A smart meter data transmission method, system and storage medium

Abstract

The present invention discloses a smart meter data transmission method, system and storage medium, and relates to the technical field of smart meters. The present invention comprises the following steps: an analysis unit obtains an image inside an elevator through an elevator visual module; the analysis unit executes a user identification program to obtain a user portrait, wherein the user portrait includes a left-face user portrait, a right-face user portrait and a front-face user portrait; the analysis unit presets a similarity threshold; the analysis unit compares the user portrait with a facial image stored in a database and calculates a similarity Si; if the similarity between the user portrait and the facial image is less than the similarity threshold, the present invention performs identification statistics on each person using the elevator through the visual unit, and then calculates the association between each person and the portal, thereby effectively incorporating outsiders into the elevator telegram billing system; the apportionment index can directly reflect the use efficiency of the elevator, making the elevator's apportioned electricity charges more reasonable.

Description

Technical Field

[0001] The present invention relates to the technical field of smart meters, and in particular to a smart meter data transmission method, system and storage medium. Background Art

[0002] With the increasing number of high-rise buildings, elevator usage and energy consumption are also increasing. Residents on different floors use elevators to varying degrees. A simple average share of the cost would be unfair to residents on lower floors or those who use the elevator less frequently. Therefore, finding a reasonable way to share elevator meter charges has always been a technical challenge.

[0003] Patent A [Publication No.: CN112707259B] provides an elevator billing device and a billing method thereof. The device calculates the amount of electricity required by the passengers in the elevator according to the weight of the passengers and the floors they leave, thereby calculating the electricity charges required. However, it is not reasonable to calculate the elevator based solely on the weight of the passengers and the floors they arrive at. The efficiency of the elevator is not taken into account. If a single user rides from the lowest floor to the highest floor, the elevator's load capacity is not high but the elevator vacancy rate is high, and the elevator's electricity consumption is also huge. Patent A's electricity billing for this situation is low and cannot reasonably share the electricity charges. In addition, Patent A's billing method is only for users within the community, and the electricity gap caused by outsiders such as couriers, takeouts, and visitors cannot be included in the statistics. Therefore, adding the elevator's efficiency factor and the outsider factor to the elevator meter billing is a technical problem that needs to be solved by those skilled in the art. Summary of the Invention

[0004] In view of the deficiencies in the prior art, the present invention provides a smart meter data transmission method, system and storage medium, which solve the problems raised in the above background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A smart meter data transmission method is implemented based on a user meter, an elevator meter, an analysis unit, an elevator vision module, a portal vision module and a load module, comprising the following steps:

[0006] Step 1: Analyze the user's avatar. The analysis unit obtains the image inside the elevator through the elevator vision module. The analysis unit executes the user recognition program to obtain the user's avatar. The user's avatar includes a left-face user avatar, a right-face user avatar, and a front-face user avatar. The analysis unit presets a similarity threshold of 90%. The analysis unit compares the user's avatar with the facial images stored in the database and calculates the similarity Si. If the similarity between the user's avatar and the facial image is less than the similarity threshold of 90%, the analysis unit marks the user's avatar with a similarity threshold less than 90% as a new facial image and assigns a user ID. The user ID is unique and corresponds one-to-one with the facial image. The analysis unit binds the newly marked facial image and the corresponding user ID and saves it in the database. If the similarity Si between the user's avatar and the facial image is greater than or equal to the similarity threshold of 90%, the analysis unit classifies the user's avatar into the facial image. Each time the analysis unit calculates the similarity Si between the user's avatar and the facial image, it accumulates the number of times the facial image appears in the image inside the elevator, Nu.

[0007] The analysis unit presets a recording period of 7 days. If the number of times a face image appears Nu is 0 within the recording period, the analysis unit deletes the face image with the number of times Nu appears 0 and the corresponding user ID from the database. The analysis unit recycles the deleted user ID and assigns it to the user portrait with a similarity Si below the similarity threshold when the user recognition program is executed next time.

[0008] Step 2: Calculate the user usage index. The analysis unit obtains the elevator's load capacity g and number of floors f through the load module. The analysis unit executes the elevator usage analysis program to obtain the user usage index Use corresponding to each user ID. The visual unit identifies and counts each person using the elevator, and then calculates the association between each person and the portal. This can effectively include outsiders in the elevator telegram billing system, making the elevator's shared electricity charges more reasonable.

[0009] Step 3: Matching associations: the analysis unit numbers the portals on each floor, executes a user identification program to obtain user portraits, records the number of times each user ID appears in the image of the portal visual module, and the duration Mt, and executes a association analysis program to obtain the association index Rel between the user ID and each numbered portal.

