Method and system for extracting influence factors of installation and connection time of three-phase electric energy meter

Abstract

The invention discloses a three-phase electric energy meter installation time influence factor extraction method and system, and the method comprises the steps: collecting the historical equipment installation time of each piece of equipment in the installation operation, obtaining the average installation time of each piece of equipment according to the historical equipment installation time of each piece of equipment, and selecting the equipment with the average installation time greater than a first preset value, the method comprises the following steps: taking a plurality of to-be-analyzed equipment as the to-be-analyzed equipment, constructing an evaluation system of the installation time of the to-be-analyzed equipment, obtaining the installation time of the to-be-analyzed equipment corresponding to each evaluation index, obtaining the influence degree of each evaluation index on the installation time according to each installation time, and analyzing each influence degree to obtain an influence factor result. According to the method, the influence factors of the installation time of the three-phase electric energy meter are extracted, so that an improved strategy is obtained, and the installation time of the three-phase electric energy meter is shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of electric meter installation, and in particular to a method and system for extracting factors affecting the installation time of a three-phase electric energy meter. Background Art

[0002] In the process of electricity use and power supply, whether it is to ensure the quality of power supply or the safety of users' electricity use, it is directly related to the installation and connection of meters. In the entire power supply system, the installation and connection of meters represents the last step of the power supply process. The installation and connection of meters is related to whether users can directly use electricity and when they can use electricity. Especially in the face of urgent electricity demand, the completion speed and accuracy of the installation and connection of meters are directly related to the safe operation of the power grid. However, in actual installation, due to the influence of various factors, the efficiency of the installation and connection of meters in the power grid is low and the progress is slow. Summary of the invention

[0003] In order to solve the above technical problems, an embodiment of the present invention provides a method and system for extracting factors affecting the installation time of a three-phase electric energy meter, thereby realizing the extraction of factors affecting the installation time of a three-phase electric energy meter.

[0004] A first aspect of an embodiment of the present invention provides a method for extracting factors affecting the installation time of a three-phase electric energy meter, the method comprising:

[0005] Collect the historical equipment installation time of each equipment in the assembly operation, and select the equipment whose historical equipment installation time is greater than the first preset value as the equipment to be analyzed;

[0006] Construct an evaluation system for the installation time of the equipment to be analyzed, obtain the installation time of multiple equipment to be analyzed corresponding to each evaluation indicator, and obtain the influence of each evaluation indicator on the installation time according to each installation time. Analyze each influence degree and obtain the influencing factor results.

[0007] In a possible implementation manner of the first aspect, after obtaining the influencing factor result, the method further includes:

[0008] Collect the historical installation time of the assembly work, perform frequency analysis on the historical installation time, obtain the frequency distribution, calculate the mean value and standard deviation of the historical installation time, and obtain the normal distribution probability based on the mean value and standard deviation;

[0009] According to the normal analysis probability and frequency distribution, the probability that the installation time of the assembly operation is greater than the second preset value is obtained. If the probability is less than the third preset value, a new average value is calculated, and an improvement strategy for the assembly operation is obtained based on the new average value and the influencing factor results.

[0010] In a possible implementation manner of the first aspect, obtaining a normal distribution probability according to a mean value and a standard deviation includes:

[0011] Through the NORMD I ST function, we can get the normal distribution probability corresponding to the mean and standard deviation.

[0012] In a possible implementation manner of the first aspect, the evaluation system includes a personnel quality evaluation index, an installation tool evaluation index, an installation process evaluation index, a work environment evaluation index, and a lead length evaluation index.

[0013] A second aspect of an embodiment of the present invention provides a system for extracting factors affecting installation time of a three-phase electric energy meter, the system comprising:

[0014] A collection module, used for collecting the historical equipment installation time of each equipment in the assembly operation, obtaining the average installation time of each equipment according to the historical equipment installation time of each equipment, and selecting the equipment whose average installation time is greater than a first preset value as the equipment to be analyzed;

[0015] The analysis module is used to construct an evaluation system for the installation time of the equipment to be analyzed, obtain the installation time of multiple equipment to be analyzed corresponding to each evaluation indicator, obtain the influence of each evaluation indicator on the installation time according to each installation time, analyze each influence degree, and obtain the influencing factor results.

