Real-time monitoring method and system for sludge solidification stirring equipment

Abstract

The invention relates to the technical field of electric digital data processing, in particular to a real-time monitoring method and system for sludge solidification stirring equipment, and the method comprises the steps: obtaining multi-dimensional parameter data of the sludge solidification stirring equipment at a plurality of historical time nodes; constructing the parameter data of the same dimension into a parameter sequence in a set time window; acquiring the local fluctuation degree of the parameter data of any time node in the parameter sequence to which the parameter data belong, and the linear correlation degree between the parameter data and other parameter sequences; determining the parameter data greater than a linear correlation degree threshold as strong cooperation parameter data; and respectively calculating first abnormal scores of the parameter data and the strong cooperation parameter data of any time node by using an improved local outlier factor algorithm, averaging the first abnormal scores to obtain a second abnormal score, and responding to the parameter data of which the second abnormal score is greater than a set threshold value as abnormal parameter data. The problem that an existing algorithm is not high in detection precision is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and more particularly to a real-time monitoring method and system for sludge solidification and mixing equipment. Background Art

[0002] In the process of sludge treatment and resource utilization, solidification and mixing equipment is used to improve the mechanical properties and environmental characteristics of sludge. Existing sludge solidification and mixing equipment usually reduces the water content and increases the strength of the sludge by adding a solidifying agent and performing mechanical stirring. However, during the solidification and mixing process, the operating state of the equipment and the stirring effect are affected by multi-dimensional parameters, such as speed, torque, power, temperature, and stirring resistance. If there is a lack of real-time monitoring and dynamic regulation, it is easy to cause uneven stirring, excessive energy consumption or unstable solidification effect. In addition, traditional monitoring methods mostly rely on a single parameter or post-detection, and cannot fully perceive and immediately feedback the complex changes in the stirring process, and it is difficult to meet the needs of refined control and intelligent management. Therefore, it is urgent to propose a method and system that can monitor the sludge solidification and mixing process in real time to achieve dynamic acquisition, analysis and feedback of multi-dimensional parameters, thereby improving the solidification effect and the reliability of equipment operation.

[0003] In existing technologies, the local outlier factor algorithm, a density-based anomaly detection method, is used in fields such as industrial equipment operation monitoring and medical data anomaly detection due to its ability to effectively identify local density outliers in a dataset. The local outlier factor algorithm calculates the local reachable density between each data point and its neighbors, thereby assessing the degree of outlier status within the neighborhood and identifying outliers.

[0004] However, the traditional local outlier factor algorithm has exposed many deficiencies in practical applications. First, the local outlier factor algorithm is highly dependent on the number of neighbors. Selection, fixed The value is difficult to adapt to the changing characteristics of parameter data, especially in multi-dimensional dynamic parameter environments such as sludge solidification mixing equipment. The fluctuation amplitude and data distribution of different parameters vary significantly. Setting a single number of neighbors often leads to insufficient sensitivity of the algorithm to abnormal signals or an increased false detection rate, seriously affecting the effectiveness of anomaly detection. Secondly, traditional local outlier factor algorithms mainly focus on the local density changes of a single parameter and lack the ability to model the complex synergistic relationships between multiple parameters, which in turn leads to low detection accuracy. Summary of the Invention

[0005] In order to solve the problem of low detection accuracy raised in the above background technology, the present invention provides solutions in the following aspects.

[0006] In a first aspect, the present application provides a real-time monitoring method for sludge solidification and mixing equipment, comprising: obtaining multi-dimensional parameter data of the sludge solidification and mixing equipment at a plurality of historical time nodes; for the parameter data of any time node, constructing the parameter data of the same dimension within a set time window as a parameter sequence to obtain a plurality of parameter sequences; obtaining the local fluctuation degree of the parameter data of any time node in the parameter sequence thereof, and obtaining the linear correlation degree between the parameter sequence of any parameter data of any time node and other parameter sequences; determining the parameter data with a linear correlation degree greater than a threshold as strong collaborative parameter data; calculating the first abnormal score of the parameter data and the strong collaborative parameter data of any time node respectively by using an improved local outlier factor algorithm, taking the average of the first abnormal scores to obtain a second abnormal score, and determining the parameter data with a second abnormal score greater than a set threshold as abnormal parameter data; wherein the improved local outlier factor algorithm comprises The local fluctuation degree is calculated based on the mean deviation and normalization processing. The local fluctuation degree is positively correlated with the initial value, the local fluctuation degree, and the number of strong collaborative parameter data.

