient is compared with the preset predicted state coefficient gradient threshold, and the information device is managed according to the comparison result.

[0066] Based on a full life cycle management method for information equipment for a power grid provided by an embodiment of the present invention, through the above-mentioned method, it is possible to specifically determine which information equipment can add new power grid operation information, ensure that the equipment will not encounter resource bottlenecks, equipment overload, data loss or processing delays, and other problems, thereby reducing the possibility of confusion in the data corresponding to the power grid operation information originally stored in the information equipment; in addition, it is possible to determine whether the information equipment can add new power grid operation information even under the current circumstances, and whether the information equipment can store the power grid operation information stably and accurately over time, ensuring that the information equipment will not frequently fail, ensuring its service life, reducing the waste of resources, and ensuring that the stable operation of the power grid is accurately judged; in addition, it is possible to determine in advance whether the information equipment is scrapped, reducing the frequent performance degradation and increased failures of the equipment throughout its life cycle, and reducing the impact on the stability of the power grid system.

[0067] In one embodiment, storing the coefficients includes:

[0068] Obtaining a total storage capacity T and a used storage capacity of the first device, and subtracting the used storage capacity from the total storage capacity to obtain a remaining storage capacity A;

[0069] Obtaining a storage capacity corresponding to the fragmented space of the first device, and dividing the storage capacity corresponding to the fragmented space by the total storage capacity T to obtain a fragmentation coefficient F;

[0070] Obtain the storage capacity R occupied by the newly added power grid operation information and calculate the storage coefficient. The calculation formula is: Where SC is the storage coefficient.

[0071] It should be noted that the total storage capacity and used storage capacity of the device can usually be obtained through hardware monitoring tools, the storage management module of the operating system, or through resource reports in the device management system; the detection of fragmented space requires scanning and analysis through storage management software or system tools to identify and evaluate the degree of fragmentation of the storage space; the demand for new data is usually estimated by the power grid operator or the data management system, and is set according to the specific storage requirements of the power grid data type to be added; these data are obtained in real time through system interfaces, sensors or database records, and are used to dynamically update the storage status of the device; fragmented space refers to the space in the device storage medium that cannot be efficiently used, which may be caused by frequent read and write operations and data deletion; due to fragmentation, the device may not be able to effectively write new data, which will further affect whether the device can easily add new power grid data;

[0072] It should be noted that the larger the current storage coefficient of the first device is, the less likely the first device is to add data information corresponding to the new grid operation type, and the greater the possibility that the corresponding first device has been scrapped. The reason is that the larger the current storage coefficient of the first device is, the more likely the storage resource of the device is close to or has reached its maximum capacity, which means that the load of the device is close to the limit. With the further occupation of the storage space, the processing capacity and response speed of the device may drop significantly, and the efficiency of storage management and data processing capacity will also decrease accordingly. At this time, if the device adds new grid operation information, it may exceed its design capacity, resulting in storage overflow, data loss or processing delay, thereby affecting the normal operation of the device and the integrity of the data; and the current storage coefficient of the device is too large, the device may no longer meet the system's requirements for stability and availability, and frequently fails, and even cannot continue to support new functions or data requirements, so the possibility of scrapping the device also increases. Being in an overloaded state for a long time not only increases maintenance costs, but also may cause more serious equipment failures, and ultimately make the equipment unable to continue to be used, and must be scrapped in advance to ensure the normal operation of the grid.

[0073] In one implementation, the benefits of analyzing the current storage coefficient of the first device for determining whether the first device can add new grid operation information and whether the current information device is already in a scrapped state are as follows: First, the storage coefficient reflects the utilization of the current storage resources of the device. By evaluating this indicator, it is possible to accurately determine whether the device can carry new grid operation data, thereby avoiding device failure or data loss caused by overloading. Secondly, the storage coefficient can effectively reveal whether the device is in a resource bottleneck period. If the storage coefficient is too large, it means that the device may have approached or reached the maximum storage capacity, which not only increases the complexity of data management, but also increases the risk of equipment scrapping. By timely understanding the storage status of the device, the enterprise can adopt a reasonable equipment adjustment or replacement strategy to avoid grid operation interruptions caused by equipment failures and ensure the stability and security of the grid. At the same time, this analysis helps to discover potential equipment problems in advance, optimize the life cycle management of equipment, improve the efficiency of resource use, and reduce operation and maintenance costs.

