A digital-based intelligent analysis, evaluation and management platform for housekeeping service quality

Abstract

The present invention discloses a digital-based intelligent analysis and evaluation management platform for housekeeping service quality, comprising a customer ordering module, a housekeeping service database, a service video collection and segmentation module, a service step standardization analysis module, a window cleanliness analysis module, a window damage analysis module, a service behavior risk detection and analysis module, an early warning terminal and a comprehensive service quality analysis and evaluation module. The module comprehensively considers the influence of service steps, window cleanliness, window appearance defects and service risk behaviors on service quality based on a number of operation pictures segmented from a target service personnel's cleaning work video on a designated window, analyzes and evaluates the housekeeping service quality of the service personnel from multiple dimensions, solves the problem of inaccurate evaluation of the service personnel, and simultaneously detects and analyzes the risk behaviors of the service personnel when cleaning the windows, reasonably avoids the risks of the service personnel, and further ensures the personal safety of the service personnel in the entire service process.

Description

Technical Field

[0001] The present invention relates to the technical field of housekeeping services, and in particular to a digital-based intelligent analysis, evaluation and management platform for housekeeping service quality. Background Art

[0002] In recent years, with the rapid development of the Internet and the accelerated pace of life, people have higher requirements for family life, and the domestic service industry has developed rapidly. Domestic service projects generally include: domestic cleaning, home appliance cleaning, air treatment, commercial cleaning, home beauty, etc. Among the many domestic service projects, people choose domestic cleaning the most. Among them, domestic window cleaning is not only difficult but also dangerous for most people, so many people will choose window cleaning in the domestic service project. Nowadays, various domestic service platforms are emerging in the market. If you want to stand out from them, intelligent analysis and evaluation of domestic service quality is particularly important, and window cleaning is the focus of the service project. Therefore, it is more important for the domestic service platform to conduct intelligent analysis and evaluation of window cleaning service quality.

[0003] The existing domestic service quality analysis and evaluation technology only analyzes and evaluates the cleanliness of windows. The evaluation dimension is relatively single and does not comprehensively consider the impact of service steps, window cleanliness, window appearance defects and dangerous service behaviors on service quality, thus affecting the accuracy of the evaluation of service personnel. Summary of the invention

[0004] In order to overcome the shortcomings of the background technology, an embodiment of the present invention provides a digital-based intelligent analysis, evaluation and management platform for housekeeping service quality, which can effectively solve the problems involved in the above-mentioned background technology.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A digital-based intelligent analysis and evaluation management platform for housekeeping service quality, including a customer ordering module, a housekeeping service database, a service video acquisition and segmentation module, a service step standardization analysis module, a window cleanliness analysis module, a window appearance defect analysis module, a service behavior danger detection analysis module, an early warning terminal and a comprehensive service quality analysis and evaluation module;

[0007] The customer ordering module is used for the user to submit an order for cleaning windows on the housekeeping service platform, and the housekeeping service staff who receives the order is recorded as the target service staff;

[0008] The service video acquisition and segmentation module is used to acquire a cleaning work video of a target service personnel on a designated window, and segment the video into a plurality of operation pictures according to a preset number of video frames, and then number the segmented operation pictures as 1, 2, ..., o, ..., j;

[0009] The service step standardization analysis module is used to perform a service step standardization analysis of the target service personnel according to the segmented operation pictures, and then obtain the service step standardization coefficient corresponding to the target service personnel;

[0010] The window cleanliness analysis module is used to collect appearance images of the designated window after the target service personnel completes the cleaning work of the designated window, and identify the cleaning parameters corresponding to the designated window based on the collected appearance images of the designated window, wherein the cleaning parameters include the type of dirt and the area of ​​dirt, wherein the type of dirt includes dust, stains and water marks, and then analyze the cleaning degree coefficient of the designated window by the target service personnel based on the above;

[0011] The window appearance defect analysis module is used to obtain an appearance image of a designated window before cleaning, and compare it with an appearance image of the designated window after the target service personnel completes the cleaning work of the designated window, thereby identifying appearance defect parameters corresponding to the designated window, and then analyzing the damage coefficient of the cleaning service of the designated window by the target service personnel based on the parameters;

