Remote control system and method for sludge low-temperature drying machine

Abstract

The present invention discloses a remote control system and method for a sludge low-temperature dryer, which relates to the field of heat pump drying. It solves the problem of poor control effect existing in the remote control system of the existing sludge low-temperature dryer. It includes a data acquisition module: respectively acquiring the cumulative sludge drying duration, the sludge moisture content periodic monitoring coefficient, and the sludge humidity periodic monitoring coefficient to obtain the drying mesh belt operation monitoring data; a data analysis module: used to obtain the dryer working state analysis data by analyzing the cumulative sludge drying duration, the sludge moisture content periodic monitoring coefficient, and the sludge humidity periodic monitoring coefficient; a control and warning module: used to remotely control and warn the low-temperature dryer according to the dryer working state monitoring data. The present invention can improve the automatic control level of the low-temperature dryer.

Description

Technical Field

[0001] The present invention belongs to the field of heat pump drying, relates to remote control technology, and specifically is a remote control system and method for a sludge low-temperature dryer. Background Art

[0002] When the existing remote control system for a sludge low-temperature dryer conducts remote control, it has the following specific defects:

[0003] The existing remote control system for a sludge low-temperature dryer requires manual remote control of the drying equipment and cannot perform automatic drying pre-alarm, resulting in low control efficiency;

[0004] The existing sludge low-temperature dryer can only monitor the real-time moisture content and humidity data in a specific sensor area and cannot perform periodic analysis on the moisture content data and humidity data within a period, resulting in inaccurate feedback of sludge drying data.

[0005] Therefore, we propose a remote control system and method for a sludge low-temperature dryer. Summary of the Invention

[0006] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to provide a remote control system and method for a sludge low-temperature dryer, and the present invention aims to improve the accuracy and automation degree of the remote control system for a sludge low-temperature dryer.

[0007] To achieve the above purpose, the present invention adopts the following technical solution: A remote control system for a sludge low-temperature dryer, and the specific working processes of each module are as follows:

[0008] Data acquisition module: Used to monitor the operation of the drying mesh belt of the low-temperature dryer, and respectively acquire the cumulative sludge drying duration, sludge moisture content period monitoring coefficient, and sludge humidity period monitoring coefficient to obtain the operation monitoring data of the drying mesh belt;

[0009] Data analysis module: Used to obtain the working state monitoring coefficient corresponding to the low-temperature dryer by analyzing the cumulative sludge drying duration, sludge moisture content period monitoring coefficient, and sludge humidity period monitoring coefficient, respectively acquire the first working state monitoring threshold and the second working state monitoring threshold, compare them with the working state monitoring coefficient numerically, and obtain the working state monitoring data of the dryer according to the numerical comparison result to obtain the working state analysis data of the dryer;

[0010] Control and warning module: Used to remotely control and give warnings to the low-temperature dryer according to the working state monitoring data of the dryer.

[0011] Furthermore, the data acquisition module acquires the operation monitoring data of the drying mesh belt as follows:

[0012] Divide the sludge that needs to be dried by the low-temperature sludge dryer into several different drying batches, and arbitrarily select one drying batch from the several divided drying batches as the sample drying batch;

[0013] During the process of the low-temperature dryer drying the sludge of the sample drying batch, mark the time value corresponding to the current moment as the first characteristic time value. In the time period before the first characteristic time value, select a second characteristic time value, and mark the time period between the first characteristic time value and the second characteristic time value as the real-time drying monitoring period;

[0014] Obtain the time value when the low-temperature dryer starts to dry the sludge of the sample drying batch, get the drying start time value, calculate the difference between the drying start time value and the first characteristic time value, and take the absolute value of the obtained difference to get the cumulative sludge drying duration;

[0015] In the real-time drying monitoring period, mark several characteristic monitoring time points with equal time intervals, and name the several marked characteristic time monitoring points as the first characteristic monitoring time point to the a-th characteristic monitoring time point in chronological order;

[0016] Divide the drying mesh belt loaded with the sludge of the sample drying batch into several mesh belt areas with equal areas, and select one mesh belt area from the several divided mesh belt areas as the mesh belt characteristic area;

[0017] Monitor the moisture content of the sludge of the sample drying batch in the real-time drying monitoring period to obtain the sludge moisture content periodic monitoring coefficient;

[0018] Monitor the humidity of the sludge of the sample drying batch in the real-time drying monitoring period to obtain the sludge humidity periodic monitoring coefficient;

[0019] Define the cumulative sludge drying duration, the sludge moisture content periodic monitoring coefficient, and the sludge humidity periodic monitoring coefficient as the drying mesh belt operation monitoring data.

[0020] Further, the data acquisition module obtains the sludge moisture content periodic monitoring coefficient as follows:

[0021] Obtain the moisture content of the sludge corresponding to the first characteristic monitoring time point in the mesh belt characteristic area to get the first sludge moisture content;

[0022] Respectively obtain the moisture content of the sludge corresponding to the second characteristic monitoring time point to the a-th characteristic monitoring time point in the mesh belt characteristic area to get the second sludge moisture content to the a-th sludge moisture content;

[0023] Calculate the variance of the sludge moisture content from the first sludge moisture content to the a-th sludge moisture content to obtain the variance of the sludge moisture content corresponding to the characteristic area of the mesh belt;

[0024] Calculate the average value of the sludge moisture content from the first sludge moisture content to the a-th sludge moisture content to obtain the average sludge moisture content corresponding to the characteristic area of the mesh belt;

[0025] Calculate the sludge moisture content monitoring coefficient corresponding to the characteristic area of the mesh belt by calculating the variance of the sludge moisture content and the average sludge moisture content;

[0026] Calculate the sludge moisture content monitoring coefficient, and the specific formula is as follows:

[0027] ;

[0028] Among them, Hsx is the sludge moisture content monitoring coefficient, Hsp is the average sludge moisture content, and Hfc is the variance of the sludge moisture content;

[0029] Obtain the sludge moisture content monitoring coefficients corresponding to each mesh belt area respectively to obtain multiple sludge moisture content monitoring coefficients;

[0030] Calculate the average value of the obtained multiple sludge moisture content monitoring coefficients to obtain the sludge moisture content cycle monitoring coefficient.

[0031] Furthermore, the data acquisition module obtains the first sludge moisture content as follows:

[0032] Obtain the pressure value applied to the sludge of the sample drying batch in the characteristic area of the mesh belt to obtain the sludge monitoring pressure value;

[0033] Obtain the weight value of the sample drying batch sludge loaded in the characteristic area of the mesh belt after drying to obtain the sludge drying weight value;

[0034] Calculate the first sludge moisture content, and the specific formula is as follows:

[0035] ;

[0036] Among them, Hsl1 is the first sludge moisture content, Njy is the sludge monitoring pressure value, Whg is the sludge drying weight value, and g is the gravitational constant.

