A method, system, medium and program product for detecting solid content in recycled water

Abstract

The present application relates to the field of solid content detection technology, and in particular to a solid content detection method, detection system, medium and program product for recycled water, the method comprising: identifying solid particle characteristics of the recycled water to be detected, and dividing the recycled water to be detected into regions based on the characteristic recognition results, obtaining at least one divided region and the particle distribution type of each divided region, the characteristic recognition results including the regional particle distribution characteristics of each divided region, and the particle distribution type including normal distribution and abnormal distribution; obtaining light measurement data of each divided region, and determining the regional solid content of each divided region based on the particle distribution type of each divided region and the corresponding regional light measurement data; integrating the regional solid content of each divided region, determining the final solid content of the recycled water to be detected, and generating an early warning prompt when the final solid content is higher than the preset solid content threshold. The present application facilitates improving the accuracy of solid content detection of recycled water.

Description

Technical Field

[0001] The present application relates to the technical field of solid content detection, and in particular to a method, a detection system, a medium and a program product for detecting the solid content of recycled water. Background Art

[0002] After using a mixing device to mix concrete, some mixing residues are often left in the mixing device. In order to prevent these residues from solidifying in the mixing device and affecting the normal use of the mixing device, a large amount of water is generally used to flush the mixing device after stopping use, which may produce sand and gravel separation slurry water. Usually, a sand and gravel separator is used to separate the gravel and coarse sand in the sand and gravel separation slurry water, and the remaining recycled water flows into the sedimentation tank to wait for subsequent production and use. However, since the recycled water may contain more solid pollutants, and the solid content of the recycled water may affect the subsequent concrete mix ratio, it is necessary to accurately detect the solid content in the recycled water.

[0003] The traditional method for solid content detection generally adopts the thermal drying method, also known as the oven method. When using the thermal drying method for solid content detection, the requirements for drying time and drying temperature are high. Factors such as the skill level and operating habits of the relevant operators may affect the accuracy of the test results. Summary of the invention

[0004] In order to improve the accuracy of solid content detection of recycled water and thus achieve timely warning, the present application provides a recycled water solid content detection method, detection system, medium and program product.

[0005] In the first aspect, the present application provides a method for detecting the solid content of recycled water, which adopts the following technical solution:

[0006] A method for detecting solid content in recycled water, comprising:

[0007] Performing solid particle feature recognition on the recovered water to be detected, and dividing the recovered water to be detected into regions based on the feature recognition result to obtain at least one divided region and a particle distribution type of each divided region, wherein the feature recognition result includes a regional particle distribution feature of each divided region, and the particle distribution type includes a normal distribution and an abnormal distribution;

[0008] Obtaining light measurement data of each divided area, and determining the regional solid content of each divided area based on the particle distribution type of each divided area and the corresponding regional light measurement data;

[0009] The regional solid content of each divided area is integrated to determine the final solid content of the recovered water to be detected, and an early warning prompt is generated when the final solid content is higher than a preset solid content threshold.

[0010] By adopting the above technical scheme, since there may be differences in the particle distribution in different areas of the recovered water to be tested, directly testing the solid content of the recovered water to be tested as a whole may lead to inaccurate test results. The solid particles in the recovered water to be tested are characterized and analyzed to divide the recovered water to be tested into different areas, and different solid content determination methods are formulated for the divided areas belonging to different particle distribution types according to the particle distribution characteristics of different divided areas, so as to improve the pertinence in determining the solid content corresponding to the divided areas, thereby facilitating the improvement of the accuracy in determining the solid content corresponding to the divided areas. Finally, the final solid content of the recovered water to be tested is determined by integrating the regional solid contents of each divided area, so as to avoid the increase of the overall detection deviation caused by the measurement of a single area, thereby facilitating the improvement of the accuracy of the solid content detection of the recovered water to be tested. Once the final solid content is detected to be higher than the preset solid content threshold, an early warning prompt is immediately generated to remind relevant personnel to promptly discover and take corresponding treatment measures.

[0011] In a possible implementation, determining the regional solid content of each divided area based on the particle distribution type of each divided area and the corresponding regional light measurement data includes:

[0012] When the particle distribution type of the divided area is normal distribution, the solid content of the area is determined based on the light measurement data corresponding to the divided area;

[0013] When the particle distribution type of the divided area is an abnormal distribution, the initial regional solid content is determined based on the light measurement data corresponding to the divided area, and the initial regional solid content is adjusted based on the particle distribution information corresponding to the divided area to obtain the final regional solid content.

[0014] By adopting the above technical scheme, since the normal distribution has symmetry and stability, the regional solid content can be reflected more accurately according to the light measurement data. Therefore, when the particle distribution type of the divided area is the normal distribution, the regional solid content is determined by directly analyzing the light measurement data. When the particle distribution type of the divided area is the abnormal distribution, the initial regional solid content corresponding to the light measurement data is adjusted according to the particle distribution information in the divided area, so as to avoid the measurement error caused by the unevenness of the particle distribution, thereby facilitating the improvement of the accuracy in determining the regional solid content.

