Photovoltaic power station operation data management method and system

Abstract

The invention provides a photovoltaic power station operation data management method and system, and the method comprises the steps: obtaining the target operation data of a target photovoltaic power station, and carrying out the analysis processing of the target operation data, so as to detect a plurality of equipment types correspondingly contained in the interior of the target photovoltaic power station in real time; detecting an equipment cluster corresponding to each equipment type in real time in the target photovoltaic power station, and detecting a target working parameter corresponding to each equipment cluster in real time in the target operation data; drawing a real-time working curve corresponding to the equipment cluster in real time according to the target working parameters based on a preset rule, and calling out a standard working curve adaptive to the equipment cluster in real time from a preset standard database; and carrying out superposition comparison processing on the real-time working curve and the standard working curve so as to judge whether the equipment cluster is abnormal or not in real time according to a comparison result. According to the invention, the manual inspection process can be omitted, and the working efficiency is correspondingly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly relates to a method and system for managing operation data of a photovoltaic power station. Background Art

[0002] With the progress of technology and the rapid development of the times, people have developed various different new types of green energy. Specifically, for example, new power generation technologies such as hydropower generation, wind power generation, and solar power generation have greatly facilitated people's lives.

[0003] Among them, significant development has been achieved in the field of solar power generation technology. Specifically, people have built corresponding photovoltaic power stations using solar energy and can transmit electricity to residents through the photovoltaic power stations to meet the electricity demand of residents.

[0004] Furthermore, in the process of daily operation of existing photovoltaic power stations, due to the large variety and quantity of production equipment, in order to ensure the continuous and stable operation of the photovoltaic power stations, most of the existing technologies need to arrange inspection personnel to regularly check the equipment inside the photovoltaic power stations to determine whether there are equipment abnormalities. However, although this inspection method can detect whether there are equipment abnormalities to a certain extent, it takes a long inspection time, and the judgment result is easily affected by the subjective judgment of the inspection personnel, so it is easy to miss or misjudge, corresponding to reducing the work efficiency. Summary of the Invention

[0005] Based on this, the purpose of the present invention is to provide a method and system for managing operation data of a photovoltaic power station to solve the problem that although the existing inspection method can detect whether there are equipment abnormalities to a certain extent, it takes a long inspection time and is prone to missing or misjudging.

[0006] The first aspect of the embodiment of the present invention proposes:

[0007] A method for managing operation data of a photovoltaic power station, wherein the method includes:

[0008] At preset time intervals, the target operation data corresponding to the target photovoltaic power station is obtained in real time through a preset acquisition device, and the target operation data is analyzed and processed to detect in real time several types of equipment included in the target photovoltaic power station;

[0009] In the target photovoltaic power station, equipment clusters corresponding to each type of equipment are detected in real time, and target working parameters corresponding to each equipment cluster are detected in real time in the target operation data;

[0010] Based on preset rules, a real-time working curve corresponding to the equipment cluster is drawn in real time according to the target working parameters, and a standard working curve adapted to the equipment cluster is retrieved in real time from a preset standard database;

[0011] The real-time working curve and the standard working curve are subjected to a coincidence comparison process to determine in real time whether the equipment cluster is abnormal according to the comparison result.

[0012] The beneficial effects of the present invention are as follows: By obtaining the target operation data of the target photovoltaic power station every preset time, the specific working data of the current target photovoltaic power station can be correspondingly obtained. Based on this, several equipment clusters included in the current target photovoltaic power station can be detected in real time, and the real-time working curve corresponding to each current equipment cluster can be immediately obtained according to the preset rules. Based on this, for the convenience of subsequent judgment, the preset standard working curve will be retrieved correspondingly at this time. Based on this, a direct comparison can be made, and it can finally be directly determined whether there is an abnormality according to the comparison result, thereby eliminating the need for manual inspection and judgment processes, correspondingly eliminating the human influence, and further improving the work efficiency.

