Industrial chain early warning method and energy service SaaS operation and maintenance management platform

Abstract

The invention relates to an industrial chain early warning method and an energy service SaaS operation and maintenance management platform, and the method comprises the steps: obtaining basic power utilization data sets of all terminals of an enterprise, and enabling each terminal to correspond to one basic power utilization data set; cleaning the basic power utilization data set to obtain an analysis power utilization data set; constructing a power consumption analysis curve by using the analysis power consumption data set; obtaining behavior data of the terminal and segmenting the power consumption analysis curve according to the behavior data to obtain a plurality of sub power consumption analysis curves; associating and analyzing the sub-power consumption analysis curve and the comparison reference curve according to the behavior data to obtain an analysis result; and giving out a judgment result according to the analysis result, wherein the judgment result comprises a normal power consumption behavior and an abnormal power consumption behavior. According to the industrial chain early warning method and the energy service SaaS operation and maintenance management platform disclosed by the invention, the relationship between the equipment operation behavior and the power consumption is found by analyzing the collected data, so that data support is provided for subsequent equipment maintenance and power consumption optimization.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an industrial chain early warning method and an energy service SaaS operation and maintenance management platform. Background Art

[0002] Industrial electricity is a crucial component of the social electricity system, characterized by high energy consumption, concentrated loads, continuous dependency, and quality sensitivity. Driven by business demands for energy conservation management, environmental monitoring, and industry chain early warning, some companies have begun installing terminals to collect data and identify electricity problems by analyzing power consumption, time-of-day loads, and abnormal power consumption. One current implementation method is as follows: Data collection: smart meters (with RS485 / 4G communication), IoT sensors (monitoring equipment temperature and vibration), SCADA systems (centralized monitoring of industrial equipment); Data processing: Python (Pandas / NumPy for processing time series data), SQL (for storing electricity consumption and production data); Analysis and visualization: Tableau / Power BI (load curve visualization); Early warning system: SMS / APP pushes abnormal information in real time, and links PLC to automatically shut down (high-risk scenarios).

[0003] The above method can realize the automation of data collection and analysis, for example, by comparing current data with historical data to discover anomalies, such as by determining through time period benchmarks, equipment benchmarks, process benchmarks, etc. These judgment methods all have errors and cannot accurately reflect the relationship between the operating behavior of the equipment and power consumption. Summary of the Invention

[0004] This application provides an industrial chain early warning method and energy service SaaS operation and maintenance management platform, which discovers the relationship between equipment operation behavior and power consumption through analysis of collected data, thereby providing data support for subsequent equipment maintenance and power consumption optimization.

[0005] The above-mentioned purpose of this application is achieved through the following technical solutions: In the first aspect, this application provides an industry chain early warning method, including: Obtain the basic electricity consumption data group of each terminal of the enterprise, each terminal corresponds to a basic electricity consumption data group; Clean the basic electricity consumption data group to obtain the analytical electricity consumption data group. Cleaning includes removing abnormal data and filling in missing data. Use the power consumption data set to construct a power consumption analysis curve; Obtaining behavioral data of the terminal and segmenting the power consumption analysis curve according to the behavioral data to obtain multiple sub-power consumption analysis curves, wherein the behavioral data includes the behavior type, start time and end time; Correlate and analyze the sub-power consumption analysis curve with the comparison reference curve according to the behavioral data to obtain the analysis results; A judgment result is given based on the analysis results, which includes normal power usage behavior and abnormal power usage behavior.

[0006] In a possible implementation of the first aspect, after constructing the power consumption analysis curve using the power consumption analysis data set, the method further includes: Calculate the area value of the power consumption analysis curve and obtain the number of production behaviors corresponding to the power consumption analysis curve; Calculate the mean value of production behavior using the area value of the power consumption analysis curve and the number of production behaviors; Compare the mean value of production behavior with the standard value of production behavior to determine the status of the mean value of production behavior, which includes normal value and abnormal value.

[0007] In a possible implementation of the first aspect, when behavioral data is missing, the power consumption analysis curve is segmented using features of the power consumption analysis curve, where the features of the power consumption analysis curve include slope change, slope change time, stable interval, and stable time.

