An intelligent operation and maintenance system and method based on the Internet of Things

Abstract

The present invention discloses an intelligent operation and maintenance system and method based on the Internet of Things, and specifically relates to the field of intelligent operation and maintenance technology for charging piles. The system includes: a loss coefficient analysis module, an acquisition frequency setting module, an environmental data acquisition and analysis module, an operation data acquisition and analysis module, a fault parameter identification module, and a fault operation and maintenance scheduling module database. The present invention sets the acquisition frequency of the charging pile monitoring equipment according to the loss coefficient of each charging pile within the target monitoring period, thereby avoiding data redundancy and improving the efficiency and stability of the intelligent operation and maintenance system. The present invention analyzes the environmental risk coefficient and the operation risk coefficient in the actual working process of the charging pile, and then generates fault work orders of different levels, thereby realizing hierarchical processing of charging pile faults, reducing the cost of manual inspections, and thus ensuring safe and stable operation during charging.

Description

Technical Field

[0001] The present invention relates to the field of intelligent operation and maintenance technology, and in particular to an intelligent operation and maintenance system and method based on the Internet of Things. Background Art

[0002] The increasing popularity of electric vehicles has led to a dramatic increase in demand for charging stations. With the rapid deployment of charging stations, traditional manual management methods are no longer able to cope with the sheer volume of equipment. Charging station maintenance and management present numerous challenges, such as rapid troubleshooting of equipment failures, the allocation of maintenance personnel, monitoring charging station status, and timely processing of fault records. Traditional O&M methods rely on manual inspections and record-keeping, which is not only inefficient but also prone to failure to detect and resolve equipment failures in a timely manner. Therefore, research on intelligent O&M for charging stations based on the Internet of Things is necessary.

[0003] The existing technology meets the basic needs for the operation and maintenance of charging piles, but there are also some potential risks. Specifically, on the one hand, the existing technology for the operation and maintenance of charging piles mostly relies on personnel inspections to repair faults, which makes it difficult to improve the operation and maintenance efficiency of charging pile equipment, while wasting manpower costs and making it difficult to resolve the actual contradiction between the market demand for the surge in the number of charging piles and the low operation and maintenance efficiency. On the other hand, there are some cases in the existing technology that use sensors to monitor and control charging piles, but they ignore the scientific setting of the monitoring data collection frequency for charging piles with different loss levels, resulting in excessive redundancy in the collected monitoring data, causing excessive system load and low data response speed, and the risk of difficulty in quickly generating corresponding operation and maintenance synchronization operations, making it difficult to efficiently discover fault problems and conduct graded response repairs, making it difficult to meet actual operation and maintenance needs. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent operation and maintenance system and method based on the Internet of Things, which solves the problems existing in the background technology.

[0005] To solve the above technical problems, the present invention provides, in a first aspect, an intelligent operation and maintenance system based on the Internet of Things, the system comprising:

[0006] The loss coefficient analysis module is used to extract the fault parameters and working parameters of each charging pile in each historical monitoring period from the database and calculate the loss coefficient of each charging pile in the target monitoring period;

[0007] The acquisition frequency setting module sets the monitoring data acquisition frequency of each charging pile within the target monitoring period based on the loss coefficient of each charging pile;

[0008] The environmental data collection and analysis module collects the environmental parameters of each charging pile within the target monitoring period based on the monitoring data collection frequency of each charging pile, and calculates the environmental risk coefficient of each charging pile within the target monitoring period;

[0009] The operation data collection and analysis module collects the operating parameters of each charging pile within the target monitoring period based on the monitoring data collection frequency of each charging pile, and calculates the operation risk coefficient of each charging pile within the target monitoring period;

[0010] The fault parameter identification module automatically identifies abnormal environmental parameters and abnormal operational parameters of each charging pile within the target monitoring period based on the environmental risk coefficient and operational risk coefficient of each charging pile within the target monitoring period;

[0011] The fault operation and maintenance scheduling module generates a fault work order for each charging pile within the target monitoring period based on the environmental abnormality parameters and operational abnormality parameters of each charging pile within the target monitoring period, and performs operation and maintenance scheduling for each charging pile within the monitoring period.

