Metering equipment operation state influence factor determination method and device, equipment and medium

Abstract

The invention discloses a metering equipment operation state influence factor determination method and device, equipment and a medium, and the method comprises the steps: carrying out the calculation according to the collection success rate data and communication delay data of each data item of metering equipment in a target power region, and obtaining the collection leakage point rate and communication delay rate of each data item; constructing a regional communication comprehensive evaluation index of the target power region according to the collection leakage point rate and the communication delay rate, constructing and calculating a single-factor communication evaluation index of each influence factor, and calculating a correlation coefficient between the regional communication comprehensive evaluation index and each single-factor communication evaluation index through a grey correlation analysis method; and screening out the target influence factors influencing the operation state of the metering equipment. According to the method, main influence factors influencing communication during operation of metering equipment are effectively identified, the intelligent level and communication stability of power grid operation are improved, energy loss is reduced, resource configuration is optimized, and sustainable and healthy development of a power system is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of power monitoring, and in particular to a method for determining influencing factors of the operating state of a metering device. Background Art

[0002] With the rapid development of information technology, metering devices, as an important part of the power grid, the real-time monitoring and accurate detection of their operating states during communication are the key links to achieve digital transformation.

[0003] Traditional detection methods are mostly manual inspections and regular tests, which are difficult to meet the management requirements of modern power grids for high efficiency, accuracy, and strong real-time performance. In recent years, a detection system based on four lines and one library has often been used for the detection of metering devices, but the relevant detection methods are mainly fixed detection schemes for the platform body, and tests are carried out through fixed programs, fixed detection items, and fixed time frequencies. However, the operating conditions of the equipment on site are very complex, and many tests need to reproduce problems in specific environments. Especially for the on-site application of some new equipment and new technologies, there is a lack of targeted and flexible detection methods. When new equipment is officially put into operation on site, various faults still emerge in an endless stream, which brings great pressure to the on-site operation and maintenance work, resulting in a large amount of manpower and material resources consumed in the on-site operation and maintenance work.

[0004] Therefore, how to optimize the detection of power metering devices during communication to meet the development needs of intelligent and automated smart grids has become a technical problem that needs to be urgently solved by those skilled in the art. Summary of the Invention

[0005] The present invention provides a method, device, equipment, and medium for determining influencing factors of the operating state of a metering device to determine the main influencing factors of a power metering device during power communication, so as to optimize the detection process of the power metering device during communication.

[0006] To solve the above technical problems, an embodiment of the present invention provides a method for determining influencing factors of the operating state of a metering device.

[0007] Collect the operating states of each data item of the metering devices in the target power area to obtain the collection success rate data of each data item, and calculate the communication delay data corresponding to each data item according to the collection success rate data of each data item.

[0008] Calculate the collection leakage rate and communication delay rate of each data item according to the collection success rate data and the communication delay data, and construct a regional communication comprehensive evaluation index for the target power area according to the collection leakage rate and the communication delay rate.

[0009] Construct and calculate the single-factor communication evaluation index of each of the influencing factors based on the influencing factors of the collection leakage rate and the communication delay rate.

[0010] Calculate the correlation coefficient between the comprehensive communication evaluation index of the region and each of the single-factor communication evaluation indexes respectively through the grey correlation analysis method.

[0011] Screen the suspected problem factors for each of the influencing factors according to the correlation coefficients, and obtain the target influencing factors affecting the operation status of the metering device.

[0012] Further, the communication delay data is:

[0013]

[0014] Among them, P(d i ,t) is the data collection result, and T ret (d i ,t) is the collection return time of data item i within the t-th time period, and T lss (d i ,t) is the specified reporting time of data item i in the reporting task, and T of (d i ) is the delay reporting range time in the reporting task of data item i.

[0015] Further, the collection leakage rate is:

[0016]

[0017] The communication delay rate of a single device of the target metering device during measurement is:

[0018]

[0019] Among them, is the number of points to be collected for data item d i ; x j is the j-th device in the device group X.