[0007] The above technical solution introduces the local fluctuation feature and the linear correlation between parameters in the multi-dimensional parameter time sequence, which not only dynamically describes the stability and coupling of each parameter, but also highlights the key influencing factors by screening strong collaborative parameter data, and realizes adaptive detection of abnormal data by combining the improved local outlier factor algorithm. It can sensitively identify sudden abnormalities under complex working conditions and maintain robustness under stable conditions, thereby effectively improving the accuracy and reliability of abnormal detection.

[0008] Further, the local fluctuation degree is calculated based on the mean deviation and normalization processing. The value of the i-th parameter data is The mean value of all parameter data in the parameter sequence to which the i-th parameter data belongs is The preset hyperparameter is The total number of all parameter data in the parameter sequence to which the i-th parameter data belongs is The above technical solution introduces the local fluctuation feature and the linear correlation between parameters in the multi-dimensional parameter time sequence, which not only dynamically describes the stability and coupling of each parameter, but also highlights the key influencing factors by screening strong collaborative parameter data, and realizes adaptive detection of abnormal data by combining the improved local outlier factor algorithm. It can sensitively identify sudden abnormalities under complex working conditions and maintain robustness under stable conditions, thereby effectively improving the accuracy and reliability of abnormal detection. Further, the linear correlation degree is calculated based on the mean deviation and normalization processing.

[0009] The above technical solution introduces the local fluctuation feature and the linear correlation between parameters in the multi-dimensional parameter time sequence, which not only dynamically describes the stability and coupling of each parameter, but also highlights the key influencing factors by screening strong collaborative parameter data, and realizes adaptive detection of abnormal data by combining the improved local outlier factor algorithm. It can sensitively identify sudden abnormalities under complex working conditions and maintain robustness under stable conditions, thereby effectively improving the accuracy and reliability of abnormal detection.

[0010] Further, the linear correlation degree is calculated based on the mean deviation and normalization processing. The mean value of all parameter data in the parameter sequence to which the i-th parameter data belongs is The total number of all parameter data in the parameter sequence to which the i-th parameter data belongs is​​​​ The parameter sequence to which the parameter data belongs, For the The parameter sequence to which the parameter data belongs, is the Pearson correlation coefficient, For the The mean of all parameter data in the first-order difference sequence of the parameter sequence to which the parameter data belongs.

[0011] By incorporating both the statistical dependencies between parameter sequences and the changing trends of the sequences themselves into correlation calculations, this technical solution can measure the strength of linear coupling between different parameters while also taking into account the dynamic characteristics of parameter changes over time, thus avoiding the bias caused by relying solely on static correlations. This more comprehensively reveals the true degree of coupling between multidimensional parameters, such as electrical, mechanical, and environmental parameters, enabling monitoring to more accurately identify key correlates affecting the sludge solidification and mixing process, improving the reliability and foresight of anomaly detection and condition assessment.

[0012] Furthermore, the The values ​​are: , is the initial value, For the The local fluctuation degree of parameter data in the parameter sequence to which it belongs, is the number of strong collaborative parameter data, is the ceiling function.

[0013] The above technical solution dynamically sets the number of neighboring points in combination with the degree of parameter fluctuation and the number of strongly collaborative parameters, and can adaptively adjust the sensitivity of the local outlier factor algorithm, so that it can more keenly capture anomalies when the parameters fluctuate greatly or there are more collaborative features, and maintain robustness when the parameters are relatively stable or there is less collaboration, thereby effectively avoiding the detection bias caused by a fixed number of neighboring points, improving the accuracy and robustness of multi-dimensional parameter anomaly identification, and thereby enhancing the real-time monitoring and intelligent analysis capabilities of the operating status of sludge solidification mixing equipment.

[0014] Furthermore, the multi-dimensional parameter data includes: current, voltage, resistance, temperature, rotation speed, torque and stirring resistance.

[0015] Furthermore, the multi-dimensional parameter data is standardized.

[0016] Furthermore, the threshold is set based on the 3σ rule.