[0015] In a possible implementation, adjusting the initial regional solid content based on the particle distribution information corresponding to the divided regions to obtain the final regional solid content includes:

[0016] Determine at least one ray re-inspection direction based on the particle distribution information corresponding to the divided area, and perform optical re-inspection on the divided area based on the ray re-inspection direction to obtain re-inspection light measurement data corresponding to each re-inspection direction;

[0017] Determine the solid content of the corresponding retest area according to each retest light measurement data;

[0018] The final regional solids content is determined based on each retested regional solids content and the initial regional solids content.

[0019] By adopting the above technical scheme, at least one re-inspection direction is determined by using the particle distribution information corresponding to the divided area, so as to more accurately locate the area where light measurement differences may exist, re-inspect the area where light measurement differences may exist by adjusting the ray angle, and adjust the initial area solid content based on the re-inspection result, so as to improve the adaptability between the final area solid content and the actual solid particle situation in the divided area, thereby facilitating the improvement of the accuracy in determining the solid content of the recovered water to be tested.

[0020] In a possible implementation, the step of integrating the regional solid contents of each divided area to determine the final solid content of the recovered water to be tested includes:

[0021] Determine the particle adhesion corresponding to each divided area based on the particle distribution information corresponding to each divided area;

[0022] Determine the regional weight ratio between the divided regions based on the regional area and particle adhesion of each divided region;

[0023] Based on the regional weight ratio and the regional solid content of each divided area, the final solid content of the recovered water to be tested is determined.

[0024] By adopting the above technical scheme, by analyzing the particle adhesion between the solid particles in each divided area, it is convenient to evaluate the distribution and aggregation state of the solid particles in the divided area, and the regional weight ratio is jointly determined based on the regional area and particle adhesion of each divided area, which is convenient to reflect the relative importance of different divided areas in the overall recovered water to be tested. Finally, the final solid content is determined according to the regional weight ratio of each divided area, which is convenient to improve the rationality and accuracy of determining the final solid content.

[0025] In one possible implementation, the method further includes:

[0026] Identifying the particle surface characteristics and real-time particle position of each solid particle in the recovered water to be detected;

[0027] Determining the surface type of each solid particle based on the particle surface characteristics of each solid particle, wherein the surface type includes easy adhesion and non-adhesion;

[0028] Based on the particle surface characteristics and real-time particle positions of each solid particle, judging whether there is a potential adhesion particle group, wherein the potential adhesion particle group includes at least two solid particles, the surface types of the at least two solid particles are different, and the trajectory similarity between the movement trajectories corresponding to the at least two solid particles within a first preset time period is higher than a preset similarity threshold;

[0029] If so, a vibration instruction is generated based on the moving trajectory of any solid particle in the potential adhesion particle group to control the relevant vibration equipment to vibrate.

[0030] By adopting the above technical solution, by analyzing the particle surface characteristics of solid particles, it is convenient to distinguish between particles that are easy to stick and particles that are not easy to stick. By analyzing the real-time particle position of each solid particle, it is convenient to predict the movement trajectory of the solid particles in the future. According to the movement trajectory and surface type of each solid particle, it is predicted whether the solid particle will stick to other solid particles in the future. If it is predicted that adhesion may occur in the future, a vibration instruction is immediately generated to prevent adhesion between the solid particles. When generating the vibration instruction, the movement trajectory of any solid particle in the potential adhesion particle group is taken into account, so as to improve the targetedness of the vibration process, thereby improving the effectiveness of the operation of preventing solid particles from sticking.

[0031] In a possible implementation, when the final solid content is higher than a preset solid content threshold, the method further includes:

[0032] Determining the type of solid particles corresponding to the recovered water to be detected based on the particle surface characteristics of each solid particle in the recovered water to be detected;

[0033] Determine the solid content of each solid particle type at each moment in the second preset time period based on the light measurement data corresponding to each divided area in the second preset time period;

[0034] The preset data axis corresponding to each solid particle type and the solid content of each solid particle type at each moment within the second preset time period are used to determine the classification data axis corresponding to each solid particle type, and all the classification data axes are fed back.

[0035] By adopting the above technical solution, by analyzing the particle surface characteristics of each solid particle, it is convenient to accurately distinguish solid particles with different particle surface characteristics, so as to facilitate the subsequent classification management of solid particles. By integrating the solid content of each solid particle type over a period of time and displaying it in the form of a classified data axis, it is convenient to intuitively display the solid content growth trend corresponding to the solid particles of each solid particle type to relevant visitors. In addition, by timely feedback of all classified data axes, it is convenient to provide relevant technical departments with data support for mix proportion production.

[0036] In a second aspect, the present application provides a detection system, which adopts the following technical solution:

[0037] A detection system, the detection system comprising:

[0038] at least one processor;

[0039] Memory;

[0040] At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the above-mentioned recycled water solid content detection method.

[0041] In a third aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution:

[0042] A computer-readable storage medium includes: a computer program that can be loaded by a processor and execute the above-mentioned method for detecting solid content in recycled water.

