Drinking water source whole-process safety traceability method and system

Abstract

The invention provides a drinking water source whole-process safety traceability method and system, and the method comprises the steps: determining each water source node based on the pipe network data of a corresponding traceability region, and carrying out the equipment configuration of corresponding water quality passive detection equipment; responding to any user side to scan the node identification code located at the water source node, and sending the generated water source tracing interface to the user side for display; responding to interaction of the user side on an operation detection area located in the water source tracing interface, performing operation detection on operation data which is uploaded by the user side and is used for performing water quality detection on the water source node by the corresponding water quality passive detection equipment, and determining an operation attribute based on a detection result; and if the response operation attribute is a correct attribute, filling the operation data into an operation detection area, filling personal evaluation data corresponding to the water source node uploaded by the user side into an evaluation detection area located in a water source tracing interface, and sending the water source tracing interface to the management side. According to the invention, the comprehensiveness is improved at least.

Description

TECHNICAL FIELD

[0001] The present application relates to data processing technology, in particular to a drinking water source whole-process safety traceability method and system. BACKGROUND

[0002] Drinking water safety is a core livelihood issue related to the health of residents and the stable development of society. In the whole process of drinking water source from water intake at the water source to pipeline transportation to the end user, the change of water quality at any node may affect the final drinking water safety. Therefore, establishing a water quality safety traceability system covering the whole process and realizing real-time monitoring and tracing of the water quality state of each water source node has become a key link to ensure drinking water safety.

[0003] In the existing drinking water quality detection and traceability technology, water quality data is mainly obtained by professional detection agencies or full-time personnel of water supply enterprises. Usually, through regular sampling at fixed points, professional detection equipment is used to analyze water quality indicators. After the detection data is summarized and arranged, it is entered into the traceability management system for monitoring and traceability analysis by the management end. Residents can only indirectly understand the water quality profile through the official reports. The existing technology has obvious limitations: on the one hand, the professional detection mode has problems such as fixed cycle and limited coverage, making it difficult to respond to water quality abnormalities at the end of the pipeline network or local special nodes in real time; on the other hand, there is a lack of effective mechanism design, which cannot mobilize any resident to participate in water quality assisted detection. That is, as direct users of drinking water, residents can intuitively perceive changes in water quality, but due to the lack of convenient participation channels and operation guidelines, it is difficult for them to effectively incorporate their observations or simple detection results into the traceability system, resulting in insufficient comprehensiveness of water quality detection.

[0004] Therefore, it is urgent to provide a drinking water source whole-process safety traceability method and system that can improve the comprehensiveness of water quality detection. SUMMARY

[0005] Based on the above problems, the present application is proposed to provide a drinking water source whole-process safety traceability method and system that overcomes the above problems or at least partially solves the above problems.

[0006] According to one aspect of the present application, a drinking water source whole-process safety traceability method is provided, comprising the following steps: determining each water source node existing in the traceability area based on the pipeline network data corresponding to the traceability area, and performing device configuration of the corresponding water quality passive detection equipment for each water source node; in response to any user end with assistance qualification scanning the node identification code located at any water source node and corresponding to the water source node, sending the generated water source traceability interface to the user end for display; In response to the user terminal interacting with the operation detection area located on the water source traceability interface, operation data of water quality detection of the water source node by the water quality passive detection equipment uploaded by the user terminal is detected, and an operation attribute is determined based on the detection result; In response to the operation attribute being a correct attribute, the operation data is filled into the operation detection area, personal evaluation data of the water source node uploaded by the user terminal is filled into the evaluation detection area located on the water source traceability interface, and the water source traceability interface is sent to the management terminal.

[0007] Optionally, in the method according to the present application, in response to any user terminal with assistance qualification scanning the node identification code located on any water source node and corresponding to the water source node, the generated water source traceability interface is sent to the user terminal for display, comprising: In response to any user terminal scanning the node identification code located on any water source node and corresponding to the water source node, a user face image uploaded by the user terminal is obtained; The user face image is evaluated, and the image score obtained based on the evaluation result is compared with a preset score threshold value; In response to the image score being greater than the preset score threshold value, a preset assistance prohibited list is called, and the user face image is compared with each prohibited face image located on the preset assistance prohibited list to obtain each image similarity; In response to each image similarity being less than a preset similarity, the user terminal is determined to have assistance qualification, and the generated water source traceability interface is sent to the user terminal for display.

[0008] Optionally, in the method according to the present application, evaluating the user face image comprises: Obtaining each direction frame line of the image frame constituting the user face image, and performing face recognition on the user face image; Pixelizing a face region located on the user face image and indicating a user face based on the recognition result to obtain each face pixel point; When it is determined that any direction frame line has a point-line overlapping relationship with each face pixel point corresponding to the continuous arrangement and greater than a preset number of pixels, a preset fixed score less than or equal to the preset score threshold value is determined as the image score; When it is determined that any direction frame line does not have a point-point line overlapping relationship with each face pixel point corresponding to the continuous arrangement and greater than a preset number of pixels, a face connection line connecting a region center point of the corresponding face region and an image center point of the corresponding user face image is generated, and a line segment length corresponding to the face connection line is determined; In response to the line segment length being greater than the preset length, the preset fixed score is determined as the image score, otherwise, feature extraction is performed on the face region, and image evaluation is performed based on the obtained different face features to obtain the image score.

[0009] Optionally, in the method according to the present application, the image evaluation based on the obtained different face features to obtain the image score comprises: obtaining an eye sub-region corresponding to an eye feature, an ear sub-region corresponding to an ear feature, and a mouth sub-region corresponding to a mouth feature in the face region; obtaining a feature number corresponding to each of the eye sub-region and the ear sub-region; in response to any feature number not being two, determining the preset fixed score as the image score; in response to each feature number being two, performing image evaluation based on a horizontal turning dimension, a vertical turning dimension, and a plane tilting dimension based on the eye sub-region, the ear sub-region, and the mouth sub-region respectively to obtain a first dimension value, a second dimension value, and a third dimension value; performing weighted summation calculation on the first dimension value, the second dimension value, and the third dimension value to obtain the image score.

[0010] Optionally, in the method according to the present application, the image evaluation based on the horizontal turning dimension, the vertical turning dimension, and the plane tilting dimension based on the eye sub-region, the ear sub-region, and the mouth sub-region respectively to obtain the first dimension value, the second dimension value, and the third dimension value comprises: performing contour comparison on a first ear contour and a second ear contour surrounding the two ear sub-regions, and performing normalization processing on the obtained contour coincidence rate to obtain the first dimension value based on the horizontal turning dimension; connecting a first eye center point and a second eye center point corresponding to the two eye sub-regions to obtain an eye connecting line; determining a center connecting point located on the eye connecting line, and generating a feature connecting line extending in a vertical direction to the mouth sub-region from the center connecting point as a starting point; obtaining a feature length corresponding to the feature connecting line, and performing normalization processing on the feature length to obtain the second dimension value based on the vertical turning dimension; generating a horizontal reference line extending in a horizontal direction from any end point of the eye connecting line as a starting point, and performing normalization processing on an included angle between the obtained eye connecting line and the horizontal reference line to obtain the third dimension value based on the plane tilting dimension.

[0011] Optionally, in the method according to the present application, in response to the user terminal interacting with the operation detection area located on the water source traceability interface, operation detection is performed on operation data of water quality detection of the water source node by the corresponding water quality passive detection device uploaded by the user terminal, and operation attributes are determined based on the detection result, including: In response to the user terminal interacting with the operation detection area located on the water source traceability interface, the device storage cabin loaded with the water quality passive detection device is controlled to be in an open state to expose the water quality passive detection device; In response to the pressing unit pre-set on the water quality passive detection device being in a pressed state at a first collection time, a collection control plug-in is loaded on the user terminal to trigger the user terminal to start data collection of the water source node based on the collection control plug-in; In response to the pressing unit being changed from the pressed state to a relaxed state at a second collection time after the first collection time, the user terminal is triggered to stop data collection of the water source node based on the collection control plug-in, and operation data of water quality detection of the water source node by the corresponding water quality passive detection device are obtained; Operation detection is performed on the operation data, and operation attributes are determined based on the detection result.

[0012] Optionally, in the method according to the present application, operation detection is performed on the operation data, and operation attributes are determined based on the detection result, including: Image sequencing is performed on each operation video frame constituting the operation data based on the chronological order, and first-level identification is performed based on the obtained operation image sequence in the corresponding forward sequence arrangement; In response to determining based on the identification result that any operation video frame located in the operation image sequence first exists a device area indicating the water quality passive detection device and a hand area indicating a hand limb connected with the water quality passive detection device, the operation image frame is determined as a first-level cut frame; Sequence cutting is performed on the operation image sequence, and second-level identification is performed on the obtained first-level processing sequence including all operation video frames located after the first-level cut frame in the corresponding reverse sequence arrangement; In response to determining based on the identification result that any operation video frame of the first-level processing sequence first exists a device area indicating the water quality passive detection device and a hand area indicating a hand limb connected with the water quality passive detection device, the operation image frame is determined as a second-level cut frame; Sequence cutting is performed on the first-level processing sequence, and third-level identification is performed based on the obtained second-level image sequence; In response to determining, based on the recognition result, that the device region indicating the water quality passive detection device and the hand region indicating the hand limb connected with the water quality passive detection device exist in each operation video frame of the secondary image sequence, the operation attribute of the operation data is determined as a correct attribute, and otherwise as an incorrect attribute.

