Community Micro-brain Digital Analysis System and Methods for Urban Governance

Abstract

This application provides a community micro-brain digital analysis system and method for urban governance, relating to the field of urban community governance technology. This application collects multi-source heterogeneous data on public facilities in urban communities, constructs a digital model of the facilities in the community micro-brain system, and records health baseline data. Through a digital sensor network of key parts of the facilities, it detects at preset cycles to obtain waveform sequences reflecting changes in internal state. By comparing these waveforms with the health baseline data, it identifies the patterns of waveform changes, establishes state change trajectories, and generates quantitative health indicators based on waveform attenuation and morphological distortion characteristics. The system then performs multi-dimensional correlation analysis between these indicators and the multi-source heterogeneous data to generate governance strategies including maintenance priorities and resource allocation suggestions. These strategies are transmitted to the community management platform to form allocation plans, and the digital model displays the full lifecycle status of the facilities, enabling intelligent monitoring and governance optimization of the entire lifecycle of community facilities.

Description

Technical Field

[0001] This application relates to the field of urban community governance technology, and in particular to a community micro-brain digital analysis system and method for urban governance. Background Technology

[0002] Utilizing community micro-brains for digital analysis is an emerging direction for achieving refined urban governance and building smart communities. This approach, through data-driven decision-making, shows broad application prospects in optimizing the allocation of public resources and improving the efficiency of community service response.

[0003] In existing technologies, the management and analysis of urban public facilities mainly rely on external operational data collected by IoT sensors or on processing repair feedback from citizens. Additionally, it also includes combining regular on-site manual inspections and historical maintenance records to develop facility maintenance plans.

[0004] However, most of these methods focus on the external appearance or usage status of facilities, lacking effective means for continuous and in-depth detection of the internal structural health of facilities. Consequently, it is difficult to detect potential risks caused by material aging or internal damage in the early stages. Therefore, existing technologies suffer from insufficient depth in diagnosing the health status of facilities. Summary of the Invention

[0005] The purpose of this application is to provide a community micro-brain digital analysis system and method for urban governance, in order to solve the problems of resource allocation disconnect and lack of refined adaptation in existing technologies.

[0006] To address the aforementioned technical problems, in a first aspect, this application provides a community micro-brain digital analysis method for urban governance, comprising:

[0007] Collect multi-source heterogeneous data on public facilities in urban communities, construct digital models of facilities in the community micro-brain system, and continuously record health baseline data of facilities through the digital models of facilities.

[0008] By deploying a digital sensor network in key parts of the facility, the internal structure of the facility is digitally detected according to a preset scanning cycle, and waveform sequences reflecting changes in the internal state of the facility are obtained.

[0009] The waveform sequence is compared and analyzed with the health benchmark data. By identifying the change pattern of waveform shape over time, the state change trajectory of the facility structure is established. Based on the waveform attenuation degree and morphological distortion characteristics in the state change trajectory, a quantitative health index of the facility is generated.

[0010] The community micro-brain system receives the health quantitative indicators and performs multi-dimensional correlation analysis between the health quantitative indicators and the multi-source heterogeneous data of public facilities to generate urban governance strategies that include maintenance priority ranking and service resource allocation suggestions.

[0011] The city governance strategy is transmitted to the community management platform via network services to generate a scheme for allocating facility maintenance and service resources, and to display the full life-cycle status of the facilities on the facility digital model.

[0012] Optionally, before collecting multi-source heterogeneous data on community public facilities, the method further includes:

[0013] By deploying server clusters and edge computing nodes, a hardware infrastructure for cloud-edge-device collaboration is built.

[0014] Install core software components on the hardware infrastructure, including a data access gateway, a model management engine, and a service bus.

[0015] Based on the core software components, configure the data channel between the data access gateway and the service bus, and establish the interface call relationship between the service bus and the model management engine to build the community microbrain system.

[0016] Optionally, the waveform sequence is compared and analyzed with the health benchmark data. By identifying the changing patterns of the waveform morphology over time, a state change trajectory of the facility structure is established. Based on the waveform attenuation degree and morphological distortion characteristics in the state change trajectory, quantitative health indicators of the facility are generated, including:

[0017] The reference waveform of the facility in a healthy state is obtained from the health baseline data. The waveform sequence obtained in the current detection cycle is aligned with the reference waveform, and the amplitude difference and phase difference of the waveform sequence relative to the reference waveform at multiple feature points are calculated.

[0018] Based on the amplitude difference and the phase difference, the morphological evolution trend of the waveform sequence in multiple consecutive detection cycles is analyzed to establish a state change trajectory describing the process of internal structural state change of the facility.

[0019] The attenuation degree of waveform energy and the distortion degree of waveform shape are quantized from the state change trajectory to obtain attenuation quantization value and distortion quantization value;

[0020] Based on the importance level of the facility in the community and predefined community governance rules, corresponding influence factors are assigned to the attenuation quantification value and the distortion quantification value through an indicator generation function, and a comprehensive health quantification indicator is output through weighted calculation.

[0021] Optionally, based on the amplitude difference and the phase difference, the morphological evolution trend of the waveform sequence over multiple consecutive detection cycles is analyzed to establish a state change trajectory describing the process of structural state change within the facility, including:

[0022] Based on the amplitude difference, the gradient of waveform amplitude change in each detection cycle is calculated as an attenuation rate index; based on the phase difference, the variance of waveform phase offset in each detection cycle is calculated as an distortion index.

[0023] Over multiple consecutive detection cycles, the attenuation rate index and the distortion index are arranged in chronological order to form a time series. The moving average and slope of the index values ​​within each time window are calculated using window sliding processing.

[0024] Based on the moving average and the slope of change, a trend line is fitted to show how the decay rate and distortion change over time.

[0025] The trend line is discretized into a series of state points corresponding to timestamps. Each state point contains a decay value and a distortion value. The state points are connected to form a state change trajectory.

[0026] Optionally, a digital sensor network deployed in key parts of the facility is used to digitally detect the internal structure of the facility according to a preset scanning cycle, acquiring waveform sequences reflecting changes in the internal state of the facility, including:

[0027] Multiple digital sensor nodes are deployed in key structural parts of the facility to form a distributed digital sensor network. The digital sensor nodes emit detection signals of a specific form into the facility according to a preset scanning cycle.

[0028] The digital sensor network receives the waveform signal returned by the detection signal, converts the waveform signal into a raw digital sequence containing time and frequency information, and extracts effective waveform features from the raw digital sequence.

[0029] Based on the effective waveform characteristics and combined with the physical attribute parameters of the facility, the transmission intensity of the detection signal and the receiving sensitivity parameters of the digital sensor network are dynamically adjusted to generate an optimized waveform sequence.

[0030] The optimized waveform sequence is associated with the spatiotemporal information of the probe to form a labeled waveform data packet. The waveform data packet is then parsed by the community microbrain system to obtain a waveform sequence reflecting the changes in the internal state of the facility.

[0031] Optionally, based on the effective waveform characteristics and in conjunction with the physical attribute parameters of the facility, the transmission intensity of the detection signal and the receiving sensitivity parameters of the digital sensor network are dynamically adjusted to generate an optimized waveform sequence, including:

[0032] The waveform energy distribution and signal-to-noise ratio parameters in the effective waveform features are matched with the physical attribute parameters obtained from the facility attribute database, the physical attribute parameters including material acoustic impedance and structural geometry;

[0033] Based on the matching results, the transmission intensity adjustment coefficient of the detection signal and the receiving sensitivity adjustment coefficient of the digital sensor network are calculated.

[0034] Based on the transmission intensity adjustment coefficient and the receiving sensitivity adjustment coefficient, the signal transmission power and receiving amplification factor of the digital sensor network are adjusted in the next detection cycle;

[0035] The modified digital sensor network is used to perform internal detection within the facility, and the resulting reflected signals are converted into digital sequences to generate optimized waveform sequences.

[0036] Optionally, the community micro-brain system receives the health quantification indicators, performs multi-dimensional correlation analysis between the health quantification indicators and the multi-source heterogeneous data of public facilities, and generates an urban governance strategy that includes maintenance priority ranking and service resource allocation suggestions, including:

[0037] The health quantification indicators are received through the data interface of the community microbrain system, and the multi-source heterogeneous data related to public facilities are retrieved from the database of the community microbrain system.

[0038] In the community micro-brain system, the health quantification indicators are compared with the multi-source heterogeneous data in terms of time, space and function to identify the correlation patterns between facility status and multi-dimensional data.

[0039] Based on the association pattern, a comprehensive status score is calculated for each facility, and a maintenance priority ranking of the facilities is generated based on the comprehensive status score.

[0040] Based on the maintenance priority ranking and the information on available service resources in the community, service resource allocation suggestions are generated for the facilities that are ranked higher.

