A conference room device iteration method, system, computing device, and storage medium

Abstract

Embodiments of the present specification provide a conference room equipment iteration method, system, computing device and storage medium, wherein the conference room equipment iteration method comprises: obtaining multi-dimensional operation and maintenance data of conference room equipment, and determining a performance threshold required by a target conference scene for the conference room equipment; aggregating the multi-dimensional operation and maintenance data to obtain aggregated operation and maintenance data; quantifying features of the aggregated operation and maintenance data to obtain health degree features and use efficiency features of the conference room equipment; constructing a performance decay curve of the conference room equipment with respect to time based on the health degree features and the use efficiency features; and determining an adaptation strength and an adaptation duration of the conference room equipment for the target conference scene based on the decay curve and the performance threshold. The cost of iterating the conference room equipment can be reduced and the timeliness of iterating the conference room equipment can be improved while ensuring the quality of the conference.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of equipment iteration and optimization technology, and in particular to a method, system, computing device and storage medium for iterating conference room equipment. Background Technology

[0002] In daily learning and work scenarios, meetings are frequently needed for notification, discussion, or sharing. As the number of meetings increases, the equipment used may gradually become inadequate for efficient and high-quality meetings, necessitating equipment replacement.

[0003] Current methods for replacing conference room equipment primarily involve replacing equipment when it malfunctions (e.g., when it frequently freezes or crashes) or replacing it based on usage experience (e.g., when the equipment's lifespan has significantly exceeded the manufacturer's recommended lifespan). However, replacing equipment only when it malfunctions can disrupt the normal flow of meetings. Replacing equipment based on usage experience relies on subjective judgment; directly replacing equipment nearing the end of its lifespan that is not frequently used may result in wasted resources, while equipment that has not reached its lifespan but has potential malfunctions may not be replaced in a timely manner, posing a risk of disrupting the meeting.

[0004] In summary, existing solutions for replacing conference room equipment suffer from problems such as untimely replacement and high replacement costs. Summary of the Invention

[0005] In view of this, embodiments of this specification provide a method for iterating conference room equipment. One or more embodiments of this specification also relate to a conference room equipment iteration system, a computing device, a computer-readable storage medium, and a computer program product, to address the technical deficiencies existing in the prior art.

[0006] According to a first aspect of the embodiments of this specification, a method for iterating conference room equipment is provided, comprising: Acquire multi-dimensional operation and maintenance data of conference room equipment and determine the performance threshold of the target meeting scenario for the conference room equipment requirements. The multi-dimensional operation and maintenance data includes at least one of the following: equipment operation data, fault handling record data, meeting usage rate data, and meeting quality feedback data. Multi-dimensional operation and maintenance data is aggregated to obtain aggregated operation and maintenance data. By performing feature quantification on aggregated operation and maintenance data, the health characteristics and usage efficiency characteristics of conference room equipment can be obtained. Based on health and usage efficiency characteristics, a performance decay curve of conference room equipment over time is constructed. Based on the attenuation curve and the performance threshold, the adaptability strength and adaptation duration of the conference room equipment to the target conference scenario are determined. The adaptation strength and adaptation duration are input into the iterative model constrained by the target meeting scenario to generate a device iteration scheme, which is used to iterate the meeting room equipment.

[0007] According to a second aspect of the embodiments of this specification, a conference room equipment iteration system is provided, comprising: The acquisition module is used to acquire multi-dimensional operation and maintenance data of conference room equipment and determine the performance threshold of the target meeting scenario's requirements for conference room equipment. The multi-dimensional operation and maintenance data includes at least one of the following: equipment operation data, fault handling record data, meeting usage rate data, and meeting quality feedback data. The feature extraction module is used to aggregate multi-dimensional operation and maintenance data to obtain aggregated operation and maintenance data; and to quantify the features of the aggregated operation and maintenance data to obtain the health features and usage efficiency features of the conference room equipment. The module is used to build performance degradation curves of conference room equipment over time based on health and usage efficiency characteristics. The determination module is used to determine the adaptability strength and duration of conference room equipment to the target conference scenario based on the attenuation curve and performance threshold. The solution generation module is used to input the adaptation strength and adaptation duration into the iterative model constrained by the target meeting scenario to generate a device iteration solution, which is used to iterate the meeting room equipment.

[0008] According to a third aspect of the embodiments of this specification, a computing device is provided, comprising: Memory and processor; The memory is used to store computer programs / instructions, and the processor is used to execute the computer programs / instructions, which implement the steps of the above method when executed by the processor.

[0009] According to a fourth aspect of the embodiments of this specification, a computer-readable storage medium is provided that stores a computer program / instructions that, when executed by a processor, implement the steps of the above-described method.

[0010] According to a fifth aspect of the embodiments of this specification, a computer program product is provided, including a computer program / instructions that, when executed by a processor, implement the steps of the above-described method.

[0011] This specification provides an embodiment of a conference room equipment iteration method, which involves acquiring multi-dimensional operation and maintenance data of the conference room equipment and determining the performance threshold of the target meeting scenario's requirements for the conference room equipment. The multi-dimensional operation and maintenance data includes at least one of equipment operation data, fault handling record data, meeting usage rate data, and meeting quality feedback data. The multi-dimensional operation and maintenance data is aggregated to obtain aggregated operation and maintenance data. Feature quantification is performed on the aggregated operation and maintenance data to obtain the health characteristics and usage efficiency characteristics of the conference room equipment. Based on the health characteristics and usage efficiency characteristics, a performance decay curve of the conference room equipment over time is constructed. Based on the decay curve and the performance threshold, the adaptation strength and adaptation duration of the conference room equipment to the target meeting scenario are determined. The adaptation strength and adaptation duration are input into an iterative model constrained by the target meeting scenario to generate an equipment iteration scheme, wherein the equipment iteration scheme is used to iterate the conference room equipment. It can use historical operation and maintenance data to predict the performance degradation trajectory of conference room equipment in the future operation cycle, quantify the adaptability strength and adaptation time between conference room equipment and specific meeting scenario requirements, and avoid meeting interruptions caused by sudden failures of conference room equipment, premature or late iteration of conference room equipment due to inaccurate experience, and the inability of the equipment to meet the performance requirements of the target meeting scenario in the short term due to insufficient adaptation time. Thus, while ensuring meeting quality, it reduces the cost of iterating conference room equipment, improves the timeliness of iterating conference room equipment, and realizes the transformation of asset management model from "experience-driven, post-replacement" to "data-driven, preventive iteration", which significantly improves asset utilization efficiency and meeting support level. Attached Figure Description

[0012] Figure 1 This is a flowchart illustrating an embodiment of a conference room equipment iteration method provided in this specification; Figure 2 This is a schematic diagram illustrating the execution process of an iterative method for conference room equipment provided in one embodiment of this specification; Figure 3 This is a structural block diagram of an iterative system for conference room equipment provided in one embodiment of this specification; Figure 4 This is a structural block diagram of a computing device provided in one embodiment of this specification. Detailed Implementation

[0013] Many specific details are set forth in the following description to provide a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0014] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of this specification. The singular forms “a,” “described,” and “the” as used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0015] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this specification, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0016] Furthermore, it should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in one or more embodiments of this specification are all information and data authorized by the user or fully authorized by all parties. Moreover, the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0017] First, the terms and concepts used in one or more embodiments of this specification will be explained.

[0018] Conference room equipment: Conference room equipment is a collective term for all kinds of electronic and mechanical devices used to support meetings in a meeting setting. Examples include projectors, microphone arrays, speakers, audio processors, video conferencing terminals, central control systems, displays, and interactive whiteboards.

[0019] Operation and maintenance data: Operation and maintenance data refers to recorded information reflecting the status and behavior of conference room equipment during operation, maintenance, and use. Examples include equipment runtime, temperature, vibration, fault alarm records, repair and replacement records, meeting usage frequency, duration of a single meeting, and user ratings of audio and video quality.

[0020] Health Status: Health status is a quantitative indicator that represents the remaining effective working capacity or remaining service life of conference room equipment relative to its brand-new condition. For example, health status values ​​can be between 0 and 1, where 1 represents complete health and 0 represents complete failure; or it can be expressed as the estimated number of hours of usable use remaining.

[0021] Energy efficiency: Energy efficiency is the overall ratio between the energy consumed by conference room equipment to perform its functions during a meeting and the resulting meeting effect. Examples include power consumption per unit meeting duration, audio and video transmission latency, and subjective ratings of image clarity.

[0022] Computing devices: Computing devices are computer devices designed to perform one or more specific tasks. Compared to personal mainframes, they are weaker in performance but have significant advantages in terms of size and power consumption. They are commonly used in various electronic and mechanical control devices.

