Urban public service facility monitoring and utilization rate evaluation method

Abstract

The invention relates to an urban public service facility monitoring and utilization rate evaluation method. The method comprises the following steps: acquiring noctilucent satellite data, GIS data, POI data and EVI data of a research area; according to the GIS data and the POI data, an urban public service facility space range is extracted, and an external buffer area of the urban public service facility space range is delimited; calculating noctilucent intensity difference time sequence data of the urban public service facility space range and the corresponding external buffer area; analyzing time-frequency characteristics of the difference time sequence data by using three-layer discrete wavelet transform based on a Haar function, and obtaining a residual sequence and a high-frequency detail sequence of the noctilucent intensity difference time sequence data; and identifying abnormal values of the residual error sequence and the high-frequency detail sequence outside a preset confidence interval, and realizing monitoring of abnormal use of the urban public service facility. According to the invention, the monitoring precision and real-time performance are improved while the monitoring cost is greatly reduced, and efficient and automatic monitoring of the utilization rate of urban public service facilities is realized.

Description

Technical Field

[0001] The present invention relates to the field of public service facility management, and in particular to a method for monitoring and evaluating the utilization rate of urban public service facilities. Background Art

[0002] Urban public service facilities are key to achieving equal access to services and enhancing residents' well-being. Accurately assessing the utilization rate of urban public service facilities is crucial for improving the refinement of urban governance and promoting sustainable development.

[0003] At present, the evaluation of urban public service facility utilization mainly relies on local reported data or manual surveys. Not only is there a time lag in data acquisition, but objectivity is also difficult to guarantee.

[0004] Chinese invention patent CN119313227A discloses a method for evaluating the utilization efficiency of urban public service facilities based on high-resolution satellite data. This method uses high-resolution satellite data to achieve the utilization rate of urban public service facilities. However, this method relies on a large number of high-resolution satellite images, which not only has high evaluation costs but also makes it difficult to ensure the real-time nature of the data.

[0005] Night-glow satellites, capable of capturing the radiation emitted by artificial light sources on Earth's surface at night in real time, have become a crucial tool for monitoring socioeconomic dynamics. Using night-glow satellite data to monitor and accurately assess the utilization of urban public service facilities in real time is crucial for optimizing their management. Summary of the Invention

[0006] To address these issues, the present invention provides a method for monitoring and assessing the utilization of urban public service facilities based on night-light satellite data. Compared to monitoring methods using high-resolution satellite data, this method significantly reduces monitoring costs while improving accuracy and real-time performance, enabling efficient and automated monitoring of the utilization of urban public service facilities.

[0007] The technical solutions for achieving the purpose of the present invention are:

[0008] In a first aspect, a method for monitoring urban public service facilities is provided, the method comprising the following steps:

[0009] S1, obtain night light satellite data, impervious surface GIS data, urban public service facility point of interest POI data and enhanced vegetation index EVI data of the study area;

[0010] S2, extracting the spatial scope of urban public service facilities based on GIS data and POI data, and delineating the outer buffer zone of the spatial scope of urban public service facilities;

[0011] S3, obtain the time series data of the night light intensity of the urban public service facilities and the corresponding external buffer zone, and calculate the time series data of the difference of the night light intensity between the urban public service facilities and the corresponding external buffer zone after desaturating the EVI data;

[0012] S4, using the three-layer discrete wavelet transform based on the Haar function to analyze the time-frequency characteristics of the difference time series data, obtain the approximate coefficients and detail coefficients, and perform inverse wavelet transform on the approximate coefficients and detail coefficients to obtain the residual sequence and high-frequency detail sequence of the night light intensity difference time series data respectively;

[0013] S5, identifies outliers in the residual sequence and high-frequency detail sequence that are outside the preset confidence interval, and monitors the abnormal use of urban public service facilities.