[0041] The maintenance priority ranking and the service resource allocation suggestions are integrated to generate an urban governance strategy.

[0042] Optionally, multi-source heterogeneous data on urban community public facilities are collected, and a digital model of the facilities is constructed in the community micro-brain system. The health baseline data of the facilities is continuously recorded through the digital model, including:

[0043] Acquire facility status and management information from multiple information collection points distributed across community public facilities to form multi-source heterogeneous data;

[0044] In the community microbrain system, the multi-source heterogeneous data is integrated and processed, and by extracting the spatial attributes, functional attributes and correlation information of the facilities, a digital model of the facilities that can represent the geometric shape and dynamic behavior of the facilities is constructed.

[0045] In the facility digital model, a benchmark reference value reflecting the ideal operating state of the facility is set;

[0046] Based on the classification characteristics of the facilities and the needs of community management, the digital model of the facilities is configured with recording rules for updating health baseline data. The recording rules define the triggering conditions, update frequency and numerical adjustment rules for data recording.

[0047] According to the recording rules, the facility's status parameters are continuously collected through the facility's digital model to form a time-stamped health baseline data sequence;

[0048] The baseline reference values ​​and the continuously updated sequence of health baseline data are associated and stored with the corresponding facility digital model to form the facility's health baseline data.

[0049] Optionally, the urban governance strategy is transmitted to the community management platform via network services to generate a resource allocation plan for facility maintenance and services, and to display the full life-cycle status of the facilities on the facility digital model, including:

[0050] The urban governance strategy is transmitted from the community micro-brain system to the community management platform using network communication protocols.

[0051] In the community management platform, the maintenance priority ranking and service resource allocation suggestions of the urban governance strategy are analyzed to generate specific facility maintenance task instructions and corresponding service resource allocation schemes;

[0052] According to the facility maintenance task instruction and the service resource allocation scheme, update the status parameters and resource binding relationships corresponding to the target facility in the community microbrain system;

[0053] The interactive interface of the facility's digital model integrates and displays the evolution trajectory of its health status from initial deployment to the current moment, as well as records of all maintenance events.

[0054] The visualization engine of the community microbrain system dynamically renders and updates the display status of the facility's digital model, making the facility's status continuously visible throughout its entire lifecycle.

[0055] Secondly, this application provides a community micro-brain digital analysis system for urban governance, comprising:

[0056] The data acquisition module is used to collect multi-source heterogeneous data of public facilities in urban communities and build a digital model of the facilities in the community micro-brain system. The digital model of the facilities is used to continuously record the health baseline data of the facilities.

[0057] The detection module is used to digitally detect the internal structure of the facility through a digital sensor network deployed in key parts of the facility according to a preset scanning cycle, and to obtain waveform sequences that reflect changes in the internal state of the facility.

[0058] The identification module is used to compare and analyze the waveform sequence with the health benchmark data, establish the state change trajectory of the facility structure by identifying the change pattern of the waveform shape over time, and generate quantitative health indicators of the facility based on the waveform attenuation degree and morphological distortion characteristics in the state change trajectory.

[0059] The analysis module is used to receive the health quantitative indicators through the community micro-brain system, perform multi-dimensional correlation analysis between the health quantitative indicators and the multi-source heterogeneous data of public facilities, and generate urban governance strategies that include maintenance priority ranking and service resource allocation suggestions.

[0060] The generation module is used to transmit the urban governance strategy to the community management platform through network services to generate a facility maintenance and service resource allocation plan and display the facility's full life cycle status on the facility digital model.

[0061] Thirdly, this application provides an electronic device, comprising:

[0062] Memory, used to store computer programs;

[0063] A processor, configured to execute the computer program to implement the steps of the community microbrain digital analysis method for urban governance as described in the first aspect above.

[0064] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the community micro-brain digital analysis method for urban governance as described in the first aspect above.

[0065] The community micro-brain digital analysis method for urban governance provided in this application lays the foundation for digital facility management and provides a status comparison benchmark by collecting multi-source heterogeneous data of community public facilities, constructing digital models of facilities, and recording health benchmark data. It captures changes in the internal state of facilities by detecting the internal structure and acquiring waveform sequences through a digital sensor network at preset cycles. By comparing waveform sequences with health benchmark data, identifying patterns of change, and generating quantitative health indicators, it accurately quantifies the health status of facilities. Through multi-dimensional correlation analysis of quantitative health indicators and multi-source heterogeneous data, it generates urban governance strategies, providing a basis for scientific maintenance and resource allocation. By transmitting governance strategies to the community management platform, generating allocation plans, and displaying the full lifecycle status, it achieves visualization and full-process optimization of facility management.

[0066] Furthermore, by acquiring status and management information from multiple data collection points of community public facilities to form multi-source heterogeneous data, and after integration and processing, relevant facility attributes and associated information are extracted to construct a digital model of the facility and set ideal operating benchmark reference values. Health benchmark data update and recording rules are configured, and status parameters are continuously collected according to the rules to form a timestamped benchmark data sequence, which is then stored in association with the digital model to form health benchmark data. This enables the standardized integration of multi-source data, constructing an accurate and dynamically updated facility health benchmark system, providing comprehensive and reliable data support for subsequent status analysis and assessment. Attached Figure Description

[0067] To more clearly illustrate the technical solutions of the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0068] Figure 1 A flowchart illustrating a community micro-brain digital analysis method for urban governance provided in this application embodiment;

[0069] Figure 2 A flowchart illustrating the specific implementation of a community micro-brain digital analysis method for urban governance, provided in this application embodiment;

[0070] Figure 3 This is a schematic diagram of the structure of a community micro-brain digital analysis system for urban governance, provided as an embodiment of this application. Detailed Implementation

[0071] Current technologies for monitoring public facilities mostly focus on their external operational status or rely on post-event feedback. This approach is akin to a doctor judging a patient's health solely by observing their appearance, failing to detect early-stage lesions in their internal organs. Due to the lack of effective methods for continuous and quantitative assessment of the structural health of facilities, managers often only intervene after obvious malfunctions or external damage occur. This not only increases maintenance costs but also introduces potential safety hazards, failing to achieve true predictive maintenance.

[0072] To address the aforementioned technical problems, this application provides a community micro-brain digital analysis method for urban governance. This method actively detects and acquires waveform sequences reflecting the internal structural state of facilities through a digital sensor network deployed within the facilities, much like performing periodic "CT scans" on the facilities. Furthermore, by analyzing the changing patterns of the waveform sequences, the internal health status of the facilities is quantitatively assessed, and a trajectory of its health status over time is established. Finally, the community micro-brain system combines external usage data and internal health indicators for comprehensive analysis, generating scientific maintenance strategies that balance urgency and importance. This achieves a shift from "passive response maintenance" to "proactive predictive maintenance," effectively solving the problem of insufficient depth in diagnosing facility health status in existing technologies.

[0073] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0074] The core of this application is to provide a community micro-brain digital analysis method for urban governance, and a flowchart of one specific implementation is shown below. Figure 1 As shown, the method includes:

[0075] S101. Collect multi-source heterogeneous data of public facilities in urban communities, and construct a digital model of the facilities in the community micro-brain system. Continuously record the health baseline data of the facilities through the digital model.

[0076] Multi-source heterogeneous data refers to a collection of data from multiple information collection points in community public facilities, with different types and structures, including facility status information and management information. A facility digital model is a digital model that can represent the geometric shape and dynamic behavior of a facility, constructed by integrating multi-source heterogeneous data and extracting the facility's spatial attributes, functional attributes, and related information. Health benchmark data serves as a reference reflecting the ideal operating state of the facility, including benchmark reference values ​​and a time-stamped sequence of health benchmark data.

[0077] Optionally, before collecting multi-source heterogeneous data from community public facilities, the method further includes: constructing a cloud-edge-device collaborative hardware infrastructure by deploying server clusters and edge computing nodes; installing core software components on the hardware infrastructure; configuring a data channel between the data access gateway and the service bus based on the core software components, and establishing an interface call relationship between the service bus and the model management engine to establish a community micro-brain system.

[0078] In the above steps, the core software components are the key software modules supporting the operation of the community micro-brain system. These include the data access gateway, the model management engine, and the service bus. The data access gateway receives and parses data transmitted from data acquisition devices and is responsible for data access and format standardization. The model management engine manages the creation, updating, storage, and retrieval of digital models of the facilities. The service bus is the central software component responsible for data transmission and interface calls between the core software components, and for component communication and collaboration. The data channel is the data flow path connecting the data access gateway and the service bus, standardizing the data transmission format and process. The interface call relationship is a predefined association rule between the service bus and the model management engine, used for function calls and data interaction.