[0016] In a possible implementation manner of the second aspect, after obtaining the influencing factor result, the method further includes:

[0017] Collect the historical installation time of the assembly work, perform frequency analysis on the historical installation time, obtain the frequency distribution, calculate the mean value and standard deviation of the historical installation time, and obtain the normal distribution probability based on the mean value and standard deviation;

[0018] According to the normal analysis probability and frequency distribution, the probability that the installation time of the assembly operation is greater than the second preset value is obtained. If the probability is less than the third preset value, a new average value is calculated, and an improvement strategy for the assembly operation is obtained based on the new average value and the influencing factor results.

[0019] In a possible implementation manner of the second aspect, obtaining a normal distribution probability according to a mean value and a standard deviation includes:

[0020] Through the NORMD I ST function, the normal distribution probability corresponding to the mean and standard deviation is obtained.

[0021] In a possible implementation manner of the second aspect, the evaluation system includes a personnel quality evaluation index, an installation tool evaluation index, an installation process evaluation index, a work environment evaluation index, and a lead length evaluation index.

[0022] A third aspect of an embodiment of the present invention provides a computer device, including:

[0023] Memory for storing computer programs;

[0024] A processor is used to implement the method for extracting factors affecting the installation time of a three-phase electric energy meter as described in the first aspect when executing the computer program.

[0025] A fourth aspect of an embodiment of the present invention provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for extracting factors affecting the installation time of a three-phase electric energy meter as in the first aspect.

[0026] The method for extracting factors affecting the installation time of a three-phase electric energy meter provided in an embodiment of the present invention collects the historical equipment installation time of each device in the installation operation, obtains the average installation time of each device based on the historical equipment installation time of each device, selects the device with an average installation time greater than a first preset value as the device to be analyzed, constructs an evaluation system for the installation time of the device to be analyzed, obtains the installation time of multiple devices to be analyzed corresponding to each evaluation indicator, obtains the degree of influence of each evaluation indicator on the installation time based on each installation time, analyzes each degree of influence, and obtains the influencing factor result. Through the above method, the extraction of factors affecting the installation time of the three-phase electric energy meter is realized, thereby obtaining an improved strategy and shortening the installation time of the three-phase electric energy meter. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 : A flow chart of a calculation method of an embodiment of a method for extracting factors affecting the installation time of a three-phase electric energy meter provided by the present invention;

[0028] Figure 2 : A schematic diagram of a crux analysis arrangement diagram for drawing an embodiment of a method for extracting factors affecting the installation time of a three-phase electric energy meter provided by the present invention;

[0029] Figure 3 : A cause analysis causal diagram of an embodiment of a method for extracting factors affecting the installation time of a three-phase electric energy meter provided by the present invention;

[0030] Figure 4 : A schematic diagram of the distribution law of the installation time of the installation work of an embodiment of the method for extracting the factors affecting the installation time of the three-phase electric energy meter provided by the present invention;

[0031] Figure 5 :A comparison diagram of NORMDI ST return and frequency distribution combination of an embodiment of the method for extracting factors affecting the installation time of a three-phase electric energy meter provided by the present invention;

[0032] Figure 6 : A bell-shaped curve diagram of an embodiment of a method for extracting factors affecting the installation time of a three-phase electric energy meter provided by the present invention;

[0033] Figure 7 : A device block diagram of an embodiment of a method for extracting factors affecting the installation time of a three-phase electric energy meter provided by the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] In actual installation, due to the influence of various factors, the efficiency of power grid meter installation and power connection is low and the progress is slow.

[0036] Based on this, an embodiment of the present application provides a method and system for extracting factors affecting the installation time of a three-phase electric energy meter, aiming to realize the extraction of factors affecting the installation time of a three-phase electric energy meter.

[0037] Please refer to Figure 1 , which is a flow chart of an embodiment of a method for extracting factors affecting the installation time of a three-phase electric energy meter provided by an embodiment of the present invention, including steps S101 to S102, each of which is specifically as follows:

[0038] S101. Collect the historical equipment installation time of each device in the assembly operation, obtain the average installation time of each device according to the historical equipment installation time of each device, and select the device whose average installation time is greater than a first preset value as the device to be analyzed.

