Solid waste management system based on intelligent weighing scale head and portable label printer

Abstract

The invention discloses a solid waste management system based on an intelligent weighing weight head and a portable label printer, and particularly relates to the technical field of solid waste management, and the system comprises the steps: obtaining the environment abnormal information, sensor response information, software fault information and cable loosening information of the intelligent weighing weight head in a weighing process, building an intelligent weighing weight head operation monitoring model, and carrying out the operation monitoring of the intelligent weighing weight head; the method comprises the following steps: generating an intelligent weighing scale head operation monitoring index, evaluating the operation hidden danger of the intelligent weighing scale head in a weighing process, determining whether the intelligent weighing scale head has the operation hidden danger or not, timely finding and processing the potential operation hidden danger problem, improving the accuracy of weighing data, and when the intelligent weighing scale head is detected to have the operation hidden danger, improving the accuracy of weighing data. The weighing frequency and the weighing duration are adjusted through an automatic regulation and control model, delay and inconsistency of manual intervention are avoided, especially when the centralized solid waste generates a peak, the processing efficiency is improved through intelligent regulation and control, and inaccurate weighing data or system overload caused by high-load operation in a short time is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of solid waste management, and more specifically, to a solid waste management system based on an intelligent weighing scale head and a portable label printer. Background Art

[0002] With the increasingly stringent requirements for environmental protection and resource management, solid waste management systems have gradually become an important part of various industrial enterprises, public facilities and environmental protection agencies. In the process of solid waste treatment, accurate waste weighing data plays a key role in waste classification, transportation, treatment and supervision. The intelligent weighing scale can record the weight information of solid waste in real time and interact with the solid waste treatment system to ensure the traceability of the entire process of waste management. At the same time, the portable label printer automatically generates and prints the corresponding labels through wireless connection after the waste weighing is completed, indicating key information such as waste type, weight, time, etc., to facilitate subsequent classification and transportation.

[0003] In the long-term operation, the intelligent weighing scale may be affected by environmental conditions and aging of hardware equipment, resulting in accidental errors in weighing data. Such accidental errors can be eliminated by manual re-weighing. In agricultural production, solid waste (such as crop residues, animal feces, waste plastic film, etc.) has obvious seasonal characteristics. In certain periods, the amount of waste generated is very concentrated, which may cause the system to face the challenge of high-load operation in the short term, and the method of manual re-weighing may not be able to cope with centralized solid waste weighing. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a solid waste management system based on an intelligent weighing scale head and a portable label printer to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] Solid waste management system based on intelligent weighing scale and portable label printer, including scale monitoring module, scale operation evaluation module, weighing control module, and output printing module;

[0007] The weighing head monitoring module is used to obtain the environmental abnormality information, sensor response information, software fault information, and cable loosening information of the intelligent weighing head during the weighing process;

[0008] The weighing head operation evaluation module is used to build an intelligent weighing head operation monitoring model, generate an intelligent weighing head operation monitoring index, evaluate the operation risks of the intelligent weighing head during the weighing process, and determine whether the intelligent weighing head has operation risks;

[0009] The weighing control module is used to automatically control the weighing frequency and total weighing time when there are hidden dangers in the operation of the intelligent weighing head;

[0010] The output printing module is used to generate label information according to the weighing results and print the label through a portable label printer.

[0011] In a preferred embodiment, the environmental abnormality information includes a humidity fluctuation abnormality coefficient and an electromagnetic disturbance coefficient, the sensor response information includes a sensor response coefficient, the software fault information includes a memory management failure coefficient, and the cable looseness information includes a cable interface looseness coefficient.

[0012] In a preferred embodiment, the logic for obtaining the humidity fluctuation anomaly coefficient is as follows:

[0013] Obtain the environmental humidity data of the intelligent weighing scale head during the weighing process, establish a humidity data set, and mark the humidity data set as SD = {sd i}, sd i represents the i-th ambient humidity value, i={1,2,...,I}, I is a positive integer;

[0014] Calculate the i-th humidity outlier sdl i , the expression is as follows Where avg represents the average value of the ambient humidity value in the humidity data set, and the expression is as follows bzh represents the standard deviation of the ambient humidity values ​​in the humidity data set, and the expression is as follows

[0015] Compare the i-th humidity outlier with the preset humidity outlier threshold, and count the number of times c1 when the humidity outlier is greater than the humidity outlier threshold;

[0016] Calculate the humidity fluctuation anomaly coefficient sdbd, the expression is as follows

[0017] The logic for obtaining the electromagnetic disturbance coefficient is as follows:

[0018] Obtain the electromagnetic interference intensity signal of the intelligent weighing scale head in the environment during the weighing process, obtain the electromagnetic interference intensity signal time series with the electromagnetic interference intensity value as the data point, and mark the electromagnetic interference intensity signal time series as DC={dc(t)}, where dc(t) represents the electromagnetic interference intensity value at the tth time point;

