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

Abstract

The invention discloses a remote control system and method for a low-temperature sludge drying machine, relates to the field of heat pump drying, and solves the problem that an existing remote control system for the low-temperature sludge drying machine is poor in control effect. The data acquisition module is used for respectively acquiring sludge accumulated drying time, a sludge moisture content period monitoring coefficient and a sludge humidity period monitoring coefficient to obtain drying mesh belt operation monitoring data; the data analysis module is used for obtaining the working state analysis data of the drying machine by analyzing the accumulated sludge drying time, the sludge moisture content periodic monitoring coefficient and the sludge humidity periodic monitoring coefficient, and the control early warning module is used for performing remote control and early warning on the low-temperature drying machine according to the working state monitoring data of the drying machine. The automatic control level of the low-temperature drying machine can be improved.

Description

Technical Field

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

[0002] The existing remote control system of the sludge low-temperature dryer has the following specific defects during remote control: The existing remote control system of the sludge low-temperature dryer requires manual remote control of the drying equipment, and cannot provide automatic drying warning, resulting in low control efficiency; Existing sludge low-temperature dryers can only monitor the real-time moisture content and humidity data of specific sensor areas, and are unable to perform periodic analysis of moisture content data and humidity data within a period of time, resulting in a lack of accuracy in the feedback sludge drying data.

[0003] To this end, we propose a remote control system and method for a sludge low-temperature dryer. Summary of the invention

[0004] In view of the deficiencies in the prior art, the object 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 of the remote control system of the sludge low-temperature dryer.

[0005] In order to achieve the above purpose, the present invention adopts the following technical solution: a remote control system of a sludge low-temperature dryer, and the specific working process of each module is as follows: 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, and obtain the operation monitoring data of the drying belt; Data analysis module: used to obtain the working state monitoring coefficient corresponding to the low-temperature dryer 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; Control and early warning module: used to remotely control and warn the low-temperature dryer based on the dryer working status monitoring data.

[0006] Furthermore, 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 time value when the low-temperature drying machine starts 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.

[0007] Furthermore, 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.

[0008] Furthermore, the data acquisition module acquires the moisture content of the first sludge 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.

[0009] Furthermore, 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; Repeat the process of obtaining the sludge moisture monitoring coefficient corresponding to the characteristic area of ​​the mesh belt, respectively obtain the sludge moisture monitoring coefficient corresponding to each mesh belt area, and 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.

[0010] Furthermore, 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 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; The first working state monitoring threshold and the second working state monitoring threshold are respectively obtained to perform numerical comparison 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 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.

[0011] Furthermore, 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.

[0012] Furthermore, 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.

[0013] Furthermore, 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.

[0014] A remote control method for a sludge low-temperature drying machine 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, 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.

[0015] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. The present invention obtains the cumulative drying time of sludge, the periodic monitoring coefficient of sludge moisture content and the periodic monitoring coefficient of sludge humidity 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; 2. The present invention obtains the working status monitoring coefficient corresponding to the low-temperature dryer by analyzing the cumulative drying time of sludge, the periodic monitoring coefficient of sludge moisture content and the periodic monitoring coefficient of sludge humidity, and remotely controls and warns the low-temperature dryer according to the working status monitoring coefficient, thereby improving the automation control level of the low-temperature dryer. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0017] Figure 1 is the overall system block diagram of the present invention; Figure 2 It is a diagram of the implementation steps of the present invention. DETAILED DESCRIPTION

[0018] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. 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.

