Intelligent management platform and management method for carbon emission

Abstract

The invention relates to the field of data processing, in particular to an intelligent management platform and management method for carbon emission, which are used for solving the problems that an existing intelligent management system for carbon emission cannot independently monitor and remind a single object in a carbon emission monitoring area and cannot integrally monitor and remind the carbon emission monitoring area, and the intelligent management platform and management method for carbon emission cannot be used for monitoring and reminding the single object in the carbon emission monitoring area. And the carbon emission reduction effect is not good. The management platform comprises the following modules: a carbon emission monitoring module, a data analysis module, a processor, an emission reduction early warning module and an over-emission early warning module. According to the management method, the monitored objects can be independently monitored through the change coefficient, then emission reduction reminding is performed on the monitored objects, all the monitored objects can be integrally monitored through the comprehensive emission coefficient, then over-emission reminding is performed on the carbon emission monitoring area, the carbon emission can be effectively reduced, and then environmental pollution is greatly reduced.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to an intelligent management platform and management method for carbon emissions. Background Art

[0002] Global warming and climate change have become the focus of global attention. With the development of science and technology, people are paying more and more attention to how to reduce carbon emissions to protect the environment. However, the relatively complex carbon emission management system makes it difficult for enterprises and governments to effectively monitor and control carbon emissions.

[0003] The patent with application number CN202211587684.0 discloses an intelligent management system and method for carbon emissions, which belongs to the field of carbon emission management. The intelligent management system for carbon emissions includes a prediction module, a comparison module, a trading module, a carbon emission index analysis module, a risk assessment module and a graded warning module; wherein the prediction module is used to predict the carbon emissions of the managed enterprise this year based on the previous carbon emissions; the comparison module is used to compare the predicted carbon emissions with the carbon emission index to generate a judgment result; the carbon emission index analysis module is used to analyze the carbon emission index of the managed enterprise according to the judgment result; the trading module is used to trade carbon emission index according to the judgment result. The present invention can accurately estimate the trend of the enterprise's own carbon emissions, and can perform risk assessment according to the risk control area, so that the remaining carbon emission indicators can be traded to the enterprises in need in time, but there are still the following shortcomings: it is impossible to monitor and remind a single object in the carbon emission monitoring area separately, and it is also impossible to monitor and remind the carbon emission monitoring area as a whole, so that the carbon emission reduction effect is not good. Summary of the invention

[0004] In order to overcome the above-mentioned technical problems, the purpose of the present invention is to provide an intelligent management platform and management method for carbon emissions: all enterprises in the carbon emission monitoring area are marked as monitoring objects in turn through the carbon emission monitoring module, and the carbon emissions of the monitoring objects are obtained, the fluctuation coefficient and the average rate value are obtained according to the carbon emissions through the data analysis module, and the variation coefficient is obtained according to the fluctuation coefficient and the average rate value, the processor generates emission reduction warning instructions and over-emission warning instructions according to the variation coefficient, and after receiving the emission reduction warning instruction through the emission reduction warning module, emission reduction prompt information is sent to the emission reduction warning object, and after receiving the over-emission warning instruction through the over-emission warning module, an over-emission alarm bell sounds, thereby solving the problem that the existing intelligent management system for carbon emissions cannot monitor and remind a single object in the carbon emission monitoring area separately, nor can it monitor and remind the carbon emission monitoring area as a whole, resulting in poor carbon emission reduction effect.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] An intelligent management platform for carbon emissions, comprising:

[0007] The carbon emission monitoring module is used to mark all enterprises in the carbon emission monitoring area as monitoring objects i in turn, obtain the carbon emission TPj of the monitoring object i, and send the carbon emission TPj to the data analysis module;

[0008] A data analysis module, used to obtain a fluctuation coefficient BD and an average rate value JL according to the carbon emission TPj, and to obtain a variation coefficient BH according to the fluctuation coefficient BD and the average rate value JL, and to send the variation coefficient BH to a processor;

[0009] A processor, for generating an emission reduction warning instruction and an over-emission warning instruction according to the variation coefficient BH, and sending the emission reduction warning instruction to the emission reduction warning module, and sending the over-emission warning instruction to the over-emission warning module;

[0010] The emission reduction warning module is used to send emission reduction prompt information to the emission reduction warning object after receiving the emission reduction warning instruction;

[0011] The over-discharge warning module is used to sound the over-discharge alarm bell after receiving the over-discharge warning instruction.