[0010] Step 4: The analysis unit multiplies the user usage index Use of each user ID by the association index Rel of each numbered portal to obtain the allocation index of each user ID and the portal with different numbers. When the association index Rel between the user ID and the portal is not zero, the analysis unit marks the user ID as the associated target of the portal. The analysis unit accumulates the allocation indexes of all associated targets of the portal to obtain the portal index. The analysis unit adds up all portal indices to obtain the total electricity consumption index.

[0011] Step 5: The analysis unit obtains the elevator's electricity consumption from the elevator meter. The analysis unit multiplies the elevator's electricity consumption by the portal index of each portal and then divides it by the total electricity consumption index to obtain the shared electricity consumption of each portal. The analysis unit sends the calculated shared electricity consumption to the user meter corresponding to the portal, so that the elevator's electricity consumption can be more reasonably allocated to the user meter of each portal to calculate the electricity bill.

[0012] Furthermore, the user identification procedure specifically includes the following steps:

[0013] The database contains three pre-stored face models at three viewing angles: left face tilted 45° downward, front face tilted 45° downward, and right face tilted 45° downward.

[0014] Step 11: The analysis unit measures the length of the elevator image as L1 and the width of the elevator image as L2. The analysis unit establishes a coordinate system in the elevator image using pixels as the unit of measurement, with the intersection of the diagonals of the elevator image rectangle as the origin of the coordinate system.

[0015] Step 12: The analysis unit establishes a vortex line in the coordinate system. The analysis unit presets the number of circles of the vortex line as a, and the value range of a is 3<a<20. The initial diameter of the inner circle of the vortex line is preset as b, and the value range of b is The expression formula of vortex line is t is a pixel point on the vortex line. The analysis unit presets a moving window. The moving window is elliptical. The vertical long axis of the moving window is The horizontal minor axis of the moving window is The analysis unit randomly selects a value from a value range based on the combination formula for the number of turns a and the initial diameter b of the inner ring to generate a vortex line. Each time the analysis unit generates a vortex line, step 121 is executed until all combinations of the combination formula are exhausted. When all combinations are exhausted, the process jumps to step 13.

[0016] Step 121: The analysis unit presets a first trigger threshold of 50%. The analysis unit moves the intersection of the vertical major axis and the horizontal minor axis of the moving window from the inside to the outside on the spiral line, pixel by pixel. Each time the moving window moves, the analysis unit calculates the similarity Si between the pixels in the moving window and the model faces from the three perspectives. The pixels in the moving window are the pixels circled by the moving window in the elevator image. When the similarity Si is greater than or equal to the first trigger threshold, the analysis unit records a trigger event. When the moving window moves to the end of the outer circle of the spiral line, the analysis unit counts the number of trigger events corresponding to the spiral line.

[0017] Step 13: The analysis unit selects the vortex line with the most trigger events and marks it as a trajectory line. The analysis unit presets a second trigger threshold of 90%. The analysis unit moves the intersection of the vertical major axis and the horizontal minor axis of the moving window from the inside to the outside pixel by pixel along the trajectory line. Each time the moving window moves, the analysis unit calculates the similarity Si between the pixels of the elevator image within the moving window and the model faces from the three perspectives. When any of the similarities Si of the model faces from the three perspectives is greater than or equal to the second trigger threshold, the analysis unit marks the pixels of the elevator image within the moving window as the user's avatar.

[0018] Step 14: The analysis unit selects the maximum value of the three model face similarities Si to classify the user portraits into left-face user portraits, right-face user portraits and front-face user portraits, and the pixel with the maximum value of the similarity Si is marked as the corresponding user portrait type.

[0019] Furthermore, the similarity Si is calculated by the following steps:

[0020] Step 15: The analysis unit converts both the user portrait and the face image into grayscale images with a grayscale value range of 0 to 255. The analysis unit marks the grayscale image of the user portrait as x and the grayscale image of the face image as y.

[0021] Step 16: The analysis unit calculates the pixel mean ux of the grayscale image x, calculates the pixel mean uy of the grayscale image y, and calculates the variance σx of the grayscale image x 2 , calculate the variance σy of the grayscale image y 2 , calculate the covariance σxy of grayscale image x and grayscale image y;

[0022] Step 17: Analyze the cells according to the formula Calculate the similarity index SSIM between the user's avatar and the face image, where C1 = (K1 × E) 2 , C2=(K2×E) 2 , C1 and C2 are constants set to avoid the denominator being zero, E is the pixel value random coefficient, the value range of E is (0~255), K1 and K2 are constants, K1 is 0.01, K2 is 0.03;

[0023] The calculation of similarity Si is affected by three factors: brightness d1, contrast d2, and structure d3. Therefore, it is necessary to comprehensively analyze the similarity index SSIM of the user portrait and the face image from the brightness d1, contrast d2, and structure d3. The calculation formula of brightness d1 is The calculation formula for contrast d2 is: The calculation formula of structure d3 is

[0024] Step 18: The numerical range of the similarity index SSIM is (-1, 1), and the analysis unit is based on the formula The similarity index SSIM is normalized to obtain the similarity Si.