[0074] In one embodiment, the processing coefficients include:

[0075] Get the maximum processing load P of the first device max and the current processing load P of the device c , and obtain the processing load P required for the newly added power grid operation information new , and the current processing load P of the device c Plus the processing load P required for the newly added power grid operation information new , and the total processing load P is obtained t ;

[0076] The total processing load P t Divide by the maximum processing load P max , get the processing coefficient CP;

[0077] It should be noted that processing load refers to the data processing tasks or computing volume carried by the device in a unit of time, which is usually related to the computing, storage, network transmission and other capabilities of the device. For example, for an information device, the processing load may include the computing burden of the CPU, the data access load of the memory, or the data transmission load of the network. Common units are MB / s (megabytes per second) or GB / s (gigabytes per second), depending on the performance of the device and the amount of data; in addition, the current processing load is obtained through the real-time monitoring system of the device. The system tracks and records the amount of data or tasks currently being processed by the device, which is usually calculated based on the CPU, memory, storage and network usage of the device. The maximum processing load is based on the specifications and design parameters of the device, usually provided by the device manufacturer, indicating the maximum load capacity that the device can withstand under normal operating conditions, which can be confirmed through the technical documentation or performance testing of the device. The new data load is the processing demand estimated based on the new power grid operation information, which usually depends on factors such as the type, scale and update frequency of the new data, and may be estimated through system analysis or based on historical data. These data can be collected and updated in real time or regularly through the monitoring interface of the device or the operation and maintenance management system.

[0078] It should be noted that the larger the current processing coefficient of the first device, the less able the first device is to add data information corresponding to the new grid operation type, and the greater the possibility that the corresponding first device has been scrapped. The reason is that the larger the current processing load of the first device, the greater the device is approaching the limit of its maximum processing capacity. Further adding new grid operation information may cause the device to be overloaded and unable to efficiently process all data, and even cause performance degradation or failure. As the load increases, the device may face resource constraints, slow processing speeds, and even delays and errors, resulting in system instability. In addition, being in a high-load state for a long time will accelerate the aging of the device hardware, reduce its service life, and increase the failure rate. Therefore, when the processing capacity load of the device is large, it means that it is more likely to be scrapped, because the device cannot continue to bear too much load, and as the load increases, the risk of failure will gradually increase.

[0079] In one implementation, the benefit of analyzing the current processing coefficient of the first device for determining whether new grid operation information can be added to the first device and whether the current information device is already in a scrapped state is that by calculating the processing coefficient, the processing capacity of the device under the existing load can be accurately evaluated, thereby avoiding performance degradation or failure due to overloading. If the processing coefficient is close to or exceeds the maximum processing capacity of the device, it indicates that the device is at risk of overload and cannot safely carry new grid data. Adding new data may aggravate the occurrence of equipment failures and affect the stability of the grid. At the same time, a higher processing coefficient also means an increased risk of equipment aging and damage, which helps to identify in advance whether the equipment is about to enter the scrap stage, so as to rationally plan the replacement or upgrade of the equipment and avoid larger-scale grid problems caused by equipment failures.

[0080] In one embodiment, the network load factor includes:

[0081] Obtain the network bandwidth load of the current device at multiple times continuously to obtain the load sequence of the current device;

[0082] Calculate the standard deviation of the load sequence and use the standard deviation as the network load fluctuation value;

[0083] Calculate the mean of the load sequence as the current network bandwidth load, obtain the average network bandwidth load required for the newly added power grid operation information, and add the current network bandwidth load to the average network bandwidth load required for the newly added power grid operation information to obtain the total network bandwidth load;

[0084] The network load coefficient is obtained according to the total network bandwidth load and the network load fluctuation value. The calculation formula is: sd=a1×uy+a2×ug, where sd is the network load coefficient, uy and ug are the total network bandwidth load and network load fluctuation values ​​respectively, a1 and a2 are the preset proportional coefficients of uy and ug respectively, and a1 and a2 are both greater than 0.

[0085] It should be noted that a1 and a2 are set by professionals based on actual conditions. Generally, the sum of a1 and a2 is 1, and the specific value is not limited. In addition, before calculating the network load factor, it is necessary to remove the unit from the total network bandwidth load and the network load fluctuation value for calculation.