[0012] As a further solution of the present invention: the specific process of the carbon emission monitoring module obtaining the carbon emission TPj is as follows:

[0013] All enterprises in the carbon emission monitoring area are marked as monitoring objects i in sequence, where i=1, ..., n, and n is a natural number;

[0014] Real-time monitoring of the carbon emissions of object i within a preset time, and marking them as carbon emissions TPj in chronological order, where j = 1, ..., m, and m is a natural number;

[0015] The carbon emissions TPj are sent to the data analysis module.

[0016] As a further solution of the present invention: the specific process of the data analysis module obtaining the variation coefficient BH is as follows:

[0017] After receiving the carbon emission TPj, all the carbon emission TPj of the preset number closest to the current moment are obtained, and are marked as analysis data FXv in chronological order, where v=1, ..., o, and o is a natural number;

[0018] Sum up all the analysis data FXv and find the average value, and mark the average value as the average displacement JP. Substitute the analysis data FXv and the average displacement JP into the formula The fluctuation coefficient BD is obtained, where μ is the error factor and μ=1.105;

[0019] Obtain the monitoring time of all carbon emissions TPj in the analysis data FXv and mark it as monitoring time JC;

[0020] An indicator coordinate system is established with the analysis data FXv as the dependent variable and the monitoring time JC as the independent variable, and a line graph is formed. The slope of each line segment is obtained and marked as the line rate value XL. All line rate values ​​XL are summed and averaged, and the average value is marked as the average rate value JL.

[0021] Substitute the volatility coefficient BD and the average rate value JL into the formula The variation coefficient BH is obtained, wherein k1 and k2 are preset proportional coefficients of the fluctuation coefficient BD and the average value JL respectively, and k1+k2=1, 0<k2<k1<1, k1=0.55, k2=0.45;

[0022] The coefficient of variation BH is sent to the processor.

[0023] As a further solution of the present invention: the specific process of the processor generating the emission reduction warning instruction and the over-emission warning instruction is as follows:

[0024] Compare the coefficient of variation BH with the threshold value of variation BHy:

[0025] If the change coefficient BH> the change threshold BHy, an emission reduction warning instruction is generated, and the monitoring object i corresponding to the change coefficient BH is marked as an emission reduction warning object, and the emission reduction warning instruction and the emission reduction warning object are sent to the emission reduction warning module;

[0026] If the variation coefficient BH ≤ the variation threshold BHy, the monitoring object i corresponding to the variation coefficient BH is marked as a normal object;

[0027] Sum the coefficients of variation BH of all monitored objects i and calculate the average value, which is marked as the mean variation value JB;

[0028] Obtain the total number of emission reduction warning objects and the total number of normal objects, and mark them as a subtraction value JS and a constant value CS respectively, obtain the ratio between the subtraction value JS and the constant value CS and mark it as a subtraction-to-constant ratio JC;

[0029] Get the product of the mean variation value JB and the normal reduction ratio JC and mark it as the comprehensive coefficient ZP;

[0030] Compare the ranking coefficient ZP with the ranking threshold ZPy:

[0031] If the comprehensive arrangement coefficient ZP>the comprehensive arrangement threshold ZPy, an over-arrangement warning instruction is generated and sent to the over-arrangement warning module.