[0037] Furthermore, the data acquisition module obtains the sludge humidity cycle monitoring coefficient as follows:

[0038] Obtain the sludge humidity corresponding to the characteristic area of the mesh belt from the first characteristic monitoring time point to the a-th characteristic monitoring time point respectively to obtain the first sludge humidity to the a-th sludge humidity;

[0039] Calculate the variance of the first sludge humidity to the a-th sludge humidity to obtain the sludge humidity variance corresponding to the characteristic area of the mesh belt;

[0040] Calculate the average value of the first sludge humidity to the a-th sludge humidity to obtain the average sludge humidity corresponding to the characteristic area of the mesh belt;

[0041] Calculate the sludge humidity monitoring coefficient corresponding to the characteristic area of the mesh belt by calculating the sludge humidity variance and the average sludge humidity;

[0042] Calculate the sludge humidity monitoring coefficient. The specific formula is as follows:

[0043] ;

[0044] Among them, Ssx is the sludge humidity monitoring coefficient, Ssp is the average sludge humidity, and Sfc is the sludge humidity variance;

[0045] Repeat the process of obtaining the sludge humidity monitoring coefficient corresponding to the characteristic area of the mesh belt, and obtain the sludge humidity monitoring coefficients corresponding to each mesh belt area respectively to obtain multiple sludge humidity monitoring coefficients;

[0046] Calculate the average value of the obtained multiple sludge humidity monitoring coefficients to obtain the sludge humidity cycle monitoring coefficient.

[0047] Furthermore, the data analysis module obtains the data for analyzing the working state of the dryer as follows:

[0048] Obtain the operation monitoring data of the drying mesh belt, and respectively obtain the cumulative sludge drying duration, the sludge moisture content cycle monitoring coefficient, and the sludge humidity cycle monitoring coefficient according to the operation monitoring data of the drying mesh belt;

[0049] Calculate the working state monitoring coefficient corresponding to the low-temperature dryer by calculating the cumulative sludge drying duration, the sludge moisture content cycle monitoring coefficient, and the sludge humidity cycle monitoring coefficient;

[0050] Calculate the working state monitoring coefficient corresponding to the low-temperature dryer. The specific formula is as follows:

[0051] ;

[0052] Among them, Gzj is the working state monitoring coefficient corresponding to the low-temperature dryer, Hsl is the sludge moisture content cycle monitoring coefficient, Wsj is the sludge humidity cycle monitoring coefficient, and Gsc is the cumulative sludge drying duration;

[0053] Obtain the first working state monitoring threshold and the second working state monitoring threshold respectively, and compare them numerically with the working state monitoring coefficient. Divide the low-temperature dryer into the first working state, the second working state, and the third working state according to the numerical comparison result, and obtain the working state monitoring data of the dryer;

[0054] Define the working state monitoring data of the dryer, the working state monitoring coefficient corresponding to the low-temperature dryer, the first working state monitoring coefficient threshold, and the second working state monitoring coefficient threshold as the working state analysis data of the dryer.

[0055] Furthermore, the data analysis module obtains the working state monitoring data of the dryer, specifically as follows:

[0056] Obtain the cumulative sludge drying duration, the first sludge moisture content cycle monitoring coefficient threshold, and the first sludge humidity cycle monitoring coefficient threshold respectively;

[0057] Calculate the first working state monitoring coefficient threshold corresponding to the low-temperature dryer from the cumulative sludge drying duration, the first sludge moisture content cycle monitoring coefficient threshold, and the first sludge humidity cycle monitoring coefficient threshold;

[0058] Obtain the cumulative sludge drying duration, the second sludge moisture content cycle monitoring coefficient threshold, and the second sludge humidity cycle monitoring coefficient threshold respectively;

[0059] Calculate the second working state monitoring coefficient threshold corresponding to the low-temperature dryer from the cumulative sludge drying duration, the second sludge moisture content cycle monitoring coefficient threshold, and the second sludge humidity cycle monitoring coefficient threshold;

[0060] Compare the first working state monitoring threshold and the second working state monitoring threshold numerically with the working state monitoring coefficient to obtain the working state monitoring data of the dryer;

[0061] Specifically as follows:

[0062] If 0 < Gzj < Gzj1, it is determined that the low-temperature dryer is in the first working state;

[0063] If Gzj1 ≤ Gzj ≤ Gzj2, it is determined that the low-temperature dryer is in the second working state;

[0064] If Gzj2 < Gzj, it is determined that the low-temperature dryer is in the third working state;

[0065] Among them, Gzj1 is the first working state monitoring threshold, and Gzj2 is the second working state monitoring threshold.

[0066] Furthermore, the control and warning module remotely controls and warns the low-temperature dryer, specifically as follows:

[0067] Obtain the monitoring data of the working status of the drying machine, and respectively obtain the monitoring data of the working status of the drying machine, the working status monitoring coefficient corresponding to the low-temperature drying machine, the first working status monitoring coefficient threshold, and the second working status monitoring coefficient threshold according to the monitoring data of the working status of the drying machine;

[0068] When the low-temperature drying machine is in the first working state, remotely control the low-temperature drying machine;

[0069] Specifically as follows:

[0070] Obtain the difference between the working status monitoring coefficient corresponding to the low-temperature drying machine and the first working status monitoring coefficient threshold, and take the absolute value of the obtained difference to obtain the first state monitoring deviation value, and obtain the state monitoring deviation threshold;

[0071] When the first state monitoring deviation value is less than the state monitoring deviation threshold, remotely control the low-temperature drying machine through the cloud platform to reduce the heating power;

[0072] When the first state monitoring deviation value is greater than or equal to the state monitoring deviation threshold, remotely give an early warning to the drying machine through the cloud platform;

[0073] When the low-temperature drying machine is in the second working state, there is no need to remotely control the low-temperature drying machine;

[0074] When the low-temperature drying machine is in the third working state, remotely control the low-temperature drying machine.

[0075] Furthermore, the control and warning module remotely controls the low-temperature drying machine in the third working state, specifically as follows:

[0076] Obtain the difference between the working status monitoring coefficient corresponding to the low-temperature drying machine and the second working status monitoring coefficient threshold, and take the absolute value of the obtained difference to obtain the second state monitoring deviation value, and obtain the state monitoring deviation threshold;

[0077] When the second state monitoring deviation value is less than the state monitoring deviation threshold, remotely control the low-temperature drying machine through the cloud platform to increase the heating power;

[0078] When the second state monitoring deviation value is greater than or equal to the state monitoring deviation threshold, remotely give an early warning to the drying machine through the cloud platform.