[0039] It is worth noting that the installation operation refers to the installation operation of connecting to a three-phase electric energy meter through a current transformer. Specifically, the electrical equipment in the installation operation includes but is not limited to meter cabinets, current transformers, information collection terminals, and three-phase electric energy meters. Among them, the meter cabinet is a cabinet for installing a special meter (device), which is called a meter cabinet. In my country, meter cabinets generally refer to electric energy meter cabinets. According to the definition of terms in the specification "Electric Energy Meter Cabinets" (GB / T16934-2013), electric energy meter cabinets: special cabinet-type electricity metering devices (electric energy metering devices). Electricity metering devices: a general term for the combination of all electrical equipment, electrical parts, and mechanical structures that measure electric energy.

[0040] The principle of current transformer is based on the principle of electromagnetic induction, and it consists of a closed iron core and winding. Its primary winding has few turns and is connected in series in the circuit of the current to be measured, with all the current of the circuit flowing through it. The secondary winding has more turns and is connected in series in the measuring instrument and protection circuit.

[0041] The power collection terminal provides other communication interfaces, such as serial ports or network ports, and transmits data to the communication management machine of the back-end manufacturer through 485 communication lines or network cables. The data collected by the communication management machine is then sent to the back-end for display, which is convenient for the operation and maintenance personnel in the station.

[0042] A three-phase watt-hour meter is an instrument used to measure electrical energy. It is also called an watt-hour meter, a fire meter, or a kilowatt-hour meter. It refers to an instrument that measures various electrical quantities.

[0043] Collect the historical equipment installation time of each device in the installation operation, and then obtain the average installation time of each device based on the historical equipment installation time of each device. For example, as shown in Table 1, the historical equipment installation time of each device during the installation of a three-phase electric energy meter is shown. Then, draw a crux analysis arrangement diagram based on the statistical data of the time consumption statistics table for each step of the metering installation operation, such as Figure 2 shown.

[0044] Table 1 Historical equipment installation schedule for each device

[0045]

[0046] Then, the equipment whose average installation time is greater than the first preset value is selected as the equipment to be analyzed. Specifically, through analysis, it can be known that the average time for the operator to install the mutual inductor is 12.1 minutes. The steps for installing the mutual inductor are simple, but the installation time is too long.

[0047] S102, construct an evaluation system for the installation time of the equipment to be analyzed, obtain the installation time of multiple equipment to be analyzed corresponding to each evaluation indicator, obtain the influence of each evaluation indicator on the installation time according to each installation time, analyze each influence degree, and obtain the influencing factor result.

[0048] After finding that the main reason for the long installation time is the installation of the transformer, the cause and effect diagram is analyzed by analyzing the reasons for the long installation time of the transformer, such as Figure 3 As shown in the figure, six key indicator factors and ten terminal factors are obtained to construct an evaluation system for the installation time of the transformer.

[0049] Then, the installation time is confirmed according to each indicator in the evaluation system, and the installation time of multiple devices to be analyzed corresponding to each evaluation indicator is obtained. According to each installation time, the influence degree of each evaluation indicator on the installation time is obtained, and each influence degree is analyzed to obtain the influencing factor results. Among them, the factor determination table is:

[0050] Table 2 Factor determination table

[0051]

[0052]

[0053] Furthermore, by consulting and analyzing the historical physical fitness test data of the statistical measurement team operators, it can be seen that all team members' physical fitness tests have met the standards (overweight personnel are not suitable for running and are tested with barbell snatches instead). Therefore, failure to meet the physical fitness test standards of the operators will not affect the installation time of the transformer.

[0054] Table 3 Statistics of physical fitness test results

[0055]

[0056] The following table shows the statistics of the health, education, and years of experience of the operators. It is found that all of them have college degrees, and the average years of experience in metering installation is 6.8 years, with the shortest being 3 years. The operators of the metering class have rich experience in metering installation, good professional quality, and good health. Therefore, "short years of experience of operators" will not affect the installation time of the transformer.

[0057] Table 4 Statistics of operating personnel

[0058]

[0059] In the statistics of metering equipment, it was found that although the operation of installing bolts on the crossbar is simpler than other operations, the installation time is much longer than other operations. According to the on-site investigation, it was found that the time was mainly spent on "operating the transformer and nut, screws and screwdriver at the same time". In order to prevent the structure or tools from falling off, the workers often need to constantly adjust their positions and postures, and because there is no fulcrum, the work of tightening the bolts is very slow and consumes a lot of physical strength.