[0019] Embodiment 1 See also Figure 1The present invention provides a technical solution: a remote control system for a sludge low-temperature drying machine, comprising a data acquisition module, a data analysis module, a control and early warning module and a server, wherein the data acquisition module, the data analysis module and the control and early warning module are respectively connected to the server, and the server controls the data acquisition module, the data analysis module and the control and early warning module respectively; The data acquisition module monitors the operation of the drying belt of the low-temperature dryer, and obtains 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, and obtains the operation monitoring data of the drying belt; The details are 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; It should be noted here that: In the present application, the volume of dried sludge corresponding to each drying batch is the same; In the present application, the sample drying batch involved here is the sludge batch dried by the sludge low-temperature dryer when the sludge low-temperature dryer is remotely controlled in the present application; 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; It should be noted here that: In the present application, as the current time value changes, the first characteristic time value and the second characteristic time value also change accordingly, thereby realizing the dynamic update of the real-time drying monitoring cycle; In this application, the specific time length corresponding to the real-time drying monitoring cycle is limited to 5 seconds; Obtain the time value when the low-temperature drying machine starts 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; It should be noted here that: In the present application, a is the numerical value corresponding to the characteristic monitoring time point in the real-time drying monitoring cycle, and a is an integer greater than 0; 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 details are 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 details are 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; It should be noted here that: The sludge drying weight value involved here needs to be obtained by analyzing historical drying data; The first sludge moisture content is obtained by calculating the sludge monitoring pressure value and 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; Repeat the process of obtaining the first sludge moisture content, respectively obtain the sludge moisture content corresponding to the second characteristic monitoring time point to the ath characteristic monitoring time point of the belt characteristic area, and 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; Repeat the process of obtaining the sludge moisture content monitoring coefficient corresponding to the characteristic area of ​​the mesh belt, respectively obtain the sludge moisture content monitoring coefficient corresponding to each mesh belt area, and obtain multiple sludge moisture content monitoring coefficients; Calculate the average of the obtained multiple sludge moisture content monitoring coefficients to obtain the sludge moisture content periodic 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 details are 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; Repeat the process of obtaining the sludge moisture monitoring coefficient corresponding to the characteristic area of ​​the mesh belt, respectively obtain the sludge moisture monitoring coefficient corresponding to each mesh belt area, and obtain multiple sludge moisture monitoring coefficients; The obtained multiple sludge moisture monitoring coefficients are averaged to obtain the sludge moisture periodic monitoring coefficient; The cumulative sludge drying time, the sludge moisture content periodic monitoring coefficient and the sludge humidity periodic monitoring coefficient are defined as the drying belt operation monitoring data; The data acquisition module acquires the operation monitoring data of the drying belt and transmits it to the data analysis module; The data analysis module obtains 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 obtains the first working state monitoring threshold and the second working state monitoring threshold and compares the working state monitoring coefficient numerically, and according to the numerical comparison result, the low-temperature dryer is put into the working state, the second working state, and the third working state to obtain the working state monitoring data of the dryer, and 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 are defined as the working state analysis data of the dryer The details are 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 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; The first working state monitoring threshold and the second working state monitoring threshold are respectively obtained to perform numerical comparison 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 to obtain the dryer working state monitoring data; The details are 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; It should be noted here that: 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 dryer 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 manually set according to the cumulative sludge drying time; The first working state monitoring coefficient threshold corresponding to the low-temperature drying machine is calculated, and the specific formula is as follows: ; Among them, Gzj1 is the working state monitoring coefficient threshold corresponding to the low-temperature dryer, Hsl1 is the first sludge moisture content period monitoring coefficient threshold, Wsj is the first sludge humidity period monitoring coefficient threshold, and Gsc is the cumulative sludge drying time; 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; It should be noted here that: The second sludge moisture content periodic monitoring coefficient threshold and the second sludge humidity periodic monitoring coefficient threshold involved here are respectively the maximum sludge moisture content periodic monitoring coefficient and the maximum sludge humidity periodic monitoring coefficient when the low-temperature dryer is in the second working state. The specific values ​​corresponding to the second sludge moisture content periodic monitoring coefficient threshold and the second sludge humidity periodic monitoring coefficient threshold need to be manually set according to the accumulated sludge drying time; 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; The second working state monitoring coefficient threshold corresponding to the low-temperature dryer is calculated, and the specific formula is as follows: ; Among them, Gzj2 is the second working state monitoring coefficient threshold corresponding to the low-temperature dryer, Hsl2 is the second sludge moisture content periodic monitoring coefficient threshold, Wsj is the second sludge humidity periodic monitoring coefficient threshold, and Gsc is the cumulative sludge drying time; 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; Wherein, Gzj1 is the first working state monitoring threshold, and Gzj2 is the second working state monitoring threshold; 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 are defined as the working state analysis data of the drying machine; The control and early warning module remotely controls and warns the low-temperature dryer based on the dryer working status monitoring data; The details are 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; The details are 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.