[0079] A remote control method for a sludge low-temperature drying machine includes the following specific steps:

[0080] Step S1: Monitor the equipment operation of the drying mesh belt of the low-temperature drying machine, and respectively obtain the cumulative sludge drying duration, the sludge moisture content periodic monitoring coefficient, and the sludge humidity periodic monitoring coefficient to obtain the drying mesh belt operation monitoring data;

[0081] Step S2: Obtain the working state monitoring coefficient corresponding to the low-temperature dryer by analyzing the cumulative sludge drying duration, the sludge moisture content periodic monitoring coefficient, and the sludge humidity periodic monitoring coefficient. Respectively obtain the first working state monitoring threshold and the second working state monitoring threshold, compare them numerically with the working state monitoring coefficient, and obtain the drying machine working state monitoring data based on the numerical comparison result to obtain the drying machine working state analysis data;

[0082] Step S3: Remotely control and give an alarm to the low-temperature dryer according to the drying machine working state monitoring data.

[0083] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present invention are as follows:

[0084] 1. The present invention respectively obtains the cumulative sludge drying duration, the sludge moisture content periodic monitoring coefficient, and the sludge humidity periodic monitoring coefficient to monitor the periodic working data of the low-temperature dryer, which can improve the accuracy of the sludge drying data fed back by the monitoring equipment and effectively ensure the drying effect;

[0085] 2. The present invention obtains the working state monitoring coefficient corresponding to the low-temperature dryer by analyzing the cumulative sludge drying duration, the sludge moisture content periodic monitoring coefficient, and the sludge humidity periodic monitoring coefficient, and remotely controls and gives an alarm to the low-temperature dryer according to the working state monitoring coefficient, which can improve the automatic control level of the low-temperature dryer. Description of the Drawings

[0086] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings.

[0087] Figure 1 It is the overall system block diagram of the present invention;

[0088] Figure 2 It is the implementation step diagram of the present invention. Detailed Embodiments

[0089] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0090] Embodiment 1

[0091] Please refer to Figure 1, the present invention provides a technical solution: a remote control system for a sludge low-temperature dryer, including a data acquisition module, a data analysis module, a control and warning module, and a server. The data acquisition module, the data analysis module, and the control and warning module are respectively connected to the server, and the server controls the data acquisition module, the data analysis module, and the control and warning module respectively;

[0092] The data acquisition module monitors the operation of the drying mesh belt of the low-temperature dryer, and respectively obtains the cumulative sludge drying duration, the sludge moisture content periodic monitoring coefficient, and the sludge humidity periodic monitoring coefficient to obtain the drying mesh belt operation monitoring data;

[0093] Specifically as follows:

[0094] The sludge to be dried by the sludge low-temperature dryer is divided into several different drying batches, and any one of the divided drying batches is selected as the sample drying batch;

[0095] It should be noted here that:

[0096] In this application, the volume of the dried sludge corresponding to each drying batch is the same;

[0097] In this application, the sample drying batch involved here is the sludge batch dried by the sludge low-temperature dryer when remotely controlling the sludge low-temperature dryer in this application;

[0098] When the low-temperature dryer is drying the sample drying batch of sludge, mark the time value corresponding to the current moment as the first characteristic time value. In the period before the first characteristic time value, select a second characteristic time value, and mark the period between the first characteristic time value and the second characteristic time value as the real-time drying monitoring period;

[0099] It should be noted here that:

[0100] In this application, as the time value of the current moment changes, the first characteristic time value and the second characteristic time value also change accordingly, so as to realize the dynamic update of the real-time drying monitoring period;

[0101] In this application, the specific time length corresponding to the real-time drying monitoring period is limited to 5 seconds;

[0102] Obtain the time value when the low-temperature dryer starts to dry the sample drying batch of sludge to obtain the drying start time value, calculate the difference between the drying start time value and the first characteristic time value, and take the absolute value of the obtained difference to obtain the cumulative sludge drying duration;

[0103] During the real-time drying monitoring period, several characteristic monitoring time points with equal time intervals are marked, and the several marked characteristic time monitoring points are named the first characteristic monitoring time point to the a-th characteristic monitoring time point in chronological order;

[0104] It should be noted here that:

[0105] In this application, a is the corresponding quantity value of the characteristic monitoring time points in the real-time drying monitoring period, and a is an integer greater than 0;

[0106] The drying mesh belt loaded with the sample dried batch of sludge is divided into several mesh belt areas with equal areas, and one of the divided mesh belt areas is selected as the mesh belt characteristic area;

[0107] The moisture content of the sample dried batch of sludge in the real-time drying monitoring period is monitored to obtain the moisture content periodic monitoring coefficient;

[0108] Specifically as follows:

[0109] Obtain the moisture content of the sludge corresponding to the mesh belt characteristic area at the first characteristic monitoring time point to obtain the first sludge moisture content;

[0110] Specifically as follows:

[0111] Obtain the pressure value exerted by the sample dried batch of sludge in the mesh belt characteristic area to obtain the sludge monitoring pressure value;

[0112] Obtain the weight value of the sample dried batch of sludge loaded in the mesh belt characteristic area after drying to obtain the sludge drying weight value;

[0113] It should be noted here that:

[0114] The sludge drying weight value involved here needs to be obtained by analyzing historical drying data;

[0115] Calculate the first sludge moisture content from the sludge monitoring pressure value and the sludge drying weight value;

[0116] Calculate the first sludge moisture content, and the specific formula is as follows:

[0117] ;

[0118] Among them, Hsl1 is the first sludge moisture content, Njy is the sludge monitoring pressure value, Whg is the sludge drying weight value, and g is the gravitational constant;

[0119] Repeat the process of obtaining the first sludge moisture content, and obtain the sludge moisture content corresponding to the characteristic area of the mesh belt from the second characteristic monitoring time point to the a-th characteristic monitoring time point respectively, so as to obtain the second sludge moisture content to the a-th sludge moisture content;

[0120] Calculate the variance of the sludge moisture content from the first sludge moisture content to the a-th sludge moisture content to obtain the sludge moisture content variance corresponding to the characteristic area of the mesh belt;

[0121] Calculate the average value of the sludge moisture content from the first sludge moisture content to the a-th sludge moisture content to obtain the average sludge moisture content corresponding to the characteristic area of the mesh belt;

[0122] Calculate the sludge moisture content monitoring coefficient corresponding to the characteristic area of the mesh belt by calculating the sludge moisture content variance and the average sludge moisture content;

[0123] Calculate the sludge moisture content monitoring coefficient, and the specific formula is as follows:

[0124] ;

[0125] Among them, Hsx is the sludge moisture content monitoring coefficient, Hsp is the average sludge moisture content, and Hfc is the sludge moisture content variance;

