Equipment-level data acquisition method, system and equipment of energy storage power station, and storage medium

Abstract

The invention discloses an equipment-level data acquisition method, system and equipment of an energy storage power station, and a storage medium. The method comprises the following steps: determining an acquisition rate and equipment state data of a target energy storage power station according to a preset acquisition period; constructing a load factor, a state factor and an environment coefficient; inputting the load factor, the state factor and the environment coefficient into a state urgency factor calculation relational expression to obtain a state urgency factor; and determining a data acquisition quantification result based on a state urgency factor, the acquisition rate and the equipment state data, analyzing the data acquisition quantification result, and when an analysis result does not meet a preset condition, continuously adjusting the acquisition period until the data acquisition quantification result corresponding to the state urgency factor meets the preset condition. Outputting an acquisition period corresponding to the current state urgency factor; and executing a data acquisition scheme matched with the acquisition period. According to the method provided by the embodiment of the invention, the state change of the equipment is captured in time through flexible data acquisition.

Description

Technical Field

[0001] The present invention relates to the technical field of data acquisition, and in particular, to a method, system, device, and storage medium for device-level data acquisition of an energy storage power station. Background Art

[0002] With the increasingly widespread application of electrochemical energy storage power stations in the fields of power grid dispatching and energy management, accurate monitoring and scientific evaluation of the operating status of energy storage power stations have become increasingly important. Device-level data of electrochemical energy storage power stations (such as state parameters of battery clusters and battery monomers) play an important role in power grid balancing, capacity regulation, and power quality guarantee.

[0003] In the prior art, most energy storage power stations adopt fixed-frequency data sampling methods, which collect data based on a preset sampling interval for real-time monitoring of device status. However, timed continuous sampling lacks a flexible adjustment mechanism and cannot capture changes in device status in a timely manner, resulting in inaccurate monitoring results.

[0004] It can be seen that how to design a flexible data acquisition method to improve the accuracy of monitoring results has become a technical problem that needs to be urgently solved by those skilled in the art. Summary of the Invention

[0005] The present invention provides a method, system, device, and storage medium for device-level data acquisition of an energy storage power station to solve the technical problem of inflexible current data acquisition, be able to capture changes in device status in a timely manner, and improve the accuracy of monitoring results.

[0006] To solve the above technical problem, an embodiment of the present invention provides a method for device-level data acquisition of an energy storage power station, and the method includes:

[0007] Determine the acquisition rate and device status data of the target energy storage power station with a preset acquisition period;

[0008] Extract the power station load data from the device status data, and construct a load factor of the target energy storage power station based on the analysis result of the power station load data;

[0009] Extract the operating status data from the device status data, analyze the operating status data, and construct a status factor of the target energy storage power station based on the analysis result;

[0010] Extract the environmental data from the device status data, and construct an environmental coefficient of the target energy storage power station based on the analysis result of the environmental data;

[0011] Input the load factor, the status factor, and the environmental coefficient into a pre-constructed calculation relationship formula of the status urgency factor to obtain the status urgency factor;

[0012] Determine the data acquisition quantization result based on the state urgency factor, the acquisition rate, and the device state data, analyze the data acquisition quantization result, and when the analysis result does not meet the preset conditions, continuously adjust the acquisition period until the data acquisition quantization result corresponding to the state urgency factor meets the preset conditions, and output the acquisition period corresponding to the current state urgency factor;

[0013] Execute the data acquisition scheme matching the acquisition period.

[0014] As one of the preferred solutions, the determination process of the acquisition rate includes:

[0015] Obtain the number of sampled battery clusters in the target energy storage power station, and obtain the total number of devices in the target energy storage power station;

[0016] Perform consistency processing on the number of sampled battery clusters and the total number of devices to obtain the difference results between several battery clusters;

[0017] Based on the difference results, obtain a typical data set matching the unsampled devices;

[0018] Based on the typical data set and the sampled data obtained from the sampled battery clusters, obtain the acquisition rate.

