A sewage treatment knowledge base construction method and system based on multi-source data analysis

Abstract

The application relates to the technical field of intelligent sewage treatment, and particularly discloses a sewage treatment knowledge base construction method and system based on multi-source data analysis, which comprises the following steps: recording the treatment operation of a worker, obtaining sewage parameters containing positions and time based on floating sampling points, statistically processing the treatment operation and the sewage parameters based on the same time scale to obtain test samples, cutting the test samples to obtain samples of the treatment operation to parameter variation, which are called mapping samples, and constructing a sewage treatment knowledge base according to the mapping samples. The application records the treatment operation based on the same time axis, simultaneously collects sewage parameters in real time, cuts the variation of the sewage parameters according to the treatment operation, obtains the sewage parameter variation corresponding to each treatment operation, carries out clustering display on the sewage parameter variation, obtains the possible results of each treatment operation, and uses the sewage parameter variation as the sewage treatment knowledge base of each treatment operation, which is extremely readable and highly convenient.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent sewage treatment, and particularly relates to a sewage treatment knowledge base construction method and system based on multi-source data analysis. BACKGROUND

[0002] The sewage treatment knowledge base is a database or document that systematically organizes sewage treatment related information, and is used to record the correspondence between treatment operation and treatment result in the sewage treatment process. On the one hand, it can be used for employee training, and on the other hand, it can also be used as a reference for the sewage treatment process. Therefore, a high-quality sewage treatment knowledge base can bring great benefits.

[0003] However, the existing sewage treatment knowledge base is actually a simple database, such as statistical processing operation and processing result according to time sequence. The data is extremely complex and has poor readability. In the application, the staff needs to manually screen, and the convenience is not high. How to provide a data statistical scheme applied to sewage treatment data to construct a more clear and intuitive sewage treatment knowledge base for the staff is a technical problem to be solved by the technical scheme of the present application. SUMMARY

[0004] The purpose of the present application is to provide a sewage treatment knowledge base construction method and system based on multi-source data analysis to solve the problems raised in the background.

[0005] To achieve the above purpose, the present application provides the following technical scheme:

[0006] A sewage treatment knowledge base construction method and system based on multi-source data analysis, the method comprising:

[0007] Setting a floating sampling point in a sewage test box, and synchronously determining the data type at the floating sampling point;

[0008] Recording the treatment operation of the staff, obtaining sewage parameters containing position and time based on the floating sampling point, and statistically processing the operation and the sewage parameters based on the same time scale to obtain test samples;

[0009] Splitting the test samples to obtain samples of operation to parameter change, referred to as mapping samples;

[0010] Constructing a sewage treatment knowledge base according to the mapping samples, verifying the treatment process in real time based on the sewage treatment knowledge base, and triggering an update instruction according to the verification result.

[0011] As a further scheme of the present application, the step of setting a floating sampling point in a sewage test box and synchronously determining the data type at the floating sampling point comprises:

[0012] Acquire a three-dimensional model of the sewage test box, and locate the inner surface in the three-dimensional model;

[0013] Select reference points on each inner surface based on a preset density; the density is the number of reference points per unit area, and the density is determined by the number of edges of the inner surface;

[0014] Obtain a ray pointing to the inside of the sewage test box through the reference point and perpendicular to the corresponding inner surface, and intercept the ray based on the box region of the sewage test box to obtain a line segment corresponding to each reference point; wherein the box region is not less than the space of the sewage test box;

[0015] Select floating sampling points on the line segment based on a preset step length, and for any floating sampling point selected, calculate the distance between it and the nearest floating sampling point, and delete the floating sampling point when the distance is less than a preset distance threshold;

[0016] For any data type, query the demand ratio of the data type, and distribute the floating sampling points according to the demand ratio.

[0017] As a further scheme of the present application: the recording of the processing operation of the worker, the sewage parameter containing position and time obtained based on the floating sampling point, and the processing operation and the sewage parameter based on the same time scale are obtained. The steps of obtaining the test sample include:

[0018] Record the processing operation of the worker at each time;

[0019] Obtain the relative coordinates of the floating sampling point in the pollution test box based on the range finder at the floating sampling point;

[0020] Obtain the sewage parameter based on the pollution collector at the floating sampling point, and record the collection time synchronously to obtain the sewage parameter containing the relative coordinates and the collection time;

[0021] Based on a preset time step, the time is regularized to obtain a time period;

[0022] Obtain the sewage parameter containing the relative coordinates in each time period, and construct a parameter matrix based on a preset matrix template;

[0023] Based on the same time axis, the processing operation and the parameter matrix are counted to obtain a test sample.