[0043] In a fourth aspect, the present application provides a computer program product, which adopts the following technical solution:

[0044] A computer program product includes a computer program, and when the computer program is executed by a processor, the above-mentioned method for detecting the solid content of recycled water is implemented.

[0045] In summary, the present application includes at least one of the following beneficial technical effects:

[0046] Since the particle distribution in different areas of the recovered water to be tested may be different, directly testing the solid content of the recovered water to be tested as a whole may lead to inaccurate test results. The recovered water to be tested is divided into different areas by performing feature recognition and analysis on the solid particles in the recovered water to be tested, and different solid content determination methods are formulated for the divided areas belonging to different particle distribution types according to the particle distribution characteristics of different divided areas, so as to improve the pertinence in determining the solid content corresponding to the divided areas, thereby facilitating the improvement of the accuracy in determining the solid content corresponding to the divided areas. Finally, the final solid content of the recovered water to be tested is determined by integrating the regional solid content of each divided area, so as to avoid the increase of the overall detection deviation caused by the measurement of a single area, thereby facilitating the improvement of the accuracy of the solid content detection of the recovered water to be tested. Once the final solid content is detected to be higher than the preset solid content threshold, an early warning prompt is immediately generated to remind relevant personnel to promptly discover and take corresponding treatment measures.

[0047] By analyzing the surface characteristics of solid particles, it is easy to distinguish between particles that are easy to stick and particles that are not easy to stick. By analyzing the real-time particle position of each solid particle, it is easy to predict the movement trajectory of the solid particles in the future. According to the movement trajectory and surface type of each solid particle, it is predicted whether the solid particle will stick to other solid particles in the future. If it is predicted that adhesion may occur in the future, a vibration command is immediately generated to prevent adhesion between solid particles. When generating vibration commands, the movement trajectory of any solid particle in the potential adhesion particle group is taken into account to improve the targetedness of the vibration process, thereby improving the effectiveness of the operation to prevent solid particles from sticking. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of a process for detecting solid content of recovered water in an embodiment of the present application;

[0049] Figure 2 It is a schematic diagram of a process of generating a vibration instruction in an embodiment of the present application;

[0050] Figure 3 It is a structural schematic diagram of a detection system in an embodiment of the present application. DETAILED DESCRIPTION

[0051] The following is combined with Figures 1 to 3 This application is described in further detail.

[0052] After reading this specification, those skilled in the art may make non-creative modifications to this embodiment as needed, but such modifications are protected by patent law as long as they are within the scope of the claims of this application.

[0053] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0054] Specifically, the embodiment of the present application provides a method for detecting the solid content of recycled water, which is performed by a detection system, and the detection system can be a server or a terminal device, wherein the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. The terminal device can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, and the embodiment of the present application does not limit this.

[0055] refer to Figure 1 , Figure 1 : is a flow chart of a method for detecting solid content in recycled water in an embodiment of the present application, the method comprising steps S110 to S130, wherein:

[0056] Step S110: solid particle characteristics are identified for the recovered water to be tested, and based on the characteristic identification results, the recovered water to be tested is divided into regions to obtain at least one divided region and a particle distribution type for each divided region. The characteristic identification results include regional particle distribution characteristics for each divided region, and the particle distribution type includes normal distribution and abnormal distribution.

[0057] Specifically, before the solid content of the recycled water to be tested is tested, preliminary solid particle feature recognition can be performed on the recycled water to be tested. The specific method can be to import the image of the recycled water to be tested into a trained deep learning model, and the trained deep learning model will extract features of the solid particles in the image of the recycled water to be tested. The training process of the deep learning model can be: select a suitable preset deep learning model, and then import the sample data into the preset deep learning model to perform feature recognition, and then adjust the model parameters based on the recognition result of the sample data by the preset deep learning model until the error value between the feature recognition result output by the preset deep learning model and the feature label in the corresponding sample data is lower than the preset error value, wherein the preset deep learning model can be a convolutional neural network VGG16 model, and can also be an implementation segmentation model. The specific preset deep learning model and the specific preset error value are not specifically limited in the embodiments of the present application, and the model parameters are adjusted based on the recognition result to improve the accuracy of the recognition result.

[0058] When the solid content of the recycled water to be tested is tested, the recycled water to be tested can be placed in a sampler for solid content testing, and the solid content of the recycled water to be tested stored in the recovery tank can also be directly tested. When it is necessary to test the solid content of the recycled water to be tested placed in the sampler, an image acquisition device arranged at the sampling table can be used to capture the image of the recycled water to be tested. When it is necessary to test the solid content of the recycled water to be tested placed in a sedimentation tank, an image acquisition device arranged at the sedimentation tank can be used to capture the image of the recycled water to be tested. However, due to the high solid content in the recycled water to be tested, the solid particles in the recycled water to be tested may settle at the bottom of the recovery pool or form a suspended layer over time. When the solid content of the recycled water to be tested is tested by sampling, only high-concentration or low-concentration areas may be sampled, which may affect the reliability of the test results. When the solid content of the recycled water to be tested in the recovery pool is directly tested, if the solid particles are precipitated or suspended, it may also affect the reliability of the solid content test results. Therefore, the stirring equipment is generally controlled to stir the recycled water to be tested in the recovery pool at a regular interval to ensure that the solid particles in the recycled water to be tested are more evenly distributed in the water.