[0020] Further, the single-factor communication evaluation index W(d i ) is:

[0021]

[0022] W(d i ) = f1 × K(d i ) + f2 × L(d i )

[0023] Among them, N is the number of device groups configured with the current corresponding data item.

[0024] Further, the correlation coefficient is as follows:

[0025]

[0026] Wherein, is the grey correlation coefficient between the comprehensive evaluation index W(p) of regional communication and the single-factor communication evaluation index W(R r ); Δ is the difference between the p-th sample of the comprehensive evaluation index of regional communication and the p-th sample of the r-th single-factor communication evaluation index, and W(R r , p) is the p-th sample of the r-th single-factor communication evaluation index. is the minimum value of Δ, is the maximum value of Δ, and ρ is the resolution coefficient.

[0027] Further, the screening of suspected problem factors for each of the influencing factors according to the respective correlation coefficients to obtain the target influencing factors affecting the operation state of the metering device includes:

[0028] Sort the correlation coefficients corresponding to each of the influencing factors, and determine the influencing factor with the highest correlation coefficient as the suspected problem factor.

[0029] In the target power region, eliminate the devices related to the suspected problem factor, reconstruct the comprehensive evaluation index of regional communication with the remaining devices, re-determine the suspected problem factor and eliminate it until the comprehensive evaluation index of regional communication reaches the preset index threshold.

[0030] Take each of the eliminated suspected problem factors as the target influencing factors affecting the operation state of the metering device.

[0031] Further, the method further includes:

[0032] Real-time detect the operation state of the metering devices in the target power region according to the target influencing factors, so as to perform a reliability assessment on the comprehensive evaluation index of regional communication in the target power region according to the detection results.

[0033] Another embodiment of the present invention provides a device for determining the influencing factors of the operation state of a metering device, including:

[0034] A data acquisition module, configured to collect the operation states of each data item of the metering devices in the target power region to obtain the acquisition success rate data of each data item, and calculate the communication delay data corresponding to each data item according to the respective acquisition success rate data.

[0035] The comprehensive index construction module is used to calculate the acquisition leakage rate and communication delay rate of each data item according to the acquisition success rate data and the communication delay data, and construct the regional communication comprehensive evaluation index of the target power area according to the acquisition leakage rate and the communication delay rate.

[0036] The influence factor evaluation module is used to construct and calculate the single-factor communication evaluation index of each influence factor according to the influence factors of the acquisition leakage rate and the communication delay rate.

[0037] The correlation degree calculation module is used to calculate the correlation degree coefficient between the regional communication comprehensive evaluation index and each single-factor communication evaluation index respectively by the grey correlation analysis method.

[0038] The influence factor determination module is used to screen out the suspected problem factors for each influence factor according to the correlation degree coefficients, and obtain the target influence factors that affect the operation state of the metering equipment.

[0039] Another embodiment of the present invention provides a computer device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the method for determining the influence factors of the operation state of the metering equipment as described above is implemented.

[0040] Another embodiment of the present invention provides a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the device where the computer-readable storage medium is located executes the computer program, the method for determining the influence factors of the operation state of the metering equipment as described above is implemented.

[0041] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following:

[0042] By collecting and analyzing various data items, calculating the acquisition success rate and communication delay, it comprehensively reflects the device performance. Using the grey correlation analysis method, it deeply analyzes the correlation between the regional communication comprehensive evaluation index and each single-factor communication evaluation index, and accurately locks the key factors affecting communication quality. On this basis, by gradually eliminating the suspected problem factors and recalculating the regional communication comprehensive evaluation index, the main influence factors are effectively identified and solved, the communication resource configuration is optimized, and a flexible communication ability detection method can reduce the labor and vehicle costs of on-site inspection equipment for operating equipment. Through the measurement of evaluation ability, it improves the quality control of equipment manufacturers, provides technical support for bidding, reduces the losses caused by equipment quality problems, not only improves the intelligent level and communication stability of power grid operation, but also reduces energy consumption, optimizes resource configuration, and at the same time provides a scientific basis and decision support for the optimization and upgrading of the regional communication system, promoting the sustainable and healthy development of the power system. Description of the Drawings