[0024] As a further scheme of the present application: the step of recording the processing operation of the worker at each time includes:

[0025] Receive the operation information input by the worker based on a preset information receiving port, the operation information containing a time period;

[0026] Based on the camera, the worker is located in real time, and when the worker is detected, an identification instruction is triggered;

[0027] acquire a trigger time of the trigger recognition instruction, when the trigger time is contained in the time period, recognize the behavior of the worker based on a preset first accuracy, and verify the operation information;

[0028] when the trigger time is not contained in the time period, recognize the behavior of the worker based on a preset second accuracy, and acquire the operation information;

[0029] wherein the camera works under a preset third accuracy condition when the recognition instruction is not triggered, the second accuracy is greater than the first accuracy, and the first accuracy is greater than the third accuracy.

[0030] As a further scheme of the present application, the step of splitting the test sample to obtain a sample of the processing operation to the parameter variation quantity, referred to as a mapping sample, comprises:

[0031] for any moment of processing operation, acquiring a parameter matrix before processing according to a preset forward span, and acquiring a parameter matrix after processing according to a preset backward span;

[0032] calculating a difference matrix of the parameter matrix after processing and the parameter matrix before processing;

[0033] constructing a sample of the processing operation to the difference matrix, referred to as a mapping sample.

[0034] As a further scheme of the present application, the step of constructing a sewage treatment knowledge base according to the mapping sample, verifying the processing process in real time based on the sewage treatment knowledge base, and triggering an update instruction according to the verification result comprises:

[0035] counting all the mapping samples, classifying the mapping samples according to the processing operation in the mapping samples, and obtaining a sample set corresponding to each processing operation;

[0036] for the sample set of each processing operation, comparing the samples in the sample set two by two, and calculating a similarity; the similarity adopts a similarity of the difference matrix;

[0037] based on the similarity, clustering the samples in the sample set, calculating a sample proportion and a mean sample of each clustering result; the sample proportion is the sample number of the clustering result divided by the total number of samples in the sample set, and the mean sample is a mean matrix of the difference matrix of all samples in the clustering result;

[0038] taking the sample proportion and the mean sample thereof as a data item, arranging the data items in descending order based on the sample proportion, and obtaining a data table of the processing operation;

[0039] counting the data tables of all the processing operations, and obtaining a sewage treatment knowledge base;

[0040] In actual application, any processing operation of the staff is based on the sewage treatment knowledge base to obtain a theoretical prediction result, the accuracy of the actual processing result is determined based on the theoretical prediction result, when the accuracy is less than a preset accuracy threshold, the processing operation and the actual processing result are taken as a mapping sample, and an updating instruction is triggered;Wherein, the process of taking the processing operation and the actual processing result as the mapping sample at least includes normalizing the actual processing result based on a matrix template.

[0041] The technical scheme of the application also provides a sewage treatment knowledge base construction system based on multi-source data analysis, the system comprises:

[0042] A sampling point setting module is configured to set a floating sampling point in the sewage test box and synchronously determine the data type at the floating sampling point.

[0043] A test sample generation module is configured to record the processing operation of the staff, obtain sewage parameters containing position and time based on the floating sampling point, and obtain test samples by statistically processing the processing operation and the sewage parameters based on the same time scale.

[0044] A test sample segmentation module is configured to segment the test samples to obtain samples of processing operation to parameter change, which are referred to as mapping samples.

[0045] A knowledge base construction module is configured to construct a sewage treatment knowledge base according to the mapping samples, verify the processing process in real time based on the sewage treatment knowledge base, and trigger an updating instruction according to the verification result.

[0046] As a further scheme of the application, the sampling point setting module comprises:

[0047] An inner surface positioning unit is configured to obtain a three-dimensional model of the sewage test box and position the inner surface in the three-dimensional model.

[0048] A reference point selection unit is configured to select reference points on each inner surface based on a preset density, wherein the density is the number of reference points per unit area, and the density is determined based on the number of edges of the inner surface.

[0049] A line segment intercepting unit is configured to obtain a ray that passes through the reference point and is perpendicular to the corresponding inner surface and points to the inside of the sewage test box, intercept the ray based on the box region of the sewage test box to obtain a line segment corresponding to each reference point, and wherein the box region is not less than the space of the sewage test box.

[0050] A sampling point deleting unit is configured to select floating sampling points on the line segment based on a preset step length, calculate the distance between any selected floating sampling point and the nearest floating sampling point, and delete the floating sampling point when the distance is less than a preset distance threshold.

[0051] The sampling point distribution unit is configured to distribute the floating sampling points according to the demand proportion of any data type.

[0052] As a further scheme of the present application, the test sample generation module comprises:

[0053] The operation recording unit is configured to record the processing operation of the staff at each time point.

[0054] The coordinate acquisition unit is configured to acquire the relative coordinates of the floating sampling points in the pollution test box based on the range finder at the floating sampling points.