[0019] Select the wavelet basis function ψ(t) to perform continuous wavelet transform on the electromagnetic interference intensity signal time series. The transformation formula is as follows Where W(a,b) represents the wavelet coefficient, a represents the scale parameter used to control the degree of expansion and contraction of the wavelet, and b represents the displacement parameter used to control the translation on the time axis;

[0020] The energy E(a1,a2) of the electromagnetic interference intensity signal is obtained within the preset scale range [a1,a2]. The acquisition formula is as follows:

[0021] Calculate the overall energy E of the electromagnetic interference intensity signal total , the expression is as follows

[0022] Calculate the electromagnetic disturbance coefficient dcrd, the expression is as follows

[0023] In a preferred embodiment, the logic for obtaining the sensor response coefficient is as follows:

[0024] Obtain the initial time T1 when the weighing sensor receives the weighing signal and the end time T2 when the weighing sensor outputs the weighing result, and calculate the response time TX, the expression is as follows TX = T2-T1;

[0025] The response time of the weighing sensor is calculated each time it receives the weighing signal to establish a response time sequence, and the response time sequence is marked as XY = {TX j}, TX j represents the response time calculated when the weighing sensor receives the weighing signal for the jth time, j = {1, 2, ..., J};

[0026] Compare the response time with the preset response time threshold, mark the response time greater than the response time threshold as the delay time TY, and establish the delay time sequence YC = {TY n}, TY n Indicates the order number of the nth occurrence of the response time being greater than the response time threshold, n = {2, 3, ..., N}, N is a positive integer;

[0027] The response time threshold is used as the initial smoothing value, and the delay time series is exponentially smoothed to calculate the smoothing value phz n , the expression is as follows phz n =α*TY n +(1-α)*phz n-1 , where α represents the smoothing coefficient, phz n-1 represents the initial smoothing value;

[0028] The smoothing value can be used to measure the degree of deviation of the delay time from the response time threshold. By calculating the smoothing value, the delay problem and response instability of the sensor can be indicated;

[0029] Calculate the sensor response coefficient cgqx, the expression is as follows

[0030] In a preferred implementation, the logic for obtaining the memory management failure coefficient is as follows:

[0031] Get the memory fragmentation count SL when the intelligent weighing scale is running, and calculate the fragmentation rate Where ZN represents the total memory;

[0032] Get the memory leakage amount NC, the number of memory allocation failures CS, the total memory usage CSY, and calculate the memory management failure coefficient ncgl. The expression is as follows

[0033] In a preferred embodiment, the logic for obtaining the cable interface looseness coefficient is as follows:

[0034] The vibration amplitude value fdu of the intelligent weighing head during operation is obtained through the vibration sensor, and the displacement wyl of the cable interface of the intelligent weighing head is measured through the displacement sensor to calculate the looseness coefficient of the cable interface In the formula wyl m Indicates the cable interface displacement of the mth cable interface.

[0035] In a preferred embodiment, the acquired humidity fluctuation anomaly coefficient, electromagnetic disturbance coefficient, sensor response coefficient, memory management failure coefficient, and cable interface looseness coefficient are normalized, and an intelligent weighing scale head operation monitoring model is constructed according to the normalized humidity fluctuation anomaly coefficient, electromagnetic disturbance coefficient, sensor response coefficient, memory management failure coefficient, and cable interface looseness coefficient to generate an intelligent weighing scale head operation monitoring index zncz, according to the following formula: Wherein a1, a2, a3, a4, and a5 represent preset proportional coefficients of humidity fluctuation anomaly coefficient, electromagnetic disturbance coefficient, sensor response coefficient, memory management failure coefficient, and cable interface looseness coefficient, respectively, and a1, a2, a3, a4, and a5 are all greater than 0.

[0036] In a preferred embodiment, the operation monitoring index of the smart weighing pound head is compared with a preset smart weighing pound head operation monitoring index threshold. If the operation monitoring index of the smart weighing pound head is greater than the smart weighing pound head operation monitoring index threshold, an abnormal operation signal is generated; if the operation monitoring index of the smart weighing pound head is less than or equal to the smart weighing pound head operation monitoring index threshold, a stable operation signal is generated.