[0008] In a possible implementation of the first aspect, after obtaining the multiple sub-power consumption analysis curves, the method further includes: Divide the sub-power consumption analysis curve using a fixed time length to obtain sub-power consumption analysis curve segments; Analyze the sub-power consumption analysis curve segment to obtain stability data; Analyze the comparative reference curve corresponding to the sub-power consumption analysis curve segment to obtain reference stability data; The stability data is compared with the reference stability data to determine the operation state of the terminal, where the operation state includes a stable operation state and an unstable operation state.

[0009] In a possible implementation of the first aspect, analyzing the sub-power consumption analysis curve segment and obtaining stability data includes: Obtaining a data point group corresponding to a sub-power consumption analysis curve segment and constructing an analysis reference curve using the data point group; The analysis reference curve is transferred into the time domain for decomposition, and a continuous group and a discontinuous group are obtained; The ratio of the continuous group to the non-continuous group was calculated and recorded as stability data. The ratio included the quantity ratio and the intensity ratio.

[0010] In a possible implementation of the first aspect, after comparing the stability data with the reference stability data, the method further includes comparing the state distributions of the stability data and the reference stability data and providing a judgment result based on the state distributions, where the judgment result includes a same state and a different state.

[0011] In a possible implementation of the first aspect, comparing the state distribution of the stability data with the reference stability data includes: Obtaining a data point group corresponding to a sub-power consumption analysis curve segment and dividing the data point group according to time length to obtain multiple sub-data point groups; Determining the surrounding data density of the data points in each sub-data point group and classifying the data points in the sub-data point group according to the surrounding data density; The distribution results of different types of data points in the time series are recorded as the stability data distribution results; The reference stability data distribution results were obtained using the same method; Compare the stability data distribution results with the reference stability data distribution results to obtain the judgment results.

[0012] In a second aspect, the present application provides an industrial chain early warning device, comprising: A data acquisition unit is used to acquire a basic electricity consumption data group of each terminal of the enterprise, where each terminal corresponds to a basic electricity consumption data group; A data cleaning unit is used to clean the basic electricity consumption data group to obtain the analytical electricity consumption data group. The cleaning includes removing abnormal data and filling in missing data. a first data processing unit, configured to construct a power consumption analysis curve using the power consumption analysis data set; A second data processing unit is configured to obtain behavioral data of the terminal and segment the power consumption analysis curve according to the behavioral data to obtain multiple sub-power consumption analysis curves, wherein the behavioral data includes a behavior type, a start time, and an end time; a third data processing unit, configured to correlate and analyze the sub-power consumption analysis curve with the comparison reference curve according to the behavior data to obtain an analysis result; The result judgment unit is used to give a judgment result based on the analysis result, and the judgment result includes normal power consumption behavior and abnormal power consumption behavior.

[0013] In a third aspect, the present application provides an energy service SaaS operation and maintenance management platform, which includes: one or more memories for storing instructions; and One or more processors, configured to call and execute the instructions from the memory to perform the method as described in the first aspect and any possible implementation of the first aspect.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, the computer-readable storage medium comprising: The program, when the program is executed by a processor, the method described in the first aspect and any possible implementation of the first aspect is executed.

[0015] In a fifth aspect, the present application provides a computer program product, comprising program instructions. When the program instructions are executed by a computing device, the method described in the first aspect and any possible implementation of the first aspect is executed.

[0016] In a sixth aspect, the present application provides a chip system comprising a processor for implementing the functions involved in the above aspects, such as generating, receiving, sending, or processing the data and / or information involved in the above methods.

[0017] The chip system may be composed of chips, or may include chips and other discrete devices.

[0018] In one possible design, the chip system also includes a memory for storing necessary program instructions and data. The processor and the memory can be decoupled and provided on different devices, connected via wired or wireless means, or the processor and the memory can be coupled on the same device.

[0019] The beneficial effects of this application are: The industrial chain early warning method and energy service SaaS operation and maintenance management platform provided in this application discover the relationship between equipment operation behavior and power consumption through analysis of collected data. Through analysis, normal power consumption behavior and abnormal power consumption behavior can be discovered. The results obtained from the analysis provide data support for subsequent equipment maintenance and power consumption optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a step flow diagram of an industrial chain early warning method provided by this application.