[0013] Optionally, in the method according to the present application, in response to the operation attribute being the correct attribute, the operation data is filled into an operation detection region, personal evaluation data corresponding to the water source node uploaded by the user end is filled into an evaluation detection region located in a water source traceability interface, and the water source traceability interface is sent to a management end, comprising: In response to the operation attribute being the correct attribute, each operation video frame constituting the operation data is filled into an operation detection region. In response to the user end interacting with the operation video frame located in the operation detection region, the operation video frame is determined as an abnormal identification frame, and an abnormal dimension selected by the user end based on the abnormal identification frame is obtained. The abnormal identification frame is detected by calling a dimension detection strategy corresponding to the abnormal dimension, and in response to determining, based on the detection result, that the abnormal identification frame is in an abnormal state of the corresponding abnormal dimension, the abnormal identification frame is determined as personal evaluation data. The personal evaluation data is filled into an evaluation detection region located in a water source traceability interface, and the water source traceability interface is sent to a management end.

[0014] Optionally, in the method according to the present application, the abnormal identification frame is detected by calling a dimension detection strategy corresponding to the abnormal dimension, comprising: In response to the abnormal dimension being a color dimension, the device region located in the abnormal identification frame is subjected to pixelization processing to obtain each regional pixel point constituting the device region. The mean value of the corresponding pixel value of each regional pixel point is calculated, and the obtained regional pixel mean value is compared with the pure water pixel value of the corresponding pure water obtained by calling. In response to determining, based on the comparison result, that the pixel difference between the regional pixel mean value and the pure water pixel value is greater than a preset pixel value, it is determined that the abnormal identification frame is in an abnormal state of the corresponding abnormal dimension. Or In response to the abnormal dimension being an impurity dimension, the abnormal identification frame is determined as an initial comparison frame, and the user end is triggered to perform voice broadcast corresponding to shaking the water quality passive detection device based on a collection control plug-in. In response to completing the voice broadcast, the user end is triggered to perform data collection on the water quality passive detection device based on the collection control plug-in to obtain a current comparison frame. performing device area-based area comparison between the initial comparison frame and the current comparison frame, and in response to a comparison result indicating that there is a difference, determining that the abnormal identification frame is in an abnormal state of a corresponding abnormal dimension.

[0015] Optionally, in the method according to the present application, the method further comprises: in response to determining that the abnormal identification frame is not in an abnormal state of a corresponding abnormal dimension based on the detection result, generating invalid identification information, and establishing a qualification buffer period with an identification time corresponding to the invalid identification information as an end point of the period; filling the invalid identification information into a historical assistance library corresponding to the user terminal, and traversing the historical assistance library; in response to the historical assistance library having each invalid identification information corresponding to a location within the qualification buffer period and greater than a preset first invalid number, canceling the assistance qualification possessed by the user terminal; in response to the historical assistance library having each invalid identification information greater than a preset second invalid number, canceling the assistance qualification possessed by the user terminal.

[0016] Optionally, in the method according to the present application, the method further comprises: in response to receiving a collaboration request signal of the user terminal, generating a collaboration participation area filled with a collaboration identification code based on the water source traceability interface; in response to any collaboration terminal scanning the collaboration identification code, sending a collaboration traceability interface corresponding to the water source traceability interface to the collaboration terminal, wherein the collaboration traceability interface includes an operation collaboration area filled with the operation data and an evaluation collaboration area; filling collaboration evaluation data corresponding to the water source node uploaded by the collaboration terminal into the evaluation collaboration area, and filling the evaluation collaboration area into the water source traceability interface.

[0017] According to another aspect of the present application, a drinking water source whole-process safety traceability system is provided, comprising: a detection configuration module configured to determine each water source node existing in a traceability area based on pipe network data of the traceability area, and perform device configuration of a water quality passive detection device for each water source node; an interface display module configured to, in response to any user terminal with an assistance qualification scanning a node identification code corresponding to any water source node, send a generated water source traceability interface to the user terminal for display; The operation detection module is configured to, in response to the user terminal interacting with the operation detection area on the water source traceability interface, detect the operation data uploaded by the user terminal on the water quality detection of the water quality passive detection device on the water source node, and determine the operation attribute based on the detection result; The data filling module is configured to, in response to the operation attribute being a correct attribute, fill the operation data to the operation detection area, fill the personal evaluation data uploaded by the user terminal on the water source node to the evaluation detection area on the water source traceability interface, and send the water source traceability interface to the management terminal.

[0018] According to the scheme of the present application, first of all, the present application realizes the assisted detection of water quality by any resident. By configuring water quality passive detection devices for each water source node and setting node identification codes, residents can quickly obtain a water source traceability interface by scanning the identification codes with a user terminal, and can upload detection data through simple interactive operation, breaking the barrier of professional detection and enabling residents to become assistants in water quality detection, greatly expanding the scope of participants in water quality detection and improving the overall comprehensiveness. Secondly, the present application improves the coverage and real-time performance of water quality detection. With the extensive participation of residents, the detection range is extended from professional agency fixed-point detection to each water source node in the whole process of the pipe network, especially the terminal node, realizing the global coverage of the detection network. At the same time, residents can respond to detection requirements at any time, so that water quality data can be uploaded to the management terminal in real time, solving the problem of hysteresis in traditional periodic detection. Thirdly, the present application ensures the effectiveness of the assisted detection data. By detecting the operation data uploaded by the user terminal and confirming the operation attribute, the present application ensures the standardization of resident detection operation and the accuracy of data, avoiding invalid data interference caused by non-professional detection and providing reliable supplementary data for the traceability system. Finally, the present application strengthens the collaboration of the whole process traceability. The operation data and personal evaluation data uploaded by the residents are integrated and synchronized to the management terminal, enriching the information dimension of the traceability and promoting the two-way interaction between the management terminal and the residents, improving the fine level of drinking water safety management, and realizing the organic combination of professional detection and resident assisted detection. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 A flowchart of a drinking water source whole-process safety traceability method according to an embodiment of the present application is shown; Figure 2 A structural schematic diagram of the water quality passive detection device in the present embodiment is shown; Figure 3 A structural block diagram of a drinking water source whole-process safety traceability system according to another embodiment of the present application is shown. DETAILED DESCRIPTION

[0020] Exemplary embodiments of the present disclosure will be described in greater detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be thoroughly and completely understood, and so that the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0021] To solve the problems existing in the prior art, the inventors propose the solutions of the present disclosure. One embodiment of the present disclosure provides a method for tracing the safety of the whole process of a drinking water source, which can be executed in a computing device, wherein the computing device can be understood as a terminal with data processing function, such as a mobile phone or a computer.

[0022] Figure 1 A flow chart of the method for tracing the safety of the whole process of a drinking water source according to one embodiment of the present disclosure is shown, as shown in FIG. 1, the method starts from step S1, in which the following contents are included: Figure 1 Based on the pipe network data of the corresponding tracing area, determine the water source nodes existing in the tracing area, and configure the corresponding water quality passive detection device for each water source node.

[0023] For example, in the present embodiment, in the process of tracing the safety of the whole process of a drinking water source, in order to achieve the purpose of assisting residents in detecting water quality, first, the determination of water source nodes in the tracing area and the configuration of corresponding detection devices need to be completed, wherein the specific scheme can be as follows: Firstly, the server can obtain the pipe network data of the corresponding tracing area, and through the information such as pipe network layout and node distribution determined based on the pipe network data, the server can comb and analyze the information, accurately locate and determine each water source node existing in the tracing area. It can be explained that these water source nodes cover various key points involved in the process of drinking water from the source to the terminal, such as water intake points, water pipeline connection points, water storage facilities, terminal water supply points, etc., to ensure that no drinking water related nodes that residents may contact are missed, and to provide clear operation objects for residents to assist in detection; ​Then, after determining each water source node, the device configuration of the corresponding water quality passive detection equipment for each water source node can be performed. It can be explained that the water quality passive detection equipment can complete the detection of the water quality of the node under the assistance of the residents. By equipping each water source node with a dedicated detection device, the detection specificity and accuracy are ensured, and the residents can use the appropriate tool when assisting in the detection, effectively improving the reliability of the detection data, laying a foundation for subsequent resident participation in water quality assistance detection. When residents believe that any water source node has a corresponding water quality problem, they can use the on-site configured water quality passive detection equipment to carry out detection work and upload corresponding data, which opens up the channel for resident assistance detection from the hardware configuration level, making it feasible for residents to participate in water quality safety supervision, and further strengthening the comprehensiveness and real-time nature of the safety traceability of the drinking water source.