[0079] Specifically, the process of establishing a community microbrain system begins with the deployment of hardware infrastructure. This step includes: deploying a server cluster consisting of multiple servers to provide centralized high-performance computing and storage capabilities, based on the scale and real-time requirements of data processing; simultaneously, deploying edge computing nodes near the data acquisition source to perform low-latency local computing. The server cluster and the edge computing nodes are then interconnected via a network to construct a cloud-edge collaborative hardware architecture.

[0080] Secondly, the core software components are installed and configured on the hardware infrastructure. This step includes deploying core software components such as the data access gateway, model management engine, and service bus on the server cluster or edge computing nodes, and completing the initialization settings of each component to ensure stable operation on the hardware infrastructure.

[0081] Finally, integration and collaboration among the various software components are achieved. This step specifically includes: First, establishing a data channel from the data access gateway to the service bus by configuring data transmission protocols and paths to ensure standardized inflow of front-end collected data. Second, establishing interface call relationships between the service bus and the model management engine by defining application programming interfaces (APIs) and calling rules to achieve cross-component functional collaboration and data interaction.

[0082] Through the three steps of hardware deployment, software configuration, and component integration, a community microbrain system with data acquisition, model management, and intelligent analysis capabilities is successfully built.

[0083] After completing the construction of the community microbrain system described above, the specific process of step S101 can be executed. Optionally, step S101 may specifically include the following steps:

[0084] S1011. Obtain facility status information and management information from multiple information collection points distributed on community public facilities to form multi-source heterogeneous data.

[0085] Among them, information collection points are specific locations or devices deployed on community public facilities to collect relevant data; facility status information is various types of data reflecting the current operation of the facilities; and management information is management data related to facility operation and maintenance, registration, etc.

[0086] S1012. In the community microbrain system, the multi-source heterogeneous data is integrated and processed, and by extracting the spatial attributes, functional attributes and related information of the facilities, a digital model of the facilities that can represent the geometric shape and dynamic behavior of the facilities is constructed.

[0087] Among them, spatial attributes are geometric feature information such as the location and size of the facility in space, functional attributes are information related to the use functions of the facility, and association information is data on the relationship between the facility and other facilities or systems.

[0088] Specifically, in step S1012, multi-source heterogeneous data is integrated and processed in the community micro-brain system. Data cleaning and format standardization algorithms are used to remove invalid data and unify the data format. Then, feature extraction technology is used to extract the spatial attributes, functional attributes and related information of the facilities, and finally a digital model of the facilities that can represent the geometric shape and dynamic behavior of the facilities is constructed.

[0089] S1013. In the facility digital model, a benchmark reference value reflecting the ideal operating state of the facility is set.

[0090] The benchmark reference value is a pre-set standard value that reflects the ideal operating state of the facility.

[0091] Secondly, in step S1013, benchmark reference values ​​reflecting the ideal operating state of the facility are set in the facility digital model. For example, in conjunction with the design standards and industry specifications for this type of fitness facility, benchmark reference values ​​are set for the aforementioned fitness facility digital model, such as an ideal operating temperature range of 15-35℃, a maximum allowable wear of components of 0.5 mm, and an operating noise level not exceeding 60 decibels.

[0092] S1014. Based on the classification characteristics of the facilities and the needs of community management, configure recording rules for updating health baseline data for the digital model of the facilities. The recording rules define the triggering conditions, update frequency and numerical adjustment rules for data recording.

[0093] For example, considering that the fitness facility is a high-frequency use facility, and the community management needs to monitor the changes in the facility's health status every month, the recording rules are configured as follows: data recording is triggered when the cumulative usage time of the facility reaches 100 hours or after an interval of 7 days, the update frequency is set to once a week, and the value adjustment rule is that if the status parameters collected for 3 consecutive times are stable within ±5% of the baseline reference value, the baseline reference value is maintained; if it exceeds the range, the upper and lower limits of the baseline reference value are slightly adjusted by 5%.

[0094] S1015. Based on the recording rules, continuously collect the status parameters of the facility through the facility digital model to form a health baseline data sequence with timestamps.

[0095] The recording rules are used to standardize the settings for updating health baseline data, including the triggering conditions for data recording, the update frequency, and the rules for adjusting values. The health baseline data sequence is a continuous set of timestamped data formed by continuously collecting facility status parameters according to the recording rules.

[0096] S1016. The baseline reference value and the continuously updated health baseline data sequence are associated and stored with the corresponding facility digital model to form the facility's health baseline data.

[0097] Finally, in step S1016, the set facility baseline reference values ​​and the time-stamped facility health baseline data sequence that are updated over time are integrated together. Then, these integrated data are bound and stored with the corresponding facility digital model, ultimately forming health baseline data that can be used as a basis for subsequent facility status comparison.

[0098] S102. By deploying a digital sensor network in key parts of the facility, the internal structure of the facility is digitally detected according to a preset scanning cycle to obtain waveform sequences that reflect changes in the internal state of the facility.

[0099] The digital sensor network is a distributed network consisting of multiple digital sensor nodes deployed at key structural locations within the facility. It is used to digitally probe the internal structure of the facility according to a preset scanning cycle. The preset scanning cycle is a pre-defined time interval for the sensor network to perform probe operations. Digital probe operation is a technical means of acquiring information about the internal structure of the facility by transmitting probe signals and receiving return signals. The waveform sequence is a signal sequence reflecting changes in the internal state of the facility. It is obtained by converting and processing the waveform signals returned from the probe signals and contains time and frequency-related information.

[0100] Optionally, step S102 may specifically include the following steps:

[0101] S1021. Deploy multiple digital sensor nodes in key structural parts of the facility to form a distributed digital sensing network. The digital sensor nodes transmit detection signals of a specific form into the facility according to a preset scanning cycle.

[0102] Digital sensor nodes are terminal devices capable of signal transmission and reception, deployed at critical structural locations within the facility to achieve precise detection. The detection signal is a specific type of signal emitted by the digital sensor node into the facility to acquire internal structural information.

[0103] S1022. Receive the waveform signal returned by the detection signal through the digital sensor network, convert the waveform signal into a raw digital sequence containing time and frequency information, and extract effective waveform features from the raw digital sequence.

[0104] Among them, the waveform signal is the signal returned after the probe signal contacts the internal structure of the facility, and the original digital sequence is a data sequence containing time and frequency information formed after the waveform signal is converted. The effective waveform feature is the key signal feature extracted from the original digital sequence that can reflect the internal state of the facility.

[0105] In one specific implementation, in step S1022, the waveform signal returned by the probe signal is received by the digital sensor network, the waveform signal is converted into an original digital sequence containing time and frequency information by analog-to-digital conversion technology, and then effective waveform features are extracted from the original digital sequence by signal filtering and feature extraction algorithms.

[0106] For example, for monitoring the internal structure of community fitness facilities, an ultrasonic sensor in a digital sensor network first transmits a 100kHz detection signal into the inside of the single bar's load-bearing bar. After the signal contacts the welds and rusted areas inside the bar, it reflects to form a waveform signal. The sensor's signal receiving module then transmits this analog waveform signal to the edge computing node.

[0107] Moving to the analog-to-digital conversion stage, the edge computing nodes employ a 12-bit resolution analog-to-digital converter (ADC) to process the analog signal at a sampling frequency of 500kHz. The core formula is the sampling interval calculation formula:

[0108]

[0109] in, Sampling interval (unit: seconds) Sampling frequency (unit: Hertz). Substitute , can be obtained Second A microsecond means that the voltage value of the analog signal is sampled every 2 microseconds. Then, through a quantization process, the voltage value at each sampling moment is converted into a 12-bit binary number, completing the conversion from analog to digital, and finally generating the original digital sequence, as shown in Table 1.

[0110] Table 1

[0111] Sampling time (microseconds) Voltage value (volts) 12-bit binary number 0 2.3 011101011000 2 2.5 011111010000 4 1.8 011000011000 6 2.1 011010101000

[0112] After analog-to-digital conversion, a low-pass filtering algorithm is used to remove interference signals. Finally, feature extraction is performed. First, a sliding window method is used, with the window size set to 5 sampling points. The filtered data is traversed, and the maximum voltage value within each window is calculated to locate the waveform peak.

[0113] With a certain segment of filtered data ( Taking this example, the maximum value within the window is 2.5V, corresponding to a sampling time of 2 microseconds, which is the first peak. Subsequent windows are calculated sequentially, ultimately identifying three core peaks: 3.1V at 10 microseconds, 2.2V at 30 microseconds, and 1.9V at 50 microseconds. The time-domain data is then converted to frequency-domain data using a Fast Fourier Transform (FFT). The calculated frequency distribution of the waveform is concentrated in the 95-105kHz range, ultimately extracting the effective waveform features of "three characteristic peaks + the 95-105kHz frequency range." The above example is merely one illustration of this application. In practical applications, parameters such as sampling frequency, filter coefficients, and window size can be adjusted according to requirements; this application does not impose any limitations on these adjustments.