[0043] Figure 1 It is a flowchart of the steps of the method for determining the influencing factors of the operation state of the metering device provided by the embodiment of the present invention;

[0044] Figure 2 It is a structural block diagram of the device for determining the influencing factors of the operation state of the metering device provided by the embodiment of the present invention;

[0045] Figure 3 It is a structural diagram of the computer device provided by the embodiment of the present invention. Detailed Embodiments

[0046] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0047] In the description of the present application, the terms "first", "second", "third", etc. are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first", "second", "third", etc. may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0048] In the description of the present application, it should be noted that unless otherwise clearly specified and limited, the terms "installed", "connected", "connected" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected, or indirectly connected through an intermediate medium, and it may be the internal communication of two components. The terms "vertical", "horizontal", "left", "right", "up", "down" and similar expressions used herein are only for the purpose of illustration, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be understood as a limitation of the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0049] In the description of the present application, it should be noted that unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0050] An embodiment of the present invention provides a method for determining influence factors of the operation state of a metering device. Specifically, please refer to Figure 1 , Figure 1 which is shown as the step flowchart of the method for determining influence factors of the operation state of a metering device in one embodiment of the present invention, including steps S11 to S15:

[0051] With the rapid development and intelligent transformation of the power system, grid metering devices, as key components in the power network, the accuracy and reliability of their operation states are directly related to the stability of power supply, the accuracy of metering data, and the economy of grid operation. In recent years, with the continuous progress of advanced technologies such as the Internet of Things, big data, and artificial intelligence, new technical means and solutions have been provided for the operation monitoring and management of grid metering devices. Therefore, constructing a set of efficient and accurate methods for detecting the operation state of grid metering devices is not only crucial for improving the intelligent level of grid operation, but also an important way to ensure the safe and stable operation of the power system, optimize resource allocation, and reduce energy consumption.

[0052] Step S11: Collect the operation states of each data item of the metering devices in the target power area to obtain the collection success rate data of each data item, and calculate the communication delay data corresponding to each data item according to the collection success rate data.

[0053] When performing operation state detection, it is necessary to obtain the collection success rate data of the target metering device. The type of the target metering device is a variety of new devices in the digital metering and simulation test environment in a substation area (a partition in the power distribution area or system), and these devices include but are not limited to power metering devices (such as smart meters), data collection and communication devices, and software and hardware devices related to the main station application, etc.; specifically, the target metering devices in this embodiment mainly include electric energy meters, voltage meters and ammeters, current transformers, metering cabinets and metering boxes, and other devices such as resistance testers and resistance meters.

[0054] Specifically, the process of obtaining the collection success rate data of the target metering device in this embodiment is as follows:

[0055] Based on the data information collected during the historical operation of the target metering device, calculate the collection success rate of each data item of the device at different time periods.

[0056] Specifically, the data items collected in this embodiment include key data items such as the total positive active energy indication value, instantaneous active power, phase A voltage, phase B voltage, phase C voltage, phase A current, phase B current, and phase C current of each metering device, which can comprehensively reflect the operating status and performance of the metering device, ensure the representativeness and practicality of the collected data, and provide a reliable basis for subsequent data analysis and equipment status evaluation.

[0057] The collection period is set to 15 minutes each time, including 00:00 - 00:15, 00:15 - 00:30, ……, 00:45 - 00:00, etc., to ensure that data can be collected at different time periods within a day. The data can be divided according to the time dimension to capture the operating characteristics of the device at different time periods, which helps to discover potential operating problems or anomalies.

[0058] The data collection modes include communication methods such as micro-power wireless, narrowband carrier, dual-mode communication, HPLC, and RS-485. Multiple communication methods are used as backups for each other to reduce the risk of data collection failure caused by the failure of a single communication method.