[0019] As one of the preferred solutions, the extraction of the operating state data from the device state data and the analysis of the operating state data, and the construction of the state factor of the target energy storage power station based on the analysis result include:

[0020] Extract the current data, voltage data, and fault state data from the operating state data;

[0021] Perform binary analysis on the fault state data to obtain the first state factor;

[0022] Perform normalization analysis on the current data and voltage data to obtain the second state factor;

[0023] Perform entropy value processing on the first state factor and the second state factor to obtain the state factor of the target energy storage power station.

[0024] As one of the preferred solutions, the determination of the data acquisition quantization result based on the state urgency factor, the acquisition rate, and the device state data includes:

[0025] Based on the status urgency factor, entropy weight calculation is performed on the obtained data cost result and data quality result to obtain the data acquisition quantization result. Among them, the data cost result is obtained by processing the acquisition period and the acquisition rate, and the similarity matrix is calculated for several pieces of the device status data to obtain the data quality result.

[0026] As one of the preferred solutions, the data cost result obtained by processing the acquisition period and the acquisition rate includes:

[0027] Within the preset acquisition period, obtain the actual data volume collected by the sampled battery cluster and the total collectable data volume of the devices in the target energy storage power station;

[0028] Process the actual data volume collected by the sampled battery cluster and the total collectable data volume of the devices in the target energy storage power station to obtain the data usage ratio;

[0029] Based on the data usage ratio, obtain the data cost result.

[0030] As one of the preferred solutions, for the calculation of the similarity matrix for several pieces of the device status data to obtain the data quality result, the calculation process includes:

[0031] Perform dimension elimination processing on the obtained several initial device statuses to obtain several pieces of device status data;

[0032] Based on the Euclidean distance between each pair of the device status data, construct a similarity matrix;

[0033] Based on the similarity matrix, perform data generation processing on the typical data set to obtain a prediction data set;

[0034] According to the discrete degree between the prediction data set and the sampled data obtained from the sampled battery cluster, obtain the prediction error;

[0035] Obtain the system error of the target energy storage power station, and based on the prediction error and the system error, obtain the data quality result.

[0036] As one of the preferred solutions, the entropy weight calculation is performed on the obtained data cost result and data quality result based on the status urgency factor to obtain the data acquisition quantization result, including:

[0037] Based on the historical data of the target energy storage power station in the normal state, obtain the normal state factor;

[0038] Process the status urgency factor and the normal state factor to respectively obtain the weight relational expressions based on the data cost result and the data quality result;

[0039] Based on the weight relationship formula, entropy weight calculation is performed on the obtained data cost result and data quality result to obtain a data acquisition quantization result.

[0040] Another embodiment of the present invention provides an equipment-level data acquisition system for an energy storage power station, including:

[0041] A determination module, configured to determine the acquisition rate and equipment status data of a target energy storage power station at a preset acquisition period;

[0042] A load extraction module, configured to extract the power station load data in the equipment status data and construct a load factor of the target energy storage power station based on the analysis result of the power station load data;

[0043] A status extraction module, configured to extract the operation status data in the equipment status data, analyze the operation status data, and construct a status factor of the target energy storage power station based on the analysis result;

[0044] An environment extraction module, configured to extract the environmental data in the equipment status data and construct an environmental coefficient of the target energy storage power station based on the analysis result of the environmental data;

[0045] A calculation module, configured to input the load factor, the status factor, and the environmental coefficient into a pre-constructed state urgency factor calculation relationship formula to obtain a state urgency factor;

[0046] An iteration module, configured to determine a data acquisition quantization result based on the state urgency factor, the acquisition rate, and the equipment status data, analyze the data acquisition quantization result, and when the analysis result does not meet the preset condition, continuously adjust the acquisition period until the data acquisition quantization result corresponding to the state urgency factor meets the preset condition, and output the acquisition period corresponding to the current state urgency factor;

[0047] An execution module, configured to execute a data acquisition scheme matching the acquisition period.