[0114] S1023. Based on the effective waveform characteristics and combined with the physical attribute parameters of the facility, dynamically adjust the transmission intensity of the detection signal and the receiving sensitivity parameters of the digital sensor network to generate an optimized waveform sequence.

[0115] Specifically, step S1023 may include the following processes: matching the waveform energy distribution and signal-to-noise ratio parameters in the effective waveform features with the physical attribute parameters obtained from the facility attribute database, wherein the physical attribute parameters include material acoustic impedance and structural geometry; based on the matching results, using a parameter optimization algorithm to calculate the transmission intensity adjustment coefficient of the detection signal and the receiving sensitivity adjustment coefficient of the digital sensor network; adjusting the signal transmission power and receiving amplification factor of the digital sensor network in the next detection cycle according to the transmission intensity adjustment coefficient and the receiving sensitivity adjustment coefficient; using the adjusted digital sensor network to perform facility internal detection, converting the obtained reflected signal into a digital sequence to generate an optimized waveform sequence.

[0116] Among them, physical property parameters are the physical characteristics of the facility itself, including material acoustic impedance and structural geometry. The emission intensity adjustment coefficient is a parameter used to adjust the emission intensity of the detection signal, and the receiver sensitivity adjustment coefficient is a parameter used to adjust the receiver sensitivity of the sensor network. The optimized waveform sequence is a more accurate waveform sequence obtained after re-detection following parameter adjustments.

[0117] As an example, taking the monitoring of the internal structure of a horizontal bar in a community fitness facility as an example, the first step is to determine the waveform energy distribution and signal-to-noise ratio parameters from the previously extracted effective waveform features. Specifically, this process involves: performing a Fast Fourier Transform on the effective waveform features to obtain their spectrum; analyzing the spectrum to determine that the main signal energy distribution is in the 10-20kHz range; and simultaneously calculating the useful signal power within the 10-20kHz frequency band. With the noise signal power within a preset noise frequency band And through the formula: The current signal-to-noise ratio (SNR) parameter is calculated. For example, by integrating the spectrum, the useful signal power in the 10-20kHz frequency band is calculated to be 3.16 units of power, and the noise signal power in the preset noise band is calculated to be 0.01 units of power. Substituting these values ​​into the formula, the SNR parameter is obtained as 25dB.

[0118] Subsequently, the waveform energy distribution and signal-to-noise ratio parameters are matched with the physical attribute parameters obtained from the facility attribute database. Specifically, the waveform energy distribution (10-20kHz) is matched with the material acoustic impedance (Q235 steel, 3.5×10^6 kg / (m²·s)), and the built-in acoustic model is used to determine whether the theoretical attenuation rate of the current energy distribution in the material exceeds a preset threshold. Simultaneously, the signal-to-noise ratio (25dB) is matched with the structural geometry (1.2m length of the load-bearing rod), and the theoretical signal-to-noise ratio margin after 1.2 meters of propagation is estimated to be lower than a safety threshold. If any matching result is negative, the parameter fit is deemed insufficient.

[0119] Next, based on the matching results (e.g., matching results with insufficient adaptability), a parameter optimization algorithm is used to calculate the transmission intensity adjustment coefficient of the detection signal and the receiving sensitivity adjustment coefficient of the digital sensor network. This algorithm (e.g., multivariate linear regression) calculates preliminary adjustment coefficients based on the difference between the current parameters and preset reference values, and then corrects them in conjunction with the overall detection target, ultimately determining the transmission intensity adjustment coefficient to be 1.1 and the receiving sensitivity adjustment coefficient to be 1.05.

[0120] The parameter optimization employs a multivariate linear regression algorithm, with the core formula being:

[0121]

[0122] in, This is the emission intensity adjustment coefficient. This is the adjustment factor for receiver sensitivity; , This is a preset reference value; This is an empirical coefficient.

[0123] Substitute the current parameter value into the calculation:

[0124]

[0125]

[0126] The calculation results are corrected based on the detection requirements, for example, when When the transmit strength is 0.86, since the preset threshold (e.g., the preset threshold is 1) is not met, the system still follows the "minimum gain strategy" and forcibly corrects the transmit strength adjustment coefficient to 1.1 to ensure that the transmit power is effectively enhanced. Similarly, the receive sensitivity adjustment coefficient is set to 1.05 according to the preset rules. Based on this, the transmit power of the signal in the next cycle is increased by 10%, the receive amplification factor is increased by 5%, and the energy ratio and signal-to-noise ratio of the optimized waveform sequence generated after re-detection are significantly improved, which can more clearly reflect the internal structural state of the single bar. The above example is only one example of this application. In practical applications, the parameter reference values ​​and algorithm coefficients can be adjusted according to the facility type, and this application does not limit this.

[0127] S1024. The optimized waveform sequence is associated with the spatiotemporal information of the detection to form a labeled waveform data packet. The waveform data packet is then analyzed by the community microbrain system to obtain a waveform sequence reflecting the changes in the internal state of the facility.

[0128] Among them, the detection spatiotemporal information is the time and spatial location information of the detection operation, the labeled waveform data packet is a set of waveform data associated with the detection spatiotemporal information, and the community microbrain system is used to parse the data packet and obtain the final waveform sequence.

[0129] Finally, in step S1024, the optimized waveform sequence is associated with the detection spatiotemporal information, which includes the specific time of this detection and the deployment location of the sensor nodes, forming a labeled waveform data packet. This data packet is then transmitted to the community microbrain system, where the system extracts the effective information from the data packet using a data parsing algorithm to obtain a waveform sequence that reflects the changes in the internal state of the facility.

[0130] The overall solution in step S102 above achieves breakthroughs in multiple dimensions compared to traditional monitoring methods: First, it solves the problem of incomplete coverage by traditional single sensors, enabling comprehensive monitoring of the facility's internal structure and ensuring no omissions in status capture; second, it overcomes the shortcomings of traditional data being susceptible to interference and lacking accuracy, with the output waveform sequence more accurately reflecting the facility's true state; third, it compensates for the shortcomings of traditional data sources being vague and difficult to trace, assigning traceable tags to the data through spatiotemporal correlation to ensure data credibility. Ultimately, it enables accurate and comprehensive perception of changes in the facility's internal state.

[0131] S103. The waveform sequence is compared and analyzed with the health benchmark data. By identifying the change pattern of waveform shape over time, the state change trajectory of the facility structure is established. Based on the waveform attenuation degree and morphological distortion characteristics in the state change trajectory, a quantitative health index of the facility is generated.

[0132] The comparative analysis involves comparing the waveform sequence with health baseline data to identify differences and determine the facility's condition. Waveform morphology variation refers to the evolution pattern of amplitude, phase, and other characteristics of the waveform over a continuous detection period. The state change trajectory describes the trajectory of the facility's internal structural state over time, formed by connecting multiple state points corresponding to timestamps. Waveform attenuation is the degree to which waveform energy weakens over time; morphological distortion characteristics are the features of the waveform shape deviating from the healthy state reference waveform; and health quantification indicators are quantitative values ​​that comprehensively reflect the facility's health status, used for intuitive assessment of the facility's health level.

[0133] Optionally, such as Figure 2 As shown, step S103 may specifically include the following steps:

[0134] S1031. Obtain a reference waveform of the facility in a healthy state from the health baseline data, align the waveform sequence obtained in the current detection cycle with the reference waveform, and calculate the amplitude difference and phase difference of the waveform sequence relative to the reference waveform at multiple feature points.

[0135] The reference waveform is a standard waveform under healthy facility conditions, obtained from health baseline data for comparison with the current waveform sequence. Alignment is the process of ensuring the current waveform sequence is consistent with the reference waveform along the time axis or feature dimension. Feature points are representative key points on the waveform; amplitude difference is the difference in amplitude values ​​between the current waveform sequence and the reference waveform at these feature points; and phase difference is the difference in phase values ​​between the two at these feature points.

[0136] In this embodiment of the application, firstly, a reference waveform of the facility in a healthy state is obtained from the health baseline data through step S1031. Then, a waveform alignment algorithm is used to align the waveform sequence obtained in the current detection cycle with the reference waveform to ensure that the two are consistent at key feature positions. Then, a feature point matching algorithm is used to select multiple feature points such as the peak point and zero point of the waveform, and the amplitude difference and phase difference of the current waveform sequence relative to the reference waveform at these feature points are calculated.