[0021] Figure 2 This is a schematic diagram provided by the present application for constructing a power consumption analysis curve using an analysis power consumption data set.

[0022] Figure 3 This is a schematic diagram provided by the present application for segmenting a power consumption analysis curve according to behavioral data.

[0023] Figure 4 This is a schematic diagram of the principle of determining the surrounding data density of a data point in each sub-data point group provided by the present application.

[0024] Figure 5This is a schematic diagram comparing a stability data distribution result provided in this application and a reference stability data distribution result.

[0025] Figure 6 It is a schematic diagram comparing another stability data distribution result provided in this application and a reference stability data distribution result. DETAILED DESCRIPTION

[0026] The technical solution in this application is further described in detail below with reference to the accompanying drawings.

[0027] This application discloses an industry chain early warning method, please refer to Figure 1 In some examples, the industry chain early warning method disclosed in this application includes the following contents: S101, obtaining a basic electricity consumption data group of each terminal of the enterprise, where each terminal corresponds to a basic electricity consumption data group; S102, cleaning the basic electricity consumption data group to obtain an analysis electricity consumption data group, wherein the cleaning includes removing abnormal data and filling in missing data; S103, constructing a power consumption analysis curve using the power consumption analysis data set; S104, acquiring behavior data of the terminal and segmenting the power consumption analysis curve according to the behavior data to obtain multiple sub-power consumption analysis curves, wherein the behavior data includes a behavior type, a start time, and an end time; S105, correlating and analyzing the sub-power consumption analysis curve with the comparison reference curve according to the behavior data to obtain an analysis result; S106: providing a judgment result based on the analysis result, where the judgment result includes normal power usage behavior and abnormal power usage behavior.

[0028] In step S101, the basic electricity consumption data group of each terminal of the enterprise is first collected. The terminal here refers to the industrial production equipment involved in production within the enterprise. The collection method is performed using the method described in the background technology. In this application, the collected data includes voltage and current. The basic electricity consumption data group has voltage and current categories. These two types of basic electricity consumption data groups are processed in the same way. Voltage and current can be collected at the same time, or one of them can be collected selectively.

[0029] In step S102 , the basic electricity consumption data group is cleaned to obtain the analysis electricity consumption data group. There are two cleaning methods: removing abnormal data (data less than zero) and filling missing data (difference method or mean method).

[0030] The reasons for generating data less than zero are: During measurement, the "positive direction" of the current is preset (for example, the current flowing from the device input terminal is defined as positive). If the actual current flows in the opposite direction due to changes in load characteristics or operating conditions (such as motor reversal or the load becoming power supply feedback energy), the collected value will be displayed as negative.

[0031] The negative value of AC current is a natural result of the periodic change of the sinusoidal waveform: in the negative half-cycle of the sine wave, the current direction is opposite to that in the positive half-cycle, and the collected value will alternate between positive and negative with the cycle (such as the current waveform of ordinary AC power).

[0032] Power factor effect: When the load is capacitive or inductive and the power factor is low, the current may be opposite to the voltage, resulting in a negative instantaneous current (but the effective value is still positive).

[0033] Acquisition module calibration issue: If the ADC (analog-to-digital converter) input range is not set correctly (for example, a unipolar module is mistakenly connected to a bipolar signal), the positive voltage may be mistakenly judged as a negative value.

[0034] The specific reasons for data loss are as follows: Sensor failure: The internal coil of the current transformer (CT), voltage transformer (PT) or Hall sensor is broken or the magnetic core is saturated, resulting in the inability to output valid signals and no data input to the acquisition module.

[0035] The acquisition chip is damaged: the ADC (analog-to-digital converter) chip is overheated, the solder joint is poor, or the pins are oxidized, and the conversion from "analog signal to digital signal" cannot be completed, resulting in the direct loss of original data.

[0036] Unstable module power supply: The acquisition module power supply voltage fluctuates (for example, lower than the rated voltage) or the ripple is too large, causing the module to periodically reset or stop working, interrupting data acquisition.

[0037] In step S103, the power consumption analysis curve is constructed using the power consumption data set. Figure 2 As shown, the specific method is to use the generation time of the data point in the electricity consumption data as the horizontal coordinate and the value as the vertical coordinate to obtain discrete points, and then connect these discrete points together in sequence.