[0012] A second aspect of the present invention provides a method for executing an intelligent operation and maintenance system based on the Internet of Things, comprising:

[0013] Step 1: Calculate the loss coefficient of each charging pile within the target monitoring period;

[0014] Step 2: Set the monitoring data collection frequency of each charging pile within the target monitoring period;

[0015] Step 3: Collect environmental parameters and operating parameters of each charging pile within the target monitoring period;

[0016] Step 4: Calculate the environmental risk coefficient and operation risk coefficient of each charging pile within the target monitoring period;

[0017] Step 5: diagnose abnormal environmental parameters and abnormal operating parameters of each charging pile within the target monitoring period;

[0018] Step 6: Execute operation and maintenance scheduling of each charging pile within the monitoring period.

[0019] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention calculates the loss coefficient of each charging pile in the target monitoring period based on the fault parameters and working parameters of each charging pile in each historical monitoring period, thereby setting the collection frequency of the charging pile monitoring equipment, thereby realizing high-frequency monitoring of charging piles with high loss coefficients, avoiding data redundancy, and improving the efficiency and stability of the intelligent operation and maintenance system.

[0020] 2. The present invention analyzes various environmental parameters and operating parameters during the actual operation of the charging pile, effectively monitors the risk parameters during the operation of the charging pile, and timely discovers potential risks during the operation of the charging pile, thereby providing reliable data support for subsequent intelligent operation and maintenance, and realizing the accuracy of intelligent monitoring during the operation of the charging pile.

[0021] 3. The present invention analyzes the environmental risk coefficient and the operational risk coefficient during the actual working process of the charging pile, thereby obtaining various parameters of environmental abnormalities and operational abnormalities during the actual working process of the charging pile, and then generates fault work orders of different levels, thereby realizing hierarchical processing of charging pile faults, reducing the cost of manual inspections, thereby ensuring safe and stable operation during charging, and realizing the scientific and timely intelligent operation and maintenance management of charging piles. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0023] Figure 1 This is a system structure connection diagram of the present invention.

[0024] Figure 2 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0026] Example 1:

[0027] Reference Figure 1 As shown, the first aspect of the present invention provides an intelligent operation and maintenance system based on the Internet of Things, which includes: a loss coefficient analysis module, an acquisition frequency setting module, an environmental data acquisition and analysis module, an operation data acquisition and analysis module, a fault parameter identification module, a fault operation and maintenance scheduling module and a database.

[0028] It should be noted that, in a specific embodiment, the database is used to store the fault parameters and working parameters of each charging pile in each historical monitoring period, store the first interval, second interval, and third interval of the loss coefficient, store the suitable temperature interval, standard operating voltage interval, and standard power factor difference interval of the charging pile, store the abnormality coefficient threshold, store the first environmental risk interval, the second environmental risk interval, the first operational risk interval, and the second operational risk interval.

[0029] The loss coefficient analysis module is used to extract the fault parameters and working parameters of each charging pile in each historical monitoring period from the database and calculate the loss coefficient of each charging pile in the target monitoring period.

[0030] In a specific embodiment of the present invention, the loss coefficient of each charging pile in the target monitoring period is calculated, and the specific analysis method is as follows: based on the fault loss coefficient Q of each charging pile in each historical monitoring period ij And the working loss coefficient W of each charging pile in each historical monitoring period ij , where i = 1, 2, ..., n, i represents the number of each historical monitoring period, n represents the total number of historical monitoring periods, j = 1, 2, ..., m, j represents the number of each charging pile, m represents the total number of charging piles, through the formula: Calculate the loss coefficient μ of each charging pile within the target monitoring period j .

[0031] It should be noted that, in a specific embodiment, the calculation method of the loss coefficient of each charging pile within the target monitoring period is obtained by comprehensively evaluating the impact of the fault loss coefficient and working loss coefficient of each charging pile in each historical monitoring period on the loss of each charging pile within the target monitoring period.

[0032] It should be noted that, in a specific embodiment, the purpose of calculating the loss coefficient of each charging pile within the target monitoring period is to provide a reference value for the subsequent setting of the monitoring data collection frequency of each charging pile within the target monitoring period, and to set corresponding levels of monitoring data collection frequencies for charging piles with different levels of loss coefficients. On the one hand, the accuracy of data collection of each charging pile is improved, and on the other hand, the data redundancy of the intelligent operation and maintenance system is reduced, thereby ensuring the efficient operation of the intelligent operation and maintenance system.

[0033] In a specific embodiment of the present invention, the failure loss coefficient of each charging pile in each historical monitoring period is analyzed by the following method: the failure parameters of each charging pile in each historical monitoring period, including the number of failures a ij 、Fault duration b ij and fault repair time c ij .