[0032] As a further solution of the present invention: a smart management method for carbon emissions, comprising the following steps:

[0033] Step A1: The carbon emission monitoring module marks all enterprises in the carbon emission monitoring area as monitoring objects i in sequence, where i=1, ..., n, and n is a natural number;

[0034] Step A2: The carbon emission monitoring module monitors the carbon emission of object i in real time within a preset time, and marks the carbon emission TPj in chronological order, where j=1, ..., m, and m is a natural number;

[0035] Step A3: The carbon emission monitoring module sends the carbon emission TPj to the data analysis module;

[0036] Step A4: after receiving the carbon emissions TPj, the data analysis module obtains all the carbon emissions TPj of a preset number closest to the current moment, and marks them in chronological order as analysis data FXv, where v=1, ..., o, and o is a natural number;

[0037] Step A5: The data analysis module sums up all the analysis data FXv and calculates the average value, and marks the average value as the average displacement JP. Substitute the analysis data FXv and the average displacement JP into the formula The fluctuation coefficient BD is obtained, where μ is the error factor and μ=1.105;

[0038] Step A6: the data analysis module obtains the monitoring time of all carbon emissions TPj in the analysis data FXv and marks it as monitoring time JC;

[0039] Step A7: The data analysis module establishes an indicator coordinate system with the analysis data FXv as the dependent variable and the monitoring time JC as the independent variable, and forms a line graph, obtains the slope of each line segment, and marks it as the line rate value XL, sums up all the line rate values ​​XL and calculates the average value, and marks the average value as the average rate value JL;

[0040] Step A8: The data analysis module substitutes the fluctuation coefficient BD and the average value JL into the formula The variation coefficient BH is obtained, wherein k1 and k2 are preset proportional coefficients of the fluctuation coefficient BD and the average value JL respectively, and k1+k2=1, 0<k2<k1<1, k1=0.55, k2=0.45;

[0041] Step A9: The data analysis module sends the variation coefficient BH to the processor;

[0042] Step A10: The processor compares the variation coefficient BH with the variation threshold BHy:

[0043] If the change coefficient BH> the change threshold BHy, an emission reduction warning instruction is generated, and the monitoring object i corresponding to the change coefficient BH is marked as an emission reduction warning object, and the emission reduction warning instruction and the emission reduction warning object are sent to the emission reduction warning module;

[0044] If the variation coefficient BH ≤ the variation threshold BHy, the monitoring object i corresponding to the variation coefficient BH is marked as a normal object;

[0045] Step A11: The processor sums up the variation coefficients BH of all monitored objects i and obtains an average value, and marks the average value as a mean variation value JB;

[0046] Step A12: the processor obtains the total number of emission reduction warning objects and the total number of normal objects, and marks them as a subtraction value JS and a constant value CS respectively, obtains the ratio between the subtraction value JS and the constant value CS and marks it as a reduction-to-normal ratio JC;

[0047] Step A13: the processor obtains the product of the mean variation value JB and the reduction ratio JC and marks it as the comprehensive arrangement coefficient ZP;

[0048] Step A14: The processor compares the comprehensive ranking coefficient ZP with the comprehensive ranking threshold ZPy:

[0049] If the comprehensive discharge coefficient ZP> the comprehensive discharge threshold ZPy, an over-discharge warning instruction is generated and sent to the over-discharge warning module;

[0050] Step A15: After receiving the emission reduction warning instruction and the emission reduction warning object, the emission reduction warning module sends emission reduction prompt information to the emission reduction warning object, and the emission reduction prompt information includes SMS prompt, pop-up prompt and voice phone prompt;

[0051] Step A16: After receiving the over-discharge warning instruction, the over-discharge warning module sounds an over-discharge alarm bell to give a prompt.

[0052] Beneficial effects of the present invention:

[0053] The present invention provides an intelligent management platform and method for carbon emissions. The carbon emission monitoring module marks all enterprises in the carbon emission monitoring area as monitoring objects in turn, and obtains the carbon emissions of the monitoring objects. The data analysis module obtains the fluctuation coefficient and the average rate value according to the carbon emissions, and obtains the variation coefficient according to the fluctuation coefficient and the average rate value. The processor generates an emission reduction warning instruction and an over-emission warning instruction according to the variation coefficient. After receiving the emission reduction warning instruction through the emission reduction warning module, an emission reduction prompt message is sent to the emission reduction warning object. After receiving the over-emission warning instruction through the over-emission warning module, an over-emission alarm ringtone is sounded. The management method first obtains the fluctuation coefficient and the average rate value of the monitoring object. The fluctuation coefficient is used to measure the stability of the carbon emissions of the monitoring object. The larger the fluctuation coefficient, the worse the stability. The larger the fluctuation, the higher the average rate value. It is used to measure the growth of carbon emissions of the monitored objects, and the larger the average value, the greater the growth rate. Therefore, the change coefficient obtained by analyzing the two can comprehensively measure the changes in carbon emissions of the monitored objects. If the change is greater, it means that the degree of change in the carbon emissions of the monitored objects is greater, and it is urgent to remind them to reduce emissions. Then the comprehensive emission coefficient is obtained. The comprehensive emission coefficient is used to comprehensively measure the degree of growth change of all monitored objects, and the larger the comprehensive emission coefficient, the more emissions reductions are needed for the monitored objects. This management method can monitor the monitored objects individually through the change coefficient and then remind them to reduce emissions. It can monitor all monitored objects as a whole through the comprehensive emission coefficient and then remind the carbon emission monitoring area of ​​over-emissions, which can effectively reduce carbon emissions and thus significantly reduce environmental pollution. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The present invention will be further described below in conjunction with the accompanying drawings.

[0055] Figure 1 It is a principle block diagram of an intelligent management platform for carbon emissions in the present invention. DETAILED DESCRIPTION

[0056] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] Embodiment 1:

[0058] See also Figure 1 As shown, this embodiment is an intelligent management platform for carbon emissions, including the following modules: a carbon emission monitoring module, a data analysis module, a processor, an emission reduction warning module, and an over-emission warning module;

[0059] The carbon emission monitoring module is used to mark all enterprises in the carbon emission monitoring area as monitoring objects i in turn, obtain the carbon emission TPj of the monitoring object i, and send the carbon emission TPj to the data analysis module;

[0060] The data analysis module is used to obtain the fluctuation coefficient BD and the average rate value JL according to the carbon emission TPj, and obtain the variation coefficient BH according to the fluctuation coefficient BD and the average rate value JL, and send the variation coefficient BH to the processor;

[0061] Wherein, the processor is used to generate an emission reduction warning instruction and an over-emission warning instruction according to the variation coefficient BH, and send the emission reduction warning instruction to the emission reduction warning module, and send the over-emission warning instruction to the over-emission warning module;

[0062] Wherein, the emission reduction warning module is used to send emission reduction prompt information to the emission reduction warning object after receiving the emission reduction warning instruction;

[0063] Wherein, the over-displacement warning module is used to sound an over-displacement alarm bell after receiving an over-displacement warning instruction.

[0064] Embodiment 2:

[0065] See also Figure 1 As shown, this embodiment is a smart management method for carbon emissions, comprising the following steps:

[0066] Step A1: The carbon emission monitoring module marks all enterprises in the carbon emission monitoring area as monitoring objects i in sequence, where i=1, ..., n, and n is a natural number;

[0067] Step A2: The carbon emission monitoring module monitors the carbon emission of object i in real time within a preset time, and marks the carbon emission in chronological order as carbon emission TPj, where j=1, ..., m, and m is a natural number;

[0068] Step A3: The carbon emission monitoring module sends the carbon emission TPj to the data analysis module;

[0069] Step A4: after receiving the carbon emissions TPj, the data analysis module obtains all the carbon emissions TPj of a preset number closest to the current moment, and marks them in chronological order as analysis data FXv, where v=1, ..., o, and o is a natural number;

[0070] Step A5: The data analysis module sums up all the analysis data FXv and calculates the average value, and marks the average value as the average displacement JP. Substitute the analysis data FXv and the average displacement JP into the formula The fluctuation coefficient BD is obtained, where μ is the error factor and μ=1.105;

[0071] Step A6: the data analysis module obtains the monitoring time of all carbon emissions TPj in the analysis data FXv and marks it as monitoring time JC;

[0072] Step A7: The data analysis module establishes an indicator coordinate system with the analysis data FXv as the dependent variable and the monitoring time JC as the independent variable, and forms a line graph, obtains the slope of each line segment, and marks it as the line rate value XL, sums up all the line rate values ​​XL and calculates the average value, and marks the average value as the average rate value JL;

