Data management system for smart grid

Abstract

The present invention relates to the technical field of data management, and particularly to a data management system for a smart grid. The system collects the inventory information of several devices in the facility layer through the collection layer, the processing layer preprocesses the inventory information, the prediction layer predicts the predicted voltage of each device based on the preprocessed inventory information, the inspection layer receives the prediction results of the prediction layer and generates an inspection plan based on this, and obtains the actual voltage of each device according to the inspection plan. The analysis layer determines whether the prediction process for the device is qualified based on the comparison result between the predicted voltage and the actual voltage. The instruction layer generates corresponding instructions based on the unqualified reasons, and the control layer determines the operating parameters of each device based on the instructions, thereby improving the qualification rate of the prediction process for the device, and being able to combine the prediction results with the set maintenance technical solutions to issue a warning maintenance notice to ensure the efficiency of the safe operation of the device, so as to improve the stability and operation efficiency of the smart grid.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and particularly to a data management system for a smart grid. Background Art

[0002] The smart grid, also known as "Grid 2.0", is built on the basis of a communication network and can achieve the safe, reliable use and operation and maintenance of the power grid through technologies such as sensing, measurement, equipment, control, and decision support. However, there is a lack of intelligent data management for the operation and maintenance process of the smart grid.

[0003] Regarding the data management of the smart grid, the Chinese patent publication number in the prior art: CN115271249A discloses an intelligent system for power grid maintenance operation arrangement and operation ticket generation based on fault prediction. By constructing each module to form an intelligent system for power grid maintenance operation arrangement and operation ticket generation, it uses a neural network and a large amount of historical data to generate a sequence prediction model, thereby accurately predicting the future faults of nodes. Using the dynamic programming algorithm, it arranges maintenance schedules for the occurred and unoccurred faults and automatically generates corresponding operation tickets and work tickets to complete fault repair or elimination at the optimal economic cost. Although this technical solution generates a prediction model through historical data and arranges maintenance processes for the occurred or unoccurred faults based on the prediction model, this technical solution needs to collect a large amount of historical data and generate and train a prediction model based on this, and then perform fault prediction based on the prediction model to generate corresponding processes. Thus, not only is the amount of data used huge, the algorithms used are complex, but also there are a large number of invalid data and interference data, thus affecting the accuracy of the prediction results and the prediction efficiency. Summary of the Invention

[0004] Therefore, the present invention provides a data management system for a smart grid to solve the problems in the prior art that the accuracy of the prediction results is affected by obtaining a large amount of historical data for fault prediction and the accuracy of its own prediction process cannot be guaranteed.

[0005] To achieve the above object, the present invention provides a data management system for a smart grid, including:

[0006] An infrastructure layer, which includes several devices for operation;

[0007] An acquisition layer for acquiring the ledger information of each of the devices;

[0008] A processing layer, which is connected to the acquisition layer and is used for preprocessing the acquired ledger information;

[0009] An estimation layer, which is connected to the processing layer and is used for estimating the estimated voltage of each of the devices based on the preprocessed ledger information;

[0010] The inspection layer, which includes several inspection vehicles and is connected to the prediction layer, is used to generate corresponding inspection plans according to the prediction results of the prediction layer, and obtain the actual voltage of the corresponding equipment based on the inspection plans;

[0011] The analysis layer, which is respectively connected to the prediction layer and the inspection layer, is used to determine whether the prediction process of the system for the equipment is qualified based on the comparison result between the actual voltage and the predicted voltage, and determine the reasons for unqualified;

[0012] The instruction layer, which is connected to the analysis layer, generates corresponding instructions based on the reasons;

[0013] The control layer, which is respectively connected to the acquisition layer, the processing layer, the prediction layer, the inspection layer and the instruction layer, is used to adjust the quantity of acquisition information and data acquisition frequency of the acquisition layer, determine the cleaning standard of the processing layer, determine the predicted voltage of the prediction layer, determine the moving speed of the inspection vehicle, or determine to issue maintenance notices for each piece of equipment based on the instructions.

[0014] Further, the analysis layer is also used to make a determination based on the comparison result between the voltage mean value and the preset voltage mean value pre-stored in the analysis layer, or re-determine whether the prediction process for the equipment is qualified based on several time nodes when collecting the ledger information within a preset period, and determine the reasons for unqualified based on the voltage mean value ratio in the case of determining that the prediction process for the equipment is unqualified;

[0015] Wherein, the voltage mean value is calculated by taking the average value of the absolute values of the differences between each actual voltage and the corresponding predicted voltage, the voltage mean value ratio is the ratio between the voltage mean value and the preset voltage mean value, and the time nodes include prediction time nodes and inspection time nodes.