[0025] Furthermore, the elevator usage analysis program specifically includes the following steps:

[0026] Step 21: The analysis unit establishes a load curve graph, where the horizontal axis of the load curve graph is time, with the time unit being seconds, and the vertical axis is weight, with the weight unit being kilograms. The analysis unit obtains the load weight g from the load module and inputs it into the load curve graph to generate a load curve. The analysis unit determines the increase or decrease of users based on the changes in the load curve. When the load curve rises in a step-by-step manner, it is determined that a user has been added to the elevator. When the load curve drops in a step-by-step manner, it is determined that a user has been reduced in the elevator. When the number of users in the elevator increases or decreases, the analysis unit executes a user analysis process to obtain the floor index Fu corresponding to each user ID. The increase or decrease of users in the elevator indicates that the elevator has stopped at one of the floors. The floor index Fu at this time is a factor that affects the user usage index Use of each user ID. The user analysis process is not executed for a floor where the elevator has not stopped, and the floor index Fu of that floor will not be calculated accordingly.

[0027] Step 22: The analysis unit defines each round trip from the lowest floor to the highest floor as a lift cycle. The analysis unit adds the floor index Fu of each user ID within a lift cycle to obtain the lift index Sj corresponding to each user ID.

[0028] Step 23: The analysis unit adds the lifting index Sj of each user ID in each recording period to obtain the user usage index Use corresponding to each user ID. The analysis unit multiplies the user usage index Use of each user ID by the association index Rel of each numbered portal to obtain the allocation index of each user ID and different numbered portals. By identifying the number of users in the elevator, the allocation index of each user is calculated. Based on the consideration of the load capacity and the number of floors, the allocation index can directly reflect the usage efficiency of the elevator, making the elevator meter billing more reasonable.

[0029] The user analysis process specifically includes the following steps:

[0030] Step 221: The analysis unit presets a condition coefficient λ, the value range of λ is 0-1, and the analysis unit presets adjustment coefficients h, w1, w2, w3 and w4, and the specific value is determined according to the actual specifications of the elevator, and the value range of h is 2-3;

[0031] Step 222: The analysis unit presets a load weight coefficient wg and a floor weight coefficient wf. The analysis unit obtains the total number of floors and the maximum load value of the elevator from the load module. The analysis unit divides the maximum load value of the elevator by the load g to obtain the specific value of the load weight coefficient wg, and divides the number of floors f by the total number of floors to obtain the specific value of the floor weight coefficient wf.

[0032] Step 223: Analyze the unit according to the formula Calculate the floor index Fu corresponding to each user ID.

[0033] Furthermore, the correlation analysis procedure specifically includes the following steps:

[0034] Step 31: The analysis unit presets a frequency proportional coefficient wn and a duration proportional coefficient wt. The value range of the frequency proportional coefficient wn and the duration proportional coefficient wt are both 0 to 1, and the specific values ​​are determined according to the number of portals on a floor. The analysis unit marks the maximum value of the number of occurrences Mn as Max(Mn) and the maximum value of the duration Mt as Max(Mt).

[0035] Step 32: Analyze the cells according to the formula The correlation index Rel between each user ID and each numbered portal is calculated, the value of the time proportional coefficient wt is 0.3, and the value of the duration proportional coefficient wt is 0.1.

[0036] A smart meter data transmission system includes a user meter, an elevator meter, an analysis unit, a visual unit, and a load module. The visual unit includes an elevator visual module and a portal visual module. The output of the elevator meter is connected to the input of the analysis unit, and the output of the analysis unit is connected to the input of the user meter. The outputs of the elevator visual module, the portal visual module, and the load module are all connected to the input of the analysis unit.

[0037] The elevator vision module is installed on the top surface of the elevator interior, with the image acquisition direction of the elevator vision module facing away from the elevator door and tilted 45° downward. The portal vision module is installed on the top of the building aisle, with the image acquisition direction of the portal vision module facing the door and tilted 45° downward. The elevator vision module acquires the image inside the elevator and transmits it to the analysis unit. The portal vision module acquires the image of the aisle in front of the door and transmits it to the analysis unit. The load module is installed at the bottom of the elevator. The load module acquires the load g and the number of floors f of the elevator and transmits them to the analysis unit.

