A method and system for monitoring the operation status of gas station equipment

Abstract

The present invention discloses a method and system for monitoring the operating status of gas station equipment, which relates to the technical field of equipment health monitoring, including collecting and preprocessing load and behavior data, calculating load fluctuation values ​​according to the preprocessed load and behavior data; calculating health indicators according to the load fluctuation values, and calculating the remaining life and maintenance priority according to the health indicators; constructing and solving the objective function according to the maintenance priority, generating allocation results, and storing, collecting and analyzing the generated load and behavior data. The present invention constructs a weighted function and calculates the load fluctuation value through a set of high-coupling frequency points to improve the accuracy of load fluctuation prediction, uses a nonlinear weighted synthesis method to calculate the health indicators of gas station equipment, solves the problem of not being able to fully utilize the relationship between consumer behavior data and equipment load data, calculates the maintenance priority of gas station equipment, and improves the operating efficiency and maintenance accuracy of gas station equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment health monitoring, and in particular to an operating status monitoring method and system based on gas station equipment. Background Art

[0002] With the continuous increase in energy consumption, gas stations, as important places for energy supply, the monitoring and maintenance of their equipment operating status are particularly important. Traditional gas station equipment monitoring methods mostly rely on the static status of the equipment or simple sensor data collection, and cannot deeply analyze the dynamic changes of the equipment and the impact of consumer behavior on equipment load. With the advancement of data collection technology and analysis methods, more and more gas stations have begun to try to use data analysis-based methods to predict equipment failures and performance changes. These methods usually rely on sensor data, behavioral data and other external factors, and use data mining and machine learning technologies to predict the health status of equipment. Existing monitoring methods often lack in-depth mining of the complex relationship between equipment and consumer behavior, especially in load fluctuations and equipment operating status analysis, and have certain limitations.

[0003] Existing methods for monitoring the operating status of gas station equipment still have significant shortcomings. Existing frequency domain analysis methods usually only focus on the frequency domain characteristics of a single signal, fail to effectively integrate load data and consumer behavior data, and cannot fully reflect the interaction between the two. When dealing with load fluctuation predictions, traditional methods mostly rely on empirical thresholds or simple statistical analysis, which makes the prediction results often lack accuracy when facing complex dynamic changes. The existing technology's processing methods for equipment health assessment and remaining life prediction are too simple, and fail to take into account the dynamic changes in equipment load fluctuations and behavior patterns, resulting in the evaluation results being unable to reflect the health status of the equipment in real time, thereby affecting the accuracy and timeliness of maintenance decisions. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an operation status monitoring method and system based on gas station equipment, which solves the problem that the existing frequency domain analysis method usually only focuses on the frequency domain characteristics of a single signal, fails to effectively integrate load data and consumer behavior data, and cannot fully reflect the interaction between the two. When processing load fluctuation prediction, traditional methods mostly rely on empirical thresholds or simple statistical analysis, which makes the prediction results often lack accuracy when facing complex dynamic changes. The processing methods of the existing technology in equipment health assessment and remaining life prediction are too simple, and fail to take into account the dynamic change characteristics of equipment load fluctuations and behavior patterns, resulting in the evaluation results unable to reflect the health status of the equipment in real time, thereby affecting the accuracy and timeliness of maintenance decisions.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides an operating status monitoring method based on gas station equipment, which includes collecting and preprocessing load and behavior data, calculating load fluctuation values ​​based on the preprocessed load and behavior data; calculating health indicators based on the load fluctuation values, and calculating remaining life and maintenance priorities based on the health indicators; constructing and solving an objective function based on the maintenance priority, generating an allocation result, and storing, collecting and analyzing the generated load and behavior data.

[0008] As a preferred solution of the operating status monitoring method based on gas station equipment of the present invention, wherein: the collection of load and behavior data and preprocessing refers to using intelligent sensors to collect load data of gas station equipment and using intelligent terminals to collect user behavior data;

[0009] Preprocess the collected load and behavior data;

[0010] The smart sensor includes a current and a voltage sensor;

[0011] The load data includes current and voltage data;

[0012] The behavior data includes refueling time period and refueling amount;

[0013] The preprocessing includes using time window alignment technology to time align the load and behavior data, using Gaussian filters to denoise the load and behavior data, using standard deviation method to identify and delete abnormal data, using interpolation method to fill missing data, and standardizing the load and behavior data.