[0126] Repeat the process of obtaining the sludge moisture content monitoring coefficient corresponding to the characteristic area of the mesh belt, and obtain the sludge moisture content monitoring coefficients corresponding to each mesh belt area respectively to obtain multiple sludge moisture content monitoring coefficients;

[0127] Calculate the average value of the obtained multiple sludge moisture content monitoring coefficients to obtain the sludge moisture content cycle monitoring coefficient;

[0128] Monitor the sludge moisture of the sample drying batch sludge in the real-time drying monitoring cycle to obtain the sludge moisture cycle monitoring coefficient;

[0129] Specifically as follows:

[0130] Obtain the sludge moisture corresponding to the characteristic area of the mesh belt from the first characteristic monitoring time point to the a-th characteristic monitoring time point respectively to obtain the first sludge moisture to the a-th sludge moisture;

[0131] Calculate the variance of the sludge moisture from the first sludge moisture to the a-th sludge moisture to obtain the sludge moisture variance corresponding to the characteristic area of the mesh belt;

[0132] Calculate the average value of the sludge moisture from the first sludge moisture to the a-th sludge moisture to obtain the average sludge moisture corresponding to the characteristic area of the mesh belt;

[0133] Calculate the sludge moisture monitoring coefficient corresponding to the characteristic area of the mesh belt by calculating the sludge moisture variance and the average sludge moisture;

[0134] Calculate the sludge humidity monitoring coefficient, and the specific formula is as follows:

[0135] ;

[0136] Among them, Ssx is the sludge humidity monitoring coefficient, Ssp is the average sludge humidity, and Sfc is the variance of sludge humidity;

[0137] Repeat the process of obtaining the sludge humidity monitoring coefficient corresponding to the characteristic area of the mesh belt, and obtain the sludge humidity monitoring coefficients corresponding to each mesh belt area respectively, so as to obtain multiple sludge humidity monitoring coefficients;

[0138] Calculate the average of the obtained multiple sludge humidity monitoring coefficients to obtain the periodic monitoring coefficient of sludge humidity;

[0139] Define the cumulative sludge drying time, the periodic monitoring coefficient of sludge moisture content, and the periodic monitoring coefficient of sludge humidity as the operation monitoring data of the drying mesh belt;

[0140] The data acquisition module acquires the operation monitoring data of the drying mesh belt and transports it to the data analysis module;

[0141] The data analysis module obtains the working state monitoring coefficient corresponding to the low-temperature dryer by analyzing the cumulative sludge drying time, the periodic monitoring coefficient of sludge moisture content, and the periodic monitoring coefficient of sludge humidity. Respectively obtain the first working state monitoring threshold and the second working state monitoring threshold and compare them with the working state monitoring coefficient numerically. According to the numerical comparison result, the low-temperature dryer is in the working state, the second working state, and the third working state, and the working state monitoring data of the dryer is obtained. Define the working state monitoring data of the dryer, the working state monitoring coefficient corresponding to the low-temperature dryer, the first working state monitoring coefficient threshold, and the second working state monitoring coefficient threshold as the working state analysis data of the dryer

[0142] Specifically as follows:

[0143] Obtain the operation monitoring data of the drying mesh belt, and respectively obtain the cumulative sludge drying time, the periodic monitoring coefficient of sludge moisture content, and the periodic monitoring coefficient of sludge humidity according to the operation monitoring data of the drying mesh belt;

[0144] Calculate the working state monitoring coefficient corresponding to the low-temperature dryer by calculating the cumulative sludge drying time, the periodic monitoring coefficient of sludge moisture content, and the periodic monitoring coefficient of sludge humidity;

[0145] Calculate the working state monitoring coefficient corresponding to the low-temperature dryer, and the specific formula is as follows:

[0146] ;

[0147] Among them, Gzj is the working state monitoring coefficient corresponding to the low-temperature drying machine, Hsl is the sludge moisture content periodic monitoring coefficient, Wsj is the sludge humidity periodic monitoring coefficient, and Gsc is the cumulative sludge drying duration;

[0148] Respectively obtain the first working state monitoring threshold and the second working state monitoring threshold, and conduct a numerical comparison with the working state monitoring coefficient. According to the numerical comparison result, divide the low-temperature drying machine into the first working state, the second working state, and the third working state to obtain the working state monitoring data of the drying machine;

[0149] Specifically as follows:

[0150] Respectively obtain the cumulative sludge drying duration, the first sludge moisture content periodic monitoring coefficient threshold, and the first sludge humidity periodic monitoring coefficient threshold;

[0151] Calculate the first working state monitoring coefficient threshold corresponding to the low-temperature drying machine through the cumulative sludge drying duration, the first sludge moisture content periodic monitoring coefficient threshold, and the first sludge humidity periodic monitoring coefficient threshold;

[0152] It should be noted here that:

[0153] The first sludge moisture content periodic monitoring coefficient threshold and the first sludge humidity periodic monitoring coefficient threshold involved here are respectively the minimum sludge moisture content periodic monitoring coefficient and the minimum sludge humidity periodic monitoring coefficient when the low-temperature drying machine is in the second working state. The specific values corresponding to the first sludge moisture content periodic monitoring coefficient threshold and the first sludge humidity periodic monitoring coefficient threshold need to be specifically set manually according to the cumulative sludge drying duration;

[0154] Calculate the first working state monitoring coefficient threshold corresponding to the low-temperature drying machine. The specific formula is as follows:

[0155] ;

[0156] Among them, Gzj1 is the working state monitoring coefficient threshold corresponding to the low-temperature drying machine, Hsl1 is the first sludge moisture content periodic monitoring coefficient threshold, Wsj is the first sludge humidity periodic monitoring coefficient threshold, and Gsc is the cumulative sludge drying duration;

[0157] Respectively obtain the cumulative sludge drying duration, the second sludge moisture content periodic monitoring coefficient threshold, and the second sludge humidity periodic monitoring coefficient threshold;

[0158] It should be noted here that:

[0159] The second sludge moisture content cycle monitoring coefficient threshold and the second sludge humidity cycle monitoring coefficient threshold involved here are respectively the maximum sludge moisture content cycle monitoring coefficient and the maximum sludge humidity cycle monitoring coefficient when the low-temperature drying machine is in the second working state. The specific values corresponding to the second sludge moisture content cycle monitoring coefficient threshold and the second sludge humidity cycle monitoring coefficient threshold need to be specifically set manually according to the cumulative sludge drying duration;

[0160] Calculate the monitoring coefficient threshold of the second working state corresponding to the low-temperature drying machine through the cumulative sludge drying duration, the second sludge moisture content cycle monitoring coefficient threshold, and the second sludge humidity cycle monitoring coefficient threshold;