[0055] The parameter acquisition unit is configured to acquire the sewage parameters based on the pollution collector at the floating sampling points, synchronously record the acquisition time, and obtain the sewage parameters containing the relative coordinates and the acquisition time.

[0056] The time normalization unit is configured to normalize the time based on a preset time step to obtain a time period.

[0057] The parameter matrix construction unit is configured to acquire the sewage parameters containing the relative coordinates in each time period, and construct a parameter matrix based on a preset matrix template.

[0058] The operation statistical unit is configured to statistically process the operation and the parameter matrix based on the same time axis to obtain a test sample.

[0059] As a further scheme of the present application, the operation recording unit comprises:

[0060] The information receiving subunit is configured to receive the operation information containing the time period input by the staff based on a preset information receiving port.

[0061] The staff positioning subunit is configured to position the staff in real time based on the camera, and trigger the recognition instruction when the staff is detected.

[0062] The first recognition subunit is configured to acquire the trigger time of the triggered recognition instruction, recognize the behavior of the staff based on a preset first accuracy when the trigger time is contained in the time period, and verify the operation information.

[0063] The second recognition subunit is configured to recognize the behavior of the staff based on a preset second accuracy when the trigger time is not contained in the time period, and acquire the operation information.

[0064] The camera works under a preset third accuracy condition when the recognition instruction is not triggered, the second accuracy is greater than the first accuracy, and the first accuracy is greater than the third accuracy.

[0065] Compared with the prior art, the present application has the following advantages:

[0066] The application records treatment operation at the same time scale, collects sewage parameters in real time, divides sewage parameter change amount according to treatment operation, obtains sewage parameter change amount corresponding to each treatment operation, clusters and displays sewage parameter change amount, obtains possible results of each treatment operation, and uses the results as a sewage treatment knowledge base of each treatment operation, which is highly readable and extremely convenient to use. BRIEF DESCRIPTION OF DRAWINGS

[0067] In order to more clearly illustrate the technical solutions in the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only some embodiments of the application.

[0068] Figure 1 A total flow chart of a sewage treatment knowledge base construction method based on multi-source data analysis is shown.

[0069] Figure 2 A structure diagram of a sewage treatment knowledge base construction system based on multi-source data analysis is shown. DETAILED DESCRIPTION

[0070] In order to make the technical problems to be solved by the application, technical solutions and beneficial effects more clearly, the following will further describe the application in combination with the drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the application, and are not used to limit the application.

[0071] Figure 1 For the total flow chart of the sewage treatment knowledge base construction method and system based on multi-source data analysis, in the embodiments of the application, a sewage treatment knowledge base construction method based on multi-source data analysis, the method comprises:

[0072] Step S100: setting a floating sampling point in a sewage test box, and synchronously determining the data type at the floating sampling point;

[0073] The technical solution of the application is used to construct a sewage treatment knowledge base, therefore, a large amount of test work needs to be performed, the test work is generated in a sewage test box, sewage is put into the sewage test box, a floating sampling point is set in the sewage test box, which is used to collect the state of the sewage at each time, when a worker takes a treatment measure, the sewage data before and after the treatment is completely recorded and stored in a preset database; it should be noted that the state of the sewage at each time is actually a high-level probability, which is actually parameters of different data types, such as concentrations of different components.

[0074] Step S200: recording the treatment operation of the worker, obtaining sewage parameters containing positions and times based on the floating sampling point, and obtaining test samples based on the same time scale statistics of the treatment operation and the sewage parameters;

[0075] The treatment operation of the staff refers to the sewage treatment process, and in actual application, mainly refers to when and how much treatment reagent is put, and of course, some physical means such as when the filtering salvage operation is used, which are collectively referred to as treatment operations, and the treatment operations are some numerical or text data in the computer; then, the collection device is installed at the floating sampling point, and the sewage parameters containing position and time can be obtained, a time axis is constructed at the starting moment of the test process, all the treatment operations and sewage parameters in a test process are counted based on the time axis, and a sample corresponding to a test process is obtained, which is called a test sample.

[0076] It should be noted that the meaning of the floating sampling point in the technical scheme of the application is that the floating sampling point is a sampling point whose position changes, for example, the collection device that can float is connected to the bottom of the sewage test tank by a flexible material (wire), and the position of the collection device changes constantly with the change of the water flow, so that when the sewage parameters are obtained, the position needs to be recorded in addition to the time; the position identification process is that a range finder pointing to three mutually perpendicular directions is installed on the collection device, one of which points to the bottom of the collection device, so that the position of the collection device in the sewage test tank can be obtained in real time.