[0017] The technical solution sets an abnormality judgment threshold based on the 3σ rule, can effectively utilize the standard deviation characteristics in statistics, scientifically distinguish normal fluctuations and abnormal changes, and ensure that only parameters significantly deviating from the normal range are identified as abnormalities. This threshold setting method balances the sensitivity and stability of detection, helps to reduce false positives caused by random fluctuations or noise, while ensuring efficient capture of real abnormal signals, thereby improving the accuracy and reliability of overall abnormality monitoring.

[0018] Further, the length of the time window is 10.

[0019] Further, the multi-dimensional parameter data is subjected to data interpolation processing.

[0020] In a second aspect, the present application provides a real-time monitoring system for sludge solidification and mixing equipment, comprising a memory and a processor, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the real-time monitoring method for sludge solidification and mixing equipment according to any one of the above embodiments is realized.

[0021] The present application has the following beneficial effects: The present application introduces local fluctuation characteristics, dynamic correlation between parameters and improved abnormality detection algorithms based on multi-dimensional parameter time series, which can more accurately capture instability and potential abnormalities in the operation of the equipment while ensuring computational efficiency, and highlight key influencing factors through the screening of strongly correlated parameters, thereby improving the sensitivity and robustness of abnormality identification. The present application can comprehensively analyze multi-source parameters such as electrical, mechanical and material characteristics, realize real-time monitoring, intelligent diagnosis and fault warning of the operation state of the sludge solidification and mixing equipment, and significantly improve the safety of the equipment operation. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 is a flow chart schematically showing a real-time monitoring method for sludge solidification and mixing equipment according to an embodiment of the present application; Figure 2 is a structural block diagram schematically showing a real-time monitoring system for sludge solidification and mixing equipment according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] A real-time monitoring method for sludge solidification and mixing equipment embodiment.

[0024] As Figure 1 shown, the flow chart of the real-time monitoring method for sludge solidification and mixing equipment according to an embodiment of the present application comprises the following steps: S1: Obtain multi-dimensional parameter data of the sludge solidification and mixing equipment at multiple historical time nodes; for the parameter data of any time node, construct the parameter data of the same dimension within a set time window as a parameter sequence to obtain multiple parameter sequences.

[0025] In a preferred embodiment, in order to realize accurate monitoring and intelligent analysis of the running state of the sludge solidification and mixing equipment, first, multi-dimensional parameter data within a set time window is collected, and the length of the time window is 10, of course, the length of the time window can be flexibly adjusted according to the actual application scene to balance the detection sensitivity and calculation efficiency. The collected multi-dimensional parameter data includes but is not limited to the following key indicators: current, voltage, resistance, temperature, speed, torque and mixing resistance. The current is used to reflect the motor load and energy consumption level, and its fluctuation can reveal the changes of mixing resistance and material state; the voltage is mainly used to evaluate the power supply stability, and combined with the current to calculate the power to ensure efficient operation of the equipment; the resistance can reflect the health status of the motor coil and electrical elements, and its abnormal increase often means heating or aging hazard; the temperature parameter not only reflects the thermal load of the motor and bearing, but also indirectly reflects the mixing intensity and material friction condition; the speed directly determines the motion state of the stirring paddle, and deviation from the set value may indicate overload or control abnormality; the torque is a key indicator of mixing load and material viscosity, and its change can reveal the distribution of solidifying agent and mixing uniformity; and the mixing resistance directly reflects the interaction between the paddle and the sludge from the material property level, and is the core basis for judging the mixing effect and the stability of the solidification process. For the parameter data of any time node, the parameter data of the same dimension within a set time window is constructed as a parameter sequence to obtain multiple parameter sequences; for example: assuming that at time nodes , the current dimension is taken as an example, and the current data collected within a set time window to is {10.2A, 10.5A, 10.1A, 9.9A, 10.3A, 10.7A, 10.4A, 10.6A, 10.2A, 10.5A}, which can be constructed as a current parameter sequence; similarly, the voltage data collected within the same time window is {220V, 219V, 221V, 220V, 218V, 222V, 221V, 220V, 219V, 220V}, which is constructed as a voltage parameter sequence. Through the above method, for any time node, the same dimension parameter data within a set time window can be organized as a parameter sequence, thereby obtaining multiple parameter sequences for subsequent analysis, realizing dynamic monitoring and feature extraction of the equipment running state.