[0014] As a preferred solution of the operating status monitoring method based on gas station equipment of the present invention, wherein: the calculation of the load fluctuation value based on the pre-processed load and behavior data refers to using fast Fourier transform to convert the pre-processed load and behavior data into frequency domain respectively, and obtain the load frequency domain Frequency Domain , use the periodogram method to calculate the load power spectrum density Power Spectral Density ;

[0015] Calculate the joint power spectral density of preprocessed load and behavioral data using synchronized Fourier transform ;

[0016] According to the load power spectrum density , behavioral power spectral density And the joint power spectral density , calculate the frequency coupling ;

[0017] According to the frequency coupling , using the quantile regression method to set the dynamic threshold and the frequency coupling Compare with the dynamic threshold and filter out the frequency coupling degree greater than the dynamic threshold , generate a set of high coupling frequency points F;

[0018] According to the load frequency domain , behavioral frequency domain And the high coupling frequency point set F, construct the weighting function , the formula is:

[0019] ,

[0020] Where f is the frequency;

[0021] According to the weighting function , calculate the load fluctuation value of the gas station equipment, the formula is:

[0022] ,

[0023] in is the load fluctuation value of the gas station equipment, t is the time, and i is the gas station equipment.

[0024] As a preferred solution of the operating status monitoring method based on gas station equipment of the present invention, wherein: the health index calculated according to the load fluctuation value refers to the calculation of the health index according to the behavior frequency domain. , calculate the amplitude through FFT transformation and phase information , generating periodic features H, ;

[0025] According to the periodic characteristic H, the change amplitude is calculated using the time difference method ;

[0026] The active power of the gas station equipment is calculated using the power calculation method according to the pre-processed load data, and the load of the gas station equipment is calculated using the load calculation method;

[0027] According to the load of gas station equipment, the average load is calculated using the moving average method, and the average load is calculated using the fluctuation range adjustment method to obtain the ideal load. ;

[0028] According to the change , calculate the Gaussian function ;

[0029] According to the load fluctuation value of gas station equipment , Ideal load And Gaussian function , using nonlinear weighted synthesis method to calculate the health index of gas station equipment , the formula is:

[0030] ,

[0031] in is the length of the time window for evaluation.

[0032] As a preferred solution of the operating status monitoring method based on gas station equipment described in the present invention, wherein: the remaining life and maintenance priority are calculated based on the health index of the gas station equipment. , the decay rate is calculated using the discrete time derivative method ;

[0033] Using device performance benchmarking to set thresholds ;

[0034] According to the decay rate and critical value , using the linear decay prediction method to calculate the remaining life of gas station equipment ;

[0035] Health indicators of gas station equipment and remaining life , calculate the maintenance priority of gas station equipment , the formula is:

[0036] .

[0037] As a preferred solution of the operating status monitoring method based on gas station equipment of the present invention, wherein: the objective function is constructed and solved according to the maintenance priority, and the allocation result is generated according to the maintenance priority of the gas station equipment. , use the sorting algorithm to sort the maintenance priorities in descending order to obtain the maintenance priority list of the equipment;

[0038] The objective function is constructed using linear programming, and the linear programming algorithm is used to solve it to obtain the allocation result of the optimal maintenance plan.

[0039] As a preferred solution of the operation status monitoring method based on gas station equipment described in the present invention, wherein: the storage of the load and behavior data generated by collection and analysis refers to sorting the collected load and behavior data and the allocation results generated in the analysis process according to timestamps and storing them in a database, marking the stored data, synchronously backing up the stored data regularly, regularly performing security and integrity checks on the stored data and backup data, and generating a test report.

[0040] In a second aspect, the present invention provides an operation status monitoring system based on gas station equipment, comprising:

[0041] A load collection module is used to collect load and behavior data and perform preprocessing, and calculate the load fluctuation value based on the preprocessed load and behavior data;

[0042] Health maintenance module, used to calculate health indicators according to load fluctuation values, and calculate remaining life and maintenance priority according to health indicators;

[0043] The allocation storage module is used to construct and solve the objective function according to the maintenance priority, generate the allocation results, and store the load and behavior data generated by the collection and analysis.

[0044] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the method for monitoring the operating status of a gas station device based on the first aspect of the present invention is implemented.

[0045] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the method for monitoring the operating status of a gas station equipment based on the first aspect of the present invention is implemented.