[0023] This specification provides a method for iterating conference room equipment, and also relates to a conference room equipment iteration system, a computing device, a computer-readable storage medium, and a computer program product, which are described in detail in the following embodiments.

[0024] See Figure 1 , Figure 1 A flowchart of an iterative method for conference room equipment according to an embodiment of this specification is shown, including the following specific steps: Step 102: Obtain multi-dimensional operation and maintenance data of the conference room equipment and determine the performance threshold of the target meeting scenario's requirements for the conference room equipment. The multi-dimensional operation and maintenance data includes at least one of the following: equipment operation data, fault handling record data, meeting usage rate data, and meeting quality feedback data.

[0025] The examples in this manual are applicable to scenarios where iterative optimization of conference room equipment is required, such as updating conference room equipment in corporate office buildings or multimedia classrooms in schools.

[0026] Conference room equipment refers to various electronic or mechanical devices used to support meetings in a meeting setting. Examples include projectors, microphone arrays, speakers, audio processors, video conferencing terminals, central control systems, displays, and interactive whiteboards.

[0027] Operation and maintenance data refers to recorded information reflecting the status and behavior of conference room equipment during operation, maintenance, and use. For example, operation and maintenance data may include runtime, temperature, vibration, fault alarm records, repair and replacement records, conference usage frequency, duration of a single conference, and user ratings of audio and video quality.

[0028] A target meeting scenario is a meeting scenario set by the user based on meeting needs, possessing at least one meeting parameter. These parameters may include meeting duration, meeting importance level, and required audio / video quality (such as noise level and device volume). For example, a user could set a meeting scenario with a duration of 2 hours, an importance level of "important," and requirements for background noise below 40 decibels and device volume above 80 decibels. A target meeting scenario can also be a meeting scenario specific to a meeting room. For example, the first meeting room might be used for video conferencing, requiring projector brightness to be at least 80% of its rated value, audio latency to be no more than 100 milliseconds, and microphone pickup distance to be at least 3 meters.

[0029] The performance threshold is a quantitative indicator of the minimum performance required by the conference room equipment for the target conference scenario. It may include at least one of the health threshold and the usage efficiency threshold, or it may be a quantitative score obtained by weighting the two.

[0030] Equipment operation data is a collection of data on the physical state and working parameters of the conference room equipment during operation, which may include equipment temperature, vibration amplitude, current, voltage, running time, fan speed, cumulative lamp lighting time, etc.

[0031] The fault handling record data is a collection of data on the faults that occurred in the conference room equipment and the subsequent maintenance and handling. It may include fault type, fault occurrence time, fault duration, maintenance measures, and information on replaced parts.

[0032] Meeting usage data is statistical information on the frequency and intensity of use of meeting room equipment, including but not limited to one or more of the following: the number of meetings per day or week, the average duration of a single meeting, and the continuous working time of the equipment.

[0033] Meeting quality feedback data reflects the feedback from meeting participants on the equipment's performance, and may include audio and video clarity ratings, sound latency, video synchronization ratings, and user satisfaction scores.

[0034] Optionally, one way to obtain multi-dimensional operation and maintenance data of conference room equipment and determine the performance threshold of the target meeting scenario's requirements for the conference room equipment can be: retrieve historical operation and maintenance records from the operation and maintenance server via an IoT gateway, and read the corresponding performance threshold from a preset scenario-threshold mapping table based on the meeting type input by the user. (Illustratively,) Figure 2 This specification illustrates a schematic diagram of the execution process of an iterative method for conference room equipment according to an embodiment of the present specification, such as... Figure 2As shown, equipment operation data, fault handling record data, meeting usage rate data, and meeting quality feedback data are obtained from the operation and maintenance server. Users can set the performance thresholds corresponding to the meeting room equipment required for the target meeting scenario via mobile terminals. Another way to obtain multi-dimensional operation and maintenance data of meeting room equipment and determine the performance thresholds required by the meeting room equipment for the target meeting scenario can be: receiving data reported by equipment sensors in real time, extracting scenario requirements from meeting notification text using natural language processing, and dynamically calculating the performance thresholds. This specification does not limit this approach.

[0035] For example, for a projector in a conference room, data from the past 6 months is exported from the equipment management system: the cumulative lamp operating time is 800 hours, the average operating temperature in the past month is 42 degrees Celsius, there have been 2 instances of automatic shutdown due to overheating, and the maintenance record shows that the cooling fan has been replaced; the meeting usage record shows that 10 meetings are held per week, with an average session duration of 1.5 hours; the most recent 5 ratings in the quality feedback system are 8, 7, 6, 6, and 5 (out of 10). The user selects the upcoming meeting type as a "product launch," requiring the projector brightness to be no less than 75% of the brightness of a new projector, and the remaining lifespan to be no less than 100 hours.

[0036] Step 102 involves acquiring multi-dimensional operation and maintenance data of the conference room equipment and determining the performance threshold of the target meeting scenario's requirements for the conference room equipment, thus providing a data foundation for subsequent aggregation of multi-dimensional operation and maintenance data.

[0037] Step 104: Aggregate the multi-dimensional operation and maintenance data to obtain aggregated operation and maintenance data.

[0038] refer to Figure 2 The system aggregates equipment operation data, fault handling records, meeting usage data, and meeting quality feedback data to obtain aggregated operation and maintenance data.

[0039] Aggregated operations and maintenance data is a comprehensive data representation formed by merging multiple dimensions of operations and maintenance data according to specific rules. It characterizes the overall operating status or comprehensive performance level of conference room equipment. For example, it involves weighted summation of values ​​from each dimension or calculation of their principal component scores.

[0040] Optionally, one way to aggregate multi-dimensional operation and maintenance data to obtain aggregated operation and maintenance data is to normalize the operation and maintenance data of each dimension and then calculate its weighted arithmetic mean, with the weights pre-set according to the degree of influence of each dimension on equipment performance. Another way is to use principal component analysis algorithm to reduce the dimensionality of high-dimensional data to a low-dimensional feature space to obtain aggregated feature vectors. The embodiments in this specification do not limit this approach.

[0041] For example, the operating temperature (42 degrees Celsius), number of failures (2 times), failure repair time (cumulative 4 hours), meeting usage frequency (10 times per week), and quality feedback score (6 points) of the above projector are respectively min-max normalized, and then weighted and summed according to the weights (temperature weight 0.2, failure weight 0.3, usage frequency weight 0.1, feedback score weight 0.4) to obtain the aggregated operation and maintenance data of 0.65.

[0042] Step 104 aggregates the multi-dimensional operation and maintenance data to obtain aggregated operation and maintenance data, which provides a data foundation for subsequent quantification of the aggregated operation and maintenance data.

[0043] Step 106: Perform feature quantification on the aggregated operation and maintenance data to obtain the health characteristics and usage efficiency characteristics of the conference room equipment.

[0044] refer to Figure 2 After obtaining the aggregated operation and maintenance data, its features are quantified to obtain the health characteristics and usage efficiency characteristics of the conference room equipment.

[0045] Feature quantization is the process of extracting features characterizing the health and energy efficiency of conference room equipment from aggregated operation and maintenance data. For example, a regression model can be used to map aggregated data to percentage of remaining lifespan and energy consumption per unit time.

[0046] Health characteristics are feature representations that characterize the degree of wear and tear on the mechanical and / or electronic components of conference room equipment. For example, health characteristics may include predicted remaining useful life, failure probability scores, normalized cumulative runtime, and wear indicators of key components.

[0047] Performance characteristics are used to represent the resources consumed and output of conference room equipment during a meeting. Examples include power consumption per unit meeting duration, audio and video transmission latency, user satisfaction prediction scores, and the signal-to-noise ratio of the equipment's output signal.

[0048] Optionally, one way to quantify the aggregated operation and maintenance data to obtain the health and performance characteristics of the conference room equipment is to input the aggregated operation and maintenance data into a pre-trained neural network and output health and performance scores. Another way is to fit the parameters of the aggregated operation and maintenance data based on a physical degradation model (such as an exponential decay model) to obtain health and performance characteristics. This specification does not limit the specific implementation of this method.

[0049] For example, the above aggregated operation and maintenance data of 0.65 is input into a linear regression model trained with historical data, and the health characteristics are 65% remaining service life, and the usage efficiency characteristics are 0.3 kWh of electricity consumption per meeting hour and 7.2 points of expected user satisfaction.