[0059] Based on the feature recognition result, the recycled water to be tested is divided into regions, that is, the recycled water to be tested is divided into regions according to the distribution characteristics of solid particles in the recycled water to be tested. Different divided regions may correspond to different particle distribution types. The regional particle distribution characteristics of the solid particles in the divided regions are identified to determine the particle distribution type of the corresponding divided regions. If the regional particle distribution characteristics of the divided regions include solid particles with a diameter greater than a preset diameter threshold, or solid particles with a preset irregular edge morphology, the particle distribution type of the corresponding divided region can be determined as an abnormal distribution, that is, there may be larger solid particles or solid particles that are stuck together in the divided region with the particle distribution type of the abnormal distribution. Correspondingly, when there are solid particles with a diameter greater than a preset diameter threshold, or solid particles with a preset irregular edge morphology in the divided region, the particle distribution type of the corresponding divided region can be determined as a normal distribution.

[0060] Step S120: acquiring light measurement data of each divided area, and determining the regional solid content of each divided area based on the particle distribution type of each divided area and the corresponding regional light measurement data.

[0061] Specifically, the light measurement data is the data obtained after using an optical sensor to perform ray measurement on the recovered water to be tested. The optical sensor can be a fiber optic sensor, an ultraviolet-visible spectrum sensor, an infrared sensor, etc., which detects the solid content in the recovered water based on the absorption, reflection, scattering or emission characteristics of solid particles to light. However, larger solid particles or adhered solid particles may exceed the measurement range of the optical sensor or affect the measurement accuracy of the optical sensor, resulting in low measurement accuracy. In addition, if the light signal emitted by the optical sensor encounters larger solid particles or adhered solid particles when propagating in the recovered water to be tested, it may cause uneven reflection or scattering signals. This unevenness will increase the noise of the signal and reduce the signal-to-noise ratio of the signal, which may affect the accuracy of the measurement results. Therefore, after obtaining the light measurement data of each divided area, the corresponding regional solid content is not determined directly based on the light measurement data of the divided area, but it is necessary to perform a directional analysis of the light measurement data of the divided area according to the particle distribution type of the different divided areas, and then determine the corresponding regional solid content based on the directional analysis results.

[0062] Different particle distribution types of divided areas have different determination methods when determining the corresponding regional solid content. Specifically, based on the particle distribution type of each divided area and the corresponding regional light measurement data, the regional solid content of each divided area is determined, which may include:

[0063] When the particle distribution type of the divided area is normal distribution, the solid content of the area is determined based on the light measurement data corresponding to the divided area.

[0064] Specifically, when the particle distribution type of the divided area is a normal distribution, it indicates that the solid particles contained in the corresponding divided area are evenly distributed, and there are no large solid particles and solid particles that are adhered. At this time, the solid content of the area corresponding to the divided area can be determined by directly analyzing the light measurement data, that is, the size and shape of the solid particles in the divided area will not affect the solid content detection results, or may have a small impact on the solid content detection results, which can be ignored. Before determining the regional solid content based on the light measurement data, the light measurement data can be pre-processed by denoising, filtering, calibration and other operations to eliminate noise and interference in the light measurement data, thereby facilitating the improvement of the accuracy and reliability of the light measurement data, and then the concentration, size and other parameters of the solid particles in the divided area are determined based on the pre-processed light measurement data. Finally, the regional solid content corresponding to the divided area can be calculated based on the preset mathematical model and the solid particle parameters obtained by analysis, wherein the preset mathematical model can be a light scattering model, a light absorption model, etc. The specific preset mathematical model is not specifically limited in the embodiment of the present application, as long as the regional solid content can be determined based on the light measurement data.

[0065] When the particle distribution type of the divided area is an abnormal distribution, the initial regional solid content is determined based on the light measurement data corresponding to the divided area, and the initial regional solid content is adjusted based on the particle distribution information corresponding to the divided area to obtain the final regional solid content.

[0066] Specifically, when the particle distribution type of the divided area is an abnormal distribution, it indicates that the solid particles contained in the corresponding divided area are unevenly distributed, and there are larger solid particles and sticky solid particles. At this time, if the corresponding regional solid content is determined directly based on the light measurement data, a large error may occur. Therefore, the initial regional solid content can be first determined based on the light measurement data corresponding to the divided area, and then the initial regional solid content can be adjusted based on the particle distribution information corresponding to the divided area. By optimizing the initial regional solid content, the error caused by the uneven distribution of solid particles can be reduced. The specific method of determining the initial regional solid content based on the light measurement data corresponding to the divided area will not be elaborated here, and the implementation steps of determining the regional solid content based on the light measurement data corresponding to the divided area in the above embodiment can be referred to.