[0086] It should be noted that the network bandwidth load data of the current device is obtained in real time through the network monitoring system of the device, usually through the bandwidth usage recorded by the network traffic monitoring tool or the network management platform. The bandwidth load data at multiple moments in the load sequence can be obtained through periodic sampling, such as collecting data once every hour or every minute; the average network bandwidth load required for the new power grid operation information is based on the bandwidth demand estimate for the new data type, which is usually provided by the data transmission plan or the power grid system design team, taking into account factors such as the transmission rate, data volume and frequency of the new information.

[0087] It should be noted that the larger the current network load coefficient of the first device is, the less likely the first device is to add data information corresponding to the new grid operation type, and the greater the possibility that the corresponding first device has been scrapped. The reason is that the larger the current network load coefficient is, the network transmission capacity of the first device is already in a high load state. If the data information corresponding to the new grid operation type is added, it may cause network bandwidth overload, resulting in data transmission delays, packet loss, and even equipment crashes. In addition, frequent high-load fluctuations will aggravate the wear of equipment hardware, increase the probability of failure, and reduce the stability and reliability of the equipment. This excessive use may shorten the service life of the equipment in advance, and in severe cases, it may cause the equipment to be scrapped prematurely. Therefore, the larger the network load coefficient is, it not only means that the current equipment has a worse ability to carry new data, but also indicates that the risk of the equipment entering the scrap stage is increased.

[0088] In one implementation, the benefits of analyzing the current network load coefficient of the first device for judging whether the first device can add new power grid operation information and whether the current information device is already in a scrapped state are as follows: First, the network load coefficient can reflect the current network bandwidth usage and load fluctuation of the device in real time. If the network load coefficient of the device is too high, it means that the network resources of the device are close to the limit. Adding new power grid operation information will significantly increase the pressure of network transmission, which may cause device failure or data loss. Therefore, avoiding overload judgment can effectively prevent problems caused by overload of network resources. Secondly, by monitoring the fluctuation of network load, it can also help predict the long-term stability of the device. When the network load fluctuates greatly, it indicates that the device may be close to its performance limit. Identifying this state in advance helps to avoid sudden failure of the device, extend the service life of the device and reduce the risk of unexpected downtime. In general, judgment based on the network load coefficient helps to optimize the resource allocation of the device, improve the efficiency of power grid operation and maintenance, and ensure that the device can operate in the best state to avoid premature scrapping or unnecessary failures.

[0089] In one embodiment, obtaining the current storage coefficient, processing coefficient, and network load coefficient of the first device to obtain the non-incremental coefficient of the first device includes:

[0090]

[0091] Wherein, HJU is the non-incremental coefficient of the first device, SC, CP, sd are respectively the current storage coefficient, processing coefficient and network load coefficient of the first device, b1, b2, b3 are respectively the preset proportional coefficients of SC, CP, sd, and b1, b2, b3 are all greater than 0.

[0092] It should be noted that b1, b2, and b3 are set by professionals based on actual conditions. Generally, the sum of b1, b2, and b3 is 1, and the specific values ​​are not limited. In addition, before calculating the non-increasable coefficient of the first device, it is necessary to remove the units of the storage coefficient, processing coefficient, and network load coefficient and normalize them before calculation. Commonly used normalization methods include Min-Max normalization, Z-Score standardization, etc., which are selected based on actual conditions and are not limited or elaborated.

[0093] In one embodiment, dividing the first device into a state where the grid operation information cannot be added, a state where the grid operation information can be temporarily added, and a scrapped state according to the comparison result includes:

[0094] Comparing the non-increaseable coefficient of the first device with a preset non-increaseable coefficient gradient threshold, if the non-increaseable coefficient is less than the preset non-increaseable coefficient first threshold, recording the first device as a temporarily increaseable power grid operation information state;

[0095] If the non-increaseable coefficient is not less than the preset non-increaseable coefficient first threshold value but less than the preset non-increaseable coefficient second threshold value, the first device is recorded as a non-increaseable power grid operation information state;

[0096] If the non-increaseable coefficient is not less than a preset non-increaseable coefficient second threshold value, the first device is recorded as scrapped and a replacement warning signal is issued.

[0097] It should be noted that the preset first threshold of the non-increasable coefficient and the preset second threshold of the non-increasable coefficient are set by professionals according to actual conditions, and the preset first threshold of the non-increasable coefficient is smaller than the preset second threshold of the non-increasable coefficient, and no specific limitation or elaboration is made.