[0161] Calculate the monitoring coefficient threshold of the second working state corresponding to the low-temperature drying machine. The specific formula is as follows:

[0162] ;

[0163] Among them, Gzj2 is the monitoring coefficient threshold of the second working state corresponding to the low-temperature drying machine, Hsl2 is the second sludge moisture content cycle monitoring coefficient threshold, Wsj is the second sludge humidity cycle monitoring coefficient threshold, and Gsc is the cumulative sludge drying duration;

[0164] If 0 < Gzj < Gzj1, it is determined that the low-temperature drying machine is in the first working state;

[0165] If Gzj1 ≤ Gzj ≤ Gzj2, it is determined that the low-temperature drying machine is in the second working state;

[0166] If Gzj2 < Gzj, it is determined that the low-temperature drying machine is in the third working state;

[0167] Among them, Gzj1 is the monitoring threshold of the first working state, and Gzj2 is the monitoring threshold of the second working state;

[0168] Define the drying machine working state monitoring data, the working state monitoring coefficient corresponding to the low-temperature drying machine, the first working state monitoring coefficient threshold, and the second working state monitoring coefficient threshold as the drying machine working state analysis data;

[0169] The control and warning module remotely controls and warns the low-temperature drying machine according to the drying machine working state monitoring data;

[0170] Specifically as follows:

[0171] Obtain the drying machine working state monitoring data, and respectively obtain the drying machine working state monitoring data, the working state monitoring coefficient corresponding to the low-temperature drying machine, the first working state monitoring coefficient threshold, and the second working state monitoring coefficient threshold according to the drying machine working state monitoring data;

[0172] When the low-temperature dryer is in the first working state, remotely control the low-temperature dryer;

[0173] Specifically as follows:

[0174] Obtain the difference between the working state monitoring coefficient corresponding to the low-temperature dryer and the first working state monitoring coefficient threshold value, and take the absolute value of the obtained difference to obtain the first state monitoring deviation value, and obtain the state monitoring deviation threshold value;

[0175] When the first state monitoring deviation value is less than the state monitoring deviation threshold value, remotely control the low-temperature dryer to reduce the heating power through the cloud platform;

[0176] When the first state monitoring deviation value is greater than or equal to the state monitoring deviation threshold value, remotely give an early warning to the dryer through the cloud platform;

[0177] When the low-temperature dryer is in the second working state, there is no need to remotely control the low-temperature dryer;

[0178] When the low-temperature dryer is in the third working state, remotely control the low-temperature dryer;

[0179] Specifically as follows:

[0180] Obtain the difference between the working state monitoring coefficient corresponding to the low-temperature dryer and the second working state monitoring coefficient threshold value, and take the absolute value of the obtained difference to obtain the second state monitoring deviation value, and obtain the state monitoring deviation threshold value;

[0181] When the second state monitoring deviation value is less than the state monitoring deviation threshold value, remotely control the low-temperature dryer to increase the heating power through the cloud platform;

[0182] When the second state monitoring deviation value is greater than or equal to the state monitoring deviation threshold value, remotely give an early warning to the dryer through the cloud platform.

[0183] In this application, if there are corresponding calculation formulas, the above calculation formulas are all dimensionless and take their numerical values for calculation. The coefficient such as the weight coefficient and the proportionality coefficient in the formula is set to obtain a result value by quantifying each parameter. Regarding the magnitudes of the weight coefficient and the proportionality coefficient, as long as the proportional relationship between the parameters and the result value is not affected.

[0184] Embodiment 2

[0185] Please refer to Figure 2 , based on another concept of the same invention, a remote control method for a sludge low-temperature dryer is proposed, including the following steps:

[0186] Step S1: Monitor the operation of the drying mesh belt of the low-temperature dryer, and respectively obtain the cumulative sludge drying duration, the periodic monitoring coefficient of sludge moisture content, and the periodic monitoring coefficient of sludge humidity to obtain the operation monitoring data of the drying mesh belt;

[0187] Specifically as follows:

[0188] Step S11: Divide the sludge to be dried by the low-temperature sludge dryer into several different drying batches, and arbitrarily select one drying batch from the several divided drying batches as the sample drying batch;

[0189] Step S12: When the low-temperature dryer is drying the sludge of the sample drying batch, mark the time value corresponding to the current moment as the first characteristic time value. In the period before the first characteristic time value, select a second characteristic time value, and mark the period between the first characteristic time value and the second characteristic time value as the real-time drying monitoring period;

[0190] Step S13: Obtain the time value when the low-temperature dryer starts to dry the sludge of the sample drying batch to obtain the drying start time value, calculate the difference between the drying start time value and the first characteristic time value, and take the absolute value of the obtained difference to obtain the cumulative sludge drying duration;

[0191] Step S14: In the real-time drying monitoring period, mark several characteristic monitoring time points with equal time intervals, and name the several marked characteristic time monitoring points as the first characteristic monitoring time point to the a-th characteristic monitoring time point in chronological order;

[0192] Step S15: Divide the drying mesh belt loaded with the sludge of the sample drying batch into several mesh belt areas with equal areas, and select one mesh belt area from the several divided mesh belt areas as the mesh belt characteristic area;

[0193] Step S16: Monitor the moisture content of the sludge of the sample drying batch in the real-time drying monitoring period to obtain the periodic monitoring coefficient of sludge moisture content;

[0194] Specifically as follows:

[0195] Step S161: Obtain the moisture content of the sludge corresponding to the first characteristic monitoring time point in the mesh belt characteristic area to obtain the first sludge moisture content;

[0196] Specifically as follows:

[0197] Step S1611: Obtain the pressure value applied to the sludge of the sample drying batch in the mesh belt characteristic area to obtain the sludge monitoring pressure value;

[0198] Step S1612: Obtain the weight value of the dried sample batch sludge loaded in the mesh belt characteristic area to get the sludge drying weight value;

[0199] Calculate the first sludge moisture content. The specific formula is as follows:

[0200] ;

[0201] Where, Hsl1 is the first sludge moisture content, Njy is the sludge monitoring pressure value, Whg is the sludge drying weight value, and g is the gravitational constant;

[0202] Step S162: Respectively obtain the sludge moisture contents corresponding to the mesh belt characteristic area from the second characteristic monitoring time point to the a-th characteristic monitoring time point to get the second sludge moisture content to the a-th sludge moisture content;

[0203] Step S164: Calculate the variance of the first sludge moisture content to the a-th sludge moisture content to get the sludge moisture content variance corresponding to the mesh belt characteristic area;

[0204] Step S165: Calculate the average value of the first sludge moisture content to the a-th sludge moisture content to get the average sludge moisture content corresponding to the mesh belt characteristic area;