[0059] The target metering device manufacturers include Dingxin, Qianjing Wuyou, Zhenzhong Electronics, Zhongchen Microelectronics, etc., covering metering devices from different manufacturers to ensure the extensiveness and representativeness of the data, which helps to discover performance differences and problems between different devices and provides a reference for device selection and maintenance.

[0060] The process of data collection is as follows: The 24 hours of each day are divided into 96 points at 15-minute intervals. According to the return situation of the downlink message, the data collection situation every 15 minutes is tracked and recorded. When the downlink message has a return and the data item collection is not empty, it is recorded as a successful collection; otherwise, it is recorded as a failed collection. The formula for determining the data collection result within a 15-minute period is:

[0061]

[0062] where t is the serial number of the collection period, that is, t = 1 represents the 00:00 - 00:15 period, t = 2 represents the 00:15 - 00:30 period, and so on. In the actual detection process, the collection personnel can adjust the period according to the actual situation; d i is the i-th data item in the data item set, and the data item set contains all the data items collected by the corresponding device.

[0063] Calculate the collection success rate data of the data item according to the collection result data of each data item at different time periods obtained.

[0064] In the same metering device, due to differences in device functions, there may be significant differences in the acquisition success rates of different data items. Therefore, based on the acquisition success rate data of each data item of the target metering device, it is necessary to calculate the communication delay data D(d i ,t) of the device within a certain period:

[0065]

[0066] where T ret (d i ,t) is the acquisition return time of data item i in the t-th period, and T lss (d i ,t) is the specified reporting time of data item i in the reporting task, and T of (d i ) is the delay reporting range time of data item i in the reporting task.

[0067] By calculating the communication delay data, the delay problems in the data acquisition process can be discovered and processed in a timely manner, thereby improving the accuracy and integrity of data acquisition. The communication delay data can be used as an important reference index for equipment maintenance, helping operation and maintenance personnel to more accurately locate the reasons for equipment failures or performance degradation, and thus formulate more effective equipment detection methods.

[0068] Step S12: Calculate the acquisition leakage rate and communication delay rate of each data item based on the acquisition success rate data and the communication delay data, and construct a regional communication comprehensive evaluation index for the target power region according to the acquisition leakage rate and the communication delay rate.

[0069] Based on the acquisition success rate data and communication delay data obtained in the previous steps, calculate the leakage rate and delay rate of different data items of the device. The leakage rate refers to the proportion of the number of data points that the target metering device fails to successfully acquire during the measurement process to the total number of points that should be acquired, reflecting the integrity and reliability of the device in data acquisition. By calculating the leakage rate, problems such as sensor failures and communication interruptions that may exist in the data acquisition process of the device can be intuitively understood. The delay rate refers to the proportion of the time required for the data to be transmitted from the device to the system exceeding the predetermined threshold after the target metering device has acquired the data. It reflects the efficiency and real-time performance of the device in data transmission. By calculating the delay rate, the performance of the device in the data transmission process can be evaluated, and potential communication bottlenecks or network delay problems can be discovered.

[0070] The leakage rate of a single device of the target metering device during measurement is:

[0071]

[0072] The delay rate of a single device of the target metering device during measurement is:

[0073]

[0074] Among them, is the number of sampling points for data item d, which is determined according to the sampling frequency of the data item. For example, if it is sampled once every 15 minutes according to this embodiment, then i x j is the j-th device in device group X. For example, if there are 100 electricity meters in a certain area, then device group X ∈ [x1, x2, x j 100 …, x 100 .

[0075] Establish a comprehensive evaluation index for regional communication within the substation area based on the leakage point rate and the delay rate:

[0076]

[0077] Among them, is the average leakage point rate of all devices in the area, is the average delay rate of all devices in the area. f1 and f2 are the weighting coefficients of the leakage point rate and the delay rate respectively. In this embodiment, f1 = 0.7 and f2 = 0.3 are taken.