[0013] Further, the step of drawing a real-time working curve corresponding to the equipment cluster based on preset rules according to the target working parameters includes:

[0014] When the target working parameters are obtained in real time, a full scan is performed on the equipment cluster to detect in real time several sub-devices included in the equipment cluster;

[0015] A corresponding target identifier is added to each sub-device, and a parameter subset corresponding to each sub-device is respectively matched in the target working parameters, and the parameter subset is unique;

[0016] A real-time working curve corresponding to the equipment cluster is drawn in real time according to each sub-device and its corresponding parameter subset.

[0017] Further, the step of drawing a real-time working curve corresponding to the equipment cluster in real time according to each sub-device and its corresponding parameter subset includes:

[0018] When each sub-device and its corresponding parameter subset are obtained in real time, a reference plane is created in real time inside a preset program;

[0019] A target two-dimensional coordinate system adapted to each sub-device is created in real time inside the reference plane, and the data generation time period corresponding to the target working parameters is detected in real time;

[0020] According to the data generation time period, map the parameter subset of each sub-device to the interior of the target two-dimensional coordinate system, so as to correspondingly form several device working curves inside the target two-dimensional coordinate system, and generate a real-time working curve corresponding to the device cluster in real time according to the several device working curves. Each device working curve is unique.

[0021] Further, the step of generating a real-time working curve corresponding to the device cluster in real time according to the several device working curves includes:

[0022] When several device working curves are obtained in real time, perform a full scan on the several device working curves to detect in real time several extreme points contained inside the several device working curves;

[0023] Correspondingly select the maximum extreme point and the minimum extreme point from the several extreme points, and set the device working curve corresponding to the maximum extreme point as the first working curve and the device working curve corresponding to the minimum extreme point as the second working curve;

[0024] Perform an integration process on the first working curve and the second working curve to correspondingly generate the real-time working curve of the device cluster.

[0025] Further, the step of performing a coincidence comparison process on the real-time working curve and the standard working curve to determine in real time whether the device cluster is abnormal according to the comparison result includes:

[0026] When the real-time working curve is obtained in real time, perform a full scan on the real-time working curve to detect the starting point and the ending point of the real-time working curve correspondingly;

[0027] Within the range of the starting point and the ending point, overlap the standard working curve on the real-time working curve to detect in real time the overlapping part and the non-overlapping part generated between the real-time working curve and the standard working curve;

[0028] Hide the overlapping part and correspondingly retain the non-overlapping part to determine in real time whether the device cluster is abnormal according to the non-overlapping part in real time.

[0029] Further, the step of determining in real time whether the device cluster is abnormal according to the non-overlapping part in real time includes:

[0030] When the non-overlapping part is obtained in real time, in the direction from the starting point to the ending point, sequentially detect several maximum extreme points and several minimum extreme points that appear inside the non-overlapping part;

[0031] Add corresponding first marks to several of the maximum points respectively, and add corresponding second marks to several of the minimum points respectively;

[0032] Construct a mapping relationship between the first mark and the second mark in real time, and determine whether the device cluster is abnormal based on the mapping relationship according to several of the maximum points and several of the minimum points.

[0033] Further, the step of determining whether the device cluster is abnormal based on the mapping relationship according to several of the maximum points and several of the minimum points includes:

[0034] Calculate the target difference between each of the maximum points and its corresponding minimum point one by one according to the mapping relationship, and determine in real time whether the target difference is within the range of a preset difference threshold;

[0035] If it is determined in real time that the target difference is within the range of the preset difference threshold, directly determine that the device cluster is not abnormal.

[0036] The second aspect of the embodiments of the present invention proposes:

[0037] A photovoltaic power station operation data management system, wherein the system includes:

[0038] An acquisition module, configured to obtain target operation data corresponding to a target photovoltaic power station in real time through a preset acquisition device at preset time intervals, and perform parsing processing on the target operation data to detect several device types included inside the target photovoltaic power station in real time;

[0039] A detection module, configured to detect device clusters corresponding to each of the device types inside the target photovoltaic power station in real time, and detect target working parameters corresponding to each of the device clusters in the target operation data in real time;

[0040] A processing module, configured to draw a real-time working curve corresponding to the device cluster in real time based on preset rules according to the target working parameters, and retrieve a standard working curve adapted to the device cluster from a preset standard database in real time;

[0041] An execution module, configured to perform a coincidence comparison process on the real-time working curve and the standard working curve, so as to determine in real time whether the device cluster is abnormal according to the comparison result.