[0137] For example, for parallel bars in community fitness facilities, the ultrasonic reference waveform under healthy conditions is first retrieved from their health baseline data. This waveform is a standard waveform calibrated during the initial installation of the parallel bars, with a peak value stable at around 2.5V, uniform zero-point distribution, and no phase shift. After acquiring the actual waveform sequence of the parallel bars in the current detection cycle, a dynamic time warping algorithm is used for alignment. This algorithm calculates the distance matrix between each sampling point of the reference waveform and the actual waveform, finding the matching path with the minimum cumulative distance. This solves the time axis shift problem caused by slight deformation of the internal structure of the parallel bars after use, ensuring accurate correspondence of key feature points between the two types of waveforms.

[0138] After waveform alignment, an adaptive feature point detection algorithm is used to select feature points. First, the aligned waveform is decomposed into multiple scales using wavelet transform to filter high-frequency noise interference. Then, feature points are selected based on the curvature changes of the waveform. When the curvature exceeds a preset threshold of 0.8, it is determined to be a feature point. Finally, 6 peak points and 4 zero points are automatically identified. The peak points are located at sampling times of 10ms, 30ms, 50ms, 70ms, 90ms, and 110ms, respectively, and the zero points are located at sampling times of 20ms, 40ms, 60ms, and 80ms, respectively.

[0139] The amplitude difference and phase difference are then calculated: the amplitude difference is obtained by subtracting the voltage value of the corresponding feature point of the reference waveform from the voltage value of the current waveform feature point. For example, at the peak point at 10ms, the current voltage is 2.2V, the reference voltage is 2.5V, and the amplitude difference is -0.3V; at the peak point at 30ms, the current voltage is 2.25V, the reference voltage is 2.5V, and the amplitude difference is -0.25V. The phase difference is calculated after converting the time-domain waveform to the frequency domain using Fourier transform. For example, at the peak point at 10ms, the frequency is 15kHz, the current phase is 102°, the reference phase is 92°, and the phase difference is 10°; at the peak point at 30ms, the frequency is 15kHz, the current phase is 114°, the reference phase is 102°, and the phase difference is 12°. The above example is only one example of this application. In practical applications, different alignment algorithms and feature point detection logic can be selected according to the facility type, and this application does not limit this.

[0140] S1032. Based on the amplitude difference and the phase difference, analyze the morphological evolution trend of the waveform sequence in multiple consecutive detection cycles to establish a state change trajectory describing the internal structural state change process of the facility.

[0141] Specifically, step S1032 may include the following processes: based on the amplitude difference, calculate the gradient of waveform amplitude change in each detection cycle as an attenuation rate index; based on the phase difference, calculate the offset variance of waveform phase in each detection cycle as a distortion degree index; over multiple consecutive detection cycles, arrange the attenuation rate index and the distortion degree index in chronological order into a time series, and calculate the moving average and slope of the index values ​​within each time window through window sliding processing; based on the moving average and slope, fit a trend line of attenuation rate and distortion degree changing with time; discretize the trend line into a series of state points corresponding to timestamps, each state point containing attenuation value and distortion value, and connect the state points to form a state change trajectory.

[0142] In the above steps, the attenuation rate index is a parameter calculated based on the amplitude difference, reflecting the rate of change of waveform amplitude in each detection cycle. The distortion index is a parameter calculated based on the phase difference, reflecting the degree of dispersion of waveform phase shift in each detection cycle. The time series is a data sequence formed by arranging the attenuation rate index and the distortion index in chronological order of detection time. The sliding window processing is a method of selecting a fixed-length time window and sequentially traversing the time series to calculate the statistical values ​​of the data within the window. The moving average is the average value of the index within the time window, the slope of change is the degree of inclination of the moving average as it changes over time, and the trend line is a curve reflecting the trend of the index changing over time, obtained by fitting the moving average and the slope of change. The state point is a data point obtained after discretizing the trend line, containing the attenuation value and distortion value corresponding to a specific timestamp.

[0143] In one specific implementation, based on the calculated amplitude difference and phase difference, the gradient of waveform amplitude change in each detection cycle is calculated as an attenuation rate index, and the variance of waveform phase offset in each detection cycle is calculated as an index of distortion degree.

[0144] Next, these indicators are arranged in the order of detection time to form a time series. The time window length is set to 3 detection periods. The time series is traversed sequentially by a window sliding algorithm to calculate the moving average and slope of the decay rate and distortion index within each window.

[0145] Then, based on the moving average and the slope of change, a trend line is fitted to show how the decay rate and distortion change over time. The trend line is discretized into a series of state points corresponding to a series of timestamps according to the detection period. Each state point contains the decay value and distortion value at that timestamp. Finally, these state points are connected to form a state change trajectory describing the process of state change of the internal structure of the facility.

[0146] For example, combining the aforementioned amplitude difference and phase difference data of the parallel bars:

[0147] In the first detection cycle, the attenuation rate index was calculated to be 0.05V / cycle based on the amplitude difference of peak points such as 10ms and 30ms, and the distortion degree index was calculated to be 2.1 square degrees based on the phase difference of each feature point.

[0148] In the second detection cycle, the attenuation rate was 0.07V / cycle and the distortion was 2.3 square degrees; in the third detection cycle, the attenuation rate was 0.06V / cycle and the distortion was 2.2 square degrees.

[0149] After forming a time series of these indicators in cyclical order, three periods are set as time windows. The moving average of the decay rate indicator in the first window is calculated as (0.05+0.07+0.06) / 3=0.06V / cycle, and the slope of change is (0.07-0.05) / (2-1)=0.01V / cycle². The moving average of the distortion degree indicator is (2.1+2.3+2.2) / 3=2.2 square degrees, and the slope of change is (2.3-2.1) / (2-1)=0.1 square degrees / cycle.

[0150] After obtaining the trend line of attenuation rate and distortion degree through linear fitting, the trend line is discretized into state points for each detection cycle (e.g., the state point of the first cycle: attenuation 0.05V, distortion 2.1 square degrees). Finally, all state points are connected to form the state change trajectory of the internal structure of the double bar. The above example is only one example of this application, and it can be set according to requirements in practical applications.

[0151] S1033. Quantize the attenuation degree of waveform energy and the distortion degree of waveform shape from the state change trajectory to obtain attenuation quantization value and distortion quantization value.

[0152] In one specific implementation, taking the state monitoring of a community double bar as an example, firstly, the attenuation quantization value is calculated using an energy integration algorithm based on the double bar state change trajectory formed in step S1032. The core formula for energy integration is:

[0153]

[0154] in, Waveform energy corresponding to a certain timestamp (unit: ), This is the voltage function of the timestamp waveform signal. The time interval of the waveform signal is set here to one detection period, i.e. .

[0155] The waveform data at three key time points in the state change trajectory are selected for calculation. For example, by substituting the waveform data at the three key time points into the energy integral formula above, the waveform energy of the initial period at the first time point is calculated. The middle period of the second timestamp The third timestamp of the current period Through the attenuation quantization formula:

[0156]

[0157] in, This is the attenuation quantization value. For the current cycle energy, Let's assume the initial periodic energy. Substitute it into... , We can obtain: .

[0158] Subsequently, the distortion value is calculated, and the shape similarity is compared using the Dynamic Time Warping Distance (DTW distance). The current period waveform and the initial healthy state waveform of the reference waveform are selected, and their DTW distance is calculated. (Unit: V), the reference waveform's own DTW distance from the reference value. Through the distortion formula:

[0159]

[0160] in, This is a distorted value. Substitute it into the numerical calculation: The higher the value, the greater the difference between the waveform shape and the reference waveform, i.e., the more obvious the distortion. The final attenuation quantization value of the double bar is 0.6, and the distortion quantization value is 1.4, clearly reflecting the continuous attenuation of waveform energy and gradual shape distortion of the internal structure of the double bar as the usage period increases. The above example is only one example of this application. In practical applications, the energy integration interval and similarity calculation method can be adjusted according to the facility type, and this application does not limit this.

[0161] S1034. Based on the importance level of the facility in the community and the predefined community governance rules, assign corresponding influence factors to the attenuation quantification value and the distortion quantification value through the indicator generation function, and output a comprehensive health quantification index through weighted calculation.

[0162] In the above steps, the attenuation quantization value is the specific numerical value of waveform energy attenuation quantified from the state change trajectory, and the distortion quantization value is the specific numerical value of waveform shape distortion quantified from the waveform. The importance level is a classification based on the function and role of the facility in the community, the community governance rules are pre-established rules to guide facility management, and the indicator generation function is a function used to assign influence factors to the attenuation quantization value and the distortion quantization value. The influence factor is a weighted parameter that reflects the degree of influence of the attenuation quantization value and the distortion quantization value on the health quantification indicator.

