A new energy power plant management method, device, equipment, medium and product

Abstract

The application discloses a new energy power plant management method, device, equipment, medium and product, and relates to the technical field of data processing. The method comprises the following steps: determining initial detection information of a power plant; processing the initial detection information by using a pre-set information mapping rule to obtain target detection information, wherein the target detection information comprises at least one target type information and target detection data corresponding to the target type information; determining a target fault diagnosis model of the power plant based on the at least one target type information, and processing the target detection data corresponding to the at least one target type information by using the target fault diagnosis model to obtain a fault diagnosis result of the power plant. The technical scheme of the application does not need to stop downstream applications, reduces the modification and adaptation difficulty and shutdown frequency of downstream machine learning applications in the new energy field, reduces the equipment maintenance cost of the new energy power plant, and improves the real-time performance of equipment detection and operation and maintenance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a new energy power plant management method, device, equipment, medium and product. BACKGROUND

[0002] With the expansion of new energy asset scale, the production and operation management of enterprises such as power plants is facing problems such as large number of equipment installations, old model transformation, limited staff, and difficult management, therefore, how to realize large-scale data access and convenient application of new energy assets, improve production efficiency and reduce operation and maintenance cost is an urgent problem to be solved.

[0003] In the field of new energy power generation, power plant equipment is constructed in multiple stages, with various models, large number of data measurement points of equipment, and different monitoring points, and the equipment points are often changed, and in addition, the maintenance and technical transformation of equipment will also cause frequent changes of data measurement points of equipment. At present, in the process of power plant construction, the downstream services depend on the upstream equipment points, that is, the downstream big data and artificial intelligence services need to frequently stop and adjust services according to the changes of upstream equipment point data, but the stop will cause the equipment monitoring business to be completely suspended, and once the downstream services cannot respond to the upstream data changes in time, it will cause deviation in the monitoring and analysis of equipment data, and further make wrong decision guidance for production and operation. SUMMARY

[0004] The present application provides a new energy power plant management method, device, equipment, medium and product, aiming to reduce the modification and adaptation difficulty and the number of stoppages of downstream machine learning applications in the new energy field, reduce the equipment maintenance cost of the new energy power plant, and improve the real-time performance of equipment detection and operation and maintenance.

[0005] According to an aspect of the present application, a new energy power plant management method is provided, which comprises:

[0006] determining initial detection information of the power plant, wherein the initial detection information comprises at least one initial type information and initial detection data corresponding to the at least one initial type information;

[0007] processing the at least one initial type information and the initial detection data corresponding to the at least one initial type information by using a pre-set information mapping rule to obtain target detection information, wherein the target detection information comprises at least one target type information and target detection data corresponding to the at least one target type information;

[0008] determining a target fault diagnosis model of the power plant based on the at least one target type information, and processing the target detection data corresponding to the at least one target type information by using the target fault diagnosis model to obtain a fault diagnosis result of the power plant.

[0009] According to another aspect of the present invention, a management device for a new energy power plant is provided. This device is used to implement the management method for a new energy power plant in any embodiment of the present invention. The device includes:

[0010] The acquisition module is used to determine the initial detection information of the power plant, wherein the initial detection information includes at least one initial type information and initial detection data corresponding to at least one initial type information;

[0011] The determination module is used to process at least one initial type information and the initial detection data corresponding to at least one initial type information using pre-set information mapping rules to obtain target detection information, wherein the target detection information includes at least one target type information and the target detection data corresponding to the target type information;

[0012] The diagnostic module is used to determine the target fault diagnosis model of the power plant based on at least one target type information, and to process the target detection data corresponding to at least one target type information using the target fault diagnosis model to obtain the fault diagnosis result of the power plant.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and a memory communicatively connected to the at least one processor;

[0015] The memory stores a computer program that can be executed by at least one processor, which enables the at least one processor to execute the management method of the new energy power plant in any embodiment of the present invention.

[0016] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the management method of a new energy power plant in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer program product is provided, which includes a computer program that, when executed by a processor, implements the management method of a new energy power plant according to any embodiment of the present invention.