[0078] The comprehensive evaluation index for regional communication within the substation area is an index that comprehensively reflects the performance of all target metering devices in the area in terms of data collection and transmission. It combines two key parameters, the leakage point rate and the delay rate, and takes into account their weighting coefficients to comprehensively evaluate the data communication performance of the devices. It can uniformly quantify and evaluate the data communication performance of all devices in the area, which is convenient for determining the influencing factors in the subsequent detection process.

[0079] Step S13, construct and calculate the single-factor communication evaluation index of each of the influencing factors according to the influencing factors of the collected leakage point rate and the communication delay rate.

[0080] Taking factors such as each data item, collection time period, device manufacturer, collection mode, etc., statistically calculate the single-factor communication evaluation index of each factor in the area. The data items include the total positive active energy indication value, instantaneous active power, phase A voltage, phase B voltage, phase C voltage, phase A current, phase B current, phase C current, etc. The data item factors reflect the communication performance of different data types. For example, the total positive active energy indication value, instantaneous active power, voltages and currents of each phase, etc. These data items play a crucial role in power grid monitoring, energy consumption analysis, etc. By calculating the single-factor communication evaluation index of these data items respectively, it can be identified which data items are more likely to have problems during communication.

[0081] The collection periods include 00:00 - 00:15, 00:15 - 00:30, ……, 00:45 - 00:00, etc. The collection period factor takes into account the data communication performance in different time periods. Factors such as the load condition of the power grid and the congestion degree of the communication network may vary significantly in different time periods, thus affecting the quality of data communication. By calculating the single-factor communication evaluation index for different collection periods respectively, it can be revealed which periods have poor communication performance.

[0082] The equipment manufacturers include: Dingxin, Qianjing Wuyou, Zhenzhong Electronics, Zhongchen Microelectronics, etc. The equipment manufacturer factor reflects the performance differences of equipment from different manufacturers in data communication. Different manufacturers may adopt different technical standards and manufacturing processes when producing equipment, thus affecting the communication performance of the equipment. By calculating the single-factor communication evaluation index for equipment from different manufacturers respectively, the performance advantages and disadvantages of equipment from each manufacturer can be compared and evaluated, providing a reference for equipment selection.

[0083] The collection modes include: micro-power wireless, narrowband carrier, dual-mode communication, HPLC, RS-485, etc. The collection mode factor takes into account the performance differences of different communication technologies in data communication. Collection modes such as micro-power wireless, narrowband carrier, dual-mode communication, HPLC, RS-485, etc. each have their own advantages and disadvantages and are suitable for different equipment scenarios.

[0084] Specifically, taking the data item d i ∈[d1, d2, d3, d n as an example, the calculation formula for the single-factor communication evaluation index W(d i ) of this data item is as follows:

[0085]

[0086] W(d i ) = f1 × K(d i ) + f2 × L(d i )

[0087] Where N is the number of device groups configured with the current corresponding data item. For example, there are 100 device groups in the current area, and 50 of them are configured with devices that need to collect instantaneous active power. Then for the data item instantaneous active power, N = 50.

[0088] Similarly, calculate the single-factor communication evaluation index W(t i ∈[t1, t2, t3, t 96 , for each collection period t i ∈[c1, c2, c3, c M , for each equipment manufacturer c i ∈[m1, m2, m3, m Q , and for each collection mode m i ∈[m1, m2, m3, m Q respectively.i ) W(c i ) W(m i ).

[0089] The calculation formula of the single-factor communication evaluation index takes the above factors into consideration, and the single-factor communication evaluation index under each factor is obtained through calculation. These indexes can comprehensively reflect the performance status of data communication, provide a quantitative evaluation result, and facilitate the comparison and analysis of the communication performance under different factors.

[0090] Step S14: Calculate the correlation coefficient between the comprehensive regional communication evaluation index and each single-factor communication evaluation index respectively through the grey relational analysis method.

[0091] The grey relational analysis method (Grey Relational Analysis, GRA) is a kind of grey system analysis method. It measures the degree of association between factors according to the similarity or dissimilarity of the development trends between factors, that is, the "grey relational degree".

