Kitchen waste processor intelligent monitoring system based on data analysis

Abstract

The present application relates to kitchen waste processor monitoring technical field, especially in based on data analysis's kitchen waste processor intelligent monitoring system, including server, dynamic operation analysis unit, feedback analysis unit, supervision unit, state evaluation unit, maintenance analysis unit and management unit;The present application is through to the garbage processor's operation state carries out analysis, and is through to the operation data carries out in-depth and feedback type mode analysis, judges whether the garbage processor is normal when processing garbage, to ensure the working efficiency of garbage processor, and through in-depth, comparative analysis mode judges whether the early warning performance of supervision unit is normal and complete, helps timely replace early warning mode, improves the early warning effect of garbage processor, at the same time ensures the early warning timeliness and effectiveness of garbage processor, and judges the operation state level of garbage processor, according to different operation state level, the garbage processor is reasonably managed.

Description

TECHNICAL FIELD

[0001] The present application relates to the kitchen waste processor monitoring technical field, especially to the intelligent monitoring system of kitchen waste processor based on data analysis. BACKGROUND

[0002] With the development of society, free 'convenient bag' is limited, dirty garbage can only be carried with a bucket, the stench is sky-high, environmental protection has become a problem that everyone is concerned about, and the household garbage processor is one of the necessary household kitchen appliances. This kind of appliance is installed under the kitchen sink and connected with the sink outlet. Food waste such as leftover food, vegetable leaves and stems can be directly poured into the machine and instantly ground and crushed, and then flow into the sewage pipe. The household garbage processor is mainly used for some garbage in daily life, such as garbage generated by families, restaurants, hotels and canteens;

[0003] The traditional garbage disposal directly crushes the food waste in the inner container by using high-speed rotating blades, and then flushes it into the sewer. However, the existing garbage processor has low supervision over its own running state, cannot timely alarm, and cannot ensure whether the alarm is complete and effective, thereby directly affecting the subsequent maintenance of the garbage processor, and cannot reasonably manage the garbage processor according to different running state levels of the garbage processor, resulting in unreasonable maintenance;

[0004] In view of the above technical defects, a solution is proposed. SUMMARY

[0005] The present application aims to provide an intelligent monitoring system of kitchen waste processor based on data analysis to solve the above technical defects. The present application analyzes the running state of the garbage processor and analyzes the running data in a deep and feedback manner to determine whether the garbage processor works normally when processing garbage, so as to ensure the working efficiency of the garbage processor. The deep and comparison analysis method is used to determine whether the alarm performance of the supervision unit is normal and complete, which helps to timely replace the alarm method, improves the alarm effect of the garbage processor, ensures the timeliness and effectiveness of the alarm of the garbage processor, determines the running state level of the garbage processor, reasonably manages the garbage processor according to different running state levels, improves the working efficiency and maintenance rationality of the garbage processor, and determines whether the garbage processor generates a secondary state signal due to non-standard daily maintenance of the garbage processor, so as to timely adjust and manage and improve the running state of the garbage processor.

[0006] The purpose of the application can be realized by the technical scheme: the intelligent monitoring system of kitchen waste processor based on data analysis, comprising a server, a dynamic operation analysis unit, a feedback analysis unit, a supervision unit, a state evaluation unit, a maintenance analysis unit and a management unit;

[0007] When the server produces supervision instructions and sends them to the dynamic operation analysis unit, the dynamic operation analysis unit immediately collects the operation data of the garbage processor after receiving the supervision instructions, including motor speed, line temperature value and working voltage value, and analyzes the operation data, sends the obtained risk signal to the feedback analysis unit and the supervision unit, and sends the obtained evaluation signal and operation state coefficient S to the state evaluation unit;

[0008] The state evaluation unit immediately retrieves the operation state coefficient S from the dynamic operation analysis unit after receiving the evaluation signal, and analyzes the operation state coefficient S, sends the obtained first-level state signal, second-level state signal and third-level state signal to the management unit, and sends the second-level state signal to the maintenance analysis unit;

[0009] The maintenance analysis unit immediately collects the basic data of the garbage processor after receiving the second-level state signal, including the number of maintenance and the number of failures, analyzes the basic data, and sends the unqualified signal and qualified signal to the management unit through the state evaluation unit;

[0010] The feedback analysis unit immediately collects the feedback data of the supervision unit after receiving the risk signal, including the working current of the display panel and the area of the display text corresponding to the risk signal in the display panel, analyzes the feedback data, and sends the abnormal signal to the supervision unit through the dynamic operation analysis unit.