[0024] It should be noted that in the present embodiment, the water quality passive detection equipment can specifically include a cavity for storing water sources, such as a transparent water cup, and then the corresponding water quality detection can be performed based on the stored water source.

[0025] In step S2, the following content is included: In response to any user terminal with assistance qualification scanning the node identification code located at any water source node and corresponding to the water source node, the generated water source traceability interface is sent to the user terminal for display.

[0026] For example, in the present embodiment, when a resident with assistance qualification believes that a certain water source node has a corresponding water quality problem, he / she can use the user terminal to scan the node identification code located at the water source node and corresponding to the water source node, and then the server will immediately respond to this scanning operation. It can be explained that the node identification code is a unique identification for each water source node. The node identification code can exist in the form of a two-dimensional code or a bar code, and can be formed on the wall or ground near the water source node by printing or pasting. The resident scans the identification code through the user terminal (such as a mobile phone or other equipment), which is equivalent to sending a request for participation in water quality assistance detection to the server. After receiving this request, the server generates a preset water source traceability interface and sends it to the user terminal. Here, the water source traceability interface contains various areas required for subsequent resident water quality detection operations and related information filling. Through the interaction between the node identification code and the user terminal, the association between the resident and the water source node is quickly established, allowing the resident to conveniently enter the detection process, greatly reducing the operation threshold for residents to participate in assistance detection, so that more residents are willing and able to participate in water quality detection. At the same time, the targeted display of the water source traceability interface provides clear operation guidance for residents, ensuring that residents can clearly understand the operation direction when assisting in the detection, improving the standardization and efficiency of the detection operation, thereby effectively achieving the purpose of resident assistance detection of water quality.

[0027] In addition, in order to identify different water source nodes, the water source traceability interface generated corresponding to any water source node can carry water source identification information corresponding to the water source node, such as the corresponding water source number or water source location, etc., so that the server can locate the corresponding water source node based on the water source identification information for each received water source traceability interface in the subsequent process.

[0028] Further, in the embodiment, the above-mentioned "in response to any user terminal scanning the node identification code located at any water source node and corresponding to the water source node, the generated water source traceability interface is sent to the user terminal for display" can further include the following steps: In response to any user terminal scanning the node identification code located at any water source node and corresponding to the water source node, the user terminal uploads a user face image; The image evaluation is performed on the user face image, and the image score obtained based on the evaluation result is compared with the preset score threshold value; In response to the image score being greater than the preset score threshold value, the preset assistance prohibited list is called, and the user face image is compared with each prohibited face image located in the preset assistance prohibited list to obtain the image similarity; In response to each image similarity being less than a preset similarity, the user terminal is determined to have assistance qualification, and the generated water source traceability interface is sent to the user terminal for display.

[0029] For example, in the embodiment, the specific process of the user terminal scanning the node identification code of any water source node to send the corresponding water source traceability interface to the user terminal for display can be realized based on the following technical solutions: First, in response to any user terminal scanning the node identification code located at any water source node and corresponding to the water source node, the server acquires the user face image uploaded by the user terminal, thereby providing basic data for subsequent verification of user identity rationality, ensuring that the identity of residents participating in assistance detection is traceable, and reducing the risk of malicious operation or invalid detection from the source; Then, the server performs image evaluation on the acquired user face image, and compares the image score obtained based on the evaluation result with the preset score threshold value. It can be explained that the image evaluation aims to judge whether the quality of the face image meets the subsequent verification requirement, for example, by setting the score standard and comparing with the threshold value, the clear and effective face image can be selected, and the verification error caused by the image quality problem is avoided, and the accuracy of subsequent identity verification is ensured; When the image score is greater than the preset score threshold, the server calls a preset assistance prohibited list, and performs image comparison between the user face image and each prohibited face image in the preset assistance prohibited list to obtain each image similarity. Here, the preset assistance prohibited list is used to exclude personnel who are not suitable for participating in assistance detection, such as personnel with low integrity. Through strict list comparison, the prohibited personnel can be prevented from participating in detection, thereby maintaining the standardization and seriousness of the assistance detection system. Finally, if each image similarity is less than a preset similarity, the server determines that the user terminal has assistance qualification, and sends the generated water source tracing interface to the user terminal for display. It can be explained that through double verification (image quality meets the standard and non-prohibited personnel), the qualified assistants can be accurately identified, and only legal and compliant residents can enter the detection process, thereby protecting the right of residents to participate in assistance detection, and improving the credibility of detection data through the standard access mechanism, thereby providing a reliable mass foundation for drinking water source safety tracing.

[0030] Further, in the embodiment, the image evaluation on the user face image can further include the following steps: Obtaining each direction frame line of the image frame constituting the user face image, and performing face recognition on the user face image; Performing pixelization processing on the face region in the user face image based on the recognition result, to obtain each face pixel point; When it is determined that any direction frame line has a point-line overlapping relationship with each face pixel point corresponding to the continuous arrangement and greater than a preset number of pixels, a preset fixed score less than or equal to the preset score threshold is determined as the image score; When it is determined that any direction frame line does not have a point-point line overlapping relationship with each face pixel point corresponding to the continuous arrangement and greater than a preset number of pixels, a face connection line connecting the region center point of the corresponding face region and the image center point of the corresponding user face image is generated, and a line segment length corresponding to the face connection line is determined; In response to the line segment length being greater than a preset length, a preset fixed score is determined as the image score, otherwise, feature extraction is performed on the face region, and image evaluation is performed based on each different face feature obtained to obtain the image score.

[0031] For example, in the embodiment, the image evaluation on the obtained user face image can be implemented based on the following technical content: Firstly, the server can obtain each directional frame line of the image framework of the user face image, and perform face recognition on the user face image, wherein each directional frame line of the image framework provides a reference basis for subsequent judgment of the face region position, and the face recognition can accurately locate the face region indicating the user's face in the image. Through clear region positioning, a clear analysis object is provided for subsequent image evaluation, ensuring that the evaluation process focuses on effective face information and avoids irrelevant region interference. Here, since the image framework of the user face image is generally quadrilateral, the corresponding directional frame line can correspond to each directional frame line based on the upward direction, downward direction, left direction and right direction; Then, the server can perform pixelization processing on the face region indicating the user's face in the user face image based on the recognition result to obtain each face pixel point. It can be explained that the pixelization processing can convert the face region into a quantifiable pixel point set, providing a data basis for subsequent judgment of point-line relationship, making the evaluation process more objective and accurate; Wherein, when the server determines that any directional frame line has a point-line overlapping relationship with each face pixel point corresponding to the continuous arrangement and greater than the preset pixel number, a preset fixed score less than or equal to the preset score threshold can be determined as the image score, that is, in this case, it is indicated that the face region may have problems such as being blocked or truncated, resulting in that the image quality does not meet the requirements, therefore, by directly assigning a lower score, unqualified face images can be quickly screened out, reducing invalid detection process and improving the efficiency of assisting detection; On the contrary, when it is determined that any directional frame line does not have a point-line overlapping relationship with each face pixel point corresponding to the continuous arrangement and greater than the preset pixel number, it is indicated that the face region may not have problems such as being blocked or truncated, then the face connection line connecting the region center point of the corresponding face region and the image center point of the corresponding user face image can be further generated, and the length of the line segment corresponding to the face connection line is determined, and then the position of the face region in the image is further analyzed to judge the image quality, ensuring that the face region is in a suitable image position, providing a good basis for subsequent feature extraction; Afterwards, in response to the length of the line segment being greater than the preset length, a preset fixed score is determined as the image score; otherwise, feature extraction is performed on the face region, and image evaluation is performed based on the obtained different face features to obtain the image score. It can be explained that the length of the line segment exceeding the preset length means that the face region deviates far from the center of the image, that is, it is not in a relatively central position, which may lead to poor image quality. Therefore, the preset fixed score can be directly determined as the image score. If the length of the line segment does not exceed the preset length, it means that the face region is in a relatively central position. At this time, feature extraction can be performed on the face region and evaluation can be performed, which can more comprehensively judge whether the image is clear and complete, ensure the accuracy of subsequent face comparison, and thus enable residents who meet the conditions to more smoothly participate in assisting detection and maintain the order of the assisting detection system.

[0032] Furthermore, in the present embodiment, the above-mentioned "image evaluation based on the obtained different face features to obtain the image score" can further include the following steps: obtaining an eye sub-region corresponding to the eye feature, an ear sub-region corresponding to the ear feature, and a mouth sub-region corresponding to the mouth feature in the face region; obtaining the number of each feature corresponding to the eye sub-region and the ear sub-region; in response to any feature number being other than two, determining the preset fixed score as the image score; in response to each feature number being two, performing image evaluation based on the horizontal turning dimension, the vertical turning dimension, and the plane tilting dimension based on the eye sub-region, the ear sub-region, and the mouth sub-region respectively to obtain a first dimension value, a second dimension value, and a third dimension value; performing weighted summation calculation on the first dimension value, the second dimension value, and the third dimension value to obtain the image score.