[0038] In step S104, the behavior data of the terminal is obtained and the power consumption analysis curve is segmented according to the behavior data, such as Figure 3 As shown, the terminal's behavioral data includes starting, stopping, and performing production actions, all of which will have a direct impact on the electricity consumption data group.

[0039] Behavior data includes three parameters: behavior type, start time, and end time. Segmenting the power consumption analysis curve based on the behavior data will generate multiple sub-power consumption analysis curves. The sub-power consumption analysis curves obtained here have a start time and end time, as well as a label (behavior type).

[0040] In step S105 , the sub-power consumption analysis curve is associated and analyzed with the comparison reference curve according to the behavior data to obtain an analysis result. The analysis result here includes normal and abnormal, but the analysis result is only for a certain sub-power consumption analysis curve.

[0041] Finally, in step S106, a judgment result is given based on the analysis results. The judgment result includes normal power usage behavior and abnormal power usage behavior. The way to give the judgment result here is to determine the proportion of normal analysis results in all analysis results. For example, for some precision equipment, the tolerance for abnormal analysis results is low. When giving the judgment result here, the reference value is relatively low. Otherwise, the reference value is relatively high.

[0042] This is because the economic feasibility of electricity consumption needs to be comprehensively considered, as the processing involves production adjustments, personnel allocation, etc., which will be directly fed back into production costs.

[0043] Determining electricity usage behavior based on time period, equipment, or process benchmarks has significant errors. Any anomaly in a behavior is eliminated by averaging. However, behavioral analysis, due to the accuracy of the comparison samples, can directly identify the problem location. Furthermore, even if problems can be identified using time period, equipment, or process benchmarks, their location cannot be directly determined.

[0044] In some examples, after constructing a power consumption analysis curve using the power consumption data set, the following steps are added: S201, calculating the area value of the power consumption analysis curve and obtaining the number of production behaviors corresponding to the power consumption analysis curve; S202, calculating the average value of the production behavior using the area value of the power consumption analysis curve and the number of production behaviors; S203, comparing the production behavior mean value and the production behavior standard value, and determining the state of the production behavior mean value, where the state includes a normal value and an abnormal value.

[0045] In steps S201 to S203, whether abnormal power consumption behavior occurs is determined based on the average power consumption of the production behavior. This method can be used to preliminarily determine whether obvious abnormal power consumption behavior occurs. For example, if the power consumption of a certain production behavior of a certain product (the standard value of the production behavior) is 1, if the power consumption (the average value of the production behavior) calculated at this time is 1.1, it means that this production behavior is abnormal.

[0046] The above method can be used for basic judgment. Of course, after completing this basic judgment, other steps need to be continued. This is because errors will cause this judgment method to have natural inaccuracies. Generally, in order to facilitate judgment, the error setting is relatively large.

[0047] In some possible implementations, when behavioral data is missing, the power consumption analysis curve is segmented using features of the power consumption analysis curve, including slope change, slope change time, stable interval, and stable time.

[0048] In some examples, after obtaining multiple sub-power consumption analysis curves, they are processed in the following manner: S301, dividing the sub-power consumption analysis curve using fixed time lengths to obtain sub-power consumption analysis curve segments; S302, analyzing the sub-power consumption analysis curve segment to obtain stability data; S303, analyzing the comparative reference curve corresponding to the sub-power consumption analysis curve segment to obtain reference stability data; S304 , comparing the stability data with the reference stability data to determine the operation state of the terminal, where the operation state includes a stable operation state and an unstable operation state.

[0049] In steps S301 to S304, the sub-power consumption analysis curve is first segmented to obtain multiple sub-power consumption analysis curve segments. Stability data for the sub-power consumption analysis curve segments is then determined. Then, the reference curve corresponding to the sub-power consumption analysis curve segments is analyzed to obtain reference stability data. The stability data of the sub-power consumption analysis curve segment is obtained in the same way as the reference stability data.

[0050] Finally, the stability data is compared with the reference stability data to determine the operating state of the terminal, which includes a stable operating state and an unstable operating state.

