CN-122015360-A - Commercial display cabinet remote intelligent operation and maintenance management system based on big data

CN122015360ACN 122015360 ACN122015360 ACN 122015360ACN-122015360-A

Abstract

The invention relates to the technical field of remote monitoring, in particular to a commercial display cabinet remote intelligent operation and maintenance management system based on big data. According to the invention, by constructing an edge feature extraction and cloud voltage coupling clustering cooperative processing flow, a dynamic voltage impedance relative evaluation system is constructed by utilizing wide-area big data, starting energy required by static friction force and rotor inertia is quantitatively overcome, a group baseline of similar display cabinets under similar voltage working conditions is combined, the starting energy of the target display cabinet is transversely compared with the group level, under the condition that the influence of power grid quality difference is eliminated, accurate relative quantification and early warning are carried out on the abrasion of mechanical parts in the compressor, the fault diagnosis accuracy of the display cabinet in a voltage unstable region is improved, false overload alarm caused by low voltage is avoided, and passive maintenance is converted into predictive maintenance.

Inventors

FAN YANPENG
JIANG YUJIAO
SUN KUN
CUI YANPING
LI YONGSHENG

Assignees

山东锡海冷链科技有限公司

Dates

Publication Date: 20260512
Application Date: 20260129

Claims (10)