[0077] Step S300: the test sample is divided to obtain a sample of treatment operation to parameter change amount, which is called a mapping sample;

[0078] The test sample contains treatment operation and pollution parameter, and their time relationship is very clear, for each treatment operation, the change of the pollution parameter before and after it is obtained, which is called parameter change amount, and then a sample of treatment operation and parameter change amount is constructed, which is called a mapping sample.

[0079] Step S400: constructing a sewage treatment knowledge base according to the mapping sample, verifying the treatment process in real time based on the sewage treatment knowledge base, and triggering an update instruction according to the verification result;

[0080] Each test can obtain a test sample, and then a plurality of mapping samples are obtained, and a plurality of tests are continuously performed (these tests can be performed in a plurality of plants), a large number of mapping samples can be obtained, a sewage treatment knowledge base is constructed according to the mapping samples, and the sewage treatment knowledge base is used to represent the relationship between the treatment operation and the sewage treatment result, in actual application, the sewage treatment knowledge base can be used as a treatment reference, and in the treatment, the treatment process is verified in real time based on the sewage treatment knowledge base, the application accuracy of the sewage treatment knowledge base can be obtained, if the application accuracy is low, the sewage treatment knowledge base needs to be updated, that is, the update instruction is triggered.

[0081] It should be noted that the goal of updating the technical solution of this invention is to test the sample. The actual application process is also used as a test, and the acquired data is used as a new test sample. When the test sample changes, the wastewater treatment knowledge base will also change.

[0082] Regarding step S100, the step of setting floating sampling points in the wastewater testing chamber and simultaneously determining the data type at the floating sampling points includes:

[0083] Obtain a 3D model of the wastewater test chamber and locate the inner surface in the 3D model;

[0084] Reference points are selected on each inner surface based on a preset density; the density is the number of reference points per unit area, and the density is determined by the number of sides of the inner surface.

[0085] A ray passing through a reference point and perpendicular to the inner surface of the wastewater test chamber is obtained, pointing towards the interior of the wastewater test chamber. The ray is then intercepted based on the chamber area of ​​the wastewater test chamber to obtain the line segment corresponding to each reference point; wherein, the chamber area is not smaller than the space of the wastewater test chamber.

[0086] Floating sampling points are selected on the line segment based on a preset step size. For any selected floating sampling point, the distance between it and the nearest floating sampling point is calculated. If the distance is less than a preset distance threshold, the floating sampling point is deleted.

[0087] For any data type, query the demand ratio for that data type and allocate floating sampling points according to the demand ratio.

[0088] Wastewater testing chambers generally have a simple structure; they are boxes used to hold wastewater. The term "box" is a broader concept; for example, a pit that can hold wastewater can also serve as a box. The wastewater testing chamber is modeled to obtain a 3D model. The inner surface is located within the 3D model, and reference points are selected on each inner surface based on a preset density. This process is used to select points on the inner surface of the wastewater testing chamber, called reference points. Rays passing through the reference points and perpendicular to the corresponding inner surface are obtained, pointing towards the interior of the wastewater testing chamber. The portion of the ray contained within the wastewater testing chamber is obtained, called a line segment. Floating sampling points are selected on the line segments based on a preset step size. For any selected floating sampling point, its distance to the nearest floating sampling point is calculated. If the distance is less than a preset distance threshold, the floating sampling point is deleted (only one is retained to reduce the number of floating sampling points). For any data type, the demand ratio for that data type is queried, and floating sampling points are allocated according to the demand ratio. The demand ratio indicates how many floating sampling points are needed for each type of data. The number of floating sampling points for each data type is obtained by multiplying the demand ratio by the total number. The data type generally refers to the type of pollutant that needs to be monitored in the wastewater.

[0089] Regarding step S200, the steps of recording the staff's processing operations, obtaining wastewater parameters containing location and time based on floating sampling points, and obtaining test samples based on the statistical processing operations and wastewater parameters at the same time scale include:

[0090] Record the actions taken by staff at various times;

[0091] The relative coordinates of the floating sampling point in the contamination test chamber are obtained using a rangefinder at the floating sampling point.

[0092] Wastewater parameters are acquired using a pollution collector at a floating sampling point, and the collection time is recorded synchronously to obtain wastewater parameters containing relative coordinates and collection time.

[0093] The time is regularized based on a preset time step to obtain a time period.

[0094] Obtain wastewater parameters with relative coordinates for each time period, and construct a parameter matrix based on a preset matrix template;

[0095] Test samples are obtained based on statistical processing operations and parameter matrices along the same time axis.