[0012] The service behavior risk detection and analysis module is used to perform service behavior risk detection and analysis of the target service personnel according to the segmented operation pictures, obtain the risk coefficient of the service behavior corresponding to the target service personnel in each operation picture, and then obtain the service behavior risk coefficient corresponding to the target service personnel;

[0013] The comprehensive service quality analysis and evaluation module is used to evaluate the comprehensive service quality coefficient corresponding to the target service personnel according to the service step standard coefficient corresponding to the target service personnel, the cleaning degree coefficient for the designated window, the cleaning service damage coefficient for the designated window and the service behavior risk coefficient;

[0014] The housekeeping service database is used to store operation pictures of each standard service step corresponding to cleaning windows, and store weight coefficients of each standard service step corresponding to cleaning windows;

[0015] The early warning terminal is used to issue corresponding early warnings according to the risk factor of the corresponding service behavior of the target service personnel in each operation picture.

[0016] Furthermore, the specific method for collecting the target service personnel's cleaning work video of the designated window is to collect the video of the target service personnel carrying a portable camera to perform cleaning work when performing door-to-door service.

[0017] Furthermore, the specific method for the standardized analysis of the target service personnel's service steps is:

[0018] S1: Matching the segmented operation pictures with the operation pictures of each standard service step of window cleaning stored in the housekeeping service database, and determining whether there is a successfully matched operation picture. If there is, recording the successfully matched operation picture as the target picture;

[0019] S2: Count the number of target images and obtain the number corresponding to each target image, which is recorded as 1, 2, ..., i, ..., n;

[0020] S3: Obtaining the standard service steps corresponding to each target image, and comparing them with the weight coefficients of the standard service steps corresponding to window cleaning stored in the housekeeping service database, thereby selecting the weight coefficients of the standard service steps corresponding to each target image;

[0021] S4: Analyze the service step standard coefficient corresponding to the target service personnel based on the weight coefficient of the standard service step corresponding to each target image. The calculation formula is: Φ represents the service step standardization coefficient corresponding to the target service personnel, θ i Represents the weight coefficient of the standard service step corresponding to the i-th target image.

[0022] Furthermore, the specific method for analyzing the cleaning degree coefficient of the target service personnel for the designated window is:

[0023] SS1: Get the actual area of ​​the specified window;

[0024] SS2: Extract the dirt type from the cleaning parameters and match it with the influencing factors corresponding to the preset dirt types, thereby matching the influencing factor of the dirt type corresponding to the specified window;

[0025] SS3: Analyze the cleaning degree coefficient of the designated window by the target service personnel according to the influencing factors of the actual area, dirty area and dirt type corresponding to the designated window. The calculation formula is: Where E represents the cleaning degree coefficient of the target service personnel for the specified window, s0 represents the actual area of ​​the specified window, s′ represents the dirty area corresponding to the specified window, and λ represents the influencing factor of the specified window corresponding to the type of dirt.

[0026] Furthermore, the appearance defect parameters include appearance defect type and appearance defect area, wherein the appearance defect type includes scratches, cracks and breakages.

[0027] Furthermore, the specific method for analyzing the damage coefficient of the cleaning service of the target service personnel on the designated window is:

[0028] SSS1: Get the actual area of ​​the specified window;

[0029] SSS2: Extract the appearance defect type from the appearance defect parameters, and match it with the corresponding proportion coefficients of various preset appearance defect types, and match the proportion coefficient of the appearance defect type corresponding to the specified window;

[0030] SSS3: Analyze the cleaning service damage coefficient of the target service personnel for the specified windows according to the actual area, appearance defect area and proportion coefficient of appearance defect type corresponding to the specified windows. The calculation formula is: Where H represents the cleaning service damage coefficient of the target service personnel for the specified window, s″ represents the appearance defect area corresponding to the specified window, and λ represents the proportion coefficient of the specified window corresponding to the appearance defect type.