[0018] The management method of the new energy power plant provided by the application comprises the following steps: determining initial detection information of the power plant, wherein the initial detection information comprises at least one initial type information and initial detection data corresponding to the at least one initial type information; processing the at least one initial type information and the initial detection data corresponding to the at least one initial type information by using a pre-set information mapping rule to obtain target detection information, wherein the target detection information comprises at least one target type information and target detection data corresponding to the at least one target type information; determining a target fault diagnosis model of the power plant based on the at least one target type information, and processing the target detection data corresponding to the at least one target type information by using the target fault diagnosis model to obtain a fault diagnosis result of the power plant. The technical scheme of the application can integrate and map the types of equipment of the power plant and the measurement point data in a unified node measurement mode, process the target detection information after standardization and unification to obtain the equipment diagnosis result of the power plant, map different data in a unified reference vector through the mapping mode of data flow, that is, directly map the upstream information that has changed to the pre-set reference vector, do not need to stop the downstream application, keep the change rhythm of the cluster node in synchronization, reduce the modification and adaptation difficulty and shutdown frequency of the downstream machine learning application in the new energy field, reduce the equipment maintenance cost of the new energy power plant, and improve the real-time performance of equipment detection and operation and maintenance. The problems that the downstream big data and artificial intelligence services need to frequently change and shut down the upstream equipment point data and adjust the services, the shutdown causes the equipment monitoring business to be completely suspended, and once the downstream service cannot respond to the upstream data change in time, the monitoring and analysis of the equipment data will deviate, and then the production and operation will make wrong decision guidance are solved.

[0019] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the application, nor is it used to limit the scope of the application. Other features of the application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the application, the following will briefly introduce the drawings needed to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0021] Figure 1 is a flowchart of a new energy power plant management method provided by the application;

[0022] Figure 2 is a schematic diagram of a device model hierarchy provided by the application;

[0023] Figure 3 is a schematic diagram of a processing flow provided by the present application;

[0024] Figure 4 is a structural schematic diagram of a new energy power generation field management device provided by the present application;

[0025] Figure 5 is a structural schematic diagram of an electronic device provided by the present application. DETAILED DESCRIPTION

[0026] In order to make the personnel in the art better understand the present application, the technical solutions in the present application will be clearly and completely described below in combination with the drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by the person of ordinary skill in the art without making creative labor should belong to the protection scope of the present application.

[0027] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0028] Stream processing is a data processing system for continuous analysis of essentially infinite business data sets. In the context of large-scale stream data analysis, Apache Flink is the most mature and reliable distributed stream batch integrated data processing open source framework. Specifically, if the device measurement point data is used as the business data source, all device data is pushed to the center in a low-latency manner for real-time uninterrupted analysis and processing. Due to the non-uniformity of device measurement points and the existence of variable objective conditions (including but not limited to measurement point name changes and measurement point increases and decreases), it will cause problems such as stream processing data missing, frequent data algorithm adjustment, data analysis result distortion, and difficult operation and maintenance. If the device data points are modeled according to the machine type in advance, only the problem of non-uniformity of data points can be solved, and when some devices are debugged, repaired or technically improved, the problem of frequent model modification will still exist. At the same time, the adjustment of the monitoring and early warning algorithm for some devices will cause the monitoring of all devices to be suspended, which cannot solve the problem of uninterrupted production monitoring.

[0029] The present application classifies and models new energy equipment and records and manages the mapping relationship of device original points, provides a unified measurement point data reading interface, uses broadcast control flow to adapt to changes in point mapping in real-time machine learning, and uses hot loading to update algorithm models without stopping, so as to solve the problem of frequent modification and adaptation of downstream machine learning applications and high equipment maintenance cost caused by inconsistent device measurement points of different manufacturers in the new energy field and changes in device measurement points.

[0030] Figure 1 The present application provides a flowchart of a management method of a new energy power plant, which can be used to reduce the modification and adaptation difficulty and downtime frequency of downstream machine learning applications in the new energy field, reduce the equipment maintenance cost of the new energy power plant, and improve the real-time performance of equipment detection and operation and maintenance. The method can be executed by the new energy power plant management device provided by the present application, which can be realized in the form of hardware and / or software. In a specific embodiment, the device can be integrated in an electronic device. The following embodiments will be described with reference to the device integrated in an electronic device, and the device can be realized in the form of software and / or hardware. Figure 1 The method specifically includes the following steps:

[0031] S101, determine the initial detection information of the power plant.

[0032] The power plant includes a plurality of devices, and determining the operation of the power plant or whether a fault exists in the devices in the power plant requires obtaining operation parameters of a plurality of devices or a plurality of positions of a device in the power plant. For example, when analyzing a machine cabin fault, the temperature information of a plurality of machine cabins needs to be determined, including but not limited to the A fan machine cabin temperature, the B fan machine cabin temperature, and the C fan machine cabin temperature in the power plant. The initial detection information can be understood as the operation information detected in the power plant for determining a fault. The initial detection information includes at least one initial type information and initial detection data corresponding to the at least one initial type information. The initial type information can be understood as an identifier of the information, and is used to distinguish the obtained information. The initial detection data corresponding to the initial type information can be understood as a specific parameter value of the initial type information. For example, if the A fan machine cabin temperature is X1, the initial type information is the A fan machine cabin temperature, and the initial detection data is X1. It is worth noting that there is more than one device in the power plant, and therefore, the obtained information is at least one piece. The initial type information and the initial detection data have a corresponding relationship. The corresponding relationship can be that one initial type information corresponds to one piece of initial detection data, or one initial type information corresponds to a plurality of pieces of initial detection data. The specific corresponding relationship is related to the data acquisition method, the acquisition time length, and the fault detection logic, and is not limited in the embodiment. The advantage of such a setting is that it can obtain data with high real-time performance and comprehensiveness, and improve the accuracy and effectiveness of fault detection.