[0016] Further, the analysis layer is also used to determine whether to optimize the inspection process or update the predicted voltage based on the comparison result between the average time interval and the preset average time interval pre-stored in the analysis layer;

[0017] Wherein, several absolute values of time intervals are obtained for the prediction time nodes and the inspection time nodes of each piece of equipment, and the average time interval is obtained by taking the average value of the several absolute values of time intervals.

[0018] Further, the analysis layer is also used to increase the moving speed of the inspection vehicle when the node quantity ratio is greater than a preset node quantity ratio, and the increasing range of the moving speed is in direct proportion to the node quantity ratio, where the node quantity ratio is the ratio between the quantity of abnormal estimated time nodes and the total quantity of the estimated time nodes, and the abnormal estimated time nodes are the estimated time nodes earlier than the corresponding inspection time nodes.

[0019] Further, the analysis layer is also used to update the estimated voltage when the node quantity ratio is less than or equal to the preset node quantity ratio.

[0020] Further, the analysis layer is also used to determine the reason for the unqualified estimated process of the device based on the comparison result between the voltage mean ratio and the preset voltage mean ratio pre-stored in the analysis layer, and determine to increase the quantity of the acquisition information of the acquisition layer, determine to correct the cleaning standard of the processing layer, or determine to issue a maintenance notice for each device based on the reason.

[0021] Further, the analysis layer is also used to increase the quantity of the acquisition information of the acquisition layer based on the comparison result between the quantity of the estimated information and the preset quantity of the estimated information pre-stored in the analysis layer, and the increasing range of the quantity of the acquisition information is in inverse proportion to the quantity of the estimated information, where the quantity of the estimated information is the quantity of the information remaining after the preprocessing of the ledger information by the processing layer.

[0022] Further, when the adjustment of increasing the quantity of the acquisition information is completed, the analysis layer is also used to determine to increase the data acquisition frequency of the acquisition layer based on the increased quantity of the acquisition information, and the increasing range of the data acquisition frequency is in direct proportion to the increased quantity of the acquisition information.

[0023] Further, the analysis layer is also used to reduce the cleaning standard of the processing layer based on the comparison result between the quantity of the estimated information and the preset quantity of the estimated information pre-stored in the analysis layer, and the reducing range of the cleaning standard is in direct proportion to the quantity of the estimated information.

[0024] Further, the analysis layer is also used to determine to replace the device or perform maintenance on the device based on the comparison result between the maintenance time interval and the preset maintenance time interval pre-stored in the analysis layer, where the maintenance time interval is the time difference between the previous maintenance record time node and the current maintenance record time node in the ledger information.

[0025] Compared with the prior art, the beneficial effects of the data management system for the smart grid according to the present invention are as follows. The system collects the ledger information of several devices in the facility layer through the collection layer, preprocesses the obtained ledger information through the processing layer to obtain ledger information that can be normally recognized, estimates the estimated voltage of each device based on the preprocessed ledger information through the estimation layer, receives the estimation results of the estimation layer through the inspection layer and generates an inspection plan based on this, obtains the actual voltage of each device in turn according to the inspection plan through the inspection vehicle, accurately determines whether the estimation process for the device is qualified based on the comparison result between the estimated voltage and the actual voltage through the analysis layer, generates a corresponding correction method when determining the unqualified reason, generates a corresponding instruction based on the reason through the instruction layer, and sends the instruction to the control layer, and then adjusts the operating parameters of the devices in each layer based on the instruction through the control layer, so as to improve the qualification rate of the estimation process for the device; and on the premise of ensuring the estimation accuracy, it can give an early warning of the fault problems of each device in advance, and then determine to issue a maintenance warning notice for each device, so that it can determine whether there is a fault in the device in advance based on the estimation process and provide a credibility warning of the estimation result to the operation and maintenance personnel, and, based on this, conduct targeted operation and maintenance management, so as to realize the intelligent management and optimization of the power grid operation state, thereby improving the stability and operation efficiency of the power grid.

[0026] Further, the present invention further specifically determines whether the estimation process for the device is qualified based on the comparison result between the voltage mean value and the pre-stored preset voltage mean value, and, determines the reason when the estimation is unqualified based on the voltage mean value ratio, so that the estimation process for the device can be accurately judged.

[0027] Further, the present invention also conducts a secondary determination based on the comparison result between the average time interval and the pre-stored preset average time interval to improve the determination accuracy of the estimation process for the device and reduce misjudgment results.