[0067] When adjusting the initial regional solid content based on the particle distribution information corresponding to the divided area, the light measurement data corresponding to different ray angles can be obtained by adjusting the ray angle, and then the final regional solid content can be determined by integrating the light measurement data of multiple different ray angles. When the solid particles are large, the solid particles may block or scatter more measurement light, thereby forming a shadow in the detection area. This shadow effect may cause the optical sensor to be unable to accurately measure the area behind the solid particles, thereby introducing errors. By adjusting the ray angle, the measurement light can be incident on the detection area at different angles, reducing the blocking and scattering of the measurement light by larger solid particles, thereby reducing the impact of the shadow effect. When the solid particles are stuck together, the stuck solid particles may be regarded as a larger solid particle, which may also cause deviations in the detection results. By adjusting the ray angle, the interaction between the measurement light and the stuck solid particles can be changed, so that the measurement light can more effectively penetrate or scatter into the gaps between the stuck solid particles, so that the optical sensor can accurately identify the stuck solid particles, thereby reducing the detection error caused by particle adhesion.

[0068] Step S130: Integrate the regional solid contents of each divided area to determine the final solid content of the recovered water to be tested, and generate an early warning prompt when the final solid content is higher than a preset solid content threshold.

[0069] Specifically, the regional solid content corresponding to each divided area is determined according to the particle distribution corresponding to different divided areas, so as to improve the accuracy of determining the regional solid content, and then the regional solid content corresponding to each divided area is integrated to obtain the final solid content corresponding to the recovered water to be detected, so as to realize the solid content detection of the recovered water to be detected, wherein, when integrating the regional solid content of each divided area, the regional solid content of each divided area can be directly superimposed, and the integration weight of each divided area can also be determined according to the distribution of solid particles in each divided area, and then the regional solid content corresponding to each divided area is summed based on the integration weight and the regional solid content of each divided area to obtain the final solid content, and the specific method of integrating the regional solid content of each divided area is not specifically limited in the embodiment of the present application. When the final solid content is higher than the preset solid content threshold, it indicates that the solid particle content of the recovered water to be detected is high and it is not suitable for direct use. At this time, an early warning prompt will be generated in time to remind the relevant staff to perform secondary treatment on the recovered water to be detected. The secondary treatment content can be filtration, precipitation, etc. The specific preset solid content threshold and secondary treatment content are not specifically limited in the embodiment of the present application, and can be determined by the relevant staff according to historical experimental data.

[0070] For the embodiments of the present application, since there may be differences in the particle distribution in different areas of the recycled water to be tested, directly testing the solid content of the recycled water to be tested as a whole may lead to inaccurate test results. By performing feature recognition and analysis on the solid particles in the recycled water to be tested, the recycled water to be tested is divided into different areas, and different solid content determination methods are formulated for the divided areas belonging to different particle distribution types according to the particle distribution characteristics of different divided areas, so as to improve the pertinence in determining the solid content corresponding to the divided areas, thereby facilitating the improvement of the accuracy in determining the regional solid content corresponding to the divided areas. Finally, the final solid content of the recycled water to be tested is determined by integrating the regional solid contents of each divided area, so as to avoid the increase of the overall detection deviation caused by the measurement of a single area, thereby facilitating the improvement of the accuracy of the solid content detection of the recycled water to be tested. Once the final solid content is detected to be higher than the preset solid content threshold, an early warning prompt is immediately generated to remind relevant personnel to promptly discover and take corresponding treatment measures.

[0071] Furthermore, in order to improve the accuracy of determining the solid content in the recovered water to be detected, the initial regional solid content is adjusted based on the particle distribution information corresponding to the divided area to obtain the final regional solid content, which may specifically include:

[0072] Based on the particle distribution information corresponding to the divided area, at least one ray re-inspection direction is determined, and based on the ray re-inspection direction, the divided area is optically re-inspected to obtain re-inspection light measurement data corresponding to each re-inspection direction; the solid content of the corresponding re-inspection area is determined according to each re-inspection light measurement data; the final regional solid content is determined based on the solid content of each re-inspection area and the solid content of the initial area.

[0073] Specifically, since larger solid particles or solid particles that are stuck together may have uneven surfaces, such as surface protrusions, irregular shapes, etc., such surface unevenness may cause inaccurate light measurement results due to blocking of rays during light measurement. Therefore, it is necessary to first determine the surface uneven position based on the particle distribution information corresponding to the divided area, and then determine at least one ray re-inspection direction based on the surface uneven position. Different ray re-inspection directions in at least one ray re-inspection direction correspond to different measurement directions, and are different from the original measurement direction passing through the surface uneven position. When determining the surface uneven position based on the particle distribution information corresponding to the divided area, the particle edge information of the solid particles can be determined from the particle distribution information through a preset edge recognition algorithm, and then at least one surface uneven position can be determined based on the particle edge information. The specific preset edge recognition algorithm is not specifically limited in the embodiments of the present application, as long as the surface uneven position of the solid particles can be identified based on the particle distribution information. In addition, it can also be identified through equipment such as a laser particle size analyzer, and the specific method is not specifically limited in the embodiments of the present application.