[0034] By formula: Calculate the fault loss coefficient Q of each charging pile in each historical monitoring period ij , where e represents a natural constant, τ1 represents the impact factor of the number of unit failures extracted from the database, τ2 represents the impact factor of the unit failure duration extracted from the database, and τ3 represents the impact factor of the unit failure repair time extracted from the database.

[0035] It should be noted that, in a specific embodiment, the calculation method of the failure loss coefficient of each charging pile in each historical monitoring period is obtained by comprehensively evaluating the negative impact of the number of failures, duration of failures and duration of failure repairs of each charging pile in each historical monitoring period on each charging pile.

[0036] In a specific embodiment of the present invention, the working loss coefficient of each charging pile in each historical monitoring period is analyzed by the following method: the working parameters of each charging pile in each historical monitoring period, including the environmental risk coefficient α ij , Operational risk coefficient β ij .

[0037] By formula: Calculate the working loss coefficient W of each charging pile in each historical monitoring period ij , where θ1 represents the influencing factor of the unit environmental risk coefficient extracted from the database, and θ2 represents the influencing factor of the unit work risk coefficient extracted from the database.

[0038] It should be noted that, in a specific embodiment, the method for calculating the working loss coefficient of each charging pile in each historical monitoring period is obtained by comprehensively evaluating the negative impact of the environmental risk coefficient and operation risk coefficient of each charging pile in each historical monitoring period on each charging pile.

[0039] The acquisition frequency setting module is used to set the monitoring data acquisition frequency of each charging pile within the target monitoring period according to the loss coefficient of each charging pile.

[0040] In a specific embodiment of the present invention, the monitoring data collection frequency of each charging pile within the target monitoring period is set, and the specific analysis method is as follows: based on the loss coefficient μ of each charging pile within the target monitoring period j ,like The monitoring data collection frequency of the jth charging pile is set to f1, where represents the first interval of the loss coefficient extracted from the database, and f1 represents the first data monitoring and acquisition frequency extracted from the database.

[0041] like Then the monitoring data collection frequency of the jth charging pile is set to f2, where represents the second interval of the loss coefficient extracted from the database, and f2 represents the second data monitoring and acquisition frequency extracted from the database.

[0042] like Then the monitoring data collection frequency of the jth charging pile is set to f3, where represents the third interval of the loss coefficient extracted from the database, and f3 represents the third data monitoring and collection frequency extracted from the database.

[0043] It should be noted that, in a specific embodiment, the first interval, the second interval and the third interval of the loss coefficient are manually set by technical personnel based on the historical loss coefficients of each charging pile and subsequent actual working parameters, and stored in the database.

[0044] The present invention calculates the loss coefficient of each charging pile in the target monitoring period according to the fault parameters and working parameters of each charging pile in each historical monitoring period, thereby setting the collection frequency of the charging pile monitoring equipment, thereby realizing high-frequency monitoring of charging piles with high loss coefficients, avoiding data redundancy, and improving the efficiency and stability of the intelligent operation and maintenance system.

[0045] The environmental data collection and analysis module collects the environmental parameters of each charging pile within the target monitoring period based on the monitoring data collection frequency of each charging pile, and calculates the environmental risk coefficient of each charging pile within the target monitoring period.

[0046] In a specific embodiment of the present invention, the environmental risk coefficient of each charging pile within the target monitoring period is calculated, and the specific analysis method is as follows: the environmental parameters of each charging pile within the target monitoring period include: temperature collected by a temperature sensor, humidity collected by a humidity sensor, dust concentration collected by a dust sensor, magnetic field strength collected by an electromagnetic sensor, and vibration frequency collected by a vibration sensor.

[0047] By formula: Calculate the temperature anomaly judgment value (QT) of each collection point of each charging pile within the target monitoring period jk , where T jk represents the temperature of each collection point of each charging pile within the target monitoring period, k = 1, 2, ..., p, k represents the number of each collection point within the target monitoring period, p represents the total number of collection points within the target monitoring period, (T - ,T + ) represents the suitable temperature range of the charging pile extracted from the database.

[0048] It should be noted that, in a specific embodiment, the method for calculating the temperature anomaly judgment value of each collection point of each charging pile within the target monitoring period is to set a reference value corresponding to the judgment result by judging whether the temperature value of each collection point of each charging pile within the target monitoring period is within the suitable temperature range of the charging pile. For example, if the temperature value of each collection point of each charging pile within the target monitoring period is within the suitable temperature range of the charging pile, the temperature anomaly judgment value of the collection point of the charging pile within the target monitoring period is set to 0.