[0028] Further, the present invention also determines whether to increase the moving speed of the inspection vehicle or update the estimated voltage based on the comparison between the node number ratio and the preset node number ratio, so as to adapt to the estimation model of the estimation layer, thereby improving the qualification rate of the estimation process for the device.

[0029] Further, the present invention also determines the reason why the estimation process for the device is unqualified based on the comparison result between the voltage mean value ratio and the pre-stored preset voltage mean value ratio, and generates a corresponding correction method based on the reason, thereby improving the qualification rate of the estimation process for the device and making the data management for the device more reliable.

[0030] Furthermore, when modifying the control system, the present invention can increase the number of acquisition information in the acquisition layer and increase the data acquisition frequency of the acquisition layer, so as to improve the qualification rate of the prediction process for the equipment, thereby making the data management for the equipment more reliable.

[0031] Furthermore, the present invention also reduces the cleaning standard of the processing layer based on the comparison result between the number of prediction information and the pre-stored number of prediction information, so that the prediction layer can obtain more ledger information, thereby improving the accuracy of the prediction model and making the prediction process for the equipment more accurate.

[0032] Furthermore, the present invention can also determine whether to replace the equipment or repair the equipment according to the comparison result between the maintenance time interval and the pre-stored preset maintenance time interval. In this way, it can ensure that the equipment can operate normally, thereby realizing the intelligent management of the smart grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the modules of the data management system for the smart grid of the present invention;

[0034] Figure 2 It is a schematic diagram of the process of the data management system for the smart grid of the present invention;

[0035] Figure 3 It is a logic flowchart of the present invention for determining whether the prediction process for the equipment is qualified based on the average voltage;

[0036] Figure 4 It is a logic flowchart of the present invention for modifying the prediction process of the equipment based on the node number ratio;

[0037] Figure 5 It is a logic flowchart of the present invention for determining the reason for the unqualified prediction process of the equipment and the modification based on the average voltage ratio. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] In order to make the objectives and advantages of the present invention clearer, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0039] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0040] It should be noted that in the description of the present invention, the terms indicating the direction or positional relationship such as "upper", "lower", "left", "right", "inner", "outer", etc. are based on the direction or positional relationship shown in the drawings. This is only for convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention.

[0041] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and defined, the terms "installed", "connected", and "coupled" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0042] In this embodiment, a data management system for a smart grid is provided. Through this system, the data management during the operation of, including but not limited to, smart grid facilities can be improved, and based on the data management, the intelligent management and self-regulation of the power grid can be realized. It can generate a prediction model based on the inventory information in the acquired devices, and quickly and accurately determine whether the prediction process for the device is qualified according to the comparison result between the predicted voltage and the actual voltage in the prediction model, and adjust the operating parameters of each device in the system according to the reasons for unqualified, so as to improve the qualification rate of the prediction process of the device, thereby improving the management efficiency of the smart grid and ensuring the safe operation of the power grid.

[0043] Please refer to Figure 1As shown, it is a schematic diagram of the modules of the data management system for the smart grid in this embodiment. The system includes a facility layer, a collection layer, a processing layer, a prediction layer, a patrol inspection layer, an analysis layer, an instruction layer, and a control layer; among them, the facility layer includes several devices for operation; the collection layer is used to collect the ledger information of each of the devices; the processing layer is connected to the collection layer and is used to preprocess the obtained ledger information; the prediction layer is connected to the processing layer and is used to predict the predicted voltage of each of the devices based on the preprocessed ledger information; the patrol inspection layer includes several patrol inspection vehicles and is connected to the prediction layer, and is used to generate a corresponding patrol inspection plan according to the prediction result of the prediction layer, and obtain the actual voltage of the corresponding device based on the patrol inspection plan; the analysis layer is respectively connected to the prediction layer and the patrol inspection layer, and is used to determine whether the prediction process of the system for the device is qualified based on the comparison result between the actual voltage and the predicted voltage, and determine the reason for the unqualified; the instruction layer is connected to the analysis layer and generates corresponding instructions based on the reason; the control layer is respectively connected to the collection layer, the processing layer, the prediction layer, the patrol inspection layer, and the instruction layer, and is used to determine and adjust the operation parameters of each device based on the instructions, and the parameters include: the number of collection information and the data collection frequency of the collection layer, the cleaning standard of the processing layer, the predicted voltage of the prediction layer, the moving speed of the patrol inspection vehicle, and can also determine and issue a maintenance notice for each of the devices based on the instructions.