[0048] Another embodiment of the present invention provides an equipment-level data acquisition device for an energy storage power station, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the equipment-level data acquisition method for the energy storage power station as described above is implemented.

[0049] Another embodiment of the present invention provides a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the device where the computer-readable storage medium is located executes the computer program, the equipment-level data acquisition method for the energy storage power station as described above is implemented.

[0050] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following:

[0051] Determine the acquisition rate and equipment status data of the target energy storage power station at a preset acquisition period; extract the power station load data from the equipment status data to construct the load factor of the target energy storage power station based on the analysis result of the power station load data; extract the operation status data from the equipment status data, analyze the operation status data, and construct the status factor of the target energy storage power station based on the analysis result; extract the environmental data from the equipment status data to construct the environmental coefficient of the target energy storage power station based on the analysis result of the environmental data; input the load factor, the status factor, and the environmental coefficient into a pre-constructed calculation relation of the status urgency factor to obtain the status urgency factor; determine the data acquisition quantization result based on the status urgency factor, the acquisition rate, and the equipment status data, analyze the data acquisition quantization result, and when the analysis result does not meet the preset condition, continuously adjust the acquisition period until the data acquisition quantization result corresponding to the status urgency factor meets the preset condition, output the acquisition period corresponding to the current status urgency factor, and execute the data acquisition scheme matching the acquisition period. Compared with the prior art, the present invention provides a flexible data acquisition method, which can timely capture the changes in the equipment status, thereby improving the accuracy of the monitoring result. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a schematic flow chart of the equipment-level data acquisition method of the energy storage power station in one embodiment of the present invention;

[0053] Figure 2 is a schematic structural diagram of the equipment-level data acquisition system of the energy storage power station in one embodiment of the present invention;

[0054] Figure 3 is a schematic structural diagram of the equipment-level data acquisition device of the energy storage power station in one embodiment of the present invention.

[0055] REFERENCE SIGNS:

[0056] Among them, 11, determination module; 12, load extraction module; 13, status extraction module; 14, environment extraction module; 15, calculation module; 16, iteration module; 17, execution module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0058] In the description of the present application, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first", "second", "third", etc. may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0059] In the description of the present application, it should be noted that unless otherwise clearly defined and limited, the terms "installed", "connected", "connected" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two components. The terms "vertical", "horizontal", "left", "right", "up", "down" and similar expressions used herein are only for the purpose of illustration and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be understood as a limitation of the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0060] In the description of the present application, it should be noted that unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0061] An embodiment of the present invention provides a method for collecting equipment-level data of an energy storage power station. Specifically, please refer to Figure 1 , Figure 1 which shows a schematic flow chart of the method for collecting equipment-level data of an energy storage power station in one of the embodiments of the present invention. The method includes:

[0062] S1: Determine the acquisition rate and equipment status data of the target energy storage power station at a preset acquisition period;

[0063] S2: Extract the power station load data from the equipment status data, and construct the load factor of the target energy storage power station based on the analysis result of the power station load data;

[0064] S3: Extract the operation status data from the equipment status data, analyze the operation status data, and construct the status factor of the target energy storage power station based on the analysis result;

[0065] S4: Extract the environmental data from the equipment status data, and construct the environmental coefficient of the target energy storage power station based on the analysis result of the environmental data;

[0066] S5: Input the load factor, the status factor, and the environmental coefficient into a pre-constructed calculation relation of the status urgency factor to obtain the status urgency factor;

[0067] S6: Determine the data acquisition quantization result based on the status urgency factor, the acquisition rate, and the equipment status data, analyze the data acquisition quantization result, and when the analysis result does not meet the preset condition, continuously adjust the acquisition period until the data acquisition quantization result corresponding to the status urgency factor meets the preset condition, and output the acquisition period corresponding to the current status urgency factor;

[0068] S7: Execute a data acquisition scheme matching the acquisition period.