[0049] According to the calculation method of the temperature abnormality judgment value of each collection point of each charging pile within the target monitoring period, the humidity H of each collection point of each charging pile within the target monitoring period is calculated. jk , dust concentration U jk , magnetic field strength R jk and vibration frequency S jk Analyze and obtain the humidity abnormality judgment value (QH) of each collection point of each charging pile within the target monitoring period jk , Dust concentration abnormality judgment value (QN) jk , Magnetic field strength abnormality judgment value (QR) jk and vibration frequency abnormality judgment value (QS) jk .

[0050] By formula: Calculate the environmental risk coefficient of each charging pile in the target monitoring period

[0051] It should be noted that, in a specific embodiment, the method for calculating the risk coefficient of each charging pile within the target monitoring period is obtained by comprehensively evaluating the impact of the humidity abnormality judgment value, dust concentration abnormality judgment value, magnetic field intensity abnormality judgment value and vibration frequency abnormality judgment value of each collection point of each charging pile within the target monitoring period on each charging pile within the target monitoring period. When the number of abnormal judgment values ​​of each environmental parameter of each charging pile within the target monitoring period is greater, the environmental risk coefficient of each charging pile within the target monitoring period is greater, so that the potential risks of each charging pile can be discovered in time.

[0052] The operation data collection and analysis module collects the operating parameters of each charging pile within the target monitoring period based on the monitoring data collection frequency of each charging pile, and calculates the operation risk coefficient of each charging pile within the target monitoring period.

[0053] In a specific embodiment of the present invention, the operation risk coefficient of each charging pile within the target monitoring period is calculated, and the specific analysis method is as follows: the operation parameters of each charging pile within the target monitoring period include: the output voltage and input voltage collected by the voltage sensor, the output current and input current collected by the current sensor, and the active power collected by the power sensor.

[0054] Based on the output voltage U of each collection point of each charging pile within the target monitoring period jk , through the formula: Calculate the output voltage abnormality judgment value (QU) of each collection point of each charging pile within the target monitoring period jk , where (U - ,U + ) represents the standard output voltage range of the charging pile extracted from the database.

[0055] It should be noted that, in a specific embodiment, the method for calculating the output voltage abnormality judgment value of each collection point of each charging pile within the target monitoring period is to set a reference value corresponding to the judgment result by judging whether the output voltage of each collection point of each charging pile within the target monitoring period is within the standard output voltage range of the charging pile. For example, if the output voltage of each collection point of each charging pile within the target monitoring period is within the standard output voltage range of the charging pile, the output voltage abnormality judgment value of the collection point of the charging pile within the target monitoring period is set to 0.

[0056] According to the calculation method of the output voltage abnormality judgment value of each collection point of each charging pile within the target monitoring period, the output current of each collection point of each charging pile within the target monitoring period is analyzed to obtain the output current abnormality judgment value (QI) of each collection point of each charging pile within the target monitoring period. jk .

[0057] By formula: Calculate the power factor X of each collection point of each charging pile within the target monitoring period jk , where P jk Represents the active power of each collection point of each charging pile within the target monitoring period, V jk It represents the input voltage of each collection point of each charging pile within the target monitoring period, Z jk Indicates the input current of each collection point of each charging pile within the target monitoring period.

[0058] It should be noted that, in a specific embodiment, the method for calculating the power factor of each collection point of each charging pile within the target monitoring period is obtained by calculating the ratio of the active power of each collection point of each charging pile within the target monitoring period to the output power, wherein the output power refers to the product of the output voltage and the output current of each collection point of each charging pile within the target monitoring period.

[0059] By formula: Calculate the power factor abnormality judgment value (QX) of each collection point of each charging pile within the target monitoring period jk , where (X - ,X+ ) represents the standard power factor difference range of the charging pile extracted from the database.

[0060] It should be noted that, in a specific embodiment, the method for calculating the power factor abnormality judgment value of each collection point of each charging pile within the target monitoring period is based on whether the difference between the power factor of each collection point of each charging pile within the target monitoring period and the standard power factor is within the standard power factor difference interval of the charging pile, thereby setting a reference value corresponding to the judgment result. For example, if the difference between the power factor of each collection point of each charging pile within the target monitoring period and the standard power factor is within the standard power factor difference interval of the charging pile, then the power factor abnormality judgment value of the collection point of the charging pile within the target monitoring period is set to 0.