[0046] The beneficial effects of the present invention are as follows: the present invention collects load and behavior data and performs preprocessing, calculates the load fluctuation value according to the preprocessed load and behavior data; calculates the health index according to the load fluctuation value, and calculates the remaining life and maintenance priority according to the health index; constructs and solves the objective function according to the maintenance priority to generate an allocation result, thereby solving the problem of not being able to fully utilize the relationship between consumer behavior data and equipment load data, improving the operating efficiency and maintenance accuracy of gas station equipment, and improving the accuracy of equipment failure prediction and maintenance decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0048] Figure 1 This is a flow chart of the operating status monitoring method based on gas station equipment in Example 1.

[0049] Figure 2 This is a schematic diagram of the operating status monitoring system based on gas station equipment in Example 1. DETAILED DESCRIPTION

[0050] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0051] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0052] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0053] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a method for monitoring the operating status of gas station equipment, comprising the following steps:

[0054] S1, collect and preprocess load and behavior data, and calculate load fluctuation value according to the preprocessed load and behavior data;

[0055] Specifically, collecting load and behavior data and preprocessing them means using smart sensors to collect load data of gas station equipment and using smart terminals to collect user behavior data;

[0056] Preprocess the collected load and behavior data;

[0057] The smart sensor includes a current and a voltage sensor;

[0058] The load data includes current and voltage data;

[0059] The behavior data includes refueling time period and refueling amount;

[0060] The preprocessing includes using time window alignment technology to time align the load and behavior data, using Gaussian filters to denoise the load and behavior data, using standard deviation method to identify and delete abnormal data, using interpolation method to fill missing data, and standardizing the load and behavior data.

[0061] By collecting the load data of gas station equipment through intelligent sensors, the operating status of the equipment and its energy consumption can be reflected in real time and accurately. Using intelligent terminals to collect user behavior data, including refueling time and refueling amount, helps to analyze equipment load fluctuations from the perspective of consumer behavior. By aligning the load data with the behavior data in time, it can ensure that data analysis is performed at the same time point to avoid inaccurate analysis results due to data time offset. The Gaussian filter can effectively remove noise from the load and behavior data, maintain the smoothness of the data, and reduce unnecessary interference. The standard deviation method can ensure the purity of the data set by identifying outliers in the data and exclude abnormal data caused by factors such as faults and sensor errors. The interpolation method can effectively fill in the missing parts in the load and behavior data to avoid analysis bias or incomplete data caused by missing data. By standardizing the load and behavior data, the dimensional differences between different data items are eliminated, so that each data item has the same scale and weight.

[0062] Further, calculating the load fluctuation value according to the preprocessed load and behavior data means using fast Fourier transform to convert the preprocessed load and behavior data into frequency domain respectively to obtain the load frequency domain Frequency Domain , use the periodogram method to calculate the load power spectrum density Power Spectral Density ;

[0063] Calculate the joint power spectral density of preprocessed load and behavioral data using synchronized Fourier transform , the formula is:

[0064] ,

[0065] in and They are pre-processed load and behavior data, is the time window length of Fourier transform, f is the frequency, t is the time, and j is the virtual unit;

[0066] According to the load power spectrum density , behavioral power spectral density And the joint power spectral density , calculate the frequency coupling , the formula is:

[0067] ,

[0068] According to the frequency coupling , using the quantile regression method to set the dynamic threshold and the frequency coupling Compare with the dynamic threshold and filter out the frequency coupling degree greater than the dynamic threshold , generate a set of high coupling frequency points F;

[0069] According to the load frequency domain , behavioral frequency domain And the high coupling frequency point set F, construct the weighting function , the formula is:

[0070] ,

[0071] According to the weighting function , calculate the load fluctuation value of the gas station equipment, the formula is:

[0072] ,

[0073] in is the load fluctuation value of the gas station equipment, and i is the gas station equipment.