[0037] In a preferred embodiment, when an abnormal operation signal is generated, the weighing frequency and the total weighing time of the same solid waste are automatically regulated as follows:

[0038] A1. The intelligent weighing scale head operation monitoring index and the intelligent weighing scale head operation monitoring index threshold are used as the control factors of the total weighing time to control the total weighing time of the same solid waste. The control function of the total weighing time is as follows: The function output is the total weighing time after regulation, czs represents the total weighing time before regulation, zncz z Indicates the threshold value of the intelligent weighing head operation monitoring index;

[0039] A2, according to the adjusted total weighing time and the initial weighing frequency, the same solid waste is weighed multiple times, and the operation status of the intelligent weighing head is evaluated in each weighing process according to the intelligent weighing head operation monitoring model, and the generated intelligent weighing head operation monitoring index is added to the data analysis set, and the data analysis set is marked as sjjh = {zncz y}, zncz y Represents the intelligent weighing head operation monitoring index generated by the y-th weighing process, y = {1,2,...,Y}; calculates the standard deviation qfr of the intelligent weighing head operation monitoring index in the data analysis set, and uses the standard deviation qfr of the intelligent weighing head operation monitoring index in the data analysis set as the regulating factor of the weighing frequency, and regulates the weighing frequency of the same solid waste. The regulating function of the weighing frequency is as follows: f(czp) = ln(1+qfr*czp), and the function output is the weighing frequency after regulation, and czp represents the weighing frequency before regulation.

[0040] Technical effects and advantages of the present invention:

[0041] 1. The present invention obtains environmental abnormality information, sensor response information, software fault information, and cable looseness information of the intelligent weighing head during the weighing process, constructs an intelligent weighing head operation monitoring model, generates an intelligent weighing head operation monitoring index, evaluates the operational risks of the intelligent weighing head during the weighing process, determines whether the intelligent weighing head has operational risks, promptly discovers and handles potential operational risk problems, improves the accuracy of weighing data, and when it is detected that the intelligent weighing head has operational risks, adjusts the weighing frequency and weighing time through an automated control model, avoiding delays and inconsistencies in manual intervention, especially when facing a peak in centralized solid waste generation, improves processing efficiency through intelligent control, and avoids inaccurate weighing data or system overload due to high-load operation in a short period of time. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0043] Figure 1 Schematic diagram of the structure of the system of the embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in 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.

[0045] Embodiment: The present invention provides as Figure 1 The solid waste management system based on the intelligent weighing scale head and portable label printer shown includes a scale head monitoring module, a scale head operation evaluation module, a weighing control module, and an output printing module;

[0046] The weighing head monitoring module is used to obtain the environmental abnormality information, sensor response information, software fault information, and cable loosening information of the intelligent weighing head during the weighing process;

[0047] The weighing head operation evaluation module is used to build an intelligent weighing head operation monitoring model, generate an intelligent weighing head operation monitoring index, evaluate the operation risks of the intelligent weighing head during the weighing process, and determine whether the intelligent weighing head has operation risks;

[0048] The weighing control module is used to automatically control the weighing frequency and total weighing time when there are hidden dangers in the operation of the intelligent weighing head;

[0049] An output printing module is used to generate label information according to the weighing result and print the label through a portable label printer;

[0050] The weighing head monitoring module is used to obtain the environmental abnormality information, sensor response information, software fault information, and cable loosening information of the intelligent weighing head during the weighing process;

[0051] The environmental abnormality information includes humidity fluctuation abnormality coefficient and electromagnetic disturbance coefficient, the sensor response information includes sensor response coefficient, the software fault information includes memory management failure coefficient, and the cable looseness information includes cable interface looseness coefficient;

[0052] The humidity fluctuation anomaly coefficient is used to measure whether there is an abnormal fluctuation in the ambient humidity of the intelligent weighing head during the weighing process. Humidity fluctuations may have an adverse effect on the performance of the weighing head, resulting in a decrease in the accuracy of the weighing data. The humidity fluctuation anomaly coefficient can be used to evaluate the extent of this adverse effect. The larger the humidity fluctuation anomaly coefficient, the greater the adverse effect of humidity fluctuations on the performance of the intelligent weighing head, the greater the probability that the intelligent weighing head has operating hazards, and the greater the degree of reduction in the accuracy of the weighing data. Conversely, the smaller the humidity fluctuation anomaly coefficient, the smaller the impact of environmental humidity fluctuations on the performance of the intelligent weighing head. At this time, the stability and accuracy of the weighing data are relatively high, and the probability of operating hazards of the intelligent weighing head is low.

[0053] The logic for obtaining the humidity fluctuation anomaly coefficient is as follows:

[0054] Obtain the environmental humidity data of the intelligent weighing scale head during the weighing process, establish a humidity data set, and mark the humidity data set as SD = {sd i}, sd i represents the i-th ambient humidity value, i={1,2,...,I}, I is a positive integer;

[0055] It should be noted that by selecting appropriate humidity sensors, such as capacitive humidity sensors and resistive humidity sensors, these sensors can measure the ambient humidity in real time and provide data. Humidity sensors can be installed near the weighing head or in the weighing area to accurately capture humidity data.