[0096] During operation, the technical solution of this invention records the staff's actions in real time, capturing the processing at each moment. The relative coordinates of the floating sampling point within the pollution testing chamber are obtained using a rangefinder at the floating sampling point; these relative coordinates represent the position. Wastewater parameters are acquired using a pollution collector at the floating sampling point, with the collection time recorded synchronously, resulting in wastewater parameters containing relative coordinates and collection time. Since the collection time is relatively instantaneous and affected by the transmission process, this technical solution requires time normalization, normalizing it to units of one minute, 30 seconds, or 10 seconds. Based on the time step, several time periods are determined, and wastewater parameters belonging to the same time period are considered as wastewater parameters of the same time. Wastewater parameters containing relative coordinates are obtained for each time period. A parameter matrix is ​​constructed based on a preset matrix template. This operation yields the parameter matrix for each time period. Statistical processing of the operation and parameter matrix along the same time axis yields the test sample.

[0097] Specifically, since the number of floating sampling points is limited and they need to be allocated to different data types, each data type has very few floating sampling points, resulting in very little actual data being obtained. To address this, the present invention introduces a data expansion process: for any position in the parameter matrix, the nearest actual data is queried and the actual data is used to fill the unknown position. Furthermore, regarding the matrix template, it is a three-dimensional matrix with fixed length, width, and height. Each data type corresponds to a matrix template, and each data type can ultimately obtain parameter matrices for different time periods.

[0098] As a preferred embodiment of the technical solution of the present invention, the step of recording the processing operations of the staff at various times includes:

[0099] The system receives operation information containing time periods from staff input via a preset information receiving port.

[0100] Based on real-time location of staff using cameras, a recognition command is triggered when a staff member is detected;

[0101] The trigger time of the trigger recognition command is obtained. When the trigger time is included in a time period, the behavior of the staff is recognized based on the preset first precision to verify the operation information.

[0102] When the trigger time is not included in the time period, the operator's behavior is identified based on the preset second precision to obtain the operation information;

[0103] The camera operates under a preset third precision condition when no recognition command is triggered, where the second precision is greater than the first precision, and the first precision is greater than the third precision.

[0104] Before the staff performs any operation, they input the operation information into the information receiving port. For the entity executing the technical solution of this invention, only the data receiving process needs to be executed. In one example of the technical solution of this invention, in addition to receiving the data, a verification process is also introduced. Based on the real-time positioning of the staff by the camera, when the staff is detected, a recognition command is triggered. The process of the camera locating the staff is real-time. Considering cost issues, its accuracy is relatively low, which is the third accuracy mentioned above. Furthermore, the trigger time of the recognition command is obtained. When the trigger time is included in a time period, the staff's behavior is identified based on a preset first accuracy to verify the operation information. The first accuracy is higher than the third accuracy, but not significantly higher. This indicates that staff operate according to preset rules, requiring only verification. When the trigger time is not included in the time period, the staff's behavior is identified based on the preset second precision to obtain operation information. The second precision is the highest because it means that the staff did not upload the task in advance and appeared in the work scene. In this case, it is necessary to identify the staff in real time to obtain whether they have performed the unuploaded operation. In fact, when the staff did not upload the task in advance and appeared in the work scene, a very important reason for monitoring them with higher precision is that they may not be staff members. The background needs to identify them in real time. Once a certain risk is found (such as the person appearing in a risky location), an alert message is generated.

[0105] Regarding step S300, the step of segmenting the test samples to obtain samples of parameter changes resulting from the processing operation, referred to as mapping samples, includes:

[0106] For any given moment, the parameter matrix before processing is obtained based on a preset forward span, and the parameter matrix after processing is obtained based on a preset backward span.

[0107] Calculate the difference matrix between the parameter matrix after processing and the parameter matrix before processing;

[0108] The sample that is constructed from the processing operation to the difference matrix is ​​called the mapping sample.

[0109] Forward span and backward span are both time differences, representing how long of data to acquire forward and how long of data to acquire backward, respectively. For any processing operation at any given moment, the parameter matrix before processing is acquired based on the preset forward span, and the parameter matrix after processing is acquired based on the preset backward span. The difference matrix between the parameter matrix before processing and the parameter matrix after processing is calculated, and a sample of the difference matrix from the processing operation is constructed, called the mapping sample.

[0110] Regarding step S400, the steps of constructing a wastewater treatment knowledge base based on the mapping samples, verifying the treatment process in real time based on the wastewater treatment knowledge base, and triggering an update command based on the verification results include:

[0111] Count all mapped samples, classify the mapped samples according to the processing operations in the mapped samples, and obtain the sample set corresponding to each processing operation;

[0112] For each processing operation's sample set, the samples are compared pairwise to calculate the similarity; the similarity is calculated using the difference matrix similarity.

[0113] The samples in the sample set are clustered based on similarity, and the sample proportion and mean sample of each cluster result are calculated. The sample proportion is the number of samples in the cluster result divided by the total number of samples in the sample set, and the mean sample is the mean matrix of the difference matrix of all samples in the cluster result.

[0114] The sample proportion and its mean sample are treated as one data item. The data items are sorted in descending order based on the sample proportion to obtain the data table for processing operations.