[0074] Existing technologies only calculate load fluctuations through simple statistics of time domain signals (such as mean and variance), which makes it difficult to capture the complex frequency-related characteristics of the equipment's operating status. Time domain methods cannot identify the power distribution of different frequency components, so they have poor ability to distinguish between periodic load fluctuations and random load fluctuations. Existing load fluctuation calculation methods usually only focus on the physical operating status of the equipment, while ignoring the potential impact of consumer behavior on equipment load fluctuations and correlation analysis, resulting in the calculation results being unable to reflect the dynamic relationship between consumer behavior and equipment load. Using frequency domain analysis can accurately locate the power fluctuation contribution at different frequency points, thereby more comprehensively analyzing the impact of fluctuations on load. This improvement can capture the periodic characteristics of equipment load fluctuations, and has a stronger ability to identify random fluctuations and nonlinear effects, improving the analysis accuracy of complex load fluctuation characteristics. The introduction of frequency coupling can quantify the impact of consumer behavior on equipment load fluctuations from a frequency perspective, enhancing the practical applicability of the calculation model. The use of weighted functions to differentiate frequency points can more accurately highlight the contribution of key frequency components, while reducing the interference of non-key frequency components, improving the formula's ability to distinguish the importance of different frequency points. The intensity evaluation based on the square power spectrum enhances the interpretability and practical significance of load fluctuation values, and is suitable for precise analysis in complex scenarios.

[0075] By using fast Fourier transform (FFT), the present invention can effectively convert load and behavior data from time domain to frequency domain, revealing their characteristics at different frequencies. Through frequency domain analysis, the relationship between load fluctuation and consumer behavior can be more clearly understood. The periodogram method can further reveal the energy distribution of signals at different frequencies by calculating the power spectrum density of load and behavior data, and can clearly understand the frequency domain characteristics of load and consumer behavior, thus laying the foundation for subsequent frequency coupling calculation. Through synchronous Fourier transform, the present invention can accurately capture the mutual relationship and coupling characteristics between load and behavior data in the frequency domain. This coupling The combined analysis not only improves the understanding of load fluctuation patterns, but also provides key data for the subsequent calculation of frequency coupling. The beneficial effect of calculating frequency coupling is that it can identify which frequency components in load fluctuations are significantly affected by consumer behavior, thereby providing more accurate frequency domain characteristics for subsequent load fluctuation predictions. By introducing weighting factors, it is possible to perform refined processing based on the contribution of each frequency point to the load fluctuation, thereby improving the calculation accuracy of the fluctuation value. Accurate load fluctuation values ​​can not only help determine the current load condition of the equipment, but also provide an accurate basis for subsequent equipment health assessments, maintenance priority determination, and remaining life predictions.

[0076] S2. Calculate the health index according to the load fluctuation value, and calculate the remaining life and maintenance priority according to the health index;

[0077] Specifically, calculating the health index based on the load fluctuation value refers to calculating the health index based on the behavior frequency domain. , calculate the amplitude through FFT transformation and phase information , generating periodic features H, ;

[0078] According to the periodic characteristic H, the change amplitude is calculated using the time difference method , the formula is:

[0079] ,

[0080] in is the time difference;

[0081] The active power of the gas station equipment is calculated using the power calculation method according to the pre-processed load data, and the load of the gas station equipment is calculated using the load calculation method;

[0082] According to the load of gas station equipment, the average load is calculated using the moving average method, and the average load is calculated using the fluctuation range adjustment method to obtain the ideal load. ;

[0083] According to the change , calculate the Gaussian function , the formula is:

[0084] ,

[0085] in is the standard deviation, is the peak point, indicating the neutral or critical point of the change amplitude;

[0086] According to the load fluctuation value of gas station equipment , Ideal load And Gaussian function , using nonlinear weighted synthesis method to calculate the health index of gas station equipment , the formula is:

[0087] ,

[0088] in is the length of the time window for evaluation.

[0089] The equipment health assessment method is based on single-point calculation or simple statistics within a fixed period, such as the mean or maximum value, which cannot effectively capture the load fluctuation trend of the equipment during long-term operation. The introduction of time window integration can comprehensively evaluate the cumulative impact of load fluctuations on the equipment in the past, improving the timeliness and accuracy of the health index. The existing health assessment method is only based on actual load fluctuations and lacks reference to equipment design standards or operating benchmarks, resulting in the inability to accurately judge the actual health impact of load fluctuations. The ideal load is introduced as an ideal benchmark, and the deviation between the actual load and the ideal load is calculated to make the health index more physically meaningful. Traditional health assessment methods often ignore the dynamic impact of the external environment (such as consumer behavior) and believe that load fluctuations are completely determined by the internal characteristics of the equipment. The health index is dynamically adjusted and consumer behavior fluctuations are included in the assessment as an independent factor, which improves the external adaptability of the health assessment model. The linear weighted method simply accumulates different factors, but cannot handle the complex nonlinear relationship between load fluctuations and consumer behavior. The use of nonlinear weighted synthesis makes the impact of load fluctuations and consumer behavior smoother and more natural, improving the flexibility of the model and the adaptability to complex scenarios.