[0098] It should be noted that the non-incremental coefficient of the first device is compared with the preset non-incremental coefficient gradient threshold. If the non-incremental coefficient of the first device is less than the preset non-incremental coefficient first threshold, it means that the current resource load of the device is at a low level and has not yet reached an overload state. At this time, the device may be able to carry the newly added power grid operation information, so the device will be assigned to the "temporary power grid operation information state", that is, it temporarily has the ability to add new power grid information and conduct subsequent analysis.

[0099] If the non-increase coefficient of the first device is not less than the preset non-increase coefficient first threshold, but less than the preset non-increase coefficient second threshold, it means that the resource load of the device is not high but not low. Although it has not reached the fully saturated state, adding new grid operation information may cause certain performance pressure. Therefore, the device is judged to be in the "non-increase grid operation information state" and only needs to store the grid operation information originally set to be stored.

[0100] If the non-increment coefficient of the first device is not less than the preset non-increment coefficient second threshold, it means that the resource load of the device is very close to the limit or has reached it, and the information device has contained a hidden fault, and the hidden fault is no longer repairable; at this time, the device is identified as "scrapped" and a replacement warning signal is triggered. The judgment at this stage not only helps to timely discover and eliminate equipment with poor performance and faults to avoid their impact on the stability of the power grid, but also provides a decision-making basis for subsequent equipment updates and replacements, thereby ensuring the normal operation of the power grid and information security.

[0101] In one embodiment, obtaining the usage time of the first device and the environment data thereof and combining the non-incremental coefficient to obtain the predicted state coefficient includes:

[0102] Obtain the used time and average usable time of the first device, and divide the used time by the average usable time to obtain a service life coefficient;

[0103] Obtain the number of times the first equipment is maintained and the duration of each maintenance, and calculate the average maintenance time;

[0104] Obtain an average maintenance time interval of the first device, obtain the time interval from the current time to the last maintenance, and divide the time interval from the current time to the last maintenance by the average maintenance time interval to obtain a maintenance interval coefficient;

[0105] A maintenance factor is obtained according to the service life factor, the mean maintenance time and the maintenance interval factor, and a predicted state factor is obtained according to the maintenance factor, the environmental data of the first device and the corresponding non-increase factor.

[0106] It should be noted that the maintenance coefficient obtained according to the service life coefficient, average maintenance time and maintenance interval coefficient is: DF=c1×sw+c2×wq+c3×fg, where DF is the maintenance coefficient, sw, wq, and fg are the service life coefficient, average maintenance time and maintenance interval coefficient, respectively, c1, c2, and c3 are the preset proportional coefficients of sw, wq, and fg, respectively, and c1, c2, and c3 are all greater than 0.

[0107] It should be noted that c1, c2, and c3 are set by professionals based on actual conditions. Generally, the sum of c1, c2, and c3 is 1, and the specific values ​​are not limited. In addition, before calculating the maintenance coefficient, it is necessary to remove the units from the service life coefficient, average maintenance time, and maintenance interval coefficient.

[0108] It should be noted that the used time and the average usable time can be obtained from the operation record of the equipment, which is usually tracked and recorded through the monitoring system or operation log of the equipment. The number of repairs and the duration of each repair can usually be obtained through the equipment maintenance management system (such as the CMMS system), and the maintenance personnel will record the maintenance events and duration after the repair. The average maintenance time interval can be statistically calculated through historical maintenance data, and the average time between each two repairs is calculated. Finally, the time interval between the current time and the last maintenance can be obtained through the real-time data monitoring system, and the system can record the timestamp of each maintenance and calculate the difference between the current time and the last maintenance time. The used time and the average usable time can be obtained from the operation record of the equipment, which is usually tracked and recorded through the monitoring system or operation log of the equipment. The number of repairs and the duration of each repair can usually be obtained through the equipment maintenance management system (such as the CMMS system), and the maintenance personnel will record the maintenance events and duration after the maintenance. The average maintenance time interval can be statistically calculated through historical maintenance data, and the average time between each two repairs is calculated. Finally, the time interval between the current time and the last maintenance can be obtained through the real-time data monitoring system, and the system can record the timestamp of each maintenance and calculate the difference between the current time and the last maintenance time.