[0092] Specifically, in this embodiment, the comprehensive regional communication evaluation index and the single-factor communication evaluation index are continuously observed and calculated for a period of time. Through grey relational analysis, the correlation coefficient between the sequence of the comprehensive regional evaluation index and the sequence of each single-factor communication evaluation index in the continuous period is calculated respectively. The specific evaluation index record information is shown in Table 1:

[0093] Table 1 Continuous Observation Period Record Table

[0094]

[0095] Calculate the correlation coefficient between the comprehensive regional communication evaluation index and each single-factor communication evaluation index respectively through the grey relational analysis method:

[0096]

[0097] Among them, is the grey relational coefficient between the comprehensive regional communication evaluation index W(p) at time p and a single single-factor communication evaluation index W(R r ); Δ is the difference between the p-th sample of the comprehensive regional communication evaluation index and the p-th sample of the r-th single-factor communication evaluation index, and W(R r , p) is the p-th sample of the r-th single-factor communication evaluation index, is the minimum value of Δ in all observation periods, is the maximum value of Δ in all observation periods, ρ is the resolution coefficient, ρ ∈ [0.1, 0.5], and in this embodiment, ρ = 0.2 is preferably selected.

[0098] According to the grey correlation degree coefficients at each moment, take the average value as the grey correlation degree between the comprehensive evaluation index W(p) of regional communication and the comprehensive evaluation index W(R r ) within the continuous observation period. The calculation formula of the grey correlation degree λ(R r ) is as follows:

[0099]

[0100] where T is the observation period.

[0101] The calculation of the correlation degree coefficient can reflect the correlation degree between each single-factor communication evaluation index and the comprehensive evaluation index of regional communication at a certain moment. Based on the calculated correlation degree coefficient and correlation degree, a comprehensive evaluation and analysis of the correlation degree between the comprehensive evaluation index of regional communication and each single-factor communication evaluation index can identify the single-factor communication evaluation index that has a greater impact on the comprehensive evaluation index of regional communication, as well as the mutual influence relationship between each index.

[0102] Step S15, screen the suspected problem factors for each of the said influence factors according to the respective correlation degree coefficients to obtain the target influence factors affecting the operation state of the metering device.

[0103] Arrange the corresponding to each single-factor communication evaluation index in sequence, lock the factor with the highest correlation coefficient as the suspected problem factor. After removing the devices related to the suspected factor from all device groups in the region, recalculate the comprehensive evaluation index of regional communication with the remaining device groups as the analysis object. If the comprehensive evaluation index of communication is lower than a certain threshold, lock the suspected problem factor as the main factor; otherwise, include the factor with the second-highest correlation coefficient in the suspected problem factors and conduct index calculation until the comprehensive evaluation index of regional communication meets a certain threshold.

[0104] In the case where the final regional comprehensive evaluation index is lower than the threshold, the removed suspected problem factor is the main influencing factor affecting the communication index of the entire region.

[0105] Through the above screening steps, gradually remove the relevant devices and recalculate the comprehensive evaluation index of regional communication until the threshold requirement is met, effectively and accurately identifying and solving the main influencing factors affecting the quality of regional communication, thereby optimizing the communication resource allocation, improving the communication stability and overall performance, ensuring the smoothness and satisfaction of the user experience, and providing a scientific basis and decision support for the optimization and upgrading of the regional communication system.

[0106] The method for determining the influencing factors of the operation state of the metering device of the present invention comprehensively reflects the device performance by collecting and analyzing various data items, calculating the acquisition success rate and communication delay. By using the grey relational analysis method, it deeply analyzes the correlation between the comprehensive regional communication evaluation index and each single-factor communication evaluation index, and accurately locks the key factors affecting communication quality. On this basis, by gradually eliminating suspected problem factors and recalculating the comprehensive regional communication evaluation index, the main influencing factors are effectively identified and solved, and the communication resource allocation is optimized. This solution not only improves the intelligent level and communication stability of the power grid operation, but also reduces energy consumption and optimizes resource allocation. At the same time, it provides a scientific basis and decision-making support for the optimization and upgrading of the regional communication system, and promotes the sustainable and healthy development of the power system.