[0115] By compiling data from all processing operations, a wastewater treatment knowledge base is obtained.

[0116] In one example of the technical solution of this invention, the application and updating process of the wastewater treatment knowledge base is described. All mapped samples are statistically analyzed. The mapped samples are classified according to the treatment operations within them, resulting in a sample set corresponding to each treatment operation. For each treatment operation's sample set, the samples are compared pairwise, and the similarity of the difference matrix is ​​calculated. Based on the similarity, the samples in the sample sets are clustered to obtain clustering results. Each clustering result is a subset of the sample set, and the ratio of the number of samples in each clustering result to the total number of samples in the sample set is calculated. The number of samples in each clustering result is then... The values ​​are converted to a range of zero to one, called the sample proportion. Then, for each clustering result, the mean matrix of all difference matrices is calculated (the matrix composed of the mean values ​​at each row and column position). The mean matrices are arranged according to the sample proportion in descending order. After the arrangement is completed, a data table is obtained, called the wastewater treatment knowledge base. After the above processing, a wastewater treatment knowledge base can be obtained for each processing operation. The actual meaning of the wastewater treatment knowledge base is that it represents the possible situations of each processing operation. The earlier the situation appears, the more likely it is to occur (the corresponding clustering result has a larger number of samples).

[0117] Furthermore, regarding the update process, in practical applications, for any processing operation performed by staff, theoretical prediction results are obtained based on the wastewater treatment knowledge base. The accuracy of the actual processing result is determined based on the theoretical prediction results. When the accuracy is less than a preset accuracy threshold, the processing operation and its actual processing result are used as mapping samples to trigger an update command.

[0118] It should be noted that the process of using the processing operation and its actual processing result as a mapping sample includes at least normalizing the actual processing result based on the matrix template. The normalization method is simple: the data of the actual processing result is transformed into the matrix template. The transformation process can use some conventional upsampling and downsampling methods. In fact, in the technical solution of this invention, since matrices need to be frequently operated on, their sizes need to be the same. For matrices of different sizes, it is necessary to adjust the matrix size through upsampling operations (increasing the number of rows and columns) and downsampling operations (decreasing the number of rows and columns).

[0119] Figure 2 A structural diagram of a wastewater treatment knowledge base construction system based on multi-source data analysis is shown. In a preferred embodiment of the technical solution of the present invention, a wastewater treatment knowledge base construction system based on multi-source data analysis is also provided, the system 10 comprising:

[0120] The sampling point setting module 11 is used to set floating sampling points in the wastewater test chamber and simultaneously determine the data type at the floating sampling points;

[0121] The test sample generation module 12 is used to record the processing operations of the staff, obtain sewage parameters containing location and time based on floating sampling points, and obtain test samples based on the statistical processing operations and sewage parameters at the same time scale.

[0122] The test sample segmentation module 13 is used to segment the test sample to obtain a sample of parameter changes from the processing operation, which is called the mapping sample;

[0123] The knowledge base construction module 14 is used to build a wastewater treatment knowledge base based on the mapping samples, verify the treatment process in real time based on the wastewater treatment knowledge base, and trigger update instructions based on the verification results.

[0124] Furthermore, the sampling point setting module 11 includes:

[0125] The inner surface positioning unit is used to acquire a three-dimensional model of the wastewater test chamber and to locate the inner surface in the three-dimensional model.

[0126] A reference point selection unit is used to select reference points on each inner surface based on a preset density; the density is the number of reference points per unit area, and the density is determined by the number of sides of the inner surface.

[0127] The line segment extraction unit is used to obtain rays that pass through the reference point and are perpendicular to the inner surface of the wastewater test chamber and point towards the inside of the wastewater test chamber. The rays are extracted based on the chamber area of ​​the wastewater test chamber to obtain the line segment corresponding to each reference point; wherein, the chamber area is not smaller than the space of the wastewater test chamber.

[0128] The sampling point deletion unit is used to select floating sampling points on the line segment based on a preset step size. For any selected floating sampling point, it calculates the distance between it and the nearest floating sampling point. When the distance is less than a preset distance threshold, the floating sampling point is deleted.

[0129] The sampling point allocation unit is used to query the demand ratio of any data type and allocate floating sampling points according to the demand ratio.

[0130] Specifically, the test sample generation module 12 includes:

[0131] The operation recording unit is used to record the operations performed by staff at various times.

[0132] The coordinate acquisition unit is used to acquire the relative coordinates of the floating sampling point in the pollution test chamber based on the rangefinder at the floating sampling point;

[0133] The parameter acquisition unit is used to acquire wastewater parameters based on the pollution collector at the floating sampling point, and synchronously record the acquisition time to obtain wastewater parameters containing relative coordinates and acquisition time.