[0051] Here, the operating status of the terminal is determined by comparing the stability data with the reference stability data. It is a sub-operating status. This is because the sub-power consumption analysis curve is divided. After obtaining all the sub-operating statuses, the proportion of normal sub-operating statuses in all sub-operating statuses will be calculated. For example, the ratio set at this time is 0.8. When the calculated proportion is greater than or equal to 0.8, it means that the operating status of the terminal corresponding to the sub-power consumption analysis curve is normal, that is, the behavioral data of the terminal corresponding to the sub-power consumption analysis curve is normal.

[0052] In some examples, the specific method of analyzing the sub-power consumption analysis curve segment and obtaining stability data is as follows: Obtaining a data point group corresponding to a sub-power consumption analysis curve segment and constructing an analysis reference curve using the data point group; The analysis reference curve is transferred into the time domain for decomposition, and a continuous group and a discontinuous group are obtained; The ratio of the continuous group to the non-continuous group was calculated and recorded as stability data. The ratio included the quantity ratio and the intensity ratio.

[0053] In the above method, the stability data is determined by the ratio of continuity to discontinuity, and the stability data includes two data: quantity ratio and intensity ratio.

[0054] The quantity here refers to the number of curves after the analysis reference curve is transferred to the time domain for decomposition, and the intensity refers to the intensity of the curve after the analysis reference curve is transferred to the time domain for decomposition.

[0055] The quantity ratio and intensity ratio are both obtained by accumulation.

[0056] Continuous here means that the start time and end time of the curve obtained after the analysis reference curve is transferred to the time domain for decomposition are the same as the start time and end time of the analysis reference curve; discontinuous means that the start time and / or end time of the curve obtained after the analysis reference curve is transferred to the time domain for decomposition are different from the start time and end time of the corresponding analysis reference curve.

[0057] It should be understood that the current signal itself is a typical time domain signal (an amplitude curve that changes with time). By extracting characteristic parameters through time domain decomposition, the abnormal changes in the operating status of the equipment can be intuitively reflected.

[0058] When the equipment is operating normally, the current curve usually exhibits stable time-domain characteristics (such as stable amplitude, regular fluctuations, and clear periodicity); however, abnormal power consumption (such as mechanical obstruction, component wear, poor electrical contact, etc.) will disrupt this pattern, resulting in abnormal current in dimensions such as amplitude, fluctuation frequency, duration, and pulse characteristics.

[0059] A sudden jump in current (such as a sudden increase from 10A to 30A and sustained) may be a short circuit or a fault between winding turns; frequent current on and off (such as periodic current interruption and recovery after 0.5 seconds) may be due to poor contact (such as loose terminals) or intermittent equipment shutdown.

[0060] For example, a centrifugal pump's current normally remains stable at 25A ± 1A. If time-domain decomposition reveals current fluctuations expanding to 25A ± 5A, with frequent spikes exceeding 30A, this could be due to load instability caused by impeller wear or pipe blockage. If the window mean gradually increases during long-term operation (for example, due to increased resistance and a slow increase in current caused by aging motor windings), this could be a precursor to electrical performance degradation.

[0061] From the above content, we can see that when the equipment operates abnormally, the corresponding current signal and / or voltage signal will change synchronously, and its energy consumption data will also change accordingly. Of course, these changes cannot be reflected through time period benchmarks, equipment benchmarks, process benchmarks, etc. However, in the long-term operation process, these behaviors can cause the time period benchmarks, equipment benchmarks, process benchmarks, etc. to gradually deviate.

[0062] In some examples, after comparing the stability data with the reference stability data, the method further includes comparing the state distribution of the stability data with the reference stability data and providing a judgment result based on the state distribution, where the judgment result includes the same state and the different state.

[0063] The specific method for comparing the state distribution of stability data with the reference stability data is as follows: S401, obtaining a data point group corresponding to a sub-power consumption analysis curve segment and dividing the data point group according to time length to obtain multiple sub-data point groups; S402, determining the surrounding data density of the data points in each sub-data point group and classifying the data points in the sub-data point group according to the surrounding data density; S403, statistically analyzing the distribution results of different types of data points in the time series, and recording them as stability data distribution results; S404, obtaining a reference stability data distribution result using the same method; S405 , comparing the stability data distribution result with the reference stability data distribution result to obtain a judgment result.