[0033] For example, in the present embodiment, the image evaluation based on the obtained face features can be specifically implemented based on the following technical content: Firstly, the server can obtain an eye sub-region corresponding to the eye feature, an ear sub-region corresponding to the ear feature, and a mouth sub-region corresponding to the mouth feature in the face region, so as to accurately position the sub-regions of the three key face features, provide a clear object for subsequent feature analysis and evaluation, ensure that the evaluation process focuses on the most recognizable part of the face, and lay a foundation for accurately judging the image quality; Then, the number of each feature corresponding to the eye sub-region and the ear sub-region can be further obtained. It can be explained that the number of features is the number of eye sub-regions and ear sub-regions, that is, by counting the number, it can be preliminarily judged whether the face image is complete; When the number of any feature is not two, it indicates that the image is missing a key feature (e.g., missing an eye sub-region or missing an ear sub-region), and a preset fixed score can be determined as the image score. It can be explained that, under normal circumstances, the eyes and ears of a human body exist in pairs, and if the number does not meet the requirement, it indicates that the image may have problems such as occlusion or improper shooting angle, resulting in incomplete features. Therefore, a preset fixed score can be directly assigned to quickly screen out images that do not meet the requirements, reduce invalid detection processes, and improve the efficiency of assisting detection. When the number of each feature is two, it indicates that the image does not miss a key feature, and image evaluation based on the horizontal turning dimension, the vertical turning dimension, and the plane tilting dimension can be further performed based on the eye sub-region, the ear sub-region, and the mouth sub-region, respectively, to obtain a first dimension value, a second dimension value, and a third dimension value. It can be explained that evaluating the face image from multiple dimensions can comprehensively reflect the pose and integrity of the face in the image, avoid the limitations of single dimension evaluation, and improve the accuracy of evaluation. Finally, the first dimension value, the second dimension value, and the third dimension value can be weighted and summed to calculate, that is, the evaluation results of each dimension are integrated by weighted summation, so that the image score can more objectively reflect the overall quality of the face image, provide a reliable basis for subsequent judgment of whether the user has the assisting qualification, and ensure the standardization and effectiveness of resident assistance detection.

[0034] It can be explained that image evaluation based on the horizontal turning dimension, the vertical turning dimension, and the plane tilting dimension can specifically include the following evaluation contents: Horizontal turning dimension: evaluating whether the eye sub-region, the ear sub-region, and the mouth sub-region are symmetrical, that is, determining whether the primary user has a corresponding turning action based on the horizontal direction when performing image acquisition, and determining a first evaluation value according to the evaluation result; Vertical turning dimension: evaluating whether the length between the eye sub-region and the mouth sub-region meets the standard, that is, determining whether the primary user has a head-bowing or head-raising action when performing image acquisition, and determining a second evaluation value according to the evaluation result; Plane tilting dimension: measuring the tilting angle of the entire face relative to the image plane, including head tilting, to calculate a third evaluation value.

[0035] Therefore, in the present embodiment, the above-mentioned "image evaluation based on the horizontal turning dimension, the vertical turning dimension, and the plane tilting dimension based on the eye sub-region, the ear sub-region, and the mouth sub-region, respectively, to obtain a first dimension value, a second dimension value, and a third dimension value" can further include the following steps: The first ear contour and the second ear contour surrounding the two ear sub-regions are compared, and the obtained contour coincidence rate is normalized to obtain a first dimension value based on the horizontal turning dimension; The first eye center point and the second eye center point corresponding to the two eye sub-regions are connected to obtain an eye connection line; A center connection point is determined on the eye connection line, and a feature connection line extending to the mouth sub-region in the vertical direction is generated with the center connection point as a starting point; A feature length corresponding to the feature connection line is obtained, and the feature length is normalized to obtain a second dimension value based on the vertical turning dimension; A horizontal reference line extending in the horizontal direction is generated with any endpoint of the eye connection line as a starting point, and the included angle between the obtained eye connection line and the horizontal reference line is normalized to obtain a third dimension value based on the plane tilt dimension.

[0036] For example, in the present embodiment, the specific process of image evaluation based on the eye sub-region, the ear sub-region and the mouth sub-region in the horizontal turning dimension, the vertical turning dimension and the plane tilt dimension is as follows: First, the server can compare the first ear contour and the second ear contour surrounding the two ear sub-regions in the horizontal turning dimension, and after obtaining the contour coincidence rate based on the comparison result, the contour coincidence rate is normalized to obtain the first dimension value based on the horizontal turning dimension. It can be explained that by comparing the coincidence degree of the contours of the two ears and normalizing, the turning situation of the face in the horizontal direction can be accurately reflected, and the standardization of the evaluation result is ensured, providing reliable horizontal dimension basis for subsequent comprehensive scoring; Secondly, for the vertical turning dimension, the server can first connect the first eye center point and the second eye center point corresponding to the two eye sub-regions to obtain an eye connection line; then determine a center connection point on the eye connection line, and generate a feature connection line extending to the mouth sub-region in the vertical direction with the center connection point as a starting point; finally, the feature length corresponding to the feature connection line is obtained, and it is normalized to obtain the second dimension value based on the vertical turning dimension. That is, the vertical connection feature of the eyes and the mouth can objectively quantify the turning state of the face in the vertical direction, and the normalization processing ensures the comparability of the vertical dimension evaluation between different images, and improves the fairness of the evaluation; Finally, for the plane tilt dimension, any endpoint of the eye connection line can be taken as the starting point to generate a horizontal reference line extending in the horizontal direction, and then the angle between the eye connection line and the horizontal reference line is obtained. The third dimension value based on the plane tilt dimension is obtained by normalizing the angle. Here, by calculating and normalizing the angle between the eye connection line and the horizontal reference line, the tilt degree of the face in the plane is effectively captured, making the evaluation of the plane tilt dimension more scientific, and further improving the multi-dimensional evaluation system.

[0037] It can be explained that through the above respective evaluation and normalization processing of the horizontal, vertical and plane tilt three dimensions, a comprehensive and standard unified dimension value is formed, which provides a solid foundation for subsequent image score calculation, and ensures that only users with qualified image quality can participate in assisted detection, thereby ensuring the orderly development of resident assisted detection and the effectiveness of detection data from a technical level.

[0038] In step S3, the following content is included: In response to the user terminal interacting with the operation detection area located on the water source traceability interface, the operation data uploaded by the user terminal corresponding to the water quality passive detection equipment detecting the water quality of the water source node is detected, and the operation attribute is determined based on the detection result.

[0039] For example, in the present embodiment, when the user terminal obtains corresponding operation data based on the water quality passive detection equipment detecting the water quality of the water source node, the user terminal can interact with the operation detection area located on the water source traceability interface, so that the server detects the operation data of the user terminal. Through strict detection of the operation data, it can be judged whether the detection operation of the resident conforms to the standard, thereby ensuring the scientificity of the assisted detection from the data level, and after completing the operation detection, the server can determine the operation attribute based on the detection result. Here, the operation attribute is a qualitative description of whether the resident's detection operation is correct. By explicitly specifying the operation attribute, valid detection and invalid detection can be distinguished, ensuring that only data generated by correct detection operation is included in the traceability system, improving the accuracy of drinking water source safety traceability, and also making the resident clear about the standardization of his / her operation, thereby facilitating better participation in assisted detection in the future.

[0040] Further, in the present embodiment, the above-mentioned "in response to the user terminal interacting with the operation detection area located on the water source traceability interface, detecting the operation data uploaded by the user terminal corresponding to the water quality passive detection equipment detecting the water quality of the water source node, and determining the operation attribute based on the detection result" can further include the following steps: In response to the user terminal interacting with the operation detection area on the water source tracing interface, the device storage cabin loading the water quality passive detection device is controlled to be in an open state to expose the water quality passive detection device. In response to the pressing unit preset on the water quality passive detection device being in a pressing state at a first collection time, a collection control plug-in is loaded on the user terminal to trigger the user terminal to start data collection on the water source node based on the collection control plug-in. In response to the pressing unit changing from the pressing state to a relaxed state at a second collection time after the first collection time, the user terminal is triggered to stop data collection on the water source node based on the collection control plug-in, and operation data of the water quality passive detection device detecting the water quality of the water source node is obtained. The operation data is detected for operation detection, and the operation attribute is determined based on the detection result.