[0134] The time warping unit is used to warp time based on a preset time step to obtain a time period;

[0135] The parameter matrix construction unit is used to obtain wastewater parameters with relative coordinates for each time period and construct the parameter matrix based on a preset matrix template.

[0136] The operation statistics unit is used to statistically process the operation and parameter matrix based on the same time axis to obtain test samples.

[0137] Furthermore, the operation recording unit includes:

[0138] The information receiving subunit is used to receive operation information containing time periods input by staff based on a preset information receiving port;

[0139] The personnel positioning subunit is used to locate staff in real time based on cameras. When a staff member is detected, a recognition command is triggered.

[0140] The first identification subunit is used to obtain the trigger time of the trigger identification command. When the trigger time is included in the time period, the behavior of the staff is identified based on the preset first precision to verify the operation information.

[0141] The second identification subunit is used to identify the staff’s behavior and obtain operation information based on a preset second precision when the trigger time is not included in the time period.

[0142] The camera operates under a preset third precision condition when no recognition command is triggered, where the second precision is greater than the first precision, and the first precision is greater than the third precision.

[0143] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for constructing a wastewater treatment knowledge base based on multi-source data analysis, characterized in that, The method includes: Set up floating sampling points in the wastewater testing chamber and simultaneously determine the data type at the floating sampling points; Record the staff's processing operations, obtain sewage parameters containing location and time based on floating sampling points, and obtain test samples based on the processing operations and sewage parameters at the same time scale; The test samples are segmented to obtain samples of parameter changes resulting from the processing operation; these are called mapped samples. A wastewater treatment knowledge base is constructed based on the mapping samples. The treatment process is verified in real time based on the wastewater treatment knowledge base, and update instructions are triggered based on the verification results. The steps of setting floating sampling points in the wastewater testing chamber and simultaneously determining the data type at the floating sampling points include: Obtain a 3D model of the wastewater test chamber and locate the inner surface in the 3D model; Reference points are selected on each inner surface based on a preset density; the density is the number of reference points per unit area, and the density is determined by the number of sides of the inner surface. A ray passing through a reference point and perpendicular to the inner surface of the wastewater test chamber is obtained, pointing towards the interior of the wastewater test chamber. The ray is then intercepted based on the chamber area of ​​the wastewater test chamber to obtain the line segment corresponding to each reference point; wherein, the chamber area is not smaller than the space of the wastewater test chamber. Floating sampling points are selected on the line segment based on a preset step size. For any selected floating sampling point, the distance between it and the nearest floating sampling point is calculated. If the distance is less than a preset distance threshold, the floating sampling point is deleted. For any data type, query the demand ratio for that data type and allocate floating sampling points according to the demand ratio.

2. The method for constructing a wastewater treatment knowledge base based on multi-source data analysis according to claim 1, characterized in that, The steps for recording the processing operations of the staff, obtaining wastewater parameters containing location and time based on floating sampling points, and statistically processing the operations and wastewater parameters at the same time scale to obtain test samples include: Record the actions taken by staff at various times; The relative coordinates of the floating sampling point in the contamination test chamber are obtained using a rangefinder at the floating sampling point. Wastewater parameters are acquired using a pollution collector at a floating sampling point, and the collection time is recorded synchronously to obtain wastewater parameters containing relative coordinates and collection time. The time is regularized based on a preset time step to obtain a time period. Obtain wastewater parameters with relative coordinates for each time period, and construct a parameter matrix based on a preset matrix template; Test samples are obtained based on statistical processing operations and parameter matrices along the same time axis.

3. The method for constructing a wastewater treatment knowledge base based on multi-source data analysis according to claim 2, characterized in that, The steps for recording the staff's actions at various times include: The system receives operation information containing time periods from staff input via a preset information receiving port. Based on real-time location of staff using cameras, a recognition command is triggered when a staff member is detected; The trigger time of the trigger recognition command is obtained. When the trigger time is included in a time period, the behavior of the staff is recognized based on the preset first precision to verify the operation information. When the trigger time is not included in the time period, the operator's behavior is identified based on the preset second precision to obtain the operation information; The camera operates under a preset third precision condition when no recognition command is triggered, where the second precision is greater than the first precision, and the first precision is greater than the third precision.

4. The method for constructing a wastewater treatment knowledge base based on multi-source data analysis according to claim 1, characterized in that, The step of segmenting the test samples to obtain samples of parameter changes resulting from the processing operation, referred to as mapped samples, includes: For any given moment, the parameter matrix before processing is obtained based on a preset forward span, and the parameter matrix after processing is obtained based on a preset backward span. Calculate the difference matrix between the parameter matrix after processing and the parameter matrix before processing; The sample that is constructed from the processing operation to the difference matrix is ​​called the mapping sample.