[0050] Step 106 performs feature quantification on the aggregated operation and maintenance data to obtain the health characteristics and usage efficiency characteristics of the conference room equipment. This transforms the raw operation and maintenance data into equipment status indicators with clear physical meaning, facilitating subsequent performance degradation analysis.

[0051] Step 108: Based on health and usage efficiency characteristics, construct the performance decay curve of the conference room equipment over time.

[0052] refer to Figure 2 After obtaining the health and usage efficiency characteristics, a performance decay curve of the conference room equipment over time is constructed based on these characteristics.

[0053] The decay curve is a function curve that reflects the change in the overall performance of conference room equipment (determined by both health and efficiency) over time. For example, a curve with time on the horizontal axis and the performance index of the conference room equipment on the vertical axis.

[0054] Optionally, one way to construct the performance decay curve of conference room equipment over time based on health and usage efficiency characteristics is to weight and synthesize the decay curve by combining the two based on the exponential decay laws that health and usage efficiency each follow. Another way is to quantify the health and usage efficiency characteristics, multiply the quantification results to obtain the decay curve. For example, health can be quantified as the percentage of remaining lifespan between 0 and 1, and usage efficiency can be quantified as the energy efficiency ratio between 0 and 1. The performance decay curve can be represented as the product of the two over time as a function P(t) = H(t) × E(t). Yet another way is to construct the decay curve in a segmented manner based on health and usage efficiency characteristics. For example, when health is above a first threshold, performance is mainly affected by efficiency decay, using a first decay function; when health is below the first threshold, performance is affected by the combined decay of health and efficiency, using a second decay function. The two functions are then concatenated to obtain the complete decay curve. This specification does not limit the embodiments in this way.

[0055] For example, continuing from the example in step 106, the projector's health characteristics are obtained as a sequence of remaining lifespan percentages (100%, 97%, 91%, 82%, 72%, 65%) corresponding to the end-of-month values ​​of each of the past 6 months, and the performance characteristics are obtained as a sequence of power consumption per meeting hour (0.25 kWh, 0.26 kWh, 0.28 kWh, 0.31 kWh, 0.35 kWh, 0.40 kWh). An exponential decay function H(t) = 100 × e is fitted based on the health characteristics. -0.0035t Where t represents hours, the performance degradation function E(t) = 0.25 × e is fitted based on performance characteristics. 0.0012tThe weighted composite performance function is P(t) = 60 × e 0.0035t +80-40×e 0.0012t (When t=0, P(t)=60+80-40=100), thus obtaining the decay curve.

[0056] Step 108 constructs a time-dependent performance decay curve for conference room equipment based on health and usage efficiency characteristics. This transforms discrete state indicators into continuous degradation trajectories, providing an intuitive basis for predicting the future availability of the equipment.

[0057] Step 110: Based on the attenuation curve and performance threshold, determine the adaptation strength and adaptation duration of the conference room equipment to the target conference scenario.

[0058] refer to Figure 2 After constructing the attenuation curve, the adaptation strength and adaptation time of the conference room equipment to the target conference scenario are determined based on the attenuation curve and the performance threshold.

[0059] Adaptability strength is a quantitative indicator that reflects the degree of fit between conference room equipment and the target conference scenario. It reflects the degree of match between the current performance of the conference room equipment and the performance required by the target conference scenario for the conference room equipment.

[0060] The adaptation time is the duration from the current moment when the degradation curve first drops below the performance threshold. In other words, it's the remaining time the device can still meet the needs of the target meeting scenario from the moment it's deployed. If the current performance of the meeting room device is already below the threshold, the adaptation time is 0.

[0061] Optionally, one way to determine the adaptation strength and duration of the conference room equipment to the target conference scenario based on the attenuation curve and the performance threshold is to take the ratio of the current performance value to the threshold as the adaptation strength, calculate the time point from the curve where the performance equals the threshold, and take the difference between that time and the current time as the adaptation duration. Another way is to calculate the slope of the tangent line of the curve at the current time, and estimate the remaining time by combining the difference between the current performance and the threshold. This specification does not limit the embodiments in this way.

[0062] For example, given the performance threshold required for the target meeting scenario (overall performance score not lower than 60), the current projector's performance score is 78. Therefore, the adaptation strength is 78 divided by 60, which equals 1.3. Solving the attenuation curve equation, the time point when the performance score first drops to 60 is 120 hours later. Since the current time is 0, the adaptation duration is 120 hours.

[0063] Step 110, based on the attenuation curve and the performance threshold, determines the adaptability strength and duration of the conference room equipment to the target conference scenario. This quantifies whether the equipment can currently meet the conference requirements and for how long, thus providing a direct basis for iterative decision-making.

[0064] Step 112: Input the adaptation strength and adaptation duration into the iterative model of the target meeting scenario constraints to generate a device iteration scheme, which is used to iterate the meeting room equipment.

[0065] refer to Figure 2 The adaptation strength and adaptation duration are input into the iterative model constrained by the target meeting scenario to generate a device iteration scheme.

[0066] The iterative model constrained by the target meeting scenario is a pre-built decision model. This model takes the adaptation strength and adaptation duration as inputs and embeds the constraints of the target meeting scenario on the device iterative operation (such as the maximum allowable cost and the longest acceptable downtime) as constraints, and outputs specific device processing actions.

[0067] Equipment iteration plans are specific plans for upgrading, replacing, repairing, or adjusting the deployment of current conference room equipment. Examples include immediate equipment replacement, equipment replacement at a future date, continued use of existing equipment after component repair, or downgrading equipment for secondary conference rooms.

[0068] Optionally, one way to generate a device iteration scheme by inputting the adaptation strength and adaptation duration into an iterative model constrained by the target meeting scenario is to use a rule-based decision tree to output different schemes based on whether the adaptation strength and duration are lower than preset thresholds. Another way is to construct a linear programming model to minimize the iteration cost and solve for the optimal iteration time while satisfying the constraint that the adaptation duration is not lower than the meeting duration. This specification does not limit the specific implementation of this method.

[0069] For example, the aforementioned adaptation strength of 1.3 and adaptation duration of 120 hours are input into a pre-built iterative model for a "product launch" scenario. This model incorporates constraints such as an iteration cost not exceeding 3000 yuan, downtime not exceeding 20 minutes, and a meeting duration of 2 hours. Since the adaptation duration is much longer than the meeting duration and the adaptation strength is greater than 1, the model determines that the current equipment fully meets the requirements and outputs "Do not iterate for now, continue using, and it is recommended to prepare backup equipment in advance when the remaining 20 hours of adaptation time are available." This solution also includes a budget assessment of 0 yuan and an expected benefit of saving on equipment procurement costs.

[0070] Step 112 inputs the adaptation strength and adaptation duration into the iterative model constrained by the target meeting scenario to generate a device iteration plan. This plan can automatically and scientifically determine the timing and method of device iteration, avoiding meeting interruptions due to malfunctions or resource waste caused by blind replacement.

[0071] In this embodiment, multi-dimensional operation and maintenance data of the conference room equipment is acquired, and the performance threshold of the target meeting scenario's requirements for the conference room equipment is determined. The multi-dimensional operation and maintenance data includes at least one of equipment operation data, fault handling record data, meeting usage rate data, and meeting quality feedback data. The multi-dimensional operation and maintenance data is aggregated to obtain aggregated operation and maintenance data. Feature quantification is performed on the aggregated operation and maintenance data to obtain the health characteristics and usage efficiency characteristics of the conference room equipment. Based on the health characteristics and usage efficiency characteristics, a performance decay curve of the conference room equipment over time is constructed. Based on the decay curve and the performance threshold, the adaptation strength and adaptation duration of the conference room equipment to the target meeting scenario are determined. The adaptation strength and adaptation duration are input into an iterative model constrained by the target meeting scenario to generate equipment... The iterative solution can use historical operation and maintenance data to predict the performance degradation trajectory of conference room equipment in the future operation cycle, quantify the adaptability strength and adaptation time between conference room equipment and specific meeting scenario requirements, and avoid meeting interruptions caused by sudden failures of conference room equipment, premature or late iteration of conference room equipment due to inaccurate experience, and the inability of the equipment to meet the performance requirements of the target meeting scenario in the short term due to insufficient adaptation time. Thus, while ensuring meeting quality, it reduces the cost of iterating conference room equipment, improves the timeliness of iterating conference room equipment, and realizes the transformation of asset management model from "experience-driven, post-replacement" to "data-driven, preventive iteration", which significantly improves asset utilization efficiency and meeting support level.

[0072] In one optional embodiment of this specification, prior to step 104, the method further includes: The importance weights of operational data in each dimension are determined through machine learning networks; Step 104 includes: The multi-dimensional operation and maintenance data are aggregated according to their importance weights to obtain aggregated operation and maintenance data.