[0056] Calculate the i-th humidity outlier sdl i , the expression is as follows Where avg represents the average value of the ambient humidity value in the humidity data set, and the expression is as follows bzh represents the standard deviation of the ambient humidity values ​​in the humidity data set, and the expression is as follows

[0057] Compare the i-th humidity outlier with the preset humidity outlier threshold, and count the number of times c1 when the humidity outlier is greater than the humidity outlier threshold;

[0058] Calculate the humidity fluctuation anomaly coefficient sdbd, the expression is as follows

[0059] The electromagnetic disturbance coefficient is used to measure the degree of disturbance of the intelligent weighing head by electromagnetic interference during operation. These electromagnetic interferences may cause signal distortion or noise increase of the intelligent weighing head, thereby affecting the accuracy and stability of the weighing data. The larger the electromagnetic disturbance coefficient, the more it indicates that the electromagnetic interference has a significant adverse effect on the performance of the intelligent weighing head. In this case, the interference may cause an increase in the error of the weighing data, aggravate the instability of the weighing results, and the intelligent weighing head has a higher probability of operating hidden dangers. The smaller the electromagnetic disturbance coefficient, the less impact the electromagnetic interference has on the performance of the intelligent weighing head. At this time, the stability and accuracy of the weighing data are relatively high, the probability of operating hidden dangers of the intelligent weighing head is low, and the impact of electromagnetic interference on the intelligent weighing head is within an acceptable range.

[0060] The logic for obtaining the electromagnetic disturbance coefficient is as follows:

[0061] Obtain the electromagnetic interference intensity signal of the intelligent weighing scale head in the environment during the weighing process, obtain the electromagnetic interference intensity signal time series with the electromagnetic interference intensity value as the data point, and mark the electromagnetic interference intensity signal time series as DC={dc(t)}, where dc(t) represents the electromagnetic interference intensity value at the tth time point;

[0062] It should be noted that the electromagnetic interference intensity value in the environment where the intelligent weighing scale head is located can be obtained in real time through the electromagnetic interference detector;

[0063] Select the wavelet basis function ψ(t) to perform continuous wavelet transform on the electromagnetic interference intensity signal time series. The transformation formula is as follows Where W(a,b) represents the wavelet coefficient, a represents the scale parameter used to control the degree of expansion and contraction of the wavelet, and b represents the displacement parameter used to control the translation on the time axis;

[0064] It should be noted that the parameters a and b are usually determined by the selected wavelet basis function. Common wavelet basis functions include Mor l et wavelet, Daubech ies wavelet, etc.

[0065] The energy E(a1,a2) of the electromagnetic interference intensity signal is obtained within the preset scale range [a1,a2]. The acquisition formula is as follows:

[0066] Electromagnetic interference is usually concentrated in certain specific frequency ranges. By setting the scale range [a1, a2], the frequency information of the electromagnetic interference signal can be mapped to the scale information in the wavelet transform, so as to identify which parts of the signal are in the frequency band of the electromagnetic interference. This helps to determine the frequency characteristics of the interference and the scope of its influence. Extracting the energy within a specific scale range can quantify the interference intensity within the frequency band. By calculating the energy within this range, the intensity and impact of the electromagnetic interference within a specific frequency band can be understood, which is very important for evaluating the severity of the interference and formulating corresponding interference suppression measures.

[0067] Calculate the overall energy E of the electromagnetic interference intensity signal total , the expression is as follows

[0068] Calculate the electromagnetic disturbance coefficient dcrd, the expression is as follows

[0069] The sensor response coefficient is used to measure the response performance of the weighing sensor in the intelligent weighing head to the weighing signal. It reflects whether there is a delay in the response speed of the weighing sensor after receiving the weighing signal and the degree of the delay. The sensor response coefficient is a key indicator for evaluating the performance of the weighing sensor in the intelligent weighing head. It combines the response speed, accuracy and stability to help determine whether the weighing sensor can reliably perform weighing operations. By monitoring and analyzing the sensor response coefficient, the performance of the system can be optimized to ensure the accuracy of the data and the stability of the equipment. The smaller the sensor response coefficient, the higher the response performance of the weighing sensor, the lower the probability of delay, and the probability of operating hidden dangers in the intelligent weighing head is lower. Conversely, the larger the sensor response coefficient, the worse the response performance of the weighing sensor, the higher the probability of delay, and the higher the probability of operating hidden dangers in the intelligent weighing head.