[0060] In actual operations, operators usually wear cotton gloves when operating, and use their left hand to fix the nut. Due to the small diameter of the nut, the required pressure and friction are large when facing the torque N applied by the screwdriver. When installing the second transformer, the nut will rotate with the screwdriver.

[0061] Since the rear nut is difficult to fix, it turns with the screwdriver, which increases the difficulty of the operation and the installation time. Therefore, the rear of the training distribution cabinet (non-powered simulation training distribution cabinet) was opened (this type of operation is prohibited during actual operation), and the nut was fixed with a wrench tool. It was found that the installation time was significantly less than the installation time of the main operator alone, with an average time difference of 67 seconds. Three transformers were installed in one operation, and the total installation time difference exceeded 200 seconds.

[0062] The team compared the screwdriver and bolt slots and found that the new bolts basically matched the cross tip of the screwdriver and the bolt. Under normal operation, the bolt slot and the cross tip of the screwdriver will not be disengaged.

[0063] Then, for safety reasons, the auxiliary workers cannot replace the main operators during installation. During the operation process, for safety reasons, the auxiliary workers should cooperate with the main operators and should not replace the main operators. The auxiliary workers helping the main operators will indeed speed up the operation progress. When the auxiliary workers and the main operators operate at the same time, the installation speed can be increased by more than 3 minutes.

[0064] Auxiliary workers can replace the main workers to complete some operations to speed up the progress of the work, and it is easier to make auxiliary workers smoothly replace the main workers to carry out some work in the simulation site. However, for safety reasons, it is believed that this regulation cannot be changed, so the impact is relatively low.

[0065] In view of high temperature working conditions, based on the endurance test data of workers in 15 degrees Celsius and 30 degrees Celsius environments, the test personnel were asked to lift as many dumbbells (2x10 kg) as possible within 30 minutes by adjusting the air conditioning temperature to 15 degrees Celsius. Then the air conditioning room temperature was adjusted to 30 degrees Celsius, and the same group of people were asked to lift as many dumbbells as possible within 30 minutes.

[0066] Table 5 Endurance test data

[0067] Serial number 01 02 03 04 05 15 degrees Celsius 101 97 86 85 97 30 degrees Celsius 104 96 89 102 105

[0068] It can be seen from the statistical data that the endurance test results of the test personnel at 15 degrees Celsius and 30 degrees Celsius are not significantly different (considering that the muscle recovery period is 72 hours, the results after three days of exercise are often slightly better than those three days ago, so the data error is within an acceptable range). Therefore, the loss of physical strength of high-temperature workers will not lead to a long installation time for the transformer.

[0069] The narrow space of the metering cabinet is not conducive to the coordination of the team. At the work site of the power distribution cabinet, the narrow space has a certain impact on the coordination of the work. In order to confirm the degree of impact, the metering class used simulated training power distribution cabinets and simulated equipment in open training venues to conduct operations. In order to further verify, the team allowed auxiliary operators to replace the main operators to operate. In the case of large space, it will indeed speed up the work progress (but it is a violation). Since the work of the main operators cannot be replaced by auxiliary operators, in actual operation, the space of the work site has a very limited impact on the installation time of the operation.

[0070] In view of the large measurement error of the lead by the assistant personnel, the distance of the transformer lead was measured and the standard deviation of the statistical data was analyzed. The standard deviation sigma was 0.24. The statistical results are shown in Table 6. Since the numerical differences of the 30 measurement results are not large, after experiments in the training ground, according to the Six Sigma management method, it is believed that the influence of this factor is limited.

[0071] Table 6 The distance measurement table of the lead wire when the auxiliary personnel process the wiring

[0072]

[0073] In view of the fact that the main personnel's measurement error of the distance of the device was too large, the auxiliary personnel were asked to measure multiple groups of lead wires of specified lengths, and the difference between the average value of the measured lead wires and the specified value, as well as the variance of the measured values, were statistically analyzed. The standard deviation sigma was 0.44. After analyzing the statistical data, it was found that the numerical differences of the 30 measurement results were not large. According to the Six Sigma management method, it is believed that the error in the measurement length of the wiring has limited impact on the actual operation.