[0073] Machine learning networks are computational or rule-based models, such as neural networks, random forests, and gradient boosting trees, used to determine the importance of various dimensions of operational data within a target meeting scenario. For example, in the case of a neural network with an attention layer, operational data from various dimensions are input into the network, and the attention layer automatically learns the attention scores for each dimension, using these scores as importance weights. In the case of a rule-based machine learning network, the importance weights of each dimension of operational data are determined through pre-built mapping rules between the meeting scenario and the operational data from various dimensions.

[0074] Importance weights are numerical representations reflecting the degree of impact of each dimension of operation and maintenance data on the health and usability of conference room equipment. For example, the sum of the weight coefficients of each dimension's data is 1, and the larger the weight, the greater the contribution of that dimension to the equipment performance evaluation.

[0075] Alternatively, one approach to determining the importance weights of operational data across dimensions using machine learning networks is to train historical operational data using a feature importance assessment algorithm to obtain the weight value for each dimension. For example, when using a random forest model, operational data across dimensions are used as input features, and equipment health scores or performance scores are used as target variables for training. After training, the amount of impurity reduction (i.e., Gini importance) brought about by each feature during decision tree splitting is read, normalized, and used as the importance weight for each dimension. Another approach is to use a neural network with an attention mechanism, embedding an attention layer in the network. Operational data across dimensions are input into the network, and the attention layer automatically calculates an attention score for each dimension. This score reflects the contribution of that dimension to the network output, and the attention score is used as the importance weight for each dimension. Another implementation approach is to use factor loadings from principal component analysis as weights. Specifically, after standardizing the operational data across all dimensions, principal component analysis is performed to extract the first principal component or the top few principal components whose cumulative contribution rate reaches a preset threshold. The absolute value or sum of squares of the loading coefficients of each original dimension on these principal components are calculated, and the importance weights of each dimension are obtained after normalization. This specification does not limit the specific implementation of this method.

[0076] Optionally, one way to aggregate multi-dimensional operation and maintenance data based on importance weights is to calculate the weighted arithmetic mean of the normalized data for each dimension. Specifically, the normalized value of each dimension is multiplied by its corresponding weight, and the products of each dimension are summed to obtain the weighted arithmetic mean, which serves as the aggregated operation and maintenance data. This method is simple and intuitive, and suitable for scenarios where the data of each dimension are independent and linearly superimposed. Another way to implement this is to calculate the weighted geometric mean. Specifically, the normalized value of each dimension is raised to the power of its corresponding weight, and the results of the power operations of each dimension are multiplied to obtain the weighted geometric mean, which serves as the aggregated operation and maintenance data. This method is suitable for scenarios where the multiplication of data of each dimension has physical meaning, such as the product of multiple independent probabilities representing a joint probability. The embodiments in this specification do not limit this approach.

[0077] For example, for a certain projector, four dimensions are selected: operating temperature (device operation data), number of failures (fault handling record data), usage frequency (meeting usage rate data), and feedback score (meeting quality feedback data). The weights of each dimension are calculated using a random forest model to be 0.2, 0.3, 0.1, and 0.4, respectively. Then, the aggregated operation and maintenance data = 0.2 × normalized temperature value + 0.3 × normalized number of failures value + 0.1 × normalized usage frequency value + 0.4 × normalized feedback score value.

[0078] In the embodiments of this specification, a machine learning network is used to determine the importance weight of each dimension of operation and maintenance data. Based on the importance weight, the multi-dimensional operation and maintenance data is aggregated to obtain aggregated operation and maintenance data. This allows the system to learn and allocate the contribution of each dimension of operation and maintenance data in the data aggregation process for different target meeting scenarios, making the aggregated operation and maintenance data more in line with the actual needs of the target meeting scenario, thereby improving the accuracy of subsequent feature quantification and scenario adaptability.

[0079] In one optional embodiment of this specification, step 108 includes the following specific steps: Based on health characteristics, a first degradation function is determined to describe the change in the health of conference room equipment over time. Based on usage effectiveness characteristics, a second degradation function is determined for the usage effectiveness of conference room equipment over time. Based on health characteristics and usage efficiency characteristics, the coupling relationship between the health and usage efficiency of conference room equipment is determined. Based on the first degradation function, the second degradation function, and the coupling relationship, a performance decay curve of the conference room equipment with respect to time is constructed.

[0080] The first degradation function is a mathematical expression representing the decrease in health over time. The second degradation function is a mathematical expression representing the change in usage effectiveness over time.

[0081] Coupling relationships are data structures that characterize the mutual influence, constraint, or synergy between health and performance. For example, the product of the two can represent how a decline in health leads to an accelerated decline in performance, or how the need to maintain performance leads to an accelerated decline in health.

[0082] Optionally, one way to determine the first degradation function of the conference room equipment's health status over time based on health status characteristics is to fit an exponential decay model using the least squares method. Specifically, the health status feature sequence is used as the dependent variable, and the corresponding time points are used as independent variables. Assuming that the health status decays exponentially over time, the decay coefficient and initial health status parameters are solved by minimizing the sum of squared errors between the fitted value and the actual value, thus obtaining the first degradation function. Another approach is to predict future health status sequences using a Long Short-Term Memory (LSTM) network and then fit it to a function. Specifically, the historical health status feature sequence is used as input to train a LSTM model. This model is used to predict health status values ​​at multiple future time points, resulting in a predicted health status sequence. This sequence is then fitted with a function (such as an exponential or polynomial function) to obtain a continuous first degradation function. This specification does not limit the specific implementation of this method.

[0083] Optionally, one way to determine the second degradation function of the meeting room equipment's usage effectiveness over time based on usage effectiveness characteristics is to use exponential fitting or power-law fitting. Specifically, based on the changing trend of usage effectiveness characteristics over time, an exponential function form (effectiveness increases or decreases exponentially over time) or a power-law function form (effectiveness changes exponentially over time) is selected, and the parameters in the function are determined through regression methods to obtain the second degradation function. Another approach is to fit the effectiveness changes at different stages based on piecewise linear regression. Specifically, the usage effectiveness characteristic sequence is divided into multiple stages (such as the break-in period, stable period, and decline period) according to time, and linear regression is used to fit each stage separately. Then, the linear functions of each stage are concatenated into a piecewise continuous second degradation function. Yet another approach is to dynamically update the effectiveness degradation parameters using Kalman filtering. Specifically, a state-space model of effectiveness degradation is established, using effectiveness characteristics as observations. The state parameters (such as decay rate) in the degradation model are estimated recursively through Kalman filtering. The parameter estimates are updated as new observation data is obtained, thus obtaining the second degradation function that dynamically adjusts over time. The embodiments in this specification do not limit this.

[0084] Optionally, one approach to determining the coupling relationship between the health and usage effectiveness of conference room equipment, based on health and usage effectiveness characteristics, is to establish a system of simultaneous differential equations and identify the coefficients of cross-terms from historical data. Specifically, a system of differential equations is constructed with health and effectiveness as state variables, where the rate of change of each state variable depends not only on itself but also on another variable (i.e., containing cross-terms). Historical data is used to identify the parameters of the coefficients of cross-terms in the system of equations, resulting in a quantitative coupling relationship. Another approach is to use a structural equation model to model the causal path between the two. Specifically, health and usage effectiveness characteristics are used as latent variable measurement indicators, and a structural equation model containing causal paths is constructed. Through goodness-of-fit tests and path coefficient estimation, the direct impact strength of health on effectiveness and the inverse impact strength of effectiveness on health are determined, thereby obtaining the coupling relationship. This specification does not limit the embodiments described herein.

[0085] Optionally, one way to construct the performance degradation curve of the conference room equipment over time based on the first degradation function, the second degradation function, and the coupling relationship is to introduce the coupling relationship as a correction term into the first and second degradation functions to obtain an adjusted degradation function, and then perform a weighted fusion of the two. Another way is to treat health and usage efficiency as system states, solve the coupled state-space model, and obtain the degradation curve characterizing the overall performance of the conference room equipment. Yet another way is to use a Bayesian network to couple the first and second degradation functions and sample to obtain the performance degradation curve. This specification does not limit the embodiments in this way.