[0011] Preferably, the operation data process of the dynamic operation analysis unit is as follows:

[0012] First step: collect the time length from the start time to the end time of the equipment working, and mark it as time threshold, divide the time threshold into o sub-time nodes, o is a natural number greater than zero, obtain the motor speed of the garbage processor in each sub-time node, obtain the difference between the motor speeds corresponding to the connected two sub-time nodes, mark it as speed floating value, and compare and analyze the speed floating value with the preset speed floating value threshold recorded in it, mark the speed floating value greater than the preset speed floating value threshold as abnormal speed floating value, obtain the mean value of the abnormal speed floating value, and mark it as average abnormal speed floating value PF;

[0013] Second step: Obtain the line temperature value of the garbage disposal machine within the time threshold, and mark the part with the line temperature value greater than the preset line temperature value as an abnormal temperature value, and obtain the line real-time current value corresponding to the time when the line temperature value is greater than the preset line temperature value, and then mark the sum between the abnormal temperature value and the line real-time current value as the line risk value XF;

[0014] Third step: Obtain the working voltage value of the garbage disposal machine in each sub-time node, construct a set A of working voltage values, obtain the maximum subset and the minimum subset in set A, and mark the difference between the maximum subset and the minimum subset as the maximum voltage span value ZD;

[0015] Fourth step: and get the running state coefficient S by formula, and compare and analyze the running state coefficient S with the preset running state coefficient threshold recorded and stored in it:

[0016] If the running state coefficient S is greater than or equal to the preset running state coefficient threshold, a risk signal is generated;

[0017] If the running state coefficient S is less than the preset running state coefficient threshold, an evaluation signal is generated.

[0018] Preferably, the state evaluation unit analyzes the running state coefficient S as follows:

[0019] Step one: Obtain the difference between the running state coefficient S and the preset running state coefficient threshold, and mark it as the running state difference value YC;

[0020] Step two: and get the state evaluation coefficient P by formula , wherein b1 is a preset compensation correction factor of the running state difference value, and the value is 1.342, P is the state evaluation coefficient, and the state evaluation coefficient P is compared and analyzed with the preset state evaluation coefficient interval recorded and stored in it:

[0021] If the state evaluation coefficient P is greater than the maximum value in the preset state evaluation coefficient interval, a first-level state signal is generated;

[0022] If the state evaluation coefficient P is within the preset state evaluation coefficient interval, a second-level state signal is generated;

[0023] If the state evaluation coefficient P is less than the minimum value in the preset state evaluation coefficient interval, a third-level state signal is generated.

[0024] Preferably, the basic data analysis process of the maintenance analysis unit is as follows:

[0025] The time length from the time when the garbage disposal machine starts to be used to the current time is obtained, and is marked as a use time length. The maintenance times of the garbage disposal machine within the use time length are obtained, and the time length corresponding to each maintenance time is also obtained. The product of the maintenance times and the time length corresponding to each maintenance time is marked as a maintenance coefficient. In addition, the fault times of the garbage disposal machine within the use time length are obtained, and the unit time fault times of the garbage disposal machine are obtained. The unit time fault times are compared with the preset unit time fault times threshold stored in the internal recording storage. The part of the unit time fault times exceeding the preset unit time fault times threshold is marked as a fault risk value. The ratio of the fault risk value to the maintenance coefficient is marked as a maintenance evaluation coefficient WP.

[0026] The maintenance evaluation coefficient WP is compared with the preset maintenance evaluation coefficient threshold stored in the internal recording storage.