[0061] By formula: Calculate the operating risk coefficient of each charging pile within the target monitoring period

[0062] It should be noted that, in a specific embodiment, the method for calculating the operating risk coefficient of each charging pile within the target monitoring period is obtained by comprehensively evaluating the impact of the output voltage abnormality judgment value, output current abnormality judgment value and power factor abnormality judgment value of each collection point of each charging pile within the target monitoring period on each charging pile within the target monitoring period. When the number of abnormal judgment values ​​of each operating parameter of each charging pile within the target monitoring period is greater, the operating risk coefficient of each charging pile within the target monitoring period is greater, thereby enabling timely discovery of the potential risks of each charging pile.

[0063] The present invention analyzes various environmental parameters and operating parameters during the actual operation of the charging pile, effectively monitors the risk parameters during the operation of the charging pile, and timely discovers potential risks during the operation of the charging pile, thereby providing reliable data support for subsequent intelligent operation and maintenance, and realizing the accuracy of intelligent monitoring during the operation of the charging pile.

[0064] The fault parameter identification module automatically identifies abnormal environmental parameters and abnormal operational parameters of each charging pile within the target monitoring period based on the environmental risk coefficient and operational risk coefficient of each charging pile within the target monitoring period.

[0065] In a specific embodiment of the present invention, the specific analysis method of the environmental abnormality parameters and operational abnormality parameters of each charging pile within the automatic target monitoring period is as follows: based on the environmental risk level and operational risk level of each charging pile within the target monitoring period, if the environmental risk level of the j-th charging pile within the target monitoring period is low risk, extract the abnormality judgment value of each environmental parameter of each collection point of each charging pile within the target monitoring period, and use the formula: Calculate the temperature anomaly coefficient (ηT) of each charging pile within the target monitoring period j , if (ηT) j >η′, the temperature of the jth charging pile within the target monitoring period is recorded as the first-level abnormal parameter, where η′ represents the abnormal coefficient threshold extracted from the database.

[0066] It should be noted that, in a specific embodiment, the calculation method of the temperature anomaly coefficient of each charging pile within the target monitoring period is obtained by performing mathematical calculations by analyzing the ratio of the number of collection points of temperature anomalies of each charging pile within the target monitoring period to the total number of collection points of each charging pile within the target monitoring period.

[0067] It should be noted that, in a specific embodiment, the specific calculation method of the temperature anomaly coefficient of each charging pile within the target monitoring period is, for example, the temperature anomaly judgment value of each collection point of each charging pile within the target monitoring period is (QT) 11 =1, (QT) 12 =1, (QT) 13 =1, (QT) 14 =0, (QT) 15 =1, (QT) 16 =1, (QT) 17 =0, (QT) 18 =1, through the formula: Calculated (ηT) j =75%.

[0068] According to the analysis method of recording the temperature of the j-th charging pile within the target monitoring period as the first-level abnormal parameter, the humidity, dust concentration, magnetic field strength and vibration frequency of the j-th charging pile within the target monitoring period are analyzed to obtain the first-level abnormal environmental parameters of the j-th charging pile within the target monitoring period, and the first-level abnormal environmental parameters of each charging pile within the target monitoring period are obtained.

[0069] If the environmental risk level of the j-th charging pile within the target monitoring period is high risk, the abnormal judgment values ​​of the environmental parameters of each charging pile within the target monitoring period are analyzed according to the analysis method of the environmental level 1 abnormal parameters of each charging pile within the target monitoring period to obtain the environmental level 2 abnormal parameters of each charging pile within the target monitoring period.

[0070] According to the method for automatically identifying the first-level environmental abnormality parameters and the second-level environmental abnormality parameters of each charging pile within the target monitoring period, the operation risk level of each charging pile within the target monitoring period is analyzed, and the first-level operating abnormality parameters and the second-level operating abnormality parameters of each charging pile within the target monitoring period are obtained.

[0071] In a specific embodiment of the present invention, the environmental risk level and operational risk level of each charging pile within the target monitoring period are analyzed by the following method: like If the environmental risk level of the j-th charging pile in the target monitoring period is in the first environmental risk interval extracted from the database, it is determined that the environmental risk level of the j-th charging pile in the target monitoring period is low risk.