[0109] It should be noted that, when the maintenance coefficient of the first device is larger, it means that even if the first device can currently add the newly added power grid operation information, it may cause the information device to frequently fail with the long-term operation of the information network equipment, resulting in a decrease in service life, waste of resources, and may also make maintenance more complicated, ultimately affecting the judgment of the stable operation of the power grid; because a larger maintenance coefficient indicates that the device is closer to the limit of its life cycle, and frequent maintenance records and shorter maintenance intervals imply that the performance of the device is gradually declining; if it is included in the newly added power grid operation information, it may increase the burden on the device, cause more failures, further shorten its service life, and increase the difficulty of maintenance. This will not only cause a waste of resources, but also may cause the failure of the equipment to have a serious impact on the stability of the power grid and increase the risk of the entire power grid. Therefore, in this case, it is unwise to continue to use the device to carry the newly added power grid operation information.

[0110] In one implementation, the benefit of analyzing the maintenance coefficient for predicting whether the first device will frequently fail as the first device is used after carrying the newly added grid operation information, resulting in a decrease in service life, waste of resources, and possibly making maintenance more complicated, which ultimately affects the stable operation of the grid is: analyzing the maintenance coefficient for predicting the impact of the first device carrying the newly added grid operation information can help identify the potential risks of the equipment in long-term operation in advance. By evaluating the maintenance coefficient, the current health status and remaining life of the equipment can be accurately understood, and the failure frequency and maintenance requirements that may occur after carrying a higher load can be predicted. This analysis helps avoid over-reliance on equipment that has aged or is frequently repaired, thereby preventing equipment from failing under the newly added load, reducing unnecessary waste of resources, and optimizing maintenance plans, reducing the complexity and cost of grid maintenance. Ultimately, through early warning, the stability and reliability of the grid are ensured, and power outages or system crashes caused by equipment failures are avoided.

[0111] In one embodiment, obtaining the predicted state coefficient according to the maintenance coefficient, the environmental data of the first device and the corresponding non-incremental coefficient includes:

[0112] The real-time temperature (t) and real-time humidity S(t) of the environment in which the first device is located from the time the first device is put into use to the current time period are obtained, and the environmental anomaly coefficient is calculated. The calculation expression is as follows:

[0113]

[0114] Wherein, [t1, t2] represents the time period when the real-time temperature is outside the preset optimal temperature range, and [t3, t4] represents the time period when the real-time humidity is outside the preset optimal humidity range; α and β are preset weight coefficients of temperature and humidity, respectively, and both α and β are greater than 0;

[0115] The predicted state coefficient is obtained according to the maintenance coefficient, the environmental abnormality coefficient and the corresponding non-increase coefficient.

[0116] It should be noted that α and β are set by professionals according to actual conditions, and are not specifically limited or elaborated; in addition, the preset optimal temperature range and the preset optimal humidity range are also set by professionals according to actual conditions, and are not specifically limited or elaborated;

[0117] It should be noted that the environmental anomaly coefficient refers to the degree to which the real-time temperature is outside the preset optimal temperature range and the real-time humidity is outside the preset optimal humidity range in the environment where the information equipment used for the power grid is located; if the environmental anomaly coefficient is larger, it means that even if the first device can currently add the newly added power grid operation information, it may cause the information equipment to frequently fail with the long-term operation of the information network equipment, resulting in a decrease in service life, waste of resources, and may also make maintenance more complicated, ultimately affecting the judgment of the stable operation of the power grid; because the larger the environmental anomaly coefficient, it means that the ambient temperature and humidity of the equipment have greatly deviated from the optimal operating range of the equipment, which will increase the load of the equipment and cause frequent failures. In an unsuitable environment for a long time, the internal components of the equipment are prone to overheating, corrosion or performance degradation, which in turn affects its stability and reliability. With the increase of equipment failures, the maintenance cost will gradually increase, and the service life of the equipment will be significantly shortened. More importantly, when the environmental conditions are too harsh, the failure rate of the equipment will directly affect the normal operation of the power grid and may cause instability of the power grid. Therefore, if the environmental anomaly coefficient is too high, even if the equipment can carry the newly added power grid operation information in the short term, it is not suitable to continue to bear these additional loads in the long run.

[0118] In one implementation, the benefit of analyzing the environmental anomaly coefficient in predicting whether the first device will frequently fail as the first device is used after carrying the newly added grid operation information, resulting in a reduction in service life, waste of resources, and possibly making maintenance more complicated, which ultimately affects the stable operation of the grid is that analyzing the environmental anomaly coefficient helps to identify in advance the failure risks that the equipment may face in harsh environments, thereby avoiding putting the equipment into operation with excessive loads. By evaluating the impact of the environment on the equipment, timely measures can be taken to optimize the use of the equipment, prevent frequent failures caused by environmental factors, reduce waste of resources, and reduce the difficulty of maintenance. This not only prolongs the service life of the equipment, but also ensures the stable operation of the grid and avoids system interruptions or instability due to equipment failures.