[0069] In S1, the preset acquisition period is a time interval preset according to factors such as the operation characteristics of the energy storage power station, data requirements, and system resources. For example, for some application scenarios with high requirements for data real-time performance, the acquisition period is set to a shorter time, such as every minute or every five minutes; while for some scenarios with lower requirements for data real-time performance, the acquisition period is appropriately extended, such as every hour or every day.

[0070] Specifically, the process of determining the acquisition rate is as follows:

[0071] Obtain the number of sampled battery clusters in the target energy storage power station, obtain the total number of equipment in the target energy storage power station, perform consistency processing on the number of sampled battery clusters and the total number of equipment to obtain the difference results between several battery clusters; based on the difference results, obtain a typical data set matching the non-sampled equipment; based on the typical data set and the sampled data obtained from the sampled battery clusters, obtain the acquisition rate.

[0072] Through the equipment list or monitoring system of the power station, accurately obtain the number of battery clusters selected as sampling objects in the target energy storage power station. These sampled battery clusters should be representative and able to reflect the operating characteristics of the battery clusters in the entire power station. The total number of equipment is the total number of all relevant equipment in the target energy storage power station, including various types of equipment such as battery clusters, inverters, and transformers.

[0073] Since the characteristics of different battery clusters may vary, to ensure the comparability and accuracy of data, it is necessary to perform consistency processing on the number of sampled battery clusters and the total number of equipment, eliminate the dimensional and scale differences of the data. Based on the difference results, select representative characteristic data from the data of the sampled battery clusters. These characteristic data should be able to reflect the operating status and performance characteristics of different types of battery clusters. Integrate and process the selected characteristic data to construct a typical data set that matches the unsampled equipment. The typical data set serves as a reference model for the data of the unsampled equipment and is used to infer and estimate the operating status of the unsampled equipment.

[0074] According to the typical data set and the sampled data, combined with the total number of equipment and the number of sampled battery clusters, calculate the acquisition rate of the entire target energy storage power station. The acquisition rate can be defined as the ratio of the actually acquired data volume to the theoretically acquirable data volume.

[0075] The higher the load, the greater the operating pressure of the power station. Usually, higher data quality assurance is required. When the load is large, more frequent or more accurate data collection is required to ensure the stability and safety of the power station. At high loads, the power station usually requires higher data quality assurance, so the load factor can be appropriately increased.

[0076] Obtain the current charge value, historical maximum power station load value, and historical minimum power station load value of the target energy storage power station, and perform normalization analysis on the obtained data to obtain the load factor of the target energy storage power station.

[0077] Specifically, the construction process of the state factor includes:

[0078] Extract the current data, voltage data, and fault status data from the operating status data; perform binary analysis on the fault status data to obtain the first state factor; perform normalization analysis on the current data and voltage data to obtain the second state factor; perform entropy processing on the first state factor and the second state factor to obtain the state factor of the target energy storage power station.

[0079] When the power station is in an abnormal state, higher data quality is required, so the first state factor can be appropriately increased.

[0080] For key parameters such as current and voltage, the weight of the second state factor is higher.

[0081] Specifically, at least extract the temperature, humidity, and wind level in the target energy storage power station, perform normalized analysis and calculation on the temperature, humidity, and wind level to obtain the external environment coefficient.

[0082] For example, in extreme weather, a higher requirement for data accuracy is needed, and when the environment is unstable, the data collection frequency or accuracy should be increased.

[0083] In S5, input the load factor, the state factor, and the environment coefficient into a pre-constructed calculation relationship of the state urgency factor to obtain the state urgency factor.