[0072] like If the environmental risk level of the j-th charging pile in the target monitoring period is in the second interval of the environmental risk extracted from the database, it is determined that the environmental risk level of the j-th charging pile in the target monitoring period is high risk.

[0073] According to the analysis method of the environmental risk level of each charging pile within the target monitoring period, the operation risk coefficient of each charging pile within the target monitoring period is analyzed to obtain the operation risk level of each charging pile within the target monitoring period.

[0074] It should be noted that, in a specific embodiment, the first and second environmental risk intervals are artificially divided by technical personnel based on the historical environmental risk coefficients of each charging pile and subsequent actual working conditions, and stored in a database.

[0075] The fault operation and maintenance scheduling module generates a fault work order for each charging pile within the target monitoring period based on the environmental abnormality parameters and operational abnormality parameters of each charging pile within the target monitoring period, and performs operation and maintenance scheduling for each charging pile within the monitoring period.

[0076] In a specific embodiment of the present invention, a fault work order for each charging pile within the target monitoring period is generated, and operation and maintenance scheduling of each charging pile within the monitoring period is executed. The specific analysis method is: based on the first-level environmental abnormality parameters and first-level operational abnormality parameters of each charging pile within the target monitoring period, a first-level fault work order for each charging pile within the target monitoring period is generated.

[0077] Based on the environmental secondary abnormality parameters and the operational secondary abnormality parameters of each charging pile within the target monitoring period, a secondary fault work order for each charging pile within the target monitoring period is generated.

[0078] Based on the first-level fault work order and the second-level fault work order of each charging pile within the target monitoring period, the fault work order of each charging pile within the target monitoring period is obtained.

[0079] Assign the fault work orders of each charging pile within the target monitoring period to maintenance personnel for repair.

[0080] The present invention analyzes the environmental risk coefficient and the operational risk coefficient during the actual working process of the charging pile, thereby obtaining various parameters of environmental abnormalities and operational abnormalities during the actual working process of the charging pile, and then generates fault work orders of different levels, thereby realizing hierarchical processing of charging pile faults, reducing the cost of manual inspections, thereby ensuring safe and stable operation during charging, and realizing scientific and timely intelligent operation and maintenance management of charging piles.

[0081] Example 2:

[0082] Reference Figure 2 As shown, the second aspect of the present invention provides a method for executing an intelligent operation and maintenance system based on the Internet of Things, the method comprising: step 1, calculating the loss coefficient of each charging pile within the target monitoring period.

[0083] Step 2: Set the monitoring data collection frequency for each charging pile within the target monitoring period.

[0084] Step 3: Collect the environmental parameters and operating parameters of each charging pile within the target monitoring period.

[0085] Step 4: Calculate the environmental risk coefficient and operation risk coefficient of each charging pile within the target monitoring period.

[0086] Step 5: Diagnose various abnormal environmental parameters and abnormal operating parameters of each charging pile within the target monitoring period.

[0087] Step 6: Execute operation and maintenance scheduling of each charging pile within the monitoring period.

[0088] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the scope of protection of the present invention.