1. Commercial display cabinet remote intelligent operation and maintenance management system based on big data, which is characterized in that the system comprises: The edge waveform acquisition module monitors the closed action state of a relay control signal of a terminal compressor of the commercial display cabinet, acquires transient current and real-time voltage waveform data, and generates a starting transient waveform data packet; The starting energy calculation module performs integral operation on the transient current waveform data packet to obtain starting energy, performs root mean square calculation on real-time voltage waveform data to obtain a voltage effective value, and associates the starting energy and the voltage effective value to generate a starting characteristic data pair; the same-voltage working condition clustering module analyzes the model identification, retrieves active data with the model identification, sets a voltage deviation range based on the voltage effective value, screens the active data which accords with the voltage deviation range, extracts starting energy, and establishes a same-voltage and same-type display cabinet data cluster; the group baseline generation module sorts the starting energy in the data clusters of the same-voltage similar display cabinets, calculates the median index and the quarter-bit distance index, and constructs a group impedance distribution benchmark; the wear outlier judging module calculates the starting energy and the median index difference in the population impedance distribution standard, divides the median index difference by the quarter bit distance index to obtain a deviation index, compares the deviation index with a preset abnormal threshold, and generates a display cabinet wear early warning judging result.
2. The big data-based commercial display cabinet remote intelligent operation and maintenance management system according to claim 1, wherein the starting transient waveform data packet comprises a collection trigger time stamp, a double-channel sampling point array and a sensor calibration parameter, the starting characteristic data pair comprises a mechanical work representation value, a power grid power supply capacity representation value and a time sequence correlation label, the same-voltage same-class display cabinet data cluster comprises a same-model display cabinet identification set, an approximate voltage working condition record set and a starting energy numerical value sequence, the group impedance distribution reference comprises a group energy median line, an impedance dispersion range and a statistical sample capacity identification, and the display cabinet abrasion early warning judging result comprises a mechanical impedance abnormal grade, a display cabinet maintenance suggestion code and a fault occurrence probability evaluation value.
3. The big data based commercial display cabinet remote intelligent operation and maintenance management system according to claim 1, wherein the edge waveform acquisition module comprises: the trigger signal capturing submodule monitors the closed action state of a relay control signal of the commercial display cabinet terminal, acquires real-time point data as a reference when a signal ascending state locking clock representing the closed action is identified, and logically correlates the time point data with a hardware starting request signal to generate an acquisition synchronous trigger instruction; The dual-channel synchronous acquisition submodule responds to the acquisition synchronous trigger instruction, synchronously activates a current transformer and a voltage sensor which are connected to a power supply line of the compressor, continuously reads analog signals in a fixed time window of a starting process according to a preset sampling frequency, converts the analog signals into digital quantized data through an analog-to-digital converter, establishes a corresponding time index and generates an electric transient waveform sequence; The data package sub-module invokes the electrical transient waveform sequence, performs time domain synchronous alignment operation on the current change track and the voltage fluctuation track according to the time index, extracts sampling frequency of the waveform sequence and a display cabinet identifier as head metadata, performs binary serialization integration on the aligned two-channel waveform data and the head metadata, and constructs a starting transient waveform data package.
4. The big data based commercial display counter remote intelligent operation and maintenance management system according to claim 3, wherein the start-up energy calculation module comprises: The current integration operation sub-module calls the starting transient waveform data packet, a transient current waveform data sequence and a time stamp index are deconstructed, product operation is carried out on the current transient amplitude of each discrete sampling point and the time interval between adjacent sampling points, accumulation and summation operation is carried out on product items in a preset starting time window, and the total amount of integration of current to time in the process of accelerating the compressor rotor from a static state to a rated rotating speed is quantized, so that a starting charge accumulation value is generated; The voltage root mean square calculation submodule responds to the calculation completion signal of the starting charge accumulation value, extracts real-time power grid voltage waveform data synchronous with the time span of the energy value, carries out square operation on each discrete voltage sampling point in the real-time power grid voltage waveform data sequence, calculates the arithmetic average of the square value, carries out square processing on the average, acquires the voltage physical quantity representing the real-time load capacity of the starting instant power supply network, and generates a power grid voltage effective value; the feature association packing sub-module establishes a logic mapping relation between two values according to the power grid voltage effective value and a unique identifier of a display cabinet starting event, marks a starting charge accumulation value as a mechanical load characteristic index, marks the power grid voltage effective value as an input voltage working condition index, combines the indexes into a structured data unit with space-time association according to a preset communication protocol format, and generates a starting feature data pair.
5. The big data based commercial display cabinet remote intelligent operation and maintenance management system according to claim 4, wherein the same-pressure working condition clustering module comprises: The homotypic data retrieval submodule calls the starting characteristic data pair, analyzes the included model identifier information of the target display cabinet, performs condition matching retrieval on the display cabinet records within the whole network range, locks the records which have the same model identifier and have effective uploading behaviors in a preset recent time window, extracts the running state parameters and the identity index information corresponding to the records, and generates a homotypic active display cabinet data set; the voltage window setting submodule calls the effective value of the power grid voltage in the starting characteristic data pair as a datum reference point, performs addition and subtraction operation according to a preset voltage fluctuation tolerance parameter, calculates the floating and sinking limit values of the datum voltage respectively, and generates a same-voltage screening deviation interval; And the working condition alignment sub-module invokes the same-voltage screening deviation interval, extracts the voltage value corresponding to the record and executes the value inclusion relation judgment of the same-voltage screening deviation interval, reserves the specific record of which the voltage value falls into the interval, separates the corresponding starting energy value from the specific record and gathers the starting energy value into a single-dimensional value set, and establishes a same-voltage same-class display cabinet data cluster.
6. The big data based remote intelligent operation and maintenance management system for commercial display cases according to claim 5, wherein an inverted index structure is established for the display case records within the whole network range, and candidate data with timestamp attributes falling into continuous time intervals are positioned through the inverted index structure; And detecting whether the candidate data comprises a complete starting current waveform file and corresponding compressor running impedance data, and only marking the candidate data comprising the complete data as a record with effective uploading behavior.
7. The big data based commercial display cabinet remote intelligent operation and maintenance management system of claim 5, wherein the group baseline generation module comprises: The numerical ordering and sorting submodule invokes the same-voltage similar display cabinet data cluster, traverses the included independent display cabinet starting energy numerical values, executes numerical comparison operation, rearranges the energy data according to monotonically increasing logic, establishes a corresponding relation between the numerical values and sequence positions, removes time dimension index interference in the original data, and generates an ascending energy numerical value list; The distribution characteristic calculation sub-module calls the ascending energy value list, positions the value points at the positive middle position according to the total length of the ascending energy value list as central trend characterization, positions the value points at the quarter position and the three-quarter position of the ascending energy value list respectively, performs subtraction operation on the values of the two quantile points, acquires the difference value reflecting the discrete width of the data, combines the value of the central position and the difference value of the discrete width into mathematical parameters of the distribution state of the quantized group, and generates a distribution statistical characteristic set; The reference model construction submodule maps a median value into a standard mechanical impedance reference line of the model display cabinet under a specific voltage based on the distribution statistical feature set, maps a quarter bit distance into an allowable reasonable fluctuation interval width, and constructs a multi-dimensional reference data structure by combining real-time sample capacity information to generate a group impedance distribution reference.
8. The remote intelligent operation and maintenance management system for the commercial display cabinet based on big data according to claim 7, wherein the process of constructing the multi-dimensional reference data structure by combining real-time sample capacity information comprises the steps of calling logarithmic calculation logic to calculate logarithmic values of list length values by taking ten as a base, and multiplying the logarithmic values by a preset confidence weight coefficient to obtain a sample credibility score; And opening up a structured storage space in the memory, sequentially writing a standard mechanical impedance reference line, an allowed reasonable fluctuation interval width and a sample reliability score, and writing the minimum value and the maximum value in the ascending energy numerical value list into the structured storage space as boundary constraint parameters to form the group impedance distribution standard comprising five-dimensional data fields.
9. The big data based commercial display counter remote intelligent operation and maintenance management system of claim 7, wherein the wear outlier determination module comprises: The impedance difference quantization submodule takes a median index stored in the group impedance distribution benchmark as a reference anchor point, combines the starting charge accumulation values recorded in the starting characteristic data pair, performs subtraction algebra operation on the median index and the starting charge accumulation values, calculates the numerical value difference between real-time consumed energy of a target display cabinet in a starting event and the central trend of the group of similar display cabinets, and generates an energy benchmark deviation value; Based on the energy reference deviation value, the outlier degree normalization submodule calls a quartile range index recorded in a group impedance distribution reference as a scale for measuring the discrete degree of data, takes the energy reference deviation value as a numerator and the quartile range index as a denominator to execute division operation, converts the absolute difference of physical energy dimension into the relative distance of statistical dimension, and generates an impedance deviation coefficient; and the wear state judging submodule reads a preset mechanical wear abnormality judging threshold value by adopting the impedance deviation coefficient, performs numerical comparison operation on the impedance deviation coefficient and the abnormality judging threshold value, triggers an abnormality marking logic if the impedance deviation coefficient is larger than the abnormality judging threshold value, and generates a display cabinet wear early warning judging result according to the corresponding risk severity level matched with the amplitude of the impedance deviation coefficient exceeding the abnormality judging threshold value.
10. The remote intelligent operation and maintenance management system for the commercial showcase based on big data according to claim 9 is characterized in that the process of performing subtraction algebra operation on the median index and the starting charge accumulation value comprises the steps of reading the starting charge accumulation value recorded in the starting characteristic data pair as a subtracted number, reading the median index stored in the group impedance distribution reference as a subtracted number, and performing double-precision floating point subtraction operation to obtain an original difference value; Constructing unidirectional wear drift filtering logic, judging whether an original difference value is larger than 0, and when the original difference value is larger than 0, characterizing that the display cabinet has forward impedance increase, and assigning the original difference value as an energy reference deviation value; when the original difference value is smaller than or equal to 0, the display cabinet is characterized to be in a low-impedance running state, the energy reference deviation value is forcedly set to 0, and negative deviation caused by non-abrasion factors is eliminated.