[0042] Further, the processing module is specifically configured to:

[0043] When the target working parameters are obtained in real time, perform a full scan on the device cluster to detect in real time a number of sub-devices contained inside the device cluster;

[0044] Add corresponding target identifiers to each of the sub-devices, and respectively match parameter subsets corresponding to each of the sub-devices in the target working parameters, where the parameter subsets are unique;

[0045] According to each of the sub-devices and its corresponding parameter subset, draw a real-time working curve corresponding to the device cluster in real time.

[0046] Further, the processing module is specifically configured to:

[0047] When each of the sub-devices and its corresponding parameter subset are obtained in real time, create a reference plane in real time inside a preset program;

[0048] Create a target two-dimensional coordinate system adapted to each of the sub-devices inside the reference plane, and detect in real time a data generation time period corresponding to the target working parameters;

[0049] According to the data generation time period, map the parameter subset of each of the sub-devices to the inside of the target two-dimensional coordinate system, so as to form a number of device working curves inside the target two-dimensional coordinate system, and generate a real-time working curve corresponding to the device cluster in real time according to the number of device working curves, and each device working curve is unique.

[0050] Further, the processing module is specifically configured to:

[0051] When a number of device working curves are obtained in real time, perform a full scan on the number of device working curves to detect in real time a number of extreme points contained inside the number of device working curves;

[0052] Screen out the maximum extreme point and the minimum extreme point from the number of extreme points, and set the device working curve corresponding to the maximum extreme point as the first working curve and the device working curve corresponding to the minimum extreme point as the second working curve;

[0053] Perform integration processing on the first working curve and the second working curve to generate the real-time working curve of the device cluster correspondingly.

[0054] Further, the execution module is specifically configured to:

[0055] When the real-time working curve is obtained in real time, a full scan is performed on the real-time working curve to correspondingly detect the starting point and the ending point of the real-time working curve;

[0056] Within the range of the starting point and the ending point, the standard working curve is overlapped on the real-time working curve to detect in real time the overlapping part and the non-overlapping part generated between the real-time working curve and the standard working curve;

[0057] Hide the overlapping part and correspondingly retain the non-overlapping part to determine in real time whether the device cluster is abnormal according to the non-overlapping part in real time.

[0058] Further, the execution module is specifically configured to:

[0059] When the non-overlapping part is obtained in real time, in the direction from the starting point to the ending point, several maximum points and several minimum points correspondingly appearing inside the non-overlapping part are detected in sequence;

[0060] Add corresponding first marks to several of the maximum points and add corresponding second marks to several of the minimum points;

[0061] Construct a mapping relationship between the first mark and the second mark in real time, and determine whether the device cluster is abnormal based on the mapping relationship according to several of the maximum points and several of the minimum points.

[0062] Further, the execution module is specifically configured to:

[0063] Calculate the target difference between each of the maximum points and its corresponding minimum point one by one according to the mapping relationship, and determine in real time whether the target difference is within the range of a preset difference threshold;

[0064] If it is determined in real time that the target difference is within the range of the preset difference threshold, it is directly determined that the device cluster is not abnormal.

[0065] The third aspect of the embodiments of the present invention proposes:

[0066] A computer, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the computer program, the photovoltaic power station operation data management method described above is implemented.

[0067] The fourth aspect of the embodiments of the present invention proposes:

[0068] A readable storage medium stores a computer program thereon, wherein when the program is executed by a processor, the photovoltaic power station operation data management method as described above is implemented.

[0069] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 is a flowchart of the photovoltaic power station operation data management method provided in the first embodiment of the present invention;

[0071] Figure 2 is a structural block diagram of the photovoltaic power station operation data management system provided in the third embodiment of the present invention.

[0072] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. SPECIFIC EMBODIMENTS

[0073] To facilitate the understanding of the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. Several embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, these embodiments are provided so that the disclosure of the present invention is thorough and comprehensive.