[0070] The logic for obtaining the sensor response coefficient is as follows:

[0071] Obtain the initial time T1 when the weighing sensor receives the weighing signal and the end time T2 when the weighing sensor outputs the weighing result, and calculate the response time TX, the expression is as follows TX = T2-T1;

[0072] The response time of the weighing sensor is calculated each time it receives the weighing signal to establish a response time sequence, and the response time sequence is marked as XY = {TX j}, TX j represents the response time calculated when the weighing sensor receives the weighing signal for the jth time, j = {1, 2, ..., J};

[0073] Compare the response time with the preset response time threshold, mark the response time greater than the response time threshold as the delay time TY, and establish the delay time sequence YC = {TY n}, TY n Indicates the order number of the nth occurrence of the response time being greater than the response time threshold, n = {2, 3, ..., N}, N is a positive integer;

[0074] The response time threshold is used as the initial smoothing value, and the delay time series is exponentially smoothed to calculate the smoothing value phz n , the expression is as follows phz n =α*TY n +(1-α)*phz n-1 , where α represents the smoothing coefficient, phz n-1 represents the initial smoothing value;

[0075] It should be noted that α is used to control the emphasis on the latest delay time, and the most suitable α value for the delay time series can be selected by technicians in this field according to actual conditions;

[0076] The smoothing value can be used to measure the degree of deviation of the delay time from the response time threshold. By calculating the smoothing value, the delay problem and response instability of the sensor can be indicated;

[0077] Calculate the sensor response coefficient cgqx, the expression is as follows

[0078] The memory management failure coefficient is used to measure the effectiveness and stability of memory management of the smart weighing scale during operation. Memory management failure usually refers to problems such as unreasonable memory resource allocation, memory leakage, or memory fragmentation. These problems may cause the performance of the smart weighing scale to degrade, respond slowly, or fail. The memory management failure coefficient can quantify the severity of these problems and evaluate their impact on the overall performance of the smart weighing scale. The larger the memory management failure coefficient, the higher the operating risk of the smart weighing scale. The smaller the memory management failure coefficient, the lower the probability of the existence of operating risks of the smart weighing scale.

[0079] The logic for obtaining the memory management failure coefficient is as follows:

[0080] Get the memory fragmentation count SL when the intelligent weighing scale is running, and calculate the fragmentation rate Where ZN represents the total memory;

[0081] Get the memory leakage amount NC, the number of memory allocation failures CS, the total memory usage CSY, and calculate the memory management failure coefficient ncgl. The expression is as follows

[0082] It should be noted that before calculating the memory management failure coefficient, the amount of memory leakage, the number of memory allocation failures, the total memory usage, and the fragmentation rate need to be normalized. Common normalization methods include Min-Max normalization and Z-Score normalization, which will not be described in detail again.

[0083] Memory leak refers to the total amount of unreleased memory, memory allocation failures refer to the number of memory allocation failures, memory fragmentation occurs when the requested memory size is smaller than the allocated memory block size during memory block allocation, resulting in some unused space in the memory block. Since more memory blocks are allocated than actually required, the remaining unused memory space is wasted;

[0084] It should be noted that the memory monitoring tool can be used to obtain the memory usage of the smart weighing scale, including the number of memory fragmentation, total memory, memory leakage, number of memory allocation failures, and total memory usage;

[0085] The cable interface looseness coefficient is used to measure the degree of looseness of the interface of the intelligent weighing scale head connected to the cable due to vibration, collision or improper installation during operation, resulting in signal transmission interruption or poor contact, which may lead to errors in weighing data. The main functions of the cable interface looseness coefficient include evaluating the looseness of the interface connecting the cable, warning of potential problems and performing maintenance. The larger the cable interface looseness coefficient, the greater the degree of looseness of the cable connected to the intelligent weighing scale head during operation, and the higher the probability of operation hidden dangers of the intelligent weighing scale head. Conversely, the smaller the cable interface looseness coefficient, the smaller the degree of looseness of the cable connected to the intelligent weighing scale head during operation, and the lower the probability of operation hidden dangers of the intelligent weighing scale head.

[0086] The logic for obtaining the cable interface looseness coefficient is as follows:

[0087] The vibration amplitude value fdu of the intelligent weighing head during operation is obtained through the vibration sensor, and the displacement wyl of the cable interface of the intelligent weighing head is measured through the displacement sensor to calculate the looseness coefficient of the cable interface In the formula wyl m represents the cable interface displacement of the mth cable interface;

[0088] It should be noted that the vibration amplitude and the cable interface displacement need to be normalized before calculating the cable interface looseness coefficient, which will not be elaborated again;

[0089] The weighing head operation evaluation module is used to build an intelligent weighing head operation monitoring model, generate an intelligent weighing head operation monitoring index, evaluate the operation risks of the intelligent weighing head during the weighing process, and determine whether the intelligent weighing head has operation risks;