[0044] Specifically, in this embodiment, by monitoring data such as the device status and operation status of each node in the power grid, intelligent operation and maintenance management is achieved. Among them, the power grid nodes include: intelligent substations, intelligent distribution grids, intelligent electricity meters, and intelligent interaction terminals. By collecting, predicting, and managing data for the devices in each node of the power grid, the reliable and safe goals of the power grid are achieved, and at the same time, the management of the power grid enters the intelligent and digital fields.In this embodiment, the operating voltage of the equipment is monitored, and the ledger information stored in each equipment is collected through the acquisition layer. Among them, the ledger information of the equipment includes: equipment name, equipment classification, total number, equipment location, work permit, technical parameters, as well as usage conditions, maintenance conditions, etc.; the processing layer connected to the acquisition layer preprocesses the obtained ledger information. The preprocessing includes data cleaning work to remove invalid data such as incomplete information data and abnormal information data, so as to obtain complete and clearly distinguishable ledger information, reduce the amount of data and relieve the subsequent estimation calculation pressure; then the estimation layer connected to the processing layer obtains the preprocessed ledger information, specifically obtains the historical operating voltage data in the ledger information of each equipment, and combines the linear regression model in the estimation layer to estimate the estimated voltage of each equipment in subsequent work; several inspection vehicles in the inspection layer generate corresponding inspection plans according to the estimation results in the estimation layer, and then sequentially inspect each equipment based on the inspection plans to obtain the actual voltage at the corresponding time node; the estimated voltage and the actual voltage are sent to the analysis layer, and the analysis layer determines whether the estimation process of the data management system for each equipment is qualified based on the comparison result of the actual voltage and the estimated voltage. When it is determined that the estimation process is unqualified, the reason can be determined through this system, and then the instruction layer connected to the analysis layer generates corresponding instructions based on the reason and sends the instructions to the control layer, and then the control layer adjusts the operating parameters of the corresponding device based on the corresponding instructions. The parameters include: the number of acquisition information in the acquisition layer, the cleaning standard in the processing layer, the estimated voltage in the estimation layer, the moving speed of the inspection vehicle, and based on the instruction, a maintenance notice for each equipment is determined to be sent. Among them, the corresponding device includes the data acquisition device in the acquisition layer, the data preprocessing module in the processing layer, the inspection vehicle in the inspection layer, and the control module in the control layer; by adjusting the operating parameters of the devices in each layer, the qualification rate of the estimation process for the equipment is improved, and thus the operating state of the equipment can be accurately estimated; and on the premise of ensuring the accuracy of the estimation process, the possible faults and performance degradation of the equipment can be warned in advance. Further, the estimation result can be combined with the pre-set maintenance technical plan to solve the equipment failure or equipment performance degradation situation. The maintenance technical plan here includes adjusting the moving speed of the inspection vehicle and sending a maintenance notice for each equipment, and, based on this, carrying out targeted operation and maintenance management, so as to provide a higher-precision estimation service for the equipment, ensure the efficiency of the safe operation of the equipment, thereby improving the stability and operation efficiency of the smart grid, and, can improve the intelligent data management efficiency of the smart grid.

[0045] Please refer to Figure 2 as shown, which is a schematic flow chart of the data management system for the smart grid in this embodiment. The process includes:

[0046] S1: Collect the ledger information of each device through the acquisition layer.

[0047] S2: The processing layer connected to the acquisition layer preprocesses the obtained ledger information.

[0048] S3: The prediction layer connected to the processing layer predicts the predicted voltage of each device based on the preprocessed ledger information.

[0049] S4: The inspection layer includes several inspection vehicles and is connected to the prediction layer, used to generate corresponding inspection plans according to the prediction results of the prediction layer, and obtain the actual voltage of the corresponding device based on the inspection plan.

[0050] S5: The analysis layer connected to the prediction layer and the inspection layer respectively obtains the predicted voltage and the actual voltage, and determines whether the prediction process of the system for the device is qualified based on the comparison result of the predicted voltage and the actual voltage, and determines the reason for unqualified.

[0051] S6: The instruction layer connected to the analysis layer generates corresponding instructions based on the reason.

[0052] S7: The control layer is connected to the acquisition layer, the processing layer, the prediction layer, the inspection layer and the instruction layer respectively, and is used to adjust the operation parameters of each device based on the instruction. The parameters include: the number of acquisition information and the data acquisition frequency of the acquisition layer, the cleaning standard of the processing layer, the predicted voltage of the prediction layer, and the moving speed of the inspection vehicle.