[0086] For example, following the example in step 106, the health feature sequence (100%, 97%, 91%, 82%, 72%, 65%) is fitted with a first degradation function H(t) = 100 × e -0.0035t t represents hours; the second degradation function E(t) = 0.25 × e is fitted using the efficiency feature sequence (0.25, 0.26, 0.28, 0.31, 0.35, 0.40 degrees / hour). 0.0012t Analysis of historical data revealed that when health falls below 80%, the rate of performance degradation increases by approximately 20%. Based on this, the coupling relationship was determined as: Actual performance degradation rate = Original degradation rate × (1 + 0.2 × I (H < 80)). Adjusting the parameters of the second degradation function according to this coupling relationship yielded the adjusted performance function E'(t) = 0.25 × e (0.0012+0.00024×I(H<80))t Then, the adjusted health function and the performance function are weighted together to form the decay curve P(t) = 0.6H(t) + 0.4(100 - 100 × (E'(t) - 0.25) / 0.25).

[0087] In this embodiment, a first degradation function of the health status of the conference room equipment over time is determined based on health status characteristics; a second degradation function of the usage efficiency of the conference room equipment over time is determined based on usage efficiency characteristics; and the coupling relationship between the health status and usage efficiency of the conference room equipment is determined based on the health status characteristics and usage efficiency characteristics. Based on the first degradation function, the second degradation function, and the coupling relationship, a performance decay curve of the conference room equipment over time is constructed. This method can simultaneously capture the nonlinear decay patterns of both health status and usage efficiency over time, and reveal and quantify the nonlinear coupling relationship between them (e.g., a decrease in health status leads to accelerated efficiency decay, or accelerated health decay is achieved to maintain efficiency). This avoids performance prediction biases caused by ignoring the nonlinear joint decay pattern (e.g., overestimating remaining effective lifespan or underestimating the risk of sudden failure). It unifies the modeling of the independent time degradation of health status and efficiency and their mutual influence, making the decay curve more closely match the actual degradation pattern of the equipment, and providing a more accurate basis for subsequent calculations of adaptation strength and adaptation duration.

[0088] In one optional embodiment of this specification, the performance degradation curve of the conference room equipment with respect to time is constructed based on a first degradation function, a second degradation function, and a coupling relationship, including: Based on the coupling relationship, the correction factor between the first degradation function and the second degradation function is determined; By using a correction factor, the parameters of the first and second degenerate functions are adjusted to obtain the first degenerate function and the second degenerate function after parameter adjustment. The decay curve is obtained by fitting the first degradation function after parameter adjustment and the second degradation function after parameter adjustment.

[0089] The correction factor is an adjustment coefficient used to correct various degradation functions. It applies the interaction between health and usability to the degradation process of the function. For example, a decline in the health of conference room equipment leads to an accelerated decline in its usability, or the equipment operates under high pressure to maintain usability, thus accelerating the decline in its health. The correction factor can be a real number greater than 0. A correction factor greater than 1 indicates an accelerated rate of degradation, while a correction factor less than 1 indicates a decelerated rate of degradation.

[0090] Optionally, one way to determine the correction factor between the first and second degradation functions based on the coupling relationship is to represent the coupling relationship as a multiplicative influence coefficient of health on performance decay rate, and directly use it as the correction factor. Specifically, when health decreases, performance decay rate increases by a certain proportion, and this proportion coefficient is the correction factor. A value greater than 1 indicates accelerated decay, equal to 1 indicates no effect, and less than 1 indicates decelerated decay. Another way to implement this is to use the correction factor as a parameter to be optimized, substitute it into the adjusted degradation function, calculate the error (such as root mean square error) between the fitted value and the actual historical data, and use methods such as gradient descent or grid search to find the correction factor value that minimizes the error. Yet another way to implement this is to analyze the relationship between health and performance decay rate based on historical data, fit a mathematical relationship (such as a linear function, piecewise function, or exponential function), so that the correction factor is dynamically adjusted with changes in health. For example, when health is above a certain threshold, the correction factor is close to 1, and when it is below the threshold, the correction factor gradually increases. This specification does not limit this aspect in the embodiments.

[0091] Optionally, one implementation of adjusting the parameters of the first and second degradation functions using a correction factor to obtain the parameter-adjusted first and second degradation functions can be achieved by multiplying the correction factor by the decay rate parameter of the degradation function. Specifically, assuming the decay rate of the original performance degradation function is a base value, multiplying the correction factor by this base value yields the adjusted decay rate, causing the performance degradation rate to accelerate or slow down proportionally with the magnitude of the correction factor. A similar correction factor can be introduced for the health degradation function to reflect the inverse effect of performance on health. Another implementation can be achieved by re-initializing the initial value of the degradation function using a correction factor. Specifically, the value of the degradation function at the initial moment is adjusted according to the direction and strength of the coupling relationship. For example, when performance has an accelerated decay effect on health, the initial value of the health degradation function is reduced by an offset related to the correction factor. This specification does not limit this implementation.

[0092] Optionally, fitting the first and second degradation functions after parameter adjustment to obtain the decay curve can be achieved by weighted summation of the two degradation functions, with the weights preset according to the device type or scenario requirements. Specifically, a weight between 0 and 1 is assigned to the health function and the performance function, and the sum of the two weights is 1. Each function value is multiplied by its corresponding weight and then summed to obtain the overall performance value as a function of time. For example, for devices that prioritize stability, the health weight can be set higher; for scenarios that prioritize energy saving, the performance weight can be set higher. Another implementation method is to take the smaller of the health function value and the performance function value at each time point as the overall performance value at that moment. This method reflects that the overall performance of the device is limited by the poorer performance of health and performance. For example, for a device with acceptable health but poor performance, its overall performance is determined by performance. Yet another implementation method is to multiply the health function value and the performance function value at each time point to obtain the overall performance value. This method is applicable when health and efficacy are independent yet act simultaneously; overall performance decreases as either decreases, and the rate of decline accelerates due to the multiplicative effect. The embodiments in this specification do not limit this.

[0093] For example, continuing from the previous example, the coupling relationship manifests as a 20% increase in performance degradation rate when the health level is below 80%. Based on this, the correction factor is determined as δ = 1 + 0.2 × I (H(t) < 80). The original second degradation function is E(t) = 0.25 × e 0.0012t Its attenuation rate parameter is 0.0012. After adjustment using a correction factor, the second degradation function E'(t) = 0.25 × e is obtained after parameter adjustment. 0.0012×δ×t The first degenerate function H(t) = 100 × e -0.0035t By weighted summation fitting with the adjusted second degradation function, the decay curve P(t) = 0.6H(t) + 0.4(100 - 100 × (E'(t) - 0.25) / 0.25) is obtained.

[0094] In the embodiments of this specification, a correction factor is determined between the first degradation function and the second degradation function based on the coupling relationship. Using the correction factor, the parameters of the first and second degradation functions are adjusted to obtain the parameter-adjusted first degradation function and the parameter-adjusted second degradation function. The parameter-adjusted first degradation function and the parameter-adjusted second degradation function are fitted to obtain a decay curve. This quantifies the originally abstract coupling relationship into a calculable parameter correction amount, allowing the rate of health degradation to be dynamically adjusted according to the performance status. Simultaneously, the rate of performance degradation can also be dynamically adjusted according to the health status (for example, when the health is below a threshold, the correction factor increases the performance decay rate accordingly). This avoids the prediction bias caused by ignoring the interaction between the two in traditional independent degradation models, thus enabling the decay curve to more accurately reflect the true mutual influence between health and performance, improving the accuracy of performance prediction for conference room equipment.

[0095] In one optional embodiment of this specification, the iterative model is configured with iteration cost constraints, downtime constraints, and service duration constraints corresponding to the target meeting scenario; Step 112 includes the following specific steps: The adaptation strength and adaptation duration are input into the iterative model constrained by the target meeting scenario. Based on the adaptation strength and adaptation duration, the iterative model solves the device iteration scheme that satisfies the constraints of iteration cost, downtime, and service duration.

[0096] The iteration cost constraint represents the maximum economic expenditure for iterating on conference room equipment. This could be the upper limit of the budget for purchasing new equipment, the upper limit of maintenance costs, or the upper limit of overall operation and maintenance costs. The downtime constraint represents the maximum allowed duration of meeting interruptions or equipment unavailability during conference room equipment iteration operations. For example, the maximum number of minutes a conference room can be unusable during projector replacement. The service duration constraint represents the minimum time that the conference room equipment must be able to continuously provide service for the target meeting scenario. For example, if the target meeting scenario requires the conference room equipment to be stably operational for at least two weeks, then the service duration constraint is two weeks, and the adaptation time must be no less than this value.