[0074] Table 7 Measurement statistics of the distance from the transformer to the terminal block

[0075]

[0076] In some embodiments, "after obtaining the influencing factor results" in step S102 includes but is not limited to the following steps:

[0077] Collect the historical installation time of the assembly work, perform frequency analysis on the historical installation time, obtain the frequency distribution, calculate the mean value and standard deviation of the historical installation time, and obtain the normal distribution probability based on the mean value and standard deviation;

[0078] According to the normal analysis probability and frequency distribution, the probability that the installation time of the assembly operation is greater than the second preset value is obtained. If the probability is less than the third preset value, a new average value is calculated, and an improvement strategy for the assembly operation is obtained based on the new average value and the influencing factor results.

[0079] Specifically, the installation time of the assembly work in the past two years is analyzed, and a frequency distribution histogram is drawn to explore the distribution pattern of the installation time of the assembly work, such as Figure 4 Then the mean and standard deviation of the historical installation time are calculated, and the NORMD I ST function is used to return the normal distribution function of the mean and standard deviation and generate a smooth bell curve to test the rationality of the normal distribution assumption.

[0080] Combine the frequency distribution histogram and the bell curve graph returned by the NORMD I ST function, such as Figure 5As shown, we get the NORMD I ST return and frequency distribution combination comparison chart. Through analysis, we find that the average value of the installation time of the assembly operation is 38.52 minutes, which is less than 40 minutes. However, due to the normal distribution, 38.31% of the installation time of the operation is still more than 40 minutes. If the normal distribution curve integral with the upper and lower lines within 40 minutes is more than 95%, then it is necessary to control the installation time of the assembly operation from the average value, and use the density function to solve it. The density function is:

[0081]

[0082] In the formula, x represents probability.

[0083] Let F(x)≦0.95 and solve to get x=1.6449. Then, after operation from standard normal distribution, converting to general normal distribution, we can get unequal equation N+1.6449, σ≦40, and solve to get N=34.69 minutes. That is, the new average value is 34.69. Then, according to the new average value and the influencing factors, we can get the improvement strategy of assembly operation.

[0084] According to the normal analysis probability and frequency distribution, the probability that the installation time of the assembly operation is greater than the second preset value is obtained. If the probability is less than the third preset value, a new average value is calculated, and an improvement strategy for the assembly operation is obtained based on the new average value and the influencing factor results.

[0085] In some embodiments, "obtaining a normal distribution probability according to the mean value and the standard deviation" in step S102 includes but is not limited to the following steps:

[0086] The normal distribution probability corresponding to the mean value and standard deviation is obtained through the NORMD I ST function.

[0087] Specifically, the NORMD I ST function returns the average value, standard deviation, cumulative distribution probability and normal distribution probability, as shown in Table 8. Then, the bell curve is drawn after the NORMD I ST function returns the data, as shown in Table 8. Figure 6 shown.

[0088] Table 8 Frequency statistics of installation time of assembly operation and data returned by NORMD I ST function

[0089]

[0090] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

[0091] In some embodiments, Figure 7 As shown, it shows a block diagram of a three-phase electric energy meter installation time influencing factor extraction system 700 provided in an embodiment of the present application, including: a collection module 701 and an analysis module 702, wherein:

[0092] The collection module 701 is used to collect the historical equipment installation time of each equipment in the assembly operation, and select the equipment whose historical equipment installation time is greater than a first preset value as the equipment to be analyzed;

[0093] The analysis module 702 is used to construct an evaluation system for the installation time of the equipment to be analyzed, obtain the installation time of multiple equipment to be analyzed corresponding to each evaluation indicator, obtain the influence of each evaluation indicator on the installation time according to each installation time, analyze each influence degree, and obtain the influencing factor result.

[0094] The specific implementation of the system for extracting factors affecting installation time of a three-phase electric energy meter is basically the same as the specific implementation of the method for extracting factors affecting installation time of a three-phase electric energy meter mentioned above, and will not be repeated here.