[0163] Finally, in step S1034, based on the importance level of the facility in the community and the predefined community governance rules, the indicator generation function is called to assign corresponding influence factors to the attenuation quantification value and the distortion value. Assuming that the fitness facility is a high-frequency facility that affects the safety of residents, and has a high importance level, and that the community governance rules clearly state that attenuation and distortion have equally important impacts on facility safety, the influence factor for the attenuation quantification value is assigned as 0.5, and the influence factor for the distortion value is assigned as 0.5.

[0164] Then, using the weighted summation formula:

[0165]

[0166] in, This represents a quantitative indicator of health, where A represents a quantitative value of decline. The quantized value represents the influencing factor corresponding to the attenuation, and D represents the distorted value. This represents the influencing factors corresponding to the distorted values, ultimately outputting a comprehensive health quantitative index. For example, substituting the aforementioned fitness facility attenuation quantification value of 0.8 and distorted value of 0.6 into the formula, the calculated health quantitative index is: This visually reflects the current health status of the facility. The above example is just one illustration; in practical applications, it can be configured according to specific needs.

[0167] Compared to traditional facility health assessments, this solution represents a multi-dimensional advancement: it transforms abstract waveform data into intuitive, quantifiable health indicators, shifting facility health from a vague perception to precise quantification; it accurately captures the evolutionary trends of the facility's internal state, providing a basis for hazard prediction and addressing the problem of traditional assessments neglecting patterns of change; and it integrates the facility's importance with governance rules, ensuring assessment results align with actual community management and preventing a disconnect between decision-making and needs. Ultimately, it achieves precise, scenario-based, and forward-looking facility health assessments, providing reliable support for management decisions and driving the upgrade of community facility management from passive maintenance to proactive prediction.

[0168] S104. Receive the health quantification indicators through the community micro-brain system, perform multi-dimensional correlation analysis between the health quantification indicators and the multi-source heterogeneous data of public facilities, and generate urban governance strategies.

[0169] Among them, urban governance strategies include urban governance strategies that prioritize maintenance and provide suggestions for the allocation of service resources.

[0170] Among them, multi-dimensional correlation analysis refers to an analytical method that compares and correlates health quantitative indicators with multi-source heterogeneous data across multiple dimensions to uncover correlation patterns between data. Maintenance priority ranking is the order in which facilities are maintained based on the comprehensive facility status score. Service resource allocation recommendations are resource allocation plans formulated for facilities with higher priority. Urban governance strategies are strategic plans that integrate maintenance priority ranking and service resource allocation recommendations to guide community facility management.

[0171] Optionally, step S104 may specifically include the following steps:

[0172] S1041. Receive the health quantification indicators through the data interface of the community microbrain system, and simultaneously retrieve the multi-source heterogeneous data related to public facilities from the database of the community microbrain system.

[0173] Among them, the data interface is the technical interface used in the community micro-brain system to receive health quantitative indicators, and the database is the storage unit for storing multi-source heterogeneous data related to public facilities.

[0174] Specifically, in step S1041, the community micro-brain system first receives health quantitative indicators of various public facilities such as fitness facilities and lighting facilities within the jurisdiction through a dedicated data interface. At the same time, it retrieves multi-source heterogeneous data from the database, such as the usage frequency, installation location, past maintenance records, and distribution of surrounding residents for these facilities.

[0175] S1042. In the community micro-brain system, the health quantification indicators are compared with the multi-source heterogeneous data in terms of time, space and function to identify the correlation pattern between facility status and multi-dimensional data.

[0176] Among them, time-dimensional association matches and associates health quantitative indicators with multi-source heterogeneous data in chronological order; spatial-dimensional association associates data based on the spatial location information of facilities; and functional-dimensional association associates data based on the functional attributes of facilities.

[0177] Secondly, in step S1042, a multi-dimensional data association algorithm is used in the community microbrain system to compare and correlate health quantitative indicators with multi-source heterogeneous data in the time dimension, spatial dimension and functional dimension.

[0178] The multi-dimensional data association algorithm used here has dimensional hierarchical matching and cross-dimensional fusion analysis as its core logic: First, data matching is completed within a single dimension. The time dimension aligns health quantitative indicators with facility usage data for the same time period through timestamps, such as matching daily health indicators with daily usage duration and frequency. The spatial dimension divides regions based on geographical coordinates and matches facility health data with resident distribution data within the same region, such as matching the health indicators of fitness facilities in the eastern part of a community with the resident population density of the eastern part. The functional dimension classifies facilities by function type and associates the health status and maintenance resource data of facilities with similar functions, such as matching the health indicators of all fitness facilities with the number of dedicated maintenance personnel and tools for fitness facilities. Then, through cross-dimensional fusion analysis, the matching results of the three dimensions are integrated, and combined with pattern recognition algorithms such as association rule mining algorithms, high-frequency association relationships between data are filtered out, ultimately identifying the association patterns between facility status and multi-dimensional data.

[0179] S1043. Calculate a comprehensive status score for each facility based on the association pattern, and generate a maintenance priority ranking for the facilities based on the comprehensive status score.

[0180] Among them, the correlation pattern is the inherent correspondence between facility status and multidimensional data identified through multi-dimensional correlation comparison. The comprehensive status score is a score that comprehensively reflects the facility status, calculated by combining quantitative health indicators and multi-source heterogeneous data. The community's available service resource information is information related to the human, material, and financial resources that the community can currently use for facility maintenance.

[0181] Next, in step S1043, based on the identified association patterns, a comprehensive scoring algorithm is used to assign corresponding weights to influencing factors such as health quantitative indicators, usage frequency, and resident density, calculate the comprehensive status score of each facility, and then generate the maintenance priority ranking of facilities according to the comprehensive status scores from high to low.

[0182] For example, based on the association model, the weights of the health quantification indicators are set to 0.5, usage frequency to 0.3, and resident density to 0.2. A fitness facility's health quantification indicator corresponds to a score of 80, usage frequency to 90, and resident density to 85. The overall status score is then calculated using the comprehensive scoring formula.

[0183]

[0184] in, Represents the overall status score. This represents the score corresponding to a quantitative health indicator. Represents the weight of quantitative health indicators. The score represents the frequency of use. This represents the use of frequency weighting. The score represents the population density. The weight representing resident density is substituted into the numerical values ​​to obtain the result. The fitness facility will be ranked as a top maintenance priority based on its score.

[0185] S1044. Based on the maintenance priority ranking and the information on available service resources in the community, generate service resource allocation suggestions for facilities with higher rankings.

[0186] Then, in step S1044, information on available community service resources is obtained from the community resource management module, including the number of maintenance personnel, types of repair tools, and amount of repair funds. Based on the maintenance priority ranking, a resource allocation algorithm is used to generate service resource allocation suggestions for the facilities ranked higher, specifying the maintenance personnel, tools, funds, and allocation time required for each facility.

[0187] For example, if the community currently has 5 maintenance personnel, 3 sets of fitness equipment repair tools, and 100,000 yuan in maintenance funds, the following service resource allocation suggestions are generated for the top 3 priority fitness facilities: "Allocate 2 maintenance personnel, 1 set of repair tools, and 30,000 yuan to fitness facility No. 1 to complete maintenance within 3 days; allocate 2 maintenance personnel, 1 set of repair tools, and 40,000 yuan to fitness facility No. 2 to complete maintenance within 5 days."

[0188] S1045. Integrate the maintenance priority ranking and the service resource allocation suggestions to generate an urban governance strategy.

[0189] Finally, in step S1045, the generated maintenance priority ranking and service resource allocation suggestions are structurally integrated using a strategy integration algorithm to clarify the order of facility maintenance, resource allocation and time nodes for each maintenance task, thus forming a complete urban governance strategy.

[0190] For example, the maintenance priority ranking table of all public facilities in the jurisdiction is integrated with the corresponding service resource allocation details to form an urban governance strategy that includes "a list of facilities with maintenance priority from 1 to 10, resource allocation plans for each facility, and completion time requirements".

[0191] The overall solution in step S104 above addresses the issues of subjectivity and disconnect in traditional sorting; it breaks through the one-size-fits-all approach to resource allocation, and formulates recommendations based on facility health, usage needs, and actual resources to avoid resource waste or shortages; the resulting governance strategy precisely matches management demands, promotes maintenance from blind response to precise policy implementation, and upgrades resource allocation from extensive distribution to efficient adaptation, effectively improving the level of refined community facility management and urban governance efficiency.

[0192] S105. The urban governance strategy is transmitted to the community management platform via network services to generate a facility maintenance and service resource allocation plan, and the full life cycle status of the facility is displayed on the facility digital model.