[0053] Please refer to Figure 3 As shown, it is a logic flowchart for determining whether the prediction process for a device is qualified based on the voltage mean in this embodiment. The analysis layer is also used to make a determination based on the comparison result of the voltage mean and the preset voltage mean, or re-determine whether the prediction process for the device is qualified based on several time nodes when collecting the ledger information within a preset period, and determine the reason for unqualified based on the voltage mean ratio in the case of determining that the prediction process for the device is unqualified;

[0054] Among them, the voltage mean is calculated by averaging the absolute values of the differences between each actual voltage and the corresponding predicted voltage. The voltage mean ratio is the ratio between the voltage mean and the preset voltage mean. The time nodes include the prediction time node and the inspection time node.

[0055] Specifically, in this embodiment, the actual voltages of each device and the preset voltages estimated at the corresponding time nodes are obtained, and then the absolute values of the voltage differences corresponding to each device are calculated. Next, the average value of several absolute values of voltage differences is calculated to obtain the voltage mean value V. The preset voltage mean value V0 can be divided into a first preset voltage mean value V1 and a second preset voltage mean value V2, where V1 < V2. The specific process of comparing the voltage mean value V with the preset voltage mean value V0 is as follows:

[0056] If V ≤ V1, it indicates that the numerical gap between the actual voltages obtained for the current devices and the estimated voltages at the corresponding time nodes is relatively small. Therefore, it can be determined that the estimation process for the devices is qualified. If V1 < V ≤ V2, at this time, the numerical size relationship between the obtained actual voltage and the estimated voltage cannot be accurately determined, and the estimation process for the devices can be re-determined based on several time nodes obtained within the preset period; the time nodes at this time include the estimated time nodes generated when the estimation layer estimates the operating voltage and the inspection time nodes generated when the inspection layer inspects the devices. If V2 < V, it indicates that the numerical gap between the actual voltages obtained for the current devices and the estimated voltages at the corresponding time nodes is relatively large. Therefore, it can be determined that the estimation process for the devices is unqualified. At this time, the reason for the unqualified can be determined based on the ratio between the voltage mean value V and the preset voltage mean value V0. In this example, specifically, the ratio between the voltage mean value V and the second preset voltage mean value V2 is calculated. Specifically, in this embodiment, V1 = 0.95×V3, V2 = 1.15×V3, where V3 is the set voltage mean value standard for device estimation, and V3 is set to 15 kV; it should be noted that the setting of V3 is determined according to the devices for data collection in the power grid, and in this embodiment, the preset values pre-stored in each device at each level can be modified.

[0057] Furthermore, the analysis layer is also used to determine whether to optimize the inspection process or update the estimated voltage based on the comparison result between the average time interval and the preset average time interval;

[0058] Among them, several absolute values of time intervals are obtained for the estimated time nodes and the inspection time nodes of each device, and the average time interval is obtained by calculating the average value of several absolute values of time intervals.

[0059] Specifically, in this embodiment, the specific process of comparing the average time interval T with the preset average time interval T0 is as follows:

[0060] If T≤T0, it indicates that when inspecting each device within the current preset cycle, the numerical difference between the inspection time node and the estimated time node is relatively small. Therefore, if the average voltage is between the first preset average voltage and the second preset average voltage at this time, it can be determined that the estimated voltage for the device is unqualified, and the reason for the unqualified needs to be determined based on the voltage average ratio. If T0<T, it indicates that when inspecting each device within the current preset cycle, the numerical difference between the inspection time node and the estimated time node is relatively large. Therefore, due to the inconsistency of the two time nodes, the average voltage is between the first preset average voltage and the second preset average voltage, making it impossible to accurately determine the estimated result for the device. It is necessary to determine the corresponding processing method through the analysis layer based on the sequence of the estimated time node and the inspection time node, generate the corresponding instruction through the instruction layer based on the processing method, and then adjust the corresponding device through the control layer based on the instruction to improve the determination accuracy. The processing methods include: optimizing the inspection process, or updating the estimated voltage; optimizing the inspection process is to adjust the inspection speed of the inspection vehicle to adapt to the estimated time node, and updating the estimated voltage is to re-adjust the estimated voltage of the estimation layer to adapt to the actual voltage at the current inspection time node. In this embodiment, the estimated time node earlier than the corresponding inspection time node is recorded as the abnormal estimated time node. Specifically, in this embodiment, T0 is set to 3 seconds.

[0061] Please refer to Figure 4 As shown, it is the logic flowchart of the estimation process for correcting the device based on the node quantity ratio in this embodiment. The analysis layer is also used to increase the moving speed of the inspection vehicle when the node quantity ratio is greater than the preset node quantity ratio, and the increase amplitude of the moving speed is in direct proportion to the node quantity ratio, where the node quantity ratio is the ratio between the quantity of abnormal estimated time nodes and the total quantity of the estimated time nodes, and the abnormal estimated time node is the estimated time node earlier than the corresponding inspection time node.