[0074] After determining at least one ray re-inspection direction, a re-inspection instruction can be generated based on at least one ray re-inspection direction, and the optical sensor can be controlled to perform optical re-inspection on the solid particles in the divided area according to the re-inspection instruction. The re-inspection instruction contains the re-inspection rays corresponding to each ray re-inspection direction and the re-inspection time of each re-inspection ray. After performing optical re-inspection based on the re-inspection instruction, each re-inspection light measurement data can be converted into a sub-re-inspection solid content, and then the solid content of the re-inspection area is determined by adding and summing all the sub-re-inspection solid contents. After determining the solid content of the re-inspection area, the solid content of the re-inspection area can be directly replaced with the solid content of the initial area, that is, the solid content of the re-inspection area can be directly determined as the final regional solid content of the divided area, or the merging weight can be determined according to the number of solid particles in the divided area, and finally the final regional solid content is determined based on the merging weight, the initial regional solid content and the re-inspection area solid content.

[0075] Furthermore, in order to improve the rationality and accuracy of determining the final solid content, the regional solid content of each divided area is integrated to determine the final solid content of the recovered water to be tested, including:

[0076] Based on the particle distribution information corresponding to each divided area, the particle adhesion corresponding to each divided area is determined; based on the regional area and particle adhesion of each divided area, the regional weight ratio between each divided area is determined; based on the regional weight ratio and the regional solid content of each divided area, the final solid content of the recovered water to be tested is determined.

[0077] Specifically, when integrating the regional solid contents corresponding to each divided area, the regional weight ratio corresponding to each divided area can be jointly determined by simultaneously considering and analyzing the regional area and particle adhesion of each divided area, wherein the larger the regional area of ​​the divided area, the larger the corresponding regional weight ratio, and the higher the particle adhesion in the divided area, the larger the corresponding regional weight ratio. The regional weight ratio corresponding to each divided area can be determined by a preset weight parameter mapping relationship, and the preset weight parameter mapping relationship is the correspondence between the parameter combination between the regional area and the particle adhesion and the regional weight ratio. The specific content of the preset weight parameter mapping relationship is not specifically limited in the embodiment of the present application, and can be determined by relevant staff based on historical experimental data and uploaded to the detection system.

[0078] Particle adhesion refers to the ability of solid particles to maintain their mutual bonding or adhesion when subjected to external forces or environmental conditions change. It is used to reflect the strength of the mutual attraction and bonding between solid particles. High adhesion means that the particles are tightly bound and not easy to separate; while low adhesion means that the particles are easy to separate or break. When the divided area contains solid particles that are adhered, it is necessary to analyze the particle adhesion. That is, if the divided area does not contain solid particles that are adhered, it is not necessary to identify the particle adhesion and directly determine the regional weight ratio according to the area of ​​the divided area. The contact area and contact angle between solid particles can be identified from the detection image containing solid particles through a preset feature recognition algorithm to determine the particle adhesion. The specific preset feature recognition algorithm is not specifically limited in the embodiment of this application. The particle adhesion can also be measured using a rheometer. The rheometer is a commonly used particle viscosity test method that can test the shear deformation of particles, thereby measuring the flow characteristics and deformation characteristics of particles. The adhesion between solid particles can be indirectly evaluated through the test of the rheometer.

[0079] Further, in order to improve the effectiveness of the operation of preventing solid particles from sticking, the method provided in the embodiment of the present application further includes steps S210 to S240, such as Figure 2 As shown, where:

[0080] Step S210: Identify the particle surface characteristics and real-time particle position of each solid particle in the recovered water to be detected.

[0081] Specifically, the particle surface features of each solid particle can be identified from the real-time image corresponding to the recovered water to be detected based on a preset feature recognition algorithm, and a particle surface feature analyzer can be used to collect and identify the particle surface features of each solid particle in the recovered water to be detected. The particle surface feature analyzer can measure and analyze the surface features and particle positions of solid particles with high precision and efficiency. The particle surface features include shape, size, roughness, surface attachments, etc.

[0082] Step S220: Based on the particle surface characteristics of each solid particle, determine the surface type of each solid particle, where the surface type includes easy-to-stick and non-easy-to-stick.

[0083] Specifically, when the particle surface characteristics of solid particles include preset characteristics, the surface type of the solid particles can be characterized as an easy-to-adhere type; when the particle surface characteristics of solid particles do not include preset characteristics, the surface type of the solid particles can be characterized as a non-sticky type. The preset characteristics include but are not limited to surface microscopic protrusions, surface microscopic depressions, grease, clay, and colloids. The specific preset characteristics can be determined by relevant staff based on historical experimental data and uploaded to the detection system.

[0084] Step S230: Based on the particle surface characteristics and real-time particle positions of each solid particle, determine whether there is a potential adhesion particle group, where the potential adhesion particle group contains at least two solid particles, the surface types of at least two solid particles are different, and the trajectory similarity between the corresponding movement trajectories of at least two solid particles within a first preset time period is higher than a preset similarity threshold.