[0119] In one embodiment, the predicted state coefficient is obtained according to the maintenance coefficient, the environmental abnormality coefficient and the corresponding non-incremental coefficient, including:

[0120]

[0121] Wherein, Dax is the predicted state coefficient of the first equipment, DF, BH, HJU are the maintenance coefficient, environmental abnormality coefficient and non-increase coefficient of the first equipment respectively, f1, f2, f3 are the preset proportional coefficients of DF, BH, HJU respectively, and f1, f2, f3 are all greater than 0.

[0122] It should be noted that f1, f2, and f3 are set by professionals based on actual conditions. Generally, the sum of f1, f2, and f3 is 1, and the specific values ​​are not limited. In addition, before calculating the predicted state coefficient of the first equipment, it is necessary to remove the units of the maintenance coefficient, environmental abnormality coefficient, and non-increase coefficient and normalize them before calculation. Commonly used normalization methods include Min-Max normalization, Z-Score standardization, etc., which are selected based on actual conditions and are not limited or elaborated.

[0123] In one embodiment, comparing the predicted state coefficient with a preset predicted state coefficient gradient threshold, and managing the information device according to the comparison result includes:

[0124] If the preset prediction state coefficient of the first device is less than the preset prediction state coefficient first threshold, the corresponding first device may add the newly added power grid operation information;

[0125] If the preset prediction state coefficient of the first device is not less than the preset prediction state coefficient first threshold value but less than the preset prediction state coefficient second threshold value, the corresponding first device cannot add the newly added power grid operation information;

[0126] If the preset prediction state coefficient of the first device is not less than the preset prediction state coefficient second threshold value, the corresponding first device cannot add the newly added power grid operation information, and at the same time sends a maintenance warning signal.

[0127] It should be noted that the preset prediction state coefficient first threshold value and the preset prediction state coefficient second threshold value are set by professionals according to actual conditions, and the preset prediction state coefficient first threshold value is smaller than the preset prediction state coefficient second threshold value, and no specific limitation or elaboration is made.

[0128] If the preset prediction state coefficient of the first device is less than the preset prediction state coefficient first threshold, this means that the health status of the device is good, and under the current environment, the device can carry the newly added power grid operation information. In this case, the first device can normally join the extended load of the power grid, process the newly added information, and ensure the normal operation and load distribution of the power grid;

[0129] If the preset prediction state coefficient of the first device is not less than the preset prediction state coefficient first threshold but less than the preset prediction state coefficient second threshold, it indicates that although the device is in a good state, there are certain potential risks. Carrying the newly added power grid information may cause frequent failures of the device and affect its stability. In this case, it is recommended not to add the device to the newly added power grid operation information for the time being;

[0130] If the preset prediction state coefficient of the first device is not less than the second threshold value of the preset prediction state coefficient, this indicates that the health of the device has significantly declined, and carrying the newly added grid information may accelerate the occurrence of failures, greatly affecting the service life of the equipment and the stable operation of the grid. In this case, not only should the device not add the newly added grid operation information, but it should also immediately issue a maintenance warning signal, suggesting that the equipment be maintained or replaced. At the same time, this also means that the probability of equipment failure is high, and emergency measures need to be prepared in advance to prevent failures from causing grid interruptions or performance degradation;

[0131] In one implementation, this management method can dynamically monitor the health and status of equipment, take timely measures to extend the life of equipment, avoid affecting the stability of the power grid due to equipment failure, and optimize the allocation and use of resources to ensure the efficiency and safety of power grid operation.

[0132] Based on the same inventive concept, the embodiment of the present invention also provides a full life cycle management device for information equipment in a power grid. Figure 2 , Figure 2 A framework diagram of a device for managing the entire life cycle of information equipment for a power grid provided by an embodiment of the present invention, the device comprising:

[0133] Non-incremental module: each information device used in the power grid is recorded as a first device, and the current storage coefficient, processing coefficient and network load coefficient of the first device are obtained to obtain the non-incremental coefficient of the first device;

[0134] A state classification module: comparing the non-increase coefficient of the first device with a preset non-increase coefficient gradient threshold, and classifying the first device into a non-increase grid operation information state, a temporary increase grid operation information state, and a scrapped state according to the comparison result;

[0135] Prediction state module: for a first device that can temporarily increase the state of a power grid data type, obtain the usage time and the environment data of the first device and combine them with the non-incremental coefficient to obtain a prediction state coefficient;

[0136] Management module: compares the predicted state coefficient with the preset predicted state coefficient gradient threshold, and manages the information equipment according to the comparison result.