[0026] Before formal analysis, the above-mentioned original multi-dimensional parameter data is subjected to necessary preprocessing operations, mainly including standardization processing and data interpolation processing. The purpose of standardization processing is to eliminate the deviation caused by the difference in dimensions between different parameters, so that the data in each dimension has comparability, ensures that the subsequent anomaly detection algorithm has a uniform input scale, thereby improving the detection accuracy and algorithm convergence. The standardization method can adopt Z-score standardization, minimum-maximum normalization, etc., and the specific method can be selected according to the actual data distribution.

[0027] At the same time, due to the possibility of data loss, communication delay or sensor error at individual moments during equipment operation, in order to ensure the integrity and continuity of the parameter sequence, data interpolation technology is needed to fill in the missing values. The data interpolation method can adopt linear interpolation, spline interpolation or trend-based prediction interpolation, which not only improves the data continuity, but also lays a foundation for the accurate calculation of the local fluctuation degree. This processing process can effectively reduce the false judgment caused by data loss or breakpoints, and enhance the robustness and stability of the entire anomaly recognition process.

[0028] S2: Obtain the local fluctuation degree of the parameter data at any time node in the parameter sequence and the linear correlation degree between the parameter sequence and other parameter sequences, and screen to obtain strong collaborative parameter data.

[0029] In a preferred embodiment, taking the current time node current as an example, the local fluctuation degree of the current data in the parameter sequence is calculated, and the local fluctuation degree is: , is the value of the first parameter data, is the mean value of all parameter data in the parameter sequence to which the first parameter data belongs, is a preset hyperparameter, is the total number of all parameter data in the parameter sequence to which the first parameter data belongs.

[0030] By calculating the local fluctuation degree of the current data in the parameter sequence at the current time node, the deviation of the current value from the average level can be sensitively captured on the basis of the overall level, so that the stability and abnormality of the current with time can be more accurately described. This method not only effectively weakens the interference caused by the difference in overall numerical size, but also enhances the ability to perceive subtle fluctuations, so that monitoring can discover potential abnormal loads, uneven stirring or electrical faults in equipment operation earlier and more accurately, thereby improving the real-time monitoring accuracy and intelligent analysis ability of the sludge solidification and stirring process.

[0031] The linear correlation degree between the parameter series of the current data at the current time node and the parameter series of the voltage, resistance, temperature, speed, torque and stirring resistance data is calculated respectively.

[0032] The degree of linear correlation for: , For the The parameter sequence to which the parameter data belongs, For the The parameter sequence to which the parameter data belongs, is the Pearson correlation coefficient, For the The mean of all parameter data in the first-order difference sequence of the parameter sequence to which the parameter data belongs. By calculating the linear correlation between the current and other multidimensional parameter sequences at the current time node, it is possible to maintain sensitivity to the interdependence between different parameters while combining the overall trend information of sequence changes to achieve a multi-angle comprehensive analysis of the equipment operating status. This method can reveal the dynamic coupling characteristics between electrical parameters and mechanical parameters and environmental parameters, thereby more accurately reflecting the inherent connection between energy consumption level, load status and material characteristics during the mixing process, and helping to identify potential anomalies and unstable factors in advance.

[0033] Parameter data with a linear correlation threshold above the threshold is identified as strongly synergistic. By highlighting the key coupling relationships between multidimensional parameters, the core parameters that significantly impact equipment operation and mixing performance under complex mixing conditions can be effectively identified. This not only helps reduce the interference of noise parameters and improves the accuracy and efficiency of data analysis, but also provides a reliable basis for real-time monitoring, anomaly detection, and intelligent control, thereby enhancing the accuracy and reliability of operational status assessments of sludge solidification mixing equipment.

[0034] S3: Based on the improved local outlier factor algorithm, the first anomaly score of the parameter data of any time node and its strongly coordinated parameter data is calculated.

[0035] In a preferred embodiment, the improved local outlier factor algorithm includes value, The values ​​are: , is the initial value, For the The local fluctuation degree of parameter data in the parameter sequence to which it belongs, is the number of strong collaborative parameter data, is the ceiling function.