[0014] In a second aspect, a city public service facility monitoring system is provided, the city public service facility monitoring system comprising:

[0015] Data acquisition module, used to obtain night light satellite data, impervious surface GIS data, urban public service facility point of interest POI data and enhanced vegetation index EVI data of the study area;

[0016] The regional delineation module is used to extract the spatial scope of urban public service facilities based on GIS data and POI data, and to delineate the outer buffer zone of the spatial scope of urban public service facilities;

[0017] The difference calculation module is used to obtain the time series data of the night light intensity of the spatial scope of urban public service facilities and the corresponding external buffer zone. After desaturating the EVI data, the time series data of the night light intensity difference between the spatial scope of urban public service facilities and the corresponding external buffer zone is calculated.

[0018] The wavelet transformation module is used to analyze the time-frequency characteristics of the difference time series data using a three-layer discrete wavelet transform based on the Haar function to obtain the approximate coefficients and detail coefficients, and to perform inverse wavelet transformation on the approximate coefficients and detail coefficients to obtain the residual sequence and high-frequency detail sequence of the night light intensity difference time series data respectively;

[0019] The anomaly monitoring module is used to identify abnormal values ​​of the residual sequence and high-frequency detail sequence that are outside the preset confidence interval, thereby monitoring the abnormal use of urban public service facilities.

[0020] According to a third aspect, an electronic device is provided, including:

[0021] memory for storing computer programs;

[0022] The processor is used to implement the steps of the urban public service facility monitoring method as described above when executing the computer program.

[0023] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the urban public service facility monitoring method as described above are implemented.

[0024] In a fifth aspect, a method for evaluating the utilization rate of urban public service facilities is provided, wherein the method for evaluating the utilization rate of urban public service facilities comprises the following steps:

[0025] According to the monitoring results of the urban public service facilities monitoring method described above, the number of abnormal usage moments and the number of normal usage moments of the urban public service facilities are calculated respectively, and then the abnormal usage ratio and the normal usage ratio of the urban public service facilities are obtained, thereby realizing the utilization rate evaluation of the urban public service facilities.

[0026] Compared with the existing technology, the present invention has the following significant advantages: the present invention utilizes the technical advantages of night light remote sensing satellites, such as wide coverage, high spatial resolution, and strong real-time performance, and through multi-dimensional spatiotemporal feature fusion technology, obtains the night light intensity of urban public service facilities and their surrounding areas in real time and desaturates the night light intensity, uses discrete wavelet transform to obtain multi-dimensional time-frequency information of night light intensity, and uses preset thresholds to achieve dynamic monitoring of the utilization rate of urban public service facilities. Compared with traditional methods that rely on local reporting or manual investigations, the present invention breaks through the spatiotemporal limitations of data collection and ensures the objectivity of monitoring data. Compared with monitoring methods using high-resolution satellites, the present invention significantly reduces monitoring costs while improving monitoring accuracy and real-time performance, achieving efficient and automated monitoring of the utilization rate of urban public service facilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of the method of the present invention.

[0028] Figure 2 It is a diagram of the research area of ​​the embodiment of the present invention.

[0029] Figure 3 It is the time series data of the night light intensity difference in an embodiment of the present invention.

[0030] Figure 4 is a low-frequency trend sequence in an embodiment of the present invention.

[0031] Figure 5 It is the night light intensity residual sequence of the embodiment of the present invention.

[0032] Figure 6 The embodiment of the present invention corresponds to High-frequency detail sequence.

[0033] Figure 7 The embodiment of the present invention corresponds to High-frequency detail sequence.

[0034] Figure 8 The embodiment of the present invention corresponds to High-frequency detail sequence.

[0035] Figure 9 This is the monitoring result of abnormal use of urban public service facilities in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the following will be a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work shall fall within the scope of protection of the present invention.

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which this invention pertains. The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the invention. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0038] like Figure 1 As shown, a method for monitoring and evaluating the utilization rate of urban public service facilities includes the following steps:

[0039] S1, obtain night light satellite data, impervious surface (GIS, Global Impervious Surface) data, urban public service facility point of interest (POI, Point of Interest) data and Enhanced Vegetation Index (EVI, Enhanced Vegetation Index) data of the study area.

[0040] S2, based on GIS data and POI data, extract the spatial scope of urban public service facilities and delineate the external buffer zone of this spatial scope.