[0062] Furthermore, the analysis layer is also used to update the estimated voltage when the node quantity ratio is less than or equal to the preset node quantity ratio.

[0063] Specifically, in this embodiment, the analysis layer obtains the inspection moving speed and inspection time nodes of the inspection vehicle in real time, and obtains the estimated time nodes in the estimation layer. The analysis layer records the estimated time nodes that are earlier than the corresponding inspection time nodes in terms of time as abnormal estimated time nodes, then counts the number of abnormal estimated time nodes, and calculates the ratio of the number of abnormal estimated time nodes to the total number of estimated time nodes to obtain the node number ratio W. When the node number ratio W is greater than the preset node number ratio W0, it can be determined that the inspection moving speed of the inspection vehicle for most devices is too slow at this time, resulting in the actual inspection time being later than the estimated time. At this time, the moving speed of the inspection vehicle can be increased, and the greater the node number ratio, that is, the more abnormal estimated time nodes, the greater the increase in the moving speed of the inspection vehicle. It should be noted that the inspection device in the inspection layer is not limited to the inspection vehicle. When the node number ratio W is less than or equal to the preset node number ratio W0, it can be determined that the inspection moving speed of the inspection vehicle for most devices is too fast, resulting in the actual inspection time being earlier than the estimated time. At this time, the estimated time nodes in the estimation layer can be adjusted to update the estimated voltage. First, a time-estimated voltage curve is drawn based on several estimated time nodes and corresponding estimated voltage values for a single device during the estimation process. When the inspection time is earlier than the estimated time, the inspection time node is brought into the curve to obtain the corresponding corrected estimated voltage, and the corrected estimated voltage is sent to the analysis layer to update the estimated voltage. Then, after the inspection vehicle obtains the actual voltage, it is compared and analyzed with the corrected estimated voltage, and the above processing is performed for each device in turn to update the estimated voltage. In this embodiment, W0 is set to 40%.

[0064] Please refer to Figure 5 As shown, it is a logic flow chart for determining the reason for the unqualified estimation process of the device and the correction based on the voltage mean ratio in this embodiment. The analysis layer is also used to determine the reason for the unqualified estimation process of the device based on the comparison result between the voltage mean ratio and the preset voltage mean ratio pre-stored in the analysis layer, and determine to increase the number of acquisition information in the acquisition layer, determine to correct the cleaning standard in the processing layer, or determine to issue a maintenance notice for each device based on the reason.

[0065] Specifically, in this embodiment, the preset voltage mean ratio M0 can be divided into the first preset voltage mean ratio M1 and the second preset voltage mean ratio M2, M1 = 1.2×M3, M2 = 1.5×M3, M3 is the set voltage mean ratio standard pre-stored in the analysis layer, and M3 is set to 1.05; the specific process of comparing the voltage mean ratio M with the preset voltage mean ratio M0 is as follows:

[0066] If 1 < M ≤ M1, the value of M is relatively small at this time. It can be determined that the reason for the unqualified estimation process of the device is that the number of acquisition information of each device in the facility layer by the acquisition layer is insufficient, that is, the account information of each device collected is relatively small. Therefore, the number of acquisition information of the acquisition layer can be increased. If M1 < M ≤ M2, the value of M is relatively moderate at this time. It can be determined that the reason for the unqualified estimation process of the device is that there is a problem in the preprocessing of the data collected by the processing layer, which leads to too much acquisition information being cleared. Therefore, the cleaning standard of the processing layer can be corrected, and further, the cleaning standard interval can be reduced to correspondingly increase the number of acquisition information after preprocessing. If M2 < M, the value of M is relatively large at this time. The large value of M leads to a large voltage mean value, which results in the unqualified estimation process of the device. At this time, it can be determined that the reason for the unqualified estimation process of the device is that the device itself has a fault, and a maintenance notice is sent to each device. It should be noted that M3 can be set as needed, and the value coefficients of M1 and M2 can also be adjusted.

[0067] Furthermore, the analysis layer is also used to increase the number of acquisition information of the acquisition layer based on the comparison result between the estimated information quantity and the preset estimated information quantity pre-stored in the analysis layer. The increase amplitude of the acquisition information quantity is inversely proportional to the estimated information quantity, where the estimated information quantity is the quantity of the information remaining after the processing layer preprocesses the account information.