[0041] For example, in this embodiment, when the user terminal interacts with the operation detection area on the water source tracing interface, the server will respond and complete the detection of the operation data and the determination of the operation attribute based on the following content: First, in response to the user terminal interacting with the operation detection area on the water source tracing interface, the server will control the device storage cabin loading the water quality passive detection device to be in an open state to expose the water quality passive detection device. It can be explained that the water quality passive detection device can be pre-installed in the device storage cabin for storage, and through the automatic opening operation of the device storage cabin, the process of residents taking the device is simplified, so that residents can quickly enter the detection link and improve the convenience of assisting detection. Next, in response to the pressing unit preset on the water quality passive detection device being in a pressing state at a first collection time, the server loads a collection control plug-in on the user terminal to trigger the user terminal to start data collection on the water source node based on the collection control plug-in. It can be explained that the state triggering mechanism of the pressing unit provides a clear signal for the start of data collection, ensures that data collection starts only when the resident correctly operates the device, avoids the generation of invalid data, and ensures the pertinence of the detection data. Then, in response to the pressing unit changing from the pressing state to a relaxed state at a second collection time after the first collection time, the server can trigger the user terminal to stop data collection on the water source node based on the collection control plug-in, and obtain operation data of the water quality passive detection device detecting the water quality of the water source node. It can be explained that the change of the pressing state controls the stop of data collection, so that the period of data collection is synchronized with the actual detection operation of the resident, and the operation data can record the detection process completely, providing a comprehensive data basis for subsequent detection. Finally, the server performs operation checks on the operation data and determines the operation attributes based on the check results. Here, operation checks can verify the validity of the data and the standardization of the operation. By clarifying the operation attributes, it can be determined whether the residents' testing operations meet the requirements, allowing the standard-compliant testing data to enter the subsequent process, ensuring the quality of residents' assistance in testing, and providing reliable public participation data for the traceability of drinking water source safety.

[0042] It can be explained that the equipment storage compartment can be, for example, a hollow shell with an opening, through which residents can access the passive water quality monitoring equipment. Accordingly, in order to improve the safety of the passive water quality monitoring equipment, a sliding sealing plate can be installed at the opening. The sliding sealing plate can be in an open state in response to the user's interaction with the operation detection area located on the water source tracing interface.

[0043] like Figure 2 As shown, Figure 2 A schematic diagram of the passive water quality monitoring device in this embodiment is shown, wherein, based on Figure 2 As can be seen from the content, the passive water quality monitoring equipment is equipped with a corresponding pressing unit.

[0044] Furthermore, in this embodiment, the aforementioned "performing operation detection on the operation data and determining operation attributes based on the detection results" may further include the following steps: The operation video frames that make up the operation data are sorted according to the time sequence, and the corresponding first-level recognition is performed based on the obtained operation image sequence in ascending order. Based on the recognition results, if any operation video frame in the operation image sequence first contains an indication of the equipment area of ​​the passive water quality detection device and a hand area indicating a hand limb that is connected to the passive water quality detection device, then the operation image frame is determined as a first-level capture frame. The operation image sequence is segmented, and the resulting first-level processing sequence, which includes all operation video frames after the first-level segmented frame, is subjected to a second-level recognition process that reverses the order of the corresponding frames. The response determines that any operation video frame containing the primary processing sequence first contains an indication of the equipment area of ​​the passive water quality detection device and a hand area indicating a hand limb that is connected to the passive water quality detection device, and the operation image frame is determined as a secondary cropping frame. The first-level processing sequence is truncated, and the third-level recognition is performed based on the obtained second-level image sequence; In response to determining, based on the recognition result, that the device region indicating the water quality passive detection device and the hand region indicating the hand limb connected with the water quality passive detection device exist in each operation video frame in the secondary image sequence, the operation attribute of the operation data is determined as a correct attribute, and vice versa.

[0045] For example, in the embodiment, the implementation manner of operation detection on the operation data and determination of the operation attribute based on the detection result can be as follows: Firstly, the server can sort the operation video frames constituting the operation data in image based on the time sequence, and perform first-order recognition on the obtained operation image sequence in corresponding positive sequence. It can be explained that by arranging the operation video frames in time sequence and performing positive sequence recognition, key information can be captured from the complete process from the beginning to the end of detection, providing a time dimension reference for subsequent extraction of effective video segments, and ensuring the comprehensiveness of detection. Secondly, in response to determining, based on the recognition result, that the device region indicating the water quality passive detection device and the hand region indicating the hand limb connected with the water quality passive detection device exist in any operation video frame in the operation image sequence for the first time, the operation image frame is determined as a first-level extraction frame. Here, the determination of the first-level extraction frame indicates that the resident starts to correctly operate the detection device. By locking the frame in which the device and the hand are associated for the first time, the invalid preparation process before detection can be excluded, and the starting point of the actual detection operation can be focused on, thereby improving the detection pertinence. Then, the operation image sequence is subjected to sequence extraction, and the obtained first-level processing sequence including all operation video frames after the first-level extraction frame is subjected to second-order recognition in corresponding reverse sequence. It can be explained that reverse sequence recognition of the first-level processing sequence can trace back from the end of the detection process, which facilitates quick positioning of the key frame before the operation ends, thereby improving the detection efficiency. Subsequently, in response to determining, based on the recognition result, that the device region indicating the water quality passive detection device and the hand region indicating the hand limb connected with the water quality passive detection device exist in any operation video frame in the first-level processing sequence for the first time, the operation image frame is determined as a second-level extraction frame. It can be explained that the determination of the second-level extraction frame clearly defines the end boundary of the effective operation, and by twice extraction in positive and reverse sequences, the complete period of the resident's actual operation of the detection device can be accurately framed, and irrelevant content after the operation ends can be excluded. After that, the first-level processing sequence is subjected to sequence extraction, and third-order recognition is performed based on the obtained secondary image sequence. It can be known that the secondary image sequence concentrates the core video frames from the beginning to the end of the operation, and the third-order recognition of the sequence can focus on the key operation process, thereby ensuring the accuracy of detection. Finally, in response to determining based on the recognition result that the device region indicating the water quality passive detection device and the hand region indicating the hand limb connected with the water quality passive detection device exist in each operation video frame of the secondary image sequence, the operation attribute of the operation data is determined as a correct attribute, and vice versa. That is, the resident needs to ensure that the hand limb is always connected with the water quality passive detection device (for example, by holding the water quality passive detection device with the hand limb) when performing water quality detection, so as to determine that the resident is not disturbed by other irrelevant personnel during the water quality detection process, strictly judge whether the operation of the resident is whole-process standard, ensure that only the detection data with correct operation is recognized, guarantee the quality of the resident's assistance detection, and provide reliable operation basis for the safety traceability of the drinking water source.

[0046] In addition, it should be noted that, based on the above content, the water quality passive detection device includes a cavity for storing the water source, such as a water cup with transparent texture. In actual operation, any resident can use the water quality passive detection device to receive the water source flowing out of the water source node for corresponding water quality detection. Since the collection control plug-in mentioned in the embodiment can trigger the user end to start data collection of the water source node, that is, each operation video frame obtained subsequently needs to exist a node region indicating the water source node to ensure that the water quality detection process is based on the water source generated by the water source node, rather than the water source generated by other places. Therefore, in order to determine whether the node region indicating the water source node exists, the embodiment can compare the node comparison image pre-shooted on the water source node with each operation video frame included in the secondary image sequence to determine whether the corresponding node region exists based on the comparison result. If any operation video frame does not exist the node region, the operation attribute thereof is determined as an error attribute to ensure that the operation data is obtained based on the corresponding water source node, thereby improving the data accuracy.

[0047] In step S4, the following steps are included: In response to the operation attribute being a correct attribute, the operation data is filled into the operation detection area, the personal evaluation data corresponding to the water source node uploaded by the user end is filled into the evaluation detection area located in the water source traceability interface, and the water source traceability interface is sent to the management end.

[0048] For example, in the embodiment, when the operation attribute is determined as the correct attribute, the server can fill the operation data to the operation detection area of the water source traceability interface. As can be seen from the above, the operation data is effective data generated in the water quality detection process of the water source node by the resident using the water quality passive detection device. By filling the operation data to the operation detection area, the detection operation process of the resident can be intuitively displayed, and a clear original basis is provided for subsequent management end review, so as to ensure that the detection process is traceable. At the same time, the server can also fill the personal evaluation data corresponding to the water source node uploaded by the user end to the evaluation detection area located in the water source traceability interface. It can be explained that the personal evaluation data can be the evaluation information of the water quality of the water source node based on the detection experience of the resident. By filling the personal evaluation data to the evaluation detection area, the subjective evaluation of the resident and the objective operation data can be supplemented, the information dimension of the water quality detection is enriched, so that the management end receiving the water source traceability interface can more comprehensively understand the situation of the water source node, the efficient transmission of the resident assisted detection information to the management end is realized, the management end can timely acquire and process the water quality detection related data provided by the resident, and the effective use of the resident assisted detection result is ensured from the mechanism, and the synergy and timeliness of the drinking water source safety traceability are strengthened.