[0041] like Figure 2 As shown, the dotted inner circle is the spatial range of the public service facilities to be monitored in the embodiment of the present invention, and the annular area between the dotted inner circle and the outer circle is the external buffer zone.

[0042] S3, obtain the time series data of the night light intensity of the spatial range of urban public service facilities and the corresponding external buffer zone, use the EVI data to desaturate, and calculate the time series data of the difference in night light intensity between the spatial range of urban public service facilities and the corresponding external buffer zone.

[0043] S4, using a three-layer discrete wavelet transform (DWT) based on the Haar function to analyze the time-frequency characteristics of the difference time series data, obtain the approximation coefficients and detail coefficients, and perform inverse wavelet transform on the approximation coefficients and detail coefficients to obtain the residual sequence and high-frequency detail sequence of the night light intensity difference time series data respectively;

[0044] S5, identifies outliers in the residual sequence and high-frequency detail sequence that are outside the preset confidence interval, and monitors the abnormal use of urban public service facilities.

[0045] S6, calculate the annual utilization rate of urban public service facilities and realize dynamic monitoring of the efficiency of the use of public service facilities.

[0046] In the step S1, the following steps are specifically included:

[0047] S11, cleaning, outlier removal, and aggregation of POI data to retain urban public service facilities in the POI data;

[0048] S12, obtain night-light satellite data, perform image rectification, georeferencing, and mask extraction;

[0049] S13, acquiring GIS data, performing projection transformation, image registration, outlier removal, and mask extraction;

[0050] S14, obtaining EVI data, performing projection transformation, image registration, outlier removal, and mask extraction.

[0051] In the step S2, the following steps are specifically included:

[0052] S21, using the cleaned POI data and map data to delineate the spatial scope and external buffer zone of urban public service facilities;

[0053] S22, using GIS data, clip the spatial extent of public service facilities and the impervious surface of the external buffer zone.

[0054] In the step S3, the following steps are specifically included:

[0055] S31, crop the night light satellite data of the urban public service facilities space range, and obtain the original night light intensity time series data of the urban public service facilities space range ,right Normalize and obtain normalized night light intensity time series data ,in is the observation window length, Observation points for the spatial scope of urban public service facilities The original night light intensity value, Observation points for the normalized spatial range of urban public service facilities It should be noted that, in this embodiment, the observation time is one year, but the actual number of observation values ​​obtained is about 240, so the observation length T here is 240.

[0056] S32, using EVI data Desaturation, obtain the time series data of night light intensity of urban public service facilities after desaturation ,in Observation points for the spatial scope of urban public service facilities The desaturated night light intensity value, , It is the average enhanced vegetation index of the spatial range of urban public service facilities within the observation window.

[0057] S33, crop the night light satellite data of the outer buffer zone of urban public service facilities to obtain the original night light intensity time series data of the outer buffer zone ,right Perform normalization to obtain the normalized night light intensity time series data of the external buffer ,in For external buffer observation points The original night light intensity value, Normalized external buffer observation point The night light intensity value.

[0058] S34, using EVI data Desaturation, obtain the night light intensity time series data of the external buffer after desaturation ,in For the rear external buffer observation point The desaturated night light intensity value, , is the average enhanced vegetation index of the outer buffer zone within the observation window.

[0059] S35: Calculate the difference in desaturated night light intensity between the spatial range of urban public service facilities and the corresponding external buffer zone, and obtain the difference time series data. ,in The spatial scope of urban public service facilities and the corresponding external buffer observation points The desaturated night light intensity difference, .

[0060] like Figure 3 Shown is the time series data of the night light intensity difference according to an embodiment of the present invention.

[0061] In the step S4, the following steps are specifically included:

[0062] S41, using the first layer of Haar scale series set For difference time series data Perform the first layer of low-pass filtering to obtain the first layer of approximate coefficient set .in, is the normalized Haar scale series, , is the characteristic function, when The value is 1 when , otherwise it is 0. ; Difference time series data and The inner product of .

[0063] S42, using the first layer of Haar wavelet series set For difference time series data Perform the first layer of high-pass filtering to obtain the first layer detail coefficient set .in, is the first layer normalized Haar wavelet sequence, , 、 are all characteristic functions, Difference time series data and The inner product of .