[0068] Specifically, in this embodiment, the preset estimated information quantity P0 can be divided into the first preset estimated information quantity P1 and the second preset estimated information quantity P2. P1 = 0.5 × P3, P2 = 1.2 × P3, and P3 is the set estimated information quantity standard pre-stored in the analysis layer, and P3 is set to 100 pieces. The specific process of comparing the estimated information quantity P with the preset estimated information quantity P0 is as follows:

[0069] If 40 < P ≤ P1, the control module in the control layer uses the first acquisition information quantity adjustment coefficient to increase the acquisition information quantity in the acquisition layer to 3 times the initial value; if P1 < P ≤ P2, the control module in the control layer uses the second acquisition information quantity adjustment coefficient to increase the acquisition information quantity in the acquisition layer to 2.5 times the initial value; if P2 < P, the control module in the control layer uses the third acquisition information quantity adjustment coefficient to increase the acquisition information quantity in the acquisition layer to 2 times the initial value. It should be noted that P3 can be set as needed without specific limitation, and P1, P2, and the increase multiple of the acquisition information quantity can all be adjusted accordingly to improve the qualified rate of the device estimation process.

[0070] Further, in the case where the adjustment for increasing the quantity of the collected information is completed, the analysis layer is further configured to determine, based on the increased quantity of the collected information, to increase the data collection frequency of the collection layer, and the increase amplitude of the data collection frequency is in a proportional relationship with the increased quantity of the collected information.

[0071] Specifically, in this embodiment, in the case where the adjustment for increasing the quantity of the collected information is completed, it is also necessary to determine the data collection frequency of the collection devices in the collection layer based on the total quantity of the increased collected information. By increasing the data collection frequency, the increased demand for the quantity of information collected by the collection layer within a unit time can be satisfied, and the more the increased quantity of the collected information, the greater the amplitude of the increase in the data collection frequency required.

[0072] Further, the analysis layer is further configured to reduce the cleaning standard of the processing layer based on the comparison result between the estimated information quantity and the preset estimated information quantity pre-stored in the analysis layer, and the reduction amplitude of the cleaning standard is in a proportional relationship with the estimated information quantity.

[0073] Specifically, in this embodiment, the analysis layer can also determine the change situation of the cleaning standard interval of the processing layer through the comparison result between P and P0. When the quantity of P is small, the cleaning standard interval can be appropriately reduced, so that the estimation layer can obtain more ledger information. The specific comparison process between P and P0 is as follows:

[0074] If 40 < P ≤ P1, the control module in the control layer uses the first standard reduction coefficient to reduce the cleaning standard interval of the processing layer to 0.9 times the initial value; if P1 < P ≤ P2, the control module in the control layer uses the second standard reduction coefficient to reduce the cleaning standard interval of the processing layer to 0.85 times the initial value; if P2 < P, the control module in the control layer uses the third standard reduction coefficient to reduce the cleaning standard interval of the processing layer to 0.8 times the initial value. It should be noted that the reduction ratio of the cleaning standard interval can be adjusted accordingly to improve the qualification rate of the estimation process of the equipment.

[0075] Further, the analysis layer is further configured to determine to perform replacement processing on the equipment based on the comparison result between the maintenance time interval and the preset maintenance time interval pre-stored in the analysis layer, where the maintenance time interval is the time difference between the previous maintenance record time node and the current maintenance record time node in the ledger information.

[0076] Specifically, in this embodiment, it can be clearly seen that when a certain device is used more frequently, the maintenance time interval appears shorter, and the device is more likely to malfunction. Therefore, by comparing the maintenance time interval with the preset maintenance time interval, it is possible to determine in advance that there is a problem with the device, so as to provide a credibility warning to the operation and maintenance personnel in advance, and help the operation and maintenance personnel adopt a more efficient processing method. The time node of each maintenance record is determined by the usage situation of the work ticket recorded in the ledger information of any device. When performing maintenance on a single device this time, the analysis layer obtains the usage situation of the previous work ticket to determine the time node of the previous maintenance record, and then calculates the maintenance time interval through the previous maintenance record time node and the current maintenance time node, and compares this maintenance time interval with the preset maintenance time interval; if the maintenance time interval is less than or equal to the preset maintenance time interval, it indicates that the current device has a relatively high failure frequency. At this time, for work efficiency, the device can be directly replaced. If the maintenance time interval is greater than the preset maintenance time interval, a maintenance notice is normally issued. Specifically, in this embodiment, the preset maintenance time interval is set to 48 hours.