[0084] Based on the state urgency factor, the collection rate, and the device state data, determine the data collection quantization result. Analyze the data collection quantization result. When the analysis result does not meet the preset conditions, continuously adjust the collection period until the data collection quantization result corresponding to the state urgency factor meets the preset conditions, output the collection period corresponding to the current state urgency factor, and execute the data collection scheme matching the collection period.

[0085] The adjustment method is preferably a genetic algorithm.

[0086] The collection scheme includes the optimal sampling period, the optimal data collection device, and the optimal sampling frequency.

[0087] Specifically, based on the state urgency factor, perform entropy weight calculation on the obtained data cost result and data quality result to obtain the data collection quantization result. Among them, the data cost result is obtained by processing the collection period and the collection rate, and calculate the similarity matrix of several pieces of the device state data to obtain the data quality result.

[0088] Within the preset collection period, obtain the actual data volume collected by the sampling battery cluster and the total collectable data volume of the devices in the target energy storage power station; process the actual data volume collected by the sampling battery cluster and the total collectable data volume of the devices in the target energy storage power station to obtain the data usage ratio; based on the data usage ratio, obtain the data cost result. The specific calculation process is as follows:

[0089]

[0090] Among them, z is the data cost result, T is the collection period, and S is the calculated collection rate.

[0091] Perform dimensionless processing on the obtained initial device states to obtain a number of device state data; construct a similarity matrix based on the Euclidean distance between each pair of the device state data; perform data generation processing on the typical data set based on the similarity matrix to obtain a prediction data set; obtain a prediction error according to the degree of dispersion between the prediction data set and the sampling data obtained from the sampled battery cluster; obtain the system error of the target energy storage power station, and obtain the data quality result based on the prediction error and the system error. The calculation process is as follows:

[0092]

[0093] Among them, SumRMSE represents the degree of dispersion between the prediction data set and the sampling data obtained from the sampled battery cluster. The smaller its value, the higher the accuracy of the prediction data. ThrRMSE represents the system error of the target energy storage power station. ThrRMSE (system error) is the maximum tolerance error value set based on the accuracy of the device, measurement error, and performance limitations of the system. First, by analyzing the technical specifications of the device and experimental data, determine the maximum error range that the system may have, which includes factors such as the accuracy of the sensor and signal noise. Then, according to the design requirements of the device and the operating characteristics of the battery system, set an error tolerance upper limit, usually obtained through historical data or simulation experiments. Finally, ThrRMSE is used as a theoretical maximum error threshold to measure the upper limit of data quality, ensuring that the error does not exceed this value during the data acquisition process, thereby guaranteeing the reliability and accuracy of the data.

[0094] Based on the ln function, this function realizes dual regulation: maintaining the slow-varying characteristic of the scoring curve in the low-error interval to avoid misjudgment caused by normal fluctuations; while triggering a scoring acceleration decay mechanism in the high-error interval to significantly amplify the impact of abnormal deviations on the score. This piecewise response mechanism not only ensures the scoring stability under normal working conditions but also effectively identifies and punishes significant deterioration of data quality, providing a reliable decision-making basis for optimizing acquisition parameters.

[0095] Based on the historical data of the target energy storage power station under normal conditions, obtain the state urgency factor under normal conditions; process the state urgency factor and the normal state factor to obtain weight relationships based on the data cost result and the data quality result respectively; perform entropy weight calculation on the obtained data cost result and data quality result based on the weight relationships to obtain the data acquisition quantization result. The calculation process is as follows:

[0096]

[0097] Among them, α is an adjustment coefficient, which determines the sensitivity of the state urgency factor H to weight adjustment.

[0098] H0 is a reference value representing the state urgency factor of the power station under normal conditions, which can usually be set according to the historical data of the power station.