[0031] Furthermore, the steps for analyzing the risk factor of the service behavior of the target service personnel in each operation picture are as follows:

[0032] SSSS1: Focus each operation image on the area where the target service personnel is located, and then extract the corresponding body contour of the target service personnel;

[0033] SSSS2: Based on the body contour of the target service personnel in each operation image, the distance between the center of gravity of the target service personnel and the ground is obtained, and the distance is recorded as the center of gravity distance;

[0034] SSSS3: Overlap and compare the body contours of the target service personnel in each operation image with the preset standard body contours corresponding to various behavior posture types, and identify the behavior posture type corresponding to the target service personnel in each operation image;

[0035] SSSS4: Compare the behavior posture type corresponding to the target service personnel in each operation picture with the safety center distances corresponding to various preset behavior posture types, and select the safety center distances of the service behaviors corresponding to the target service personnel in each operation picture;

[0036] SSSS5: Compare the gravity center distance of the target service personnel corresponding to the service behavior in each operation picture with the safe gravity center distance, and calculate the gravity center hazard coefficient of the target service personnel corresponding to the service behavior in each operation picture. The calculation formula is: where Y o represents the gravity risk coefficient of the target service personnel’s corresponding service behavior in the oth operation picture, l o重 represents the centroid distance of the target service personnel’s corresponding service behavior in the oth operation picture, l o安重 represents the safe center of gravity distance of the target service personnel corresponding to the service behavior in the o-th operation picture;

[0037] SSSS6: Divide the body contour corresponding to the target service personnel in each operation image into the body contour inside the window and the body contour outside the window, and obtain the total area of ​​the body contour corresponding to the target service personnel in each operation image and the area of ​​the body contour outside the window;

[0038] SSSS7: Based on the total body contour area corresponding to the target service personnel in each operation picture and the body contour area outside the window, the body contour area risk coefficient of the target service personnel corresponding to the service behavior in each operation picture is calculated. The calculation formula is: T m represents the body contour area risk factor of the target service personnel corresponding to the service behavior in the mth operation picture, R m外 represents the body contour area outside the window corresponding to the service behavior of the target service personnel in the mth operation picture, R 总 Represents the total body contour area of ​​the target service personnel;

[0039] SSSS8: The risk factor of the target service personnel's corresponding service behavior in each operation picture is calculated based on the risk factor of the center of gravity of the target service personnel's corresponding service behavior in each operation picture and the risk factor of the body contour area of ​​the target service personnel's corresponding service behavior in each operation picture. The calculation formula is: Where L m Represents the risk factor of the target service personnel's corresponding service behavior in the mth operation picture.

[0040] Furthermore, the calculation formula of the service behavior risk coefficient corresponding to the target service personnel is: Where L′ represents the service behavior risk coefficient corresponding to the target service personnel.

[0041] Furthermore, the calculation formula of the comprehensive service quality coefficient corresponding to the target service personnel is: Among them, Ψ represents the comprehensive service quality coefficient corresponding to the target service personnel.

[0042] Furthermore, the specific method for issuing corresponding warnings based on the risk coefficient of the service behavior of the target service personnel in each operation picture is: comparing the risk coefficient of the service behavior of the target service personnel in each operation picture with the preset risk coefficient of the warning service behavior; if the risk coefficient of the service behavior of the target service personnel in a certain operation picture is greater than or equal to the preset risk coefficient of the warning service behavior, then obtaining the time point corresponding to the operation picture and issuing a warning to the target service personnel at that time point.

[0043] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0044] The present invention analyzes and evaluates the housekeeping service quality of the target service personnel who receive window cleaning, including service steps, window cleaning degree, window appearance defects and service risk behaviors, thereby solving the problem of inaccurate evaluation of the service personnel.

[0045] The present invention can detect and analyze the dangerous behaviors of service personnel when cleaning windows, thereby providing early warning of the dangerous behaviors of service personnel, and further providing timely warnings when service personnel engage in dangerous behaviors during the process of cleaning windows, so that service personnel can adjust their service postures at any time to avoid the occurrence of dangerous situations, which is beneficial to ensuring the personal safety of service personnel throughout the entire service process. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0047] Figure 1 This is a schematic diagram of a digital-based intelligent analysis, evaluation and management platform for housekeeping service quality according to the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] Reference Figure 1 As shown, the present invention provides a digital-based intelligent analysis and evaluation management platform for housekeeping service quality, including a customer ordering module, a housekeeping service database, a service video acquisition and segmentation module, a service step standardization analysis module, a window cleanliness analysis module, a window appearance defect analysis module, a service behavior danger detection and analysis module, an early warning terminal and a comprehensive service quality analysis and evaluation module;