[0077] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0078] The above are only the preferred embodiments of the present invention and are not used to limit the present invention; for those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A data management system for a smart grid, characterized in that Including: The facility layer, which includes several devices for operation; The acquisition layer, which is used to acquire the ledger information of each of the said devices; The processing layer, which is connected to the acquisition layer and is used to preprocess the acquired ledger information; The prediction layer, which is connected to the processing layer and is used to predict the predicted voltage of each of the said devices based on the preprocessed ledger information; The inspection layer, which includes several inspection vehicles and is connected to the prediction layer, is used to generate a corresponding inspection plan according to the prediction result of the prediction layer, and to obtain the actual voltage of the corresponding said devices based on the inspection plan; The analysis layer, which is respectively connected to the prediction layer and the inspection layer, is used to determine whether the prediction process of the system for the said devices is qualified based on the comparison result between the actual voltage and the predicted voltage, and to determine the reason for unqualified; The instruction layer, which is connected to the analysis layer and generates corresponding instructions based on the reason; The control layer, which is respectively connected to the acquisition layer, the processing layer, the prediction layer, the inspection layer and the instruction layer, is used to determine the acquisition information quantity and data acquisition frequency of the acquisition layer, determine the cleaning standard of the processing layer, determine the predicted voltage of the prediction layer, determine the moving speed of the inspection vehicle, or determine to issue a maintenance notice for each of the said devices based on the instruction; 2. The data management system for a smart grid according to claim 1, wherein The analysis layer is also used to make a determination based on the comparison result between the voltage mean value and the preset voltage mean value pre-stored in the analysis layer, or to re-determine whether the prediction process for the said devices is qualified based on several time nodes during the acquisition of the ledger information within a preset period, and to determine the reason for unqualified based on the voltage mean value ratio in the case where the prediction process for the devices is determined to be unqualified; Wherein, the voltage mean value is calculated by averaging the absolute values of the differences between each of the actual voltages and the corresponding predicted voltages, the voltage mean value ratio is the ratio between the voltage mean value and the preset voltage mean value, and the time nodes include the prediction time node and the inspection time node.

3. The data management system for a smart grid according to claim 2, wherein The analysis layer is also used to determine whether to optimize the inspection process or update the predicted voltage based on the comparison result between the average time interval and the preset average time interval pre-stored in the analysis layer; Wherein, several absolute values of time intervals are obtained for the prediction time node and the inspection time node of each of the said devices, and the average time interval is obtained by averaging the several absolute values of time intervals.

4. The data management system for a smart grid according to claim 3, characterized in that, The analysis layer is also used to increase the moving speed of the inspection vehicle when the node quantity ratio is greater than the preset node quantity ratio, and the increasing amplitude of the moving speed is in a direct proportion relationship with the node quantity ratio, wherein the node quantity ratio is the ratio between the number of abnormal prediction time nodes and the total number of the prediction time nodes, and the abnormal prediction time node is the prediction time node earlier than the corresponding inspection time node.

5. The data management system for a smart grid according to claim 4, wherein, The analysis layer is also used to update the predicted voltage when the node quantity ratio is less than or equal to the preset node quantity ratio.

6. The data management system for a smart grid according to claim 2, wherein The analysis layer is further configured to determine the reason for the unqualified estimated process of the device based on the comparison result between the voltage mean ratio and the preset voltage mean ratio pre-stored in the analysis layer, and determine to increase the number of collected information in the collection layer, determine to correct the cleaning standard of the processing layer, or determine to issue a maintenance notice for each device based on the reason.

7. The data management system for a smart grid according to claim 6, wherein The analysis layer is further configured to increase the number of collected information in the collection layer based on the comparison result between the estimated information quantity and the preset estimated information quantity pre-stored in the analysis layer, and the increase amplitude of the number of collected information is inversely proportional to the estimated information quantity, where the estimated information quantity is the quantity of information retained after the processing layer preprocesses the ledger information.

8. The data management system for a smart grid according to claim 7, wherein, When the adjustment of increasing the number of collected information is completed, the analysis layer is further configured to determine to increase the data collection frequency of the collection layer based on the increased number of collected information, and the increase amplitude of the data collection frequency is directly proportional to the increased number of collected information.

9. The data management system for a smart grid according to claim 7, characterized in that, The analysis layer is further configured to reduce the cleaning standard of the processing layer based on the comparison result between the estimated information quantity and the preset estimated information quantity pre-stored in the analysis layer, and the reduction amplitude of the cleaning standard is directly proportional to the estimated information quantity.

10. The data management system for a smart grid according to claim 1, characterized in that, The analysis layer is further configured to determine to replace the device or perform maintenance on the device based on the comparison result between the maintenance time interval and the preset maintenance time interval pre-stored in the analysis layer, where the maintenance time interval is the time difference between the previous maintenance record time node and the current maintenance record time node in the ledger information.

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