[0038] The elevator electricity meter records the electricity consumption of the elevator and transmits it to the analysis unit, and the user electricity meter is used to receive the shared electricity consumption sent by the analysis unit.

[0039] A smart meter storage medium includes a database, a port of the database establishes data communication with a port of an analysis unit, and a computer program or instruction is stored in the database. When the computer program or instruction is executed, the method described in any one of the contents is implemented.

[0040] The present invention has the following beneficial effects:

[0041] 1. By identifying and counting each person using the elevator through the visual unit, and then calculating the correlation between each person and the portal, outsiders can be effectively included in the elevator telegram billing system, making the elevator's shared electricity charges more reasonable.

[0042] 2. By identifying the number of users in the elevator, the allocation index of each user is calculated. Taking into account the load capacity and the number of floors, the allocation index can directly reflect the efficiency of elevator use, making the elevator electricity meter billing more reasonable.

[0043] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 This is a block diagram of a smart meter data transmission system of the present invention. DETAILED DESCRIPTION

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making any creative efforts shall fall within the scope of protection of the present invention.

[0047] The present invention provides a technical solution: a smart meter data transmission method, which is implemented based on a user meter, an elevator meter, an analysis unit, an elevator vision module, a portal vision module and a load module, and includes the following steps:

[0048] Step 1: Analyze the user's avatar. The analysis unit obtains the image inside the elevator through the elevator vision module. The analysis unit executes the user recognition program to obtain the user's avatar. The user's avatar includes a left-face user avatar, a right-face user avatar, and a front-face user avatar. The analysis unit presets a similarity threshold of 90%. The analysis unit compares the user's avatar with the facial images stored in the database and calculates the similarity Si. If the similarity between the user's avatar and the facial image is less than the similarity threshold of 90%, the analysis unit marks the user's avatar with a similarity threshold less than 90% as a new facial image and assigns a user ID. The user ID is unique and corresponds one-to-one with the facial image. The analysis unit binds the newly marked facial image and the corresponding user ID and saves it in the database. If the similarity Si between the user's avatar and the facial image is greater than or equal to the similarity threshold of 90%, the analysis unit classifies the user's avatar into the facial image. Each time the analysis unit calculates the similarity Si between the user's avatar and the facial image, it accumulates the number of times the facial image appears in the image inside the elevator, Nu.

[0049] The analysis unit presets a recording period of 7 days. If the number of times a face image appears Nu within the recording period is 0, the analysis unit will delete the face image with the number of times Nu appears 0 and the corresponding user ID from the database to prevent user portraits that appear too rarely from occupying database storage resources. The analysis unit recycles the deleted user ID and assigns it to the user portrait with a similarity Si below the similarity threshold when the user recognition program is executed next time.

[0050] Step 2: Calculate the user usage index. The analysis unit obtains the elevator's load capacity g and number of floors f through the load module. The analysis unit executes the elevator usage analysis program to obtain the user usage index Use corresponding to each user ID.

[0051] Step 3: Matching correlation, the analysis unit numbers the portals on each floor, executes a user identification program to obtain user portraits, records the number of times each user ID appears in the image of the portal visual module, and the duration Mt, and executes a correlation analysis program to obtain the correlation index Rel between the user ID and each numbered portal;

[0052] Step 4: The analysis unit multiplies the user usage index Use of each user ID by the association index Rel of each numbered portal to obtain the allocation index of each user ID and the portal with different numbers. When the association index Rel between the user ID and the portal is not zero, the analysis unit marks the user ID as the associated target of the portal. The analysis unit accumulates the allocation indexes of all associated targets of the portal to obtain the portal index. The analysis unit adds up all portal indices to obtain the total electricity consumption index.

[0053] Step 5: The analysis unit obtains the elevator's electricity consumption from the elevator meter. The analysis unit multiplies the elevator's electricity consumption by the portal index of each portal and then divides it by the total electricity consumption index to obtain the shared electricity consumption of each portal. The analysis unit sends the calculated shared electricity consumption to the user meter corresponding to the portal, so that the elevator's electricity consumption can be more reasonably allocated to the user meter of each portal to calculate the electricity bill.

[0054] The user identification procedure specifically includes the following steps:

[0055] The database contains three pre-stored face models at three viewing angles: left face tilted 45° downward, front face tilted 45° downward, and right face tilted 45° downward.