[0074] It should be noted that when an element is referred to as being "fixedly provided on" another element, it can be directly on the other element or there can also be an intermediate element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intermediate element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0075] Unless otherwise defined, all technical and scientific terms used herein 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 herein 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. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0076] Please refer to Figure 1 , which shows the photovoltaic power station operation data management method provided in the first embodiment of the present invention. The photovoltaic power station operation data management method provided in this embodiment can eliminate the process of manual inspection, thereby eliminating the phenomena of missed judgment and misjudgment, and correspondingly improving the work efficiency.

[0077] Specifically, this embodiment provides:

[0078] A method for managing operation data of a photovoltaic power station, the method comprising the following steps:

[0079] Step S10, at preset time intervals, obtain in real time target operation data corresponding to a target photovoltaic power station through a preset acquisition device, and perform parsing and processing on the target operation data to detect in real time several types of devices included inside the target photovoltaic power station;

[0080] Step S20, detect in real time inside the target photovoltaic power station device clusters respectively corresponding to each type of device, and detect in real time in the target operation data target working parameters respectively corresponding to each device cluster;

[0081] Step S30, based on a preset rule, draw in real time a real-time working curve corresponding to the device cluster according to the target working parameters, and retrieve in real time in a preset standard database a standard working curve adapted to the device cluster;

[0082] Step S40, perform a coincidence comparison process on the real-time working curve and the standard working curve to judge in real time whether the device cluster is abnormal according to the comparison result.

[0083] Specifically, in this embodiment, it should be noted first that in order to quickly and effectively determine whether there is an abnormality inside the photovoltaic power station, it is necessary to obtain in real time the operation data generated by the photovoltaic power station. Based on this, it is necessary to preset a corresponding acquisition device inside the current photovoltaic power station. Specifically, the acquisition device can be set as various different types of sensors, and can collect in real time the working data generated by various production devices inside the current photovoltaic power station through this acquisition device. Based on this, this acquisition device can integrate the working data of current various devices and can further form the operation data of the current photovoltaic power station. Based on this, in the actual application process, the server set in the background will obtain in real time the target operation data generated by the current photovoltaic power station once every six hours through the above acquisition device. Based on this, it will immediately perform parsing and processing on the current target operation data and can detect in real time several device clusters included inside the current photovoltaic power station. Among them, it should be noted that each device cluster contains several devices of the same type for subsequent management. Based on this, for the convenience of subsequent judgment, the present invention will further detect in real time in the above target operation data the target working parameters corresponding to each current device cluster for subsequent processing.

[0084] Further, after the required device cluster and corresponding target working parameters are detected in real time through the above steps, the current target working parameters will be further subjected to fitting processing. Specifically, the server will immediately fit the current target working parameters into corresponding real-time working curves according to the pre-set rules. Correspondingly, in order to facilitate subsequent judgment and shorten the data processing time, the server will further match the standard working curve adapted to the current device cluster in the standard database pre-set inside it in real time. Based on this, the current real-time working curve and the standard working curve will be finally compared and overlapped, and the required comparison result can be obtained accordingly. According to this comparison result, it can be directly judged whether the current device cluster is abnormal. Correspondingly, it can be judged whether there is an abnormality inside the current photovoltaic power station, thus eliminating the need for manual inspection, eliminating the phenomena of missed judgment and misjudgment, and improving work efficiency at the same time.

[0085] Second Embodiment

[0086] Further, the step of drawing a real-time working curve corresponding to the device cluster according to the target working parameters based on a preset rule includes:

[0087] When the target working parameters are obtained in real time, perform a full scan of the device cluster to detect in real time a number of sub-devices included inside the device cluster;

[0088] Add corresponding target identifiers to each of the sub-devices, and match out a parameter subset corresponding to each sub-device in the target working parameters based on the target identifier. The parameter subset is unique;

[0089] Draw a real-time working curve corresponding to the device cluster according to each sub-device and its corresponding parameter subset in real time.