[0033] The application can obtain the operation of the devices in the power plant at regular intervals or irregular intervals, and determine the fault condition of the power plant according to the obtained information. Further, during the construction of the power plant, the devices are continuously increased. The application can also add the data acquisition method of the new devices to the original device acquisition method. The advantage of such a setting is that it can reduce the difficulty and time length of data acquisition work as much as possible, and improve the efficiency of detection data acquisition.

[0034] In one embodiment, S101 can specifically include: determining the historical data measuring points and the new data measuring points of the power plant; and determining at least one initial type information and initial detection data corresponding to the at least one initial type information based on the historical data measuring points, the detection rules of the historical data measuring points, the new data measuring points, and the detection rules of the new data measuring points.

[0035] The historical data measuring point can be understood as the location of the original equipment in the power plant, the new data measuring point can be understood as the location of the new equipment in the power plant, the detection rule of the historical data measuring point can be understood as the data acquisition scheme of the original equipment in the power plant, including but not limited to data acquisition equipment, data acquisition interval length and data acquisition mode, and the detection rule of the new data measuring point can be understood as the data acquisition scheme of the new equipment in the power plant, including but not limited to data acquisition equipment, data acquisition interval length and data acquisition mode. Specifically, the data acquisition task of the new equipment is superimposed on the data acquisition task of the original equipment to obtain detection information with high real-time performance according to the current equipment distribution of the power plant. The information contains the equipment measuring point change of the power plant, which can better indicate the operation of the power plant, and thus improve the quality of fault analysis work.

[0036] S102, using a pre-set information mapping rule, processing at least one initial type information and initial detection data corresponding to the at least one initial type information to obtain target detection information.

[0037] The pre-set information mapping rule can be understood as an information processing scheme set in advance according to the fault analysis logic, and the information mapping rule includes a type mapping rule and a data adjustment rule. Different devices and different manufacturers have certain differences in naming methods, data generation methods and formats of relative parameters. The type mapping rule is used to standardize and unify the naming of each device, that is, to unify each initial type information, and the data adjustment rule is used to standardize and unify the data of each device, that is, to unify the initial detection data. The target detection information includes at least one target type information and target detection data corresponding to the target type information. The target detection information can be understood as the initial detection information after standardization and unification, the target type information is the initial type information after standardization and unification, and the target detection data can be understood as the initial detection data after standardization and unification. The advantage of such setting is that it can reduce the difficulty of data processing, reduce the workload of the processor, and improve the fault diagnosis efficiency.

[0038] Specifically, assuming that the fault diagnosis work is performed by a fault diagnosis model, the temperature identifier identified by the fault diagnosis model is temperature X, the temperature identifier of device A is temperature 1, and the temperature identifier of device B is temperature 2. This step will map the temperature identifiers of devices A and B to temperature X for processing by the fault diagnosis model. Similarly, assuming that the temperature value format identified by the fault diagnosis model is format X, the temperature value format of device A is format 1, and the temperature value format of device B is format 2. This step will map the temperature value formats of devices A and B to format X for processing by the fault diagnosis model.

[0039] In an embodiment, S102 can specifically include: processing the at least one initial type information by using an information mapping rule to obtain at least one target type information, wherein the target type information and the initial type information are one-to-one corresponding, or the target type information corresponds to the at least one initial type information; processing the initial detection data corresponding to the at least one initial type information by using a data adjustment rule to obtain intermediate detection data; determining the type information of the intermediate detection data, and determining the target detection data corresponding to each target type information based on the corresponding relationship between the type information of the intermediate detection data and the target type information.

[0040] Wherein, the initial type information is mapped to obtain the target type information, each initial type information is corresponding to its target type information, but the target type information corresponds to more than one initial type information, specifically, the temperature of each device needs to be mapped to temperature X, the humidity information of each device needs to be mapped to humidity X, if there is only one device temperature in the initial detection information, the initial type information and the target type information of the device temperature parameter are one-to-one corresponding, if there are multiple device temperatures in the initial detection information, the initial type information and the target type information of the device temperature parameter are one-to-many, that is, one target type information corresponds to multiple initial type information, it is worth noting that although it is mapped to one target type information, the device data has its own characteristic information, the advantage of this setting is that when a fault occurs, the fault equipment can be quickly located and the fault reason can be analyzed according to the characteristic information of the fault data.