[0056] Step 11: The analysis unit measures the length of the elevator image as L1 and the width of the elevator image as L2. The analysis unit establishes a coordinate system in the elevator image using pixels as the unit of measurement, with the intersection of the diagonals of the elevator image rectangle as the origin of the coordinate system.

[0057] Step 12: The analysis unit establishes a vortex line in the coordinate system. The analysis unit presets the number of circles of the vortex line as a, and the value range of a is 3<a<20. The initial diameter of the inner circle of the vortex line is preset as b, and the value range of b is The expression formula of vortex line is t is a pixel point on the vortex line. The analysis unit presets a moving window. The moving window is elliptical. The vertical long axis of the moving window is The horizontal minor axis of the moving window is The analysis unit randomly selects a value from a value range based on the combination formula for the number of turns a and the initial diameter b of the inner ring to generate a vortex line. Each time the analysis unit generates a vortex line, step 121 is executed until all combinations of the combination formula are exhausted. When all combinations are exhausted, the process jumps to step 13.

[0058] The combination formula values ​​are not ordered. The average aspect ratio of a face is 34:21. To increase the chance of successfully matching the moving window with the model face, the moving window is set to an ellipse that is close to the face aspect ratio, which can improve the recognition error tolerance.

[0059] Step 121: The analysis unit presets a first trigger threshold of 50%. The analysis unit moves the intersection of the vertical major axis and the horizontal minor axis of the moving window from the inside to the outside on the spiral line, pixel by pixel. Each time the moving window moves, the analysis unit calculates the similarity Si between the pixels in the moving window and the model faces from the three perspectives. The pixels in the moving window are the pixels circled by the moving window in the elevator image. When the similarity Si is greater than or equal to the first trigger threshold, the analysis unit records a trigger event. When the moving window moves to the end of the outer circle of the spiral line, the analysis unit counts the number of trigger events corresponding to the spiral line.

[0060] Step 13: The analysis unit selects the vortex line with the most trigger events and marks it as a trajectory line. The analysis unit presets a second trigger threshold of 90%. The analysis unit moves the intersection of the vertical major axis and the horizontal minor axis of the moving window from the inside to the outside pixel by pixel along the trajectory line. Each time the moving window moves, the analysis unit calculates the similarity Si between the pixels of the elevator image within the moving window and the model faces from the three perspectives. When any of the similarities Si of the model faces from the three perspectives is greater than or equal to the second trigger threshold, the analysis unit marks the pixels of the elevator image within the moving window as the user's avatar.

[0061] Step 14: The analysis unit selects the maximum value of the three model face similarities Si to classify the user portraits into left-face user portraits, right-face user portraits and front-face user portraits, and the pixel with the maximum value of the similarity Si is marked as the corresponding user portrait type.

[0062] The similarity Si is calculated by the following steps:

[0063] Step 15: The analysis unit converts both the user portrait and the face image into grayscale images with a grayscale value range of 0 to 255. The analysis unit marks the grayscale image of the user portrait as x and the grayscale image of the face image as y.

[0064] Step 16: The analysis unit calculates the pixel mean ux of the grayscale image x, calculates the pixel mean uy of the grayscale image y, and calculates the variance σx of the grayscale image x 2 , calculate the variance σy of the grayscale image y 2 , calculate the covariance σxy of grayscale image x and grayscale image y;

[0065] Step 17: Analyze the cells according to the formula Calculate the similarity index SSIM between the user's avatar and the face image, where C1 = (K1 × E) 2 , C2=(K2×E) 2 , C1 and C2 are constants set to avoid the denominator being zero, E is the pixel value random coefficient, the value range of E is (0~255), K1 and K2 are constants, K1 is 0.01, K2 is 0.03;

[0066] The calculation of similarity Si is affected by three factors: brightness d1, contrast d2, and structure d3. Therefore, it is necessary to comprehensively analyze the similarity index SSIM of the user portrait and the face image from the brightness d1, contrast d2, and structure d3. The calculation formula of brightness d1 is The calculation formula for contrast d2 is: The calculation formula of structure d3 is

[0067] Step 18: The numerical range of the similarity index SSIM is (-1, 1), and the analysis unit is based on the formula The similarity index SSIM is normalized to obtain the similarity Si.