[0205] Step S166: Calculate the sludge moisture content monitoring coefficient corresponding to the mesh belt characteristic area through the sludge moisture content variance and the average sludge moisture content;

[0206] Calculate the sludge moisture content monitoring coefficient. The specific formula is as follows:

[0207] ;

[0208] Where, Hsx is the sludge moisture content monitoring coefficient, Hsp is the average sludge moisture content, and Hfc is the sludge moisture content variance;

[0209] Step S167: Respectively obtain the sludge moisture content monitoring coefficients corresponding to each mesh belt area to get multiple sludge moisture content monitoring coefficients;

[0210] Step S168: Calculate the average value of the obtained multiple sludge moisture content monitoring coefficients to get the sludge moisture content cycle monitoring coefficient;

[0211] Step S17: Monitor the sludge humidity of the sample batch sludge in the real-time drying monitoring cycle to get the sludge humidity cycle monitoring coefficient;

[0212] Specifically as follows:

[0213] Step S171: Obtain the sludge humidity corresponding to the mesh belt characteristic area from the first characteristic monitoring time point to the a-th characteristic monitoring time point, and obtain the first sludge humidity to the a-th sludge humidity;

[0214] Step S172: Calculate the variance of the first sludge humidity to the a-th sludge humidity to obtain the sludge humidity variance corresponding to the mesh belt characteristic area;

[0215] Step S173: Calculate the average value of the first sludge humidity to the a-th sludge humidity to obtain the average sludge humidity corresponding to the mesh belt characteristic area;

[0216] Step S174: Calculate the sludge humidity monitoring coefficient corresponding to the mesh belt characteristic area by calculating the sludge humidity variance and the average sludge humidity;

[0217] Calculate the sludge humidity monitoring coefficient, and the specific formula is as follows:

[0218] ;

[0219] Where, Ssx is the sludge humidity monitoring coefficient, Ssp is the average sludge humidity, and Sfc is the sludge humidity variance;

[0220] Step S175: Obtain the sludge humidity monitoring coefficients corresponding to each mesh belt area respectively to obtain multiple sludge humidity monitoring coefficients;

[0221] Step S176: Calculate the average value of the obtained multiple sludge humidity monitoring coefficients to obtain the sludge humidity cycle monitoring coefficient;

[0222] Step S18: Define the sludge cumulative drying duration, the sludge moisture content cycle monitoring coefficient, and the sludge humidity cycle monitoring coefficient as the drying mesh belt operation monitoring data;

[0223] Step S2: Obtain the working state monitoring coefficient corresponding to the low-temperature dryer by analyzing the sludge cumulative drying duration, the sludge moisture content cycle monitoring coefficient, and the sludge humidity cycle monitoring coefficient. Respectively obtain the first working state monitoring threshold and the second working state monitoring threshold, compare them numerically with the working state monitoring coefficient, and obtain the drying machine working state monitoring data according to the numerical comparison result to obtain the drying machine working state analysis data;

[0224] Specifically as follows:

[0225] Step S21: Obtain the drying mesh belt operation monitoring data, and respectively obtain the sludge cumulative drying duration, the sludge moisture content cycle monitoring coefficient, and the sludge humidity cycle monitoring coefficient according to the drying mesh belt operation monitoring data;

[0226] Step S22: Calculate the working state monitoring coefficient corresponding to the low-temperature dryer based on the cumulative sludge drying duration, the periodic monitoring coefficient of sludge moisture content, and the periodic monitoring coefficient of sludge humidity;

[0227] Calculate the working state monitoring coefficient corresponding to the low-temperature dryer. The specific formula is as follows:

[0228] ;

[0229] Where, Gzj is the working state monitoring coefficient corresponding to the low-temperature dryer, Hsl is the periodic monitoring coefficient of sludge moisture content, Wsj is the periodic monitoring coefficient of sludge humidity, and Gsc is the cumulative sludge drying duration;

[0230] Step S23: Obtain the first working state monitoring threshold and the second working state monitoring threshold respectively, and compare them numerically with the working state monitoring coefficient. Divide the low-temperature dryer into the first working state, the second working state, and the third working state according to the numerical comparison result to obtain the working state monitoring data of the dryer;

[0231] Specifically as follows:

[0232] Step S231: Obtain the cumulative sludge drying duration, the first threshold of the periodic monitoring coefficient of sludge moisture content, and the first threshold of the periodic monitoring coefficient of sludge humidity respectively;

[0233] Step S232: Calculate the first working state monitoring coefficient threshold corresponding to the low-temperature dryer based on the cumulative sludge drying duration, the first threshold of the periodic monitoring coefficient of sludge moisture content, and the first threshold of the periodic monitoring coefficient of sludge humidity;

[0234] Step S233: Obtain the cumulative sludge drying duration, the second threshold of the periodic monitoring coefficient of sludge moisture content, and the second threshold of the periodic monitoring coefficient of sludge humidity respectively;

[0235] Step S234: Calculate the second working state monitoring coefficient threshold corresponding to the low-temperature dryer based on the cumulative sludge drying duration, the second threshold of the periodic monitoring coefficient of sludge moisture content, and the second threshold of the periodic monitoring coefficient of sludge humidity;

[0236] Step S235: If 0 < Gzj < Gzj1, then determine that the low-temperature dryer is in the first working state;

[0237] Step S236: If Gzj1 ≤ Gzj ≤ Gzj2, then determine that the low-temperature dryer is in the second working state;

[0238] Step S237: If Gzj2 < Gzj, then determine that the low-temperature dryer is in the third working state;

[0239] Step S24: Define the working state monitoring data of the dryer, the working state monitoring coefficient corresponding to the low-temperature dryer, the first working state monitoring coefficient threshold, and the second working state monitoring coefficient threshold as the dryer working state analysis data;

[0240] Step S3: Remotely control and give early warnings to the low-temperature dryer according to the working state monitoring data of the dryer;

[0241] Specifically as follows:

[0242] Step S31: Obtain the working state monitoring data of the dryer, and respectively obtain the working state monitoring data of the dryer, the working state monitoring coefficient corresponding to the low-temperature dryer, the first working state monitoring coefficient threshold, and the second working state monitoring coefficient threshold according to the working state monitoring data of the dryer;

[0243] Step S32: When the low-temperature dryer is in the first working state, remotely control the low-temperature dryer;

[0244] Specifically as follows:

[0245] Step S321: Obtain the difference between the working state monitoring coefficient corresponding to the low-temperature dryer and the first working state monitoring coefficient threshold, and take the absolute value of the obtained difference to get the first state monitoring deviation value, and obtain the state monitoring deviation threshold;