[0073] Step A8: The data analysis module substitutes the fluctuation coefficient BD and the average value JL into the formula The variation coefficient BH is obtained, wherein k1 and k2 are preset proportional coefficients of the fluctuation coefficient BD and the average value JL respectively, and k1+k2=1, 0<k2<k1<1, k1=0.55, k2=0.45;

[0074] Step A9: The data analysis module sends the variation coefficient BH to the processor;

[0075] Step A10: The processor compares the variation coefficient BH with the variation threshold BHy:

[0076] If the change coefficient BH> the change threshold BHy, an emission reduction warning instruction is generated, and the monitoring object i corresponding to the change coefficient BH is marked as an emission reduction warning object, and the emission reduction warning instruction and the emission reduction warning object are sent to the emission reduction warning module;

[0077] If the variation coefficient BH ≤ the variation threshold BHy, the monitoring object i corresponding to the variation coefficient BH is marked as a normal object;

[0078] Step A11: The processor sums up the variation coefficients BH of all monitored objects i and obtains an average value, and marks the average value as a mean variation value JB;

[0079] Step A12: the processor obtains the total number of emission reduction warning objects and the total number of normal objects, and marks them as a subtraction value JS and a constant value CS respectively, obtains the ratio between the subtraction value JS and the constant value CS and marks it as a reduction-to-normal ratio JC;

[0080] Step A13: the processor obtains the product of the mean variation value JB and the reduction ratio JC and marks it as the comprehensive arrangement coefficient ZP;

[0081] Step A14: The processor compares the comprehensive ranking coefficient ZP with the comprehensive ranking threshold ZPy:

[0082] If the comprehensive discharge coefficient ZP> the comprehensive discharge threshold ZPy, an over-discharge warning instruction is generated and sent to the over-discharge warning module;

[0083] Step A15: After receiving the emission reduction warning instruction and the emission reduction warning object, the emission reduction warning module sends emission reduction prompt information to the emission reduction warning object, and the emission reduction prompt information includes SMS prompt, pop-up prompt and voice phone prompt;

[0084] Step A16: After receiving the over-discharge warning instruction, the over-discharge warning module sounds an over-discharge alarm bell to give a prompt.

[0085] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0086] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the invention or exceed the scope defined by the claims, they shall all fall within the protection scope of the present invention.

Claims

1. An intelligent management platform for carbon emissions, characterized in that: include: The carbon emission monitoring module is used to mark all enterprises in the carbon emission monitoring area as monitoring objects in turn, obtain the carbon emissions of the monitoring objects, and send the carbon emissions to the data analysis module; A data analysis module, used to obtain a fluctuation coefficient and an average rate value according to the carbon emission, obtain a variation coefficient according to the fluctuation coefficient and the average rate value, and send the variation coefficient to a processor; The processor is used to generate an emission reduction warning instruction and an over-emission warning instruction according to the variation coefficient, and send the emission reduction warning instruction to the emission reduction warning module, and send the over-emission warning instruction to the over-emission warning module; the specific process of the processor generating the emission reduction warning instruction and the over-emission warning instruction is as follows: Compare the coefficient of variation to the variation threshold: If the change coefficient is greater than the change threshold, an emission reduction warning instruction is generated, and the monitoring object corresponding to the change coefficient is marked as an emission reduction warning object, and the emission reduction warning instruction and the emission reduction warning object are sent to the emission reduction warning module; If the variation coefficient is less than or equal to the variation threshold, the monitored object corresponding to the variation coefficient is marked as a normal object; Sum the coefficients of variation of all monitored objects and calculate the average value, and mark the average value as the mean variation value; Obtain the total number of emission reduction warning objects and the total number of normal objects, and mark them as a decrement value and a constant value respectively, obtain the ratio between the decrement value and the constant value and mark it as a decrement-to-constant ratio; Get the product of the mean variation and the normal ratio and label it as the comprehensive coefficient; Compare the ranking factor to the ranking threshold: If the comprehensive control coefficient is greater than the comprehensive control threshold, an over-control warning instruction is generated and sent to the over-control warning module; The emission reduction warning module is used to send emission reduction prompt information to the emission reduction warning object after receiving the emission reduction warning instruction; The over-discharge warning module is used to sound the over-discharge alarm bell after receiving the over-discharge warning instruction.