Claims

1. An intelligent operation and maintenance system based on the Internet of Things, characterized in that: The system comprises: The loss coefficient analysis module is used to extract the fault parameters and working parameters of each charging pile in each historical monitoring period from the database and calculate the loss coefficient of each charging pile in the target monitoring period; The acquisition frequency setting module sets the monitoring data acquisition frequency of each charging pile within the target monitoring period based on the loss coefficient of each charging pile; The environmental data collection and analysis module collects the environmental parameters of each charging pile within the target monitoring period based on the monitoring data collection frequency of each charging pile, and calculates the environmental risk coefficient of each charging pile within the target monitoring period; The operation data collection and analysis module collects the operating parameters of each charging pile within the target monitoring period based on the monitoring data collection frequency of each charging pile, and calculates the operation risk coefficient of each charging pile within the target monitoring period; The fault parameter identification module automatically identifies abnormal environmental parameters and abnormal operational parameters of each charging pile within the target monitoring period based on the environmental risk coefficient and operational risk coefficient of each charging pile within the target monitoring period; The fault operation and maintenance scheduling module generates a fault work order for each charging pile within the target monitoring period based on the environmental abnormality parameters and operational abnormality parameters of each charging pile within the target monitoring period, and performs operation and maintenance scheduling for each charging pile within the monitoring period; The specific analysis method for setting the monitoring data collection frequency of each charging pile within the target monitoring period is as follows: Based on the loss coefficient of each charging pile within the target monitoring period ,like , then the The monitoring data collection frequency of each charging pile is set to ,in represents the first interval of loss coefficients extracted from the database, Indicates the first data monitoring collection frequency extracted from the database; like , then the The monitoring data collection frequency of each charging pile is set to ,in represents the second interval of loss coefficient extracted from the database, Indicates the second data monitoring collection frequency extracted from the database; like , then the The monitoring data collection frequency of each charging pile is set to ,in represents the third interval of loss coefficient extracted from the database, Indicates the third data monitoring collection frequency extracted from the database; Also includes temperature abnormality judgment value ; The specific analysis method for the abnormal environmental parameters and abnormal operating parameters of each charging pile within the target monitoring period is as follows: Based on the environmental risk level and operation risk level of each charging pile within the target monitoring period, if the first The environmental risk level of each charging pile is low risk. The abnormal judgment values ​​of the environmental parameters of each collection point of each charging pile within the target monitoring period are extracted, and the formula is used: , calculate the temperature anomaly coefficient of each charging pile within the target monitoring period ,like , then the first The temperature of each charging pile is recorded as the first-level abnormal parameter, where represents the anomaly coefficient threshold extracted from the database; According to the target monitoring period The temperature of each charging pile is recorded as the first-level abnormal parameter analysis method, and the first The humidity, dust concentration, magnetic field strength and vibration frequency of each charging pile are analyzed to obtain the first The first-level abnormal parameters of the environment of each charging pile are obtained, and the first-level abnormal parameters of the environment of each charging pile within the target monitoring period are obtained; If the target monitoring period The environmental risk level of each charging pile is high risk. According to the analysis method of each level one abnormal environmental parameter of each charging pile within the target monitoring period, the abnormal judgment value of each environmental parameter of each charging pile within the target monitoring period is analyzed to obtain each level two abnormal environmental parameter of each charging pile within the target monitoring period. According to the method for automatically identifying the first-level environmental abnormality parameters and the second-level environmental abnormality parameters of each charging pile within the target monitoring period, the operation risk level of each charging pile within the target monitoring period is analyzed, and the first-level operating abnormality parameters and the second-level operating abnormality parameters of each charging pile within the target monitoring period are obtained.

2. The intelligent operation and maintenance system based on the Internet of Things according to claim 1, characterized in that: The specific analysis method for calculating the loss coefficient of each charging pile within the target monitoring period is as follows: Based on the fault loss coefficient and working loss coefficient of each charging pile in each historical monitoring period, the formula is: , calculate the loss coefficient of each charging pile within the target monitoring period ,in, , Indicates the number of each historical monitoring period, Indicates the total number of historical monitoring cycles, , Indicates the number of each charging pile, Indicates the total number of charging piles, Indicates the failure loss coefficient of each charging pile in each historical monitoring period, Indicates the working loss coefficient of each charging pile in each historical monitoring period.

3. The intelligent operation and maintenance system based on the Internet of Things according to claim 2, characterized in that: The specific analysis method of the failure loss coefficient and working loss coefficient of each charging pile in each historical monitoring period is as follows: The fault parameters of each charging pile in each historical monitoring period, including the number of faults , Fault duration and troubleshooting time ; By formula: , calculate the failure loss coefficient of each charging pile in each historical monitoring period ,in represents a natural constant, represents the impact factor of the number of unit failures extracted from the database, represents the impact factor of unit fault duration extracted from the database, Represents the impact factor of the unit fault repair time extracted from the database; The working parameters of each charging pile in each historical monitoring period, including the environmental risk coefficient , Operational risk factor ; By formula: , calculate the working loss coefficient of each charging pile in each historical monitoring period ,in represents the impact factor of the unit environmental risk coefficient extracted from the database, Represents the influencing factors of the unit work risk coefficient extracted from the database.