[0050] The customer ordering module is connected to the service video acquisition and segmentation module, the service video acquisition and segmentation module is respectively connected to the service step standardization analysis module and the service behavior risk detection and analysis module, the service step standardization analysis module, the window cleanliness analysis module, the window appearance defect analysis module and the service behavior risk detection and analysis module are all connected to the comprehensive service quality analysis and evaluation module, the early warning terminal is connected to the service behavior risk detection and analysis module, and the housekeeping service database is connected to the service step standardization analysis module;

[0051] The customer ordering module is used for the user to submit an order for cleaning windows on the housekeeping service platform, and the housekeeping service staff who receives the order is recorded as the target service staff;

[0052] The service video acquisition and segmentation module is used to acquire a cleaning work video of a target service personnel on a designated window, and segment the video into a plurality of operation pictures according to a preset number of video frames, and then number the segmented operation pictures as 1, 2, ..., o, ..., j;

[0053] The specific method for collecting the cleaning work video of the target service personnel on the designated window mentioned in the above embodiment is to collect the video of the target service personnel carrying a portable camera to perform cleaning work when performing door-to-door service.

[0054] The service step standardization analysis module is used to perform a service step standardization analysis of the target service personnel according to the segmented operation pictures, and then obtain the service step standardization coefficient corresponding to the target service personnel;

[0055] The specific method for standardized analysis of the service steps of the target service personnel mentioned in the above embodiment is:

[0056] S1: Matching the segmented operation pictures with the operation pictures of each standard service step of window cleaning stored in the housekeeping service database, and determining whether there is a successfully matched operation picture. If there is, recording the successfully matched operation picture as the target picture;

[0057] S2: Count the number of target images and obtain the number corresponding to each target image, which is recorded as 1, 2, ..., i, ..., n;

[0058] S3: Obtaining the standard service steps corresponding to each target image, and comparing them with the weight coefficients of the standard service steps corresponding to window cleaning stored in the housekeeping service database, thereby selecting the weight coefficients of the standard service steps corresponding to each target image;

[0059] S4: Analyze the service step standard coefficient corresponding to the target service personnel based on the weight coefficient of the standard service step corresponding to each target image. The calculation formula is: Φ represents the service step standardization coefficient corresponding to the target service personnel, θ i Represents the weight coefficient of the standard service step corresponding to the i-th target image.

[0060] The window cleanliness analysis module is used to collect appearance images of the designated window after the target service personnel completes the cleaning work of the designated window, and identify the cleaning parameters corresponding to the designated window based on the collected appearance images of the designated window, wherein the cleaning parameters include the type of dirt and the area of ​​dirt, wherein the type of dirt includes dust, stains and water marks, and then analyze the cleaning degree coefficient of the designated window by the target service personnel based on the above;

[0061] The specific method for analyzing the cleaning degree coefficient of the designated window by the target service personnel mentioned in the above embodiment is:

[0062] SS1: Get the actual area of ​​the specified window;

[0063] It should be noted that the specific method for obtaining the actual area of ​​the specified window is to obtain the image area of ​​the specified window based on the acquired appearance image of the specified window, and calculate the actual area of ​​the specified window according to the image area of ​​the specified window and the shooting ratio when acquiring the appearance image of the specified window;

[0064] SS2: Extract the dirt type from the cleaning parameters and match it with the influencing factors corresponding to the preset dirt types, thereby matching the influencing factor of the dirt type corresponding to the specified window;

[0065] SS3: Analyze the cleaning degree coefficient of the designated window by the target service personnel according to the influencing factors of the actual area, dirty area and dirt type corresponding to the designated window. The calculation formula is: Where E represents the cleaning degree coefficient of the target service personnel for the specified window, s0 represents the actual area of ​​the specified window, s′ represents the dirty area corresponding to the specified window, and λ represents the influencing factor of the specified window corresponding to the type of dirt.