[0041] Further, after the initial detection information is standardized, the target type information associated with the initial type information needs to be analyzed and used as the target detection data of the target type information, the advantage of this setting is that the type information and the detection data after processing can be quickly associated according to the corresponding relationship, the information of the same device of different manufacturers is uniformly processed, the standardized data is obtained, and the processing time of the data is reduced under the premise of ensuring the completeness, correspondence and standardization of the data.

[0042] The application not only includes online fault detection, but also can set an offline fault detection scheme and a data integration scheme. The offline fault detection can be applicable to a case of no network, remote area or periodic fault analysis, etc. The data integration scheme can periodically integrate data, standardize the data, store and present the data, improve the data standardization and the data viewing experience of technicians, and can also save the data processing time of the processor for secondary use of the data. Specifically, after processing at least one initial type information and initial detection data corresponding to the at least one initial type information by using a pre-set information mapping rule to obtain target detection information, the method further includes: processing the target detection information according to a pre-set data processing scheme to obtain offline detection information, wherein the data processing scheme includes a data integration scheme and a data storage scheme; when receiving an offline data analysis instruction, processing the offline detection information based on a data processing requirement in the offline data analysis instruction to obtain an offline data processing result, wherein the offline data processing result is used to indicate a device adjustment scheme and / or a device operation condition of the power plant.

[0043] The offline data analysis instruction can be understood as a processing instruction of offline data, including but not limited to a data integration instruction, a data analysis instruction, a fault analysis instruction and a data storage instruction. The offline data analysis instruction can be triggered by a user according to a use requirement, or can be triggered by a server at a fixed time, which is not limited in the embodiment.

[0044] Specifically, the offline data processing result can be understood as a processing conclusion of offline data. For example, when the offline data analysis instruction is a data integration instruction, the offline data processing result can be a data integration success identifier, a data integration report, a data integration table, a data integration failure identifier and an abnormal reason, etc. When the offline data analysis instruction is a data storage instruction, the offline data processing result can be a data storage success identifier, a data storage location, a data storage failure identifier and an abnormal reason, etc.

[0045] In one embodiment, the data is stored monthly. The temperature information of the A device in January to March is named as temperature 1, and the temperature information of the A device in April to December is named as temperature 2. If the temperature arrangement scheme is to arrange the temperature and use the temperature uniformly.

[0046] Table 1

[0047]

[0048] Table 2

[0049]

[0050] Table 1 is the temperature data of the A device before sorting, and Table 2 is the temperature data of the A device before sorting. From the above two tables, it can be seen that the integrated data has higher neatness and standardization, and requires lower space.

[0051] S103, based on at least one target type information, determine the target fault diagnosis model of the power plant, and process the target detection data corresponding to the at least one target type information by using the target fault diagnosis model to obtain the fault diagnosis result of the power plant.

[0052] Different types of faults require the use of different fault diagnosis models. The present application determines the fault diagnosis model to be used according to the fault diagnosis requirement. The target fault diagnosis model can be understood as an algorithm for detecting the target type information direction fault in the fault diagnosis model. For example, when the target type information is the cabin temperature, the target fault diagnosis model is the cabin fault analysis model. The advantage of such setting is that it can save the use degree of non-target diagnosis model, and does not need to update multiple models, saving unnecessary work and improving the operation efficiency of the processor. The fault diagnosis method is to input the target detection data corresponding to the target type information into the target fault diagnosis model for processing, and determine the fault diagnosis result of the power plant according to the model output. The contents of the model output include but are not limited to fault diagnosis conclusion, different fault identification information and fault report. According to the model output, the fault diagnosis conclusion of the power plant can be obtained. The direct mapping and model control method does not need to stop the downstream application service, which can avoid system downtime and reduce maintenance cost.

[0053] In one embodiment, based on at least one target type information, the target fault diagnosis model of the power plant is determined, including: based on at least one target type information, searching in the corresponding relationship between type information and diagnosis model to obtain a candidate fault diagnosis model; determining whether the candidate fault diagnosis model exists an update indication; if the candidate fault diagnosis model does not exist the update indication, determining that the candidate fault diagnosis model is the target fault diagnosis model; if the candidate fault diagnosis model exists the update indication, updating the candidate fault diagnosis model based on the update indication, and determining that the updated candidate fault diagnosis model is the target fault diagnosis model.

[0054] Wherein, the candidate fault diagnosis model can be understood as a model matched with the target type information, i.e. an algorithm for determining the fault in the direction of the target type information. The update indication is used to indicate whether the candidate fault diagnosis model needs to be optimized, i.e. whether the program package of the current model needs to be updated and reloaded. If there is an update indication, it proves that the candidate fault diagnosis model is not the latest and needs to be optimized and updated. If there is no update indication, it proves that the candidate fault diagnosis model is the latest and does not need to be optimized and updated, which can be directly used to analyze the equipment fault of the power plant.