[0064] S43, using the second layer Haar wavelet series set For difference time series data Perform the second layer of high-pass filtering to obtain the second layer detail coefficient set .in, is the second-layer normalized Haar wavelet sequence, , 、 are all characteristic functions, Difference time series data and The inner product of .

[0065] S44, using the third layer Haar wavelet series set For difference time series data Perform the third layer of high-pass filtering to obtain the third layer detail coefficient set .in, is the third-layer normalized Haar wavelet sequence, , 、 are all characteristic functions, Difference time series data and The inner product of .

[0066] S45, the detail coefficient 、 and Set to zero, for the approximate coefficient Upsample three times and use a low-pass reconstruction filter Reconstruct and obtain differential time series data Low-frequency trend series , calculate the night light intensity residual sequence ,in is the observation point after reconstruction Low frequency value of night light intensity, For observation points The residual value of the night light intensity; Figure 4 The following is a low-frequency trend sequence according to an embodiment of the present invention, as shown in FIG. Figure 5 FIG. 4 shows a night light intensity residual sequence according to an embodiment of the present invention.

[0067] S46, the approximate coefficients are set Set to zero, respectively 、 and Upsampling is performed three times, twice, and once, respectively, and a high-pass reconstruction filter is used. Reconstruct and obtain differential time series data High-frequency detail sequence 、 、 ,in 、 、 Corresponding to 、 and The reconstructed observation points High-frequency value of night light intensity. Figures 6 to 8 The following are the corresponding embodiments of the present invention. 、 and High-frequency detail sequence.

[0068] In the step S5, the following steps are specifically included:

[0069] S51, for the residual sequence and high-frequency detail sequences 、 、 If the residual or high-frequency value of the night light intensity at a certain observation point is less than the lower limit of the confidence interval, the residual or high-frequency value of the night light intensity at that observation point is considered to be an abnormally small value; if the residual or high-frequency value of the night light intensity at a certain observation point is greater than the upper limit of the confidence interval, the residual or high-frequency value of the night light intensity at that observation point is considered to be an abnormally large value; otherwise, the residual or high-frequency value of the night light intensity at that observation point is considered to be a normal value;

[0070] S52: If more than half of the residuals and high-frequency values ​​of the night light intensity at a certain observation point are identified as abnormally small values, it is considered that the urban public service facilities at the observation point are underused; if more than half of the residuals and high-frequency values ​​of the night light intensity at a certain observation point are identified as abnormally large values, it is considered that the urban public service facilities at the observation point are overloaded; otherwise, it is considered that the urban public service facilities at the observation point are in normal use. Figure 9 1 shows the monitoring results of abnormal usage of urban public service facilities according to an embodiment of the present invention, wherein “▲” indicates overloaded usage and “▼” indicates underused usage.

[0071] In step S51, a sliding window is selected , calculate the approximate coefficients respectively and detail coefficient Confidence interval within the window ,in, for or or or The mean value within the sliding window, for or or or The standard deviation within the sliding window.

[0072] In the step S6, the following steps are specifically included:

[0073] S51, based on the above monitoring results, the number of observation points for underutilization of public service facilities U=5, the number of observation points for overload utilization O=5, and the number of observation points for normal utilization N=TUO=240-5-5=230 are obtained respectively;

[0074] S52, calculation of the annual underutilization ratio of public service facilities , normal usage ratio , overload utilization ratio

[0075] It should be noted that the order of the embodiments of the present application described above is for descriptive purposes only and does not represent the superiority or inferiority of the embodiments. The above description is of specific embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0076] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the device, equipment, and storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simple. For relevant portions, refer to the descriptions of the method embodiments.