[0107] An embodiment of the present invention further provides a device for determining the influencing factors of the operation state of a metering device, which is used to execute the method for determining the influencing factors of the operation state of the metering device as described above. Figure 2 The following is a structural block diagram of the device for determining the influencing factors of the operation state of the metering device according to the embodiment of the present invention. The device includes:

[0108] A data acquisition module 21, configured to collect the operation states of various data items of the metering devices in the target power region, obtain the acquisition success rate data of each data item, and calculate the communication delay data corresponding to each data item according to the acquisition success rate data of each data item.

[0109] A comprehensive index construction module 22, configured to calculate the acquisition leakage rate and communication delay rate of each data item according to the acquisition success rate data and the communication delay data, and construct a comprehensive regional communication evaluation index of the target power region according to the acquisition leakage rate and the communication delay rate.

[0110] An influencing factor evaluation module 23, configured to construct and calculate the single-factor communication evaluation index of each influencing factor according to the influencing factors of the acquisition leakage rate and the communication delay rate.

[0111] A correlation degree calculation module 24, configured to calculate the correlation degree coefficient between the comprehensive regional communication evaluation index and each single-factor communication evaluation index respectively by using the grey relational analysis method.

[0112] An influencing factor determination module 25, configured to screen suspected problem factors for each influencing factor according to each correlation degree coefficient, and obtain the target influencing factors affecting the operation state of the metering device.

[0113] The technical features and effects of the device proposed in the embodiments of the present invention are the same as those of the method proposed in the embodiments of the present invention, and will not be elaborated herein. Each module in the above device can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in the processor in the computer device in the form of hardware or be independent of it, or can be stored in the memory of the computer device in the form of software, so as to facilitate the processor to call and execute the operations corresponding to the above modules.

[0114] The embodiments of the present invention also provide a computer-readable storage medium, which includes a stored computer program; wherein, when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the method for determining the influencing factors of the operation state of the metering device as described above.

[0115] The embodiments of the present invention also provide a computer device. Figure 3 As shown in the structure block diagram of a preferred embodiment of a computer device provided by the present invention, the computer device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the method for determining the influencing factors of the operation state of the metering device as described above.

[0116] Preferably, the computer program can be divided into one or more modules / units (such as computer program 1, computer program 2,...), and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the computer device.

[0117] The processor can be a Central Processing Unit (CPU), or can also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or the processor can also be any conventional processor. The processor is the control center of the computer device, and connects various parts of the computer device through various interfaces and lines.

[0118] The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function, etc., and the data storage area can store relevant data, etc. In addition, the memory can be a high-speed random access memory, or a non-volatile memory, such as a plug-in hard disk, a SmartMedia Card (SMC), a Secure Digital (SD) card, a Flash Card, etc., or the memory can also be other volatile solid-state storage devices.

[0119] It should be noted that the above computer device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that Figure 3 The structural block diagram is only an example of the computer device and does not constitute a limitation on the computer device. It may include more or fewer components than shown, or combine certain components, or different components.

[0120] The above-described embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.

Claims

1. A method for determining influencing factors of the operating state of a metering device, characterized in that, Including: Collect the operating status of each data item of the metering equipment in the target power area to obtain the collection success rate data of each data item, and calculate the communication delay data corresponding to each data item according to the collection success rate data of each data item; Calculate the collection leakage rate and communication delay rate of each data item according to the collection success rate data and the communication delay data, and construct the regional communication comprehensive evaluation index of the target power area according to the collection leakage rate and the communication delay rate; Construct and calculate the single-factor communication evaluation index of each influencing factor according to the influencing factors of the collection leakage rate and the communication delay rate; Calculate the correlation coefficient between the regional communication comprehensive evaluation index and each single-factor communication evaluation index respectively through the grey relational analysis method; Screen the suspected problem factors for each influencing factor according to the correlation coefficients, and obtain the target influencing factors affecting the operating status of the metering equipment.