[0066] The window appearance defect analysis module is used to obtain an appearance image of a designated window before cleaning, and compare it with an appearance image of the designated window after the target service personnel completes the cleaning work of the designated window, thereby identifying appearance defect parameters corresponding to the designated window, and then analyzing the damage coefficient of the cleaning service of the designated window by the target service personnel based on the parameters;

[0067] The appearance defect parameters mentioned in the above embodiments include appearance defect type and appearance defect area, wherein the appearance defect type includes scratches, cracks and breakages.

[0068] The specific method for analyzing the damage coefficient of the cleaning service of the target service personnel for the designated window mentioned in the above embodiment is:

[0069] SSS1: Get the actual area of ​​the specified window;

[0070] SSS2: Extract the appearance defect type from the appearance defect parameters, and match it with the corresponding proportion coefficients of various preset appearance defect types, and match the proportion coefficient of the appearance defect type corresponding to the specified window;

[0071] SSS3: Analyze the cleaning service damage coefficient of the target service personnel for the specified windows according to the actual area, appearance defect area and proportion coefficient of appearance defect type corresponding to the specified windows. The calculation formula is: Where H represents the cleaning service damage coefficient of the target service personnel for the specified window, s″ represents the appearance defect area corresponding to the specified window, and λ represents the proportion coefficient of the specified window corresponding to the appearance defect type.

[0072] The service behavior risk detection and analysis module is used to perform service behavior risk detection and analysis of the target service personnel according to the segmented operation pictures, obtain the risk coefficient of the service behavior corresponding to the target service personnel in each operation picture, and then obtain the service behavior risk coefficient corresponding to the target service personnel;

[0073] The steps for analyzing the risk factor of the service behavior of the target service personnel in each operation picture mentioned in the above embodiment are as follows:

[0074] SSSS1: Focus each operation image on the area where the target service personnel is located, and then extract the corresponding body contour of the target service personnel;

[0075] SSSS2: Based on the body contour of the target service personnel in each operation image, the distance between the center of gravity of the target service personnel and the ground is obtained, and the distance is recorded as the center of gravity distance;

[0076] SSSS3: Overlap and compare the body contours of the target service personnel in each operation image with the preset standard body contours corresponding to various behavior posture types, and identify the behavior posture type corresponding to the target service personnel in each operation image;

[0077] It should be noted that various behavioral posture types include standing, sitting, squatting, and walking;

[0078] It should be noted that the specific process of identifying the behavior posture type corresponding to the target service personnel in each operation picture is as follows:

[0079] Obtain the overlapped area of ​​the total area of ​​the body contour corresponding to the target service personnel in each operation picture and the total area of ​​the standard body contour corresponding to each behavior posture type, and record it as the overlapped area of ​​the body contour corresponding to each behavior posture type of the target service personnel in each operation picture;

[0080] The overlap area of ​​the body contours of the target service personnel corresponding to various behavior posture types in each operation picture is divided by the total area of ​​the standard body contours corresponding to the behavior posture type to obtain the overlap coefficient of the target service personnel corresponding to various behavior posture types in each operation picture, and then the behavior posture type with the largest overlap coefficient is selected as the behavior posture type corresponding to the target service personnel in each operation picture;

[0081] SSSS4: Compare the behavior posture type corresponding to the target service personnel in each operation picture with the safety center distances corresponding to various preset behavior posture types, and select the safety center distances of the service behaviors corresponding to the target service personnel in each operation picture;

[0082] SSSS5: Compare the gravity center distance of the target service personnel corresponding to the service behavior in each operation picture with the safe gravity center distance, and calculate the gravity center hazard coefficient of the target service personnel corresponding to the service behavior in each operation picture. The calculation formula is: where Y o represents the gravity risk coefficient of the target service personnel’s corresponding service behavior in the oth operation picture, l o重 represents the centroid distance of the target service personnel’s corresponding service behavior in the oth operation picture, l o安重 represents the safe center of gravity distance of the target service personnel corresponding to the service behavior in the o-th operation picture;

[0083] SSSS6: Divide the body contour corresponding to the target service personnel in each operation image into the body contour inside the window and the body contour outside the window, and obtain the total area of ​​the body contour corresponding to the target service personnel in each operation image and the area of ​​the body contour outside the window;