[0090] The obtained humidity fluctuation anomaly coefficient, electromagnetic disturbance coefficient, sensor response coefficient, memory management failure coefficient, and cable interface looseness coefficient are normalized, and the intelligent weighing head operation monitoring model is constructed according to the normalized humidity fluctuation anomaly coefficient, electromagnetic disturbance coefficient, sensor response coefficient, memory management failure coefficient, and cable interface looseness coefficient to generate the intelligent weighing head operation monitoring index zncz. The formula is as follows: Wherein, a1, a2, a3, a4, and a5 represent preset proportional coefficients of humidity fluctuation anomaly coefficient, electromagnetic disturbance coefficient, sensor response coefficient, memory management failure coefficient, and cable interface looseness coefficient, respectively, and a1, a2, a3, a4, and a5 are all greater than 0;

[0091] It should be noted that a1, a2, a3, a4, and a5 are set according to actual conditions. For example, the expert empowerment method is adopted, that is, experts in related fields are invited to determine the preset proportion coefficients of various indicators through professional opinion surveys and comprehensive evaluations. In addition, various methods such as hierarchical analysis method and fuzzy comprehensive evaluation method can also be considered to determine the preset proportion coefficients to ensure the objectivity and scientificity of the preset proportion coefficients, which will not be elaborated here.

[0092] It can be seen from the above calculation expression that the greater the humidity fluctuation anomaly coefficient, the greater the electromagnetic disturbance coefficient, the greater the sensor response coefficient, the greater the memory management failure coefficient, and the greater the cable interface loose coefficient, the greater the intelligent weighing head operation monitoring index, indicating that the operation state of the intelligent weighing head is more unstable; conversely, the smaller the humidity fluctuation anomaly coefficient, the smaller the electromagnetic disturbance coefficient, the smaller the sensor response coefficient, the smaller the memory management failure coefficient, and the smaller the cable interface loose coefficient, the smaller the intelligent weighing head operation monitoring index, indicating that the operation state of the intelligent weighing head is more stable;

[0093] The operation monitoring index of the intelligent weighing pound head is compared with the preset operation monitoring index threshold of the intelligent weighing pound head. If the operation monitoring index of the intelligent weighing pound head is greater than the operation monitoring index threshold of the intelligent weighing pound head, it indicates that the operation state of the intelligent weighing pound head has exceeded the stable operation range, there is an operation risk, and an abnormal operation signal is generated; if the operation monitoring index of the intelligent weighing pound head is less than or equal to the operation monitoring index threshold of the intelligent weighing pound head, it indicates that the operation state of the intelligent weighing pound head is within the normal controllable range, and an operation stable signal is generated;

[0094] The weighing control module is used to automatically control the weighing frequency and total weighing time when there are hidden dangers in the operation of the intelligent weighing head;

[0095] When an abnormal operation signal is generated, the weighing frequency and total weighing time of the same solid waste are automatically regulated as follows:

[0096] A1. The intelligent weighing scale head operation monitoring index and the intelligent weighing scale head operation monitoring index threshold are used as the control factors of the total weighing time to control the total weighing time of the same solid waste. The control function of the total weighing time is as follows: The function output is the total weighing time after regulation, czs represents the total weighing time before regulation, zncz z Indicates the threshold value of the intelligent weighing head operation monitoring index;

[0097] Weighing frequency refers to the number of times the intelligent weighing scale weighs the same solid waste within the total weighing time;

[0098] A2, according to the adjusted total weighing time and the initial weighing frequency, the same solid waste is weighed multiple times, and the operation status of the intelligent weighing head is evaluated in each weighing process according to the intelligent weighing head operation monitoring model, and the generated intelligent weighing head operation monitoring index is added to the data analysis set, and the data analysis set is marked as sjjh = {zncz y}, zncz y represents the intelligent weighing head operation monitoring index generated by the yth weighing process, y = {1, 2, ..., Y}; calculates the standard deviation qfr of the intelligent weighing head operation monitoring index in the data analysis set, and uses the standard deviation qfr of the intelligent weighing head operation monitoring index in the data analysis set as the regulating factor of the weighing frequency, and regulates the weighing frequency of the same solid waste. The regulating function of the weighing frequency is as follows: f(czp) = ln(1+qfr*czp), and the function output is the weighing frequency after regulation, and czp represents the weighing frequency before regulation;

[0099] The output printing module is used to generate label information according to the weighing results and print the label through a portable label printer, as follows:

[0100] The final weighing result obtained from the weighing control module includes data such as the actual weight of the solid waste, the total weighing time, and the weighing frequency;

[0101] Generate label content based on the final weighing result. The label usually includes:

[0102] Solid Waste Number: A unique identifier for solid waste.

[0103] Weighing date and time: The specific time of the weighing operation.

[0104] Final weighing weight: the actual weight of the solid waste.

[0105] Total weighing time: The total time of the weighing operation.

[0106] Weighing frequency: The number of weighings performed within a specified time.

[0107] Other relevant information: such as solid waste type, treatment requirements, operator information, etc.