2. The method for determining the influencing factor of the operation state of the metering device according to claim 1, characterized in that, The communication delay data is: Among them, P(d i , t) is the data acquisition result, T ret (d i , t) is the acquisition return time of data item i within the t-th time period, T lss (d i , t) is the specified reporting time of data item i in the reporting task, T of (d i ) is the delayed reporting range time of data item i in the reporting task.

3. The method for determining the influencing factor of the operation state of the metering device according to claim 2, characterized in that, The collection leakage rate is: The communication delay rate of a single device of the target metering equipment during measurement is: Among them, is the number of sampling points for data item d i ; x j is the j-th device in device group X.

4. The method for determining the influencing factors of the operation state of the metering device according to claim 3, wherein, The single-factor communication evaluation index W(d i ) is as follows: W(d i ) = f1×K(d i ) + f2×L(d i ) Where N is the number of device groups configured with the current corresponding data item.

5. The method for determining the influencing factors of the operation state of the metering device according to claim 4, wherein, The correlation coefficient is: Among them, W(R r ) is the grey correlation coefficient between the comprehensive regional communication evaluation index W(p) and the single-factor communication evaluation index W(R r ); Δ is the difference between the p-th sample of the comprehensive regional communication evaluation index and the p-th sample of the r-th single-factor communication evaluation index, and W(R r , p) is the p-th sample of the r-th single-factor communication evaluation index, is the minimum value of Δ, is the maximum value of Δ, and ρ is the resolution coefficient.

6. The method for determining the influencing factor of the operating state of the metering device according to claim 1, characterized in that The screening of the suspected problem factors for each influencing factor according to the correlation coefficients to obtain the target influencing factors affecting the operating status of the metering equipment includes: Sort the correlation coefficients corresponding to each influencing factor, and determine the influencing factor with the highest correlation coefficient as the suspected problem factor; Eliminate the devices related to the suspected problem factor in the target power area, reconstruct the regional communication comprehensive evaluation index with the remaining devices, re-determine the suspected problem factor and eliminate it until the regional communication comprehensive evaluation index reaches the preset index threshold; Take each of the eliminated suspected problem factors as the target influencing factors affecting the operating status of the metering equipment.

7. The method for determining the influencing factors of the operating state of the metering device according to any one of claims 1-6, characterized in that, The method further includes: Real-time detect the operating status of the metering equipment in the target power area according to the target influencing factors, so as to evaluate the reliability of the regional communication comprehensive evaluation index of the target power area according to the detection results.

8. A device for determining influence factors of the operating state of a metering device, characterized in that, Including: A data collection module for collecting the operating status of each data item of the metering equipment in the target power area to obtain the collection success rate data of each data item, and calculating the communication delay data corresponding to each data item according to the collection success rate data of each data item; A comprehensive index construction module for calculating the collection leakage rate and communication delay rate of each data item according to the collection success rate data and the communication delay data, and constructing the regional communication comprehensive evaluation index of the target power area according to the collection leakage rate and the communication delay rate; An influencing factor evaluation module for constructing and calculating the single-factor communication evaluation index of each influencing factor according to the influencing factors of the collection leakage rate and the communication delay rate; A correlation calculation module for calculating the correlation coefficient between the regional communication comprehensive evaluation index and each single-factor communication evaluation index respectively through the grey relational analysis method; An impact factor determination module is configured to screen out suspected problem factors for each of the impact factors according to the respective correlation coefficients, so as to obtain target impact factors affecting the operating state of the metering device.

9. A computer device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the method for determining the impact factors of the operating state of the metering device according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program. When the device where the computer-readable storage medium is located executes the computer program, the method for determining the impact factors of the operating state of the metering device according to any one of claims 1 to 7 is implemented.

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