[0027] If the maintenance evaluation coefficient WP is greater than or equal to the preset maintenance evaluation coefficient threshold, an unqualified signal is generated.

[0028] If the maintenance evaluation coefficient WP is less than the preset maintenance evaluation coefficient threshold, a qualified signal is generated.

[0029] Preferably, the feedback data analysis process of the feedback analysis unit is as follows:

[0030] S1: The time length from the time when the risk signal is received by the supervision unit to the time when the display text is completed is obtained, and is marked as a reaction time length. The area of the display text corresponding to the risk signal in the display panel within the reaction time length is obtained, and is marked as a text area. The ratio of the reaction time length to the text area is marked as a reaction coefficient.

[0031] S2: The reaction time length is divided into k sub-time nodes, k is a natural number greater than zero. The working current of the display panel in each sub-time node is obtained. The part of the working current exceeding the preset working current threshold is obtained, and the sum of the part of the working current exceeding the preset working current threshold is marked as a risk current.

[0032] S3: The reaction coefficient and the risk current are compared with the preset reaction coefficient threshold and the preset risk current threshold stored in the internal recording storage.

[0033] If the reaction coefficient is less than the preset reaction coefficient threshold, and the risk current is less than the preset risk current threshold, no signal is generated.

[0034] If the reaction coefficient is greater than or equal to the preset reaction coefficient threshold, or the risk current is greater than or equal to the preset risk current threshold, an abnormal signal is generated.

[0035] Preferably, the management unit obtains a third-level state signal upon receiving the unqualified signal and the secondary state signal, and immediately implements a preset management scheme corresponding to the third-level state signal upon obtaining the third-level state signal.

[0036] The management unit obtains a first-level state signal upon receiving the qualified signal and the secondary state signal, and immediately implements a preset management scheme corresponding to the first-level state signal upon obtaining the first-level state signal.

[0037] The present application has the following advantages:

[0038] The present application analyzes the running state of the garbage disposal machine, and analyzes the running data in a deep and feedback manner, to determine whether the garbage disposal machine works normally when processing garbage, so as to ensure the working efficiency of the garbage disposal machine. The present application determines whether the early warning performance of the management unit is normal and complete through deep and comparison analysis, which helps to replace the early warning method in time, improve the early warning effect of the garbage disposal machine, ensure the timeliness and effectiveness of the early warning of the garbage disposal machine, and determine the running state level of the garbage disposal machine, so as to reasonably manage the garbage disposal machine according to different running state levels, improve the working efficiency and maintenance rationality of the garbage disposal machine, and determine whether the garbage disposal machine generates a secondary state signal due to non-standard daily maintenance, so as to make timely adjustment and management, and improve the running state of the garbage disposal machine. BRIEF DESCRIPTION OF DRAWINGS

[0039] The present application will be further described below with reference to the accompanying drawings;

[0040] Fig. 1 is a system flowchart of the present application;

[0041] Fig. 2 is a local analysis flowchart of the present application. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0043] Embodiment 1:

[0044] Please refer to Figs. 1-2As shown, the application is an intelligent monitoring system for kitchen waste processors based on data analysis, which comprises a server, a dynamic operation analysis unit, a feedback analysis unit, a supervision unit, a state evaluation unit, a maintenance analysis unit and a management unit. The server is in one-way communication connection with the dynamic operation analysis unit. The dynamic operation analysis unit is in one-way communication connection with the supervision unit. The dynamic operation analysis unit is in bidirectional communication connection with the feedback analysis unit and the state evaluation unit. The state evaluation unit is in one-way communication connection with the management unit. The state evaluation unit is in bidirectional communication connection with the maintenance analysis unit.