[0097] Optionally, the iterative model can be a rule-based model, a linear programming model, or a neural network model, etc. For example, when the iterative model is a rule-based model, multiple sets of threshold judgment rules are preset: if the adaptation time is greater than the service time constraint and the adaptation strength is greater than 1, then "Do not iterate for now" is output; otherwise, "Plan iterate" or "Iterate immediately" is output according to the specific range of the adaptation strength and adaptation time. When the iterative model is a neural network model, the adaptation strength, adaptation time, and various constraint parameters are taken as input, and the iterative action category is directly output through the trained network. When the iterative model is a linear programming model, the optimal iteration timing and iteration method are solved with the goal of minimizing the iteration cost, and with the constraints that the adaptation time is not less than the service time constraint and the downtime does not exceed the downtime constraint. The embodiments in this specification do not limit this.

[0098] For example, continuing from the previous example, the adaptation strength of the conference room equipment is 1.3, and the adaptation duration is 120 hours. The target meeting scenario is a 2-hour press conference, so the service duration constraint is 2 hours. The maximum allowable downtime for the conference room is 20 minutes, and the iteration cost budget is 3000 yuan. Inputting the adaptation strength of 1.3 and the adaptation duration of 120 hours into the iteration model, the model determines that 120 hours is much greater than 2 hours, and the adaptation strength is greater than 1, thus satisfying all constraints. The output equipment iteration plan is "Do not iterate for now, continue to use, and it is recommended to prepare backup equipment in advance when the adaptation duration has 20 hours remaining." At the same time, the output budget assessment is 0 yuan, and the expected benefit is a saving of 3000 yuan in procurement costs.

[0099] In the embodiments of this specification, the adaptation strength and adaptation duration are input into the iterative model constrained by the target meeting scenario. Based on the adaptation strength and adaptation duration, the iterative model solves for a device iteration scheme that satisfies the constraints of iteration cost, downtime, and service duration. This allows for the selection of the iteration strategy with the lowest cost and least impact (interruption) on the meeting while ensuring the normal conduct of the meeting, thus avoiding meeting interruptions due to sudden equipment failure or resource waste caused by premature replacement.

[0100] In one optional embodiment of this specification, the equipment iteration scheme includes reasons why the conference room equipment is suitable for the target conference scenario; or, Reasons why the conference room equipment is not suitable for the target meeting scenario, equipment models suitable for the target meeting scenario, and budget assessment and expected benefits of iterating on the conference room equipment.

[0101] For example, when the adaptation strength is 1.3 and the adaptation duration is 120 hours, which is greater than the service duration constraint of 2 hours, the model outputs the device iteration solution as: suitable for the target meeting scenario, because "the current adaptation strength is 1.3 (exceeding the threshold of 1), the adaptation duration is 120 hours (exceeding 60 times the meeting duration), and the device performance fully meets the requirements of the press conference, so no replacement is needed." If the adaptation strength is 0.6 and the adaptation duration is 0.5 hours, the model outputs the solution as: not suitable for the target meeting scenario, because "the adaptation strength is less than 1, the adaptation duration is less than 1 hour, and it is impossible to guarantee that the 2-hour meeting will not be interrupted"; the recommended device model is "LM-3000 projector"; the budget assessment is 2500 yuan; the expected benefit is "avoiding the risk of meeting interruption due to equipment failure and improving the meeting quality score by an estimated 20%".

[0102] Optionally, if the conference room equipment is suitable for the target meeting scenario, the equipment iteration plan may also include suggested maintenance cycles, the next performance test time, etc. If the adaptation intensity is much greater than the preset intensity, and / or the adaptation duration is much greater than the preset duration, the equipment iteration plan may also include scheduling the conference room equipment to a meeting scenario with higher performance requirements, or swapping the performance of the conference room equipment with that of a lower-performance conference room equipment to fully leverage its performance advantages.

[0103] Optionally, if the conference room equipment is not suitable for the target meeting scenario, the equipment replacement plan may also include temporary alternatives (such as renting spare equipment), suggestions for equipment recycling or downgrading, etc.

[0104] In the embodiments described in this specification, by outputting an iterative solution that includes reasons for applicability / inapplicability, recommended models, budget assessment, and expected benefits, users can be provided with transparent and explainable decision-making basis, assisting managers in rationally planning equipment procurement and replacement, and reducing decision-making costs.

[0105] In one optional embodiment of this specification, after step 112, the following specific steps are further included: Based on the equipment iteration plan, the conference room equipment is iterated, and an operation and maintenance process for the iterated conference room equipment is generated. Based on the operation and maintenance process, generate a digital work order for executing the operation and maintenance process; Digital work orders are sent to the front end so that users can perform maintenance on the upgraded conference room equipment based on the digital work orders.

[0106] The operation and maintenance process is a sequence of activities involving routine maintenance, inspection, cleaning, and calibration of the upgraded conference room equipment. Examples include the brightness calibration steps after replacing the projector bulb and the call quality testing process for audio equipment.

[0107] A digital work order is a task assignment form that records details of maintenance tasks, execution standards, responsible persons, and time limits in electronic form. For example, it may be an electronic form that includes equipment location, operation steps, estimated time, required tools, and a completion confirmation checkbox.

[0108] The front end is the interactive interface on the mobile terminal (such as mobile phone or computer) used by the user, including but not limited to mobile applications, web management backends, tablet maintenance applications, etc.

[0109] Optionally, one approach to iterating conference room equipment based on an equipment iteration plan and generating an operation and maintenance process for the iterated equipment can be: automatically matching a preset post-maintenance template based on replacement or repair actions in the iteration plan to generate a 5S operation and maintenance process including sorting, setting in order, sweeping, cleaning, and discipline inspection. Another approach can be: dynamically generating a customized operation and maintenance process based on the equipment model and historical maintenance records using a rule engine.

[0110] Alternatively, one way to generate digital work orders for executing maintenance processes based on the maintenance workflow is to transform each step of the maintenance workflow into a task node in the work order, and assign an estimated completion time and responsible role to each node. Another approach is to instantiate the workflow into a traceable work order through a workflow engine and automatically assign it to the corresponding maintenance personnel.

[0111] For example, the equipment iteration plan is "replace the projector bulb and calibrate the brightness". Based on this, the maintenance process is generated as follows: Step 1: Turn off the power and unplug the projector; Step 2: Remove the old bulb; Step 3: Install the new bulb; Step 4: Power on and test the brightness; Step 5: Clean the dust filter; Step 6: Fill in the maintenance record. Each step of this process is converted into a task node, generating a digital work order. This work order includes operation instructions, standard working hours, and a completion confirmation box for each node. The work order is then sent to maintenance engineer Zhang San via WeChat.

[0112] In the embodiments of this specification, the conference room equipment is iterated based on the equipment iteration scheme, and an operation and maintenance process for the iterated conference room equipment is generated. Based on the operation and maintenance process, a digital work order for executing the operation and maintenance process is generated. The digital work order is sent to the front end so that users can perform operation and maintenance on the iterated conference room equipment according to the digital work order. This realizes a closed loop from conference room equipment iteration decision-making, iteration execution, and post-iteration operation and maintenance services for conference room equipment, standardizes the operation and maintenance process, reduces human omissions or errors, and provides feedback data from the actual operation and maintenance execution quality for subsequent iteration model parameter optimization.

[0113] In one optional embodiment of this specification, after sending the digital work order to the front end so that the user can perform maintenance on the iterated conference room equipment according to the digital work order, the method further includes: Receive the execution results of each task node in the digital work order sent from the front end; The execution result is compared with the preset standard execution result to obtain the comparison result; Based on the comparison results, an operation and maintenance quality report on the iterated conference room equipment is generated.

[0114] A task node is the smallest executable and verifiable unit of operation in the maintenance process. For example, "cleaning the projector dust filter" is a task node.

[0115] The execution result is the data or status indicator returned by the operation and maintenance personnel after completing a certain task node. For example, the execution result includes "completed", "incomplete", "abnormal", and the measured value (such as the measured value of temperature and brightness after cleaning).

[0116] Optionally, comparing the execution result with the preset standard execution result to obtain the comparison result can be achieved in one way: for quantitative nodes, calculate the percentage deviation between the execution value and the standard value; for qualitative nodes, determine whether "completed" is selected. Another way is to use fuzzy comprehensive evaluation method to combine the execution results of multiple nodes into a pass / fail score.

[0117] Optionally, one way to generate an operation and maintenance quality report on the iterated conference room equipment based on the comparison results is to summarize the pass / fail status of each node and generate a text document containing information such as pass rate, average deviation, and a list of abnormal nodes. Another way is to generate a PDF report containing chart comparisons and improvement suggestions, and automatically archive it to the equipment archive library.