[0020] In this application, if corresponding calculation formulas appear, the above calculation formulas are all dimensionless and take their numerical calculations. The weight coefficients, proportional coefficients and other coefficients in the formulas are set to a result value obtained by quantifying each parameter. The size of the weight coefficient and the proportional coefficient can be determined as long as it does not affect the proportional relationship between the parameter and the result value.

[0021] Embodiment 2 See also Figure 2 Based on another concept of the same invention, a remote control method for a sludge low-temperature dryer is proposed, comprising the following steps: Step S1: monitoring the operation of the drying belt of the low-temperature drying machine, 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, and obtaining the operation monitoring data of the drying belt; The details are as follows: Step S11: dividing the sludge to be dried by the sludge low-temperature dryer into a number of different drying batches, and arbitrarily selecting one drying batch from the divided drying batches as a sample drying batch; Step S12: When the low-temperature drying machine 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; Step S13: obtaining the time value when the low-temperature drying machine starts drying the sample drying batch sludge, obtaining the drying start time value, calculating the difference between the drying start time value and the first characteristic time value, and taking the absolute value of the obtained difference to obtain the cumulative drying time of the sludge; Step S14: 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; Step S15: dividing the drying mesh belt loaded with the sample dried batch sludge into a plurality of mesh belt areas of equal area, and selecting one mesh belt area from the plurality of divided mesh belt areas as a mesh belt characteristic area; Step S16: monitoring the moisture content of the sample drying batch sludge in the real-time drying monitoring period to obtain a sludge moisture content period monitoring coefficient; The details are as follows: Step S161: obtaining the sludge moisture content corresponding to the characteristic area of ​​the mesh belt at the first characteristic monitoring time point to obtain a first sludge moisture content; The details are as follows: Step S1611: obtaining the pressure value applied by the sample drying batch sludge in the characteristic area of ​​the mesh belt to obtain the sludge monitoring pressure value; Step S1612: obtaining the weight value of the sample drying batch sludge loaded on 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; Step S162: respectively acquiring the sludge moisture content corresponding to the second characteristic monitoring time point to the ath characteristic monitoring time point of the belt characteristic area, and obtaining the second sludge moisture content to the ath sludge moisture content; Step S164: performing variance calculation on the first sludge moisture content to the ath sludge moisture content to obtain the sludge moisture content variance corresponding to the characteristic area of ​​the mesh belt; Step S165: Calculate the average of the first sludge moisture content to the ath sludge moisture content to obtain the average sludge moisture content corresponding to the characteristic area of ​​the mesh belt; Step S166: Calculate the sludge moisture content variance and the sludge average moisture content 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; Step S167: acquiring the sludge moisture content monitoring coefficient corresponding to each mesh belt area respectively, and obtaining a plurality of sludge moisture content monitoring coefficients; Step S168: Calculate the average of the multiple sludge moisture content monitoring coefficients obtained to obtain the sludge moisture content periodic monitoring coefficient; Step S17: monitoring the sludge humidity of the sample drying batch sludge in the real-time drying monitoring period to obtain the sludge humidity period monitoring coefficient; The details are as follows: Step S171: acquiring 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, respectively, to obtain the first sludge moisture to the ath sludge moisture; Step S172: Calculate the variance of the first sludge moisture to the ath sludge moisture to obtain the sludge moisture variance corresponding to the characteristic area of ​​the mesh belt; Step S173: Calculate the average humidity of the first sludge to the ath sludge to obtain the average humidity of the sludge corresponding to the characteristic area of ​​the mesh belt; Step S174: Calculate the sludge humidity variance and the sludge average humidity 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; Step S175: acquiring the sludge humidity monitoring coefficient corresponding to each mesh belt area respectively, and obtaining a plurality of sludge humidity monitoring coefficients; Step S176: Calculate the average of the obtained multiple sludge humidity monitoring coefficients to obtain the sludge humidity periodic monitoring coefficient; Step S18: defining the cumulative sludge drying time, the