[0085] Specifically, by analyzing the particle surface characteristics of solid particles, it is convenient to judge whether the solid particles will develop adhesion with other solid particles. By analyzing the real-time particle position of solid particles, it is convenient to predict and analyze the direction of solid particles in the future. If the surface types of at least two solid particles are different, and the movement trajectories of at least two solid particles in the first preset time period are similar, at this time, the at least two solid particles can be determined as a hidden danger adhesion particle group, that is, it can be characterized that the at least two solid particles may adhere in the future. By analyzing the real-time particle position corresponding to each moment of the solid particles in the first preset time period, the movement trajectory of the solid particles in the first preset time period can be determined. The preset similarity threshold can be 95% or 98%. The specific preset similarity threshold is not specifically limited in the embodiment of the present application, and can be determined by relevant staff based on historical experimental data and uploaded to the detection system. The first preset time period is a period of time after the particle surface characteristics of the solid particles are determined. The duration corresponding to the first preset time period can be 30 seconds or 25 seconds. The specific duration is not specifically limited in the embodiment of the present application.

[0086] Step S240: If yes, a vibration instruction is generated based on the moving trajectory of any solid particle in the potential adhesion particle group, so as to control the relevant vibration equipment to vibrate.

[0087] Specifically, although the stirring equipment will regularly stir the recycled water to be tested, the solid particles in the recycled water to be tested may still stick together or settle during the stirring interval. When it is determined that the recycled water to be tested contains a potential adhesion particle group, a vibration instruction can be generated based on the moving trajectory of the potential adhesion particle group. By timely controlling the relevant vibration equipment to vibrate along the moving trajectory of the potential adhesion particle group, adhesion between at least two solid particles in the potential adhesion particle group can be prevented. Since the trajectory similarity between the moving trajectories of at least two solid particles in the potential adhesion particle group is higher than the preset similarity threshold, the moving trajectory of any solid particle in the potential adhesion particle group can be regarded as the moving trajectory of the potential adhesion particle group.

[0088] Furthermore, when the final solid content is higher than the preset solid content threshold, the method provided in the embodiment of the present application further includes:

[0089] Based on the particle surface characteristics of each solid particle in the recovered water to be detected, the solid particle type corresponding to the recovered water to be detected is determined; based on the light measurement data corresponding to each divided area within the second preset time period, the solid content of each solid particle type at each moment in the second preset time period is determined; based on the preset data axis corresponding to each solid particle type, and the solid content of each solid particle type at each moment in the second preset time period, the classified data axis corresponding to each solid particle type is determined, and all the classified data axes are fed back.

[0090] Specifically, solid particle types include but are not limited to cement, minerals, fine sand and admixtures. Different solid particle types may correspond to different particle surface characteristics. For example, cement particles usually have a specific shape and texture, and may also have a certain color, such as gray or white; mineral slag powder particles may have a rougher and irregular surface feature, and the surface may have a certain gloss; fine sand particles usually have a relatively uniform size and shape, and the surface may have a certain smoothness; admixtures may form visible precipitation or crystals in the recycled water. Therefore, by analyzing the particle surface characteristics of the solid particles, the solid particle type of each solid particle in the recycled water to be tested can be determined.

[0091] By analyzing the light measurement data of each divided area within a period of time, and dividing the partial light measurement data corresponding to each solid particle type from the light measurement data, it is convenient to analyze the content of solid particles of different solid particle types in the recovered water to be detected, and then by converting each partial light measurement data into the solid content corresponding to each solid particle type at each moment in the second preset time period, it is convenient to intuitively view the soil particle content of each solid particle type contained in the recovered water to be detected at each moment. The second preset time period can be a period of time after the solid particle feature identification of the recovered water to be detected, and the duration corresponding to the second preset time period can be 30 minutes, or 60 minutes. The specific duration is not specifically limited in the embodiment of this application, and can be determined by relevant staff based on historical experimental data.

[0092] The preset data axis includes a time axis and a solid content axis, that is, a moment solid content axis. Different solid particle types correspond to different preset data axes. By importing the moment solid content corresponding to each moment of the solid particle type within the second preset time period into the corresponding preset data axis, the classification data axis corresponding to each solid particle type can be obtained. By integrating the solid content of each solid particle type over the past period of time and displaying it in the form of a classification data axis, it is convenient to intuitively display the solid content growth trend corresponding to the solid particles of each solid particle type to relevant visitors.

[0093] The present application provides a detection system, such as Figure 3 As shown, Figure 3 The detection system 300 shown includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the detection system 300 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one, and the structure of the detection system 300 does not constitute a limitation on the embodiments of the present application.

[0094] The processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0095] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 The fact that only one line is used in the diagram does not mean that there is only one bus or only one type of bus.

[0096] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0097] The memory 303 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the contents shown in the above method embodiment.

[0098] The detection system includes but is not limited to: mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle terminals (such as vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. It can also be a server, etc. Figure 3 The detection system shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0099] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding content in the aforementioned method embodiment.