[0090] The time difference method helps to accurately calculate the load fluctuation amplitude by comparing the changes in load signals at different time points. It can quantitatively evaluate the dynamic changes of equipment load and provide direct information about load fluctuations for health assessment. The fluctuation amplitude adjustment law is further optimized according to the volatility of load data to ensure more accurate load prediction. The moving average method smoothes the short-term fluctuations in load data, making the trend of load changes clearer and helping to judge the long-term health status of the equipment. The combination of these two methods provides an ideal load model that can reflect the normal operating status of the equipment load, thereby providing a stable reference point for subsequent health assessment. The Gaussian function helps to evaluate the amplitude and distribution of load fluctuations from a statistical perspective, providing an effective mathematical tool for health assessment. The nonlinear weighted synthesis method can obtain a comprehensive health indicator by combining the weighted results of load fluctuation values, ideal loads and Gaussian functions. Compared with the linear weighted method, the nonlinear method can better handle complex load fluctuation patterns and uncertainties, improve the accuracy and robustness of health indicators, and more accurately evaluate the overall health status of the equipment, providing a more accurate basis for equipment maintenance and life prediction.

[0091] Furthermore, the remaining life and maintenance priority are calculated based on the health indicators of the gas station equipment. , the decay rate is calculated using the discrete time derivative method , the formula is:

[0092] ,

[0093] Using device performance benchmarking to set thresholds ;

[0094] According to the decay rate and critical value , using the linear decay prediction method to calculate the remaining life of gas station equipment , the formula is:

[0095] ,

[0096] Health indicators of gas station equipment and remaining life , calculate the maintenance priority of gas station equipment , the formula is:

[0097] .

[0098] The maintenance priority algorithm is calculated based on a single parameter (such as remaining life or failure rate) and lacks comprehensive consideration of the health status, resulting in a delayed response to equipment with short-term health deterioration. The introduction of health index and remaining life, and the calculation of maintenance priority through nonlinear combination can more comprehensively reflect the real maintenance needs of the equipment. Traditional methods often use fixed weights (such as linear weighting) to combine health status and remaining life, but cannot adapt to the dynamic changes of weights under different states. The relative weights of health status and remaining life are dynamically adjusted through logarithmic difference. When the difference between the two is large, the priority response sensitivity is improved. Simple linear weighting or addition operations are used, lacking nonlinear processing of complex relationships of multiple factors. The use of fractional structure can more accurately describe their compound effects on priority. Many methods lack sufficient sensitivity to extreme conditions (such as health index close to 0 or remaining life close to 0), resulting in distortion of priority calculation. The reciprocal term in the first part of the formula and the absolute value term in the second part can effectively enhance the sensitivity to abnormal conditions and ensure that the priority is rapidly increased when the equipment status deteriorates.

[0099] Using the discrete time derivative method to calculate the decay rate of the equipment can accurately capture the health changes of the equipment at each time step. It can not only reflect the potential degradation process during the operation of the equipment, but also provide a quantitative basis for the subsequent life prediction. By setting the critical value, the equipment that needs to be focused on can be effectively screened out to ensure that the equipment is maintained in time before reaching the predetermined performance threshold, thereby reducing the equipment failure rate and extending the service life of the equipment. The steps of using the linear decay prediction method combined with the decay rate and the set critical value to predict the remaining life effectively convert the equipment health status into a specific time prediction. The remaining life is calculated based on the decay rate and critical value of the equipment, providing clear time guidance for the maintenance plan of the equipment. In gas station equipment, due to high frequency of use and complex operating environment, the linear decay prediction method can help operators foresee potential failures and plan repairs or replacements in advance, thereby preventing operational interruptions caused by sudden failures. The step of calculating maintenance priority can reasonably arrange the maintenance work of gas station equipment to ensure the efficiency and safety of equipment use. Through this method, maintenance measures can be taken in time before equipment failure, reducing the downtime caused by equipment failure, and can help operation and maintenance personnel concentrate resources to repair the most urgent equipment problems, thereby improving the overall efficiency of equipment management.