[0045] When the server produces supervision instructions and sends them to the dynamic operation analysis unit, the dynamic operation analysis unit immediately collects the operation data of the waste processor after receiving the supervision instructions, including motor speed, line temperature value and working voltage value, and analyzes the operation data to determine whether the waste processor is working normally when processing waste, so as to ensure the working efficiency of the waste processor. The specific operation data process is as follows:

[0046] The time length from the start time to the end time of the equipment is collected and marked as a time threshold. The time threshold is divided into o sub-time nodes, and o is a natural number greater than zero. The motor speed of the waste processor in each sub-time node is obtained. The difference between the motor speeds corresponding to the connected two sub-time nodes is obtained and marked as a speed floating value. The speed floating value is compared and analyzed with the preset speed floating value threshold stored in it. The speed floating value greater than the preset speed floating value threshold is marked as an abnormal speed floating value. The average value of the abnormal speed floating value is obtained and marked as the average abnormal speed floating value PF. It should be noted that the larger the average abnormal speed floating value PF, the greater the risk of abnormality of the waste processor during work.

[0047] The line temperature value of the waste processor in the time threshold is obtained, and the part of the line temperature value greater than the preset line temperature value is marked as an abnormal temperature value. At the same time, the line real-time current value corresponding to the time when the line temperature value is greater than the preset line temperature value is obtained, and then the sum of the abnormal temperature value and the line real-time current value is marked as the line risk value XF. It should be noted that the larger the line risk value XF, the greater the risk of line failure of the waste processor during work.

[0048] The working voltage value of the waste processor in each sub-time node is obtained, and a set A of working voltage values is constructed. The maximum subset and the minimum subset in set A are obtained, and the difference between the maximum subset and the minimum subset is marked as the maximum voltage span value ZD.

[0049] And through the formula obtained, wherein a1, a2 and a4 are preset proportional coefficients of the average abnormal rotation speed floating value, the line risk value and the maximum voltage span value respectively, a3 is a preset correction coefficient, a1, a2, a3 and a4 are positive numbers greater than zero, S is the operation state coefficient, and the operation state coefficient S is compared with a preset operation state coefficient threshold value recorded and stored therein:

[0050] If the operation state coefficient S is greater than or equal to the preset operation state coefficient threshold value, a risk signal is generated and sent to the feedback analysis unit and the supervision unit, the supervision unit displays the word "operation risk" immediately after receiving the risk signal, thereby timely managing and maintaining the garbage disposal machine and improving the operation safety and working efficiency of the garbage disposal machine;

[0051] If the operation state coefficient S is less than the preset operation state coefficient threshold value, an evaluation signal is generated and sent to the state evaluation unit;

[0052] The state evaluation unit retrieves the operation state coefficient S from the dynamic operation analysis unit immediately after receiving the evaluation signal, analyzes the operation state coefficient S, judges the operation state level of the garbage disposal machine, and manages the garbage disposal machine reasonably according to different operation state levels, so as to improve the working efficiency and maintenance rationality of the garbage disposal machine. The specific operation state coefficient S analysis process is as follows:

[0053] The difference between the operation state coefficient S and the preset operation state coefficient threshold value is obtained and marked as an operation state difference value YC;

[0054] and the state evaluation coefficient P is obtained through the formula

[0055] If the state evaluation coefficient P is greater than the maximum value in the preset state evaluation coefficient interval, a first-level state signal is generated;

[0056] If the state evaluation coefficient P is within the preset state evaluation coefficient interval, a second-level state signal is generated;

[0057] ​If the state evaluation coefficient P is less than the minimum value in the preset state evaluation coefficient interval, a third-level state signal is generated, wherein the running states of the garbage disposal machine corresponding to the first-level state signal, the second-level state signal and the third-level state signal are sequentially deteriorated, and the first-level state signal, the second-level state signal and the third-level state signal are sent to the management unit, and the second-level state signal is sent to the maintenance analysis unit. After receiving the first-level state signal, the second-level state signal and the third-level state signal, the management unit immediately makes a preset management scheme corresponding to the first-level state signal, the second-level state signal and the third-level state signal, and then reminds the staff to reasonably manage the garbage disposal machine on a daily basis, so as to improve the working efficiency of the garbage disposal machine.