[0068] The elevator usage analysis program specifically includes the following steps:

[0069] Step 21: The analysis unit establishes a load curve graph, where the horizontal axis of the load curve graph is time, with the time unit being seconds, and the vertical axis is weight, with the weight unit being kilograms. The analysis unit obtains the load weight g from the load module and inputs it into the load curve graph to generate a load curve. The analysis unit determines the increase or decrease of users based on the changes in the load curve. When the load curve rises in a step-by-step manner, it is determined that a user has been added to the elevator. When the load curve drops in a step-by-step manner, it is determined that a user has been reduced in the elevator. When the number of users in the elevator increases or decreases, the analysis unit executes a user analysis process to obtain the floor index Fu corresponding to each user ID. The increase or decrease of users in the elevator indicates that the elevator has stopped at one of the floors. The floor index Fu at this time is a factor that affects the user usage index Use of each user ID. The user analysis process is not executed for a floor where the elevator has not stopped, and the floor index Fu of that floor will not be calculated accordingly.

[0070] Step 22: The analysis unit defines each round trip from the lowest floor to the highest floor as a lift cycle. The analysis unit adds the floor index Fu of each user ID within a lift cycle to obtain the lift index Sj corresponding to each user ID.

[0071] Step 23: The analysis unit adds the rise and fall index Sj of each user ID in each recording period to obtain the user usage index Use corresponding to each user ID;

[0072] The user analysis process specifically includes the following steps:

[0073] Step 221: The analysis unit presets a condition coefficient λ, the value range of λ is 0-1, and the analysis unit presets adjustment coefficients h, w1, w2, w3 and w4, and the specific value is determined according to the actual specifications of the elevator, and the value range of h is 2-3;

[0074] Step 222: The analysis unit presets a load weight coefficient wg and a floor weight coefficient wf. The analysis unit obtains the total number of floors and the maximum load value of the elevator from the load module. The analysis unit divides the maximum load value of the elevator by the load g to obtain the specific value of the load weight coefficient wg, and divides the number of floors f by the total number of floors to obtain the specific value of the floor weight coefficient wf.

[0075] Step 223: Analyze the unit according to the formula The floor index Fu corresponding to each user ID is calculated, where h is 2, w1 is 10, w2 is 50, w3 is 100, w4 is 0.1, and λ is 0.8.

[0076] The correlation analysis procedure specifically includes the following steps:

[0077] Step 31: The analysis unit presets a frequency proportional coefficient wn and a duration proportional coefficient wt. The value range of the frequency proportional coefficient wn and the duration proportional coefficient wt are both 0 to 1, and the specific values ​​are determined according to the number of portals on a floor. The analysis unit marks the maximum value of the number of occurrences Mn as Max(Mn) and the maximum value of the duration Mt as Max(Mt).

[0078] Step 32: Analyze the cells according to the formula The correlation index Rel between each user ID and each numbered portal is calculated, the value of the time proportional coefficient wt is 0.3, and the value of the duration proportional coefficient wt is 0.1.

[0079] like Figure 1 As shown, a smart meter data transmission system includes a user meter, an elevator meter, an analysis unit, a visual unit and a load module. The visual unit includes an elevator visual module and a portal visual module. The output end of the elevator meter is connected to the input end of the analysis unit, and the output end of the analysis unit is connected to the input end of the user meter. The output ends of the elevator visual module, the portal visual module and the load module are all connected to the input end of the analysis unit.

[0080] The elevator vision module is installed on the top surface of the elevator interior. The image acquisition direction of the elevator vision module faces away from the elevator door and is tilted 45° downward. The portal vision module is installed on the top of the building aisle. The image acquisition direction of the portal vision module faces the door and is tilted 45° downward. The elevator vision module acquires images inside the elevator and transmits them to the analysis unit. The portal vision module acquires images of the aisle in front of the door and transmits them to the analysis unit. The load module is installed at the bottom of the elevator. The load module acquires the elevator's load g and number of floors f and transmits them to the analysis unit.

[0081] The elevator meter records the elevator's electricity consumption and transmits it to the analysis unit, and the user meter is used to receive the shared electricity sent by the analysis unit.

[0082] A smart meter storage medium includes a database, a port of the database establishes data communication with a port of an analysis unit, and a computer program or instruction is stored in the database. When the computer program or instruction is executed, a method as described in any one of the specific embodiments is implemented.