[0118] Optionally, after generating an operation and maintenance quality report on the iterated conference room equipment based on the comparison results, the data in the operation and maintenance quality report can also be stored in the operation and maintenance server for subsequent parameter fine-tuning of the iterative model.

[0119] For example, the standard execution result for the "Brightness Calibration" node in a digital work order is a brightness of 80% or higher. After execution, the maintenance personnel reported that the measured brightness was 85%, resulting in a "qualified" result. The system summarizes the execution results of all nodes: 4 out of 5 nodes are qualified, and 1 node, "Dust Net Cleaning," timed out. A maintenance quality report is generated after comparison: the overall pass rate is 80%, and the "Dust Net Cleaning" node timed out.

[0120] In this embodiment, the execution results of each task node in the digital work order sent by the front end are received; the execution results are compared with the preset standard execution results to obtain a comparison result; based on the comparison result, an operation and maintenance quality report on the iteratively updated conference room equipment is generated. This allows for a quantitative evaluation of the completion quality of each conference room equipment operation and maintenance task, improving the effectiveness of conference room equipment operation and maintenance. Furthermore, the operation and maintenance quality report can be used to dynamically correct the parameters in the iterative model, thereby achieving adaptive optimization of operation and maintenance quality to iterative decisions and continuously improving the overall efficiency of conference room equipment management.

[0121] In one optional embodiment of this specification, prior to step 102, the method further includes: Receive the iteration cycle for the conference room equipment sent by the user through the front end; Step 102 includes the following specific steps: According to the iteration cycle, acquire multi-dimensional operation and maintenance data of conference room equipment.

[0122] The iteration cycle is the time interval between two executions of this method to evaluate and make decisions about the conference room equipment. For example, once a week, once a month, or an interval dynamically set according to the importance of the equipment.

[0123] Optionally, one way to set the iteration cycle is for the user to manually input the specific number of hours or days through the front-end interface. Another approach is for the system to adaptively recommend an iteration cycle based on the mean time between failures (MTBF) of the conference room equipment or historical maintenance data, which takes effect after user confirmation.

[0124] For example, a user sets an iteration cycle of 30 days for a conference room projector on the enterprise operations and maintenance platform. The system automatically triggers the execution of this method every 30 days according to this cycle, obtains multi-dimensional operations and maintenance data from the past 30 days, recalculates the adaptation strength and adaptation duration, and generates the latest equipment iteration plan. This specification does not limit the scope of this embodiment.

[0125] In this embodiment of the specification, by receiving the iteration cycle of the conference room equipment sent by the user through the front end, and obtaining multi-dimensional operation and maintenance data of the conference room equipment according to the iteration cycle, the automated execution of equipment iteration evaluation is realized. This ensures that the health and usage efficiency of the conference room equipment are continuously monitored, avoids the lack of equipment status evaluation due to human omissions or improper cycles, and reduces the risk of sudden failures.

[0126] Corresponding to the above method embodiments, this application also provides a conference room equipment iteration system. Figure 3 A structural block diagram of a conference room equipment iteration system according to an embodiment of this application is shown. Figure 3 As shown, the system includes: The acquisition module 302 is used to acquire multi-dimensional operation and maintenance data of the conference room equipment and determine the performance threshold of the target meeting scenario for the conference room equipment. The multi-dimensional operation and maintenance data includes at least one of the following: equipment operation data, fault handling record data, meeting usage rate data, and meeting quality feedback data. The feature extraction module 304 is used to aggregate multi-dimensional operation and maintenance data to obtain aggregated operation and maintenance data; and to perform feature quantification on the aggregated operation and maintenance data to obtain the health characteristics and usage efficiency characteristics of the conference room equipment. Module 306 is used to construct the performance degradation curve of conference room equipment over time based on health and usage efficiency characteristics. The determination module 308 is used to determine the adaptation strength and adaptation duration of the conference room equipment to the target conference scenario based on the attenuation curve and performance threshold. The scheme generation module 310 is used to input the adaptation strength and adaptation duration into the iterative model of the target meeting scenario constraints to generate a device iteration scheme, wherein the device iteration scheme is used to iterate the meeting room equipment.

[0127] Optionally, before aggregating multi-dimensional operation and maintenance data to obtain aggregated operation and maintenance data, the following steps are also included: The importance weights of operational data in each dimension are determined through machine learning networks; The feature extraction module 304 is further used to aggregate multi-dimensional operation and maintenance data according to importance weights to obtain aggregated operation and maintenance data.

[0128] Optionally, module 306 is further used for: Based on health characteristics, a first degradation function is determined to describe the change in the health of conference room equipment over time. Based on usage effectiveness characteristics, a second degradation function is determined for the usage effectiveness of conference room equipment over time. Based on health characteristics and usage efficiency characteristics, the coupling relationship between the health and usage efficiency of conference room equipment is determined. Based on the first degradation function, the second degradation function, and the coupling relationship, a performance decay curve of the conference room equipment with respect to time is constructed.

[0129] Optionally, module 306 is further used for: Based on the coupling relationship, the correction factor between the first degradation function and the second degradation function is determined; By using a correction factor, the parameters of the first and second degenerate functions are adjusted to obtain the first degenerate function and the second degenerate function after parameter adjustment. The decay curve is obtained by fitting the first degradation function after parameter adjustment and the second degradation function after parameter adjustment.

[0130] Optionally, the iterative model is configured with iteration cost constraints, downtime constraints, and service duration constraints corresponding to the target meeting scenario; the solution generation module 310 is further used for: The adaptation strength and adaptation duration are input into the iterative model constrained by the target meeting scenario. Based on the adaptation strength and adaptation duration, the iterative model solves the device iteration scheme that satisfies the constraints of iteration cost, downtime, and service duration.

[0131] Optionally, the solution generation module 310 is further used for: Based on the equipment iteration plan, the conference room equipment is iterated, and an operation and maintenance process for the iterated conference room equipment is generated. Based on the operation and maintenance process, generate a digital work order for executing the operation and maintenance process; Digital work orders are sent to the front end so that users can perform maintenance on the upgraded conference room equipment based on the digital work orders.

[0132] Optionally, the solution generation module 310 is further used for: Receive the execution results of each task node in the digital work order sent from the front end; The execution result is compared with the preset standard execution result to obtain the comparison result; Based on the comparison results, an operation and maintenance quality report on the iterated conference room equipment is generated.

[0133] The apparatus provided in this application acquires multi-dimensional operation and maintenance data of conference room equipment and determines the performance threshold of the target meeting scenario's requirements for the conference room equipment. The multi-dimensional operation and maintenance data includes at least one of equipment operation data, fault handling record data, meeting usage rate data, and meeting quality feedback data. The apparatus aggregates the multi-dimensional operation and maintenance data to obtain aggregated operation and maintenance data. It then performs feature quantification on the aggregated operation and maintenance data to obtain the health characteristics and usage efficiency characteristics of the conference room equipment. Based on the health characteristics and usage efficiency characteristics, it constructs a performance decay curve of the conference room equipment over time. Based on the decay curve and the performance threshold, it determines the adaptation strength and adaptation duration of the conference room equipment to the target meeting scenario. Finally, it inputs the adaptation strength and adaptation duration into an iterative model constrained by the target meeting scenario to generate an equipment iteration scheme, which is used to iterate the conference room equipment. It can use historical operation and maintenance data to predict the performance degradation trajectory of conference room equipment in the future operation cycle, quantify the adaptability strength and adaptation time between conference room equipment and specific meeting scenario requirements, and avoid meeting interruptions caused by sudden failures of conference room equipment, premature or late iteration of conference room equipment due to inaccurate experience, and the inability of the equipment to meet the performance requirements of the target meeting scenario in the short term due to insufficient adaptation time. Thus, while ensuring meeting quality, it reduces the cost of iterating conference room equipment, improves the timeliness of iterating conference room equipment, and realizes the transformation of asset management model from "experience-driven, post-replacement" to "data-driven, preventive iteration", which significantly improves asset utilization efficiency and meeting support level.

[0134] The above is an illustrative scheme of a conference room equipment iteration system according to this embodiment. It should be noted that the technical solution of this system and the technical solution of the aforementioned conference room equipment iteration method belong to the same concept. Details not described in detail in the system's technical solution can be found in the description of the technical solution of the aforementioned conference room equipment iteration method. Furthermore, the components in the system embodiment should be understood as functional modules necessary to implement each step of the program flow or each step of the method; these functional modules are not actual functional divisions or separations. The system claims defined by such a set of functional modules should be understood as a functional module architecture that primarily implements the solution through the computer program described in the specification, and not as a physical system that primarily implements the solution through hardware.