[0058] Embodiment 2:

[0059] After receiving the second-level state signal, the maintenance analysis unit immediately collects the basic data of the garbage disposal machine, including the maintenance frequency and the failure frequency, and analyzes the basic data to determine whether the garbage disposal machine generates the second-level state signal due to non-standard daily maintenance of the garbage disposal machine, and then makes timely adjustments and management to improve the running state of the garbage disposal machine. The specific basic data analysis process is as follows:

[0060] The length of time from the moment when the garbage disposal machine starts to be used to the current moment is obtained and marked as the use length. The maintenance frequency of the garbage disposal machine within the use length is obtained, and the length of time corresponding to each maintenance frequency is also obtained. The product of the maintenance frequency and the length of time corresponding to each maintenance frequency is marked as the maintenance coefficient. In addition, the failure frequency of the garbage disposal machine within the use length is obtained, and then the unit time failure frequency of the garbage disposal machine is obtained. The unit time failure frequency is compared and analyzed with the preset unit time failure frequency threshold value recorded and stored in it. The part of the unit time failure frequency that exceeds the preset unit time failure frequency threshold value is marked as the failure risk value. The ratio of the failure risk value to the maintenance coefficient is marked as the maintenance evaluation coefficient WP. It should be noted that the larger the value of the maintenance evaluation coefficient WP, the worse the daily management of the garbage disposal machine. The maintenance evaluation coefficient WP is compared and analyzed with the preset maintenance evaluation coefficient threshold value recorded and stored in it.

[0061] If the maintenance evaluation coefficient WP is greater than or equal to the preset maintenance evaluation coefficient threshold value, an unqualified signal is generated, and the unqualified signal is sent to the management unit through the state evaluation unit. When the management unit receives the unqualified signal and the second-level state signal, the third-level state signal is obtained. When the third-level state signal is obtained, a preset management scheme corresponding to the third-level state signal is immediately made, and then the garbage disposal machine is reasonably managed accurately.

[0062] If the maintenance evaluation coefficient WP is less than the preset maintenance evaluation coefficient threshold, a qualified signal is generated, and the qualified signal is sent to the management unit through the state evaluation unit. When the management unit receives the qualified signal and the secondary state signal, a primary state signal is obtained. When the primary state signal is obtained, a preset management scheme corresponding to the primary state signal is immediately made, which helps to accurately and reasonably manage the garbage disposal machine and improves the rationality of the analysis result;

[0063] After the feedback analysis unit receives the risk signal, feedback data after the supervisory unit receives the risk signal is immediately collected. The feedback data includes the working current of the display panel and the area of the display text corresponding to the risk signal in the display panel. The feedback data is analyzed to determine whether the early warning performance of the supervisory unit is normal and complete. The specific feedback data analysis process is as follows:

[0064] The time length from the moment when the supervisory unit receives the risk signal to the moment when the display text is completed is obtained and marked as the reaction time length. The area of the display text corresponding to the risk signal in the display panel within the reaction time length is obtained and marked as the text area. The ratio of the reaction time length to the text area is marked as the reaction coefficient. It should be noted that the larger the value of the reaction coefficient, the worse the early warning performance of the garbage disposal machine;

[0065] The reaction time length is divided into k sub-time nodes, k is a natural number greater than zero. The working current of the display panel in each sub-time node is obtained. The part of the working current that exceeds the preset working current threshold is obtained. The sum of the part of the working current that exceeds the preset working current threshold is marked as the risk current. The reaction coefficient and the risk current are compared and analyzed with the preset reaction coefficient threshold and the preset risk current threshold recorded in the internal storage:

[0066] If the reaction coefficient is less than the preset reaction coefficient threshold, and the risk current is less than the preset risk current threshold, no signal is generated;

[0067] If the reaction coefficient is greater than or equal to the preset reaction coefficient threshold, or the risk current is greater than or equal to the preset risk current threshold, an abnormal signal is generated and sent to the supervisory unit through the dynamic operation analysis unit. The supervisory unit immediately issues a warning in the form of voice "operation abnormality" after receiving the abnormal signal, thereby timely changing the warning mode, which helps to improve the early warning effect of the garbage disposal machine and ensures the timeliness and effectiveness of the early warning of the garbage disposal machine;