[0083] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A smart meter data transmission method, implemented based on a user meter, an elevator meter, an analysis unit, an elevator vision module, a portal vision module, and a load module, characterized in that: The following steps are involved: Step 1: Analyze user profile pictures; Step 2: Calculate the user usage index. The analysis unit obtains the elevator's load capacity g and number of floors f through the load module, executes the elevator usage analysis program to obtain the user usage index Use corresponding to each user ID, and uses the visual unit to identify and count each person using the elevator. Then, the correlation between each person and the portal is calculated, which can effectively include outsiders in the elevator telegram billing system. Step 3: Matching correlation, the analysis unit executes the user identification program to obtain the user portrait, records the number of times Mn and duration Mt of each user ID appearing in the image of the portal visual module, and executes the correlation analysis program to obtain the correlation index Rel between the user ID and each numbered portal; Step 4: The analysis unit multiplies the user usage index Use of each user ID by the association index Rel of each numbered portal to obtain the allocation index of each user ID and the portal with different numbers. When the association index Rel between the user ID and the portal is not zero, the user ID is marked as the associated target of the portal. The allocation index of all associated targets of the portal is accumulated to obtain the portal index. The total electricity consumption index is obtained by adding up all portal indices. Step 5: The analysis unit obtains the elevator's electricity consumption from the elevator meter, multiplies the elevator's electricity consumption by each portal's portal index, and then divides it by the total electricity consumption index to obtain the shared electricity consumption of each portal. The calculated shared electricity consumption is sent to the user's electricity meter corresponding to the portal. The elevator usage analysis procedure specifically includes the following steps: Step 21: The analysis unit establishes a load curve graph, where the horizontal axis of the load curve graph is time and the vertical axis is weight. The analysis unit obtains the load weight g from the load module and inputs it into the load curve graph to generate a load curve. The analysis unit determines the increase or decrease of users based on the changes in the load curve. When the load curve rises in a step-by-step manner, it is determined that a user has been added to the elevator. When the load curve drops in a step-by-step manner, it is determined that a user has been removed from the elevator. When the number of users in the elevator increases or decreases, the analysis unit performs a user analysis process to obtain the floor index Fu corresponding to each user ID. Step 22: The analysis unit defines each round trip from the lowest floor to the highest floor as a lift cycle. The analysis unit adds the floor index Fu of each user ID within a lift cycle to obtain the lift index Sj corresponding to each user ID. Step 23: The analysis unit adds up the rise and fall index Sj of each user ID in each recording period to obtain the user usage index Use corresponding to each user ID.

2. The smart meter data transmission method according to claim 1, characterized in that: The specific process of analyzing the user's profile picture in step 1 is as follows: The analysis unit obtains the image inside the elevator through the elevator vision module, executes the user recognition program to obtain the user portrait, presets a similarity threshold, compares the user portrait with the facial image stored in the database to calculate the similarity Si, and if the similarity between the user portrait and the facial image is less than the similarity threshold, the user portrait less than the similarity threshold is marked as a new facial image and assigned a user ID. The user ID is unique and corresponds one-to-one to the facial image, and the newly marked facial image is bound to the corresponding user ID; if the similarity Si between the user portrait and the facial image is greater than or equal to the similarity threshold, the user portrait is classified into the facial image; each time the similarity Si between the user portrait and the facial image is calculated, the number of appearances Nu of the facial image is accumulated.

3. The smart meter data transmission method according to claim 2, characterized in that: The user identification procedure specifically includes the following steps: The database contains three pre-stored face models from different perspectives: left face down, front face down, and right face down. Step 11: The analysis unit measures the length of the elevator image as L1 and the width of the elevator image as L2. The analysis unit establishes a coordinate system in the elevator image using pixels as the unit of measurement, with the intersection of the diagonals of the elevator image rectangle as the origin of the coordinate system. Step 12: The analysis unit establishes a vortex line in the coordinate system. The analysis unit presets the number of circles of the vortex line as a, and the value range of a is 3<a<20. The initial diameter of the inner circle of the vortex line is preset as b, and the value range of b is <b< , the expression formula of vortex line is , t is a pixel point on the vortex line, the analysis unit presets a moving window, the moving window is elliptical, and the vertical major axis of the moving window is , the horizontal minor axis of the moving window is The analysis unit randomly selects values ​​from the value range according to the combination formula for the number of turns a and the initial diameter b of the inner circle to generate a vortex line. Each time the analysis unit generates a vortex line, step 121 is executed until all combinations of the combination formula are exhausted. When all combinations are exhausted, the process jumps to step 13. Step 121: The analysis unit presets a first trigger threshold. The analysis unit moves the intersection of the vertical major axis and the horizontal minor axis of the moving window from the inside to the outside pixel by pixel along the vortex line. Each time the moving window moves, the analysis unit calculates the similarity Si between the pixels in the moving window and the model faces of the three perspectives. When the similarity Si is greater than or equal to the first trigger threshold, the analysis unit records a trigger event. When the moving window moves to the end of the outer circle of the vortex line, the analysis unit counts the number of trigger events corresponding to the vortex line. Step 13: The analysis unit selects the vortex line with the most trigger events and marks it as a trajectory line. The analysis unit presets a second trigger threshold and moves the intersection of the vertical major axis and the horizontal minor axis of the moving window from the inside to the outside pixel by pixel along the trajectory line. Each time the moving window moves, the analysis unit calculates the similarity Si between the pixels in the moving window and the model faces of the three perspectives. When any of the similarities Si of the model faces of the three perspectives is greater than or equal to the second trigger threshold, the analysis unit marks the pixels in the moving window as the user's avatar. Step 14: The analysis unit selects the maximum value of the three model face similarities Si to classify the user portraits into left-face user portraits, right-face user portraits and front-face user portraits, and the pixel with the maximum value of the similarity Si is marked as the corresponding user portrait type.