[0077] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0078] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for monitoring urban public service facilities, characterized by: The urban public service facility monitoring method comprises the following steps: S1, obtain night light satellite data, impervious surface GIS data, urban public service facility point of interest POI data and enhanced vegetation index EVI data of the study area; S2, extracting the spatial scope of urban public service facilities based on GIS data and POI data, and delineating the outer buffer zone of the spatial scope of urban public service facilities; S3, obtain the time series data of the night light intensity of the spatial range of urban public service facilities and the corresponding external buffer zone, and calculate the time series data of the difference between the night light intensity of the spatial range of urban public service facilities and the corresponding external buffer zone after desaturation using the EVI data; S4, using the three-layer discrete wavelet transform based on the Haar function to analyze the time-frequency characteristics of the difference time series data, obtain the approximate coefficients and detail coefficients, and perform inverse wavelet transform on the approximate coefficients and detail coefficients to obtain the residual sequence and high-frequency detail sequence of the night light intensity difference time series data respectively; S5, identifies outliers in the residual sequence and high-frequency detail sequence that are outside the preset confidence interval, and monitors the abnormal use of urban public service facilities.

2. The urban public service facility monitoring method according to claim 1, characterized in that: The step S3 comprises: S31, crop the night light satellite data of the urban public service facilities space range, and obtain the original night light intensity time series data of the urban public service facilities space range ,right Normalize and obtain normalized night light intensity time series data ,in is the observation window length, Observation points for the spatial scope of urban public service facilities The original night light intensity value, Observation points for the normalized spatial range of urban public service facilities The original night light intensity value; S32, using EVI data Desaturation, obtain the time series data of night light intensity of urban public service facilities after desaturation ,in Observation points for the spatial scope of urban public service facilities The desaturated night light intensity value, , is the average enhanced vegetation index of the spatial extent of urban public service facilities within the observation window; S33, crop the night light satellite data of the outer buffer zone of urban public service facilities to obtain the original night light intensity time series data of the outer buffer zone ,right Perform normalization to obtain the normalized night light intensity time series data of the external buffer ,in For external buffer observation points The original night light intensity value, Normalized external buffer observation point The night light intensity value; S34, using EVI data Desaturation, obtain the night light intensity time series data of the external buffer after desaturation ,in For the rear external buffer observation point The desaturated night light intensity value, , is the average enhanced vegetation index in the outer buffer zone within the observation window; S35: Calculate the difference in desaturated night light intensity between the spatial range of urban public service facilities and the corresponding external buffer zone, and obtain the difference time series data. ,in The spatial scope of urban public service facilities and the corresponding external buffer observation points The desaturated night light intensity difference, .

3. The urban public service facility monitoring method according to claim 1, characterized in that: The step S4 comprises: S41, using a layer of Haar scale series set to compare the difference time series data Perform low-pass filtering to obtain an approximate coefficient set ,in is the observation window length, The spatial scope of urban public service facilities and the corresponding external buffer observation points The desaturated night light intensity difference; S42, using the first layer of Haar wavelet series set to compare the difference time series data Perform the first layer of high-pass filtering to obtain the first layer detail coefficient set ; S43, using the second layer of Haar wavelet series set to compare the difference time series data Perform the second layer of high-pass filtering to obtain the second layer detail coefficient set ; S44, using the third layer of Haar wavelet series set to compare the difference time series data Perform the third layer of high-pass filtering to obtain the third layer detail coefficient set ; S45, will 、 and Zero, Upsample and reconstruct using a low-pass reconstruction filter to obtain difference time series data Low-frequency trend series , calculate the night light intensity residual sequence ,in is the observation point after reconstruction Low frequency value of night light intensity, For observation points The residual value of night light intensity; S46, will Set to zero, respectively 、 and Upsample and reconstruct using a high-pass reconstruction filter to obtain difference time series data High-frequency detail sequence 、 、 ,in 、 、 Corresponding to 、 and The reconstructed observation points High-frequency values ​​of night light intensity.