[0049] Further, in the embodiment, the above-mentioned "in response to the operation attribute being the correct attribute, filling the operation data to the operation detection area, filling the personal evaluation data corresponding to the water source node uploaded by the user end to the evaluation detection area located in the water source traceability interface, and sending the water source traceability interface to the management end" can further include the following steps: In response to the operation attribute being the correct attribute, filling each operation video frame constituting the operation data to the operation detection area; In response to the user end interacting with the operation video frame located in the operation detection area, determining the operation video frame as an abnormal identification frame, and acquiring the abnormal dimension selected by the user end based on the abnormal identification frame; Calling a dimension detection strategy corresponding to the abnormal dimension to detect the abnormal identification frame, and in response to determining that the abnormal identification frame is in an abnormal state of the corresponding abnormal dimension based on the detection result, determining the abnormal identification frame as personal evaluation data; Filling the personal evaluation data to the evaluation detection area located in the water source traceability interface, and sending the water source traceability interface to the management end.

[0050] For example, in the embodiment, when the operation attribute is the correct attribute, the processing of the personal evaluation data and the sending of the water source traceability interface can be realized based on the following technical content: Firstly, in response to the operation attribute being a correct attribute, the server can fill each operation video frame constituting the operation data into an operation detection area. It can be explained that the operation video frame completely records the process of the resident detecting using the water quality passive detection device. Filling it into the operation detection area can intuitively present each detail of the detection, provide comprehensive raw materials for subsequent anomaly identification and evaluation, and ensure the traceability of the detection process. Then, in response to the user terminal interacting with the operation video frame located in the operation detection area, the server can further determine the operation video frame as an anomaly identification frame and obtain the anomaly dimension selected by the user terminal based on the anomaly identification frame. That is, the resident can identify the anomaly video frame and select the anomaly dimension through interaction, so that the observation and judgment of the resident can be directly involved in the water quality detection evaluation, fully exerting the subjective initiative of the resident in assisting detection, and making the anomaly identification more in line with the actual perception. It can be explained that the anomaly dimension can be pre-set to multiple dimensions for the user to select. Then, the server can retrieve a dimension detection strategy corresponding to the anomaly dimension to perform anomaly detection on the anomaly identification frame, and in response to determining that the anomaly identification frame is in an abnormal state of the corresponding anomaly dimension based on the detection result, determine the anomaly identification frame as personal evaluation data. Here, anomaly detection is performed by matching the corresponding dimension detection strategy, which ensures the professionalism and accuracy of anomaly judgment, and makes video frames that meet the anomaly standard become effective personal evaluation data, improving the credibility of personal evaluation data. Finally, after obtaining the corresponding personal evaluation data, the server can fill the personal evaluation data into an evaluation detection area located in a water source tracing interface and send the water source tracing interface to the management terminal, thereby completely realizing the standardized transmission of anomaly information in resident-assisted detection, enabling the management terminal to obtain verified anomaly data in a timely manner, providing precise public feedback information for drinking water source safety management, and strengthening the actual value of resident-assisted detection in the tracing system.

[0051] Furthermore, in the present embodiment, the above-mentioned "retrieving a dimension detection strategy corresponding to the anomaly dimension to perform anomaly detection on the anomaly identification frame" can further include the following steps: In response to the anomaly dimension being a color dimension, performing pixelization processing on a device area located in the anomaly identification frame to obtain each area pixel point constituting the device area; Performing mean value calculation on the corresponding pixel values of each area pixel point, and performing numerical comparison between the obtained area pixel mean value and the retrieved pure water pixel value of the corresponding pure water; In response to determining that the pixel difference between the area pixel mean value and the pure water pixel value is greater than a preset pixel value based on the comparison result, determining that the anomaly identification frame is in an abnormal state of the corresponding anomaly dimension. or In response to the abnormal dimension being the impurity dimension, the server determines the abnormal identification frame as an initial comparison frame, and triggers the user terminal to perform voice broadcast corresponding to shaking the water quality passive detection device based on the acquisition control plug-in; In response to completing the voice broadcast, the server triggers the user terminal to perform data acquisition on the water quality passive detection device based on the acquisition control plug-in, to obtain a current comparison frame; The server performs regional comparison on the initial comparison frame and the current comparison frame based on the device region, and in response to the comparison result being different, determines that the abnormal identification frame is in an abnormal state corresponding to the abnormal dimension.

[0052] For example, in the present embodiment, the abnormal dimension can specifically include a color dimension and an impurity dimension. The color dimension can be understood as the water source of the water source node presenting an abnormal color, and the corresponding impurity dimension can be understood as the water source of the water source node existing corresponding impurities. The specific process of calling the corresponding dimension detection strategy for abnormal detection based on the two different abnormal dimensions will be introduced respectively as follows: For the color dimension, the server can first perform pixelization processing on the device region located in the abnormal identification frame to obtain each regional pixel point constituting the device region. It can be explained that the pixelization processing converts the device region into a quantifiable pixel set for accurate detection of the color dimension, making the judgment of color difference more objective. Then, the server can perform mean value calculation on the pixel value of each regional pixel point, and compare the obtained regional pixel mean value with the corresponding pure water pixel value of the pure water. That is, by comparing with the pure water pixel value as a benchmark, an explicit color abnormality judgment standard is established to ensure the consistency of the detection result. Finally, in response to determining that the pixel difference between the regional pixel mean value and the pure water pixel value is greater than a preset pixel value based on the comparison result, the server determines that the abnormal identification frame is in an abnormal state corresponding to the color dimension. In this way, the color abnormality can be screened out through the quantitative pixel difference, so that the judgment of water color in the resident assistance detection is more scientific and reliable. For the impurity dimension, the server can first determine the abnormal identification frame as an initial comparison frame, and trigger the user end to perform voice broadcast corresponding to the shaking of the water quality passive detection device based on the acquisition control plug-in. The voice broadcast provides clear operation instructions for residents, ensuring that residents can correctly perform the action of shaking the device, creating uniform operation conditions for impurity detection. For example, the voice broadcast can be "Now we need to collect data from the water quality passive detection device. Please shake the water quality passive detection device appropriately." Then, in response to the completion of the voice broadcast, the server can trigger the user end to collect data from the water quality passive detection device based on the acquisition control plug-in, obtaining the current comparison frame. Here, by collecting the current comparison frame after shaking, the dynamic changes that impurities may cause can be captured, providing effective comparison materials for impurity detection. Finally, by performing regional comparison based on the device region between the initial comparison frame and the current comparison frame, and in response to the comparison result being different, it is determined that the abnormal identification frame is in an abnormal state corresponding to the impurity dimension, thereby effectively identifying possible impurities in the water source, making the results of resident assistance detection more practical and scientific, and improving the practical value of assistance detection.

[0053] It can be explained that through the above different detection strategies for color and impurity dimensions, accurate detection of key abnormal dimensions of water quality is achieved, making the results of resident assistance detection more scientific and reliable, and providing effective crowd participation data support for drinking water source safety traceability.

[0054] Based on the above content, in the present embodiment, the water source traceability interface corresponding to the node identification code can be sent to the user end for display only after the user end with assistance qualification scans the node identification code. The determination of the user end with assistance qualification can be based on comparing the obtained user face image with the preset assistance prohibited list, while the judgment of the user end without assistance qualification can be realized based on the following method steps: In response to determining that the abnormal identification frame is not in an abnormal state corresponding to the abnormal dimension based on the detection result, generating invalid identification information, and establishing a qualification buffer period with the identification time corresponding to the invalid identification information as the end of the period; Filling the invalid identification information into the historical assistance library corresponding to the user end, and traversing the historical assistance library; In response to the historical assistance library having each invalid identification information corresponding to the qualification buffer period and greater than a preset first invalid number, canceling the assistance qualification of the user end; In response to the historical assistance library having each invalid identification information greater than a preset second invalid number, canceling the assistance qualification of the user end.