[0068] To sum up, the application analyzes the running state of the garbage disposal machine, and analyzes the operation data in an in-depth and feedback manner, to determine whether the garbage disposal machine works normally when processing garbage, so as to ensure the working efficiency of the garbage disposal machine, and to determine whether the early warning performance of the supervision unit is normal and complete through in-depth and comparison analysis, which helps to replace the early warning mode in time, improve the early warning effect of the garbage disposal machine, ensure the timeliness and effectiveness of the early warning of the garbage disposal machine, and determine the running state level of the garbage disposal machine, so as to reasonably manage the garbage disposal machine according to different running state levels, improve the working efficiency and maintenance rationality of the garbage disposal machine, and determine whether the garbage disposal machine generates a secondary state signal due to non-standard daily maintenance, so as to make timely adjustment and management, and improve the running state of the garbage disposal machine.

[0069] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value, and the coefficients in the formula are set by a person skilled in the art according to the actual situation. The above is only a preferred specific embodiment of the application, but the protection scope of the application is not limited thereto. Any person skilled in the art can make equivalent replacement or change according to the technical solution and inventive concept of the application within the technical range disclosed by the application, which should be covered within the protection scope of the application.

Claims

1. An intelligent monitoring system for food waste treatment machines based on data analysis, characterized in that: It includes a server, a dynamic operation analysis unit, a feedback analysis unit, a monitoring unit, a status assessment unit, a maintenance analysis unit, and a management unit; When the server generates a monitoring instruction, it sends it to the dynamic operation analysis unit. Upon receiving the monitoring instruction, the dynamic operation analysis unit immediately collects the operating data of the waste disposal machine, including motor speed, line temperature, and operating voltage. It then analyzes the operating data, sends the obtained risk signals to the feedback analysis unit and the monitoring unit, and sends the obtained evaluation signals and operating status coefficient S to the status evaluation unit. After receiving the evaluation signal, the status assessment unit immediately retrieves the operating status coefficient S from the dynamic operation analysis unit, analyzes the operating status coefficient S, and sends the obtained first-level status signal, second-level status signal and third-level status signal to the management unit, while sending the second-level status signal to the maintenance analysis unit. Upon receiving the Level 2 status signal, the maintenance analysis unit immediately collects basic data of the waste disposal machine, including the number of maintenance operations and the number of malfunctions. The unit then analyzes the basic data and sends the resulting non-compliance and compliance signals to the management unit via the status assessment unit. After receiving a risk signal, the feedback analysis unit immediately collects the feedback data received by the regulatory unit. The feedback data includes the operating current of the display panel and the area of ​​the displayed text corresponding to the risk signal on the display panel. The unit then analyzes the feedback data and sends the obtained abnormal signal to the regulatory unit via the dynamic operation analysis unit. The basic data analysis process of the maintenance analysis unit is as follows: The system obtains the duration from the start of the waste disposal machine's operation to the current time and marks it as the usage duration. It also obtains the number of maintenance operations performed on the waste disposal machine within the usage duration, along with the duration corresponding to each maintenance operation. The product of the number of maintenance operations and the duration corresponding to each maintenance operation is marked as the maintenance coefficient. Furthermore, the system obtains the number of malfunctions performed on the waste disposal machine within the usage duration, thereby obtaining the number of malfunctions per unit time. The system compares and analyzes the number of malfunctions per unit time with a preset threshold for the number of malfunctions per unit time stored internally. The portion of the number of malfunctions per unit time that exceeds the preset threshold is marked as a malfunction risk value. The ratio of the malfunction risk value to the maintenance coefficient is marked as the maintenance evaluation coefficient WP. The maintenance evaluation coefficient WP is then compared and analyzed with its internally entered and stored preset maintenance evaluation coefficient threshold. If the maintenance evaluation coefficient WP is greater than or equal to the preset maintenance evaluation coefficient threshold, an unqualified signal is generated; If the maintenance evaluation coefficient WP is less than the preset maintenance evaluation coefficient threshold, a qualified signal is generated; The feedback data analysis process of the feedback analysis unit is as follows: S1: Obtain the time between the moment the regulatory unit receives the risk signal and the moment the text is displayed, and mark it as the reaction time. Obtain the area of ​​the text displayed on the display panel corresponding to the risk signal within the reaction time, and mark it as the text area. Mark the ratio of the reaction time to the text area as the reaction coefficient. S2: Divide the reaction time into k sub-time nodes, where k is a natural number greater than zero, obtain the operating current of the display panel in each sub-time node, obtain the portion of the operating current that exceeds the preset operating current threshold, and mark the sum of the portion of the operating current that exceeds the preset operating current threshold as the risk current. S3: Compare and analyze the reaction coefficient and risk current with the preset reaction coefficient threshold and preset risk current threshold stored internally. If the reaction coefficient is less than the preset reaction coefficient threshold and the risk current is less than the preset risk current threshold, no signal will be generated. If the reaction coefficient is greater than or equal to the preset reaction coefficient threshold, or the risk current is greater than or equal to the preset risk current threshold, an abnormal signal is generated. When the management unit receives an unqualified signal and a secondary status signal, it obtains a tertiary status signal. Upon obtaining the tertiary status signal, it immediately makes a preset management plan corresponding to the tertiary status signal. When the management unit receives the qualified signal and the secondary status signal, it obtains the primary status signal. Upon obtaining the primary status signal, it immediately makes the preset management plan corresponding to the primary status signal.