[0193] In this step, network services refer to the service form that utilizes network communication technology to achieve data transmission and interaction, used to transmit urban governance strategies from the community micro-brain system to the community management platform. The facility maintenance and service resource allocation plan is a specific plan generated based on the urban governance strategy, clearly defining facility maintenance tasks and resource allocation. The facility's full lifecycle status refers to complete information such as its health status and maintenance records throughout its entire lifecycle, from initial deployment and operation to maintenance and updates, which is visualized through a digital facility model.

[0194] Optionally, step S105 may specifically include the following steps:

[0195] S1051. The urban governance strategy is transmitted from the community micro-brain system to the community management platform using a network communication protocol.

[0196] Among them, network communication protocols are standard protocols that regulate data transmission formats and processes, ensuring the reliable transmission of urban governance strategies between different systems. The community management platform is a management platform used to receive and parse urban governance strategies and generate specific implementation plans.

[0197] Specifically, in step S1051, a secure data transmission channel is established between the community micro-brain system and the community management platform using a standardized network communication protocol. The integrated urban governance strategy is then transmitted in the form of an encrypted data stream to ensure the security and integrity of the data transmission.

[0198] For example, by using the HTTPS protocol to build an encrypted transmission channel, urban governance strategies, including priority ranking of community fitness facilities and lighting facilities maintenance and resource allocation suggestions, can be transmitted from the community micro-brain system to the community management platform, thus preventing data leakage or loss during transmission.

[0199] S1052. In the community management platform, the maintenance priority ranking and service resource allocation suggestions of the urban governance strategy are analyzed to generate specific facility maintenance task instructions and corresponding service resource allocation schemes.

[0200] Among these, maintenance priority ranking refers to the order in which facilities are maintained, as determined by the urban governance strategy; service resource allocation recommendations are resource allocation guidelines developed for priority facilities; facility maintenance task instructions are directives that clearly define the specific maintenance work content, the responsible parties, and the time requirements; and service resource allocation plans are detailed plans for allocating human, material, and financial resources.

[0201] Secondly, in step S1052, the received urban governance strategy is structured and analyzed in the community management platform to extract core information such as maintenance priority ranking and service resource allocation suggestions. Combined with the community management process and task allocation rules, specific facility maintenance task instructions and corresponding service resource allocation plans are generated, clarifying the responsible personnel, work content, required tools and materials, and completion time for each maintenance task.

[0202] For example, the community management platform analyzes and determines that fitness facility A has the highest maintenance priority, requiring the allocation of 2 maintenance personnel, 1 set of repair tools, and corresponding funds. Based on this, it generates a maintenance task instruction and resource allocation plan that "assigns maintenance personnel A and B to complete the replacement and repair of the components of fitness facility A within 3 days, and allocates B-type repair tools and 30,000 yuan of repair funds."

[0203] S1053. According to the facility maintenance task instruction and the service resource allocation scheme, update the status parameters and resource binding relationship corresponding to the target facility in the community microbrain system.

[0204] Among them, status parameters are data reflecting the current operating status of the facility, and resource binding relationships are the corresponding associations between the facility and the allocated service resources.

[0205] Next, in step S1053, the key information in the generated facility maintenance task instruction and service resource allocation plan is synchronized to the community micro-brain system, and the status parameters corresponding to the target facility are updated. For example, the status parameter of fitness facility A is updated to "to be maintained". At the same time, a resource binding relationship is established between the facility and the allocated resources to ensure that the facility status and resource configuration information in the system are consistent.

[0206] S1054. On the interactive interface of the facility digital model, integrate and display the health status evolution trajectory from initial deployment to the current moment and the records of each maintenance event.

[0207] The health status evolution trajectory is the process of the facility's health changes from its initial state to the present moment, while the maintenance event log is a detailed record of all past maintenance work performed on the facility. The interactive interface is a visual interface for users to view and operate the facility's digital model, and the visualization engine is a technical module used to dynamically render and update the displayed status of the digital model.

[0208] Then, through step S1054, data traceability and visualization technology is integrated into the interactive interface of the facility digital model to retrieve the initial deployment data, health status data and maintenance records of the facility stored in the community micro-brain system, sort them out in chronological order to form the health status evolution trajectory, and display the time, content and results of each maintenance event in a structured manner to realize the centralized presentation of the facility's full life cycle information.

[0209] S1055. Through the visualization engine of the community microbrain system, the display status of the facility digital model is dynamically rendered and updated, so that the status of the facility throughout its entire life cycle is continuously visible.

[0210] Finally, in step S1055, the visualization engine of the community microbrain system is used to dynamically update the display status of the facility's digital model using rendering technology. For example, different colors are used to indicate the current health level of the facility, and dynamic icons are used to indicate maintenance tasks, ensuring that the facility's status throughout its entire life cycle is continuously visible and easy to understand.

[0211] The overall solution in step S105 above overcomes the problems of inefficient flow and difficulty in implementation of governance strategies. It relies on reliable transmission to quickly transform strategies into executable maintenance tasks and resource plans; it solves the pain points of scattered and difficult-to-trace facility status information, realizes full life cycle status visualization, and allows managers to intuitively grasp the facility status and track maintenance progress; at the same time, it avoids the problems of disordered maintenance and unreasonable resource allocation, improves the transparency and refinement of community facility management, and provides visualized and traceable support for urban governance.

[0212] The community micro-brain digital analysis method for urban governance provided in this application overcomes the limitations of weak data foundation and lagging status perception, providing precise data support for facility management and capturing internal structural changes in real time; it solves the problems of difficulty in quantifying health status and strong subjectivity in governance strategies, reflecting facility status with intuitive health indicators and generating scientific governance strategies through multi-dimensional analysis; at the same time, it breaks the dilemma of passive management and decision-making relying on experience, promoting facility management from passive response to proactive prediction and upgrading from experience-based decision-making to data-driven, significantly improving maintenance efficiency and the rationality of resource utilization, and helping urban governance develop towards refinement and intelligence.

[0213] Figure 3 This is a schematic diagram illustrating a specific implementation of a community micro-brain digital analysis system for urban governance provided in this application. (Refer to...) Figure 3 The system may include:

[0214] The data acquisition module 31 is used to collect multi-source heterogeneous data of public facilities in urban communities and build a digital model of the facilities in the community micro-brain system, and continuously record the health benchmark data of the facilities through the digital model of the facilities.

[0215] The detection module 32 is used to digitally detect the internal structure of the facility according to a preset scanning cycle through a digital sensor network deployed in key parts of the facility, and to obtain waveform sequences that reflect changes in the internal state of the facility.

[0216] The identification module 33 is used to compare and analyze the waveform sequence with the health benchmark data, establish the state change trajectory of the facility structure by identifying the change pattern of the waveform shape over time, and generate the facility's health quantitative indicators based on the waveform attenuation degree and morphological distortion characteristics in the state change trajectory.

[0217] Analysis module 34 is used to receive the health quantitative indicators through the community micro-brain system, perform multi-dimensional correlation analysis between the health quantitative indicators and the multi-source heterogeneous data of public facilities, and generate an urban governance strategy that includes maintenance priority ranking and service resource allocation suggestions.

[0218] The generation module 35 is used to transmit the urban governance strategy to the community management platform through network services to generate a facility maintenance and service resource allocation plan and display the facility's full life cycle status on the facility digital model.

[0219] The community micro-brain digital analysis system for urban governance in this application embodiment is used to implement the aforementioned community micro-brain digital analysis method for urban governance. Therefore, the specific implementation of the community micro-brain digital analysis system for urban governance can be found in the embodiment section of the community micro-brain digital analysis method for urban governance above. The specific implementation can be referred to the description of the corresponding embodiments, and will not be repeated here.

[0220] This application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of any of the above-described community micro-brain digital analysis methods for urban governance.

[0221] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of any of the above-described community micro-brain digital analysis methods for urban governance.

[0222] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0223] Embodiments of the present invention also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the community micro-brain digital analysis method for urban governance.