[0090] Further, the step of drawing a real-time working curve corresponding to the device cluster according to each sub-device and its corresponding parameter subset includes:

[0091] When each sub-device and its corresponding parameter subset are obtained in real time, create a reference plane inside a preset program in real time;

[0092] Create a target two-dimensional coordinate system adapted to each sub-device inside the reference plane, and detect in real time the data generation time period corresponding to the target working parameters;

[0093] According to the data generation time period, map the parameter subset of each sub-device to the interior of the target two-dimensional coordinate system, so as to correspondingly form several device working curves inside the target two-dimensional coordinate system, and generate a real-time working curve corresponding to the device cluster in real time according to the several device working curves. Each device working curve is unique.

[0094] Further, the step of generating a real-time working curve corresponding to the device cluster in real time according to the several device working curves includes:

[0095] When several device working curves are obtained in real time, perform a full scan on the several device working curves to detect several extreme points correspondingly included inside the several device working curves in real time;

[0096] Correspondingly select the maximum extreme point and the minimum extreme point from the several extreme points, and set the device working curve corresponding to the maximum extreme point as the first working curve and the device working curve corresponding to the minimum extreme point as the second working curve;

[0097] Perform an integration process on the first working curve and the second working curve to correspondingly generate the real-time working curve of the device cluster.

[0098] Further, the step of performing a coincidence comparison process on the real-time working curve and the standard working curve to determine in real time whether the device cluster is abnormal according to the comparison result includes:

[0099] When the real-time working curve is obtained in real time, perform a full scan on the real-time working curve to detect the starting point and the ending point of the real-time working curve correspondingly;

[0100] Within the range of the starting point and the ending point, overlap the standard working curve on the real-time working curve to detect the overlapping part and the non-overlapping part generated between the real-time working curve and the standard working curve in real time;

[0101] Hide the overlapping part and correspondingly retain the non-overlapping part to determine in real time whether the device cluster is abnormal according to the non-overlapping part in real time.

[0102] Further, the step of determining in real time whether the device cluster is abnormal according to the non-overlapping part in real time includes:

[0103] When the non-overlapping part is obtained in real time, in the direction from the starting point to the ending point, sequentially detect several maximum extreme points and several minimum extreme points that appear correspondingly inside the non-overlapping part;

[0104] Add corresponding first marks to several of the maximum points respectively, and add corresponding second marks to several of the minimum points respectively;

[0105] Construct the mapping relationship between the first mark and the second mark in real time, and determine whether the device cluster is abnormal based on the mapping relationship according to several of the maximum points and several of the minimum points.

[0106] Further, the step of determining whether the device cluster is abnormal based on the mapping relationship according to several of the maximum points and several of the minimum points includes:

[0107] Calculate the target difference between each of the maximum points and its corresponding minimum point one by one according to the mapping relationship, and determine in real time whether the target difference is within the range of a preset difference threshold;

[0108] If it is determined in real time that the target difference is within the range of the preset difference threshold, directly determine that the device cluster is not abnormal.

[0109] In addition, in this embodiment, it should also be noted that after the device cluster and its corresponding target working parameters are detected in real time through the above steps, it is necessary to further process the current target working parameters in order to draw the real-time working curve corresponding to the current device cluster in real time. Specifically, the above server will further perform a full scan of the current device cluster. At the same time, several sub-devices included inside the current device cluster can be scanned in real time. It should be pointed out that since each sub-device works independently, it is necessary to separately obtain the parameter subsets corresponding to each sub-device to detect the working status of each sub-device. Based on this, in order to quickly and effectively draw the required curve, the time period during which the data corresponding to the above target working parameters is generated, that is, the specific data generation time, will be further detected. Based on this, taking the current data generation time as the abscissa and the current parameter subset as the ordinate, the device working curves corresponding to each current sub-device can be drawn in real time, and the device working curves of each current sub-device are simultaneously displayed inside the same target two-dimensional coordinate system, so that the change situation of each device working curve can be intuitively detected. Based on this, the above server will immediately perform a full scan of the current several device working curves, so that all the extreme points that appear inside the current several device working curves can be scanned correspondingly. It should be noted that the magnitude of the extreme point can directly determine the working conditions of each device, that is, when the extreme point is too large or too small, it can be determined that the working status of the device has an abnormality. Based on this, the above server will detect the maximum extreme point and the minimum extreme point in real time among the current several extreme points, and use the current maximum extreme point and minimum extreme point as the judgment basis. Specifically, the first working curve corresponding to the current maximum extreme point will be further detected. Correspondingly, the device working curve corresponding to the current minimum extreme point is used as the second working curve. At the same time, it is also necessary to connect the current first working curve and the second working curve into a whole, so that the real-time working curve of the above device cluster can be correspondingly generated, and this real-time working curve is used as the judgment basis for subsequent processing.