4. The urban public service facility monitoring method according to claim 3, characterized in that: The step S4 specifically includes: S41, using a layer of Haar scale series set For difference time series data Perform the first layer of low-pass filtering to obtain the approximate coefficient set ,in, is the normalized Haar scale series, , is the characteristic function, when The value is 1 when , otherwise it is 0. ; S42, using the first layer of Haar wavelet series set For difference time series data Perform the first layer of high-pass filtering to obtain the first layer detail coefficient set ,in, is the first layer normalized Haar wavelet sequence, , 、 are all indicator functions, ; S43, using the second layer Haar wavelet series set For difference time series data Perform the second layer of high-pass filtering to obtain the second layer detail coefficient set ,in, is the second-layer normalized Haar wavelet sequence, , 、 are all indicator functions, ; S44, using the third layer Haar wavelet series set For difference time series data Perform the third layer of high-pass filtering to obtain the third layer detail coefficient set ,in, is the third-layer normalized Haar wavelet sequence, , 、 are all indicator functions, ; S45, will 、 and Zero, Upsample and use a low-pass reconstruction filter Reconstruct and obtain differential time series data Low-frequency trend series , calculate the night light intensity residual sequence ; S46, will Set to zero, respectively 、 and Upsample and use a high-pass reconstruction filter Reconstruct and obtain differential time series data High-frequency detail sequence 、 、 .

5. The urban public service facility monitoring method according to claim 1, characterized in that: The step S5 comprises: Select the sliding window and calculate the residual sequence separately and high-frequency detail sequences 、 、 Confidence interval within a sliding window: ， in, for or or or The mean value within the sliding window, for or or or The standard deviation within the sliding window.

6. The urban public service facility monitoring method according to claim 1, characterized in that: The step S5 comprises: S51, for the residual sequence and high-frequency detail sequences 、 、 If the residual or high-frequency value of the night light intensity at a certain observation point is less than the lower limit of the confidence interval, the residual or high-frequency value of the night light intensity at that observation point is considered to be an abnormally small value; if the residual or high-frequency value of the night light intensity at a certain observation point is greater than the upper limit of the confidence interval, the residual or high-frequency value of the night light intensity at that observation point is considered to be an abnormally large value; otherwise, the residual or high-frequency value of the night light intensity at that observation point is considered to be a normal value; S52, if more than half of the residuals and high-frequency values ​​of the night light intensity at a certain observation point are identified as abnormally small values, it is considered that the urban public service facilities at that moment are underused; if more than half of the residuals and high-frequency values ​​of the night light intensity at a certain observation point are identified as abnormally large values, it is considered that the urban public service facilities at that moment are overloaded; otherwise, it is considered that the urban public service facilities at that observation point are in normal use.

7. A monitoring system based on the urban public service facility monitoring method according to any one of claims 1 to 6, characterized in that: The monitoring system comprises: Data acquisition module, used to obtain night light satellite data, impervious surface GIS data, urban public service facility point of interest POI data and enhanced vegetation index EVI data of the study area; The regional delineation module is used to extract the spatial scope of urban public service facilities based on GIS data and POI data, and to delineate the external buffer zone of the spatial scope of urban public service facilities; The difference calculation module is used to obtain the time series data of the night light intensity of the spatial scope of urban public service facilities and the corresponding external buffer zone. After desaturating the EVI data, the time series data of the night light intensity difference between the spatial scope of urban public service facilities and the corresponding external buffer zone is calculated. The wavelet transformation module is used to analyze the time-frequency characteristics of the difference time series data using a three-layer discrete wavelet transform based on the Haar function to obtain the approximate coefficients and detail coefficients, and to perform inverse wavelet transformation on the approximate coefficients and detail coefficients to obtain the residual sequence and high-frequency detail sequence of the night light intensity difference time series data respectively; The anomaly monitoring module is used to identify abnormal values ​​of the residual sequence and high-frequency detail sequence that are outside the preset confidence interval, thereby monitoring the abnormal use of urban public service facilities.

8. An electronic device, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of the urban public service facility monitoring method as described in any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the urban public service facility monitoring method according to any one of claims 1 to 6.

10. A method for evaluating the utilization rate of urban public service facilities, characterized by: The method for evaluating the utilization rate of urban public service facilities comprises the following steps: According to the monitoring results of the urban public service facility monitoring method as described in any one of claims 1 to 6, the number of abnormal usage observation points and the number of normal usage observation points of the urban public service facilities are calculated respectively, and then the abnormal usage ratio and normal usage ratio of the urban public service facilities are obtained, thereby realizing the utilization rate evaluation of the urban public service facilities.

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