[0137] Based on an information equipment full life cycle management system for a power grid provided by an embodiment of the present invention, through the above method, it is possible to specifically determine which information equipment can add new power grid operation information, ensure that the equipment will not encounter resource bottlenecks, equipment overload, data loss or processing delays, and other problems, thereby reducing the possibility of confusion in the data corresponding to the power grid operation information originally stored in the information equipment; in addition, it is possible to determine whether the information equipment can add new power grid operation information even under the current circumstances, and whether the information equipment can store the power grid operation information stably and accurately over time, ensuring that the information equipment will not frequently fail, ensuring its service life, reducing the waste of resources, and ensuring that the stable operation of the power grid is accurately judged; in addition, it is possible to determine in advance whether the information equipment is scrapped, reducing the frequent performance degradation and increased failures of the equipment throughout its life cycle, and reducing the impact on the stability of the power grid system.

[0138] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be used to artificially limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for managing the entire life cycle of information equipment for a power grid, characterized in that: The following steps are involved: Each information device used in the power grid is recorded as a first device, and the current storage coefficient, processing coefficient and network load coefficient of the first device are obtained to obtain a non-incremental coefficient of the first device; Comparing the non-increaseable coefficient of the first device with a preset non-increaseable coefficient gradient threshold, and classifying the first device into a non-increaseable grid operation information state, a temporarily increaseable grid operation information state, and a scrapped state according to the comparison result; For a first device that can temporarily increase the state of a power grid data type, obtain the usage time and the environment data of the first device and obtain a predicted state coefficient in combination with a non-incremental coefficient; The predicted state coefficient is compared with the preset predicted state coefficient gradient threshold, and the information device is managed according to the comparison result.

2. A method for managing the entire life cycle of information equipment for a power grid according to claim 1, characterized in that: The storage coefficients include: Obtaining a total storage capacity T and a used storage capacity of the first device, and subtracting the used storage capacity from the total storage capacity to obtain a remaining storage capacity A; Obtaining a storage capacity corresponding to the fragmented space of the first device, and dividing the storage capacity corresponding to the fragmented space by the total storage capacity T to obtain a fragmentation coefficient F; Obtain the storage capacity R occupied by the newly added power grid operation information and calculate the storage coefficient. The calculation formula is: Where SC is the storage coefficient; The processing coefficients include: Obtaining the maximum processing load of the first device and the current processing load of the device, and obtaining the processing load required by the newly added power grid operation information, and adding the current processing load of the device to the processing load required by the newly added power grid operation information to obtain a total processing load; Divide the total processing load by the maximum processing load to obtain the processing factor.

3. A method for managing the entire life cycle of information equipment for a power grid according to claim 1, characterized in that: The network load factor includes: Obtain the network bandwidth load of the current device at multiple times continuously to obtain the load sequence of the current device; Calculate the standard deviation of the load sequence and use the standard deviation as the network load fluctuation value; Calculate the mean of the load sequence as the current network bandwidth load, obtain the average network bandwidth load required for the newly added power grid operation information, and add the current network bandwidth load to the average network bandwidth load required for the newly added power grid operation information to obtain the total network bandwidth load; The network load factor is obtained according to the total network bandwidth load and the network load fluctuation value.

4. A method for managing the entire life cycle of information equipment for a power grid according to claim 1, characterized in that: Acquiring the current storage coefficient, processing coefficient, and network load coefficient of the first device to obtain the non-incremental coefficient of the first device includes: Wherein, HJU is the non-incremental coefficient of the first device, SC, CP, sd are respectively the current storage coefficient, processing coefficient and network load coefficient of the first device, b1, b2, b3 are respectively the preset proportional coefficients of SC, CP, sd, and b1, b2, b3 are all greater than 0.