[0095] In one embodiment of the present application, a computer device is provided, which includes a memory and a processor, wherein a computer program is stored in the memory, and the above steps are implemented when the processor executes the computer program; the computer device provided in this embodiment has an implementation principle and technical effects similar to those of the above method embodiments, and will not be repeated here.

[0096] In one embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored, and the above steps are implemented when the computer program is executed by a processor; the computer-readable storage medium provided in this embodiment has an implementation principle and technical effects similar to those of the above method embodiment, and will not be repeated here.

[0097] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0098] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0099] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for extracting factors affecting the installation time of a three-phase electric energy meter, characterized in that: include: Collect the historical equipment installation time of each device in the assembly operation, obtain the average installation time of each device according to the historical equipment installation time of each device, and select the device whose average installation time is greater than a first preset value as the device to be analyzed; Construct an evaluation system for the installation time of the equipment to be analyzed, obtain the installation time of multiple equipment to be analyzed corresponding to each evaluation indicator, obtain the influence of each evaluation indicator on the installation time according to each installation time, analyze each influence degree, and obtain the influencing factor result.

2. The method for extracting factors affecting the installation time of a three-phase electric energy meter according to claim 1, characterized in that: After obtaining the influencing factor results, it also includes: Collecting historical installation time of the assembly work, performing frequency analysis on the historical installation time to obtain frequency distribution, calculating the mean value and standard deviation of the historical installation time, and obtaining a normal distribution probability according to the mean value and the standard deviation; According to the normal analysis probability and frequency distribution, the probability that the installation time of the assembly operation is greater than the second preset value is obtained. If the probability is less than the third preset value, a new average value is calculated, and an improvement strategy for the assembly operation is obtained according to the new average value and the influencing factor results.

3. The method for extracting factors affecting the installation time of a three-phase electric energy meter according to claim 1, characterized in that: The obtaining a normal distribution probability according to the mean value and the standard deviation includes: The normal distribution probability corresponding to the mean value and the standard deviation is obtained through the NORMDIST function.

4. The method for extracting factors affecting the installation time of a three-phase electric energy meter according to claim 1, characterized in that: The evaluation system includes personnel quality evaluation indicators, installation tool evaluation indicators, installation process evaluation indicators, work environment evaluation indicators and lead length evaluation indicators.

5. A system for extracting factors affecting the installation time of a three-phase electric energy meter, characterized in that: include: A collection module, used for collecting the historical equipment installation time of each equipment in the assembly operation, obtaining the average installation time of each equipment according to the historical equipment installation time of each equipment, and selecting the equipment whose average installation time is greater than a first preset value as the equipment to be analyzed; The analysis module is used to construct an evaluation system for the installation time of the equipment to be analyzed, obtain the installation time of multiple equipment to be analyzed corresponding to each evaluation indicator, obtain the influence of each evaluation indicator on the installation time according to each installation time, analyze each influence degree, and obtain the influencing factor result.

6. The system for extracting factors affecting installation time of a three-phase electric energy meter according to claim 5, characterized in that: After obtaining the influencing factor results, it also includes: Collecting historical installation time of the assembly work, performing frequency analysis on the historical installation time to obtain frequency distribution, calculating the mean value and standard deviation of the historical installation time, and obtaining a normal distribution probability according to the mean value and the standard deviation; According to the normal analysis probability and frequency distribution, the probability that the installation time of the assembly operation is greater than the second preset value is obtained. If the probability is less than the third preset value, a new average value is calculated, and an improvement strategy for the assembly operation is obtained according to the new average value and the influencing factor results.

7. The system for extracting factors affecting installation time of a three-phase electric energy meter according to claim 5, characterized in that: The obtaining a normal distribution probability according to the mean value and the standard deviation includes: The normal distribution probability corresponding to the mean value and the standard deviation is obtained through the NORMDIST function.

8. The system for extracting factors affecting the installation time of a three-phase electric energy meter as described in claim 5, wherein the evaluation system includes personnel quality evaluation indicators, installation tool evaluation indicators, installation process evaluation indicators, working environment evaluation indicators and lead length evaluation indicators.

9. A computer device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the method for extracting factors affecting the installation time of a three-phase electric energy meter as described in any one of claims 1 to 4 when executing the computer program.

10. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for extracting factors affecting the installation time of a three-phase electric energy meter as described in any one of claims 1 to 4.

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