[0100] S3, construct and solve the objective function according to the maintenance priority, generate the allocation result, store, collect and analyze the generated load and behavior data;

[0101] Specifically, the objective function is constructed and solved according to the maintenance priority, and the allocation result is generated according to the maintenance priority of the gas station equipment. , use the sorting algorithm to sort the maintenance priorities in descending order to obtain the maintenance priority list of the equipment;

[0102] The objective function is constructed using linear programming, and the linear programming algorithm is used to solve it to obtain the allocation result of the optimal maintenance plan.

[0103] Constructing the objective function according to the maintenance priority of gas station equipment can ensure that the optimization process focuses on the most critical equipment, thereby effectively reducing downtime and failure risks. The solution of the objective function makes equipment maintenance no longer rely on subjective experience, but makes accurate decisions through mathematical models, thereby achieving optimal maintenance management of gas station equipment. By sorting the equipment maintenance priority in descending order, it is ensured that within the limited resources and time window, the equipment that needs maintenance most is given priority, which can significantly improve the gas station operation and maintenance efficiency, reduce the equipment failure rate, and ensure the efficient and stable operation of the equipment. In actual operation and maintenance, the maintenance sequence of equipment can be accurately arranged to minimize maintenance time and resource waste, and improve the overall operation and maintenance efficiency. The use of linear programming solution algorithm can efficiently and quickly derive the optimal maintenance plan for gas station equipment, which can effectively improve the level of intelligence of equipment management, reduce operation and maintenance costs, and extend the service life of equipment.

[0104] Furthermore, storing the load and behavior data generated by collection and analysis means sorting the collected load and behavior data and the allocation results generated during the analysis process according to timestamps and storing them in a database, marking the stored data, synchronously backing up the stored data regularly, regularly checking the security and integrity of the stored data and backup data, and generating a check report.

[0105] By using timestamps to sort the collected load and behavior data, we ensure that the data can be stored in chronological order and facilitate subsequent time series analysis. The sorted data can provide an accurate time benchmark for the operation status analysis of gas station equipment, help analyze the relationship between load fluctuations and consumer behavior, and thus provide a scientific basis for equipment maintenance and optimization. By marking the collected data, the data can be classified, labeled and tracked in the database. Tags can help distinguish different types of data, such as equipment load data, consumer behavior data, and allocation results generated during the analysis process. This process not only improves data management efficiency, but also facilitates subsequent data query and analysis. Labeled data can be used as basic information on equipment operating status, consumer behavior patterns, etc., and plays an important role in model training, predictive analysis, and report generation, enhancing the value of data utilization. The implementation of data backup not only ensures the continuous availability of load and behavior data, but also prevents data loss due to operational errors or equipment failures. Data security testing can prevent malicious attacks, unauthorized access or tampering, and ensure the confidentiality and integrity of load and behavior data during storage. By generating test reports, potential problems in data storage can be discovered in a timely manner, and effective measures can be taken to repair them, ensuring the accuracy and reliability of data, thereby improving the intelligence level and operation and maintenance efficiency of gas station equipment management systems.

[0106] This embodiment also provides an operation status monitoring system based on gas station equipment, including:

[0107] A load collection module is used to collect load and behavior data and perform preprocessing, and calculate the load fluctuation value based on the preprocessed load and behavior data;

[0108] Health maintenance module, used to calculate health indicators according to load fluctuation values, and calculate remaining life and maintenance priority according to health indicators;

[0109] The allocation storage module is used to construct and solve the objective function according to the maintenance priority, generate the allocation results, and store the load and behavior data generated by the collection and analysis.

[0110] This embodiment also provides a computer device, which is suitable for the case of an operation status monitoring method based on gas station equipment, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the operation status monitoring method based on gas station equipment as proposed in the above embodiment.

[0111] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0112] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the operating status monitoring method based on gas station equipment as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0113] In summary, the present invention solves the problem of not being able to fully utilize the relationship between consumer behavior data and equipment load data, improves the operating efficiency and maintenance accuracy of gas station equipment, and improves the accuracy of equipment failure prediction and maintenance decision-making.