[0036] By setting the number of neighbors dynamically as the product of the initial value, the local fluctuation degree and the number of strong synergy parameters, and using the ceiling function to ensure it is an integer, the adaptive ability of the local outlier factor algorithm in a multi-dimensional dynamic environment is significantly enhanced. The traditional algorithm uses a fixed number of neighbors when processing data with different fluctuation amplitudes and parameter synergy strengths, often resulting in overfitting or missed detection. By introducing the influence factors of parameter fluctuation characteristics and synergy structure, the number of neighbors can be flexibly adjusted according to the complexity of the data and the behavior characteristics of the group. This design can adaptively reflect the abnormal sensitivity of different parameters in the sequence, making the algorithm more sensitive to abnormal data in environments with large fluctuations or high synergy, while maintaining robustness in cases with small fluctuations or low correlation. This significantly improves the accuracy and reliability of multi-dimensional parameter anomaly detection for sludge solidification and mixing equipment, enabling fine-grained monitoring and intelligent analysis of equipment operating conditions.

[0037] wherein the local reachable density of each parameter data is calculated based on The abnormal score of each parameter data is obtained by calculating the local reachable density of each parameter data based on the existing technology, which will not be described in detail.

[0038] S4: Take the average of the first abnormal score to obtain a second abnormal score. The parameter data with a second abnormal score greater than a set threshold is abnormal parameter data.

[0039] In a preferred embodiment, to improve the stability and accuracy of the abnormal detection result, a second abnormal score mechanism is further introduced, that is, the first abnormal scores of a target parameter data and its strongly synergistic parameter data obtained by the improved local outlier factor algorithm are arithmetically averaged to obtain the final second abnormal score. The fluctuation or abnormality of a single parameter in a complex system may be affected by multiple factors, and its own abnormal score may be occasionally high or low in some cases, affecting the accuracy of abnormal judgment. By introducing highly synergistic parameter data to participate in abnormality measurement calculation, it is equivalent to comprehensive evaluation based on the collective behavior of parameters, thereby significantly improving the robustness and reliability of the determination result. This weighted mechanism based on synergistic behavior can effectively alleviate the misjudgment problem caused by isolated fluctuation of individual parameters, and is particularly suitable for high-dimensional, strongly coupled physical system monitoring scenarios.

[0040] Furthermore, to ensure that the anomaly determination criteria have a sound statistical basis and broad adaptability, this solution introduces an anomaly threshold setting method based on the 3σ rule. Specifically, a statistical model is generated by analyzing a large number of second anomaly score samples generated during historical normal operation, calculating their mean and standard deviation, and setting the judgment threshold as the mean plus three times the standard deviation. This method, based on the data fluctuation characteristics under the assumption of normal distribution, has a clear statistical basis and is highly practical, enabling unified standards, rapid deployment, and stable judgment in real-world engineering scenarios.

[0041] The present invention's solution serializes and models the multidimensional parameters of sludge solidification mixing equipment at multiple time points, extracts key features based on the degree of local fluctuation and the correlation between parameters, and uses an improved outlier detection algorithm to achieve adaptive anomaly identification. This approach maintains detection sensitivity while also taking into account computational efficiency, effectively enhancing real-time perception of equipment operating status and early warning capabilities for potential failures. By dynamically adjusting algorithm parameters and highlighting strong synergistic features, this method not only improves the accuracy and robustness of anomaly detection, but also comprehensively reveals the coupling patterns between electrical, mechanical, and material properties, ultimately achieving refined monitoring, intelligent analysis, and stability assurance of the mixing process.

[0042] An embodiment of a real-time monitoring system for sludge solidification and mixing equipment: like Figure 2 As shown, a structural block diagram of a real-time monitoring system for sludge solidification and mixing equipment according to an embodiment of the present invention includes a processor and a memory.

[0043] The present invention also provides a real-time monitoring system for sludge solidification and mixing equipment. Figure 2 As shown, the system includes a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, a real-time monitoring method for sludge solidification and mixing equipment according to the present invention is implemented.

[0044] The real-time monitoring system for sludge solidification and mixing equipment also includes other components familiar to those skilled in the art, such as a communication interface. The settings and functions of these components are known in the art and will not be described in detail here.