[0099] The quantization result can be expressed as:

[0100]

[0101] An embodiment of the present invention provides a device-level data acquisition system for an energy storage power station. Specifically, please refer to Figure 2 , Figure 2 which shows a schematic structural diagram of the device-level data acquisition system for the energy storage power station in one of the embodiments of the present invention. The system includes:

[0102] A determination module 11, configured to determine the acquisition rate and device status data of the target energy storage power station at a preset acquisition period;

[0103] A load extraction module 12, configured to extract the power station load data from the device status data and construct the load factor of the target energy storage power station based on the analysis result of the power station load data;

[0104] A status extraction module 13, configured to extract the operation status data from the device status data, analyze the operation status data, and construct the status factor of the target energy storage power station based on the analysis result;

[0105] An environment extraction module 14, configured to extract the environment data from the device status data and construct the environment coefficient of the target energy storage power station based on the analysis result of the environment data;

[0106] A calculation module 15, configured to input the load factor, the status factor, and the environment coefficient into a pre-constructed state urgency factor calculation relationship formula to obtain the state urgency factor;

[0107] An iteration module 16, configured to determine the data acquisition quantization result based on the state urgency factor, the acquisition rate, and the device status data, analyze the data acquisition quantization result, and continuously adjust the acquisition period when the analysis result does not meet the preset conditions until the data acquisition quantization result corresponding to the state urgency factor meets the preset conditions, and output the acquisition period corresponding to the current state urgency factor;

[0108] An execution module 17, configured to execute the data acquisition scheme matching the acquisition period.

[0109] Refer to Figure 3, which is a structural block diagram of the device-level data acquisition device of the energy storage power station provided by the embodiments of the present invention. The device-level data acquisition device 20 of the energy storage power station provided by the embodiments of the present invention includes a processor 21, a memory 22, and a computer program stored in the memory 22 and configured to be executed by the processor 21. When the processor 21 executes the computer program, it implements the steps in the method embodiment of the device-level data acquisition method of the energy storage power station as described above. For example Figure 1 the steps S1 to S7 described in

[0110] ; or, when the processor 21 executes the computer program, it implements the functions of each module in the above device embodiments. For example, the determination module 11.

[0111] The determination module 11 is configured to determine the acquisition rate and device status data of the target energy storage power station at a preset acquisition period;

[0112] The load extraction module 12 is configured to extract the power station load data in the device status data and construct the load factor of the target energy storage power station based on the analysis result of the power station load data;

[0113] The status extraction module 13 is configured to extract the operation status data in the device status data, analyze the operation status data, and construct the status factor of the target energy storage power station based on the analysis result;

[0114] The environment extraction module 14 is configured to extract the environment data in the device status data and construct the environment coefficient of the target energy storage power station based on the analysis result of the environment data;

[0115] The calculation module 15 is configured to input the load factor, the status factor, and the environment coefficient into a pre-constructed state urgency factor calculation relationship formula to obtain the state urgency factor;

[0116] The iterative module 16 is configured to determine a data acquisition quantization result based on the status urgency factor, the acquisition rate, and the device status data, analyze the data acquisition quantization result, and when the analysis result does not meet the preset condition, continuously adjust the acquisition period until the data acquisition quantization result corresponding to the status urgency factor meets the preset condition, and output the acquisition period corresponding to the current status urgency factor;

[0117] The execution module 17 is configured to execute a data acquisition scheme that matches the acquisition period.

[0118] The device-level data acquisition device 20 of the energy storage power station may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art can understand that the schematic diagram is only an example of the device-level data acquisition and processing device of the energy storage power station, and does not constitute a limitation on the device-level data acquisition device 20 of the energy storage power station. It may include more or fewer components than shown in the figure, or combine some components, or different components. For example, the device-level data acquisition device 20 of the energy storage power station may also include input / output devices, network access devices, buses, etc.

[0119] The processor 21 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor 21 is the control center of the device-level data acquisition device 20 of the energy storage power station, and connects various parts of the device-level data acquisition device 20 of the entire energy storage power station through various interfaces and lines.