[0084] SSSS7: Based on the total body contour area corresponding to the target service personnel in each operation picture and the body contour area outside the window, the body contour area risk coefficient of the target service personnel corresponding to the service behavior in each operation picture is calculated. The calculation formula is: T m represents the body contour area risk factor of the target service personnel corresponding to the service behavior in the mth operation picture, R m外 represents the body contour area outside the window corresponding to the service behavior of the target service personnel in the mth operation picture, R 总 Represents the total body contour area of ​​the target service personnel;

[0085] SSSS8: The risk factor of the target service personnel's corresponding service behavior in each operation picture is calculated based on the risk factor of the center of gravity of the target service personnel's corresponding service behavior in each operation picture and the risk factor of the body contour area of ​​the target service personnel's corresponding service behavior in each operation picture. The calculation formula is: Where L mRepresents the risk factor of the target service personnel's corresponding service behavior in the mth operation picture.

[0086] The calculation formula for the service behavior risk coefficient corresponding to the target service personnel mentioned in the above embodiment is: Where L′ represents the service behavior risk coefficient corresponding to the target service personnel.

[0087] The comprehensive service quality analysis and evaluation module is used to evaluate the comprehensive service quality coefficient corresponding to the target service personnel according to the service step standard coefficient corresponding to the target service personnel, the cleaning degree coefficient for the designated window, the cleaning service damage coefficient for the designated window and the service behavior risk coefficient;

[0088] The calculation formula for the comprehensive service quality coefficient corresponding to the target service personnel mentioned in the above embodiment is: Among them, Ψ represents the comprehensive service quality coefficient corresponding to the target service personnel.

[0089] The present invention analyzes and evaluates the housekeeping service quality of the target service personnel who receive window cleaning, including service steps, window cleaning degree, window appearance defects and service risk behaviors, thereby solving the problem of inaccurate evaluation of the service personnel.

[0090] The housekeeping service database is used to store operation pictures of cleaning windows corresponding to each standard service step, and store weight coefficients of cleaning windows corresponding to each standard service step.

[0091] The warning terminal is used to issue a corresponding warning according to the risk factor of the corresponding service behavior of the target service personnel in each operation picture;

[0092] The specific method mentioned in the above embodiment for issuing corresponding warnings based on the risk coefficient of the corresponding service behavior of the target service personnel in each operation picture is: comparing the risk coefficient of the corresponding service behavior of the target service personnel in each operation picture with the preset risk coefficient of the warning service behavior; if the risk coefficient of the corresponding service behavior of the target service personnel in a certain operation picture is greater than or equal to the preset risk coefficient of the warning service behavior, then obtaining the time point corresponding to the operation picture and issuing a warning to the target service personnel at that time point.

[0093] The present invention can detect and analyze the dangerous behaviors of service personnel when cleaning windows, thereby providing early warning of the dangerous behaviors of service personnel, and further providing timely warnings when service personnel engage in dangerous behaviors during the process of cleaning windows, so that service personnel can adjust their service postures at any time to avoid the occurrence of dangerous situations, which is beneficial to ensuring the personal safety of service personnel throughout the entire service process.