[0064] In steps S401 to S405, the surrounding data density of the data points in each sub-data point group is first determined, and then the data points in the sub-data point group are classified according to the surrounding data density. Then, the distribution results of different types of data points are statistically analyzed in the time series, and finally, the stability data distribution results are compared with the reference stability data distribution results.

[0065] In the above method, the purpose of dividing the data point group according to the time length is to reduce the number of data points in the sub-data point group. Generally, the time length corresponding to a sub-data point group is 3-5 seconds.

[0066] Specifically, if Figure 4 As shown, the specific method of determining the surrounding data density of the data points in each sub-data point group is to first select any data point in a sub-data point group, then create a circle with one data point and count the number of other data points in the circle. This method can directly reflect the distribution density of the data points.

[0067] Then, the data points are arranged according to the distribution density, with every ten or every twenty points in the sequential sequence as a group. Of course, this is just an example.

[0068] Counting the distribution results of different types of data points in a time series means sorting the data points according to the time they were generated. At this time, all the data points are on a straight line, and then counting the distribution results of the same type of data points, such as Figure 5 As shown in the figure, the distribution of data points of the same type on the straight line is mainly used to examine whether there is clustering behavior.

[0069] The reference stability data distribution results were processed in the same way.

[0070] contrast Figure 5 and Figure 6 Comparing the stability data distribution results and the reference stability data distribution results refers to whether the distribution methods of the same type of data points in the stability data distribution results and the reference stability data distribution results are the same (completely the same or the same number of proportion groups are required). If they are the same, it means that the sub-power consumption analysis curve segment and the corresponding comparison reference curve are the same. Otherwise, it means that they are different, and the sub-power consumption analysis curve segment has abnormal power consumption behavior.

[0071] This application also provides an industry chain early warning device, including: A data acquisition unit is used to acquire a basic electricity consumption data group of each terminal of the enterprise, where each terminal corresponds to a basic electricity consumption data group; A data cleaning unit is used to clean the basic electricity consumption data group to obtain the analytical electricity consumption data group. The cleaning includes removing abnormal data and filling in missing data. a first data processing unit, configured to construct a power consumption analysis curve using the power consumption analysis data set; A second data processing unit is configured to obtain behavioral data of the terminal and segment the power consumption analysis curve according to the behavioral data to obtain multiple sub-power consumption analysis curves, wherein the behavioral data includes a behavior type, a start time, and an end time; a third data processing unit, configured to correlate and analyze the sub-power consumption analysis curve with the comparison reference curve according to the behavior data to obtain an analysis result; The result judgment unit is used to give a judgment result based on the analysis result, and the judgment result includes normal power consumption behavior and abnormal power consumption behavior.

[0072] Furthermore, after constructing the power consumption analysis curve using the power consumption data set, the following steps are also included: Calculate the area value of the power consumption analysis curve and obtain the number of production behaviors corresponding to the power consumption analysis curve; Calculate the mean value of production behavior using the area value of the power consumption analysis curve and the number of production behaviors; Compare the mean value of production behavior with the standard value of production behavior to determine the status of the mean value of production behavior, which includes normal value and abnormal value.

[0073] Furthermore, when the behavior data is missing, the power consumption analysis curve is segmented using the features of the power consumption analysis curve, where the features of the power consumption analysis curve include slope change, slope change time, stable interval, and stable time.

[0074] Furthermore, after obtaining multiple sub-power consumption analysis curves, the method further includes: Divide the sub-power consumption analysis curve using a fixed time length to obtain sub-power consumption analysis curve segments; Analyze the sub-power consumption analysis curve segment to obtain stability data; Analyze the comparative reference curve corresponding to the sub-power consumption analysis curve segment to obtain reference stability data; The stability data is compared with the reference stability data to determine the operation state of the terminal, where the operation state includes a stable operation state and an unstable operation state.

[0075] Furthermore, analyzing the sub-power consumption analysis curve segment and obtaining stability data includes: Obtaining a data point group corresponding to a sub-power consumption analysis curve segment and constructing an analysis reference curve using the data point group; The analysis reference curve is transferred into the time domain for decomposition, and a continuous group and a discontinuous group are obtained; The ratio of the continuous group to the non-continuous group was calculated and recorded as stability data. The ratio included the quantity ratio and the intensity ratio.