5. A method for managing the entire life cycle of information equipment for a power grid according to claim 1, characterized in that: According to the comparison result, the first device is divided into a state where the grid operation information cannot be added, a state where the grid operation information can be temporarily added, and a scrapped state, including: Comparing the non-increaseable coefficient of the first device with a preset non-increaseable coefficient gradient threshold, if the non-increaseable coefficient is less than the preset non-increaseable coefficient first threshold, recording the first device as a temporarily increaseable power grid operation information state; If the non-increaseable coefficient is not less than the preset non-increaseable coefficient first threshold value but less than the preset non-increaseable coefficient second threshold value, the first device is recorded as a non-increaseable power grid operation information state; If the non-increaseable coefficient is not less than a preset non-increaseable coefficient second threshold value, the first device is recorded as scrapped and a replacement warning signal is issued.

6. A method for managing the entire life cycle of information equipment for a power grid according to claim 1, characterized in that: Obtaining the usage time of the first device, the environment data in which the first device is located, and combining the non-incremental coefficient to obtain the predicted state coefficient includes: Obtain the used time and average usable time of the first device, and divide the used time by the average usable time to obtain a service life coefficient; Obtain the number of times the first equipment is maintained and the duration of each maintenance, and calculate the average maintenance time; Obtain an average maintenance time interval of the first device, obtain the time interval from the current time to the last maintenance, and divide the time interval from the current time to the last maintenance by the average maintenance time interval to obtain a maintenance interval coefficient; A maintenance factor is obtained according to the service life factor, the mean maintenance time and the maintenance interval factor, and a predicted state factor is obtained according to the maintenance factor, the environmental data of the first device and the corresponding non-increase factor.

7. A method for managing the entire life cycle of information equipment for a power grid according to claim 6, characterized in that: The predicted state coefficient obtained according to the maintenance coefficient, the environmental data of the first equipment and the corresponding non-increase coefficient includes: The real-time temperature T(t) and real-time humidity S(t) of the environment in which the first device is located from the time the first device is put into use to the current time period are obtained, and the environmental anomaly coefficient is calculated. The calculation expression is as follows: Wherein, [t1, t2] represents the time period when the real-time temperature is outside the preset optimal temperature range, and [t3, t4] represents the time period when the real-time humidity is outside the preset optimal humidity range; α and β are preset weight coefficients of temperature and humidity, respectively, and both α and β are greater than 0; The predicted state coefficient is obtained according to the maintenance coefficient, the environmental abnormality coefficient and the corresponding non-increase coefficient.

8. A method for managing the entire life cycle of information equipment for a power grid according to claim 7, characterized in that: The predicted state coefficients obtained based on the maintenance coefficient, environmental abnormality coefficient and the corresponding non-increase coefficient include: Wherein, Dax is the predicted state coefficient of the first equipment, DF, BH, HJU are the maintenance coefficient, environmental abnormality coefficient and non-increase coefficient of the first equipment respectively, f1, f2, f3 are the preset proportional coefficients of DF, BH, HJU respectively, and f1, f2, f3 are all greater than 0.

9. A method for managing the entire life cycle of information equipment for a power grid according to claim 1, characterized in that: Comparing the predicted state coefficient with a preset predicted state coefficient gradient threshold, and managing the information device according to the comparison result includes: If the preset prediction state coefficient of the first device is less than the preset prediction state coefficient first threshold, the corresponding first device may add the newly added power grid operation information; If the preset prediction state coefficient of the first device is not less than the preset prediction state coefficient first threshold value but less than the preset prediction state coefficient second threshold value, the corresponding first device cannot add the newly added power grid operation information; If the preset prediction state coefficient of the first device is not less than the preset prediction state coefficient second threshold value, the corresponding first device cannot add the newly added power grid operation information, and at the same time sends a maintenance warning signal.

10. A device for managing the entire life cycle of information equipment for a power grid, used to implement a method for managing the entire life cycle of information equipment for a power grid as described in any one of claims 1 to 9, characterized in that: The device comprises: Non-incremental module: each information device used in the power grid is recorded as a first device, and the current storage coefficient, processing coefficient and network load coefficient of the first device are obtained to obtain the non-incremental coefficient of the first device; A state classification module: comparing the non-increase coefficient of the first device with a preset non-increase coefficient gradient threshold, and classifying the first device into a non-increase grid operation information state, a temporary increase grid operation information state, and a scrapped state according to the comparison result; Prediction state module: for a first device that can temporarily increase the state of a power grid data type, obtain the usage time and the environment data of the first device and combine them with the non-incremental coefficient to obtain a prediction state coefficient; Management module: compares the predicted state coefficient with the preset predicted state coefficient gradient threshold, and manages the information equipment according to the comparison result.

Images