[0100] An embodiment of the present application provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the method in any of the above embodiments is implemented.

[0101] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0102] The above description is only a partial implementation method of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for detecting solid content in recycled water, characterized in that: include: Performing solid particle feature recognition on the recovered water to be detected, and dividing the recovered water to be detected into regions based on the feature recognition results to obtain at least one divided region and a particle distribution type of each divided region, wherein the feature recognition results include regional particle distribution features of each divided region, and the particle distribution type includes normal distribution and abnormal distribution; Obtaining light measurement data of each divided area, and determining the regional solid content of each divided area based on the particle distribution type of each divided area and the corresponding regional light measurement data; Integrate the regional solid content of each divided area to determine the final solid content of the recovered water to be tested, and generate an early warning prompt when the final solid content is higher than a preset solid content threshold; Wherein, determining the regional solid content of each divided area based on the particle distribution type of each divided area and the corresponding regional light measurement data includes: When the particle distribution type of the divided area is normal distribution, the solid content of the area is determined based on the light measurement data corresponding to the divided area; When the particle distribution type of the divided area is an abnormal distribution, the initial area solid content is determined based on the light measurement data corresponding to the divided area, and the initial area solid content is adjusted based on the particle distribution information corresponding to the divided area to obtain the final area solid content; The adjusting of the initial regional solid content based on the particle distribution information corresponding to the divided regions to obtain the final regional solid content includes: At least one ray re-inspection direction is determined based on the particle distribution information corresponding to the divided area, and the divided area is optically re-inspected based on the ray re-inspection direction to obtain re-inspection light measurement data corresponding to each re-inspection direction, wherein particle edge information of solid particles is determined from the particle distribution information according to a preset edge recognition algorithm, at least one surface unevenness position is determined based on the particle edge information, and at least one ray re-inspection direction is determined based on the surface unevenness position, and different ray re-inspection directions in the at least one ray re-inspection direction correspond to different measurement directions; Determine the solid content of the corresponding retest area according to each retest light measurement data; Determine the final regional solid content based on the solid content of each retested area and the initial regional solid content, wherein a merging weight is determined according to the number of solid particles in the divided area, and the final regional solid content is determined based on the merging weight, the initial regional solid content and the retested regional solid content; Among them, it also includes: Identifying the particle surface characteristics and real-time particle position of each solid particle in the recovered water to be detected; Based on the particle surface characteristics of each solid particle, the surface type of each solid particle is determined, and the surface type includes easy adhesion and non-adhesion. When the particle surface characteristics of the solid particle include preset characteristics, the surface type of the solid particle can be characterized as an easy adhesion type; when the particle surface characteristics of the solid particle do not include preset characteristics, the surface type of the solid particle can be characterized as a non-adhesion type, wherein the preset characteristics include surface microscopic protrusions, surface microscopic depressions, grease, clay, and colloid; Based on the particle surface characteristics and real-time particle positions of each solid particle, judging whether there is a potential adhesion particle group, wherein the potential adhesion particle group includes at least two solid particles, the surface types of the at least two solid particles are different, and the trajectory similarity between the movement trajectories corresponding to the at least two solid particles in a first preset time period is higher than a preset similarity threshold, wherein the movement trajectory of the solid particle in the first preset time period is determined according to the real-time particle position corresponding to the solid particle at each moment in the first preset time period; If so, a vibration instruction is generated based on the moving trajectory of any solid particle in the potential adhesion particle group to control the relevant vibration equipment to vibrate.

2. A method for detecting solid content in recycled water according to claim 1, characterized in that: The step of integrating the regional solid contents of each divided area to determine the final solid content of the recovered water to be tested includes: Determine the particle adhesion corresponding to each divided area based on the particle distribution information corresponding to each divided area; Determine the regional weight ratio between the divided regions based on the regional area and particle adhesion of each divided region; Based on the regional weight ratio and the regional solid content of each divided area, the final solid content of the recovered water to be tested is determined.

3. A method for detecting solid content in recycled water according to claim 1, characterized in that: When the final solid content is higher than the preset solid content threshold, the method further includes: Determining the type of solid particles corresponding to the recovered water to be detected based on the particle surface characteristics of each solid particle in the recovered water to be detected; Determine the solid content of each solid particle type at each moment in the second preset time period based on the light measurement data corresponding to each divided area in the second preset time period; The preset data axis corresponding to each solid particle type and the solid content of each solid particle type at each moment within the second preset time period are used to determine the classification data axis corresponding to each solid particle type, and all the classification data axes are fed back.

4. A detection system, characterized in that: The detection system includes: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute a method for detecting solid content in recycled water according to any one of claims 1-3.

5. A computer-readable storage medium, characterized in that: include: A computer program is stored which can be loaded by a processor and execute a method for detecting solid content in recycled water as described in any one of claims 1 to 3.

6. A computer program product, characterized in that It comprises a computer program, which, when executed by a processor, implements the steps of a method for detecting solid content in recycled water according to any one of claims 1 to 3.

Images