[0055] For example, in the embodiment, when it is determined based on the detection result that the abnormal identification frame is not in the abnormal state of the corresponding abnormal dimension, the server responds and completes the related processing according to the following process: Firstly, the server can generate invalid identification information, and establish a qualification buffer period with the identification time corresponding to the invalid identification information as the end of the period. It can be explained that the invalid identification information is used to record the case that the judgment of the resident on the abnormal dimension is inconsistent with the actual detection result, and the establishment of the qualification buffer period provides a certain fault tolerance space for the resident, avoiding the loss of assistance qualification due to single or a few misjudgments, and ensuring the continuity and enthusiasm of the resident in participating in the assistance detection; Then, the server can fill the invalid identification information into the historical assistance library corresponding to the user terminal, and traverse the historical assistance library. Here, the historical assistance library is an archive for storing the behavior records of user assistance detection. By filling the invalid identification information and traversing, the judgment history of the user can be traced back comprehensively, and complete and continuous data support is provided for subsequent evaluation of assistance qualification, so as to ensure the objectivity and accuracy of the evaluation process; Finally, in response to the existence of each invalid identification information corresponding to the qualification buffer period and greater than a preset first invalid number in the historical assistance library, the server can cancel the assistance qualification possessed by the user terminal, wherein the preset first invalid number is set for the number of misjudgments within the qualification buffer period. By this condition, the user who still misjudges many times within the buffer period can be screened out, so as to avoid the continuous provision of invalid detection data and ensure the quality of assistance detection. At the same time, in response to the existence of each invalid identification information greater than a preset second invalid number in the historical assistance library, the server can also cancel the assistance qualification possessed by the user terminal, wherein the preset second invalid number is greater than the preset first invalid number, and the preset second invalid number is set for the total number of invalid identification information accumulated by the user. By strictly limiting the number of accumulated misjudgments, the user with long-term poor judgment accuracy can be further screened out, so as to improve the reliability of the assistance detection system as a whole.

[0056] In addition, based on the above content, when any user thinks that the water quality of a water source node is problematic, he / she can scan the node identification code model based on the user terminal used to perform detection on the corresponding water source quality through the water source traceability interface obtained. In order to improve the detection efficiency of the same water source node, in the embodiment, the following steps can be further included: In response to receiving the cooperation request signal of the user terminal, a cooperation participation area filled with a cooperation identification code is generated based on the water source traceability interface; In response to any cooperation terminal scanning the cooperation identification code, a cooperation traceability interface corresponding to the water source traceability interface is sent to the cooperation terminal, wherein the cooperation traceability interface includes an operation cooperation area filled with the operation data and an evaluation cooperation area. filling the evaluation co-operation region to the water source traceability interface.

[0057] For example, in the present embodiment, when there are other terminals besides the user terminal desiring to perform co-operation-based water quality detection on the water source node, the user terminal can correspondingly send a co-operation request signal to the server, and the server will respond and generate a co-operation participation region filled with a co-operation identification code based on the water source traceability interface. It can be explained that the generation of the co-operation participation region provides a clear entrance for other residents to participate in co-operation detection. Through the uniqueness of the co-operation identification code, it can be ensured that the co-operation behavior is accurately associated with the detection task of the current water source node, facilitating more residents to join the detection, expanding the participation range of the detection, and improving the comprehensiveness of the detection. Further, in response to any co-operation terminal scanning the co-operation identification code, the server sends a co-operation traceability interface corresponding to the water source traceability interface to the co-operation terminal, wherein the co-operation traceability interface also includes an operation co-operation region filled with the operation data and an evaluation co-operation region. The sending of the co-operation traceability interface enables the co-operation terminal to obtain the same operation data as the initiator, that is, the operation data filled in the operation co-operation region provides a unified detection basis for the co-operation, avoiding evaluation deviation caused by information asymmetry and ensuring the objectivity of co-operation evaluation. After receiving the corresponding co-operation traceability interface, the co-operation terminal can perform co-operation-based water quality detection based on the co-operation traceability interface and upload the obtained co-operation evaluation data. The server can fill the co-operation evaluation data to the evaluation co-operation region and fill the evaluation co-operation region to the water source traceability interface. Thus, by integrating the evaluation data of the co-operation terminal, a single detection task can collect the judgments of multiple residents, enrich the evaluation dimension, improve the credibility of the water quality detection result, and make the drinking water source safety traceability more scientific and representative under the co-operation of the masses.

[0058] To sum up, first of all, the embodiment realizes the assisted detection of any resident on water quality, the water quality passive detection device is configured for each water source node, and the node identification code is set, the resident can quickly obtain the water source traceability interface by scanning the identification code with the user terminal, and the detection data can be uploaded through simple interactive operation, the barrier of professional detection is broken, the resident becomes the assistant of water quality detection, the range of participants of water quality detection is greatly widened, and the comprehensiveness is improved; secondly, the embodiment improves the coverage and real-time performance of water quality detection, relies on the extensive participation of residents, the detection range is extended from the fixed-point detection of professional institutions to each water source node in the whole process of the pipe network, especially the terminal node, and the global coverage of the detection network is realized; meanwhile, the resident can respond to the detection demand at any time, so that the water quality data can be uploaded to the management end in real time, and the hysteresis problem existing in the traditional periodic detection is solved; thirdly, the embodiment guarantees the effectiveness of the assisted detection data, the operation data uploaded by the user terminal is detected and the operation attribute is confirmed, so that the standardization of the resident detection operation and the accuracy of the data are ensured, invalid data interference caused by non-professional detection is avoided, and reliable supplementary data is provided for the traceability system; finally, the embodiment strengthens the collaboration of the whole process traceability, the operation data and the personal evaluation data uploaded by the resident are integrated and synchronized to the management end, the information dimension of the traceability is enriched, the two-way interaction between the management end and the resident is promoted, the fine level of drinking water safety management is improved, and the organic combination of professional detection and resident assisted detection is realized.

[0059] Another embodiment of the present application provides a drinking water source whole-process safety traceability system, Figure 3 The system includes a system block diagram corresponding thereto, and the system includes: The detection configuration module is configured to determine each water source node existing in the traceability area based on the pipe network data of the corresponding traceability area, and to configure the device of the corresponding water quality passive detection device for each water source node; The interface display module is configured to respond to any user terminal with an assisted qualification to scan the node identification code located at any water source node and corresponding to the water source node, and to send the generated water source traceability interface to the user terminal for display; The operation detection module is configured to respond to the user terminal interacting with the operation detection area located in the water source traceability interface, to detect the operation data uploaded by the user terminal, to detect the operation data of the corresponding water quality passive detection device for the water quality detection of the water source node, and to determine the operation attribute based on the detection result; The data filling module is configured to respond to the operation attribute being a correct attribute, to fill the operation data to the operation detection area, to fill the personal evaluation data corresponding to the water source node uploaded by the user terminal to the evaluation detection area located in the water source traceability interface, and to send the water source traceability interface to the management end.

[0060] In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application can be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been described in detail in order to not obscure the understanding of this description.

[0061] In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application can be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been described in detail in order to not obscure the understanding of this description.

[0062] Similarly, it is to be understood that the mechanical details of the inventive features sometimes are grouped into a single embodiment, figure or description of related embodiments in this disclosure. However, this is done for the sake of brevity only, and it is not to be interpreted that the inventive features necessarily belong to the same embodiment.

[0063] It will be understood by those within the art that the modules, or units, or components of the devices in the examples disclosed herein can be arranged in a device as described in the examples, or in alternative devices, in one or more devices differing from the devices in the examples. The modules in the foregoing examples can be combined into a module or further divided into multiple sub-modules.

[0064] It will be understood by those within the art that the modules, or units, or components of the devices in the examples can be adapted and set in one or more devices differing from the devices in the examples. The modules or units or components in the examples can be combined into a module or unit or component, and further divided into multiple sub-modules or sub-units or sub-components.

[0065] Furthermore, those skilled in the art will recognize that the examples described herein can comprise one or more of the described features. The teachings provided herein can be employed in various combinations and sub-combinations thereof to produce the disclosed results.

[0066] In addition, some of the described embodiments can be implemented as a method, an apparatus, or article of manufacture using, for example, computer hardware, software, or both. The

[0067] As used herein, unless otherwise indicated, the use of the ordinal adjectives "first", "second", "third", etc., merely to distinguish different instances of a similar object do not imply a meaning that the objects must be in a given order, or that one comes before or after another.

[0068] Although the application has been described in terms of limited embodiments, those skilled in the art will appreciate that other embodiments can be devised in keeping with the scope of the application described herein. Moreover, it will be noted that the language used in the specification has been chosen principally for readability and instructional purposes and can not have been chosen to delimit or define the subject matter of the application.

Claims

1. A method for the whole-process safety traceability of a drinking water source, characterized in that, The method comprises the following steps: determining water source nodes existing in the traceable area based on the pipe network data of the corresponding traceable area, and performing device configuration on each water source node corresponding to the water quality passive detection device; in response to any user terminal with assistance qualification scanning the node identification code located at any water source node and corresponding to the water source node, sending the generated water source traceable interface to the user terminal for display; in response to the user terminal interacting with the operation detection area located in the water source traceable interface, performing operation detection on the operation data uploaded by the user terminal, which is the operation of the water quality detection device on the water source node, and determining the operation attribute based on the detection result; in response to the operation attribute being a correct attribute, filling the operation data into the operation detection area, filling the personal evaluation data uploaded by the user terminal corresponding to the water source node into the evaluation detection area located in the water source traceable interface, and sending the water source traceable interface to the management terminal.