4. The intelligent operation and maintenance system based on the Internet of Things according to claim 3, characterized in that: The specific analysis method for calculating the environmental risk coefficient of each charging pile within the target monitoring period is as follows: The environmental parameters of each charging pile within the target monitoring period include: temperature collected by a temperature sensor, humidity collected by a humidity sensor, dust concentration collected by a dust sensor, magnetic field strength collected by an electromagnetic sensor, and vibration frequency collected by a vibration sensor; By formula: , calculate the temperature anomaly judgment value of each collection point of each charging pile within the target monitoring period ,in Indicates the temperature of each collection point of each charging pile within the target monitoring period, , Indicates the number of each collection point within the target monitoring period, Indicates the total number of collection points in the target monitoring period, Indicates the suitable temperature range of the charging pile extracted from the database; According to the calculation method of the temperature abnormality judgment value of each collection point of each charging pile within the target monitoring period, the humidity of each collection point of each charging pile within the target monitoring period is calculated. , dust concentration , magnetic field strength and vibration frequency Analyze and obtain the humidity abnormality judgment value of each collection point of each charging pile within the target monitoring period , Dust concentration abnormality judgment value , Magnetic field strength abnormality judgment value and vibration frequency abnormality judgment value ; By formula: , calculate the environmental risk coefficient of each charging pile in the target monitoring period .

5. The intelligent operation and maintenance system based on the Internet of Things according to claim 4, characterized in that: The specific analysis method for calculating the operation risk coefficient of each charging pile within the target monitoring period is as follows: The operating parameters of each charging pile within the target monitoring period include: output voltage and input voltage collected by the voltage sensor, output current and input current collected by the current sensor, and active power collected by the power sensor; Based on the output voltage of each collection point of each charging pile within the target monitoring period , through the formula: , calculate the output voltage abnormality judgment value of each collection point of each charging pile within the target monitoring period ,in Indicates the standard output voltage range of the charging pile extracted from the database; According to the calculation method of the voltage abnormality judgment value of each collection point of each charging pile within the target monitoring period, the output current of each collection point of each charging pile within the target monitoring period is analyzed to obtain the output current abnormality judgment value of each collection point of each charging pile within the target monitoring period. ; By formula: , calculate the power factor of each collection point of each charging pile within the target monitoring period ,in Indicates the active power of each collection point of each charging pile within the target monitoring period, Indicates the input voltage of each collection point of each charging pile within the target monitoring period, Indicates the input current of each collection point of each charging pile within the target monitoring period; By formula: , calculate the power factor abnormality judgment value of each collection point of each charging pile within the target monitoring period ,in Indicates the standard power factor difference range of the charging pile extracted from the database; By formula: , calculate the operating risk coefficient of each charging pile within the target monitoring period .

6. The intelligent operation and maintenance system based on the Internet of Things according to claim 5, characterized in that: The specific analysis method for the environmental risk level and operational risk level of each charging pile within the target monitoring period is as follows: Environmental risk coefficient of each charging pile based on the target monitoring period ,like If the environmental risk is in the first interval extracted from the database, the first interval within the target monitoring period is determined. The environmental risk level of each charging pile is low risk; like If the environmental risk is in the second interval extracted from the database, the first The environmental risk level of each charging pile is high risk; According to the analysis method of the environmental risk level of each charging pile within the target monitoring period, the operation risk coefficient of each charging pile within the target monitoring period is analyzed to obtain the operation risk level of each charging pile within the target monitoring period.

7. The intelligent operation and maintenance system based on the Internet of Things according to claim 6, characterized in that: The specific analysis method for generating a fault work order for each charging pile within the target monitoring period and executing operation and maintenance scheduling for each charging pile within the monitoring period is as follows: Generate a first-level fault work order for each charging pile within the target monitoring period based on the first-level environmental abnormality parameters and the first-level operational abnormality parameters of each charging pile within the target monitoring period; Generate a Level 2 fault work order for each charging pile within the target monitoring period based on the Level 2 environmental anomaly parameters and Level 2 operational anomaly parameters of each charging pile within the target monitoring period; Based on the first-level fault work order and the second-level fault work order of each charging pile within the target monitoring period, the fault work order of each charging pile within the target monitoring period is obtained; Assign the fault work orders of each charging pile within the target monitoring period to maintenance personnel for repair.

8. An intelligent operation and maintenance method based on the Internet of Things, applied to the intelligent operation and maintenance system based on the Internet of Things according to any one of claims 1 to 7, characterized in that: The method comprises: Step 1: Calculate the loss coefficient of each charging pile within the target monitoring period; Step 2: Set the monitoring data collection frequency of each charging pile within the target monitoring period; Step 3: Collect environmental parameters and operating parameters of each charging pile within the target monitoring period; Step 4: Calculate the environmental risk coefficient and operation risk coefficient of each charging pile within the target monitoring period; Step 5: diagnose abnormal environmental parameters and abnormal operating parameters of each charging pile within the target monitoring period; Step 6: Execute operation and maintenance scheduling of each charging pile within the monitoring period.

Images