[0246] Step S322: When the first state monitoring deviation value is less than the state monitoring deviation threshold, remotely control the low-temperature dryer to reduce the heating power through the cloud platform;

[0247] Step S323: When the first state monitoring deviation value is greater than or equal to the state monitoring deviation threshold, remotely give an early warning to the dryer through the cloud platform;

[0248] Step S33: When the low-temperature dryer is in the second working state, there is no need to remotely control the low-temperature dryer;

[0249] Step S34: When the low-temperature dryer is in the third working state, remotely control the low-temperature dryer;

[0250] Specifically as follows:

[0251] Step S341: Obtain the difference between the working state monitoring coefficient corresponding to the low-temperature dryer and the second working state monitoring coefficient threshold, and take the absolute value of the obtained difference to get the second state monitoring deviation value, and obtain the state monitoring deviation threshold;

[0252] Step S342: When the second state monitoring deviation value is less than the state monitoring deviation threshold, remotely control the low-temperature dryer to increase the heating power through the cloud platform;

[0253] Step S343: When the second state monitoring deviation value is greater than or equal to the state monitoring deviation threshold, remotely issue a warning for the dryer through the cloud platform.

[0254] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation manners described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and utilize the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A remote control system for a low-temperature sludge drying machine, characterized in that: include: Data acquisition module: used to monitor the operation of the drying belt of the low-temperature dryer, and obtain the cumulative drying time of the sludge, the periodic monitoring coefficient of the sludge moisture content and the periodic monitoring coefficient of the sludge humidity according to the monitoring results, and obtain the operation monitoring data of the drying belt; Data analysis module: used to obtain the working state monitoring coefficient by analyzing the cumulative drying time of sludge, the periodic monitoring coefficient of sludge moisture content and the periodic monitoring coefficient of sludge humidity, respectively obtain the first working state monitoring threshold and the second working state monitoring threshold and compare the values ​​with the working state monitoring coefficient, obtain the working state monitoring data of the dryer according to the numerical comparison results, and obtain the working state analysis data of the dryer; The working status monitoring coefficient corresponding to the low-temperature dryer is calculated, and the specific formula is as follows: ； Among them, Gzj is the working state monitoring coefficient corresponding to the low-temperature dryer, Hsl is the periodic monitoring coefficient of sludge moisture content, Wsj is the periodic monitoring coefficient of sludge humidity, and Gsc is the cumulative drying time of sludge; Control and early warning module: used to remotely control and warn the low-temperature dryer based on the dryer working status monitoring data.

2. A remote control system for a sludge low-temperature drying machine according to claim 1, characterized in that: The data acquisition module acquires the operation monitoring data of the drying belt, as follows: The sludge to be dried by the sludge low-temperature dryer is divided into a number of different drying batches, and one drying batch is randomly selected from the divided drying batches as a sample drying batch; When the low-temperature dryer is drying the sample drying batch sludge, the time value corresponding to the current moment is marked as the first characteristic time value, a second characteristic time value is selected in the time period before the first characteristic time value, and the time period between the first characteristic time value and the second characteristic time value is marked as the real-time drying monitoring period; Obtain the start time value of the low-temperature drying machine for drying the sample drying batch sludge, obtain the drying start time value, calculate the difference between the drying start time value and the first characteristic time value, and take the absolute value of the obtained difference to obtain the cumulative drying time of the sludge; In the real-time drying monitoring cycle, a number of characteristic monitoring time points with equal time intervals are marked respectively, and the marked characteristic time monitoring points are named as the first characteristic monitoring time point to the ath characteristic monitoring time point in chronological order; The drying mesh belt loaded with the sample drying batch sludge is divided into a plurality of mesh belt areas with equal areas, and one mesh belt area is selected from the divided plurality of mesh belt areas as a mesh belt characteristic area; The sludge moisture content of the sample drying batch sludge in the real-time drying monitoring period is monitored to obtain the sludge moisture content period monitoring coefficient; The sludge humidity of the sample drying batch sludge in the real-time drying monitoring cycle is monitored to obtain the sludge humidity cycle monitoring coefficient; The cumulative sludge drying time, the periodic monitoring coefficient of sludge moisture content and the periodic monitoring coefficient of sludge humidity are defined as the drying belt operation monitoring data.

3. A remote control system for a sludge low temperature drying machine according to claim 2, characterized in that: The data acquisition module acquires the periodic monitoring coefficient of the sludge moisture content, as follows: Obtain the sludge moisture content corresponding to the first characteristic monitoring time point in the characteristic area of ​​the mesh belt to obtain a first sludge moisture content; The sludge moisture content corresponding to the second characteristic monitoring time point to the ath characteristic monitoring time point of the characteristic area of ​​the mesh belt is respectively obtained to obtain the second sludge moisture content to the ath sludge moisture content; The variance of the first sludge moisture content to the ath sludge moisture content is calculated to obtain the sludge moisture content variance corresponding to the characteristic area of ​​the mesh belt; The average moisture content of the first sludge to the ath sludge is calculated to obtain the average moisture content of the sludge corresponding to the characteristic area of ​​the mesh belt; The sludge moisture content variance and the sludge average moisture content are calculated to obtain the sludge moisture content monitoring coefficient corresponding to the characteristic area of ​​the mesh belt; The sludge moisture content monitoring coefficient is calculated, and the specific formula is as follows: ； Among them, Hsx is the monitoring coefficient of sludge moisture content, Hsp is the average moisture content of sludge, and Hfc is the variance of sludge moisture content; The sludge moisture content monitoring coefficient corresponding to each mesh belt area is obtained respectively to obtain multiple sludge moisture content monitoring coefficients; The average of the obtained multiple sludge moisture content monitoring coefficients is calculated to obtain the sludge moisture content periodic monitoring coefficient.

4. A remote control system for a sludge low-temperature drying machine according to claim 3, characterized in that: The data acquisition module acquires the first sludge moisture content as follows: Obtain the pressure value exerted by the sample drying batch sludge in the characteristic area of ​​the mesh belt to obtain the sludge monitoring pressure value; Obtaining the weight value of the sample drying batch sludge loaded in the characteristic area of ​​the mesh belt after drying, and obtaining the sludge drying weight value; The moisture content of the first sludge is calculated using the following formula: ； Among them, Hsl1 is the moisture content of the first sludge, Njy is the sludge monitoring pressure value, Whg is the sludge drying weight value, and g is the gravity constant.