[0224] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0225] The above provides a detailed description of the community micro-brain digital analysis system and method for urban governance provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A community micro-brain digital analysis method for urban governance, characterized in that, The method comprises the following steps: Collecting multi-source heterogeneous data of urban community public facilities, and constructing a digital model in a community micro-brain system to continuously record health benchmark data of the facilities through the digital model; Deploying a digital sensing network at key positions of the facilities, and performing digital detection on the internal structure of the facilities according to a preset scanning period to obtain a waveform sequence reflecting the internal state change of the facilities; Comparing and analyzing the waveform sequence with the health benchmark data, establishing a state change trajectory of the facility structure by identifying the change rule of the waveform form over time, and generating a health quantitative index of the facility based on the waveform attenuation degree and the form distortion characteristics in the state change trajectory; Receiving the health quantitative index through the community micro-brain system, performing multi-dimensional correlation analysis on the health quantitative index and the multi-source heterogeneous data of the public facilities, and generating an urban governance strategy including maintenance priority ranking and service resource allocation suggestions; Transmitting the urban governance strategy to a community management platform through a network service to generate an allocation scheme of facility maintenance and service resources, and displaying the full life cycle state of the facility on the digital model; Wherein, the comparison and analysis of the waveform sequence and the health benchmark data, the establishment of the state change trajectory of the facility structure by identifying the change rule of the waveform form over time, and the generation of the health quantitative index of the facility based on the waveform attenuation degree and the form distortion characteristics in the state change trajectory, comprise: Obtaining a reference waveform of the facility in a healthy state from the health benchmark data, aligning the waveform sequence obtained in the current detection period with the reference waveform, and calculating the amplitude difference and phase difference of the waveform sequence relative to the reference waveform at multiple feature points; Based on the amplitude difference and the phase difference, analyzing the form evolution trend of the waveform sequence in continuous multiple detection periods to establish a state change trajectory describing the state change process of the internal structure of the facility; Quantifying the attenuation degree of waveform energy and the distortion degree of waveform shape from the state change trajectory to obtain attenuation quantitative values and distortion quantitative values; According to the importance level of the facility in the community and the pre-defined community governance rules, assigning corresponding influence factors to the attenuation quantitative values and distortion quantitative values through an index generation function, and outputting a comprehensive health quantitative index through weighted calculation.

2. The method of claim 1, wherein, Before collecting the multi-source heterogeneous data of the community public facilities, the method further comprises the following steps: Building a cloud-edge-end collaborative hardware infrastructure by deploying a server cluster and an edge computing node; Installing core software components on the hardware infrastructure, the core software components including a data access gateway, a model management engine and a service bus; Based on the core software components, configuring a data channel between the data access gateway and the service bus, and establishing an interface calling relationship between the service bus and the model management engine to establish a community micro-brain system.

3. The method of claim 1, wherein, Based on the amplitude difference and the phase difference, analyzing the form evolution trend of the waveform sequence in continuous multiple detection periods to establish a state change trajectory describing the state change process of the internal structure of the facility, comprises: Based on the amplitude difference, the change gradient of the waveform amplitude in each detection period is calculated as an attenuation rate indicator, and based on the phase difference, the offset variance of the waveform phase in each detection period is calculated as a distortion degree indicator; In a plurality of continuous detection periods, the attenuation rate indicators and the distortion degree indicators are arranged in time sequence as time series, and the moving average value and the change slope of the indicator value in each time window are calculated through window sliding processing; Based on the moving average value and the change slope, a trend line of the attenuation rate and the distortion degree changing with time is fitted; The trend line is discretized into a series of state points corresponding to time stamps, each state point containing an attenuation value and a distortion value, and the state change trajectory is formed by connecting the state points.

4. The method of claim 1, wherein, By deploying a digital sensor network at key parts of the facility, the internal structure of the facility is digitally detected according to a preset scanning period to obtain a waveform sequence reflecting the state change of the facility, including: A plurality of digital sensor nodes are deployed at key structural parts of the facility to form a distributed digital sensor network, and the digital sensor nodes emit a specific form of detection signal to the interior of the facility according to a preset scanning period; The waveform signal returned by the detection signal is received by the digital sensor network, the waveform signal is converted into an original digital sequence containing time and frequency information, and effective waveform features are extracted from the original digital sequence; Based on the effective waveform features and in combination with the physical attribute parameters of the facility, the emission intensity of the detection signal and the receiving sensitivity parameters of the digital sensor network are dynamically adjusted to generate an optimized waveform sequence; The optimized waveform sequence is associated with the detection space-time information to form a labeled waveform data packet, and the waveform data packet is analyzed by the community micro-brain system to obtain a waveform sequence reflecting the state change of the facility interior.

5. The method of claim 4, wherein, Based on the effective waveform features and in combination with the physical attribute parameters of the facility, the emission intensity of the detection signal and the receiving sensitivity parameters of the digital sensor network are dynamically adjusted to generate an optimized waveform sequence, including: The waveform energy distribution and signal-to-noise ratio parameters in the effective waveform features are matched with the physical attribute parameters obtained from the facility attribute database, and the physical attribute parameters include material acoustic impedance and structural geometric size; Based on the matching result, the emission intensity adjustment coefficient of the detection signal and the receiving sensitivity adjustment coefficient of the digital sensor network are calculated; According to the emission intensity adjustment coefficient and the receiving sensitivity adjustment coefficient, the signal emission power and the receiving amplification multiple of the digital sensor network in the next detection period are adjusted; The digital sensor network after adjustment is used to perform internal detection of the facility, and the obtained reflected signal is converted into a digital sequence to generate an optimized waveform sequence.

6. The method of claim 1, wherein, The community micro-brain system receives the health quantification indicators, performs multidimensional correlation analysis on the health quantification indicators and the multi-source heterogeneous data of public facilities, and generates urban governance strategies containing maintenance priority ranking and service resource allocation suggestions, including: receiving the health quantification index through a data interface of the community micro-brain system, and calling the multi-source heterogeneous data related to public facilities from a database of the community micro-brain system; in the community micro-brain system, correlating and comparing the health quantification index with the multi-source heterogeneous data in time dimension, space dimension and function dimension, and identifying the correlation mode between facility state and multi-dimensional data; according to the correlation mode, calculating a comprehensive state score for each facility, and generating a maintenance priority ranking of the facility based on the comprehensive state score; according to the maintenance priority ranking and service resource information available in the community, generating a service resource allocation suggestion for the facility with high ranking; integrating the maintenance priority ranking and the service resource allocation suggestion to generate a city governance strategy.

7. The method of claim 1, wherein, collecting multi-source heterogeneous data of urban community public facilities, and constructing a facility digital model in the community micro-brain system, and continuously recording health benchmark data of the facility through the facility digital model, including: obtaining facility state information and management information from a plurality of information collection points distributed on community public facilities to constitute multi-source heterogeneous data; in the community micro-brain system, integrating and processing the multi-source heterogeneous data, and constructing a facility digital model capable of representing the geometric shape and dynamic behavior of the facility by extracting the spatial attributes, functional attributes and correlation information of the facility; in the facility digital model, setting a benchmark reference value reflecting the ideal operating state of the facility; according to the classification characteristics of the facility and the community management requirements, configuring the facility digital model with a recording rule for updating the health benchmark data, the recording rule defining the trigger condition, update frequency and value adjustment rule of data recording; according to the recording rule, continuously collecting state parameters of the facility through the facility digital model to form a health benchmark data sequence with time stamp; associating and storing the benchmark reference value and the continuously updated health benchmark data sequence with the corresponding facility digital model to form the health benchmark data of the facility.

8. The method of claim 1, wherein, transmitting the city governance strategy to a community management platform through a network service to generate an allocation scheme of facility maintenance and service resources, and displaying the full life cycle state of the facility on the facility digital model, including: transmitting the city governance strategy from the community micro-brain system to the community management platform using a network communication protocol; in the community management platform, parsing the maintenance priority ranking and service resource allocation suggestion of the city governance strategy to generate specific facility maintenance task instructions and corresponding service resource allocation scheme; according to the facility maintenance task instructions and the service resource allocation scheme, updating the state parameters and resource binding relationship corresponding to the target facility in the community micro-brain system; on the interactive interface of the facility digital model, integrating and displaying the health state evolution track from initial deployment to the current time and the record of previous maintenance events; through the visualization engine of the community micro-brain system, dynamically rendering and updating the display state of the facility digital model to make the full life cycle state of the facility continuously visible.

9. A community micro-brain digital analysis system for urban governance, used for executing the community micro-brain digital analysis method for urban governance according to any one of claims 1-8. including: The collection module is configured to collect multi-source heterogeneous data of urban community public facilities, and construct a digital model in a community micro-brain system, and continuously record health benchmark data of the facilities through the digital model; The detection module is configured to perform digital detection on internal structures of the facilities according to a preset scanning period through a digital sensor network deployed at key positions of the facilities, and obtain waveform sequences reflecting changes in internal states of the facilities; The identification module is configured to compare and analyze the waveform sequences with the health benchmark data, establish a state change trajectory of the facilities by identifying change rules of waveform forms over time, and generate health quantitative indexes of the facilities based on waveform attenuation degrees and form distortion characteristics in the state change trajectory; The analysis module is configured to receive the health quantitative indexes through the community micro-brain system, perform multi-dimensional correlation analysis on the health quantitative indexes and the multi-source heterogeneous data of the public facilities, and generate urban governance strategies including maintenance priority ranking and service resource allocation suggestions; The generation module is configured to transmit the urban governance strategies to a community management platform through a network service to generate an allocation scheme of facility maintenance and service resources, and display a full life cycle state of the facilities on the digital model.

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