[0110] Further, after the real-time working curves of each device cluster are obtained in real time through the above steps, the standard working curves adapted to the current device clusters will be immediately detected inside the above-mentioned preset standard database. Based on this, the starting point and the ending point of the current real-time working curve will be detected in real time, and within the range of the current starting point and ending point, the above-mentioned standard working curve will be overlapped on the current real-time working curve. It should be noted that when the two curves are overlapped, due to certain differences between the two curves, an overlapping part and a non-overlapping part will be formed between the two curves. It should be pointed out that the above overlapping part can indicate that the above real-time working curve is in a normal working state. Correspondingly, the above non-overlapping part can indicate that there is a certain deviation in the above real-time working curve. Based on this, the above server will correspondingly hide the current overlapping part, and correspondingly retain the current non-overlapping part, and will detect a number of maximum points and minimum points that appear in sequence from the starting point to the ending point. On this basis, the above server will further calculate the target difference generated between each maximum point and its corresponding minimum point. The magnitude of this target difference can directly reflect the working deviation degree of the current device. Based on this, it will be judged in real time whether the current target difference is within the range of the preset difference threshold. Specifically, if so, it can be directly determined that the current device has no abnormality. Correspondingly, if not, it can be directly determined that the current device has an abnormality, thus eliminating the need for manual inspection, eliminating the phenomena of missed judgment and misjudgment, and improving work efficiency at the same time.

[0111] Please refer to Figure 2 , the third embodiment of the present invention provides:

[0112] A photovoltaic power station operation data management system, wherein the system includes:

[0113] An acquisition module, configured to obtain target operation data corresponding to a target photovoltaic power station in real time through a preset acquisition device at preset time intervals, and perform parsing processing on the target operation data to detect in real time several device types included inside the target photovoltaic power station;

[0114] A detection module, configured to detect in real time device clusters corresponding to each of the device types inside the target photovoltaic power station, and detect in real time target working parameters corresponding to each of the device clusters in the target operation data;

[0115] A processing module, configured to draw in real time a real-time working curve corresponding to the device cluster based on preset rules according to the target working parameters, and retrieve in real time a standard working curve adapted to the device cluster from a preset standard database;

[0116] An execution module, configured to perform a coincidence comparison process on the real-time working curve and the standard working curve, so as to determine in real time whether the device cluster is abnormal according to the comparison result.

[0117] Further, the processing module is specifically configured to:

[0118] When the target working parameters are obtained in real time, perform a full scan on the device cluster to detect in real time a number of sub-devices included inside the device cluster;

[0119] Add corresponding target identifiers to each of the sub-devices, and respectively match parameter subsets corresponding to each of the sub-devices in the target working parameters, and the parameter subsets are unique;

[0120] Draw a real-time working curve corresponding to the device cluster in real time according to each of the sub-devices and its corresponding parameter subset.

[0121] Further, the processing module is specifically configured to:

[0122] When each of the sub-devices and its corresponding parameter subset are obtained in real time, create a reference plane in real time inside a preset program;

[0123] Create a target two-dimensional coordinate system adapted to each of the sub-devices in the reference plane, and detect in real time the data generation time period corresponding to the target working parameters;

[0124] Map the parameter subset of each sub-device to the inside of the target two-dimensional coordinate system according to the data generation time period, so as to form a number of device working curves inside the target two-dimensional coordinate system, and generate a real-time working curve corresponding to the device cluster in real time according to the number of device working curves, and each device working curve is unique.

[0125] Further, the processing module is specifically configured to:

[0126] When a number of device working curves are obtained in real time, perform a full scan on the number of device working curves to detect in real time a number of extreme points included inside the number of device working curves;

[0127] Screen out the maximum extreme point and the minimum extreme point from the number of extreme points, and set the device working curve corresponding to the maximum extreme point as the first working curve and the device working curve corresponding to the minimum extreme point as the second working curve;

[0128] Integrate the first working curve and the second working curve to correspondingly generate the real-time working curve of the equipment cluster.