[0120] The memory 22 can be used to store the computer programs and / or modules. By running or executing the computer programs and / or modules stored in the memory 22 and invoking the data stored in the memory 22, the processor 21 realizes various functions of the device-level data acquisition device 20 of the energy storage power station. The memory 22 may mainly include a program storage area and a data storage area. Among them, the program storage area can store the operating system, application programs required for at least one function (such as the sound playing function, the image playing function, etc.); the data storage area can store the data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0121] Among them, if the modules integrated in the device-level data acquisition device 20 of the energy storage power station are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disc, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0122] Those of ordinary skill in the art can understand that to implement all or part of the processes in the above-mentioned embodiment methods, it can be completed by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned method embodiments. Among them, the storage medium can be a magnetic disk, an optical disc, a read-only memory (ROM) or a random access memory (RAM), etc.

[0123] Accordingly, an embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the steps in the device-level data acquisition method of the energy storage power station in the above embodiment, such as Figure 1 the steps S1 to S7 described therein.

[0124] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following:

[0125] Determine the acquisition rate and equipment status data of the target energy storage power station at a preset acquisition period; extract the power station load data in the equipment status data, and construct the load factor of the target energy storage power station based on the analysis result of the power station load data; extract the operation status data in the equipment status data, analyze the operation status data, and construct the status factor of the target energy storage power station based on the analysis result; extract the environmental data in the equipment status data, and construct the environmental coefficient of the target energy storage power station based on the analysis result of the environmental data; input the load factor, the status factor, and the environmental coefficient into a pre-constructed calculation relationship of the status urgency factor to obtain the status urgency factor; determine the data acquisition quantization result based on the status urgency factor, the acquisition rate, and the equipment status data, analyze the data acquisition quantization result, and when the analysis result does not meet the preset conditions, continuously adjust the acquisition period until the data acquisition quantization result corresponding to the status urgency factor meets the preset conditions, output the acquisition period corresponding to the current status urgency factor, and generate a data acquisition plan with the acquisition period. Compared with the prior art, the present invention provides a flexible data acquisition method, which can timely capture the changes in the equipment status, thereby improving the accuracy of the monitoring results.

[0126] The above embodiments only represent several implementation manners of the present invention, and the description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.

Claims

1. A method for device-level data acquisition of an energy storage power station, characterized in that, Including: Determine the collection rate and equipment status data of the target energy storage power station at a preset collection period; Extract the power station load data from the equipment status data, and construct the load factor of the target energy storage power station based on the analysis result of the power station load data; Extract the operation status data from the equipment status data, analyze the operation status data, and construct the status factor of the target energy storage power station based on the analysis result; Extract the environmental data from the equipment status data, and construct the environmental coefficient of the target energy storage power station based on the analysis result of the environmental data; Input the load factor, the status factor, and the environmental coefficient into a pre-constructed calculation relationship of the status urgency factor to obtain the status urgency factor; Determine the data collection quantization result based on the status urgency factor, the collection rate, and the equipment status data, analyze the data collection quantization result, and when the analysis result does not meet the preset condition, continuously adjust the collection period until the data collection quantization result corresponding to the status urgency factor meets the preset condition, and output the current collection period corresponding to the status urgency factor; Execute the data collection scheme matching the collection period.

2. The equipment-level data acquisition method of the energy storage power station according to claim 1, characterized in that The determination process of the collection rate includes: Obtain the number of sampled battery clusters in the target energy storage power station, and obtain the total number of equipment in the target energy storage power station; Perform consistency processing on the number of sampled battery clusters and the total number of equipment to obtain the difference results between several battery clusters; Based on the difference results, obtain a typical data set matching the unsampled equipment; Based on the typical data set and the sampled data obtained from the sampled battery clusters, obtain the collection rate.