[0114] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for monitoring the operating status of gas station equipment, characterized in that: include, Collect and preprocess load and behavior data, and calculate load fluctuation values ​​based on the preprocessed load and behavior data; Calculate the health index based on the load fluctuation value, and calculate the remaining life and maintenance priority based on the health index; Construct and solve the objective function based on maintenance priorities, generate allocation results, store, collect and analyze the generated load and behavior data; The collecting and preprocessing of load and behavior data refers to using smart sensors to collect load data of gas station equipment and using smart terminals to collect user behavior data; Preprocess the collected load and behavior data; The smart sensor includes a current and a voltage sensor; The load data includes current and voltage data; The behavior data includes refueling time period and refueling amount; The preprocessing includes using a time window alignment technique to time align the load and behavior data, using a Gaussian filter to denoise the load and behavior data, using a standard deviation method to identify and delete abnormal data, using an interpolation method to fill in missing data, and standardizing the load and behavior data; The calculation of the load fluctuation value according to the pre-processed load and behavior data refers to using fast Fourier transform to convert the pre-processed load and behavior data into frequency domain to obtain the load frequency domain Frequency Domain , use the periodogram method to calculate the load power spectrum density Power Spectral Density ; Calculate the joint power spectral density of preprocessed load and behavioral data using synchronized Fourier transform ; According to the load power spectrum density , behavioral power spectral density And the joint power spectral density , calculate the frequency coupling ; According to the frequency coupling , using the quantile regression method to set the dynamic threshold and the frequency coupling Compare with the dynamic threshold and filter out the frequency coupling degree greater than the dynamic threshold , generate a set of high coupling frequency points F; According to the load frequency domain , behavioral frequency domain And the high coupling frequency point set F, construct the weighting function , the formula is: ， Where f is the frequency; According to the weighting function , calculate the load fluctuation value of the gas station equipment, the formula is: ， in is the load fluctuation value of the gas station equipment, t is the time, and i is the gas station equipment.

2. The operating status monitoring method based on gas station equipment according to claim 1, characterized in that: The calculation of the health index according to the load fluctuation value refers to the calculation of the health index according to the behavior frequency domain. , calculate the amplitude through FFT transformation and phase information , generating periodic features H, ; According to the periodic characteristic H, the change amplitude is calculated using the time difference method ; The active power of the gas station equipment is calculated using the power calculation method according to the pre-processed load data, and the load of the gas station equipment is calculated using the load calculation method; According to the load of gas station equipment, the average load is calculated using the moving average method, and the average load is calculated using the fluctuation range adjustment method to obtain the ideal load. ; According to the change , calculate the Gaussian function ; According to the load fluctuation value of gas station equipment , Ideal load And Gaussian function , using nonlinear weighted synthesis method to calculate the health index of gas station equipment , the formula is: ， in is the length of the time window for evaluation.

3. The operating status monitoring method based on gas station equipment according to claim 2, characterized in that: The calculation of the remaining life and maintenance priority according to the health indicators refers to the calculation of the remaining life and maintenance priority according to the health indicators of the gas station equipment. , the decay rate is calculated using the discrete time derivative method ; Using device performance benchmarking to set thresholds ; According to the decay rate and critical value , using the linear decay prediction method to calculate the remaining life of gas station equipment ; According to the health indicators of gas station equipment and remaining life , calculate the maintenance priority of gas station equipment , the formula is: 。 4. The operating status monitoring method based on gas station equipment according to claim 3 is characterized in that: The objective function is constructed and solved according to the maintenance priority, and the distribution result is generated according to the maintenance priority of the gas station equipment. , use the sorting algorithm to sort the maintenance priorities in descending order to obtain the maintenance priority list of the equipment; The objective function is constructed using linear programming, and the linear programming algorithm is used to solve it to obtain the allocation result of the optimal maintenance plan.

5. The operating status monitoring method based on gas station equipment according to claim 4, characterized in that: The storage of the load and behavior data generated by collection and analysis refers to sorting the collected load and behavior data and the allocation results generated during the analysis process according to timestamps and storing them in a database, marking the stored data, synchronously backing up the stored data regularly, regularly checking the security and integrity of the stored data and backup data, and generating a test report.

6. A gas station equipment-based operating status monitoring system, based on the gas station equipment-based operating status monitoring method according to any one of claims 1 to 5, characterized in that: include, A load collection module is used to collect load and behavior data and perform preprocessing, and calculate the load fluctuation value based on the preprocessed load and behavior data; Health maintenance module, used to calculate health indicators according to load fluctuation values, and calculate remaining life and maintenance priority according to health indicators; The allocation storage module is used to construct and solve the objective function according to the maintenance priority, generate the allocation results, and store the load and behavior data generated by the collection and analysis.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the operating status monitoring method based on gas station equipment described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the operating status monitoring method based on gas station equipment according to any one of claims 1 to 5 are implemented.

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