Description

Commercial display cabinet remote intelligent operation and maintenance management system based on big data Technical Field The invention relates to the technical field of remote monitoring, in particular to a commercial display cabinet remote intelligent operation and maintenance management system based on big data. Background The technical field of remote monitoring relates to a technical system for acquiring, transmitting and intensively monitoring the running state and environmental parameters of a remote display cabinet in real time by utilizing a wired or wireless communication network. The core matters in the field cover the data acquisition of the front-end sensing display cabinet, the signal transmission of the network layer and the data presentation and instruction control of the monitoring center, and integrally form the display cabinet management framework crossing the geographic space limitation. The traditional commercial display cabinet remote intelligent operation and maintenance management system is a system for maintaining and managing electrical parameters, refrigerating and heating performances of a commodity display cabinet in scenes such as supermarkets, convenience stores and the like by means of pointers. The method is characterized in that a fixed temperature interval and a compressor current threshold value are preset in an embedded controller of a display cabinet, real-time operation data are collected by a temperature sensor and a current transformer, when the collected values exceed the preset threshold value range, the controller triggers local audible and visual alarm, a communication module is controlled to send specific fault codes to a central server, an alarm log is recorded at the server, a manual dispatcher telephones to inform maintenance personnel to carry a universal meter and a pressure meter to go to the site to check the condition of the display cabinet, and manual fault checking and component replacement are carried out. In the prior art, the fault of the compressor is simply judged by means of preset fixed current threshold values, the interference to the motor starting characteristics caused by the voltage fluctuation of the power grid in different areas is ignored, false alarm is easy to occur in the voltage unstable areas, the mechanical impedance abnormality cannot be accurately identified in the early wear stage before the compressor is not completely stuck, passive response can be carried out only according to the post fault codes, quantitative evaluation means for the sub-health state of the display cabinet are lacking, the commodity loss risk caused by the shutdown of the display cabinet is difficult to reduce, and maintenance personnel are notified to go to the site by means of manual scheduling, so that the efficiency is low, the operation and maintenance cost is increased, and the operation state of the display cabinet under different voltage environments cannot be finely managed and controlled. Disclosure of Invention In order to solve the technical problems that in the prior art, the voltage fluctuation of a power grid in different areas is ignored to cause interference to the starting characteristic of a motor, false alarm is easy to occur in a voltage unstable area, mechanical impedance abnormality cannot be accurately identified in an early wear stage before a compressor is not completely blocked, passive response can be carried out only according to a post fault code, a quantitative evaluation means for a sub-health state of a display cabinet is lacking, commodity loss risk caused by shutdown of the display cabinet is difficult to reduce, and maintenance personnel is notified to go to a site to check by means of manual scheduling, efficiency is low, operation and maintenance cost is increased, and refined management and control of the operation state of the display cabinet in different voltage environments cannot be realized. The technical scheme is as follows: In one aspect, a business display cabinet remote intelligent operation and maintenance management system based on big data is provided, the system comprises: The edge waveform acquisition module monitors the closed action state of a relay control signal of a terminal compressor of the commercial display cabinet, acquires transient current and real-time voltage waveform data, and generates a starting transient waveform data packet; The starting energy calculation module performs integral operation on the transient current waveform data packet to obtain starting energy, performs root mean square calculation on real-time voltage waveform data to obtain a voltage effective value, and associates the starting energy and the voltage effective value to generate a starting characteristic data pair; the same-voltage working condition clustering module analyzes the model identification, retrieves active data with the model identification, sets a voltage deviation range based on the voltage effective