[0135] Figure 4 A structural block diagram of a computing device according to one embodiment of this specification is shown. The components of the computing device 400 include, but are not limited to, a memory 410 and a processor 420. The processor 420 is connected to the memory 410 via a bus 430, and a database 450 is used to store data.

[0136] The computing device 400 also includes an access device 440, which enables the computing device 400 to communicate via one or more networks 460. Examples of these networks include a Public Switched Telephone Network (PSTN), a Local Area Network (LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), or a combination of communication networks such as the Internet. The access device 440 may include one or more of any type of wired or wireless network interface (e.g., a Network Interface Controller (NIC)), such as an IEEE 802.11 Wireless Local Area Network (WLAN) interface, a Worldwide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, or a Near Field Communication (NFC) interface.

[0137] In one embodiment of this specification, the aforementioned components of the computing device 400 and Figure 4 Other components, not shown, can also be connected to each other, for example, via a bus. It should be understood that... Figure 4 The block diagram of the computing device shown is for illustrative purposes only and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0138] The computing device 400 can be any type of stationary or mobile computing device, including mobile computers or mobile computing devices (e.g., tablet computers, personal digital assistants, laptop computers, notebook computers, netbooks, etc.), mobile phones (e.g., smartphones), wearable computing devices (e.g., smartwatches, smart glasses, etc.) or other types of mobile devices, or stationary computing devices such as desktop computers or personal computers (PCs). The computing device 400 can also be a mobile or stationary server.

[0139] The memory 410 is used to store computer programs / instructions, and the processor 420 is used to execute the following computer programs / instructions, which, when executed by the processor, implement the steps of the above-mentioned iterative method for conference room equipment.

[0140] The above is an illustrative scheme of a computing device according to this embodiment. It should be noted that the technical solution of this computing device and the technical solution of the conference room equipment iteration method described above belong to the same concept. For details not described in detail in the technical solution of the computing device, please refer to the description of the technical solution of the conference room equipment iteration method described above.

[0141] An embodiment of this specification also provides a computer-readable storage medium storing a computer program / instructions that, when executed by a processor, implement the steps of the above-described iterative method for conference room equipment.

[0142] The above is an illustrative scheme of a computer-readable storage medium according to this embodiment. It should be noted that the technical solution of this storage medium belongs to the same concept as the technical solution of the aforementioned conference room equipment iteration method. Details not described in detail in the technical solution of the storage medium can be found in the description of the technical solution of the aforementioned conference room equipment iteration method.

[0143] An embodiment of this specification also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the above-described iterative method for conference room equipment.

[0144] The above is an illustrative scheme of a computer program product according to this embodiment. It should be noted that the technical solution of this computer program product and the technical solution of the above-described conference room equipment iteration method belong to the same concept. For details not described in detail in the technical solution of the computer program product, please refer to the description of the technical solution of the above-described conference room equipment iteration method.

[0145] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0146] The computer instructions include computer program code, which may be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium may be appropriately added or removed according to the requirements of patent practice. For example, in some regions, according to patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals.

[0147] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments in this specification are not limited to the described order of actions, because according to the embodiments in this specification, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments in this specification.

[0148] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0149] The preferred embodiments disclosed above are merely illustrative of this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the embodiments described herein. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the embodiments, thereby enabling those skilled in the art to better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. A method for iterative use of conference room equipment, characterized in that, include: Acquire multi-dimensional operation and maintenance data of conference room equipment and determine the performance threshold of the target meeting scenario for the conference room equipment. The multi-dimensional operation and maintenance data includes at least one of equipment operation data, fault handling record data, meeting usage rate data and meeting quality feedback data. The multi-dimensional operation and maintenance data are aggregated to obtain aggregated operation and maintenance data; The aggregated operation and maintenance data is subjected to feature quantification to obtain the health characteristics and usage efficiency characteristics of the conference room equipment; Based on the health characteristics and the usage efficiency characteristics, a performance decay curve of the conference room equipment with respect to time is constructed; Based on the attenuation curve and the performance threshold, the adaptation strength and adaptation duration of the conference room equipment to the target conference scenario are determined. The adaptation strength and adaptation duration are input into the iterative model of the target meeting scenario constraints to generate a device iteration scheme, wherein the device iteration scheme is used to iterate the meeting room equipment.

2. The method according to claim 1, characterized in that, Before aggregating the multi-dimensional operation and maintenance data to obtain aggregated operation and maintenance data, the method further includes: The importance weights of operational data in each dimension are determined through machine learning networks; The aggregation of the multi-dimensional operation and maintenance data to obtain aggregated operation and maintenance data includes: The multi-dimensional operation and maintenance data are aggregated according to the importance weights to obtain aggregated operation and maintenance data.

3. The method according to claim 1, characterized in that, The process of constructing a performance degradation curve of the conference room equipment over time based on the health characteristics and usage efficiency characteristics includes: Based on the health characteristics, a first degradation function is determined for the health of the conference room equipment over time; Based on the aforementioned usage performance characteristics, a second degradation function is determined for the usage performance of the conference room equipment over time. Based on the health characteristics and the usage efficiency characteristics, the coupling relationship between the health and usage efficiency of the conference room equipment is determined; Based on the first degradation function, the second degradation function, and the coupling relationship, a performance degradation curve of the conference room equipment with respect to time is constructed.

4. The method according to claim 3, characterized in that, The step of constructing the performance degradation curve of the conference room equipment over time based on the first degradation function, the second degradation function, and the coupling relationship includes: Based on the coupling relationship, a correction factor is determined between the first degradation function and the second degradation function; Using the correction factor, the parameters of the first degradation function and the second degradation function are adjusted to obtain the first degradation function and the second degradation function after parameter adjustment; The attenuation curve is obtained by fitting the first degradation function after parameter adjustment and the second degradation function after parameter adjustment.

5. The method according to claim 1, characterized in that, The iterative model is configured with iteration cost constraints, downtime constraints, and service duration constraints corresponding to the target meeting scenario. The step of inputting the adaptation strength and the adaptation duration into the iterative model constrained by the target conference scenario to generate a device iteration scheme includes: The adaptation strength and the adaptation duration are input into the iterative model constrained by the target meeting scenario. Based on the adaptation strength and the adaptation duration, the iterative model solves for a device iteration scheme that satisfies the iteration cost constraint, the downtime constraint, and the service duration constraint.

6. The method according to claim 1, characterized in that, After inputting the adaptation strength and the adaptation duration into the iterative model of the target meeting scenario constraints to generate a device iteration scheme, the method further includes: Based on the aforementioned equipment iteration scheme, the conference room equipment is iterated, and an operation and maintenance process for the iterated conference room equipment is generated. Based on the aforementioned operation and maintenance process, a digital work order for executing the operation and maintenance process is generated; The digital work order is sent to the front end so that the user can perform maintenance on the iterated conference room equipment according to the digital work order.

7. The method according to claim 6, characterized in that, After sending the digital work order to the front end so that the user can perform maintenance on the iterated conference room equipment according to the digital work order, the method further includes: Receive the execution results of each task node in the digital work order sent by the front end; The execution result is compared with the preset standard execution result to obtain the comparison result; Based on the comparison results, an operation and maintenance quality report on the iterated conference room equipment is generated.

8. A conference room equipment iteration system, characterized in that, include: The acquisition module is used to acquire multi-dimensional operation and maintenance data of the conference room equipment and determine the performance threshold of the target meeting scenario for the conference room equipment. The multi-dimensional operation and maintenance data includes at least one of equipment operation data, fault handling record data, meeting usage rate data and meeting quality feedback data. The feature extraction module is used to aggregate the multi-dimensional operation and maintenance data to obtain aggregated operation and maintenance data; and to perform feature quantization on the aggregated operation and maintenance data to obtain the health characteristics and usage efficiency characteristics of the conference room equipment. A construction module is used to construct a performance decay curve of the conference room equipment over time based on the health characteristics and the usage efficiency characteristics; The determination module is used to determine the adaptation strength and adaptation duration of the conference room equipment to the target conference scenario based on the attenuation curve and the performance threshold. The scheme generation module is used to input the adaptation strength and the adaptation duration into the iterative model of the target meeting scenario constraints to generate a device iteration scheme, wherein the device iteration scheme is used to iterate the meeting room equipment.

9. A computing device, characterized in that, include: Memory and processor; The memory is used to store computer programs / instructions, and the processor is used to execute the computer programs / instructions, which, when executed by the processor, implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program / instructions that, when executed by a processor, implement the steps of the method according to any one of claims 1 to 7.

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