[0094] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A digital-based intelligent analysis and evaluation management platform for housekeeping service quality, characterized in that: It includes customer order module, housekeeping service database, service video acquisition and segmentation module, service step standardization analysis module, window cleanliness analysis module, window appearance defect analysis module, service behavior hazard detection analysis module, early warning terminal and comprehensive service quality analysis and evaluation module; The customer ordering module is used for the user to submit an order for cleaning windows on the housekeeping service platform, and the housekeeping service staff who receives the order is recorded as the target service staff; The service video acquisition and segmentation module is used to acquire a cleaning work video of a target service personnel on a designated window, and segment the video into a plurality of operation pictures according to a preset number of video frames, and then number the segmented operation pictures as 1, 2, ..., o, ..., j; The service step standardization analysis module is used to perform a service step standardization analysis of the target service personnel according to the segmented operation pictures, and then obtain the service step standardization coefficient corresponding to the target service personnel; The window cleanliness analysis module is used to collect appearance images of the designated window after the target service personnel completes the cleaning work of the designated window, and identify the cleaning parameters corresponding to the designated window based on the collected appearance images of the designated window, wherein the cleaning parameters include the type of dirt and the area of ​​dirt, wherein the type of dirt includes dust, stains and water marks, and then analyze the cleaning degree coefficient of the designated window by the target service personnel based on the above; The window appearance defect analysis module is used to obtain an appearance image of a designated window before cleaning, and compare it with an appearance image of the designated window after the target service personnel completes the cleaning work of the designated window, thereby identifying appearance defect parameters corresponding to the designated window, and then analyzing the damage coefficient of the cleaning service of the designated window by the target service personnel based on the parameters; The service behavior risk detection and analysis module is used to perform service behavior risk detection and analysis of the target service personnel according to the segmented operation pictures, obtain the risk coefficient of the service behavior corresponding to the target service personnel in each operation picture, and then obtain the service behavior risk coefficient corresponding to the target service personnel; The comprehensive service quality analysis and evaluation module is used to evaluate the comprehensive service quality coefficient corresponding to the target service personnel according to the service step standard coefficient corresponding to the target service personnel, the cleaning degree coefficient for the designated window, the cleaning service damage coefficient for the designated window and the service behavior risk coefficient; The housekeeping service database is used to store operation pictures of each standard service step corresponding to cleaning windows, and store weight coefficients of each standard service step corresponding to cleaning windows; The warning terminal is used to issue a corresponding warning according to the risk factor of the corresponding service behavior of the target service personnel in each operation picture; The steps for analyzing the risk factor of the service behavior of the target service personnel in each operation picture are as follows: SSSS1: Focus each operation image on the area where the target service personnel is located, and then extract the corresponding body contour of the target service personnel; SSSS2: Based on the body contour of the target service personnel in each operation image, the distance between the center of gravity of the target service personnel and the ground is obtained, and the distance is recorded as the center of gravity distance; SSSS3: Overlap and compare the body contours of the target service personnel in each operation image with the preset standard body contours corresponding to various behavior posture types, and identify the behavior posture type corresponding to the target service personnel in each operation image; SSSS4: Compare the behavior posture type corresponding to the target service personnel in each operation picture with the safety center distances corresponding to various preset behavior posture types, and select the safety center distances of the service behaviors corresponding to the target service personnel in each operation picture; SSSS5: Compare the gravity center distance of the target service personnel corresponding to the service behavior in each operation picture with the safe gravity center distance, and calculate the gravity center hazard coefficient of the target service personnel corresponding to the service behavior in each operation picture. The calculation formula is: ,in represents the gravity risk coefficient of the target service personnel’s corresponding service behavior in the oth operation picture, represents the centroid distance of the target service personnel’s corresponding service behavior in the oth operation picture, represents the safe center of gravity distance of the target service personnel corresponding to the service behavior in the o-th operation picture; SSSS6: Divide the body contour corresponding to the target service personnel in each operation image into the body contour inside the window and the body contour outside the window, and obtain the total area of ​​the body contour corresponding to the target service personnel in each operation image and the area of ​​the body contour outside the window; SSSS7: Based on the total body contour area corresponding to the target service personnel in each operation picture and the body contour area outside the window, the body contour area risk coefficient of the target service personnel corresponding to the service behavior in each operation picture is calculated. The calculation formula is: , represents the body contour area risk factor of the target service personnel corresponding to the service behavior in the mth operation picture, represents the body contour area outside the window corresponding to the service behavior of the target service personnel in the mth operation picture, Represents the total body contour area of ​​the target service personnel; SSSS8: The risk factor of the target service personnel's corresponding service behavior in each operation picture is calculated based on the risk factor of the center of gravity of the target service personnel's corresponding service behavior in each operation picture and the risk factor of the body contour area of ​​the target service personnel's corresponding service behavior in each operation picture. The calculation formula is: ,in represents the risk factor of the service behavior of the target service personnel in the mth operation picture; The calculation formula of the service behavior risk coefficient corresponding to the target service personnel is: ,in Indicates the service behavior risk coefficient corresponding to the target service personnel.