[0108] Label format: The label format can be text, barcode, QR code, etc. Standardized label templates can be designed according to needs;

[0109] Send label information to printer: Send the generated label information to a portable label printer. Usually it can be transmitted through the following methods: Bluetooth, wired network, wireless network, etc.

[0110] Print labels: Use a portable label printer to print out label information. The printer will print the label content on label paper according to the preset format;

[0111] Ensure that the label information is accurate and attach the printed label to the solid waste container to complete the identification work of the weighing operation;

[0112] The present invention acquires environmental abnormality information, sensor response information, software fault information, and cable looseness information of the intelligent weighing head during the weighing process, constructs an intelligent weighing head operation monitoring model, generates an intelligent weighing head operation monitoring index, evaluates the operational risks of the intelligent weighing head during the weighing process, determines whether the intelligent weighing head has operational risks, promptly discovers and handles potential operational risk problems, improves the accuracy of weighing data, and when it is detected that the intelligent weighing head has operational risks, adjusts the weighing frequency and weighing time through an automated control model, avoiding delays and inconsistencies in manual intervention. Especially when facing a peak in centralized solid waste generation, intelligent control is used to improve processing efficiency, avoiding inaccurate weighing data or system overload due to high-load operation in a short period of time.

[0113] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0114] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0115] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0116] 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 can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0117] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A solid waste management system based on an intelligent weighing scale and a portable label printer, characterized by: It includes weighing head monitoring module, weighing head operation evaluation module, weighing control module and output printing module; The weighing head monitoring module is used to obtain the environmental abnormality information, sensor response information, software fault information, and cable loosening information of the intelligent weighing head during the weighing process; The weighing head operation evaluation module is used to build an intelligent weighing head operation monitoring model, generate an intelligent weighing head operation monitoring index, evaluate the operation risks of the intelligent weighing head during the weighing process, and determine whether the intelligent weighing head has operation risks; The weighing control module is used to automatically control the weighing frequency and total weighing time when there are hidden dangers in the operation of the intelligent weighing head; The output printing module is used to generate label information according to the weighing results and print the label through a portable label printer.

2. The solid waste management system based on the intelligent weighing scale and portable label printer according to claim 1 is characterized in that: The environmental anomaly information includes a humidity fluctuation anomaly coefficient and an electromagnetic disturbance coefficient, the sensor response information includes a sensor response coefficient, the software fault information includes a memory management failure coefficient, and the cable looseness information includes a cable interface looseness coefficient.

3. The solid waste management system based on the intelligent weighing scale and portable label printer according to claim 2 is characterized in that: The logic for obtaining the humidity fluctuation anomaly coefficient is as follows: Obtain the environmental humidity data of the intelligent weighing scale head during the weighing process, establish a humidity data set, and mark the humidity data set as SD = {sd i }, sd i represents the i-th ambient humidity value, i={1,2,...,I}, I is a positive integer; Calculate the i-th humidity outlier sdl i , the expression is as follows Where avg represents the average value of the ambient humidity value in the humidity data set, and the expression is as follows bzh represents the standard deviation of the ambient humidity values ​​in the humidity data set, and the expression is as follows Compare the i-th humidity outlier with the preset humidity outlier threshold, and count the number of times c1 that the humidity outlier is greater than the humidity outlier threshold; Calculate the humidity fluctuation anomaly coefficient sdbd, the expression is as follows The logic for obtaining the electromagnetic disturbance coefficient is as follows: Obtain the electromagnetic interference intensity signal of the intelligent weighing scale head in the environment during the weighing process, obtain the electromagnetic interference intensity signal time series with the electromagnetic interference intensity value as the data point, and mark the electromagnetic interference intensity signal time series as DC={dc(t)}, where dc(t) represents the electromagnetic interference intensity value at the tth time point; Select the wavelet basis function ψ(t) to perform continuous wavelet transform on the electromagnetic interference intensity signal time series. The transformation formula is as follows Where W(a,b) represents the wavelet coefficient, a represents the scale parameter used to control the degree of expansion and contraction of the wavelet, and b represents the displacement parameter used to control the translation on the time axis; The energy E(a1,a2) of the electromagnetic interference intensity signal is obtained within the preset scale range [a1,a2]. The acquisition formula is as follows: Calculate the overall energy E of the electromagnetic interference intensity signal total , the expression is as follows Calculate the electromagnetic disturbance coefficient dcrd, the expression is as follows 4. The solid waste management system based on the intelligent weighing scale and portable label printer according to claim 2 is characterized in that: The logic for obtaining the sensor response coefficient is as follows: Obtain the initial time T1 when the weighing sensor receives the weighing signal and the end time T2 when the weighing sensor outputs the weighing result, and calculate the response time TX, the expression is as follows TX = T2-T1; The response time of the weighing sensor is calculated each time it receives the weighing signal to establish a response time sequence, and the response time sequence is marked as XY = {TX j }, TX j represents the response time calculated when the weighing sensor receives the weighing signal for the jth time, j = {1, 2, ..., J}; Compare the response time with the preset response time threshold, mark the response time greater than the response time threshold as the delay time TY, and establish the delay time sequence YC = {TY n }, TY n Indicates the order number of the nth occurrence of the response time being greater than the response time threshold, n = {2, 3, ..., N}, N is a positive integer; The response time threshold is used as the initial smoothing value, and the delay time series is exponentially smoothed to calculate the smoothing value phz n , the expression is as follows phz n =α*TY n +(1-α)*phz n-1 , where α represents the smoothing coefficient, phz n-1 represents the initial smoothing value; The smoothing value can be used to measure the degree of deviation of the delay time from the response time threshold. By calculating the smoothing value, the delay problem and response instability of the sensor can be indicated; Calculate the sensor response coefficient cgqx, the expression is as follows 5. The solid waste management system based on the intelligent weighing scale and portable label printer according to claim 2 is characterized in that: The logic for obtaining the memory management failure coefficient is as follows: Get the memory fragmentation count SL when the intelligent weighing scale is running, and calculate the fragmentation rate Where ZN represents the total memory; Get the memory leakage amount NC, the number of memory allocation failures CS, the total memory usage CSY, and calculate the memory management failure coefficient ncgl. The expression is as follows 6. The solid waste management system based on the intelligent weighing scale and portable label printer according to claim 2 is characterized in that: The logic for obtaining the cable interface looseness coefficient is as follows: The vibration amplitude value fdu of the intelligent weighing head during operation is obtained through the vibration sensor, and the displacement wyl of the cable interface of the intelligent weighing head is measured through the displacement sensor to calculate the looseness coefficient of the cable interface In the formula wyl m Indicates the cable interface displacement of the mth cable interface.