5. The remote control system of a sludge low temperature drying machine according to claim 2, characterized in that: The data acquisition module acquires the sludge moisture periodic monitoring coefficient, as follows: The sludge moisture corresponding to the characteristic area of ​​the mesh belt from the first characteristic monitoring time point to the ath characteristic monitoring time point is obtained respectively, and the first sludge moisture to the ath sludge moisture are obtained; The variance of the first sludge humidity to the ath sludge humidity is calculated to obtain the sludge humidity variance corresponding to the characteristic area of ​​the mesh belt; The average humidity of the first sludge to the ath sludge is calculated to obtain the average humidity of the sludge corresponding to the characteristic area of ​​the mesh belt; The sludge humidity variance and the sludge average humidity are calculated to obtain the sludge humidity monitoring coefficient corresponding to the characteristic area of ​​the mesh belt; The sludge moisture monitoring coefficient is calculated, and the specific formula is as follows: ； Among them, Ssx is the sludge moisture monitoring coefficient, Ssp is the average sludge moisture, and Sfc is the sludge moisture variance; The sludge moisture monitoring coefficient corresponding to each mesh belt area is obtained respectively to obtain multiple sludge moisture monitoring coefficients; The average of the multiple sludge moisture monitoring coefficients obtained is calculated to obtain the sludge moisture periodic monitoring coefficient.

6. The remote control system of a sludge low-temperature drying machine according to claim 1, characterized in that: The data analysis module acquires the working status analysis data of the drying machine, as follows: Acquire the drying net belt operation monitoring data, and obtain the sludge cumulative drying time, the sludge moisture content periodic monitoring coefficient, and the sludge humidity periodic monitoring coefficient according to the drying net belt operation monitoring data; The corresponding working state monitoring coefficient of the low-temperature dryer is obtained by calculating the cumulative drying time of the sludge, the periodic monitoring coefficient of the sludge moisture content and the periodic monitoring coefficient of the sludge humidity; The first working state monitoring threshold and the second working state monitoring threshold are respectively obtained, and the first working state monitoring threshold and the second working state monitoring threshold are numerically compared with the working state monitoring coefficient, and the low-temperature dryer is divided into a first working state, a second working state and a third working state according to the numerical comparison result, so as to obtain the dryer working state monitoring data; The dryer working state monitoring data, the working state monitoring coefficient corresponding to the low-temperature dryer, the first working state monitoring coefficient threshold, and the second working state monitoring coefficient threshold are defined as the dryer working state analysis data.

7. A remote control system for a sludge low temperature drying machine according to claim 6, characterized in that: The data analysis module acquires the working status monitoring data of the drying machine, as follows: respectively obtaining the cumulative sludge drying time, the first sludge moisture content periodic monitoring coefficient threshold, and the first sludge humidity periodic monitoring coefficient threshold; The first working state monitoring coefficient threshold corresponding to the low-temperature dryer is obtained by calculating the accumulated sludge drying time, the first sludge moisture content periodic monitoring coefficient threshold and the first sludge humidity periodic monitoring coefficient threshold; respectively obtaining the cumulative drying time of the sludge, the second sludge moisture content periodic monitoring coefficient threshold, and the second sludge humidity periodic monitoring coefficient threshold; The second working state monitoring coefficient threshold corresponding to the low-temperature dryer is obtained by calculating the accumulated sludge drying time, the second sludge moisture content periodic monitoring coefficient threshold and the second sludge humidity periodic monitoring coefficient threshold; Numerical comparison is performed between the first working state monitoring threshold and the second working state monitoring threshold and the working state monitoring coefficient to obtain the working state monitoring data of the dryer; The details are as follows: If 0<Gzj<Gzj1, it is judged that the low-temperature drying machine is in the first working state; If Gzj1≤Gzj≤Gzj2, it is determined that the low-temperature drying machine is in the second working state; If Gzj2<Gzj, it is judged that the low-temperature drying machine is in the third working state; Among them, Gzj1 is the first working state monitoring threshold, and Gzj2 is the second working state monitoring threshold.

8. The remote control system of a sludge low-temperature drying machine according to claim 1, characterized in that: The control and early warning module performs remote control and early warning on the low-temperature drying machine, as follows: Acquire the working state monitoring data of the drying machine, and acquire the working state monitoring data of the drying machine, the working state monitoring coefficient corresponding to the low-temperature drying machine, the first working state monitoring coefficient threshold, and the second working state monitoring coefficient threshold according to the working state monitoring data of the drying machine; When the low-temperature drying machine is in the first working state, the low-temperature drying machine is remotely controlled; The details are as follows: Obtaining the difference between the working state monitoring coefficient corresponding to the low-temperature drying machine and the first working state monitoring coefficient threshold, taking the absolute value of the obtained difference, obtaining the first state monitoring deviation value, and obtaining the state monitoring deviation threshold; When the first state monitoring deviation value is less than the state monitoring deviation threshold, the low-temperature drying machine is remotely controlled to reduce the heating power through the cloud platform; When the first state monitoring deviation value is greater than or equal to the state monitoring deviation threshold, the dryer is remotely warned through the cloud platform; When the low-temperature drying machine is in the second working state, there is no need to remotely control the low-temperature drying machine; When the low-temperature drying machine is in the third working state, the low-temperature drying machine is remotely controlled.

9. A remote control system for a sludge low-temperature drying machine according to claim 8, characterized in that: The control and early warning module remotely controls the low-temperature drying machine in the third working state, as follows: Obtaining the difference between the working state monitoring coefficient corresponding to the low-temperature drying machine and the second working state monitoring coefficient threshold, taking the absolute value of the obtained difference, obtaining the second state monitoring deviation value, and obtaining the state monitoring deviation threshold; When the second state monitoring deviation value is less than the state monitoring deviation threshold, the low-temperature dryer is remotely controlled to increase the heating power through the cloud platform; When the second state monitoring deviation value is greater than or equal to the state monitoring deviation threshold, the dryer is remotely warned through the cloud platform.

10. A remote control method for a low-temperature sludge drying machine, applicable to a remote control system for a low-temperature sludge drying machine according to any one of claims 1 to 9, characterized in that: The remote control method comprises the following specific steps: Step S1: monitoring the operation of the drying belt of the low-temperature drying machine, and obtaining the cumulative drying time of the sludge, the periodic monitoring coefficient of the sludge moisture content and the periodic monitoring coefficient of the sludge humidity according to the monitoring results, so as to obtain the operation monitoring data of the drying belt; Step S2: Obtain the working state monitoring coefficient corresponding to the low-temperature dryer by analyzing the cumulative drying time of the sludge, the periodic monitoring coefficient of the sludge moisture content, and the periodic monitoring coefficient of the sludge humidity, respectively obtain the first working state monitoring threshold and the second working state monitoring threshold and compare the values ​​with the working state monitoring coefficient, obtain the working state monitoring data of the dryer according to the numerical comparison results, and obtain the working state analysis data of the dryer; Step S3: remotely control and warn the low-temperature dryer according to the dryer working status monitoring data.

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