[0055] The advantage of such an arrangement is that only the model that needs to be updated is updated, which can ensure the quality of fault diagnosis and does not waste the processing power of the processor.

[0056] The application systematically names the point information of the equipment (fan, inverter, booster station, etc.), so as to distinguish the information such as large components (logical nodes), data names, data types, etc. Specifically, in combination with the self-developed FLINK DATASOURCE & RichFlatMapFunction SDK, the four-segment regular matching configuration of the point name is considered according to the machine resources and performance, the input required by the model is obtained, and the data transmission amount is reduced. In order to represent the equipment model hierarchy and unified data analysis logic, a model path representation method is designed to represent the hierarchy of the equipment information model. The model path is a four-segment form divided by points, for example, first segment.second segment.third segment.fourth segment{fifth segment}. Among them, the first segment represents the logical node (LN), the second segment represents the data name, the third segment represents the data stream identifier, the fourth segment represents the data type, and the fifth segment in the brackets is optional content, which can be set, adjusted and increased or decreased according to the specific business and measuring point. Figure 2 is a schematic diagram of an equipment model hierarchy provided by the application. In the diagram, XXX represents the name of the power plant. As can be seen from the diagram, the power plant includes a plurality of equipment, the equipment includes a plurality of logical nodes, each logical node includes a plurality of data, and the data includes data name, data volume identifier and type information.

[0057] It is worth noting that before processing the data, the target detection information can also be filtered and discarded according to the model path and data processing logic, and only the necessary data is processed. For example, the model only needs the data of the temperature logical node, and the data other than the temperature node is discarded. The advantage of such an arrangement is that it can greatly reduce the data processing amount of the model.

[0058] Optionally, after obtaining the fault diagnosis result of the power plant, it further includes: determining whether the fault diagnosis result exists an abnormal operation indication; if the fault diagnosis result does not exist the abnormal operation indication, generating an equipment control parameter, and controlling the equipment in the power plant to operate based on the equipment control parameter; if the fault diagnosis result exists the abnormal operation indication, generating a fault adjustment report based on the fault diagnosis result, and displaying the fault adjustment report, wherein the fault adjustment report is used to indicate the equipment abnormal information and the equipment adjustment scheme of the power plant.

[0059] Wherein, determining whether the fault diagnosis result exists abnormal operation indication can be understood as determining whether the abnormal operation information of the power plant equipment exists in the fault diagnosis result, if not, it proves that the equipment in the power plant is normal, the optimization control mode of the power plant equipment (i.e. the equipment control parameter) will be generated, and the equipment in the power plant is controlled to run based on the optimization control mode to adjust the operation mode of each equipment in the power plant and improve the performance of the power plant. If it exists, it proves that there is an abnormal equipment in the power plant, and the abnormal equipment needs to be located and the reason of the equipment abnormality needs to be analyzed to generate a fault adjustment report to instruct the operation and maintenance personnel to debug the equipment and maximize the reduction of the loss of the power plant.

[0060] In the present application, the SOURCE interface of FLINK is extended to provide an SDK for reading device data, a simple configuration on-demand model data acquisition method is implemented in the SDK, and the FLINK Broadcast Process Function customization is also provided in the SDK. When the upstream data point changes and the data source cannot standardize the measurement point name, the mapping relationship management of the measurement point naming is used to issue control flow by using the Broadcast State & Stream mechanism of the FLINK framework, globally change the data measurement point name, and ensure that each node of the computing cluster is synchronized during the change, so as to achieve the effect of global consistency of the business decision of the model output. Figure 3 is a schematic diagram of a processing flow, and the implementation device of the present application is a processor between the upstream and the downstream. Instruction 1 represents the instruction issued by the upstream to the processor, data represents the data transmitted by the upstream to the processor, and instruction 2 represents the control information sent by the processor to the downstream. The present application performs four-segment regular matching configuration and measurement point coding mapping configuration information processing according to instruction 1 and data, uses the model for functional processing, and then obtains the control instruction of the downstream.

[0061] Table 3

[0062]

[0063] Table 3 represents the measurement point coding mapping configuration information. The mapping effective start event can be configured in advance in combination with the field implementation. The configuration information can be updated and overwritten when the configuration information changes, including but not limited to changing the configuration content and adjusting the effective time.

[0064] Further, in the production scene, when the device appears part replacement, technical transformation and other scenes, the data point of the device increases or decreases, the real-time model downstream also needs to make corresponding changes, considering resources and performance, multiple models often share one data stream, secondly, real-time scenarios are very sensitive to availability, real-time machine learning jobs usually need to avoid start and stop, the application can combine the CLASSLOADER mechanism of JAVA, use URLCLASSLOADER to achieve hot loading of data model without stopping, realize model hot loading management through self-developed SDK, also use the Broadcast State & Stream of FLINK to issue change commands, ensure that the change rhythm of the cluster node is synchronized, and achieve the effect of global consistency of model result output. The use and implementation mode of the configuration information and the issued are the same as above, specifically, the model configuration information is shown in Table 4.