3. The method for collecting device-level data of an energy storage power station according to claim 1, wherein The extracting the operation status data from the equipment status data, analyzing the operation status data, and constructing the status factor of the target energy storage power station based on the analysis result includes: Extract the current data, voltage data, and fault status data from the operation status data; Perform binary analysis on the fault status data to obtain the first status factor; Perform normalization analysis on the current data and voltage data to obtain the second status factor; Perform entropy value processing on the first status factor and the second status factor to obtain the status factor of the target energy storage power station.

4. The method for device-level data acquisition of an energy storage power station according to claim 2, characterized in that, The determining the data collection quantization result based on the status urgency factor, the collection rate, and the equipment status data includes: Based on the status urgency factor, perform entropy weight calculation on the obtained data cost result and data quality result to obtain the data collection quantization result, where the data cost result is obtained by processing the collection period and the collection rate, and calculate the similarity matrix of several equipment status data to obtain the data quality result.

5. The device-level data acquisition method of the energy storage power station according to claim 4, wherein The data cost result is obtained by processing the collection period and the collection rate, including: Within the preset collection period, obtain the actual data volume collected by the sampled battery clusters and the total collectable data volume of the equipment in the target energy storage power station; Process the actual data volume collected by the sampled battery cluster and the total data volume that can be collected by the equipment in the target energy storage power station to obtain a data usage ratio; Based on the data usage ratio, obtain the data cost result.

6. The method for collecting device-level data of an energy storage power station according to claim 4, wherein, The calculation process of calculating the similarity matrix for a number of the device status data to obtain the data quality result includes: Perform a dimension elimination process on the obtained number of initial device statuses to obtain a number of device status data; Construct a similarity matrix based on the Euclidean distance between each pair of the device status data; Based on the similarity matrix, perform data generation processing on the typical data set to obtain a prediction data set; Obtain a prediction error according to the degree of dispersion between the prediction data set and the sampled data obtained from the sampled battery cluster; Obtain the system error of the target energy storage power station, and based on the prediction error and the system error, obtain the data quality result.

7. The equipment-level data acquisition method for an energy storage power station according to claim 4, characterized in that, The entropy weight calculation of the obtained data cost result and data quality result based on the state urgency factor to obtain the data acquisition quantization result includes: Based on the historical data of the target energy storage power station in the normal state, obtain the normal state factor; Process the state urgency factor and the normal state factor to respectively obtain weight relational expressions based on the data cost result and the data quality result; Based on the weight relational expressions, perform entropy weight calculation on the obtained data cost result and data quality result to obtain the data acquisition quantization result.

8. An equipment-level data acquisition system for an energy storage power station, characterized in that, Includes: A determination module, configured to determine the acquisition rate and device status data of the target energy storage power station at a preset acquisition period; A load extraction module, configured to extract the power station load data from the device status data, and construct the load factor of the target energy storage power station based on the analysis result of the power station load data; A status extraction module, configured to extract the operation status data from the device status data, analyze the operation status data, and construct the status factor of the target energy storage power station based on the analysis result; An environment extraction module, configured to extract the environmental data from the device status data, and construct the environmental coefficient of the target energy storage power station based on the analysis result of the environmental data; A calculation module, configured to input the load factor, the status factor, and the environmental coefficient into a pre-constructed state urgency factor calculation relational expression to obtain the state urgency factor; An iteration module, configured to determine the data acquisition quantization result based on the state urgency factor, the acquisition rate, and the device status data, analyze the data acquisition quantization result, and when the analysis result does not meet the preset conditions, continuously adjust the acquisition period until the data acquisition quantization result corresponding to the state urgency factor meets the preset conditions, and output the acquisition period corresponding to the current state urgency factor; An execution module, configured to execute a data acquisition scheme matching the acquisition period.

9. An equipment-level data acquisition device for an energy storage power station, characterized in that, Includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the equipment-level data acquisition method of the energy storage power station according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program. When the device where the computer-readable storage medium is located executes the computer program, the method for device-level data acquisition of an energy storage power station as described in any one of claims 1 to 7 is implemented.

Images