[0129] Further, the execution module is specifically configured to:

[0130] When the real-time working curve is obtained in real time, perform a full scan on the real-time working curve to correspondingly detect the starting point and the ending point of the real-time working curve;

[0131] Within the range of the starting point and the ending point, overlap the standard working curve on the real-time working curve to detect in real time the overlapping part and the non-overlapping part generated between the real-time working curve and the standard working curve;

[0132] Hide the overlapping part and correspondingly retain the non-overlapping part to determine in real time whether the equipment cluster is abnormal according to the non-overlapping part.

[0133] Further, the execution module is specifically configured to:

[0134] When the non-overlapping part is obtained in real time, in the direction from the starting point to the ending point, sequentially detect a number of maximum points and a number of minimum points that appear inside the non-overlapping part;

[0135] Add corresponding first marks to a number of the maximum points respectively, and add corresponding second marks to a number of the minimum points respectively;

[0136] Build a mapping relationship between the first mark and the second mark in real time, and determine whether the equipment cluster is abnormal based on the mapping relationship according to a number of the maximum points and a number of the minimum points.

[0137] Further, the execution module is specifically configured to:

[0138] Calculate the target difference between each maximum point and its corresponding minimum point one by one according to the mapping relationship, and determine in real time whether the target difference is within the range of a preset difference threshold;

[0139] If it is determined in real time that the target difference is within the range of the preset difference threshold, directly determine that the equipment cluster is not abnormal.

[0140] The fourth embodiment of the present invention provides a computer, including a memory, a processor, and a computer program stored on the memory and executable on the processor. Wherein, when the processor executes the computer program, it implements the photovoltaic power station operation data management method as described above.

[0141] A fifth embodiment of the present invention provides a readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the photovoltaic power station operation data management method as described above.

[0142] In summary, the photovoltaic power station operation data management method and system provided by the above embodiments of the present invention can save the process of manual inspection, thereby eliminating the phenomenon of missed judgment and misjudgment, and correspondingly greatly improve the work efficiency.

[0143] It should be noted that the above modules can be functional modules or program modules, and can be implemented by software or hardware. For modules implemented by hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0144] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0145] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0146] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0147] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0148] The above-described embodiments merely represent several implementation manners of the present invention, and the description thereof is relatively specific and detailed, but should not be construed as a limitation on the scope 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 modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the appended claims.

Claims

1. A photovoltaic power station operation data management method, characterized in that: The method comprises: At preset intervals, target operation data corresponding to a target photovoltaic power station is acquired in real time through a preset acquisition device, and the target operation data is analyzed and processed to detect in real time several types of equipment contained in the target photovoltaic power station; Detecting in real time inside the target photovoltaic power station the device clusters corresponding to each of the device types, and detecting in real time in the target operation data the target operating parameters corresponding to each of the device clusters; Based on the preset rules, a real-time working curve corresponding to the device cluster is drawn in real time according to the target working parameters, and a standard working curve adapted to the device cluster is called out in real time in a preset standard database; The real-time working curve is overlapped and compared with the standard working curve, so as to determine in real time whether the equipment cluster is abnormal according to the comparison result.

2. The photovoltaic power station operation data management method according to claim 1, characterized in that: The step of drawing a real-time working curve corresponding to the equipment cluster in real time according to the target working parameters based on preset rules includes: When the target working parameters are obtained in real time, the device cluster is fully scanned to detect in real time a number of sub-devices contained in the device cluster; Adding a corresponding target identifier to each of the sub-devices, and matching a parameter subset corresponding to each of the sub-devices in the target working parameters based on the target identifier, wherein the parameter subset is unique; A real-time working curve corresponding to the device cluster is drawn in real time according to each of the sub-devices and the parameter subset corresponding thereto.