5. The method for constructing a wastewater treatment knowledge base based on multi-source data analysis according to claim 4, characterized in that, The steps of constructing a wastewater treatment knowledge base based on the mapping samples, verifying the treatment process in real time based on the wastewater treatment knowledge base, and triggering update instructions based on the verification results include: Count all mapped samples, classify the mapped samples according to the processing operations in the mapped samples, and obtain the sample set corresponding to each processing operation; For each processing operation's sample set, the samples are compared pairwise to calculate the similarity; the similarity is calculated using the difference matrix similarity. The samples in the sample set are clustered based on similarity, and the sample proportion and mean sample of each cluster result are calculated. The sample proportion is the number of samples in the cluster result divided by the total number of samples in the sample set, and the mean sample is the mean matrix of the difference matrix of all samples in the cluster result. The sample proportion and its mean sample are treated as one data item. The data items are sorted in descending order based on the sample proportion to obtain the data table for processing operations. By compiling data from all processing operations, a wastewater treatment knowledge base is obtained. In practical applications, for any processing operation performed by staff, theoretical prediction results are obtained based on the wastewater treatment knowledge base. The accuracy of the actual processing result is determined based on the theoretical prediction results. When the accuracy is less than a preset accuracy threshold, the processing operation and its actual processing result are used as mapping samples to trigger an update instruction. The process of using the processing operation and its actual processing result as mapping samples includes at least regularizing the actual processing result based on a matrix template.

6. A wastewater treatment knowledge base construction system based on multi-source data analysis, characterized in that, The system includes: The sampling point setting module is used to set floating sampling points in the wastewater test chamber and simultaneously determine the data type at the floating sampling points; The test sample generation module is used to record the processing operations of the staff, obtain sewage parameters containing location and time based on floating sampling points, and obtain test samples based on the statistical processing operations and sewage parameters at the same time scale. The test sample segmentation module is used to segment the test samples to obtain samples of parameter changes from the processing operation, which are called mapping samples. The knowledge base construction module is used to build a wastewater treatment knowledge base based on the mapping samples, verify the treatment process in real time based on the wastewater treatment knowledge base, and trigger update instructions based on the verification results. The sampling point setting module includes: The inner surface positioning unit is used to acquire a three-dimensional model of the wastewater test chamber and to locate the inner surface in the three-dimensional model. A reference point selection unit is used to select reference points on each inner surface based on a preset density; the density is the number of reference points per unit area, and the density is determined by the number of sides of the inner surface. The line segment extraction unit is used to obtain rays that pass through the reference point and are perpendicular to the inner surface of the wastewater test chamber and point towards the inside of the wastewater test chamber. The rays are extracted based on the chamber area of ​​the wastewater test chamber to obtain the line segment corresponding to each reference point; wherein, the chamber area is not smaller than the space of the wastewater test chamber. The sampling point deletion unit is used to select floating sampling points on the line segment based on a preset step size. For any selected floating sampling point, it calculates the distance between it and the nearest floating sampling point. When the distance is less than a preset distance threshold, the floating sampling point is deleted. The sampling point allocation unit is used to query the demand ratio of any data type and allocate floating sampling points according to the demand ratio.

7. The wastewater treatment knowledge base construction system based on multi-source data analysis according to claim 6, characterized in that, The test sample generation module includes: The operation recording unit is used to record the operations performed by staff at various times. The coordinate acquisition unit is used to acquire the relative coordinates of the floating sampling point in the pollution test chamber based on the rangefinder at the floating sampling point; The parameter acquisition unit is used to acquire wastewater parameters based on the pollution collector at the floating sampling point, and synchronously record the acquisition time to obtain wastewater parameters containing relative coordinates and acquisition time. The time warping unit is used to warp time based on a preset time step to obtain a time period; The parameter matrix construction unit is used to obtain wastewater parameters with relative coordinates for each time period and construct the parameter matrix based on a preset matrix template. The operation statistics unit is used to statistically process the operation and parameter matrix based on the same time axis to obtain test samples.

8. The wastewater treatment knowledge base construction system based on multi-source data analysis according to claim 7, characterized in that, The operation recording unit includes: The information receiving subunit is used to receive operation information containing time periods input by staff based on a preset information receiving port; The personnel positioning subunit is used to locate staff in real time based on cameras. When a staff member is detected, a recognition command is triggered. The first identification subunit is used to obtain the trigger time of the trigger identification command. When the trigger time is included in the time period, the behavior of the staff is identified based on the preset first precision to verify the operation information. The second identification subunit is used to identify the staff’s behavior and obtain operation information based on a preset second precision when the trigger time is not included in the time period. The camera operates under a preset third precision condition when no recognition command is triggered, where the second precision is greater than the first precision, and the first precision is greater than the third precision.

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