[0076] Furthermore, after comparing the stability data with the reference stability data, the method further includes comparing the state distribution of the stability data with the reference stability data and providing a judgment result according to the state distribution, wherein the judgment result includes the same state and the different state.

[0077] Furthermore, comparing the state distribution of the stability data with the reference stability data includes: Obtaining a data point group corresponding to a sub-power consumption analysis curve segment and dividing the data point group according to time length to obtain multiple sub-data point groups; Determining the surrounding data density of the data points in each sub-data point group and classifying the data points in the sub-data point group according to the surrounding data density; The distribution results of different types of data points in the time series are recorded as the stability data distribution results; The reference stability data distribution results were obtained using the same method; Compare the stability data distribution results with the reference stability data distribution results to obtain the judgment results.

[0078] In one example, the unit in any of the above devices can be one or more integrated circuits configured to implement the above method, such as: one or more application specific integrated circuits (ASICs), or, one or more digital signal processors (DSPs), or, one or more field programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0079] For another example, when the units in the device can be implemented in the form of a processing element scheduling program, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call programs. For another example, these units can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0080] Various objects such as various messages / information / equipment / network elements / systems / devices / actions / operations / processes / concepts that may appear in this application are named. It can be understood that these specific names do not constitute a limitation on the relevant objects. The names assigned may change with factors such as scenarios, contexts or usage habits. The understanding of the technical meaning of the technical terms in this application should be mainly determined from the functions and technical effects embodied / executed in the technical solutions.

[0081] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0082] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0083] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0084] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0085] It should also be understood that in various embodiments of this application, the terms "first," "second," and so on are merely used to indicate that multiple objects are distinct. For example, the terms "first time window" and "second time window" are merely used to indicate different time windows. They should not have any impact on the time windows themselves. The terms "first," "second," and so on should not limit the embodiments of this application in any way.

[0086] It should also be understood that in the various embodiments of the present application, unless otherwise specified or there is a logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced to each other, and the technical features in different embodiments can be combined to form new embodiments according to their internal logical relationships.

[0087] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a computer-readable storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned computer-readable storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0088] This application also provides an energy service SaaS operation and maintenance management platform, which includes: one or more memories for storing instructions; and One or more processors are used to call and execute the instructions from the memory to perform the method as described above.

[0089] The present application also provides a computer program product, which includes instructions. When the instructions are executed, the terminal device and the network device perform operations of the terminal device and the network device corresponding to the above method.

[0090] The present application also provides a chip system, which includes a processor for implementing the functions involved in the above content, such as generating, receiving, sending, or processing the data and / or information involved in the above method.

[0091] The chip system may be composed of chips, or may include chips and other discrete devices.

[0092] The processor mentioned in any of the above may be a CPU, a microprocessor, an ASIC, or one or more integrated circuits for executing a program for controlling the above-mentioned feedback information transmission method.

[0093] In one possible design, the chip system also includes a memory for storing necessary program instructions and data. The processor and the memory can be decoupled and provided on different devices, respectively, and connected via wired or wireless means to support the chip system in implementing the various functions of the above embodiments. Alternatively, the processor and the memory can be coupled on the same device.

[0094] Optionally, the computer instructions are stored in a memory.

[0095] Optionally, the memory is a storage unit within the chip, such as a register, cache, etc. The memory can also be a storage unit within the terminal located outside the chip, such as ROM or other types of static storage devices that can store static information and instructions, RAM, etc.

[0096] It can be understood that the memory in the present application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories.

[0097] The non-volatile memory may be ROM, programmable ROM (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory.

[0098] Volatile memory can be RAM, which is used as an external cache memory. There are many different types of RAM, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct memory bus RAM.

[0099] The embodiments of this specific implementation method are all preferred embodiments of the present application and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. An industrial chain early warning method, characterized in that: include: Obtain the basic electricity consumption data group of each terminal of the enterprise, each terminal corresponds to a basic electricity consumption data group; Clean the basic electricity consumption data group to obtain the analytical electricity consumption data group. Cleaning includes removing abnormal data and filling in missing data. Use the power consumption data set to construct a power consumption analysis curve; Obtaining behavioral data of the terminal and segmenting the power consumption analysis curve according to the behavioral data to obtain multiple sub-power consumption analysis curves, wherein the behavioral data includes the behavior type, start time and end time; Correlate and analyze the sub-power consumption analysis curve with the comparison reference curve according to the behavioral data to obtain the analysis results; A judgment result is given based on the analysis results, which includes normal power usage behavior and abnormal power usage behavior.