7. The solid waste management system based on the intelligent weighing scale and portable label printer according to claim 6 is characterized in that: The obtained humidity fluctuation anomaly coefficient, electromagnetic disturbance coefficient, sensor response coefficient, memory management failure coefficient, and cable interface looseness coefficient are normalized, and the intelligent weighing head operation monitoring model is constructed according to the normalized humidity fluctuation anomaly coefficient, electromagnetic disturbance coefficient, sensor response coefficient, memory management failure coefficient, and cable interface looseness coefficient to generate the intelligent weighing head operation monitoring index zncz. The formula is as follows: Wherein a1, a2, a3, a4, and a5 represent preset proportional coefficients of humidity fluctuation anomaly coefficient, electromagnetic disturbance coefficient, sensor response coefficient, memory management failure coefficient, and cable interface looseness coefficient, respectively, and a1, a2, a3, a4, and a5 are all greater than 0.

8. The solid waste management system based on the intelligent weighing scale and portable label printer according to claim 7 is characterized in that: The operation monitoring index of the smart weighing pound head is compared with the preset smart weighing pound head operation monitoring index threshold. If the operation monitoring index of the smart weighing pound head is greater than the smart weighing pound head operation monitoring index threshold, an abnormal operation signal is generated; if the operation monitoring index of the smart weighing pound head is less than or equal to the smart weighing pound head operation monitoring index threshold, a stable operation signal is generated.

9. The solid waste management system based on the intelligent weighing scale and portable label printer according to claim 8 is characterized in that: When an abnormal operation signal is generated, the weighing frequency and total weighing time of the same solid waste are automatically regulated as follows: A1. The intelligent weighing scale head operation monitoring index and the intelligent weighing scale head operation monitoring index threshold are used as the control factors of the total weighing time to control the total weighing time of the same solid waste. The control function of the total weighing time is as follows: The function output is the total weighing time after regulation, czs represents the total weighing time before regulation, zncz z Indicates the threshold value of the intelligent weighing head operation monitoring index; A2, according to the adjusted total weighing time and the initial weighing frequency, the same solid waste is weighed multiple times, and the operation status of the intelligent weighing head is evaluated in each weighing process according to the intelligent weighing head operation monitoring model, and the generated intelligent weighing head operation monitoring index is added to the data analysis set, and the data analysis set is marked as sjjh = {zncz y }, zncz y Represents the intelligent weighing head operation monitoring index generated by the y-th weighing process, y = {1,2,...,Y}; calculates the standard deviation qfr of the intelligent weighing head operation monitoring index in the data analysis set, and uses the standard deviation qfr of the intelligent weighing head operation monitoring index in the data analysis set as the regulating factor of the weighing frequency, and regulates the weighing frequency of the same solid waste. The regulating function of the weighing frequency is as follows: f(czp) = ln(1+qfr*czp), and the function output is the weighing frequency after regulation, and czp represents the weighing frequency before regulation.

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