3. The photovoltaic power station operation data management method according to claim 2, characterized in that: The step of drawing a real-time working curve corresponding to the device cluster in real time according to each of the sub-devices and the corresponding parameter subsets comprises: When each of the sub-devices and the corresponding parameter subsets are acquired in real time, a reference surface is created in real time inside the preset program; Creating a target two-dimensional coordinate system adapted to each of the sub-devices in real time inside the reference plane, and detecting a data generation time period corresponding to the target working parameters in real time; According to the data generation time period, a parameter subset of each sub-device is mapped to the interior of the target two-dimensional coordinate system to form a number of device working curves within the target two-dimensional coordinate system, and a real-time working curve corresponding to the device cluster is generated in real time based on the several device working curves, and each of the device working curves is unique.

4. The photovoltaic power station operation data management method according to claim 3, characterized in that: The step of generating a real-time working curve corresponding to the device cluster in real time according to the plurality of device working curves comprises: When the working curves of the equipment are obtained in real time, the working curves of the equipment are fully scanned to detect in real time the corresponding extreme value points contained in the working curves of the equipment; Selecting a maximum extreme value point and a minimum extreme value point from the plurality of extreme value points, and setting the device working curve corresponding to the maximum extreme value point as the first working curve, and setting the device working curve corresponding to the minimum extreme value point as the second working curve; The first working curve and the second working curve are integrated to generate a real-time working curve of the device cluster.

5. The photovoltaic power station operation data management method according to claim 1, characterized in that: The step of performing a coincidence comparison process on the real-time working curve and the standard working curve to determine in real time whether an abnormality occurs in the device cluster according to the comparison result includes: When the real-time working curve is acquired in real time, the real-time working curve is fully scanned to detect the starting point and the ending point of the real-time working curve accordingly; In the range between the starting point and the end point, the standard working curve is overlapped on the real-time working curve to detect in real time the overlapping part and the non-overlapping part between the real-time working curve and the standard working curve; The overlapping part is hidden, and the non-overlapping part is correspondingly reserved, so as to determine in real time whether an abnormality occurs in the device cluster according to the non-overlapping part.

6. The photovoltaic power station operation data management method according to claim 5, characterized in that: The step of determining in real time according to the non-overlapping part whether the device cluster is abnormal comprises: When the non-overlapping portion is acquired in real time, a plurality of maximum value points and a plurality of minimum value points corresponding to the inside of the non-overlapping portion are detected in sequence in a direction from the starting point to the ending point; Adding corresponding first marks to the plurality of maximum value points, and adding corresponding second marks to the plurality of minimum value points; A mapping relationship between the first mark and the second mark is constructed in real time, and based on the mapping relationship, it is determined whether the device cluster is abnormal according to a plurality of the maximum value points and a plurality of the minimum value points.

7. The photovoltaic power station operation data management method according to claim 6, characterized in that: The step of judging whether the device cluster is abnormal based on the mapping relationship according to the plurality of maximum value points and the plurality of minimum value points comprises: Calculating the target difference between each of the maximum value points and the corresponding minimum value points one by one according to the mapping relationship, and judging in real time whether the target difference is within the range of a preset difference threshold; If it is determined in real time that the target difference is within the range of the preset difference threshold, it is directly determined that no abnormality occurs in the device cluster.

8. A photovoltaic power station operation data management system, characterized in that: The system comprises: An acquisition module, used to acquire target operation data corresponding to a target photovoltaic power station in real time through a preset acquisition device at preset intervals, and parse and process the target operation data to detect in real time several types of equipment contained in the target photovoltaic power station; A detection module, used to detect in real time inside the target photovoltaic power station the device clusters corresponding to each of the device types, and to detect in real time in the target operation data the target operating parameters corresponding to each of the device clusters; A processing module, used to draw a real-time working curve corresponding to the device cluster in real time according to the target working parameters based on preset rules, and call out a standard working curve adapted to the device cluster in real time in a preset standard database; The execution module is used to perform an overlap comparison process on the real-time working curve and the standard working curve, so as to determine in real time whether an abnormality occurs in the device cluster according to the comparison result.

9. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the photovoltaic power station operation data management method according to any one of claims 1 to 7 is implemented.

10. A readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the photovoltaic power station operation data management method as described in any one of claims 1 to 7 is implemented.

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