2. According to claim 1, the intelligent management platform for carbon emissions is characterized in that: The specific process of the carbon emission monitoring module obtaining carbon emissions is as follows: Mark all enterprises in the carbon emission monitoring area as monitoring objects in turn; Real-time monitoring of the carbon emissions of the object within a preset time period, and marking them as carbon emissions in chronological order; Send the carbon emissions to the data analysis module.

3. The intelligent management platform for carbon emissions according to claim 1 is characterized in that: The specific process of the data analysis module obtaining the coefficient of variation is as follows: After receiving the carbon emissions, all the carbon emissions of the preset number closest to the current moment are obtained, and marked as analysis data in chronological order; Sum up all the analysis data and calculate the average value, and mark the average value as the average displacement. Analyze the analysis data and the average displacement to obtain the fluctuation coefficient; Obtain the monitoring time of all carbon emissions in the analysis data and mark it as monitoring time; An indicator coordinate system is established with the analysis data as the dependent variable and the monitoring time as the independent variable, and a line graph is formed. The slope of each line segment is obtained and marked as the line rate value. All line rate values ​​are summed and averaged, and the average value is marked as the average rate value. The coefficient of variation is obtained by analyzing the coefficient of fluctuation and the average value; The coefficient of variation is sent to the processor.

4. A smart management method for carbon emissions, characterized in that: The following steps are involved: Step A1: The carbon emission monitoring module marks all enterprises in the carbon emission monitoring area as monitoring objects in turn; Step A2: The carbon emission monitoring module monitors the carbon emissions of the object in real time within a preset time period, and marks them as carbon emissions in chronological order; Step A3: The carbon emission monitoring module sends the carbon emission to the data analysis module; Step A4: after receiving the carbon emissions, the data analysis module obtains all the carbon emissions of a preset number closest to the current moment, and marks them as analysis data in chronological order; Step A5: The data analysis module sums up all the analysis data and obtains the average value, and marks the average value as the average displacement, and obtains the fluctuation coefficient by analyzing the analysis data and the average displacement; Step A6: The data analysis module obtains the monitoring time of all carbon emissions in the analysis data and marks it as monitoring time; Step A7: The data analysis module establishes an indicator coordinate system with the analysis data as the dependent variable and the monitoring time as the independent variable, and forms a line graph, obtains the slope of each line segment, and marks it as a line rate value, sums up all the line rate values ​​and calculates the average value, and marks the average value as the average rate value; Step A8: The data analysis module analyzes the fluctuation coefficient and the average value to obtain the variation coefficient; Step A9: The data analysis module sends the variation coefficient to the processor; Step A10: The processor compares the variation coefficient with the variation threshold: If the change coefficient is greater than the change threshold, an emission reduction warning instruction is generated, and the monitoring object corresponding to the change coefficient is marked as an emission reduction warning object, and the emission reduction warning instruction and the emission reduction warning object are sent to the emission reduction warning module; If the variation coefficient is less than or equal to the variation threshold, the monitored object corresponding to the variation coefficient is marked as a normal object; Step A11: The processor sums up the variation coefficients of all monitored objects and obtains an average value, and marks the average value as a mean variation value; Step A12: the processor obtains the total number of emission reduction warning objects and the total number of normal objects, and marks them as a subtraction value and a constant value respectively, obtains the ratio between the subtraction value and the constant value and marks it as a subtraction-to-constant ratio; Step A13: the processor obtains the product of the mean variation value and the reduction ratio and marks it as a comprehensive coefficient; Step A14: The processor compares the ranking coefficient with the ranking threshold: If the comprehensive control coefficient is greater than the comprehensive control threshold, an over-control warning instruction is generated and sent to the over-control warning module; Step A15: After receiving the emission reduction warning instruction and the emission reduction warning object, the emission reduction warning module sends emission reduction prompt information to the emission reduction warning object, and the emission reduction prompt information includes SMS prompt, pop-up prompt and voice phone prompt; Step A16: After receiving the over-discharge warning instruction, the over-discharge warning module sounds an over-discharge alarm bell to give a prompt.

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