sludge moisture content periodic monitoring coefficient and the sludge humidity periodic monitoring coefficient as drying belt operation monitoring data; 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; The details are as follows: Step S21: Acquire the drying net belt operation monitoring data, and acquire 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; Step S22: Calculate the cumulative sludge drying time, the sludge moisture content periodic monitoring coefficient, and the sludge humidity periodic monitoring coefficient to obtain a corresponding working state monitoring coefficient of the low-temperature 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; Step S23: respectively obtaining the first working state monitoring threshold and the second working state monitoring threshold and performing numerical comparison with the working state monitoring coefficient, and dividing the low-temperature dryer into the first working state, the second working state and the third working state according to the numerical comparison results, and obtaining the dryer working state monitoring data; The details are as follows: Step S231: 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; Step S232: Calculate the first working state monitoring coefficient threshold corresponding to the low-temperature dryer by using the accumulated sludge drying time, the first sludge moisture content periodic monitoring coefficient threshold, and the first sludge humidity periodic monitoring coefficient threshold; Step S233: respectively obtaining the cumulative sludge drying time, the second sludge moisture content periodic monitoring coefficient threshold, and the second sludge humidity periodic monitoring coefficient threshold; Step S234: Calculate the second working state monitoring coefficient threshold corresponding to the low-temperature dryer by using the accumulated sludge drying time, the second sludge moisture content periodic monitoring coefficient threshold, and the second sludge humidity periodic monitoring coefficient threshold; Step S235: if 0<Gzj<Gzj1, it is determined that the low-temperature drying machine is in the first working state; Step S236: if Gzj1≤Gzj≤Gzj2, it is determined that the low-temperature drying machine is in the second working state; Step S237: if Gzj2<Gzj, it is determined that the low-temperature drying machine is in the third working state; Step S24: defining 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 as the working state analysis data of the drying machine; Step S3: remotely controlling and giving early warning to the low-temperature drying machine according to the monitoring data of the drying machine working status; The details are as follows: Step S31: 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; Step S32: when the low-temperature drying machine is in the first working state, remotely controlling the low-temperature drying machine; The details are as follows: Step S321: obtaining a difference between the working state monitoring coefficient corresponding to the low-temperature drying machine and the first working state monitoring coefficient threshold, taking an absolute value of the obtained difference to obtain a first state monitoring deviation value, and obtaining a state monitoring deviation threshold; Step S322: when the first state monitoring deviation value is less than the state monitoring deviation threshold, remotely controlling the low-temperature drying machine to reduce the heating power through the cloud platform; Step S323: when the first state monitoring deviation value is greater than or equal to the state monitoring deviation threshold, the dryer is remotely warned via the cloud platform; Step S33: when the low-temperature drying machine is in the second working state, there is no need to remotely control the low-temperature drying machine; Step S34: when the low-temperature drying machine is in the third working state, remotely controlling the low-temperature drying machine; The details are as follows: Step S341: 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 to obtain the second state monitoring deviation value, and obtaining the state monitoring deviation threshold; Step S342: when the second state monitoring deviation value is less than the state monitoring deviation threshold, remotely controlling the low-temperature drying machine to increase the heating power through the cloud platform; Step S343: When the second state monitoring deviation value is greater than or equal to the state monitoring deviation threshold, an early warning is remotely issued to the dryer through the cloud platform.

[0022] 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 only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only 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 perform numerical comparison with the working state monitoring coefficient, obtain the working state monitoring data of the dryer according to the numerical comparison result, and obtain the working state analysis data of the dryer; 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 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; 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.

Images