[0065] Table 4

[0066]

[0067] The application can classify modeling in the new energy field, standardize device measurement points, record and version manage the mapping relationship between source data measurement points and platform standardized measurement points (equivalent to the record and management of historical measurement point information and program package), use the broadcast control event stream mode in the FLINK real-time machine learning, adaptively modify the source data by using the self-developed SDK and point mapping relationship, simultaneously hot load the corresponding algorithm changes without stopping, and realize high availability in the real-time scene. It is worth noting that the point mapping relationship and version management are also used in offline machine learning to isolate upstream measurement point changes, which solves the problem of inconsistent measurement points of different manufacturers in the new energy field and the high maintenance cost of frequent repair of downstream machine learning applications caused by changes of measurement points after technical transformation of equipment, and also avoids frequent start and stop of real-time algorithm models, ensures that the change rhythm of the cluster node is synchronized, and achieves the effect of global consistency of model result output. The scheme of the application is based on FLINK, has a high-availability and scalable architecture, and can support massive data analysis and processing.

[0068] Exemplarily, the method of the present application can be used in wind power plant equipment monitoring and fault early warning, solar power plant data acquisition and intelligent optimization, etc. 1) Due to the variety of equipment types, different models and non-uniform equipment data measurement point coding provided by different manufacturers, the traditional data analysis and monitoring system needs to be frequently adjusted, and the operation and maintenance cost is high. By adopting the technical solution of the present application, the wind power plant can realize unified modeling and mapping management of equipment data measurement points, seamlessly update the algorithm model through the hot loading mechanism when the equipment is technically improved or repaired, avoid system downtime, monitor and warn the equipment operation state by using real-time data stream processing, greatly improve the operation and maintenance efficiency, and reduce the failure rate. 2) Due to long-term operation, the measurement points of some equipment in the solar power plant have changed and performance optimization is needed. The present application realizes unified reading and analysis of equipment data through the FLINK stream processing framework combined with self-developed SDK, and adjusts the algorithm model adaptively through the measurement point mapping relationship, realizes non-stop updating. In the practical application of the solar power plant, the present application can monitor the equipment operation state in real time, adjust the power generation strategy in time, and improve the energy utilization efficiency. In addition, the present application can also be applied to other industrial scenes that need to process a large amount of real-time data and have high requirements on system availability, such as smart grid, industrial Internet of Things (IIoT), etc. By adopting the technical solution of the present application, efficient data acquisition, processing and analysis can be realized, and the stability and intelligent level of the system can be improved.

[0069] The technical solution of the present embodiment can integrate and map the types and measurement point data of the equipment in the power plant, place them under a unified node measurement method, process the standardized and unified target detection information to obtain the equipment diagnosis result of the power plant, map different data in a unified reference vector through the mapping method of data stream, i.e. directly map the changed upstream information to the pre-set reference vector, without stopping the downstream application, so that the change rhythm of the cluster node is kept synchronized, the modification and adaptation difficulty of the downstream machine learning application in the new energy field is reduced, the downtime frequency is reduced, the equipment maintenance cost of the new energy power plant is reduced, and the real-time performance of equipment detection and operation and maintenance is improved. The problems that the downstream big data and artificial intelligence services need to frequently stop and adjust the services according to the changes of the upstream equipment point data, the downtime will cause the equipment monitoring business to be completely suspended, and once the downstream service cannot respond to the upstream data changes in time, the monitoring and analysis of the equipment data will deviate, and then the production and operation will be guided to make wrong decisions, etc. are solved.

[0070] Figure 4 is a structural schematic diagram of a new energy power plant management device provided by the present application. As shown in Figure 4 , the device comprises an acquisition module 201, a determination module 202 and a diagnosis module 203.

[0071] The acquisition module 201 is configured to determine initial detection information of the power plant, and the initial detection information includes at least one initial type information and initial detection data corresponding to the at least one initial type information.

[0072] The determination module 202 is configured to process the at least one initial type information and the initial detection data corresponding to the at least one initial type information by using a preset information mapping rule, to obtain target detection information, wherein the target detection information includes at least one target type information and target detection data corresponding to the at least one target type information.

[0073] The diagnosis module 203 is configured to determine a target fault diagnosis model of the power plant based on the at least one target type information, and process the target detection data corresponding to the at least one target type information by using the target fault diagnosis model, to obtain a fault diagnosis result of the power plant.

[0074] Optionally, the acquisition module 201 is specifically configured to determine a historical data measuring point and a new data measuring point of the power plant; and determine the at least one initial type information and the initial detection data corresponding to the at least one initial type information based on the historical data measuring point, a detection rule of the historical data measuring point, the new data measuring point and a detection rule of the new data measuring point.