4. The smart meter data transmission method according to claim 3, characterized in that: The similarity Si is calculated by the following steps: Step 15: The analysis unit converts both the user portrait and the face image into grayscale images with a grayscale value range of 0 to 255. The analysis unit marks the grayscale image of the user portrait as x and the grayscale image of the face image as y. Step 16: The analysis unit calculates the pixel mean ux of the grayscale image x, calculates the pixel mean uy of the grayscale image y, and calculates the variance of the grayscale image x , calculate the variance of the grayscale image y , calculate the covariance of grayscale image x and grayscale image y ; Step 17: Analyze the cells according to the formula Calculate the similarity index SSIM between the user's avatar and the face image, where , , C1 and C2 are constants, E is the pixel value random coefficient, the value range of E is 0 to 255, K1 and K2 are constants; The calculation of similarity Si is affected by three factors: brightness d1, contrast d2, and structure d3. Therefore, it is necessary to comprehensively analyze the similarity index SSIM of the user portrait and the face image from the brightness d1, contrast d2, and structure d3. The calculation formula of brightness d1 is , the calculation formula of contrast d2 is , the calculation formula of structure d3 is ; Step 18: The calculated value range of the similarity index SSIM is (-1, 1). The analysis unit is based on the formula The similarity index SSIM is normalized to obtain the similarity Si.

5. The smart meter data transmission method according to claim 2, characterized in that: The user analysis process specifically includes the following steps: Step 221: Analyze unit preset condition coefficients , The value range of is 0 to 1, and the analysis unit presets the adjustment coefficients h, w1, w2, w3 and w4; Step 222: The analysis unit presets a load weight coefficient wg and a floor weight coefficient wf. The analysis unit obtains the total number of floors and the maximum load value of the elevator from the load module. The analysis unit divides the maximum load value of the elevator by the load g to obtain the specific value of the load weight coefficient wg, and divides the number of floors f by the total number of floors to obtain the specific value of the floor weight coefficient wf. Step 223: Analyze the unit according to the formula Calculate the floor index Fu corresponding to each user ID.

6. A smart meter data transmission method according to claim 5, characterized in that: The correlation analysis procedure specifically includes the following steps: Step 31: The analysis unit presets a frequency proportional coefficient wn and a duration proportional coefficient wt, both of which range from 0 to 1. The analysis unit marks the maximum value of the number of occurrences Mn as Max(Mn), and marks the maximum value of the duration Mt as Max(Mt); Step 32: Analyze the cells according to the formula The correlation index Rel between each user ID and each numbered portal is calculated.

7. A smart meter data transmission system, characterized in that: A smart meter data transmission method applied to any one of claims 1-6, comprising a user meter, an elevator meter, an analysis unit, a visual unit, and a load module, wherein the visual unit comprises an elevator visual module and a portal visual module, the output end of the elevator meter is connected to the input end of the analysis unit, the output end of the analysis unit is connected to the input end of the user meter, and the output ends of the elevator visual module, the portal visual module, and the load module are all connected to the input end of the analysis unit; The elevator vision module is installed on the top surface of the elevator interior, with the image acquisition direction of the elevator vision module facing away from the elevator door and obliquely downward. The portal vision module is installed on the top of the building aisle, with the image acquisition direction of the portal vision module facing the door and obliquely downward. The elevator vision module acquires the image inside the elevator and transmits it to the analysis unit. The portal vision module acquires the image of the aisle in front of the door and transmits it to the analysis unit. The load module is installed at the bottom of the elevator. The load module acquires the load g and the number of floors f of the elevator and transmits them to the analysis unit. The elevator electricity meter records the electricity consumption of the elevator and transmits it to the analysis unit, and the user electricity meter is used to receive the shared electricity consumption sent by the analysis unit.

8. A smart meter storage medium, characterized in that: The method comprises a database, a port of the database establishes data communication with a port of the analysis unit, a computer program or instruction is stored in the database, and when the computer program or instruction is executed, the method according to any one of claims 1 to 6 is implemented.

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