2. The method for drinking water source whole process safety tracing according to claim 1, characterized in that, The method comprises the following steps: in response to any user terminal with assistance qualification scanning the node identification code located at any water source node and corresponding to the water source node, sending the generated water source traceable interface to the user terminal for display, comprising: in response to any user terminal scanning the node identification code located at any water source node and corresponding to the water source node, obtaining the user face image uploaded by the user terminal; performing image evaluation on the user face image, and comparing the image score obtained based on the evaluation result with the preset score threshold value; in response to the image score being greater than the preset score threshold value, calling the preset assistance prohibition list, and performing image comparison between the user face image and each prohibited face image located in the preset assistance prohibition list to obtain each image similarity; in response to each image similarity being less than a preset similarity, determining that the user terminal has assistance qualification, and sending the generated water source traceable interface to the user terminal for display.

3. The drinking water source full-process safety traceability method according to claim 2, wherein the image evaluation on the user face image comprises: obtaining each direction frame line of the image frame constituting the user face image, and performing face recognition on the user face image; performing pixelization processing on the face area located in the user face image and indicating the user's face based on the recognition result to obtain each face pixel point; when it is determined that any direction frame line has a point-line overlapping relationship with each face pixel point corresponding to the continuous arrangement and greater than a preset pixel number, a preset fixed score less than or equal to the preset score threshold value is determined as the image score; when it is determined that any direction frame line does not have a point-line overlapping relationship with each face pixel point corresponding to the continuous arrangement and greater than a preset pixel number, a face connection line connecting the area center point of the corresponding face area and the image center point of the corresponding user face image is generated, and the length of the line segment corresponding to the face connection line is determined. In response to the line segment length being greater than the preset length, the preset fixed score is determined as the image score, otherwise, feature extraction is performed on the face region, and image evaluation is performed based on the obtained different face features to obtain the image score.

4. The drinking water source whole-process safety tracing method according to claim 3, characterized in that, the image evaluation based on the obtained different face features to obtain the image score comprises: obtaining an eye sub-region corresponding to an eye feature, an ear sub-region corresponding to an ear feature, and a mouth sub-region corresponding to a mouth feature in the face region; obtaining the number of each feature corresponding to the eye sub-region and the ear sub-region; in response to any feature number not being two, determining the preset fixed score as the image score; in response to each feature number being two, performing image evaluation based on a horizontal turning dimension, a vertical turning dimension, and a plane tilting dimension based on the eye sub-region, the ear sub-region, and the mouth sub-region respectively to obtain a first dimension value, a second dimension value, and a third dimension value; performing weighted summation calculation on the first dimension value, the second dimension value, and the third dimension value to obtain the image score.

5. The drinking water source whole-process safety tracing method according to claim 4, characterized in that, the image evaluation based on the eye sub-region, the ear sub-region, and the mouth sub-region respectively based on the horizontal turning dimension, the vertical turning dimension, and the plane tilting dimension to obtain the first dimension value, the second dimension value, and the third dimension value comprises: performing contour comparison on a first ear contour and a second ear contour surrounding the two ear sub-regions, and performing normalization processing on the obtained contour coincidence rate to obtain the first dimension value based on the horizontal turning dimension; connecting a first eye center point and a second eye center point corresponding to the two eye sub-regions to obtain an eye connection line; determining a center connection point located on the eye connection line, and generating a feature connection line extending to the mouth sub-region in the vertical direction from the center connection point as a starting point; obtaining a feature length corresponding to the feature connection line, and performing normalization processing on the feature length to obtain the second dimension value based on the vertical turning dimension; generating a horizontal reference line extending in the horizontal direction from any endpoint of the eye connection line as a starting point, and performing normalization processing on the included angle between the obtained eye connection line and the horizontal reference line to obtain the third dimension value based on the plane tilting dimension.

6. The drinking water source whole-process safety tracing method according to claim 1, characterized in that, in response to the user terminal interacting with the operation detection area located on the water source tracing interface, performing operation detection on operation data uploaded by the user terminal corresponding to water quality detection of the water source node by the water quality passive detection equipment, and determining the operation attribute based on the detection result, comprising: in response to the user terminal interacting with the operation detection area located on the water source tracing interface, controlling the device storage cabin loading the water quality passive detection equipment to be in an open state to expose the water quality passive detection equipment; In response to the pressing unit being in a pressed state at a first collection time, a collection control plug-in is loaded on the user terminal to trigger the user terminal to start data collection on the water source node based on the collection control plug-in; In response to the pressing unit being changed from the pressed state to a relaxed state at a second collection time after the first collection time, the user terminal is triggered to stop data collection on the water source node based on the collection control plug-in, and operation data of water quality detection on the water source node by the water quality passive detection device is obtained; Operation detection is performed on the operation data, and operation attributes are determined based on the detection result.

7. The method of claim 6, wherein the operation detection is performed on the operation data, and the operation attributes are determined based on the detection result, comprising: image sorting of each operation video frame constituting the operation data based on time sequence, and first-order identification of corresponding normal sequence arrangement of the obtained operation image sequence; in response to determining, based on the identification result, that any operation video frame in the operation image sequence first has a device region indicating the water quality passive detection device and a hand region indicating a hand connected to the water quality passive detection device, the operation video frame is determined as a first-order cutting frame; sequence cutting of the operation image sequence, and second-order identification of a first-order processing sequence obtained by including all operation video frames after the first-order cutting frame and corresponding reverse sequence arrangement; in response to determining, based on the identification result, that any operation video frame in the first-order processing sequence first has a device region indicating the water quality passive detection device and a hand region indicating a hand connected to the water quality passive detection device, the operation video frame is determined as a second-order cutting frame; sequence cutting of the first-order processing sequence, and third-order identification based on a second-order image sequence obtained thereby; in response to determining, based on the identification result, that each operation video frame in the second-order image sequence has a device region indicating the water quality passive detection device and a hand region indicating a hand connected to the water quality passive detection device, the operation attributes of the operation data are determined as correct attributes, otherwise as incorrect attributes.

8. The method of claim 7, wherein in response to the operation attributes being correct attributes, the operation data is filled into an operation detection area, personal evaluation data corresponding to the water source node uploaded by the user terminal is filled into an evaluation detection area in a water source tracing interface, and the water source tracing interface is sent to a management terminal, comprising: in response to the operation attributes being correct attributes, each operation video frame constituting the operation data is filled into the operation detection area; in response to the user terminal interacting with the operation video frame in the operation detection area, the operation video frame is determined as an abnormal identification frame, and an abnormal dimension selected by the user terminal based on the abnormal identification frame is obtained. ​ ​ retrieve a dimension detection strategy corresponding to the abnormal dimension to perform abnormal detection on the abnormal identification frame, and determine the abnormal identification frame as personal evaluation data in response to determining that the abnormal identification frame is in an abnormal state of the corresponding abnormal dimension based on a detection result; fill the personal evaluation data to an evaluation detection area located on the water source traceability interface, and send the water source traceability interface to the management end.

9. The drinking water source whole-process safety traceability method according to claim 8, characterized in that, retrieve a dimension detection strategy corresponding to the abnormal dimension to perform abnormal detection on the abnormal identification frame, including: in response to the abnormal dimension being a color dimension, perform pixelization processing on a device area located on the abnormal identification frame to obtain each area pixel point constituting the device area; perform mean value calculation on each area pixel point corresponding to a pixel value, and perform numerical comparison between the obtained area pixel mean value and the retrieved pure water pixel value of the corresponding pure water; in response to determining that a pixel difference between the area pixel mean value and the pure water pixel value is greater than a preset pixel value based on a comparison result, determine that the abnormal identification frame is in an abnormal state of the corresponding abnormal dimension; or in response to the abnormal dimension being an impurity dimension, determine the abnormal identification frame as an initial comparison frame, and trigger the user end to perform voice broadcast corresponding to shaking the water quality passive detection device based on a collection control plug-in; in response to completing the voice broadcast, trigger the user end to perform data collection on the water quality passive detection device based on the collection control plug-in to obtain a current comparison frame; perform area comparison based on the device area between the initial comparison frame and the current comparison frame, and determine that the abnormal identification frame is in an abnormal state of the corresponding abnormal dimension in response to a comparison result being different.

10. A drinking water source whole process safety traceability system, characterized in that, including: a detection configuration module configured to determine each water source node existing in the traceability area based on pipe network data of the corresponding traceability area, and perform device configuration of the corresponding water quality passive detection device for each water source node; an interface display module configured to, in response to any user end having an assistance qualification scanning a node identification code corresponding to any water source node located at the water source node, send a generated water source traceability interface to the user end for display; an operation detection module configured to, in response to the user end interacting with an operation detection area located on the water source traceability interface, perform operation detection on operation data uploaded by the user end corresponding to water quality detection of the water source node by the corresponding water quality passive detection device, and determine an operation attribute based on a detection result; a data filling module configured to, in response to the operation attribute being a correct attribute, fill the operation data to the operation detection area, fill personal evaluation data corresponding to the water source node uploaded by the user end to an evaluation detection area located on the water source traceability interface, and send the water source traceability interface to the management end.

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