[0045] In this description, the terms "communication" and "communicate" are used broadly. For example, a device can communicate information to another device, even though the information need not be received explicitly by the other device. In other words, one device can communicate information to another device by placing the information in a location where the other device is able to retrieve the information, even though one device does not know exactly where or when another device will retrieve the information. The term "communication" can include one or both of these actions, and also can include other actions associated with these actions. For example, the process of placing information in a location where another device is able to retrieve the information can include outputting the information to memory, sending the information through a network, or any of numerous other processes that result in the information being placed in a location where another device is able to retrieve the information. Similarly, the process of retrieving information that has been placed in a location where a device is able to retrieve the information can include receiving the information from memory, receiving the information through a network, or any of numerous other processes by which a device retrieves information that has been placed in a location where the device is able to retrieve the information. Also, the information that a first device communicates to a second device can comprise more data than the second device actually receives. For example, a first device can output information to a location from which a second device is able to retrieve information even though the second device does not actually receive the information until after it has been processed by a third device. Similarly, a first device can receive information from a location in which a second device has placed the information even though the second device does not actually send the information to the first device until after it has been processed by a third device.

[0046] In the description of the specification, the meaning of "a plurality of" or "several" is at least two, for example, two, three or more, and the like, unless explicitly specifically defined otherwise.

[0047] While the present specification has illustrated and described a number of embodiments, it will be apparent to those of ordinary skill in the art that many changes, substitutions and alterations to the embodiments described can be made without departing from the spirit and scope of the present specification. It should be understood that various alternatives to the embodiments of the application described herein can be employed in practicing the application.

Claims

1. A real-time monitoring method for sludge solidification and mixing equipment, characterized in that: include: Acquire multidimensional parameter data of the sludge solidification mixing equipment at multiple historical time nodes; for the parameter data at any time node, construct the parameter data of the same dimension within a set time window into a parameter sequence to obtain multiple parameter sequences; Obtain the local fluctuation degree of the parameter data at any time node in the parameter sequence to which it belongs, and obtain the linear correlation degree between the parameter sequence to which any parameter data at any time node belongs and other parameter sequences; Parameter data greater than a linear correlation threshold is determined to be strongly synergistic parameter data; a first anomaly score of the parameter data and the strongly synergistic parameter data at any time node is calculated using an improved local outlier factor algorithm, the first anomaly scores are averaged to obtain a second anomaly score, and parameter data whose second anomaly score is greater than a set threshold is determined to be abnormal parameter data; The improved local outlier factor algorithm includes value, The value is positively correlated with the initial value, the degree of local fluctuation, and the number of strong synergistic parameter data.

2. A real-time monitoring method for sludge solidification and mixing equipment according to claim 1, characterized in that: The degree of local fluctuation for: , For the The value of the parameter data, For the The mean of all parameter data in the parameter sequence to which the parameter data belongs, To preset hyperparameters, For the The total number of all parameter data in the parameter sequence to which the parameter data belongs.

3. A real-time monitoring method for sludge solidification and stirring equipment according to claim 1, characterized in that: The degree of linear correlation for: , For the The parameter sequence to which the parameter data belongs, For the The parameter sequence to which the parameter data belongs, is the Pearson correlation coefficient, For the The mean of all parameter data in the first-order difference sequence of the parameter sequence to which the parameter data belongs.

4. A real-time monitoring method for sludge solidification and mixing equipment according to claim 1, characterized in that: described The values ​​are: , is the initial value, For the The local fluctuation degree of parameter data in the parameter sequence to which it belongs, is the number of strong collaborative parameter data, is the ceiling function.

5. The real-time monitoring method for sludge solidification and mixing equipment according to claim 1, characterized in that: The multi-dimensional parameter data includes: current, voltage, resistance, temperature, rotation speed, torque and stirring resistance.

6. A real-time monitoring method for sludge solidification and mixing equipment according to claim 1, characterized in that: The multidimensional parameter data is standardized.

7. The real-time monitoring method for sludge solidification and mixing equipment according to claim 1, characterized in that: The threshold is set based on the 3σ rule.

8. The real-time monitoring method for sludge solidification and mixing equipment according to claim 1, characterized in that: The length of the set time window is 10.

9. The real-time monitoring method for sludge solidification and mixing equipment according to claim 1, characterized in that: Perform data interpolation processing on the multidimensional parameter data.

10. A real-time monitoring system for sludge solidification and mixing equipment, characterized in that: The invention comprises a memory and a processor, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a real-time monitoring method for sludge solidification and mixing equipment as described in any one of claims 1 to 9 is implemented.

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