[0075] Optionally, the information mapping rule includes a type mapping rule and a data adjustment rule, and the determination module 202 is specifically configured to process the at least one initial type information by using the information mapping rule, to obtain the at least one target type information, wherein the target type information and the initial type information are in one-to-one correspondence, or the target type information corresponds to the at least one initial type information; process the initial detection data corresponding to the at least one initial type information by using the data adjustment rule, to obtain intermediate detection data; determine type information of the intermediate detection data, and determine the target detection data corresponding to each target type information based on a corresponding relationship between the type information of the intermediate detection data and the target type information.

[0076] Optionally, the diagnosis module 203 is specifically configured to search in a corresponding relationship between the type information and a diagnosis model based on the at least one target type information, to obtain a candidate fault diagnosis model; determine whether the candidate fault diagnosis model exists an update indication; if the candidate fault diagnosis model does not exist the update indication, determine that the candidate fault diagnosis model is the target fault diagnosis model; if the candidate fault diagnosis model exists the update indication, update the candidate fault diagnosis model based on the update indication, and determine that the updated candidate fault diagnosis model is the target fault diagnosis model.

[0077] Optionally, the diagnosis module 203 is further configured to, after obtaining the target detection information by processing the at least one initial type information and the initial detection data corresponding to the at least one initial type information according to the preset information mapping rule, process the target detection information according to a preset data processing scheme to obtain offline detection information, wherein the data processing scheme comprises a data integration scheme and a data storage scheme; and after receiving an offline data analysis instruction, process the offline detection information based on a data processing requirement in the offline data analysis instruction to obtain an offline data processing result, wherein the offline data processing result is used to indicate a device adjustment scheme and / or a device operation condition of the power plant.

[0078] Optionally, the diagnosis module 203 is further configured to, after obtaining the fault diagnosis result of the power plant, determine whether the fault diagnosis result exists an abnormal operation indication; if the fault diagnosis result does not exist the abnormal operation indication, generate a device control parameter and control a device in the power plant to operate based on the device control parameter; and if the fault diagnosis result exists the abnormal operation indication, generate a fault adjustment report based on the fault diagnosis result and display the fault adjustment report, wherein the fault adjustment report is used to indicate device abnormal information and a device adjustment scheme of the power plant.

[0079] The management device of the new energy power plant provided in the embodiment can execute the management method of the new energy power plant provided in any embodiment of the application, and has the function modules and beneficial effects corresponding to the execution method.

[0080] Figure 5 is a structural schematic diagram of an electronic device. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart phones, wearable devices (e.g., headgear, eyewear, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the applications described and / or claimed in this document.

[0081] As Figure 5As shown, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (also referred to as Random Access Memory, RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor 11, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or loaded into the Random Access Memory (RAM) 13 from the storage unit 18. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0082] Various components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc., an output unit 17, such as various types of displays, a speaker, etc., a storage unit 18, such as a magnetic disk, an optical disk, etc., and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0083] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the management method of the new energy power plant.

[0084] In some embodiments, the management method of the new energy power plant can be implemented as a computer program, which is tangibly contained in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the management method of the new energy power plant described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the management method of the new energy power plant by any other appropriate means (e.g., by means of firmware).

[0085] The various embodiments of the systems and techniques described above can be implemented in digital electronic circuitry, integrated circuitry, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system on a chip systems (SOCs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0086] Computer programs used to implement the methods of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer program

[0087] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0088] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0089] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), blockchain networks, and the Internet.

[0090] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0091] In an embodiment, the present application also includes a computer program product comprising a computer program which, when executed by a processor, implements the method for managing a new energy power plant of any embodiment of the present application.

[0092] The computer program code can also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce the computer implemented process such that the

[0093] It should be understood that the various forms of flow shown in the figures are illustrative examples of implementing the steps of the application. Several steps have been described as being performed by a single device. It will be understood that these steps can be performed by a single device or multiple devices. It will also be understood that the steps can be performed in a different order than that shown in the figures. It will also be understood that the steps can be performed concurrently or sequentially. It will also be understood that the steps can be performed by different entities. It will also be understood that the steps can be performed by a combination of hardware and software. It will also be understood that the steps can be performed by a combination of one or more devices and one or more computers.

[0094] The specific embodiments have been shown and described for purposes of illustrating the embodiments, and not for purposes of limitation. It will be understood by those skilled in the art that various modifications, combinations, sub-combinations, and alterations can occur depending on design requirements and other factors insofar as they are within the scope of the application. Accordingly, the scope of the present application is intended to embrace all such alterations, modifications, and variations.