2. According to claim 1, a digital-based intelligent analysis and evaluation management platform for housekeeping service quality, characterized in that: The specific method for collecting the cleaning work video of the target service personnel on the designated window is that the target service personnel carries a portable camera to collect the video when performing the cleaning work during the door-to-door service.

3. According to claim 1, a digital-based intelligent analysis and evaluation management platform for housekeeping service quality, characterized in that: The specific method for the standardized analysis of the target service personnel's service steps is as follows: S1: Matching the segmented operation pictures with the operation pictures of each standard service step of window cleaning stored in the housekeeping service database, and determining whether there is a successfully matched operation picture. If there is, recording the successfully matched operation picture as the target picture; S2: Count the number of target images and obtain the number corresponding to each target image, which is recorded as 1, 2, ..., i, ..., n; S3: Obtaining the standard service steps corresponding to each target image, and comparing them with the weight coefficients of the standard service steps corresponding to window cleaning stored in the housekeeping service database, thereby selecting the weight coefficients of the standard service steps corresponding to each target image; S4: Analyze the service step standard coefficient corresponding to the target service personnel based on the weight coefficient of the standard service step corresponding to each target image. The calculation formula is: , represents the service step standardization coefficient corresponding to the target service personnel, Represents the weight coefficient of the standard service step corresponding to the i-th target image.

4. According to claim 1, a digital-based intelligent analysis and evaluation management platform for housekeeping service quality, characterized in that: The specific method for analyzing the cleaning degree coefficient of the target service personnel for the designated window is: SS1: Get the actual area of ​​the specified window; SS2: Extract the dirt type from the cleaning parameters and match it with the influencing factors corresponding to the preset dirt types, thereby matching the influencing factor of the dirt type corresponding to the specified window; SS3: Analyze the cleaning degree coefficient of the designated window by the target service personnel according to the influencing factors of the actual area, dirty area and dirt type corresponding to the designated window. The calculation formula is: ,in Indicates the cleaning degree coefficient of the target service personnel for the specified window, Indicates the actual area of ​​the specified window. Indicates the dirty area corresponding to the specified window, Indicates the influence factor of the specified window for the type of dirt.

5. According to claim 1, a digital-based intelligent analysis and evaluation management platform for housekeeping service quality, characterized in that: The appearance defect parameters include appearance defect type and appearance defect area, wherein the appearance defect type includes scratches, cracks and breakages.

6. According to claim 1, a digital-based intelligent analysis and evaluation management platform for housekeeping service quality, characterized in that: The specific method for analyzing the damage coefficient of the cleaning service of the target service personnel to the designated window is as follows: SSS1: Get the actual area of ​​the specified window; SSS2: Extract the appearance defect type from the appearance defect parameters, and match it with the corresponding proportion coefficients of various preset appearance defect types, and match the proportion coefficient of the appearance defect type corresponding to the specified window; SSS3: Analyze the cleaning service damage coefficient of the target service personnel for the specified windows according to the actual area, appearance defect area and proportion coefficient of appearance defect type corresponding to the specified windows. The calculation formula is: ,in represents the damage coefficient of the cleaning service of the target service personnel on the specified window, Indicates the appearance defect area corresponding to the specified window. Indicates the proportion coefficient of the specified window corresponding to the appearance defect type.

7. According to claim 1, a digital-based intelligent analysis and evaluation management platform for housekeeping service quality, characterized in that: The calculation formula of the comprehensive service quality coefficient corresponding to the target service personnel is: ,in, Represents the comprehensive service quality coefficient corresponding to the target service personnel.

8. According to claim 1, a digital-based intelligent analysis and evaluation management platform for housekeeping service quality, characterized in that: The specific method for issuing corresponding warnings based on the risk coefficient of the corresponding service behavior of the target service personnel in each operation picture is: comparing the risk coefficient of the corresponding service behavior of the target service personnel in each operation picture with the preset risk coefficient of the warning service behavior; if the risk coefficient of the corresponding service behavior of the target service personnel in a certain operation picture is greater than or equal to the preset risk coefficient of the warning service behavior, then obtaining the time point corresponding to the operation picture and issuing a warning to the target service personnel at that time point.

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