2. The industrial chain early warning method according to claim 1, characterized in that: After using the power consumption data set to build the power consumption analysis curve, it also includes: Calculate the area value of the power consumption analysis curve and obtain the number of production behaviors corresponding to the power consumption analysis curve; Calculate the mean value of production behavior using the area value of the power consumption analysis curve and the number of production behaviors; Compare the mean value of production behavior with the standard value of production behavior to determine the status of the mean value of production behavior, which includes normal value and abnormal value.

3. The industrial chain early warning method according to claim 1, characterized in that: When behavioral data is missing, the power consumption analysis curve is segmented using the characteristics of the power consumption analysis curve. The characteristics of the power consumption analysis curve include slope change, slope change time, stable interval, and stable time.

4. The industrial chain early warning method according to claim 1 or 3, characterized in that: After obtaining multiple sub-power consumption analysis curves, it also includes: Divide the sub-power consumption analysis curve using a fixed time length to obtain sub-power consumption analysis curve segments; Analyze the sub-power consumption analysis curve segment to obtain stability data; Analyze the comparative reference curve corresponding to the sub-power consumption analysis curve segment to obtain reference stability data; The stability data is compared with the reference stability data to determine the operation state of the terminal, where the operation state includes a stable operation state and an unstable operation state.

5. The industrial chain early warning method according to claim 4, characterized in that: Analyze the sub-power consumption analysis curve segment and obtain stability data including: Obtaining a data point group corresponding to a sub-power consumption analysis curve segment and constructing an analysis reference curve using the data point group; The analysis reference curve is transferred into the time domain for decomposition, and a continuous group and a discontinuous group are obtained; The ratio of the continuous group to the non-continuous group was calculated and recorded as stability data. The ratio included the quantity ratio and the intensity ratio.

6. The industrial chain early warning method according to claim 4, characterized in that: After comparing the stability data with the reference stability data, the method further includes comparing the state distribution of the stability data with the reference stability data and providing a judgment result based on the state distribution, wherein the judgment result includes the same state and the different state.

7. The industrial chain early warning method according to claim 6, characterized in that: The state distribution of the comparative stability data and the reference stability data includes: Obtaining a data point group corresponding to a sub-power consumption analysis curve segment and dividing the data point group according to time length to obtain multiple sub-data point groups; determining the density of surrounding data for the data points in each sub-data point group and classifying the data points in the density sub-data point group based on the surrounding data; The distribution results of different types of data points in the time series are recorded as the stability data distribution results; The reference stability data distribution results were obtained using the same method; Compare the stability data distribution results with the reference stability data distribution results to obtain the judgment results.

8. An industrial chain early warning device, characterized in that: include: A data acquisition unit is used to acquire a basic electricity consumption data group of each terminal of the enterprise, where each terminal corresponds to a basic electricity consumption data group; A data cleaning unit is used to clean the basic electricity consumption data group to obtain the analytical electricity consumption data group. The cleaning includes removing abnormal data and filling in missing data. a first data processing unit, configured to construct a power consumption analysis curve using the power consumption analysis data set; A second data processing unit is configured to obtain behavioral data of the terminal and segment the power consumption analysis curve according to the behavioral data to obtain multiple sub-power consumption analysis curves, wherein the behavioral data includes a behavior type, a start time, and an end time; a third data processing unit, configured to correlate and analyze the sub-power consumption analysis curve with the comparison reference curve according to the behavior data to obtain an analysis result; The result judgment unit is used to give a judgment result based on the analysis result, and the judgment result includes normal power consumption behavior and abnormal power consumption behavior.

9. An energy service SaaS operation and maintenance management platform, characterized by: The platform includes: one or more memories for storing instructions; and One or more processors, configured to call and execute the instructions from the memory to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium comprises: The program, when executed by a processor, executes the method according to any one of claims 1 to 7.

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