Claims

1. A method of managing a new energy power plant, characterized by, The method comprises the following steps: determining initial detection information of the power plant, wherein the initial detection information comprises at least one initial type information and initial detection data corresponding to the at least one initial type information; processing the at least one initial type information and the initial detection data corresponding to the at least one initial type information by using a pre-set information mapping rule to obtain target detection information, wherein the target detection information comprises at least one target type information and target detection data corresponding to the target type information; determining a target fault diagnosis model of the power plant based on the at least one target type information, and processing the target detection data corresponding to the at least one target type information by using the target fault diagnosis model to obtain a fault diagnosis result of the power plant; the information mapping rule comprises a type mapping rule and a data adjustment rule, and the processing the at least one initial type information and the initial detection data corresponding to the at least one initial type information by using the pre-set information mapping rule to obtain target detection information comprises: processing the at least one initial type information by using the information mapping rule to obtain the at least one target type information, wherein the target type information and the initial type information correspond to each other, or the target type information corresponds to at least one initial type information; processing the initial detection data corresponding to the at least one initial type information by using the data adjustment rule to obtain intermediate detection data; determining type information of the intermediate detection data, and determining target detection data corresponding to each target type information based on a corresponding relationship between the type information of the intermediate detection data and the target type information.

2. The method of claim 1, wherein, The determining initial detection information of the power plant comprises: determining historical data measuring points and new data measuring points of the power plant; determining the at least one initial type information and the initial detection data corresponding to the at least one initial type information based on the historical data measuring points, detection rules of the historical data measuring points, the new data measuring points and detection rules of the new data measuring points.

3. The method of claim 1, wherein, The determining a target fault diagnosis model of the power plant based on the at least one target type information comprises: finding a candidate fault diagnosis model in a corresponding relationship between type information and diagnosis models based on the at least one target type information; determining whether the candidate fault diagnosis model exists an update indication; if the candidate fault diagnosis model does not exist the update indication, determining that the candidate fault diagnosis model is the target fault diagnosis model; if the candidate fault diagnosis model exists the update indication, updating the candidate fault diagnosis model based on the update indication, and determining that the updated candidate fault diagnosis model is the target fault diagnosis model.

4. The method of claim 1, wherein, After the processing the at least one initial type information and the initial detection data corresponding to the at least one initial type information by using the pre-set information mapping rule to obtain target detection information, the method further comprises: According to a preset data processing scheme, the target detection information is processed to obtain offline detection information, wherein the data processing scheme includes a data integration scheme and a data storage scheme; When receiving an offline data analysis instruction, the offline detection information is processed based on a data processing requirement in the offline data analysis instruction to obtain an offline data processing result, wherein the offline data processing result is used to indicate a device adjustment scheme and / or a device operation condition of the power plant.

5. The method of claim 1, wherein, After obtaining the fault diagnosis result of the power plant, the method further includes: determining whether the fault diagnosis result exists an abnormal operation indication; if the fault diagnosis result does not exist the abnormal operation indication, generating a device control parameter and controlling a device in the power plant to operate based on the device control parameter; if the fault diagnosis result exists the abnormal operation indication, generating a fault adjustment report based on the fault diagnosis result and displaying the fault adjustment report, wherein the fault adjustment report is used to indicate device abnormal information and a device adjustment scheme of the power plant.

6. A management device of a new energy power plant, characterized by, The method for managing the new energy power plant according to any one of claims 1 to 5, the management device of the new energy power plant includes: an acquisition module configured to determine initial detection information of a power plant, wherein the initial detection information includes at least one initial type information and initial detection data corresponding to the at least one initial type information; a determination module configured to process the at least one initial type information and the initial detection data corresponding to the at least one initial type information by using a preset information mapping rule to obtain target detection information, wherein the target detection information includes at least one target type information and target detection data corresponding to the target type information; a diagnosis module configured to determine a target fault diagnosis model of the power plant based on the at least one target type information, and process the target detection data corresponding to the at least one target type information by using the target fault diagnosis model to obtain a fault diagnosis result of the power plant; the information mapping rule includes a type mapping rule and a data adjustment rule, and the determination module is specifically configured to process the at least one initial type information by using the information mapping rule to obtain the at least one target type information, wherein the target type information and the initial type information are in one-to-one correspondence, or the target type information corresponds to at least one initial type information; process the initial detection data corresponding to the at least one initial type information by using the data adjustment rule to obtain intermediate detection data; determine type information of the intermediate detection data, and determine the target detection data corresponding to each target type information based on a corresponding relationship between the type information of the intermediate detection data and the target type information.

7. An electronic device, comprising: The electronic device includes: at least one processor; and a memory connected with the at least one processor in communication; The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the new energy power plant management method in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to implement the new energy power plant management method in any one of claims 1 to 5 when executed.

9. A computer program product comprising a computer program, characterized in that, The computer program implements the new energy power plant management method in any one of claims 1 to 5 when executed by the processor.

Images