2. The intelligent monitoring system for food waste treatment machines based on data analysis according to claim 1, characterized in that, The operational data process of the dynamic operation analysis unit is as follows: Step 1: Collect the duration between the start and end times of the equipment's operation and mark it as a time threshold. Divide the time threshold into 0 sub-time nodes, where 0 is a natural number greater than zero. Obtain the motor speed of the waste disposal machine within each sub-time node. Obtain the difference between the motor speeds corresponding to two consecutive sub-time nodes and mark it as the speed fluctuation value. Compare and analyze the speed fluctuation value with the preset speed fluctuation value threshold stored internally. Mark the speed fluctuation value corresponding to the preset speed fluctuation value threshold as an abnormal speed fluctuation value. Obtain the mean of the abnormal speed fluctuation values ​​and mark it as the average abnormal speed fluctuation value PF. Step 2: Obtain the line temperature value of the waste disposal machine within the time threshold, and mark the part of the line temperature value that is greater than the preset line temperature value as the abnormal temperature value. At the same time, obtain the real-time line current value corresponding to the moment when the line temperature value is greater than the preset line temperature value, and then mark the sum of the abnormal temperature value and the real-time line current value as the line risk value XF. Step 3: Obtain the operating voltage value of the waste disposal machine in each sub-time node, construct a set A of operating voltage values, obtain the maximum subset and minimum subset in set A, and mark the difference between the maximum subset and the minimum subset as the maximum voltage span value ZD; Step 4: Obtain the operating state coefficient S using the formula, and compare and analyze the operating state coefficient S with the preset operating state coefficient threshold stored internally. If the operating state coefficient S is greater than or equal to the preset operating state coefficient threshold, a risk signal is generated. If the operating state coefficient S is less than the preset operating state coefficient threshold, an evaluation signal is generated.

3. The intelligent monitoring system for food waste treatment machines based on data analysis according to claim 1, characterized in that, The state assessment unit analyzes the operating state coefficient S as follows: Step 1: Obtain the difference between the running state coefficient S and the preset running state coefficient threshold, and mark it as the running state difference value YC; Step Two: and through the formula The state evaluation coefficients are obtained, where b1 is the preset compensation correction factor for the difference in operating state, with a value of 1.342, and P is the state evaluation coefficient. The state evaluation coefficient P is then compared and analyzed with the preset state evaluation coefficient range stored internally. If the state evaluation coefficient P is greater than the maximum value in the preset state evaluation coefficient range, then a first-level state signal is generated. If the state evaluation coefficient P is within the preset state evaluation coefficient range, then a second-level state signal is generated; If the state evaluation coefficient P is less than the minimum value in the preset state evaluation coefficient range, a level 3 state signal is generated.

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